Photovoltaic Radiation Estimation based on Deep Fuzzy Neural Network: Case Study of a Dasht-e Lut in Iran

Phtovoltaic system is a renewable energy which works with solar radiation. Collecting energy and use it in any condition of days or nights need to use powerful controller with good efficiency and performance. There are many techniques to improve photovoltaic efficiency which one of them is Maximum Power Point Tracking (MPPT) which have load matching mechanism between their cells and the load. We can use photovoltaic system to store energy in Iran due to many desert area with good solar radiation in days. This job can supply electricity of large urban area and we can remove distributed power supply in areas. The key problem in Iranʼs photovoltaic systems is that it does not achieved much power and radiation which is due to the fact that the power of photovoltaic cells is affected by various weather conditions such as sun radiation and temperature or rainy and cloudy weather. So, there is a need to provide a suitable controller for good performance evaluation in the field of energy and radiation which is called the Maximum Power Point Tracking. The aim of this work is applying an isotropic method based on fuzzy logic controller with developed Deep Neural Networks to optimize MPPT of Iranʼs photovoltaic systems based in desert area climate condition. The case study is Dasht-e Lut in Iran which is a desert area with special climate. The main reason to propose this method is some defects in previous controllers such as low stability, sensitive to a high frequency noise and low efficiency and of course, for optimizing energy and radiation for predicting daily energy consumption and production. Simulation done in MATLAB platform and results indicated that the proposed controller has more efficiency and better dynamic response in comparison to other methods.