Deep Learning-Based Time-Varying Parameter Identification for System-Wide Load Modeling
2019; Institute of Electrical and Electronics Engineers; Volume: 10; Issue: 6 Linguagem: Inglês
10.1109/tsg.2019.2896493
ISSN1949-3061
AutoresMingjian Cui, Mahdi Khodayar, Chen Chen, Xinan Wang, Ying Zhang, Mohammad E. Khodayar,
Tópico(s)Optimal Power Flow Distribution
ResumoThe integration of uncertain power resources is causing more challenges for traditional load modeling research. Parameter identification of load modeling is impacted by a variety of load components with time-varying characteristics. This paper develops a deep learning-based time-varying parameter identification model for composite load modeling (CLM) with ZIP load and induction motor. A multi-modal long short-term memory (M-LSTM) deep learning method is used to estimate all the time-varying parameters of CLM considering system-wide measurements. It contains a multi-modal structure that makes use of different modalities of the input data to accurately estimate time-varying load parameters. An LSTM network with a flexible number of temporal states is defined to capture powerful temporal patterns from the load parameters and measurements time series. The extracted features are further fed to a shared representation layer to capture the joint representation of input time series data. This temporal representation is used in a linear regression model to estimate time-varying load parameters at the current time. Numerical simulations on the 23- and 68-bus systems verify the effectiveness and robustness of the proposed M-LSTM method. Also, the optimal lag values of parameters and measurements as input variables are solved.
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