IMPROVED HYBRID PUMPING SYSTEM WITH STOCKAGE BATTERY BASED ON PARTICLE SWARM ALGORITHM
Keywords:
Hybrid PV-Wind energy, Particle swarm algorithm, Direct torque control, Ac load, Dc loadAbstract
The combination of several renewable energy sources makes it possible to optimize the production of electricity with the aim of improving the human, social, economic, and environmental conditions of daily life. This study focuses on the use of a very promising meta-heuristic optimization technique, particle swarm optimization (PSO), applied to systems powered by hybrid renewable energy sources. This work is divided into two parts, the first part aims to present the maximum power point (MPPT) PSO MPPT-PSO technique. The latter is applied to a photovoltaic (PV) system connected to a resistive load (dc load). The MPPT-PSO technique has been compared to another meta-heuristic technique called MPPT-GWO (grey wolf optimization).
The second part aims to optimize the functioning of a system composed of a wind turbine/PV battery connected to an induction motor considered as ac load. Thus, we take a closer look at the regulation as well as its optimization using the PSO. We are particularly interested in the setting coefficients of two PI regulators, the first PI regulator is used to keep the dc voltage constant, and the second is to regulate the speed of the induction motor. To confirm the effectiveness of the proposed approach, we will compare it to the techniques of the genetic algorithm (GA) and the bat (BAT) algorithm.
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