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Adaptive Fuzzy Logic Strong Tracking Based Load Modeling
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PRE
AI
DOI：10.1109/TPWRD.2021.3092076.png)
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摘要
En 中文
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 problems of load characteristics and parameter identification of load model are 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 and the filter gain is adjusted by introducing a time-varying fading factor. 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. This method can establish an accurate load model and has good robustness.
Keyword:
Load modeling
Adaptation models
Fuzzy logic
Noise measurement
Filtering
Robustness
Mathematical model
Fuzzy logic
load modeling
robustness
strong tracking filter
state estimation
期刊
IF:
3.7
论文数:
9.1K
被引数:
2.2W
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