Evaluation of DC Power Quality Based on Empirical Mode Decomposition and One-Dimensional Convolutional Neural Network

Abstract

With the rise in the use of DC distributed energy resources and the growth of DC electricity load, the difficulty in improving DC power quality has become an important research direction. The research on DC power quality has an important impact on the development of DC power distribution theory and technology. In this paper, an evaluation method that combines empirical mode decomposition (EMD) with a one-dimensional convolutional neural network (1D-CNN) of DC power quality is proposed. As a method of data preprocessing, EMD decomposes the original electrical signal into several intrinsic mode functions (IMFs). Then, the 1-D CNN with a residual module is used to train the data obtained from EMD and conducts a comprehensive evaluation with different levels. In addition, the proposed network was compared with other state-of-the-art deep neural networks, and the experiment proved its effectiveness. Finally, an example analysis is carried out with the data provided by the Gree Photovoltaic Direct-driven Inverter Multi VRF (variable refrigerant flow) System to show the validity of the proposed method for evaluating DC power quality in a real case. INDEX TERMS DC power quality, photovoltaic direct-driven inverter multi VRF, empirical mode decomposition, one-dimensional convolutional neural network.

A power quality disturbances classification method based on multi-modal parallel feature extraction

“…The recognition of PQD consists of two parts: feature extraction and classification. Common feature extraction methods include wavelet transform 4 , 5 , empirical mode decomposition 6 , 7 , variational mode decomposition 8 , S transform 9 , 10 , etc. According to the characteristics of the appropriate classification algorithm, such as: support vector machine 11 – 14 , decision tree 15 , artificial neural networ 16 , 17 , etc.…”