MACHINE LEARNING BASED SIMULATION FOR FAULT DETECTION IN MICROGRIDS

Fault detection (FD) is crucial for a functioning microgrid (MG) but is particularly challenging since faults can stay undetected indefinitely. Hence, there is a need for real-time, accurate FD in the early phase of MG operations to mitigate small initial deviations from nominal conditions. To address this need, we propose an FD framework for MG operational planning. Our proposed framework is synthesized from i) a dataset generated by introducing faults into an MG with PV cells, ii) processing the dataset to train various machine learning (ML) models for FD, iii) benchmarking the resulting FD models using classification metrics, and iv) applying an appropriate fault mitigation strategy. Although noisy measurements were present during the experiment due to variations in ambient temperature and solar irradiance, our proposed FD model is shown to be both computationally efficient with an average training time of 1.76 seconds and accurate with a weighted F-score of 0.96.