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Preview: Industrial Informatics, IEEE Transactions on - new TOC

IEEE Transactions on Industrial Informatics - new TOC



TOC Alert for Publication# 9424



 



Table of Contents

Oct. 2017

Presents the table of contents for this issue of the publication.



IEEE Industrial Electronics Society

Oct. 2017

Presents a listing of the editorial board, board of governors



Reliability Enhancement of Redundancy Management in AFDX Networks

Oct. 2017

Avionics Full Duplex Switched Ethernet is a safety critical network in which a redundancy management mechanism is employed to enhance the reliability of the network. However, as stated in the ARINC664-P7 standard, there still exists a potential problem, which may fail redundant transmissions due to sequence inversion in the redundant channels. In this paper, we explore this phenomenon and provide its mathematical analysis. It is revealed that the variable jitter and the transmission latency difference between two successive frames are the two main sources of sequence inversion. Thus, two methods are proposed and investigated to mitigate the effects of jitter pessimism, which can eliminate the potential risk. A case study is carried out and the obtained results confirm the validity and applicability of the developed approaches.



Lossless In-Network Processing in WSNs for Domain-Specific Monitoring Applications

Oct. 2017

Internet of things (IOT) is emerging as sensing paradigms in many domain-specific monitoring applications in smart cities, such as structural health monitoring (SHM) and smart grid monitoring. Due to the large size of the monitoring objects (e.g., civil structure or the power grid), plenty of sensors need to be deployed and organized to be a large scale of multihop wireless sensor networks (WSNs), which tends to have quite high transmission cost. In-network processing is an efficient way to reduce the transmission cost in WSNs. However, implementing in-network processing for above domain-specific monitoring usually requires to losslessly distribute a dedicate domain-specific algorithm into WSNs, which is much different from most existing in-network processing works. This paper conducts a case study of a classic centralized SHM algorithm, i.e., eigensystem realization algorithm (ERA), and shows how to losslessly and optimally in-network process ERA, especially the typical feature extraction method, i.e., that is singular value decomposition (SVD) therein, in a WSN. Based on whether the intermediate data can be processed together or not by sensor nodes, we respectively implement tree-based in-network processing of SVD and chain-based in-network processing of SVD in WSNs. We prove that using an appropriate shallow light tree as routes for tree-based in-network processing of SVD, can achieve the approximation ratio ${text{1}}+sqrt{2}$ (in terms of transmission cost), while for the chain-based in-network processing of SVD, we design two efficient heuristic algorithms for searching the optimal routes. Extensive simulation results validate the efficiency of these proposed schemes that are customized for SVD-based IOT applications.



Incorporating Intelligence in Fog Computing for Big Data Analysis in Smart Cities

Oct. 2017

Data intensive analysis is the major challenge in smart cities because of the ubiquitous deployment of various kinds of sensors. The natural characteristic of geodistribution requires a new computing paradigm to offer location-awareness and latency-sensitive monitoring and intelligent control. Fog Computing that extends the computing to the edge of network, fits this need. In this paper, we introduce a hierarchical distributed Fog Computing architecture to support the integration of massive number of infrastructure components and services in future smart cities. To secure future communities, it is necessary to integrate intelligence in our Fog Computing architecture, e.g., to perform data representation and feature extraction, to identify anomalous and hazardous events, and to offer optimal responses and controls. We analyze case studies using a smart pipeline monitoring system based on fiber optic sensors and sequential learning algorithms to detect events threatening pipeline safety. A working prototype was constructed to experimentally evaluate event detection performance of the recognition of 12 distinct events. These experimental results demonstrate the feasibility of the system's city-wide implementation in the future.



An Accurate Method for MPPT to Detect the Partial Shading Occurrence in a PV System

Oct. 2017

This paper proposes an accurate detection scheme that effectively differentiates the partial shading from the uniform change of irradiance. By doing so, it avoids the unnecessary global peak search which results in a drop of the maximum power point tracker (MPPT) efficiency. The detection is achieved by calculating the irradiance at two designated points on the I-V curve namely, i.e., the short-circuit (Isc) and MPP (Impp) currents. Since the mismatch of irradiance at these two points differs greatly for the partial shading and uniform irradiance change, the occurrence of the former is easily discriminated. To prove its effectiveness, the scheme is integrated into perturb and observe and particle swarm optimization MPPT algorithms using a buck-boost converter. Its performance under several partial shading and dynamic shading condition is simulated using MATLAB/Simulink and validated using the dSpace DS1104 platform. It only requires three samples to determine if partial shading occurs; without the scheme, an unnecessary scans of the entire P-V curve is initiated. Consequently, MPPT transient efficiency is increased by 30-35%. In addition to this, the calculated irradiance is utilized to update the open-circuit voltage of the array, thus eliminating the use of temperature and irradiance sensors.



A Multilevel Inverter Structure Based on a Combination of Switched-Capacitors and DC Sources

Oct. 2017

This paper presents a switched-capacitor multilevel inverter (SCMLI) combined with multiple asymmetric dc sources. The main advantage of proposed inverter with similar cascaded MLIs is reducing the number of isolated dc sources and replacing them with capacitors. A self-balanced asymmetrical charging pattern is introduced in order to boost the voltage and create more voltage levels. Number of circuit components such as active switches, diodes, capacitors, drivers, and dc sources reduces in proposed structure. This multistage hybrid MLI increases the total voltage of used dc sources by multiple charging of the capacitors stage by stage. A bipolar output voltage can be inherently achieved in this structure without using single phase H-bridge inverter that was used in traditional SCMLIs to generate negative voltage levels. This eliminates requirements of high-voltage rating elements to achieve negative voltage levels. A 55-level step-up output voltage (27 positive levels, a zero level, and 27 negative levels) are achieved by a three-stage system that uses only three asymmetrical dc sources (with amplitude of 1 Vin, 2 V in, and 3 Vin) and seven capacitors (self-balanced as multiples of 1 Vin). MATLAB/SIMULINK simulation results and experimental tests are given to validate the performance of proposed circuit.



Neural Network Learning Adaptive Robust Control of an Industrial Linear Motor-Driven Stage With Disturbance Rejection Ability

Oct. 2017

In this paper, a neural network learning adaptive robust controller (NNLARC) is synthesized for an industrial linear motor stage to achieve good tracking performance and excellent disturbance rejection ability. The NNLARC scheme contains parametric adaption part, robust feedback part, and radial basis function (RBF) neural network (NN) part in a parallel structure. The adaptive part and the robust part are designed based on the system dynamics to meet the challenge of parametric variations and uncertain random disturbances. It must be noted that in actual industrial machining situations, precision motion equipment is always disturbed by unknown factors, which usually cannot be described by mathematical models but affect the tracking accuracy significantly. Therefore, the RBF NN part is employed to further approximate and compensate the complicated disturbances with high reconstructing accuracy and fast training rate. The stability of the proposed NNLARC strategy is analyzed and proved through the Lyapunov theorem. Comparative experiments under various external disturbances such as completely unknown disturbance added by polyfoam are conducted on an industrial linear motor stage. The experimental results consistently validate that the proposed NNLARC control strategy can excellently meet the challenge of complicated disturbance in practical applications. The proposed scheme also provides a guidance for control strategy synthesis with both good tracking performance and disturbance rejection.



Environmental Sensors-Based Occupancy Estimation in Buildings via IHMM-MLR

Oct. 2017

Occupancy estimation in buildings can benefit various applications such as heating, ventilation, and air-conditioning control, space monitoring, and emergency evacuation. Due to the consideration of temporal dependency in occupancy data, hidden Markov model (HMM) has been shown to be effective in occupancy estimation. However, the conventional HMM that assumes invariant temporal dependency of occupancy dynamics for different time instances is unrealistic. Moreover, the performance of the conventional HMM that utilizes mixture of Gaussian for emission probability in terms of continuous observations can be easily affected by the noise in sensory data. To address these problems, in this paper, we propose a new architecture, i.e., inhomogeneous hidden Markov model with multinomial logistic regression (IHMM-MLR), for building occupancy estimation using nonintrusive environmental sensors. Instead of using the time-invariant transition probability matrix, we apply a time-dependent (inhomogeneous) transition probability matrix which can capture the temporal dependency for different time instances. Meanwhile, we employ an efficient probabilistic model, i.e., MLR, for emission probability. Online and offline occupancy estimation schemes are presented for real-time and accurate long-term applications respectively. Real experiments have indicated the effectiveness of our proposed approach.



Mixed-Effect Models for the Analysis and Optimization of Sheet-Metal Assembly Processes

Oct. 2017

Assembly processes can be affected by various parameters, which is revealed by the measured geometrical characteristics (GCs) of the assembled parts deviating from the nominal values. Here, we propose a mixed-effect model (MEM) application for the purposes of analyzing variations in assembly cells, as well as for screening the input variables and characterization. MEMs make it possible to take into account statistical dependencies that originate from repeated measurements on the same assembly. The desirability functions approach was used to describe how to find corrective or control actions based on the fitted MEM. Objectives: To examine the usefulness of the MEM between the positions of the in-going parts as the input controllable variables and the measured GCs as the outputs. Methods: The data from 34 car frontal cross members (each measured three times) were experimentally collected in a laboratory environment by intentionally changing the positions of the in-going parts, assembling the parts, and subsequently measuring their GCs. A single MEM that completely describes the assembly process was fitted between the GCs and the positions of the in-going parts. Results: We present a modeling technique that can be used to establish which measured GCs are influenced by which controllable variables, and how this occurs. The fitted MEM shows evidence that the variability of some GCs changes over time. The natural variation in the system (i.e., unmodeled variations) is about two times larger than the variation between the assembled cross members. We also present two cases that demonstrate how to use the fitted MEM desirability functions to find corrective or control actions. Conclusion: MEMs are very useful tools for analyzing the assembly processes for car-body parts, which are nonlinear processes with multiple inputs and multiple correlated outputs. MEMs can potentially be applied in numerous industrial processes, since modern manufacturing plants measure all important - rocess variables, which is the sole prerequisite for MEMs applications.



Unevenly Sampled Dynamic Data Modeling and Monitoring With an Industrial Application

Oct. 2017

In this paper, a dynamic modeling method for unevenly sampled data is proposed for the monitoring of bi-layer (i.e., a process layer and a quality layer) dynamic processes. First, a novel uneven data dynamic canonical correlation analysis method with an integrated dynamic time window is proposed for interlayer latent structure modeling, which captures the dynamic relations between regularly sampled process data and quality data with slow and irregular sampling. The new model is a step toward big data modeling to deal with data irregularity and diversity. Second, after extracting covariations using an interlayer model, intralayer variations are extracted using subsequent principal component analysis on the residual subspaces of the original process data and quality data, respectively. Third, a concurrent monitoring method for unevenly sampled bi-layer data is proposed. Finally, the proposed method is demonstrated using an illustrative simulation example and applied successfully to a real blast furnace iron-making process.



Electric Vehicle Route Selection and Charging Navigation Strategy Based on Crowd Sensing

Oct. 2017

This paper has proposed an electric vehicle (EV) route selection and charging navigation optimization model, aiming to reduce EV users' travel costs and improve the load level of the distribution system concerned. Moreover, with the aid of crowd sensing, a road velocity matrix acquisition and restoration algorithm is proposed. In addition, the waiting time at charging stations is addressed based on the queue theory. The formulated objective of the presented model is to minimize the EV users' travel time, charging cost or the overall cost based on the time of use price mechanism, subject to a variety of technical constraints such as path selections, travel time, battery capacities, and charging or discharging constraints, etc. Case studies are carried out within a real-scale zone in a city where there are four charging stations and the IEEE 33-bus distribution system. The effects of real-time traffic information acquisition and different decision targets on EV users' travel route and effects of charging or discharging of EVs on the load level of the distribution system are also analyzed. The simulation results have demonstrated the feasibility and effectiveness of the proposed approach.



Bayesian Networks in Fault Diagnosis

Oct. 2017

Fault diagnosis is useful in helping technicians detect, isolate, and identify faults, and troubleshoot. Bayesian network (BN) is a probabilistic graphical model that effectively deals with various uncertainty problems. This model is increasingly utilized in fault diagnosis. This paper presents bibliographical review on use of BNs in fault diagnosis in the last decades with focus on engineering systems. This work also presents general procedure of fault diagnosis modeling with BNs; processes include BN structure modeling, BN parameter modeling, BN inference, fault identification, validation, and verification. The paper provides series of classification schemes for BNs for fault diagnosis, BNs combined with other techniques, and domain of fault diagnosis with BN. This study finally explores current gaps and challenges and several directions for future research.



Multiagent-Based Transactive Energy Framework for Distribution Systems With Smart Microgrids

Oct. 2017

The increasing population of microgrids with various kinds of plug and play energy resources and rapidly varying demand in distribution systems are multiplying the complexity involved in overall system management. This paper proposes an agent-based transactive energy management framework with a comprehensive energy management system (CEMS) as a solution to address the aggregated complexity induced by microgrids in distribution systems. In this framework, microgrids sell or buy the energy in transactive market, which is an inter-microgrid auction based electricity market, to manage the excess supply or residual demand. CEMS follows a dual phase energy management strategy. In the first stage local auxiliary resources such as demand response and distributed energy storage systems of the microgrids are optimally integrated into system operation to level off the forecasted energy imbalances in microgrids. In the latter stage, the operating configuration of the local auxiliary resources is adjusted in real time along with transactive energy to address the imbalances leftover in the former phase and the forecast errors. The efficacy of the proposed framework and CEMS is verified on a IEEE distribution test feeder system with microgrids.



Robust Visual Tracking via Collaborative Motion and Appearance Model

Oct. 2017

In this paper, robust visual tracking scheme is achieved through a novel sparse tracking via collaborative motion and appearance (TCMA). A coarse-to-fine framework with both motion and holistic appearance information is taken into consideration. In coarse search, we employ an optical flow map for the generation of motion particles. A rough estimation of target image patch is obtained using l2-regularized least square method in coarse search stage. In fine search, a novel smooth term is proposed in the cost function to improve the robustness of the tracker. With this smooth term, the object appearance in the previous frame will also affect the calculation of sparse coefficient in the current frame. It allows the tracker involving temporal information between consecutive frames instead of only considering single frame appearance information as in the conventional sparse coding-based tracking algorithms. In order to reserve the original and latest appearance information simultaneously in the template, a quadratic-function-like weight allocation scheme combining with particle contributed histogrammic correlation is developed in the updating stage. Both qualitative and quantitative studies are conducted on a set of challenging image sequences. The superior performance over other state-of-the-art algorithms is verified through the experiment.



Thermal-Aware Energy Management of an HPC Data Center via Two-Time-Scale Control

Oct. 2017

This paper presents a two-time-scale control method to optimize the energy consumption of high-performance-computing data centers through dynamic frequency scaling of processors, tasks assignment, and cooling supplement. First, the steady and dynamical models of the data center are built, which reflect the computational interactions and thermal relationship among the components of the data center. Next, the energy minimization problem for processing a parallel task is divided into two parts that correspond to the steady thermal model and the dynamic thermal one. Then, the problem is solved in a two-time-scale manner, i.e., the optimization of task assignment and processing frequency is considered in steady thermal environment, and the optimization of cooling supplement is achieved in dynamic thermal environment. Finally, simulations of a real task trace are carried out, which demonstrate that the proposed method can significantly improve energy efficiency while guaranteeing the thermal constraints of the data center.



Optimal Protection Coordination for Microgrids Considering N $-$1 Contingency

Oct. 2017

Usually, protection coordination problems are solved under the assumption that the network topology is fixed. Yet, in practice, any power system can encounter changes in the network topology due to transient events. These transient events can be in the form of line or generation source outage. Furthermore, in the presence of distributed generation, the network topology can change depending on whether the system is operating in the grid-connected or islanded mode. Thus, it is essential to consider all possible network topologies while designing a protection scheme for distribution systems with distributed generation (DG). In this paper, the protection coordination problem is solved to determine the optimal relay settings considering N-1 contingency, which can result from a single line, DG unit, or substation outage. In addition, the relays are designed taking into account both grid-connected and islanded operation modes. The problem has been formulated as a mixed integer nonlinear programming problem including coordination constraints corresponding to the various possible outages. The proposed approach is tested



Hierarchical Distributed Scheme for Demand Estimation and Power Reallocation in a Future Power Grid

Oct. 2017

The classical power allocation/reallocation faces difficult challenges in a future power grid with a great many distributed generators and fast power fluctuations caused by high percentage of renewable energy. To perform power reallocation fast in a future power grid with a large number of participants and disturbances, a hierarchical distributed scheme based on a partition framework is proposed. In the proposed scheme, the power grid is naturally partitioned into a certain number of regions, and the total energy demand in the power grid with disturbances is automatically estimated rather than given in advance. Besides, the centralized local optimizations in regions and the distributed global optimization among regions are coupled to solve the power reallocation problem, in which each region performs as a single agent. Thus, the agents in the proposed scheme are much fewer than the purely distributed ones, hence the communication load is greatly relieved and the reallocation process is significantly simplified. Effectiveness of the proposed scheme is verified by the cases.



Fully Distributed Demand Response Using the Adaptive Diffusion–Stackelberg Algorithm

Oct. 2017

In this paper, we consider the demand response problem in smart grid consisting of a retailer and multiple residential consumers, where the retailer determines consumers' payments based on their power consumption profile. Our aim is to propose a fully distributed algorithm that is able to optimize the aggregate cost, utility, and retailer's profit simultaneously. To this end, we first formulate the consumer-side trend as a constrained convex optimization problem and propose a fully distributed adaptive diffusion algorithm to solve it. In addition, we design a one-leader N-follower Stackelberg game to model interactions among the retailer and consumers. The proposed framework is able to continuously track the drifts resulting from the changes in the real-time pricing or the consumer preferences. Moreover, it is scalable and does not require network-wide information or rely on central controller. We provide comprehensive simulation results to show the effectiveness of the proposed framework.



Noise Effect and Noise-Assisted Ensemble Regression in Power System Online Sensitivity Identification

Oct. 2017

Recently developed data acquisition equipment and data processing methods have ignited the possibility of power system online sensitivity identification (OSI). Despite the existing OSI algorithms, practical issues such as data collinearity and the noise effect on the identification algorithm must be considered to realize OSI in real-power systems. In this study, the negative and positive aspects of noise to OSI are first studied. Then, under the data collinearity condition and by making use of the positive aspects of noise, a noise-assisted ensemble regression method is proposed to simultaneously solve the data collinearity problem and manage the negative aspects of noise. Moreover, the proposed method is proven equivalent to one of the most effective measures, the norm-2 regularization method, to address the collinearity problem, and therefore provides satisfactory OSI results. The proposed method is tested in an 8-generator 36-node system with original operations data from a real-power system, and the results validate its effectiveness.



Desirably Adjusting Gain Margin, Phase Margin, and Corresponding Crossover Frequencies Based on Frequency Data

Oct. 2017

This paper presents an analytical method to tune a fixed-structure fractional-order compensator for satisfying desired phase and gain margins with adjustable crossover frequencies. The proposed method is based on the measured frequency data of the plant. Since no analytical model for the plant is needed in the compensator tuning procedure, the resulted compensator does not depend on the order and complexity of the plant. Also, sufficient conditions for the existence of a compensator with no zero and pole in the right half-plane for satisfying the aforementioned objectives are analytically derived. Furthermore, different hardware-in-the-loop experimental results are presented to show the efficiency of the proposed tuning method.



Daily Clearness Index Profiles Cluster Analysis for Photovoltaic System

Oct. 2017

Due to various weather perturbation effects, the stochastic nature of real-life solar irradiance has been a major issue for solar photovoltaic (PV) system planning and performance evaluation. This paper aims to discover clearness index (CI) patterns and to construct centroids for the daily CI profiles. This will be useful in being able to provide a standardized methodology for PV system design and analysis. Four years of solar irradiance data collected from Johannesburg (26.21 S, 28.05 E), South Africa are used for the case study. The variation in CI could be significant in different seasons. In this paper, cluster analysis with Gaussian mixture models (GMM), K-Means with Euclidean distance (ED), K-Means with Manhattan distance, Fuzzy C-Means (FCM) with ED, and FCM with dynamic time warping (FCM DTW) are performed for the four seasons. A case study based on sizing a stand-alone solar PV and storage system with anaerobic digestion biogas power plants is used to examine the usefulness of the clustering results. It concludes that FCM DTW and GMM can determine the correct PV farm rated capacity with an acceptable energy storage capacity, with 36 and 46 rather than 1457 solar irradiance profiles, respectively.



Novel Three-Point Interpolation DFT Method for Frequency Measurement of Sine-Wave

Oct. 2017

This paper proposes a novel three-point interpolation discrete Fourier transform for accurate power system frequency measurement. The accurate formula of the proposed frequency measurement method is derived by using the maximum sidelobe decay windows. Moreover, the influence of white noise on the proposed frequency measurement is analyzed by deducing the expression of frequency measurement variance. The systematic errors and variances of frequency measurement are analyzed by simulation.



Optimization-Based AC Microgrid Synchronization

Oct. 2017

This paper casts the synchronization phenomena in inverter-based ac microgrids as an optimization problem solved using alternating direction method of multipliers (ADMM). Existing cooperative control techniques are based on the standard voting protocols in multiagent systems, and assume ideal communication among inverters. Alternatively, this paper presents a recursive algorithm to restore synchronization in voltage and frequency using ADMM, which results in a more robust secondary control even in the presence of noise. The performance of the control algorithm, for an islanded microgrid test system with additive noise in communication links broadcasting reference signals and communication links connecting neighboring inverters, is evaluated for a modified IEEE 34-bus feeder system. An upper bound for the deviation due to communication noise from the reference set point is analytically derived and verified by the simulated microgrid test system.



A Fast Image Retrieval Method Designed for Network Big Data

Oct. 2017

In the field of big data applications, image information is widely used. The value density of information utilization in big data is very low, and how to extract useful information quickly is very important. So we should transform the unstructured image data source into a form that can be analyzed. In this paper, we proposed a fast image retrieval method which designed for big data. First of all, the feature extraction method is necessary and the feature vectors can be obtained for every image. Then, it is the most important step for us to encode the image feature vectors and make them into database, which can optimize the feature structure. Finally, the corresponding similarity matching is used to determined the retrieval results. There are three main contributions for image retrieval in this paper. New feature extraction method, reasonable elements ranking, and appropriate distance metric can improve the algorithm performance. Experiments show that our method has a great improvement in the effective performance of feature extraction and can also get better search matching results.



FPGA Implementation of a Tone-Based Flight Termination System in a Software-Defined Radio Platform

Oct. 2017

This paper outlines the design and implementation of a tone-based flight termination system (FTS) in a software-defined radio (SDR) platform. It is completely a novel implementation of an analog FTS in an SDR platform of NI Flex-RIO system. This single platform based design appears as a substitute for the previously used multiple platforms based complex system. Ruggedization and relevance design methods are required for the FTS design. Hence, the blueprint of the FTS is carried out in a field-programmable gate array. It ensures reconfigurable, interoperable operations with precise, reliable, and future upgradable implementation. Efficient optimization methods have been adopted to minimize the use of hardware resources. LabVIEW, a graphical programming language, is used for rapid prototyping. The validation of the system was done both in subsystem level as well as the integrated level at real-time mission scenario.



Robust Intensity-Based Localization Method for Autonomous Driving on Snow–Wet Road Surface

Oct. 2017

Autonomous vehicles are being developed rapidly in recent years. In advance implementation stages, many particular problems must be solved to bring this technology into the market place. This paper focuses on the problem of driving in snow and wet road surface environments. First, the quality of laser imaging detection and ranging (LIDAR) reflectivity decreases on wet road surfaces. Therefore, an accumulation strategy is designed to increase the density of online LIDAR images. In order to enhance the texture of the accumulated images, principal component analysis is used to understand the geometrical structures and texture patterns in the map images. The LIDAR images are then reconstructed using the leading principal components with respect to the variance distribution accounted by each eigenvector. Second, the appearance of snow lines deforms the expected road context in LIDAR images. Accordingly, the edge profiles of the LIDAR and map images are extracted to encode the lane lines and roadside edges. Edge matching between the two profiles is then calculated to improve localization in the lateral direction. The proposed method has been tested and evaluated using real data that are collected during the winter of 2016-2017 in Suzu and Kanazawa, Japan. The experimental results show that the proposed method increases the robustness of autonomous driving on wet road surfaces, provides a stable performance in laterally localizing the vehicle in the presence of snow lines, and significantly reduces the overall localization error at a speed of 60 km/h.



Optimal Stochastic Design of Wind Integrated Energy Hub

Oct. 2017

This study presents a stochastic approach to design a wind integrated energy hub with multiple energy systems. Energy hub system offers significant advantages to energy services by providing the flexibility to cope with the challenging effects of intermittent renewable energy sources penetration. To this end, the wind integrated energy hub design problem would optimally determine appropriate number and size of system components that satisfy electricity and thermal demand and system constraints. To secure operation, the reliability indices such as the loss-of-load expectation and the expected energy not supplied are considered. The wind power generation and load forecasting uncertainties as well as the random outages of components are modeled as a scenario using proper scenario generation methods. The scenario reduction technique is also introduced to reduce the computational burden of the scenario-based design model. Finally, the proposed model is applied to a test case to illustrate effectiveness of the proposed approach.



Fast and Accurate Frequency-Dependent Behavioral Model of Bonding Wires

Oct. 2017

A proposed model of bonding wires is presented in this paper. For a regular double-π bonding-wire model considering the skin effect, nine parameters should be determined, including inductance (LS), series parasitic resistances (R1, R2), shunt parasitic capacitances (CPAR1 , CPAR2), and parameters for skin effects (RS1, LS1, RS2, LS2), so procedures to extract the design parameters for a bonding-wire model are complicated. To reduce the complexity, a proposed model is presented. Introducing a frequency-dependent resistor Rskinfm can significantly reduce the number of design parameters for a bonding-wire model considering the skin effect from nine to five. This can resolve the design complexity of the bonding wires and cables. Moreover, it is suitable for industrial applications. In addition, the proposed design methodology is presented and the mechanisms are validated by experiments. According to experimental results, the model accuracy with 10% difference in magnitude between measured and modeled S21 of the 2, 4, 6, and 8 mm aluminum bonding wires is at the frequencies of 5.9, 5.0, 3.5, and 2.9 GHz, respectively.



IoT-Based Techniques for Online M2M-Interactive Itemized Data Registration and Offline Information Traceability in a Digital Manufacturing System

Oct. 2017

The integration of internet-of-things (IoT) technologies in the industry benefits digital manufacturing applications by allowing ubiquitous interaction and collaborative automation between machines. Online data collection and data interaction are critical for real-time decision making and machine collaborations. However, due to the specificity of digital manufacturing applications, the technical gap between IoT techniques and practical machine operation could hinder the efficient data interactions, collaborations between machines, and the effectiveness as well as the accuracy of itemized data collection. This investigation, therefore, identifies some major technical problems and challenges that current IoT-based digital manufacturing is facing, and proposes a method to bridge the technical gap for itemized product management. The highlights of this investigation are: 1) a data-oriented system architecture toward flexible data interaction between machines, 2) a customized machine-to-machine protocol for machine discovery, presence, and messaging, (3) flexible data structure and data presentation for interoperability, and (4) versatile information tracing approaches for product management. The proposed solutions have been implemented in PicknPack digital food manufacturing line, and achieved ubiquitous data interaction, online data collection, and versatile product information tracing methods have shown the feasibility and significance of the presented methods.



Rapid MPPT for Uniformly and Partial Shaded PV System by Using JayaDE Algorithm in Highly Fluctuating Atmospheric Conditions

Oct. 2017

In photovoltaic (PV) array, the output power and the power-voltage (P-V ) characteristic of PV array are totally dependent on the temperature and solar insolation. Therefore, if these atmospheric parameters fluctuate rapidly, then the maximum power point (MPP) of the P-V curve of PV array also fluctuates very rapidly. This rapid fluctuation of the MPP may be in accordance with the uniform shading of the PV panel or may be in accordance to the partially shaded due to the clouds, tall building, trees, and raindrops. However, in both cases, the MPP tracking (MPPT) is not only a nonlinear problem, this becomes a highly nonlinear problem, which solution is time bounded. Because the highly fluctuating atmospheric conditions change the P-V characteristic after every small time duration. This paper introduces a hybrid of “Jaya” and “differential evolution (DE)” (JayaDE) technique for MPPT in the highly fluctuating atmospheric conditions. This JayaDE algorithm is tested on MATLAB simulator and is verified on a developed hardware of the solar PV system, which consists of a single peak and many multiple peaks in the voltage-power curve. Moreover, the tracking ability is compared with the recent state of the art methods. The satisfactory steady-state and dynamic performances of this new hybrid technique under variable irradiance and temperature levels show the superiority over the state-of-the-art control methods.



Shop Floor Control: A Physical Agents Approach for PLC-Controlled Systems

Oct. 2017

Multiagent technology raised great expectations from the outset, and these expectations are still high today. A technology that allows the creation of systems that are autonomous, reactive, proactive, and have entities with social skills is necessarily very appealing. Many initiatives have emerged to develop the application of this technology in different areas, including manufacturing. Multiagent-based solutions typically use this technology to create distributed systems with decentralized decision making as a way to tackle complex systems or problems by dividing the complexity among agents. Unfortunately, most works do not go further than the definition of agents or architectures, which prevents industry from adopting this technology. This paper presents an approach to implementing the shop floor control of an automated distribution center following a multiagent approach. This implementation divides the system into agents in charge of the management of the system while highlighting the communication channels with shop floor control equipment.



An Identity-Based Data Aggregation Protocol for the Smart Grid

Oct. 2017

The smart grid significantly improves the reliability, efficiency, security, and sustainability of electricity services. It plays an important role in modern energy infrastructure. A drawback of this new technique, however, is that the fine-grained metering data may leak private customer information. Thus, various public-key based data aggregation protocols for privacy protection have been proposed. However, the National Institute of Standards and Technology has recommended not using public-key based cryptography in the smart grid, since maintaining the public-key infrastructure is a heavy cost. In this paper, we propose an identity-based data aggregation protocol for the smart grid, which cannot only prevent unauthorized reading and fine-grained analyzing but can also protect against unintentional errors and maliciously altered messages. The basic building block of our protocol is an identity-based encryption and signature scheme in which an identity-based encryption scheme is combined with an identity-based signature scheme. They share the same private/public parameters, which greatly reduces the complexity of the protocol in the smart grid. Security analysis demonstrates the effectiveness of our protocol in the context of six typical attacks against the smart grid. A prototype implementation based on the Intel Edison platform shows that our protocol is efficient enough for physically constrained smart grid operators, such as smart meters.



CAN With eXtensible In-Frame Reply: Protocol Definition and Prototype Implementation

Oct. 2017

Controller area network (CAN) has been the de facto standard in the automotive industry for the past two decades. Recently, CAN with flexible data-rate (CAN FD) has been standardized, which achieves noticeably higher throughput. Further improvements are still possible for CAN, by exploiting its peculiar physical layer to carry out distributed operations among network nodes, implemented as atomic transactions mapped on quasi-conventional frame exchanges. In this paper, a proposal is made for an extension to the CAN protocol, termed CAN with eXtensible in-frame Reply (CAN XR), which enables upper protocol layers to define new custom services devoted to, e.g., network management, application-specific functions, and high-efficiency data transfer. The key point is that CAN XR retains full backward compatibility with CAN, therefore, there is no need to change the protocol specification once again.



Review of Fast Calculation Techniques for Computer-Generated Holograms With the Point-Light-Source-Based Model

Oct. 2017

Computer-generated holograms (CGHs) are a key technology in electroholography systems; however, heavy calculations are required to calculate CGHs. We review fast calculation techniques for CGH calculation of a point-light-source-based model, which is a simple and general model of a three-dimensional object in an electroholography system. To reduce the calculation time, many methods that reduce the temporal and spatial redundancy of the CGH calculation have been developed (e.g., look-up table method, the wavefront recording plane method, and other approximation techniques). The implementation of such methods on parallel computers (e.g., graphic processing unit and field programmable gate arrays) has also been reported.



Enhanced Autofocusing in Optical Scanning Holography Based on Hologram Decomposition

Oct. 2017

Optical scanning holography is a compact and powerful method for capturing hologram of a wide three-dimensional (3-D) view scene. After a hologram has been taken, it is often necessary to determine the locations of the focal plane on which the objects are residing, so that the 3-D scene can be numerically reconstructed for further analysis or processing. Recent research has shown that automatic detection of the depth (focal plane) of objects represented in a hologram can be conducted with entropy minimization method. Despite the success, the method could fail if the entropy information of objects in a hologram are interfering with each other. In this paper, we propose a method based on the hologram decomposition to overcome this problem. Briefly, the hologram is decomposed into subholograms and the focal plane distance is determined separately for each subobject. Simulation results reveal that our proposed method has good accuracy and reliability.



Cumulative Dual Foreground Differences for Illegally Parked Vehicles Detection

Oct. 2017

Illegally parked vehicles on the urban road may create a traffic flow problem as well as a potential traffic accident, such as crashing between parked and other vehicles. Thus, the intelligent traffic monitoring system should be able to prevent this situation by integrating an illegally parked vehicle detection module. However, implementing such a module becomes more challenging due to road environments, such as weather conditions, occlusion, and illumination changing. Hence, this work addresses a method to implement an illegally parked vehicle detection based on the cumulative dual foreground differences from the short- and long-term background models, temporal analysis, vehicle detector, and tracking. The extensive experiments were conducted using both iLIDS and our proposed datasets to evaluate the effectiveness of the proposed method by comparing with other methods. The results showed that the method is effective in detecting illegally parked vehicles and can be considered as part of the intelligent traffic monitoring system.



Low-Power Digital Baseband Transceiver Design for UWB Physical Layer of IEEE 802.15.6 Standard

Oct. 2017

This paper presents the design and implementation of ultrawideband digital baseband transceiver for wireless body area networks (WBAN) applications. The power dissipation and area of the proposed architecture are minimized by combining algorithmic and architectural level modifications. A new algorithm for Bose-Chaudhuri- Hocquenghem (BCH) encoding is implemented and the coding gain of the BCH decoder is improved by 0.5 dB, with a cost of only one percent area overhead. An area efficient, low-complexity, and low-power BCH decoder is implemented and it has 42% lower area and 38% lower power dissipation compared to a conventional hard-decision decoder. Other salient features of this paper include a low-complexity, low-power packet detection unit, and a low-power module for removing the shortening bits. The baseband transceiver has been designed in 90 nm CMOS technology and it has an energy efficiency of 73 pJ/bit in transmitter mode and 225 pJ/bit in receiver mode.



Bandwidth Management for Soft Real-Time Control Applications in Industrial Wireless Networks

Oct. 2017

Industrial distributed control systems would greatly benefit from the adoption of wireless communication technologies, if only guarantees could be provided on timing of time-critical data delivery over the ether. This paper presents solutions to handle single-hop deadline-constrained periodic traffic, which combine centralized transmission scheduling according to earliest deadline first (EDF) and automatic repeat request (ARQ). For each solution, an admission control test is provided, which guarantees a configurable number of retries to each data instance within its deadline, statically addressing both timeliness and reliability on a per-instance basis. Dynamic bandwidth management strategies are also introduced that use runtime information about unperformed guaranteed retries and reassign them, as extra retries, to failed instances whose deadline has not yet expired. Simulation results show that significant benefits can be obtained, in terms of both determinism and improved performance, by a careful static/dynamic management of the available communication resources.



Wrin’Tac: Tactile Sensing System With Wrinkle's Morphological Change

Oct. 2017

This paper describes an active tactile sensing system that selects sensing modalities based on specific sensing tasks, by changing its morphology, called Wrin'Tac. This paper was inspired by the human finger wet-induced wrinkle, which is usually observed when one soaks in warm water for a period, and has been indicated as an efficient transformation for enhancement of gripping stability in a wet environment. We proposed a device which is an integration of actuation (pneumatic actuator) and sensing elements (strain gauges) inside a thin, multilayered substrate. Under pressurization, the morphology of the substrate surface (both geometrical and mechanical characteristics) change with appearance of wrinkles. Especially, by formation of wrinkles, this device can change its shape so that the posture of embedded sensing elements (strain gauges) can vary and generate different responses depending on external load conditions. As a result, this device can actively select its sensing functions depending on specific sensing tasks. First, we created a model to investigate the dynamic changes in a strain gauges' mechanical response under formation of wrinkles. Then, a prototype of this sensing device and its fabrication process were proposed to accomplish sensing tasks under vertical indentation and horizontal sliding action on its surface by using one type of strain gauge. This paper is an example of soft morphological control in tactile sensing, and is expected to open a new avenue to development of tactile sensing systems.



Guest Editorial - Special Section on Emerging Informatics for Risk Hedging and Decision Making in Smart Grids

Oct. 2017

The aim of this Special Section is to attract and report the latest advances toward the trend of applying advanced informatics techniques resolving complex problems facing power system operation and planning in the new era of smart grids. Special interests are given to the new methods that can handle various tasks of risk hedging and decision making appeared in eleven system operation and planning, though the scope has been slightly expanded to other topical issues in smart grids as well. The accepted eleven high-quality papers represent how the newadvances and solutions toward resolving complex problems facing power system operation and planning can be brought forward by continuously leveraging emerging techniques in the field of data analytics and informatics. It should be highlighted that with the increased penetration of various emerging technologies such as renewables and electric vehicles (EVs), secure and economic system operation and planning deserve continuous research efforts in producing the most up-to-date methods and solutions dealing with issues of diversified natures and complexities in future power grids. Specifically, the covered topics in this Special Section are topical and broad, concerning mainly power system security analysis and electricity market planning and operation under risks and uncertainties, which are briefly summarized.



A DSE-Based Power System Frequency Restoration Strategy for PV-Integrated Power Systems Considering Solar Irradiance Variations

Oct. 2017

With power networks undergoing an unprecedented transition from traditional power systems to modern electric grids integrated with renewable energy sources, maintaining frequency stability of generators in modern power systems has become one of the major concerns. Targeting this issue, in this paper, we propose a novel frequency restoration strategy in photovoltaics (PV)-connected power systems using decentralized dynamic state estimation technique and PV power plant as a contingency power source. When a sudden increase in load demand occurs, the output power of PV panels is increased in order to compensate for the shortage of real power capacity of the generator, in order to restore the frequency of a certain generator bus bar. An unscented Kalman filter-based decentralized dynamic estimation is utilized in this study to estimate the frequency of a selected generator bus bar with local noisy voltage and current measurement data acquired by using phasor measurement units. Solar luminous intensity may vary over a period of time in different seasons, weather conditions, etc., which causes the variations in the output power of PV power plants. This irradiance uncertainty is also considered in this study. The proposed control strategy not only incorporates the frequency deviations of a generator bus-bar, but also takes into account the tie-line power deviations under disturbances. Simulation results demonstrate the capacity of proposed control schemes in restoring the frequency of generator bus-bars and also maintaining the tie-line power flowing between adjoining areas at it scheduled value.



Probabilistic Hosting Capacity for Active Distribution Networks

Oct. 2017

The increased connection of distributed generation (DG), such as photovoltaic (PV) and wind turbine (WT), has shifted the current distribution networks from being passive (consuming energy) into active (consuming/producing energy). However, there is still no consensus about how to determine the maximum amount of DGs that are allowed to be connected, i.e., how to quantify a so-called “hosting capacity” (HC). Therefore, this paper proposes a novel risk assessment tool for estimating network HC by considering uncertainties associated with PV, WT, and loads. This evaluation is performed using the likelihood approximation approach. The paper, also, proposes a utilization of clearness index for localized solar irradiance prediction of PV. In addition, we propose the use of sparse grid technique as an effective means for uncertainty computation while the use of Monte Carlo technique is taken for a comparison purpose. Two actual distribution networks (11-buses and South Australian large feeder) are considered as case studies to demonstrate the usefulness of the proposed tool.



Imbalance Learning Machine-Based Power System Short-Term Voltage Stability Assessment

Oct. 2017

In terms of machine learning-based power system dynamic stability assessment, it is feasible to collect learning data from massive synchrophasor measurements in practice. However, the fact that instability events rarely occur would lead to a challenging class imbalance problem. Besides, short-term feature extraction from scarce instability seems extremely difficult for conventional learning machines. Faced with such a dilemma, this paper develops a systematic imbalance learning machine for online short-term voltage stability assessment. A powerful time series shapelet (discriminative subsequence) classification method is embedded into the machine for sequential transient feature mining. A forecasting-based nonlinear synthetic minority oversampling technique is proposed to mitigate the distortion of class distribution. Cost-sensitive learning is employed to intensify bias toward those scarce yet valuable unstable cases. Furthermore, an incremental learning strategy is put forward for online monitoring, contributing to adaptability and reliability enhancement along with time. Simulation results on the Nordic test system illustrate the high performance of the proposed learning machine and of the assessment scheme.



Intelligent Early Warning of Power System Dynamic Insecurity Risk: Toward Optimal Accuracy-Earliness Tradeoff

Oct. 2017

Dynamic insecurity risk of a power system has been increasingly concerned due to the integration of stochastic renewable power sources (such as wind and solar power) and complicated demand response. In this paper, an intelligent early-warning system to achieve reliable online detection of risky operating conditions is proposed. The proposed intelligent system (IS) consists of an ensemble learning model based on extreme learning machine (ELM) and a decision-making process under a multiobjective programming framework. Taking an ensemble form, the randomness existing in individual ELM training is generalized and reliable classification results can be obtained. The decision making is designed for ELM ensemble whose parameters are optimized to search for the optimal tradeoff between the warning accuracy and the warning earliness of the proposed IS. The compromise solution turns out to significantly speed up the overall computation with an acceptable sacrifice in the accuracy (e.g., from 100% to 99.9%). More importantly, the proposed IS can provide multiple and switchable performances to the operators in order to satisfy different local dynamic security assessment requirements.



CVaR-Constrained Optimal Bidding of Electric Vehicle Aggregators in Day-Ahead and Real-Time Markets

Oct. 2017

An electric vehicle aggregator (EVA) that manages geographically dispersed electric vehicles offers an opportunity for the demand side to participate in electricity markets. This paper proposes an optimization model to determine the day-ahead inflexible bidding and real-time flexible bidding under market uncertainties. Based on the relationship between market price and bid price, the proposed optimal bidding model of EVA aims to minimize the conditional expectation of electricity purchase cost in two markets considering price volatility. Moreover, the penalty cost of the deviation between the bidding quantities is included to avoid large power variation and arbitrage. The conditional expectation optimization model is formulated as an expectation minimization problem with the conditional value-at-risk constraints. Based on the price data in the PJM market, simulation results verify that our model is a decision-making tool in electricity markets, which can help market players comprehend the variants of bid price, expected cost and probability of successful bidding.



The Coordinated Development Path of Renewable Energy and National Economy in China Considering Risks of Electricity Market and Energy Policy

Oct. 2017

The long-standing over-reliance on fossil fuels brings urgent environmental issues. To reduce emissions and maintain a sustained economic growth, many countries seek for energy revolution. With the help of smart grid technology, renewable energy eventually plays an indispensable role in energy production and consumption. Electricity market mechanisms and energy policies are thus developed rapidly and can pose more risks to national economy. This paper proposes a modified computable general equilibrium (CGE) model for China to evaluate these risks. Based on several economic evaluation indices, the coordinated development path of renewable energy and national economy is put forward. Industrial restructuring is considered in the modified CGE model since it is one of the key economic policies in today's China. Numerical studies are conducted based on real-world data, and sensitivity analysis further illustrates how different factors affect the results.



Modeling and Analysis of Lithium Battery Operations in Spot and Frequency Regulation Service Markets in Australia Electricity Market

Oct. 2017

Renewable share in the global total energy mix is predicted to grow, and this leads to an increase in the required capacity for frequency regulation. While an electric vehicle (EV) is gaining more popularity, a collection of retired EV battery packs provides an economic option for meeting the additional frequency regulation needs. In this paper, a battery market operation model is proposed to maximize financial return, and a battery operation cost estimator is built to evaluate the potential impacts of market operations on the battery lifespan. Specifically, the model is designed for retired EV lithium batteries under the Australian national electricity market framework. It predicts the automatic-generation-control energy due to the frequency regulation service offers. Battery cycle life cost and battery capacity degradation are considered in the model. It can be used to determine multimarket offers based on the expected profit. Nonetheless, the model can be generalized for other electricity market frameworks and battery types.



An Accurate and Fast Converging Short-Term Load Forecasting Model for Industrial Applications in a Smart Grid

Oct. 2017

Short-term load forecasting (STLF) models are very important for electric industry in the trade of energy. These models have many applications in the day-to-day operations of electric utilities such as energy generation planning, load switching, energy purchasing, infrastructure maintenance, and contract evaluation. A large variety of STLF models have been developed that trade off between forecast accuracy and convergence rate. This paper presents an accurate and fast converging STLF model for industrial applications in a smart grid. In order to improve the forecast accuracy, modifications are devised in two popular techniques: mutual information based feature selection; and enhanced differential evolution algorithm based error minimization. On the other hand, the convergence rate of the overall forecast strategy is enhanced by devising modifications in the heuristic algorithm and in the training process of the artificial neural network. Simulation results show that accuracy of the newly proposed forecast model is 99.5% with moderate execution time, i.e., we have decreased the average execution of the existing bilevel forecast strategy by 52.38%.



Historical-Data-Based Energy Management in a Microgrid With a Hybrid Energy Storage System

Oct. 2017

In a microgrid, due to potential reverse power profiles between the renewable energy source (RES) and the loads, energy storage devices are employed to achieve high self-consumption of RES and to minimize power surplus flowing back into the main grid. This paper proposes a variable charging/discharging threshold method to manage the energy storage system. In addition, an adaptive intelligence technique (AIT) is put forward to raise the power management efficiency. A battery-ultra-capacitor hybrid energy storage system (HESS) with merits of high energy and power density is used to evaluate the proposed method with on-site-measured RES output data. Compared with the particle swarm optimization (PSO) algorithm based on the precise predicted data of the load and the RES, the results show that the proposed method can achieve better load smoothing and self-consumption of the RES without the requirement of precise load and RES forecasting.



Multiple Perspective-Cuts Outer Approximation Method for Risk-Averse Operational Planning of Regional Energy Service Providers

Oct. 2017

In the smart grid and future energy internet environment, a regional energy service provider (RESP) may be able to integrate multiple energy resources such as generator units, demand response, electrical vehicle charging/swapping stations, and carbon emission trading to participate in the market. By imploring a well-known portfolio optimization theory conditional value-at-risk to tackle electricity price uncertainty, this paper formulates the risk-averse day-ahead operational planning for such a RESP as a mixed-integer quadratically constrained programming (MIQCP), named as RA-RESP. A global optimization method, named as multiple perspective-cuts outer approximation method (MPC-OAM) is proposed to solve this model efficiently. A remarkable stronger and tighter mixed integer linear programing master problem is designed to accelerate the convergence of the proposed method. Comprehensive simulation results show that, compared with existing day-ahead planning models, the RA-RESP is a good compromise between profit-based models and cost-based ones. The proposed MPC-OAM can solve complicated RA-RESP problem efficiently, and compared with state-of-the-art solution techniques, the MPC-OAM outperforms in both computing speed and solution quality, especially for scenario which includes more nonlinear factors.



Risk-Averse Energy Trading in Multienergy Microgrids: A Two-Stage Stochastic Game Approach

Oct. 2017

Multienergy microgrids are a promising solution to improve overall energy (electricity, cooling, heating, etc.) efficiency. In this paper, a new optimal energy trading strategy is developed considering the risk from uncertain energy supply and demand in a set of individual multienergy microgrids. According to the historical data about energy supply of each microgrid, an aggregator aims to maximize each microgrid's profit while minimizing the risk of overbidding for renewable energy resources trading based microgrids. A novel two-stage stochastic game model with Cournot Nash pricing mechanism and the conditional value-at-risk criterion is proposed to characterize the payoff function of each microgrid. The sample average approximation (SAA) technique is employed to approximate the stochastic Nash equilibrium of the game model. The existence of the SAA Nash equilibrium is investigated and the corresponding Nash equilibrium seeking algorithm is also realized in a distributed manner. The proposed method is validated by numerical simulations on real-world data collected in Australia, and the results show that the SAA Nash equilibrium based strategy can effectively reduce the risk of not meeting the demand and improve the economic benefits for each microgrid.



Guest Editorial - Special Section on Systems of Power Converters: Design, Modeling, Control, and Implementation

Oct. 2017

In this Special Section on Systems of Power Converters: Design, Modeling, Control, and Implementation, we have 11 high-quality papers approved for publication that cover the following three topics. 1) Converter Design and Operation. 2) Subsystem-Level Applications. 3) System-Level Applications. These topics and the corresponding papers are summarized.



Grid Interfaced Distributed Generation System With Modified Current Control Loop Using Adaptive Synchronization Technique

Oct. 2017

This paper presents real-time implementation of a grid interfaced distributed generation (DG) system with modified current control loop using three phase amplitude adaptive notch filter (AANF) based synchronization tool. A grid current feedback based modified dq-current control technique for interfacing inverter is developed in order to achieve constant loading on the grid, transient-free operation, and power factor improvement close to unity power factor (UPF) of the utility grid during sudden load variations. This technique does not require separate calculation of reference reactive component and harmonics component of currents hence reduces control circuit complexity. In addition, it requires only three voltage and three current sensors. Three phase AANF is developed and is used for online extraction of utility voltage phase angle to generate synchronized reference current signals for interfacing inverter. AANF is used because of its adjustable accuracy and amplitude adaptability even under unbalanced voltage sag and swell, frequency variation, and distorted grid conditions. Fast and accurate behavior of three phase AANF improves the dynamic response of entire DG system control performance for sudden load variations. The dynamic behavior of the proposed grid interfaced DG system is experimentally evaluated in maintaining constant loading on grid, transient-free operation, and power factor improvement close to UPF operation of the utility grid, by compensating total reactive power and harmonic current demanded by variable linear as well as nonlinear load.



Design and Implementation of Disturbance Compensation-Based Enhanced Robust Finite Control Set Predictive Torque Control for Induction Motor Systems

Oct. 2017

Finite-control-set-based predictive torque control (PTC) method has received more and more attention in recent years due to its fast torque response. However, it also has two drawbacks that could be improved. First, the torque reference in the cost function of the existing PTC method is generated by the proportional-integral speed controller, so torque reference's generation rate is not fast and its accuracy is low especially when the load torque is given suddenly and inertia value is varying. In addition, the variable prediction of the traditional PTC method depends on the system model, which also has the problem of parameter uncertainties. This paper investigates a disturbance observer (DOB)-based PTC approach for induction motor systems subject to load torque disturbances, parameter uncertainties, and time delays. Not only does the speed loop adopt a DOB-based feed-forward compensation method for improving the system disturbance rejection ability and robustness, but the flux, current, and torque predictions are also improved by using this technique. The simulation and experimental results verified the effectiveness of the proposed method.



A Cooperative Operation of Novel PV Inverter Control Scheme and Storage Energy Management System Based on ANFIS for Voltage Regulation of Grid-Tied PV System

Oct. 2017

In this paper, the voltage regulation problem in low-voltage power distribution networks integrated with increased amount of solar photovoltaics (PV) has been addressed. This paper proposes and evaluates the cooperative performance of a novel proportional-integral-derivative (PID) control scheme for PV interfacing inverter based on intelligent adaptive neuro-fuzzy inference system (ANFIS) and an ANFIS-based supervisory storage energy management system (EMS) for regulating the voltage of three-phase grid-connected solar PV system under any nonlinear and fluctuating operating conditions. The proposed ANFIS-based PID control scheme (ANFISPID) dynamically controls the PV inverter to inject/ absorb appropriate reactive power to regulate the voltage at point of common coupling (PCC) and provides robust response at any system worst case scenarios and grid faults. And the proposed ANFIS-based supervisory EMS controls the charge/discharge of the energy storage system when there is voltage deviation to cooperate with ANFISPID in PCC voltage regulation. The proposed ANFISPID-based PV inverter control scheme and ANFIS-based supervisory EMS are developed and simulated in MATLAB/ Simulink environment and their dynamic cooperative performances are compared with cooperative performances of conventional PID-based PV inverter control scheme and state-based EMS.



A Quasi-Resonant Switched-Capacitor Multilevel Inverter With Self-Voltage Balancing for Single-Phase High-Frequency AC Microgrids

Oct. 2017

In this paper, a quasi-resonant switched-capacitor (QRSC) multilevel inverter (MLI) is proposed with self-voltage balancing for single-phase high-frequency ac (HFAC) microgrids. It is composed of a QRSC circuit (QRSCC) in the frontend and an H-bridge circuit in the backend. The input voltage is divided averagely by the series-connected capacitors in QRSCC, and any voltage level can be obtained by increasing the capacitor number. The different operational mechanism and the resulting different application make up for the deficiency of the existing switched-capacitor topologies. The capacitors are connected in parallel partially or wholly when discharging to the load, thus the self-voltage balancing is realized without any high-frequency balancing algorithm. In other words, the proposed QRSC MLI is especially adapted for HFAC fields, where fundamental frequency modulation is preferred when considering the switching frequency and the resulting loss. The quasi-resonance technique is utilized to suppress the current spikes that emerge from the instantaneous parallel connection of the series-connected capacitors and the input source, decreasing the capacitance, increasing their lifetimes, and reducing the electromagnetic interference, simultaneously. The circuit analysis, power loss analysis, and comparisons with typical switched-capacitor topologies are presented. To evaluate the superior performances, a nine-level prototype is designed and implemented in both simulation and experiment, whose results confirm the feasibility of the proposed QRSC MLI.



A Simple Harmonic Reduction Method in Multipulse Rectifier Using Passive Devices

Oct. 2017

This paper proposes a novel and passive harmonic reduction method at dc link of multipulse rectifier (MPR). The proposed method uses a single-phase diode-bridge rectifier to generate circulating current, which can shape the input line current of MPR. The input side of the single-phase diode-bridge rectifier is connected with the secondary winding of interphase reactor (IPR), and its output side is connected with load, which can recycle the harmonic energy and feed to load. The operation mode of the single-phase diode-bridge rectifier is analyzed, and the turn ratio of the IPR is designed optimally. Under ideal condition, the proposed MPR operates as a 24-pulse rectifier, and its total harmonic distortion (THD) of input line current is about 7.6%. Most of all, the proposed method is easy to be used in different 12-pulse rectifier topologies, and its conduction losses are far less than that of the conventional double-tapped IPR. Simulation and experiment results show that the THD of input line current is less than 5%.



Detection of False-Data Injection Attacks in Cyber-Physical DC Microgrids

Oct. 2017

Power electronics-intensive dc microgrids use increasingly complex software-based controllers and communication networks. They are evolving into cyber-physical systems (CPS) with sophisticated interactions between physical and computational processes, making them vulnerable to cyber attacks. This paper presents a framework to detect possible false-data injection attacks (FDIAs) in cyber-physical dc microgrids. The detection problem is formalized as identifying a change in sets of inferred candidate invariants. Invariants are microgrids properties that do not change over time. Both the physical plant and the software controller of CPS can be described as Simulink/Stateflow (SLSF) diagrams. The dynamic analysis infers the candidate invariants over the input/output variables of SLSF components. The reachability analysis generates the sets of reachable states (reach sets) for the CPS modeled as hybrid automata. The candidate invariants that contain the reach sets are called the actual invariants. The candidate invariants are then compared with the actual invariants, and any mismatch indicates the presence of FDIA. To evaluate the proposed methodology, the hybrid automaton of a dc microgrid, with a distributed cooperative control scheme, is presented. The reachability analysis is performed to obtain the reach sets and, hence, the actual invariants. Moreover, a prototype tool, HYbrid iNvariant GEneratoR, is extended to instrument SLSF models, obtain candidate invariants, and identify FDIA.



On the Practical Design of a High Power Density SiC Single-Phase Uninterrupted Power Supply System

Oct. 2017

This paper proposes a high power density SiC single-phase system potential for uninterrupted power supply applications. To get the high power density, the semiconductors, packaging, circuit topology, and thermal design are synthetically considered. To increase the switching frequency and reduce the size of the passive components, the SiC MOSFETs and diodes are chosen; to minimize the parasitic inductances and eliminate the snubbers, the SiC bare dies are packaged as the half-bridge (HB) modules; to remove the bulky dc-link capacitors, the full-bridge inverter and the active power filter are designed, and they are structured by using the fabricated SiC HB modules; and finally to dissipate the heat from such a compact enclosure in the cost-efficient way, the heat sink of the modules and the forced air cool system are well designed, and the thermal 3-D finite-element analysis model is built to survey the best cooling configuration. A 2-kVA prototype is built and tested, and the power density of the system is up to 58 W/in3 and the maximal efficiency is up to 98.3%.



Robust Finite-Time Control for Autonomous Operation of an Inverter-Based Microgrid

Oct. 2017

Recently, more and more small-scale renewable generation sources based distributed generators are integrated to the existing power network through power electronic-based converters. Microgrid has been proposed as a solution to meet the challenges posed by highly intermittent renewable generations. To address the fast response and complex operating conditions of various inverters in an autonomous microgrid, this paper proposes a robust finite-time control algorithm for frequency/voltage regulation and active/reactive power control. The major advantages of the proposed control algorithm include, being robust and stable against various load disturbances, unmodeled dynamics and system parameter perturbations; enabling flexible convergence time according to user preferences and different operating conditions' requirements. The finite-time convergence of the robust control algorithm is guaranteed through rigorous analysis and the balance between control accuracy and chattering suppression is investigated. Simulation results demonstrate the effectiveness of the proposed robust finite-time control algorithm.



Implementation of High-Precision Quadrature Control for Single-Stage SECS

Oct. 2017

In this paper, a high precision quadrature control for a single-stage solar energy conversion system (SECS) is presented with power quality improvement capabilities. The SECS uses a voltage source converter (VSC) which performs multifunctions. It harvests maximum energy from the solar photovoltaic (SPV) string and it integrates the extracted energy to the grid. In addition, it utilizes a SPV feed-forward loop to improve the dynamic response and reduces the burden on the proportional-integral controller by regulating dc bus voltage. To control the switching sequences of VSC, a high precision quadrature control is used which extracts the fundamental current from the contaminated load current. The mathematical formulation of quadrature control is corroborated by the experimental results of SECS under different operating conditions.



An Implementation of Hybrid Control Strategy for Distributed Generation System Interface Using Xilinx System Generator

Oct. 2017

This paper presents an analytical study and hardware-in-loop (HIL) cosimulation design of a grid-connected inverter system with a combinational robust observer-based modified repetitive current controller. In this study, main attention is paid to improve power quality and tracking performance of a distributed generation (DG) interfacing system under various perturbations. The inherent delay in convergence of conventional repetitive controller (RC) is reduced by introducing a low pass filter in delay line and this configuration is named as modified RC (MRC). By adding an observer with MRC, system states can be reconstructed, which improve the system dynamic response. Robust stability and convergence criterion are derived in terms of linear matrix inequality using combined Lyapunov function and singular value decomposition technique, which determine the suitable parameters of feedback control and state observer gains. By utilizing these gains, the switching signals are generated to operate the DG interfacing inverter effectively. The performance of proposed controller is compared with traditional proportional integral, proportional resonant, and MRC under both normal and fault conditions. Finally, HIL cosimulation is performed by realizing the power circuit in MATLAB/Simulink as a simulation model and a control structure using Xilinx system generator platform as burnt in hardware Virtex-6 field programmable gate array (FPGA) ML605 evaluation kit.



Study of the Phase Shift Plus PWM Control Strategy Based on a Resonant Bridge Modular Switched-Capacitor Converter

Oct. 2017

Bridge modular switched-capacitor converter (BMSCC) has been reported with symmetric and modular structure, and featured less output voltage ripple, less components cost, and convenient voltage extension but weak output voltage regulation. This paper presents a phase shift plus pulse width modulation (PWM) control strategy for the novel resonant BMSCC. With this control method, output voltage regulation and the limitation of the loop peak current to a proper range are implemented. Also, output voltage ripple could be effectively controlled as well through the voltage ripple characteristics analysis in different cases. Detailed analyses of the relationships between the switching phases of MOSFETs and the output voltage, the switching-phases of MOSFETs and the loop peak current are performed. Meanwhile, through optimal switching sequence, the proposed control method ensures that almost all switching devices maintain zero current switching or zero voltage switching operation, which results in high system efficiency. Using simulation software of the saber and hardware platform, the soft switching, output voltage, and peak current characteristics are verified.



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Oct. 2017

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IEEE Industrial Electronics Society

Oct. 2017

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Oct. 2017

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