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Technologies of microgrids (MGs) help power grid evolve into one that is more efficient, less polluting, reduced losses, and more flexible to provide energy consumers' want and need. Because of the nature of various renewable energy sources (RESs) integrated into the MGs such as variability and inability to accurately predict and control, different technical problems are created. Power quality is one of the most important issues to be addressed, especially harmonic distortion and voltage stabilization. Many devices have been proposed to improve these two aspects that may result from loads nonlinearity and sources uncertainty. In this study, an adaptive switched filter compensator (ASFC) with developed proportionalintegral-derivative (PID) controller is proposed to improve the overall dynamic performance of the MGs. The PID's controller gains are optimally tuned via the application of grasshopper's optimization algorithm (GOA) to act adaptively with self-tuning as the operating conditions may subject to change during MG operation. Different case studies are proposed to reveal the robustness of the presented ASFC on harmonic mitigation, dynamic voltage stabilization, reactive power compensation and power factor improvement considering the features of RESs such as variations of wind speed, solar PV irradiation and temporary fault conditions. A distribution synchronous static compensator (D-STATCOM), as one of the most popular D-FACTS, with optimal tuned PID controller by using the GOA is also proposed. To validate both the proposed ASFC topology and the modified D-STATCOM, comparative studies including what has been published in literature are examined by using MATLAB/Simulink platform. The results advocate the effectiveness, robustness and latency of the proposed devices. INDEX TERMS Microgrids (MGs), adaptive switched filter capacitor (ASFC), distribution synchronous static compensator (D-STATCOM), distribution flexible alternative current transmission system (D-FACTS), power quality (PQ), grasshopper's optimization algorithm (GOA), PID Controller.

SUMMARY In this paper, a modified simulated annealing technique is proposed for the simultaneous improvement of power quality and optimal placement and the sizing of fixed capacitor banks in a modern distribution network. The latter is supplying a mix of linear and nonlinear loads imposing voltage and current harmonics. Besides, the network includes integrated variable‐speed wind turbines as distributed generation. The stochastic power output of the wind generation is considered by performing the Monte Carlo simulation of the distribution power flow. Operational and power quality constraints include the bounds of node voltage magnitude, the node voltage total harmonic distortion, and the number/size of installed capacitors. The net annual economic saving is defined as the associated objective function that accounts for the cost of the released power capacity because of the power losses reduction, the cost change of energy losses, and the extra cost of the searched capacitor banks. Solution results are discussed for a model‐distorted distribution system under different conditions. Copyright © 2012 John Wiley & Sons, Ltd.

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This paper proposes a new approach for rapid detection of islanding events in a microgrid (MG). The proposed approach is a two-step procedure in which the first step is to extract some valuable features from the voltage and current signals. Such signals are analyzed for finding the second harmonic by the discrete Fourier transform (DFT). Then, the symmetrical components of this second harmonic are calculated for voltage and current, resulting in six features; positive, negative and zero sequence components. In the second step, a novel deep learning classifier based on long short-term memory (LSTM) network to identify the islanding decision is applied. The LSTM is a new artificial intelligence technique which is a distinctive pattern of recurrent neural networks. To evaluate the performance of the proposed approach, simulated and practical voltage and current signals are used. The simulated signals are generated by simulating a MG consisting of inverter based wind DGs using Matlab Simulink, while the practical data are collected from an experimental model consisting of wind and PV DGs. Different intentional and unintentional islanding events are conducted and processed using the proposed approach. The results show that in comparison with other artificial intelligence algorithms such as decision tree (DT), support vector machine (SVM) and artificial neural network (ANN), the proposed approach is efficient and reliable in detecting the islanding with high accuracy, high dependability and small detection time.

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