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.
Wang, Fei; Lu, Kai-Hung; Xu, Qiangqiang; and Chen, Ziwen
"DESIGN OF AN OPTIMAL ADAPTIVE INTELLIGENT CONTROL SCHEME FOR STATCOM IN A SERIES COMPENSATED WIND FARM,"
Journal of Marine Science and Technology: Vol. 28:
4, Article 6.
Available at: https://jmstt.ntou.edu.tw/journal/vol28/iss4/6