The effectiveness of a hybrid MPPT controller based on an artificial neural network and fuzzy logic in low-light conditions

Technological advancement and economic progress have made power consumption a big issue. Concern is growing as traditional energy sources dwindle. In the future, numerous fossil fuels will be insufficient to satisfy human requirements. This motivates research into the feasibility of using renewable energy sources. Renewable energy sources offer a multitude of advantages, including their cost-effectiveness, lack of environmental impact, and sustainable nature. Sunlight is currently the most prevalent source of energy because it is both free and readily accessible. Consequently, photovoltaic (PV) energy is gaining importance in the field of electricity generation. Tracking the maximum power point (MPP) in a solar PV system is challenging due to varying meteorological conditions (irradiance and temperature). To maximise the efficiency of a solar power installation, it is essential to monitor the PV array's optimum power point. This analysis compares the perturb and observe (P&O), fuzzy logic (FL), and suggested artificial neural network (ANN)-fuzzy strategy for determining the MPP of a PV system with minimal radiation exposure. Simulation results show that at low irradiation levels, the proposed ANN-fuzzy maximum power point tracking (MPPT) unit controller is superior to the FL and P&O MPPT controllers in terms of tracking maximum power.