Transformer Monitoring and Protection in Dynamic Power Systems – A Review

Xie, Boqi; Zhao, Dongbo; Hong, Tianqi

doi:10.3389/fenrg.2020.00150

Cited by 17 publications

(4 citation statements)

References 68 publications

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“…In response to this safety hazard, some scholars have conducted research on dissolved gases in oil and developed online monitoring technology for dissolved gases in oil. This technology, by analyzing changes in the composition of dissolved gases in oil under different voltages, temperatures, and other conditions, can accurately reflect early internal defects in transformers [ 17 , 18 , 19 ]. However, this method has a long monitoring cycle, usually measured in years, months, or weeks.…”

Section: Introductionmentioning

confidence: 99%

Transformer Monitoring with Electromagnetic Energy Transmission and Wireless Sensing

Xu,

Shang,

et al. 2024

Sensors

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To ensure stable and normal transformer operation, light gas protection of the transformer Buchholz relay is essential. However, false alarms related to light gas protection are common, and troubleshooting them often requires on-site gas sampling by personnel. During this time, the transformer’s operating state may rapidly deteriorate, posing a safety threat to field staff. To tackle these challenges, this work presents the near-field, thin-sliced transformer monitoring system that uses Electromagnetic Energy Transmission and Wireless Sensing Device (ETWSD). The system leverages external wireless energy input to power gas monitoring sensors. Simultaneously, it employs Near-Field Communication to obtain real-time concentrations of light gases, along with the electrified state and temperature. In field testing conducted on transformer relays’ gas collection chambers, the ETWSD effortlessly monitors parameters within warning ranges, encompassing methane gas concentrations around 1000 ppm, leakage voltage ranging from 0–100 V, and relay working temperatures up to 90 °C. Additionally, to facilitate real-time diagnosis for electrical workers, we have developed an Android-based APP software that displays current light gas concentrations, leakage voltage collection values, and temperature, while also enabling threshold judgment, alarms, and data storage. The developed ETWSD is expected to aid on-site personnel in promptly and accurately evaluating transformer light gas protection error alarm faults. It provides a method for simultaneous, contactless, and rapid monitoring of multiple indicators.

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Section: Introductionmentioning

confidence: 99%

Transformer Monitoring with Electromagnetic Energy Transmission and Wireless Sensing

Xu,

Shang,

et al. 2024

Sensors

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“…A power transformer is one of the most important parts of the power system (Xie et al, 2020), and the health of a power transformer plays a vital role in the safe operation of power grids and the development of national economy (Wang et al, 2022). Accurate health condition assessment helps detect potential faults and reduce maintenance costs by 20-50% (Sun et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Health condition assessment of transformers based on cross message passing graph neural networks

Luo

Xi²,

Che³

et al. 2022

Front. Energy Res.

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A method is proposed to assess the health condition of transformers based on cross message passing graph neural networks (CMPGNNs) in this paper. In order to improve the accuracy of transformer health condition assessment, multiple indicators and their strong correlation are taken into account. The evaluation indicators are divided into four comprehensive state categories, and each category has several indicators. First, the correlation between indicators of a state category is extracted by the health index method and the importance of criteria is analyzed through the inter-criteria correlation (CRITIC) method, and the health index of comprehensive indicators is obtained. Then, a diagram of comprehensive indicators is constructed, and the correlation between indicators of different state categories is extracted. Finally, CMPGNN is constructed to achieve the health assessment. The experimental results show that the proposed method can improve the accuracy of transformer health condition assessment.

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“…At the same time, driven by the smart grid and energy Internet, the operation and maintenance management of power transformers by power grid companies is also facing the transformation from state maintenance to predictive maintenance (Xie et al, 2020). Liu et al (2009) proposed the design of the 220-kV substation fault information system based on a real-time detection platform.…”

Section: Introductionmentioning

confidence: 99%

Research on the Transformer Intelligent Operation and Maintenance System Based on the Graph Neural Network

Chen

Luo

et al. 2022

Front. Energy Res.

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With the development of the smart grid and energy Internet, the power industry generates huge, multi-source, heterogeneous, and highly coupled data, which are difficult to utilize. The intelligent operation and maintenance system of the power transformer based on the knowledge graph and graph neural network is developed in this article. The multi-source heterogeneous data are structured and modeled by the constructed knowledge graph, and it presents the correlation among data more intuitively. On this basis, the graph neural network is designed to achieve the prediction and excavate the deep information hidden in the data. The testing results show that the system has fully used the multi-dimensional and interrelated heterogeneous data, achieving a deep information mine. It benefits the management and strategy implementation for the system scientifically and guides the operation and maintenance of the transformer. The system is of great significance on improving the efficiency of the transformer maintenance and safe operation.

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Transformer Monitoring and Protection in Dynamic Power Systems – A Review

Cited by 17 publications

References 68 publications

Transformer Monitoring with Electromagnetic Energy Transmission and Wireless Sensing

Transformer Monitoring with Electromagnetic Energy Transmission and Wireless Sensing

Health condition assessment of transformers based on cross message passing graph neural networks

Research on the Transformer Intelligent Operation and Maintenance System Based on the Graph Neural Network

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