A General Harmonic Probability Model Based on Load Operating States Identification
Ying Wang, Yu Meng-Jie, Xiaoyang Ma, Xianyong Xiao, Chun Wang, Chunhui Liu
Abstract
Accurate harmonic modeling is key for the assessment and mitigation against harmonic issues. Existing modeling methods face difficulties in reflecting the influence of uncertain factors such as operating states of the harmonic source, resulting in poor accuracy. To address this problem, we propose in this study a general harmonic probability model (GHPM) based on operating states identification. Firstly, a load operating state identification algorithm is described, to support modelling according to the different operating states. Secondly, the proposed GHPM is presented. It includes the possible probability density function of the emission by the typical harmonic sources, which overcomes the poor generality of the traditional methods. Furthermore, this study proposes the parameter estimation method of the proposed GHPM based on the monitored data. Finally, the proposed method is verified by a 110 kV steelmaking plant, PV power station and wind farm in Central China, and the IEEE 13-bus test system. The effects of practical issues are also discussed to show the applicability of the proposed method.