Machine learning based adaptive fault diagnosis considering hosting capacity amendment in active distribution network

Augmentation of distributed energy resources (DERs) safely in distribution system termed as hosting capacity (HC) is one of the prominent needs to achieve energy sufficiency with minimum emission. However, any amendment in HC over premeditated injection sets up challenges in perspective of situational awareness (SA) of networks for precise decision-making related to fault prediction and location. In this work, authors propose histogram-based gradient boost (HGB) algorithm, an accurate machine learning (ML) technique for fault type detection and location. Due to the unique characteristic of noise cancelation, spectral-kurtosis is utilized for extraction of features of the faulted transient signals. For improved competence of the process, optimized feature importance values are considered. In order to study the efficacy of the proposed method, HC of the network is altered, leading to up-gradation of network parameters. These upgraded parameters are used for retraining the proposed ML algorithm for desired SA, with perception, comprehension, projection, and accurate decision making. The authors also considered other ML techniques to showcase a comparative study with the HGB. The entire analysis is tested on reconfigured IEEE-33 bus distribution system developed in Typhoon HIL real-time simulator. The proposed methodology is also meticulously compared with existing literature to establish its excellence.