Application of Artificial Neural Network Based Improved MPPT System for Solar Photovoltaic System Under Variable Irradiation Condition
The Indian government has set an ambitious goal of satisfying the country's fast expanding demand, which is currently fulfilled primarily by coal and oil. By 2030, the government wants renewable energy to account for 40% of total energy generation. New Delhi is working hard to develop 175 GW (GW) of renewable energy by 2022, with an aim of 100 GW of solar power and 100 GW of wind power. Increasing environmental concerns, dwindling fuel supplies, and rising energy demands have shifted our focus to an idealistic future based solely on renewable and non-polluting energy supply technology. Photovoltaic (PV) power generation is becoming more popular in contrast to other renewable energy sources due to advantages such as ease of access, low cost, less environmental contamination, and lower maintenance costs. The Maximum Power Point Tracking (MPPT) methodology was utilised to reduce the impacts of changing external conditions and improve the power delivered by the PV system. In order to enhance energy generation, it keeps track of the panel's maximum power output. MPPT controllers have a simple design, low cost, strong performance characteristics with minimum output power variability, and the ability to monitor in changing situations easily and rapidly. An MPPT system based on an enhanced neural network was developed in the current study. The suggested system provides a lower transient and steady state response than existing software computing technologies and traditional power point monitoring approaches. Extensive study has been undertaken on a freestanding solar photovoltaic system, and a model for system analysis has been developed. In comparison to traditional power point monitoring approaches, the suggested system had a lower transient and steady state response.