Improved method of non-intrusive load monitoring based on compressed sensing - Bo Yuan; Hong Liu; Shaoyun Ge; Guoping Liu (International Journal of Electrical Power & Energy Systems)
Compressed Sensing (CS) has become one of the way to solve the problem of massive monitoring data in smart grid due to its characteristics of low-frequency sampling and simple compression. However, its application in non-intrusive load monitoring (NILM) has not yet been deeply studied. In order to meet the requirements of dense data acquisition or high-frequency sampling in traditional NILM, NILM based on CS (NILM-CS) and its key elements’ improvement methods have been deeply explored for the first time. Firstly, NILM-CS model is established and its key elements is analyze. Then, event detection method and eigenvalue extraction method for load analysis of NILM-CS are proposed. On this basis, two theorems of ‘sparsity proportion’ were analyzed and Multiple Extraction Direction Pursuit based on Gradient (MEDP-G) algorithm was proposed to improve the reconstruction algorithm. At the same time, the Sparse Cosine Window (SCW) matrix was constructed to improve the measurement matrix, ultimately forming an improved NILM-CS method with MEDP-G algorithm and SCW matrix suitable for NILM. Experiment shows that the NILM-CS and its improved methods proposed in this paper are reasonable. The load decomposition accuracy of NILM-CS is greater than 92 %, and the load identification accuracy is close to 90 %, whose effect is similar to traditional compression method but reduces sampling frequency by 50 %, and is superior to traditional NILM with same-frequency sampling by 1 ∼ 5 percentage points. Even better, improved NILM-CS can further improve load decomposition accuracy by 1.2 ∼ 1.8 percentage points and improve load identification accuracy by 1.3 ∼ 4.8 percentage points compared to NILM-CS, which has a load analysis effect far superior to traditional NILM ways for solving massive data problems.