FAST FAULT LOCATION METHOD FOR A DISTRIBUTION SYSTEM WITH HIGH PENETRATION OF PV
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Jimenez Aparicio, Miguel
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Abstract
Distribution systems with high levels of solar PV may experience notable changes due to external conditions, such as temperature or solar irradiation. Fault detection methods must be developed in order to support these changes of conditions. This thesis develops a method for fast detection, location, and classification of faults in a system with a high level of solar PV. The method uses the Continuous Wavelet Transform (CWT) technique to detect the traveling waves produced by fault events. The CWT coefficients of the current waveform at the traveling wave arrival time provide a fingerprint that is characteristic of each fault type and location. Two Convolutional Neural Networks are trained to classify by node and type any new fault event in each measuring device. The method doesn’t require communication between them. The results show that for multiple fault scenarios and solar PV conditions, high accuracy for both location and type classification can be obtained, even in scenarios where measuring noise is present.
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2020-12-02
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