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Abstract

This paper proposes a static synchronous compensator (STATCOM) for use with a self-excited induction generator (SEIG)-based wind farm. The STATCOM applies a damping controller based on an optimal adaptive intelligent controller (OAIC) comprising the critical network, the functional linkbased Elman neural network (FLENN), and the genetic algorithm hybrid time-varying particle swarm optimization (GAHTVPSO) algorithm. The OAIC improves the damping power oscillations in the SEIG-based series-compensated wind farm system. The node-connecting weights of the proposed FLENN and the critical network are trained online via a backpropagation (BP) algorithm, and the GAHTVPSO adjusts the learning rates of the BP algorithm to improve the learning ability of the neural network. A performance analysis confirms the superior damping characteristics of the proposed controller. Moreover, the internal power fluctuations to the power system can be effectively alleviated under variable wind-power generation conditions.

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