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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.

To ensure the full benefits from wind generation systems, an effective maximum power point tracking (MPPT) technique should be implemented. In this article, an adaptive fuzzy logic controller (AFLC) is presented as a control methodology for producing the peak power from a permanent magnet synchronous generator (PMSG)-based wind turbine (WT). Regardless of the importance and significance of modeling and simulation processes, experimental studies occupy the most important aspect and remain a big challenge. The first part of this article is devoted to the simulation and modeling of PMSG along with the control system by MATLAB/Simulink ® package to develop the control system. To justify the simulation results, experimental validation tests are presented under the same status of wind speed profile and run period. The experimental validation is conducted using the DSPACE DS1104 control board and is compared with simulation results. Moreover, for a realistic response, actual wind speed data is utilized in this research depending on measured data from Ras Ghareb wind farm in the Gulf of Suez, Egypt. The obtained results confirm the superiority of AFLC compared with fuzzy logic controller and conventional PI control schemes. In addition, good tracking with high accuracy is obtained regarding experimental and simulations results. Moreover, an evaluation indices are employed for WT performance based on gross system efficiency and integral of the absolute error (IAE). These indices are used to demonstrate the feasibility of the AFLC methodology compared with traditional approaches under the same wind turbine status.

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Due to the continuous increase of fuel prices and pollutions, the use of renewable energy especially wind has increased. In developing countries including Egypt, squirrel cage induction generator wind turbine (SCIG-WT) represents a considerable proportion of the total capacity of installed wind farm due to its qualities such as low cost and easy availability. However, its operation has a substantial effect on system stability. In contrast, doubly fed induction generator wind turbine (DFIG-WT) is broadly penetrated the electrical grid as it keeps the system stable. In this work, the ability of WT generators to continue operating rather than tripping at the time of faults is analyzed for proper stability investigation. The detailed control and stability of a grid-connected large scale SCIG and DFIG of Zafarana, Suez Gulf area, Egypt are discussed whereas the parameters of fault ride through (FRT) curve of Egypt grid code is utilized. Moreover, a precise analytical stability argument using a proposed integrated nonlinear dynamical model is presented. Conditions for global asymptotic stability of the SCIG in the sense of Lyapunov function (LF) are given and tested by time domain simulation. The eigenvalues of the matrices of LF and its derivative are determined by which the stability boundaries are determined depending on the positivity of these matrices. The dynamic behavior of the whole system is simulated in MATLAB/ Simulink interface programming while the practical data are collected from an experimental model consisting of DFIG-WT to demonstrate the efficacy of the FRT control system.

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