On-line self-learning PID controller design of SSSC using self-recurrent wavelet neural networks

Conventionally, FACTS devices employ a proportional-integral (PI) controller as a supplementary controller. However, the conventional PI controller has many disadvantages. The present paper aims to propose an on-line self-learning PI-derivative (PID) controller design of a static synchronous series compensator for power system stability enhancement and to overcome the PI controller problems. Unlike the PI controllers, the proposed PID controller has a local nature because of its powerful adaption process, which is based on the back-propagation (BP) algorithm that is carried out through an adaptive self-recurrent wavelet neural network identifier (ASRWNNI). In fact, the PID controller parameters are updated in on-line mode using the BP algorithm based on the information provided by the ASRWNNI, which is a powerful and fast-acting identifier thanks to its local nature, self-recurrent structure, and stable training algorithm with adaptive learning rates based on the discrete Lyapunov stability theorem. The proposed control scheme is applied to a 2-machine, 2-area power system under different operating conditions and disturbances to demonstrate its effectiveness and robustness. Later on, the design problem is extended to a 4-machine, 2-area benchmark system and the results show that the interarea modes of the oscillations are well damped with the proposed approach.