Intelligent Fault Detection and Classification Schemes for Smart Grids Based on Deep Neural Networks

Abstract

Effective fault detection, classification, and localization are vital for smart grid self-healing and fault mitigation. Deep learning has the capability to autonomously extract fault characteristics and discern fault categories from the three-phase raw of voltage and current signals. With the rise of distributed generators, conventional relaying devices face challenges in managing dynamic fault currents. Various deep neural network algorithms have been proposed for fault detection, classification, and location. This study introduces innovative fault detection methods using Artificial Neural Networks (ANNs) and one-dimension Convolution Neural Networks (1D-CNNs). Leveraging sensor data such as voltage and current measurements, our approach outperforms contemporary methods in terms of accuracy and efficiency. Results in the IEEE 6-bus system showcase impressive accuracy rates: 99.99%, 99.98% for identifying faulty lines, 99.75%, 99.99% for fault classification, and 98.25%, 96.85% for fault location for ANN and 1D-CNN, respectively. Deep learning emerges as a promising tool for enhancing fault detection and classification within smart grids, offering significant performance improvements.

Recent Trends and Issues of Energy Management Systems Using Machine Learning

“…To predict faults, a fuzzy-based fault diagnosis method has been employed at the edge of the network, gathering signals from cloud-based smart sensors, and achieving high accuracy. Furthermore, using a 1D-convolutional neural network (CNN) for fault classification has been shown to improve the performance of fault detection and classification compared to traditional methods [49].…”

Artificial Intelligence for Management of Variable Renewable Energy Systems: A Review of Current Status and Future Directions

“…For smart grids, fault detection and classification are a critical component of self-healing and mitigating system failures. ANNs have been studied for intelligent fault detection, classification, and localization, with results indicating high success rates, and they have the potential to significantly improve power system reliability [123].…”

Intelligent fault detection strategy for knowledge entities in fault semantic networks of distribution network based on siamese networks

“…Confronting these challenges is of paramount importance, necessitating focused research efforts towards the development of intelligent fault detection methodologies that are specifically designed for the nuanced requirements of knowledge entities in semantic networks [ 18 , 19 ]. In this paper, we introduce a cutting-edge fault detection approach for knowledge entity variables within distribution networks.…”