Research on Intelligent Anti-misoperation Technology Applied to Substation One-button Sequential Control

Wang, Yang; Li, Hengxuan; Shiping, E; Zhang, Kanjun; Chen, Qianchen; Guo, Yao; Chang, Ye

doi:10.1109/aemcse51986.2021.00061

Cited by 3 publications

(2 citation statements)

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“…It requires full-site digitization, a networked communication platform, and standardized information sharing as basic requirements. The system automatically completes information collection, measurement, control, protection, and monitoring [1][2][3]. Currently, intelligent substations show a trend of increasing the number of connected devices and the size of substations, leading to increased complexity and danger in manual on-site switching operations [2].…”

Section: Introductionmentioning

confidence: 99%

“…The system automatically completes information collection, measurement, control, protection, and monitoring [1][2][3]. Currently, intelligent substations show a trend of increasing the number of connected devices and the size of substations, leading to increased complexity and danger in manual on-site switching operations [2]. To address this, a one-key sequential control system is proposed to ensure the personal safety of operators and transition from traditional manual step-by-step operations to remote automatic operations using computers.…”

Section: Introductionmentioning

confidence: 99%

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Fault Diagnosis of Power Transformer in One-Key Sequential Control System of Intelligent Substation Based on a Transformer Neural Network Model

Wang,

Fu,

Zhang

et al. 2024

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With the introduction of numerous technologies and equipment, the volume of data in smart substations has undergone exponential growth. In order to enhance the intelligent management level of substations and promote their efficient and sustainable development, the one-key sequential control system of smart substations is being renovated. In this study, firstly, the intelligent substation is defined and compared with the traditional substation. The one-key sequential control system is introduced, and the main issues existing in the system are analyzed. Secondly, experiments are conducted on the winding temperature, insulation oil temperature, and ambient temperature of power transformers in the primary equipment. Combining data fusion technology and transformer neural network models, a Power Transformer-Transformer Neural Network (PT-TNNet) model based on data fusion is proposed. Subsequently, comparative experiments are conducted with multiple algorithms to validate the high accuracy, precision, recall, and F1 score of the PT-TNNet model for equipment state monitoring and fault diagnosis. Finally, using the efficient PT-TNNet, Random Forest, and Extra Trees models, the cross-validation of the accuracy of winding temperature and insulation oil temperature of transformers is performed, confirming the superiority of the PT-TNNet model based on transformer neural networks for power transformer state monitoring and fault diagnosis, its feasibility for application in one-key sequential control systems, and the optimization of one-key sequential control system performance.

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