A Novel Classification and Identification Method for Complex Power Quality Disturbances Based on Confidence-Enhanced Guided Multilabel Learning

Abstract

The practical applications of current PQD classification and identification algorithms are limited due to their often poor recognition of disturbance types outside the training sets. To deal with this issue, a novel classification and identification method for complex PQDs based on confidence-enhanced guided multilabel learning (CEGML) is proposed in this article. Initially, the fully convolutional network (FCN) is employed to extract deep and shallow features, which are subsequently fused. In addition, the concept of the simple parameter-free attention module (SimAM) is combined with the gated recurrent unit (GRU) to design the synchronized recurrent attention model (SRAM), enhancing the extraction of key features and the model’s ability to fit labels. At the same time, a linear layer is utilized to predict the confidence level of each label within the PQDs. Lastly, a confidence label is designed to differentiate between single disturbances and multiple disturbances, and a confidence enhancement factor is set to elevate the confidence levels of each label in the case of multiple disturbances, enabling the network to accurately identify more complex combinations of disturbance types beyond the training sets, even with limited and basic training data. Simulation and practical test results demonstrate that the PQD classification and identification method based on CEGML proposed in this article effectively recognizes complex disturbance types not included in the training set, achieving higher identification accuracy than existing methods.