Experimental validation of double-fed induction generator in oscillating water column using circulatory system-based optimization

Abstract

Abstract This paper validates the optimal operation for a grid-connected double-fed induction generator (DFIG) in an oscillating water column power plant (OWCPP). This study presents a novel optimization technique called the circulatory system-based optimization (CSBO) approach to develop six adaptive fuzzy logic controllers (AFLCs) with 30 parameters and compare them to chaotic-billiards optimization (C-BO) and genetic algorithm (GA). The proposed controller is also compared with a proportional–integral differential (PID) controller based on a self-adaptive global-best harmony search (SGHS). CSBO-based AFLCs are fully investigated under different scenarios and experimented with using a real-time interface DSP1104. The results of using CSBO–AFLCs revealed a fast time response, fast convergence, less overshoot and minimal error compared with those achieved with C-BO–AFLC, SGHS–PID and GA–AFLC during different case studies. The CSBO-based AFLCs ensure maximum power from the DFIG in an OWCPP and enhance dynamic response with very low errors. The results show that the CSBO shows better power tracking by 25% as compared with C-BO, by 45% when compared with the GA and by 56% when compared with PID. Moreover, the integral absolute errors of six controllers are investigated to demonstrate the feasibility of CSBO–AFLC. The root mean square of the errors of six controllers using CSBO is improved by 68.27% when compared with GA, by 22.57% when compared with C-BO and by 38.42% when compared with PID. These indicators demonstrate the feasibility of CSBO when compared with other algorithms with the same OWCPP.