Non-Intrusive Load Identification Based on Complex Spectrum and Support Vector Machine

Abstract

Aiming at the problem that the load identification accuracy of non-intrusive load monitoring (NILM) is greatly affected by the power of loads and the number of background loads, a non-intrusive load identification method based on the current complex spectrum and support vector machine (SVM) is proposed. Through the high-frequency sampling of the load's voltage and current, the complex spectrum of the current is extracted by the fast Fourier transform (FFT), and the multi-class SVM load identification model is established and optimized to realize the non-intrusive load identification. The algorithm is verified using the PLAID datasets, and the load identification accuracy of the algorithm is compared with SVM classifiers based on total harmonic distortion rate (THD), harmonic component ratio and harmonic amplitude. The results of the experiments show that the proposed method not only improves the identification accuracy of low-power loads, but also has higher identification accuracy and better identification robustness of switching load in multi-load scenarios.