Deep Learning Applications for Power Quality Monitoring

Abstract

Power quality tracking is very important for making sure that electrical systems work reliably and efficiently. A branch of AI called "deep learning" has become a powerful way to look at complicated and unpredictable data trends in many fields. This paper gives an outline of how deep learning can be used to measure power supply. We talk about new developments in deep learning methods like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep autoencoders, and how they might be used in power quality research. CNNs have been used a lot for power quality tracking jobs like feature extraction and classification, because they can find spatial relationships in multivariate time-series data. RNNs, especially long short-term memory (LSTM) networks, are good at figuring out how things depend on time and guessing what will happen with power quality in the future. Deep autoencoders are a way to learn without being watched that can be used to find problems and weird patterns in power systems. This lets you do preventative maintenance and find problems early. Additionally, we talk about the problems and benefits of using deep learning to check power quality. These include getting the data ready, training the models, being able to understand the results, and being able to scale the system. Deep learning has a lot of promise, but it also has some problems and unanswered research questions. For example, we need named training data, the ability for models to work in a variety of settings, and the ability to draw conclusions in real time.