Improvement of Particle Swarm Optimization against Slow Variations of Environmental Conditions in Maximum Power Point Tracking

https://doi.org/10.58571/CNCA.AMCA.2022.053

Resumen: Particle Swarm Optimization (PSO) is an artificial intelligence technique applied to track the maximum power point of photovoltaic cells. It has been shown to be effective against irradiance and temperature variations, including partial shading condition. However, this technique suffers from unnecessary restarts as its threshold levels are adjusted. To overcome that situation, this document presents an improved PSO that focuses on the slow irradiance variations that trigger these thresholds. Comparative results between PSO, PSO combined with the Perturb and Observe (PSO-P&O) algorithm and the proposed PSO improvement technique are shown. As a result, the proposed improvement technique avoids unnecessary restarts of the original PSO without the steady-state oscillations around the maximum power point of the PSO-P&O.

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Martínez-Barbosa, A., Calleja-Gjumlich, J., Guerrero-Ramírez, G. & Guerrero-Ramírez, E. A study on performance and fragility of controllers: PR and PD. Memorias del Congreso Nacional de Control Automático, pp. 445-450, 2022. https://doi.org/10.58571/CNCA.AMCA.2022.053

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Sistemas Electrónicos de Potencia

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