الفهرس 
Chapter 1 : IntroductionOnly 14 pages are availabe for public view
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Abstract
Countries began to turn their attention to benefiting from nature and exploiting their resources to produce electricity, and this started when the global oil crisis occurred in the early seventies. One of the most used resources was solar. For a short period, solar energy was the fastest spreading technology globally, and countries had sufficient knowledge to manufacture panels. Still, there is another challenge that appeared in solar energy when problems occurred in solar energy systems and the occurrence of large fires in factories. So, the focus of countries and the world began to focus on discovering the factors that led to this problem. There were geographical problems based on temperature and solar radiation, and electrical problems resulting from incorrect connections between the solar energy system and other problems because the system did not extract the required capacity. This research shed light on the electrical problems and Studied and data are extracted for the ease of knowing the failure that occurred, and there was difficulty and inaccuracy in determining it. Artificial intelligence is used, and data are trained on it for ease and accuracy of knowing the type of failure.
As a result of the rapid expansion of photovoltaic systems, raising the efficiency and managing the maintenance became the main factors in the PV systems, and after them comes the cost and the time of repair immediately. This research provides an artificial neural network (ANN) to classify the system’s type of failure. Three types of failure have been studied: line-to-line fault with a small voltage difference, line-to-line fault with a large voltage difference, and ground fault. In addition to the fourth normal operation case, no failure is applied.