Power Quality Disturbance Classification Based on Parallel Fusion of CNN and GRU

Effective identification of complex power quality disturbances (PQDs) is the premise and key to improving power quality issues in the current complex power grid environment. However, with the increasing application of solid-state switches, nonlinear devices, and multi-energy system generation, the power grid disturbance signals are distorted and complicated. This increases the difficulty of PQDs identification. To address this issue, this paper presents a novel method for power quality disturbance classification using a convolutional neural network (CNN) and gated recurrent unit (GRU). The CNN consists of convolutional blocks, some of which come with a squeeze-and-excitation block (SE), and is used to extract the short-term features from PQDs, where the convolutional block is used to capture the spatial information from PQDs and the SE is used to enhance the feature extraction capability of the convolutional neural network. The GRU network is designed to capture the long-term feature from PQDs, and an attention mechanism connected to GRU's hidden states at different times is proposed to improve the GRU's feature capture ability in long-term sequences. The CNN and GRU are parallelly arranged to perceive the same PQDs in two different views, and the feature information extracted from them is fused and transmitted to the Softmax activation layer for classification. Based on MATLAB-Simulink, a typical multi-energy-source system is constructed to analyze PQDs, and twelve PQDs are simulated to validate the proposed method. The simulation results show that the proposed method has higher classification accuracy in both single and hybrid disturbances and significant advantages in noise immunity.