A WCA-based optimization of a fuzzy sliding-mode controller for stand-alone hybrid renewable power system

Due to their nonlinearity, hybrid renewable power systems (HRPSs) are more complex in terms of their modeling, power management and control. In this context, this paper focuses on optimizing the functionality of a stand-alone HRPS through the optimization of the input/output scaling gains of the fuzzy sliding-mode controllers. This optimization is based on a new evolutionary optimization algorithm called water cycle algorithm (WCA). In fact, in this work the introduction of WCA in renewable energy optimization systems is proposed for the first time. Furthermore, depending on the HRPS operating modes, two fitness functions are proposed: the photovoltaic-generated energy, which must be maximized, and the error between the measured DC-bus voltage and its reference, which should be minimized. The stand-alone HRPS is composed of a photovoltaic generator as a main source, a fuel cell and batteries as auxiliary sources. For the verification process, simulations have been conducted under MATLAB/Simulink/SimPower Environment. The obtained results reflect the effectiveness of our proposed technique.