Open Access

ARTICLE

STPGTN–A Multi-Branch Parameters Identification Method Considering Spatial Constraints and Transient Measurement Data

Shuai Zhang, Liguo Weng^*

Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing, 210044, China

* Corresponding Author: Liguo Weng. Email:

(This article belongs to the Special Issue: Machine Learning-Guided Intelligent Modeling with Its Industrial Applications)

Computer Modeling in Engineering & Sciences 2023, 136(3), 2635-2654. https://doi.org/10.32604/cmes.2023.025405

Received 09 July 2022; Accepted 15 November 2022; Issue published 09 March 2023

Abstract

Transmission line (TL) Parameter Identification (PI) method plays an essential role in the transmission system. The existing PI methods usually have two limitations: (1) These methods only model for single TL, and can not consider the topology connection of multiple branches for simultaneous identification. (2) Transient bad data is ignored by methods, and the random selection of terminal section data may cause the distortion of PI and have serious consequences. Therefore, a multi-task PI model considering multiple TLs’ spatial constraints and massive electrical section data is proposed in this paper. The Graph Attention Network module is used to draw a single TL into a node and calculate its influence coefficient in the transmission network. Multi-Task strategy of Hard Parameter Sharing is used to identify the conductance of multiple branches simultaneously. Experiments show that the method has good accuracy and robustness. Due to the consideration of spatial constraints, the method can also obtain more accurate conductance values under different training and testing conditions.

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Graphic Abstract

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