A Data Driven Fault Detection Approach with an Ensemble Classifier based Smart Meter in Modern Distribution System

The operation of the distribution grids is constantly being threatened by occurrence of faults caused by natural instances such as lightning and hurricane, ageing of distribution system components, human errors, etc. This faults are unpredictable and dangerous. Moreover, the introduction of distributed generations (DGs) in modern distribution networks have made the detection of these faults more complicated. Thus, to maintain the continuity of the power supply, a fast and accurate fault detection strategy is required to isolate the fault. In view of this, the paper presents a data driven fault detection approach with an ensemble classifier based smart meter in modern distribution system. To achieve this, a random forest (RF) based fault detection algorithm is programmed within the smart meter. At first, the magnitude of maximum angular difference between positive and zero sequence component of the current at the DG bus is computed. The computed value is then fed to a trained RF classifier for detecting fault conditions. The algorithm is developed for a modified IEEE 13 node test feeder in MATLAB (SIMULINK) platform. The greatest advantage of the proposed algorithm is the non-requisite of additional hardware or software kit for fault detection. The algorithm undergoes several test conditions to showcase its effectiveness. The algorithm depicts a high accuracy of 98.95%. The results signifies that the algorithm is robust, reliable and accurate with an additional benefit of augmenting situational awareness.