Controlling the energy management PV system using AI techniques

Abstract

In light of the critical need to reduce carbon emissions and address the urgent problem of climate change, renewable energy systems like photovoltaic (PV) systems have grown in importance. Static converters employ reactive energy to alter the current in solar power systems. Energy storage batteries improve power quality by storing voltage and current, and a proportional-integral controller controls reactive and active powers. Batteries also have an effect on steady-state error. Artificial intelligence (AI) is essential for enhancing PV system energy production in different climates, because traditional controllers fail to maximize solar systems' energy output. That being said, changes in the weather may impact how well PV systems work. This research introduces an innovative method for combining solar photovoltaic (PV) systems with efficient input performance by use of adaptive neuro-fuzzy inference systems (ANFIS). A controller based on fuzzy neural inference was developed and tested with respect to energy production and consumption. The relevance of artificial intelligence in energy management is shown by this research, which investigates its role in enabling a battery system to provide continuous power supply to customers. Results in energy control and management were also encouraging, according to the results.