Parameter Estimation of Solar Photovoltaic Cell and Proton Exchange Membrane Fuel Cell Using Hybrid Particle Swarm Optimization Dingo Optimizer Algorithm

Abstract

In the recent few years, renewable energy sources have gained a lot of attention due to depletion of fossil fuels, increasing demand of energy as well as due to various advantages of renewable energy sources such as availability, cleanliness and reliability. Solar energy and Proton Exchange Membrane Fuel Cells (PEMFC) are most effective and promising sources of renewable energy. Hence, the effective and precise parameter estimation of solar photovoltaic (PV) cells and PEMFC is extremely crucial for precise evaluation, control of PV systems as well as simulation and development of highly efficient fuel cells. In this work, a new hybrid (HPSODOX) algorithm based on two widely used meta-heuristic algorithms that is, Particle Swarm Optimization (PSO) and Dingo Optimizer (DOX) is developed for parameter estimation of four diode model, modified four diode model of solar PV cell under different operating conditions and PEMFC. On comparison with other well-known meta-heuristic algorithms, the results obtained show that the developed hybrid (HPSODOX) algorithm has better performance in terms of accuracy, precision and efficiency.