A new adaptive controller of facts-based FMRLC aimed at improving power system stability

Abstract

Various control techniques using Advanced Super-conducting Magnetic Energy Storage (ASMES) aimed at improving power system stability have been proposed. As fuzzy controller has proved its value in some applications, the number of investigations employing fuzzy controller with ASMES has been greatly increased over recent period. Nevertheless, it is sometimes difficult to specify the rule base for some plants, or the need can arise for tuning the rule-base parameters if the plant changes. In order to solve such problems the Fuzzy Model Reference Learning Controller (FMRLC) is proposed. This paper investigates multi-inputs/multi-outputs FMRLC for time-variant nonlinear system. This provides the motivation for adaptive fuzzy control, whereby the focus is placed on the automatic on-line synthesis and tuning of fuzzy controller parameters (i.e., the use of on-line data for continuous learning of the fuzzy controller which ensures that the performance objectives are met). The simulation results show that the proposed robust controller is able to work with nonlinear power system (i.e., single machine connected at infinite bus), under various fault conditions and significant disturbances.

Keywords

• transient power system stability
• FACTS
• ASMES
• current source inverter (CSI)
• MIMO fuzzy controllers
• reference model
• learning control