An Advanced Adaptive - Learning Controller for Facts to Improve Power System Stability

The power system models for transient stability studies are nonlinear and complex. Their parameters slowly change with time due to environmental effects or rapidly due to faults. It is preferable that the control technique in this system possesses robustness for various fault conditions and disturbances. Many control techniques that using Advanced Super-conducting Magnetic Energy Storage (ASMES) to improve power system stability have been proposed. While fuzzy controller has proven its value in some applications, the researches applying fuzzy controller with ASMES actively reported. But it is sometimes very difficult to specify the rule base for some plants, when the parameters changes. To solve this problem, a Fuzzy Model Reference Learning Controller (FMRLC) is proposed in this paper, where investigate multiinput multi – output (MIMO) FMRLC for time-variant nonlinear system. This provides the motivation for adaptive fuzzy control, where the focus is on the automatic on-line synthesis and tuning of fuzzy controller parameters (i.e., use online data to continually learn the fuzzy controller which will ensure that the performance objectives are met). Simulation results show that the proposed robust controller is able to work with non-linear and non-stationary power system (i.e., single machine infinite bus system), under various fault conditions and disturbances.