基于Fisher主元分析和核极限学习机的非侵入式电力负荷辨识模型

非侵入式电力负荷监测与辨识是实现泛在电力物联网客户侧智能感知的关键技术.针对现有辨识模型存在的特征冗余度高、辨识准确率差、计算效率低等问题,提出了一种基于Fisher主元分析(Fisher principal component analysis,FPCA)和核极限学习机(kernel extreme learning...

Non-intrusive Power Load Identification Model Based on FPCA and KELM

Non-intrusive power load monitoring and identification is the key technology to realize customer-side intelligent sensing in the ubiquitous power Internet of things. Aiming at the problems of high feature redundancy, low identification accuracy and low calculation efficiency in the existing identification model, a non-intrusive power load identification model based on Fisher principal component analysis and kernel extreme learning machine is proposed. Firstly, the steady-state characteristics such as current, power and harmonic contents are selected as the original input variables, and the Fisher principal component analysis(FPCA), which combines Fisher score and principal component analysis, is used to eliminate the invalid features with poor separability and to eliminate the correlation among the effective features at the same time. Then, radial basis function is introduced to build the network structure, and genetic algorithm(GA) is used to optimize the model parameters such as penalty coefficient, so as to build the kernel extreme learning machine(KELM) classification model for load identification. Finally, the open TIPDM load data set is used for example analysis. The simulation results show that the proposed model has better identification accuracy and calculation efficiency than the traditional load identification models, and it can effectively identify common household loads.