Non-intrusive load identification method based on VMD and PSO-SVM

Non-intrusive load monitoring is one of the important technologies of intelligent power consumption, among which load decomposition and identification is an important link to realize the technology. In view of the advantage of variational mode decomposition (VMD) in signal processing, a load identification algorithm based on variational mode decomposition and fast independent component analysis (VMD-FastICA) and variational mode decamposition-entropy-particle swanm optimization fo optimizing support vector machines (VMD-Entropy-PSOSVM) is proposed. The total load power signal is decomposed using VMD to obtain multiple intrinsic mode functions (IMF), and then, the IMF is reconstructed based on the cliff criterion and singular value decomposition to virtualize single-channel blind source separation into multi-channel blind source separation into fast independent component analysis (FastICA) for load signal separation. Then, the energy and energy entropy of the modal components of the decomposed load waveform are obtained, and the multi-dimensional feature matrix input is constructed to establish a particle swarm optimization-support vector machine particle swarm optimization for optimizing support vector machines (PSO-SVM) for classification and identification of the load. The experimental algorithm is simulated using the reduced electricity dataset (REDD), and it is verified that the algorithm has better results in both decomposition and recognition compared to other algorithms.