Application of Deep Learning in Power Quality Monitoring

Abstract

Power quality monitoring is crucial for ensuring the reliable operation of electrical systems and the delivery of high-quality electricity to consumers. With the increasing complexity of modern power grids and the proliferation of nonlinear loads, traditional methods for power quality monitoring may fall short in accurately identifying and analyzing disturbances. In recent years, deep learning techniques have emerged as powerful tools for extracting complex patterns from large datasets, making them particularly well-suited for power quality monitoring tasks.

This paper provides an overview of the application of deep learning in power quality monitoring. It discusses the challenges associated with traditional monitoring methods and how deep learning algorithms, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), can address these challenges by automatically learning features from raw data. Furthermore, the paper explores various deep learning architectures and techniques that have been proposed for power quality monitoring, including waveform classification, event detection, and anomaly detection.

Additionally, the paper highlights the advantages of deep learning approaches, such as their ability to handle nonlinear and non-stationary signals, adaptability to different types of disturbances, and potential for real-time implementation. It also discusses the importance of large-scale datasets for training deep learning models and the need for standardized benchmarks and evaluation metrics in this field.