Non-Invasive Load Identification Algorithm Based on Color Coding and Feature Fusion of Power and Current - Xiang, Yuzhu; Ding, Yifan; Luo, Qiang; Wang, Puyu; Li, Qing; Liu, Haojie; Fang, Kaijie; Cheng, Hanmiao (Frontiers in Energy Research, 2022)
In the traditional non-invasive load monitoring (NILM) algorithms, the identification accuracy is enhanced with the increased network scale while sacrificing the calculation speed, which restricts the efficiency of the load identification. In this paper, a multi-feature (active/reactive power and current peak-to-peak value) fusion algorithm, which can achieve enhanced identification accuracy with smaller network scale and the calculation speed being maintained, is proposed. The features of the power and current amplitudes of the loads are transformed into the values of red-green-blue (RGB) color channels by the color coding and then fused into the V-I trajectory features. After that, the true-color feature image with higher discrimination is generated and input into the convolutional neural network (CNN). The testing results on PLAID data set indicate that in comparison with the traditional load identification algorithm, the algorithm proposed in this paper performs higher identification accuracy with smaller neural network parameter scale, which significantly improves the identification efficiency.