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Energies 2017, 10(8), 1213; https://doi.org/10.3390/en10081213

Photovoltaic Cell Parameter Estimation Using Hybrid Particle Swarm Optimization and Simulated Annealing

School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
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Received: 29 June 2017 / Revised: 3 August 2017 / Accepted: 10 August 2017 / Published: 15 August 2017
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Abstract

Accurate parameter estimation of solar cells is vital to assess and predict the performance of photovoltaic energy systems. For the estimation model to accurately track the experimentally measured current-voltage (I-V) data, the parameter estimation problem is converted into an optimization problem and a metaheuristic optimization algorithm is used to solve it. Metaheuristics present a fairly acceptable solution to the parameter estimation but the problem of premature convergence still endures. The paper puts forward a new optimization approach using hybrid particle swarm optimization and simulated annealing (HPSOSA) to estimate solar cell parameters in single and double diode models using experimentally measured I-V data. The HPSOSA was capable of achieving a global minimum in all test runs and was significant in alleviating the premature convergence problem. The performance of the algorithm was evaluated by comparing it with five different optimization algorithms and performing a statistical analysis. The analysis results clearly indicated that the method was capable of estimating all the model parameters with high precision indicated by low root mean square error RMSE and mean absolute error MAE. The parameter estimation was accurately performed for a commercial (RTC France) solar cell. View Full-Text
Keywords: photovoltaic cell; parameter estimation; particle swarm optimization (PSO); simulated annealing (SA); hybrid PSOSA; metaheuristic photovoltaic cell; parameter estimation; particle swarm optimization (PSO); simulated annealing (SA); hybrid PSOSA; metaheuristic
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Mughal, M.A.; Ma, Q.; Xiao, C. Photovoltaic Cell Parameter Estimation Using Hybrid Particle Swarm Optimization and Simulated Annealing. Energies 2017, 10, 1213.

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