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Improving Solar Power System's Efficiency Using Artificial Neural Network

Renewable energy sources are the best solution to reduce dependence on conventional and nonrenewable sources that also cause environmental pollution. With the increase in the prices of conventional fuels globally, the increase of gas emissions resulting from its use, and the impact on the environment and the global climate; various renewable energy sources have emerged as an alternative to traditional sources of energy. Ssolar energy is one of the most important renewable energy sources used globally; The technology used is relatively simple and uncomplicated, compared to the technology used in other renewable energy sources. Solar energy is the ideal alternative to conventional energy in the Gaza Strip in Palestine, due to the relatively high solar radiation in the region, which makes its application more practical and economical compared to other parts of the world. Palestine has higher rates of total solar absorption, ranging from 4-8 kWh / m2 per day, which is high compared to other countries. This paper offers a solution to the Gaza Strip, which has suffered from a severe power shortage due to the Israeli blockade, by using solar PV as a backup system and a good alternative to diesel generators. Photovoltaic cells convert the sunlight into DC electric power. Where the major problem of the PV is that with the changing of atmospheric conditions, the voltage is changing, and so the maximum power is changing. We know that PV systems are still very expensive; therefore, the Artificial Neural Network controller is designed for the converter to secure the maximum power to the system to increase the efficiency of it. ANN controller is designed to bring out the maximum power from the solar panel. This paper uses a controller that utilizes MPPT technique to increase the efficiency of converting solar energy into electrical energy by modifying the duty cycle of Puls Width Modulation (PWM) for the boost converter to obtain the MPP energy from solar cells at all times. A solar panel applied and their components are individually modeled in the MATLAB / SIMULINK program to simulate a real PV system behavior, then an MPPT technique, including DC/DC boost converter was designed. Then an ANN controller is designed and then trained to get the maximum power 65 point from the solar panel at different atmospheric conditions. Also, this controller is compared with the direct connected method without an MPPT controller. The system performance is measured by changing solar radiation and temperature of the PV module. The findings indicate that MPPT ANN has a fast response to the variability and is more efficient, which means more power transfer to the system. The outcome shows that the photovoltaic module directly associated without MPPT technique has less efficiency because of the mismatch between the photovoltaic module and the load.