Intelligent fault detection technique for distribution network with interconnected distributed generation sources

Abstract

Machine Learning (ML) or Artificial Intelligence (AI) approaches in a distribution protection system are proposed to detect and categorize distribution network (DN) issues with Distribution Generation. This study highlights an important but generally overlooked distribution generation feature in energy transformation. Distributed generators (DGs) integrated into Distribution Network (DN) capability improve power quality, system dependability, voltage sags, and emergency backup during protracted grid outages. A technical and global analysis of DG technology's increased penetration is revealing its effects such as the utility systems undergoing elevated fault current and load flow changes, which will affect current protective relaying, particularly overcurrent relays. A protective system that can respond to the new dynamic DN is required to avoid consequences. Intelligent protection system application is a suitable distribution network solution for the explained challenge. The study conducts a thorough assessment of various methods used in detection and diagnostic systems inside distribution networks that have interconnected distributed generators (DGs). The study specifically emphasizes the implementation of intelligence-based approaches. This evaluation is conducted through a thorough literature review. A comprehensive model of a distribution network, encompassing all essential components, was constructed and subjected to simulation. This model incorporated all pertinent parameters associated with the distribution network. Subsequently, several forms of faults were intentionally introduced at a specific point inside the micro-grid. The purpose of this exercise is to gather voltage and current signals at the busbar and then the collected data is subsequently transformed into numerical values to facilitate machine-learning modelling. The implementation of intelligent approach for fault detection in distribution networks with various machine-learning techniques, allows the approach to form part of the objective to gather related signals that are pre-processed as variable features in order to extract required data that can help identify distribution network and classify faults at most efficient and accurate way. The primary objective of the protection system was to analyze the underlying failure mode, determine the fault type quickly and accurately, and identify the faulty line in the system. S shows the micro-grid's successful operation with current and voltage signals evidently shown . The current and voltage signals are transformed to numerical values for feature extraction which is key requirement for machine learning modelling. The derived variable features from feature extraction were trained and tested to validate fault diagnosis and classification to find the best machine learning fault classifier, Support vector machine (SVM) classifiers shows excellent results with 99.9% accuracy in validation and testing. These accuracy results meet the difficult requirements of a micro grid protection systems and SVM's ability to simulate non-linear decision boundaries, which is valuable in many applications.