Turkish Journal of Electrical Engineering and Computer Sciences
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.
Adaptive control, flexible AC transmission systems, power system control, power system stability, self-recurrent wavelet neural networks
GANJEFAR, SOHEIL and ALIZADEH, MOJTABA
"On-line self-learning PID controller design of SSSC using self-recurrent wavelet neural networks,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 21:
4, Article 6.
Available at: https://journals.tubitak.gov.tr/elektrik/vol21/iss4/6
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