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This paper presents an evaluation of the impacts of simulation models for high impedance faults detection in microgrids with distributed generators interfaced by voltage source converters. Detailed simulation models are used to consider the high frequency switching of power electronics and, consequently, the associated harmonic distortions. Thus, for systems with a higher degradation of the current sine waveform, the high impedance fault characteristics such as nonlinearity, asymmetry, and randomness may remain hidden, compromising the performance of some existing detection techniques. Such models are not commonly used in the high impedance fault detection literature. To evaluate the impact of the simulation model&#x2019;s complexity, a pretrained deep convolutional neural networks on the imageNet database is adapted to classify current waveform based on a time-frequency representation image. The proposed method is designed and tested using a microgrid model based on the CIGRE European medium voltage distribution network benchmark. The simulation data set considers microgrid operating in islanded and connected manner, radial and ring topology, with transients at different locations of the network. The results indicate that high impedance fault detection methods for microgrid with voltage source converters generators need to be designed using detailed simulation models to achieve better accuracy, and the proposed classifier presented accurate and robust high impedance fault detection.

High Impedance Fault Detection on Microgrids Considering the Impact of VSC Based Generation