Abstract
In this paper, a Static VAR Compensator (SVC) which is applied in power systems for enhancing dynamic voltage stability of grid-connected wind power systems is studied. For controlling the proposed SVC, the parameters of an Adaptive Neuro-Fuzzy Inference System (ANFIS) structure with the objective function of minimizing squared errors is optimized by Particle Swarm Optimization (PSO). For demonstrating the performance of the proposed hybrid ANFIS and PSO controller, simulation results in MATLAB environment were performed with the benchmark IEEE 14-bus power system with the connected wind farm. Since the Doubly Fed Induction Generator (DFIG) is one of the most important variable wind generators, in this paper a 62 × 1.6-MW DFIG-based wind turbines system is selected to study. Sometime-domain simulation results under severe faults are shown to compare the oscillation and overshoot of the voltage and current waveforms. It can be seen that the proposed hybrid model between ANFIS and PSO can be applied to enhance the dynamic voltage stability of the studied grid-connected wind power systems.
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Bui, VT., Truong, DN. (2021). Improvement of ANFIS Controller for SVC Using PSO. In: Huang, YP., Wang, WJ., Quoc, H.A., Giang, L.H., Hung, NL. (eds) Computational Intelligence Methods for Green Technology and Sustainable Development. GTSD 2020. Advances in Intelligent Systems and Computing, vol 1284. Springer, Cham. https://doi.org/10.1007/978-3-030-62324-1_25
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DOI: https://doi.org/10.1007/978-3-030-62324-1_25
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