FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine

Abstract : As per modern electrical grid rules, Wind Turbine needs to operate continually even in presence severe grid faults as Low Voltage Ride Through (LVRT). Hence, a new LVRT Fault Detection and Identification (FDI) procedure has been developed to take the appropriate decision in order to develop the convenient control strategy. To obtain much better decision and enhanced FDI during grid fault, the proposed procedure is based on voltage indicators analysis using a new Artificial Neural Network architecture (ANN). In fact, two features are extracted (the amplitude and the angle phase). It is divided into two steps. The first is fault indicators generation and the second is indicators analysis for fault diagnosis. The first step is composed of six ANNs which are dedicated to describe the three phases of the grid (three amplitudes and three angle phases). Regarding to the second step, it is composed of a single ANN which analysis the indicators and generates a decision signal that describes the function mode (healthy or faulty). On other hand, the decision signal identifies the fault type. It allows distinguishing between the four faulty types. The diagnosis procedure is tested in simulation and experimental prototype. The obtained results confirm and approve its efficiency, rapidity, robustness and immunity to the noise and unknown inputs.
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Amel Adouni, Dhia Chariag, Demba Diallo, Mouna Ben Hamed, Lassaâd Sbita. FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine. ISA Transactions, Elsevier, 2016, 64, pp.353 - 364. 〈10.1016/j.isatra.2016.05.009〉. 〈hal-01494711〉



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