Non-Intrusive Power Load Identification for High-Penetration Renewable Energy Grids - Liu, Bin; Tan, Zhukui; Su, Xiao (Frontiers in Energy Research, 2021)
With the popularity of renewable energy grids with high penetration rate, classic non-intrusive power load identification algorithms such as Hidden Markov Model (HMM) need to face the uncertainty caused by renewable energy generation. It will cause the load state like active power continue to change, and new state transitions appear during operation, resulting in the lack of robustness of state identification and power decomposition. Aiming to solve this problem, this paper proposes and constructs a Gaussian Mixture Model-Binary Parameter Hidden Markov Model (GMM-BPHMM) which takes into account the randomness of renewable energy power supply, clusters the load status based on active power and steady-state current to reduce the possibility of volatile clustering results from the renewable energy grid under high penetration rate; improves the Viterbi algorithm to take into account the updating HMM parameters to achieve the optimal prediction of the load state; considers the random volatility of load power bringing by renewable energy grid and constructs a power calculation optimization model and realizes the power decomposition of the load based on the principle of maximum likelihood estimation. Finally, on the basis of the public data set AMPds2, the paper generated simulation data based on the renewable energy generation model and verified the method, and the test case verified the effectiveness of the method.