Direct adaptive power system stabilizer design using fuzzy wavelet neural network with self-recurrent consequent part
The main disadvantage of FWNN is that the application domain is limited to static problems due to its feed-forward network structure. Therefore, we propose to use a self-recurrent wavelet neural network (SRWNN) in the consequent part of FWNN, solving the control problem for chaotic systems. Our proposed structure requires fewer wavelet nodes than the networks with feed-forward structure, due to the dynamic behavior of the recurrent network. Finding the optimal learning rates is a challenging task in the classic gradient-based learning algorithms. Hence, in our proposed framework, all of the learning rates are determined optimally based on Lyapunov stability theory. We develop a controller based on the proposed network structure and use it for damping the oscillations in the multi-machine power system.
wnn wavelet neural network fwnn fuzzy wavelet neural network srwn self-recurrent wavelet neural network tsk takagi-sugeno-kang dact direct adaptive control theory ga genetic algorithm lalr lyapunov-based adaptive learning rate alr adaptive learning rate rols recursive orthogonal least square mb-pss multi-band power system stabilizer cpss conventional power system stabilizer sfwnapss stable fuzzy wavelet neural based adaptive power system stabilizer