Spatiotemporal Correlation Analysis for Predicting Current Transformer Errors in Smart Grids

Yao Zhong, Tengbin Li, Krzysztof Przystupa (), Lin Cong, Guangrun Yang, Sen Yang, Orest Kochan and Jarosław Sikora
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Yao Zhong: Metering Center of Yunnan Power Grid Co., Ltd., Kunming 650200, China
Tengbin Li: Metering Center of Yunnan Power Grid Co., Ltd., Kunming 650200, China
Krzysztof Przystupa: Department of Automation, Lublin University of Technology, Nadbystrzycka 38D, 20-618 Lublin, Poland
Guangrun Yang: Metering Center of Yunnan Power Grid Co., Ltd., Kunming 650200, China
Sen Yang: Metering Center of Yunnan Power Grid Co., Ltd., Kunming 650200, China
Orest Kochan: Department of Measuring-Information Technologies, Lviv Polytechnic National University, 79013 Lviv, Ukraine
Jarosław Sikora: Department of Automatics and Metrology, Lublin University of Technology, Nadbystrzycka 38D, 20-618 Lublin, Poland

Energies, 2024, vol. 17, issue 7, 1-14

Abstract: The online calibration method for current transformers is an important research direction in the field of smart grids. This article constructs a transformer error prediction model based on spatiotemporal integration. This model draws inspiration from the structure of forgetting gates in gated loop units and combines it with a graph convolutional network (GCN) that is good at capturing the spatial relationships within the graph attention network to construct an adaptive GCN. The spatial module formed by this adaptive GCN is used to model the spatial relationships in the circuit network, and the attention mechanism and gated time convolutional network are combined to form a time module to learn the temporal relationships in the circuit network. The layer that combines the time and space modules is used, which consists of a gating mechanism for spatiotemporal fusion, and a transformer error prediction model based on a spatiotemporal correlation analysis is constructed. Finally, it is verified on a real power grid operation dataset, and compared with the existing prediction methods to analyze its performance.

Keywords: transformer error prediction; graph convolutional neural network; graph attention network; gating mechanism (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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