Title: Identification of partial shading in solar panels using genetic algorithms, simulated annealing, and particle swarm optimisation
Authors: Mohamed A. Awadallah
Addresses: Centre for Urban Energy, Ryerson University, Toronto ON, Canada
Abstract: The paper presents an optimisation-based methodology to identify the partial shading condition in solar panels feeding voltage-source inverter (VSI) induction motor drives. Performance characteristics of the panel are used to determine the shading factor and number of shaded modules. An optimisation problem is formulated and solved independently by genetic algorithms (GA), simulated annealing (SA), and particle swarm optimisation (PSO) techniques. The proposed method is able to distinguish between partial shading operation and uniform reduction of solar irradiation on the whole panel. Results show the effectiveness of the proposed method to identify partial shading of solar panels. A comparison study shows that PSO obviously outperforms both GA and SA in partial shading identification, especially in robustness and convergence time. The impact of partial shading on the operation of the induction motor drive and water pump load is found minor.
Keywords: solar panels; partial shading; induction motor drives; genetic algorithms; GAs; simulated annealing; particle swarm optimisation; PSO; solar energy; solar power; voltage-source inverters; VSI induction motor drives; water pump load.
International Journal of Renewable Energy Technology, 2016 Vol.7 No.2, pp.125 - 147
Received: 18 Jun 2014
Accepted: 21 Jun 2015
Published online: 23 Apr 2016 *