Verifications of Distinct defects in Solar Photovoltaic (SPV) using Computer Techniques

Solar Photovoltaic systems are really important for the world to use energy.  Solar Photovoltaic systems can have problems that make them work not so well like when part of the system is in the shade or when it gets old or when there are short circuits or when the inverter is not working right. It is very important to find and fix these problems so that Solar Photovoltaic systems keep working and last a long time. This paper is about using The Machine Intelligence of Things and new computer methods help us find and fix problems in Solar Photovoltaic systems. We can keep an eye on Solar Photovoltaic systems all the time. Analyze them because Machine Intelligence of Things uses IoT sensors with Machine Intelligence algorithms. Machine Intelligence and Learning of Machine models are good at finding patterns that show something is wrong with Solar Photovoltaic systems. These patterns are found using data from devices. When Solar Photovoltaic systems are not working right Machine Intelligence of Things and Learning of Machine models send alerts. Give us ideas on how to fix them. This way we can fix problems before they get worse. Solar Photovoltaic systems do not have to stop working for a long time. Also Solar Photovoltaic systems make energy because Machine Intelligence of Things helps us take care of them better. Machine Intelligence of Things and Learning of Machine models are very useful, for Solar Photovoltaic systems. The paper discusses various AI and computational methodologies, including supervised and unsupervised learning, neural networks, and edge computing, highlighting their effectiveness in identifying and diagnosing different SPV faults.