Cognitive Edge Computing–Based Fault Detection and Location Strategy for Active Distribution Networks

Abstract

This article proposes a fault detection and location strategy based on cognitive edge computing to harvest the benefits of cognitive edge computing and address the special needs of active distribution networks (ADNs). In the proposed strategy, an ADN smart gateway is used to compile data in a central repository where it will be processed and analyzed. The intermediary smart gateway includes a protection unit where the fault detection, location, and isolation are accomplished through a combination of virtual mode decomposition (VMD), support vector machine (SVM,) and long short-term memory (LSTM)–type deep machine learning tools. The local measurements of branch currents and bus voltages are processed through VMD, and the informative decomposed components are provided as inputs to the SVM-based fault detection unit and LSTM-based fault location unit. The smart digital relay passes trip commands to the respective circuit breaker/s and submits compiled data regarding the history of faults and protection actions to the upper-level units. The findings from simulation results demonstrate the effectiveness of the proposed strategy to provide fast and accurate fault detection and protection against all types of faults and locations in the ADN.