Frequency regulation using neural network observer based controller in power system

Abstract In this study, an artificial neural network observer based sliding mode control strategy is proposed for load frequency regulation problem in multi-area power system. In this study both unmatched disturbance estimation and its rejection is done by using artificial neural network (ANN) observer. A three layer feed forward neural network is considered for ANN observer and its weights are trained using new modified adaptive training rule. The ANN observer guarantees precise estimation of the actual variables and this leads to convergence of estimation error to zero. In order to neglect chattering in control signal, the estimated unmatched unknown disturbance via ANN observer is utilized to select switching surface boundary limits. The ANN observer based controller improves closed loop system time response when compared with well known existing GESO based NSMC and two layer active disturbance rejection control (ADRC) schemes at random unmatched and unknown disturbances. It also rejects the effects of unmatched unknown disturbances and unknown bounded power integration in the system. The ANN observer based controller is also validated on IEEE 39 bus system. The robustness of the proposed ANN observer based controller in terms of stability and effectiveness when subjected to unmatched unknown disturbance and unknown power integration is established by the simulation results.