A non-intrusive fine-grained load identification method based on three-dimensional voltage–current trajectories

Addressing issues such as high hardware costs, low recognition accuracy, and the inability to achieve fine-grained equipment classification, a non-invasive load fine-grained recognition system based on FPGA was developed and tested on a Linux system for online training. A three-dimensional (3D) image construction method based on color coding of voltage–current (V-I) trajectories is proposed to preprocess the collected voltage and current data, allowing for the distinction of features of various electrical equipment in multiple dimensions. First, high-frequency sampling data is preprocessed to extract the V-I trajectory and higher harmonic characteristics of the load. Then, the V-I trajectory is processed using RGB color coding and fused with higher-order harmonic features to construct a 3D image. This results in a 3D color V-I trajectory image that incorporates both color and harmonic features. Finally, the improved ResNet50 network is employed to identify the load characteristics, and the method is validated using the PLAID dataset and measured data. The load identification method achieves an accuracy rate of over 98%, enhancing the information conveyed by the V-I trajectory and improving the uniqueness of load characteristics, thereby enabling fine-grained equipment identification. This advancement holds significant implications for energy conservation and emission reduction in household electricity consumption, as well as for eliminating potential safety hazards associated with electrical equipment.