A novel recognition method for complex power quality disturbances based on Markov transition field and improved densely connected network

Abstract

In order to solve the difficulty that complex power quality disturbances (PQDs) are difficult to recognize accurately and efficiently under the new power system background, this paper proposes a novel PQDs recognition method based on markov transition field (MTF) and improved densely connected network (DenseNet). Firstly, the one-dimensional PQDs signal is mapped into the two-dimensional image with clear texture features by using MTF encoding method. Then, a DenseNet-S lightweight network is designed and the convolutional attention module (CBAM) is introduced to improve its feature extraction ability, so as to enhance the performance of the network. Finally, the images are input into the improved model for training and learning, and PQDs recognition is realized through the optimal model. In order to verify the effectiveness of the proposed method, experimental tests are carried out based on IEEE 1159 standard simulation dataset and real-world field measured signals dataset, and compared with existing recognition methods. The results show that the proposed method can effectively improve the recognition accuracy and noise robustness of complex PQDs, and has more advantages in disturbances recognition efficiency. It can meet the recognition accuracy and efficiency requirements of massive and complex PQDs events in engineering applications.