Machine Learning Techniques for Fault Detection in Smart Distribution Grids

Abstract

Fault detection is critical to the resilience and operational integrity of electrical power grids, particularly smart grids. In addition to requiring a lot of labeled data, traditional fault detection approaches have limited flexibility in handling unknown fault scenarios. In addition, since traditional machine learning models rely on historical data, they struggle to adapt to new fault patterns in dynamic grid environments. Due to these limitations, fault detection systems have limited resilience and scalability, necessitating more advanced approaches. This paper presents a hybrid technique that integrates supervised and unsupervised machine learning with Generative AI to generate artificial data to aid in fault identification. A number of machine learning algorithms were compared with regard to how they detect symmetrical and asymmetrical faults in varying conditions, with a particular focus on fault conditions that have not happened before. A key feature of this study is the application of the autoencoder, a new machine learning model, to compare different ML models. The autoencoder, an unsupervised model, performed better than other models in the detection of faults outside the learning dataset, pointing to its potential to enhance smart grid resilience and stability. Also, the study compared a generative AI-generated dataset (D2) with a conventionally prepared dataset (D1). When the two datasets were utilized to train various machine learning models, the synthetic dataset (D2) outperformed D1 in accuracy and scalability for fault detection applications. The strength of generative AI in improving the quality of data for machine learning is thus indicated by this discovery.By emphasizing the necessity of using advanced machine learning techniques and high-quality synthetic datasets, this research aims to increase the resilience of smart grid networks through improved fault detection and identification.