Empirical Evaluation of Microgrid Fault Identification Models from a Statistical Perspective

Abstract

Microgrid fault identification models are developed via integration of extensive data collection, pre-processing of collected data, current & voltage segmentation, feature representation, identification of variant feature sets, their classification & post-processing operations. Existing models that perform microgrid fault identification are either highly complex, or cannot be applied for heterogeneous fault types. Moreover, these models also showcase large variance in terms of their qualitative & quantitative performance levels. Due to these issues, it is difficult for researchers to identify optimum models for their performance-specific deployment use cases. To overcome these issues, a detailed review of different microgrid fault detection & mitigation models in needed, which can evaluate their performance in terms of qualitative & quantitative parameters. Thus, this text initially discusses characteristics of some of the recently proposed microgrid fault detection models in terms of their functional nuances, application specific advantages, deployment specific limitations, and context-specific future research scopes. After referring this discussion, it was observed that linear models that incorporate pattern recognition are highly useful for fault pre-emption and mitigation purposes. This text also compares these models in terms of their accuracy of detection, delay needed for fault identification, computational complexity, deployment cost, and scalability metrics.