A Load-Forecasting-Based Adaptive Parameter Optimization Strategy of STATCOM Using ANNs for Enhancement of LFOD in Power Systems

Abstract

This paper proposes a load-oriented control parameter optimization strategy for static synchronous compensator (STATCOM) to enhance low-frequency oscillation damping (LFOD) and improve stability of overall complex power systems. Frequency deviations of generators of interest are employed as the input signals of the designed supplementary damping controller of STATCOM. In order to obtain the optimal load-oriented control parameters, a day-ahead load-forecasting scheme is devised, using artificial neural network (ANN) learning techniques. The ANN is trained by a set of data over a 4-year period, and then the control parameters are optimized using Particle Swarm Optimization technique by minimizing the critical damping index. The proposed control strategy is implemented in the IEEE standard complex power system, and the numerical results demonstrate that the low-frequency oscillations (LFOs) of the power system can be effectively mitigated using the proposed controller. Compared to conventional robust controller with universal parameters, this novel load-oriented optimal control strategy shows its superiority in alleviating LFOs and enhancing the overall stability of the power system. Since the proposed control scheme aims to adaptively adjust the controller parameters in correspondence to load variations, this study is envisaged to have practical utilizations in industrial applications.