PowerCog: A Practical Method for Recognizing Power Quality Disturbances Accurately in a Noisy Environment

Currently, with the growing applicability of nonlinear electrical devices, power quality disturbances (PQDs) often occur in power systems. Previous works usually extracted statistical features from electrical signals manually and constructed classifiers with traditional machine learning methods for PQD monitoring. Furthermore, noisy tags or unlabeled data (different from noisy signals) are usually ignored in the traditional training stage, and these methods fail to meet the high accuracy and automation demands of real-world scenarios. To overcome the shortcomings of existing methods, this article proposes a practical method called PowerCog for accurately recognizing PQDs in noisy environments. First, an input voltage waveform signal is divided into several intrinsic mode functions by an empirical wavelet transform, and these functions are then aligned into columns to form a matrix. Second, tritraining is utilized for label refactoring to improve the generalization ability of the model in a noisy environment. Then, an optimized convolutional neural network structure combined with principal component analysis is deployed to extract and select the universal features automatically. Finally, a support vector machine classifier is constructed to recognize PQD patterns. Several comparative experiments are performed to verify the effectiveness and accuracy of PowerCog in complex environments.

Automated summary

This article proposes a practical method called PowerCog for accurately recognizing PQDs in noisy environments, utilizing techniques such as wavelet transform, tritraining, neural network structures, and a support vector machine classifier. The method improves accuracy and automation in real-world scenarios through feature extraction and selection. Multiple comparative experiments verify the effectiveness of PowerCog in complex environments.