Frequency regulation using neural network observer based controller in power system. (September 2020)

• Record Type: Journal Article
• Authors: Prasad, Sheetla; Ansari, Mohammad Rashid
• Is Part Of: Control engineering practice. Volume 102(2020)
• Publication Date: 2020-09
• Subjects: Artificial neural network observer -- Load frequency control -- Unmatched disturbance rejection -- Linear matrix inequality (LMI) -- Non-linear sliding mode controller; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2020.104571
• Languages: English
• ISSNs: 0967-0661

Abstract

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. Highlights: Estimate unmeasured states and any class of disturbances adaptively, and precisely via modified adaptive learning rule ANN observer. Shrink the effect of any class of disturbances on closed loop system response via ANN observer based variable switching boundary limits of a sliding mode control scheme. Attain both reduced over/undershoot and settling time simultaneously with negligible chattering phenomenon in control signals. Minimize the effect of fluctuations in plant operating characteristics and also validated on IEEE 39 bus large power system.