Hybrid PSO-Optimized ANFIS-Based Model to Improve Dynamic Voltage Stability

D. N. Truong, V. T. Bui


The objective of this paper is to perform a hybrid design for an Adaptive Neuro-Fuzzy Inference System (ANFIS) optimized by Particle Swarm Optimization (PSO) to improve the dynamic voltage stability of a grid-connected wind power system. An onshore 99.2MW wind farm using Doubly Fed Induction Generator (DFIG) is studied. To compensate the reactive power absorbed from the power grid of the wind farm, a Static VAR Compensator (SVC) is proposed. To demonstrate the performance of the proposed hybrid PSO–ANFIS controller, simulations of the voltage response in time-domain are performed in Matlab to evaluate the effectiveness of the designed controller. From the results, it can be concluded that the proposed hybrid PSO-optimized ANFIS-based model can be applied to enhance the dynamic voltage stability of the studied grid-connected wind power system.


adaptive neuro-fuzzy inference system; particle swarm optimization; static var compensator; voltage stability

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