Intelligent Fault Detection Algorithm of PV System Based on Cloud Computing

Using Photovoltaic (PV) panel systems is increasingly gaining popularity as a viable alternative energy source. In order to optimize the utilization of dependable energy resources, the photovoltaic panel system must be maintained in an optimal state. Continuous maintenance and monitoring are necessary in this context. However, in the event of fluctuations in weather patterns affecting the reliability of energy production, it becomes necessary to determine if these changes are within the expected range owing to environmental factors or if they deviate from the norm due to issues such as malfunctioning equipment, shading, or accumulation of dust on solar panels. To address this need, the implementation of an intelligent monitoring system is essential. This research includes an integrated system for remote monitoring via the use of cloud computing techniques for remote monitoring through temperature, radiation, voltage, and current sensors, as well as analyzing the type of fault using Artificial Neural Network (ANN) and fuzzy theory in the MATLAB program. Ensuring energy continuity and reducing time, effort, and cost for maintenance through early fault detection and diagnosis are crucial for maintaining the performance and longevity of PV systems and for rapid decision-making.