Leveraging Deep Learning Algorithms for Predicting Power Outages and Detecting Faults: A Review

Power outage prediction and fault detection play crucial roles in ensuring the reliability and stability of electrical power systems. Traditional methods for predicting power outages and detecting faults rely on rule-based approaches and statistical analysis, which often fall short of accurately capturing the complex patterns and dynamics of power systems. Deep learning algorithms, with their ability to learn automatically representations from large amounts of data, have emerged as promising solutions for addressing these challenges. In this literature review, we present an overview of deep learning algorithms applied to power outage prediction and fault detection. The purpose of this literature review is to explore the uses, effectiveness and advantages and disadvantages of utilizing deep learning algorithms in this domain. Various deep-learning models were explored in the context of power outage prediction and fault detection. Convolutional Neural Networks (CNNs) are effective in analyzing spatial dependencies and patterns in power system data, such as voltage levels and load distributions. Recurrent Neural Networks (RNNs), particularly Long Short Term Memory (LSTM) networks, excel in capturing temporal dependencies and patterns in time series data such as power demand and line currents. Generative Adversarial Networks (GANs) offer a unique approach by generating synthetic power system data for training purposes. This literature review involve collecting historical power system data from various sites. Deep-learning algorithms demonstrated promising results in power outage prediction and fault detection. It achieved a high accuracy rate of 95% in predicting power outages and accurately classified various fault types with an average precision of 92%. The findings highlight the advantages of deep learning algorithms in power outage prediction and fault detection. This literature review provides a comprehensive review of deep learning algorithms in the context of power outage prediction and fault detection.