Dynamic Load Model Parameter Prediction Using Confidence-Interval-Based Fuzzy Linear Regression
Huang Yulong1, Liu Mingbo2, Zheng Wenjie3, Liu Xindong1, Chen Xun3
1. Jinan University Zhuhai 519070 China; 2. South China University of Technology Guangzhou 510640 China; 3. Electric Power Research Institute of Guangdong Power Grid Corporation Guangzhou 510600 China
Abstract：The accuracy of load model greatly impacts power system stability simulation and control. Since real load characteristics are time-varying all along, load model parameters built on some historical data are only valid within limited scopes. Currently classification and synthesis approach, multi-curve identification method, and dominant parameter online identification technique are mainly adopted to deal with time-variation problem of load characteristics. However these methods decrease model fitting precision to some degree. In addition, because system operating conditions and real load characteristics are changing all along, limited classifications fail to obtain good boundaries, even may have overlap. After analyzing the influencing factors of load model parameters, this paper proposes a prediction method of dynamic load model parameters using confidence-interval-based fuzzy linear regression, to recurrently find dynamic load model parameters from historical database that match the operating condition of real time system. Thus the selected load model parameters will be more suitable to the dynamic characteristics of real load as the database size grows. The proposed method is verified by the field measurement data at two 220kV substations from a certain large city.
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2015, Vol. 30 
