Mr. Xu Zhang Profile

Xu Zhang

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Proceedings Article | 25 September 2023 Paper

Power grid fault diagnosis method based on temporal convolutional network

Zirui Wang, Ziqi Zhang, Mingxuan Du, Xu Zhang

Proceedings Volume 12788, 1278854 (2023) https://doi.org/10.1117/12.3004281

KEYWORDS: Power grids, Convolution, Statistical modeling, Education and training, Tunable filters, Diagnostics

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With the development trend of digitization and intelligence of power grid dispatching, intelligent fault diagnosis methods are of great significance for timely locating and handling faults. When there is a fault or disturbance in the power grid, a large amount of alarm information will be uploaded to the monitoring alarm window through the monitoring system. The alarm information usually appears on different levels and in chronological order and lacks effective classification and processing. It is difficult to dig out effective information, and it is difficult for operation and maintenance personnel to detect alarms in a short time. The key content of the information cannot make a quick judgment on the fault, thus reducing the efficiency of fault diagnosis. Therefore, this paper proposes a new method based on a temporal convolutional network (TCN). By extracting the unified vectorized representation of the alarm information samples of fault events under different fault conditions, the TCN-based fault category classification is developed. Finally, the model is trained and tested using the samples generated by the TS2000 simulation system. Experimental results show that this method can effectively determine the category of the fault, which can meet the needs of intelligent online diagnosis.

Proceedings Article | 29 March 2023 Paper

Power grid fault diagnosis method based on alarm information and PMU fusion

Jiang Liu, Ling Zheng, Xu Zhang, Zixing Guo

Proceedings Volume 12566, 1256609 (2023) https://doi.org/10.1117/12.2668200

KEYWORDS: Data modeling, Power grids, Data fusion, Information fusion, Education and training, Computer programming, Image fusion, Gallium nitride, Data integration, Binary data

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With the rapid development of smart grid technology, more and more attention has been paid to the stability of the power grid. When the power grid fails, the dispatch center will receive various types of data, including the PMU data collected by the PMU (Power Management Unit) device used by the Wide Area Monitoring System (WAMS) and the data collected by the Supervisory Control System for Data Acquisition and Control (SCADA). Alarm information data, they describe the same grid fault from different dimensions. At present, the power grid fault diagnosis based on PMU data and alarm information texts has been fully studied, but the information source is single, and it is difficult to ensure the accuracy of the diagnosis results when complex faults and multiple faults occur in the power grid. This paper starts with the information source of power grid fault diagnosis and develops a power grid fault diagnosis method that integrates the dual data sources of PMU and alarm information: 1. effectively connect alarm message text and PMU based on fault events. 2. complete the fusion of PMU and alarm information to improve the scope and accuracy of power grid fault diagnosis. Finally, the simulation fault data is used to test. The experimental results show that the accuracy of fault diagnosis of dual data source fusion is higher than that of single data.

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