Adaptive Fuzzy Logic Strong Tracking Based Load Modeling

Abstract

The randomness and time variability of power load are the main problems in the load modeling, in which the model error and measurement noise greatly affect the modeling accuracy. In this paper, an adaptive fuzzy logic strong tracking based load modeling method is proposed. The problem of load characteristics and parameter identification of load model is transformed into a real-time tracking of load. The optimal model set is first selected from the initial load model set, and according to the mixing probability, the state estimation and error covariance are mixed as the input of the filter. In order to reduce the model error in the parallel filtering, the strong tracking filter (STF) is constructed based on the orthogonality principle. The adaptive fuzzy logic strong tracking filter is constructed by combining the fuzzy logic with the STF to adjust the covariance matrix of the STF according to the change of measurement noise. Moreover, the weights of each sub model are updated in real-time by the parallel filtering, and the sub models are fused to get a comprehensive load model which can reflect the actual load characteristics. The effectiveness of the method is verified by the simulation of IEEE 39-bus system.