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Wide-area composite load parameter identification based on multi-residual deep neural network

journal contribution
posted on 2024-06-18, 21:56 authored by S Afrasiabi, M Afrasiabi, MA Jarrahi, M Mohammadi, Jamshid Aghaei, MS Javadi, M Shafie-Khah, JPS Catalao
Accurate and practical load modeling plays a critical role in the power system studies including stability, control, and protection. Recently, wide-area measurement systems (WAMSs) are utilized to model the static and dynamic behavior of the load consumption pattern in real-time, simultaneously. In this article, a WAMS-based load modeling method is established based on a multi-residual deep learning structure. To do so, a comprehensive and efficient load model founded on combination of impedance-current-power and induction motor (IM) is constructed at the first step. Then, a deep learning-based framework is developed to understand the time-varying and complex behavior of the composite load model (CLM). To do so, a residual convolutional neural network (ResCNN) is developed to capture the spatial features of the load at different location of the large-scale power system. Then, gated recurrent unit (GRU) is used to fully understand the temporal features from highly variant time-domain signals. It is essential to provide a balance between fast and slow variant parameters. Thus, the designed structure is implemented in a parallel manner to fulfill the balance and moreover, weighted fusion method is used to estimate the parameters, as well. Consequently, an error-based loss function is reformulated to improve the training process as well as robustness in the noisy conditions. The numerical experiments on IEEE 68-bus and Iranian 95-bus systems verify the effectiveness and robustness of the proposed load modeling approach. Furthermore, a comparative study with some relevant methods demonstrates the superiority of the proposed structure. The obtained results in the worst-case scenario show error lower than 0.055% considering noisy condition and at least 50% improvement comparing the several state-of-art methods.

History

Volume

34

Issue

9

Start Page

6121

End Page

6131

Number of Pages

11

eISSN

2162-2388

ISSN

2162-237X

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Language

eng

Peer Reviewed

  • Yes

Open Access

  • No

Acceptance Date

2021-12-04

Era Eligible

  • Yes

Medium

Print-Electronic

Journal

IEEE Transactions on Neural Networks and Learning Systems