Wide-Area Composite Load Parameter Identification Based on Multi-Residual Deep Neural Network

• Type: Article in International Scientific Journal
• Date: 2023

Authors

Afrasiabi, S (Author) Other; Afrasiabi, M (Author) Other; Jarrahi, MA (Author) Other; Mohammadi, M (Author) Other; Aghaei, J (Author) Other; Javadi, MS (Author) Other; Shafie-Khah, M (Author) Other; Catalao, JPS (Author) FEUP

Journal

Title: IEEE Transactions on Neural Networks and Learning Systems

Vol. 34

Pages: 6121-6131

ISSN: 2162-237X

Publisher: IEEE

Indexing

ISI Web of Knowledge - 9 Citations

Scopus - 12 Citations

Other information

Authenticus ID: P-00V-WA2

DOI: 10.1109/tnnls.2021.3133350

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11