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With the increase in the attention towards renewable sources of energy, the integration of solar PV Systems with Power Grids is increased substantially. This is of particular importance in those areas which receives good temperature and sunlight for most of the days in the year, like the states of northern India. As the power grid is catering the power need of a number of industries including the critical ones like healthcare and production, it is critical to have an accurate forecast of the solar energy output. The parameter that affects the output to greatest extent is the failure rate. In this research paper, high frequency sensor data is analyzed for Fault Detection (FD). Data consisting of 2.2X106 measurements from a GPV (Grid Connected PV), both under Maximum PPT (MPPT) and Intermediate PPT (IPPT) modes is considered. The seven types of faults which are considered in the dataset include:open circuit, voltage sags, partial shading, inverter, current feedback sensor, and MPPT/IPPT controller in boost converter faults. The regression model isproved to be computationally efficient and very accurate for successful FD under large temperature and irradiancevariations with noisy measurements.