Optimal design of renewable sources of PV/wind/FC generation for power system reliability and cost using MA‐RBFNN approach

Summary This manuscript proposes a hybrid control method for optimal design of hybrid renewable energy sources (RES) like solar photovoltaic (PV)‐wind energy and fuel cell (FC) for power system reliability and cost. The proposed hybrid method is the joint execution of Mayfly algorithm (MA) and radial basis function neural network (RBFNN) technique and hence, it is named as MA‐RBFNN. Here, the MA‐RBFNN algorithm is utilized to minimize the cost function and maximize the reliability of the power system. The major objective of the work is “minimizing the cost under several conditions like, wind, PV and FC and also it reliable for the supply of load, with high reliability and less cost.” Here, the MA approach is trained with the inputs viz, the previous instantaneous energy of the obtainable sources, the required reliability, and the related target reference source power. The MA‐RBFNN control scheme makes the gain parameters to provide the optimal control signal and manage the energy of the RES. Finally, the proposed MA‐RBFNN method gives the optimum control of outage components on reliability with system cost. The MA‐RBFNN method is executed in matrix laboratory/Simulink site and the performance is analyzed with different existing methods.