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Fig. 11 provides a schematic representation of the suggested artificial intelligence control of energy management PV systems. A photovoltaic (PV) generator, a battery management system (BMS), a boost converter, and an alternating current (AC) load fitted with a neurofuzzy control system make up the primary elements of the power system.
In 11 the energy management system was implemented for a stand-alone hybrid system with two sustainable energy sources: wind, solar, and battery storage. To monitor maximum energy points efficiently, the P&O algorithm was used to control photovoltaic and wind power systems. The battery storage system is organized via PI controller.
The utilization of artificial intelligence (AI) is crucial for improving the energy generation of PV systems under various climatic circumstances, as conventional controllers do not effectively optimize the energy output of solar systems. Nevertheless, the performance of PV systems can be influenced by fluctuations in meteorological conditions.
This would involve developing advanced intelligent techniques that can predict and analyze real-time data to ensure an efficient energy management system. Additionally, the integration of intelligent algorithms in controller design may improve the dynamic performance of power systems.
Several recently published research works emphasize significant aspects of wind, PV, and energy storage system (ESS) integration in power systems. In Kumar (2022), a control approach is proposed to achieve maximum point tracking (MPPT) of a hybrid wind–PV system.
An intelligent generation control strategy for ESS is proposed in Mahmoud et al. (2019) that focuses on enhancing battery lifetime, optimal power distribution, and minimizing costs. The charging/discharging time for batteries is analyzed by the FLC scheme.
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