A New Fuzzy Stabilizer Based on Online Learning Algorithm for Damping of Low-Frequency Oscillations

A multi objective Honey Bee Mating Optimization (HBMO) designed by online learning mechanism is proposed in this paper to optimize the double Fuzzy-Lead-Lag (FLL) stabilizer parameters in order to improve low-frequency oscillations in a multi machine power system. The proposed double FLL stabilizer consists of a low pass filter and two fuzzy logic controllers whose parameters can be set by the proposed multi objective optimization process. A multilayer adaptive network is employed to design the fuzzy logic controller with self-learning capability that does not require another controller to tune the fuzzy inference rules and membership functions. In the proposed online learning algorithm, two artificial neural networks are employed which this system makes the FLL stabilizer adaptive to changes in the operating conditions. Therefore, variation in the power system response, under a wide range of operating conditions, is less compared to the system response with a fixed-parameter conventional controller. The effectiveness of the proposed stabilizer has been employed by simulation studies. The effectiveness of the proposed stabilizer is demonstrated on Two-Area Four-Machine (TAFM) power system under different loading conditions.

Online Learning algorithm; multi objective optimization; multi machine; small signal stability; HBMO; Fuzzy stabilizer