Power Quality Disturbance Identification and Optimization Based on Machine Learning

Abstract

In order to improve the electrical quality disturbance recognition ability of the neural network, this paper studies a depth learning-based power quality disturbance recognition and classification method: constructing a power quality perturbation model, generating training set; construct depth neural network; profit training set to depth neural network training; verify the performance of the depth neural network; the results show that the training set is randomly added 20DB-50DB noise, even in the most serious 20dB noise conditions, it can reach more than 99% identification, this is a tradition. The method is impossible to implement. Conclusion: the deepest learning-based power quality disturbance identification and classification method overcomes the disadvantage of the selection steps of artificial characteristics, poor robustness, which is beneficial to more accurately and quickly discover the category of power quality issues.

Detection and Classification of Power Quality Disturbances Using Stock Well Transform and Improved Grey Wolf Optimization-Based Kernel Extreme Learning Machine

“…With advancements in sensor technology and data analytics, real-time monitoring of PQDs has become more feasible. Several methods and technologies, such as deep learning (DL) [4], machine learning (ML) [5], and the internet of things (IoT) [6], have been proposed for power quality research. To detect and classify PQ disturbance, a range of techniques have been developed, including time-domain analysis, frequency-domain analysis, statistical methods, and artificial intelligence (AI) techniques.…”