Optical sensors for power transformer monitoring: A review

Guo, Ma; Wang, Yuan; Qin, Weiqi; Zhou, Hongyang; Yan, Chao; Jiang, Jun; Ju, Yiguang

doi:10.1049/hve2.12021

Cited by 74 publications

(36 citation statements)

References 111 publications

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“…However, it has two disadvantages: (1) The unnecessary maintenance occurs when the equipment may be in good condition, that is, excessive maintenance. (2) The untimely equipment maintenance results in the long-time failure, which damages the insulation property of the power transformer by acceleration. Therefore, in order to reduce equipment maintenance cost and transformer failure rate, reasonable maintenance for the transformer according to correct fault diagnosis is especially important.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, in order to reduce equipment maintenance cost and transformer failure rate, reasonable maintenance for the transformer according to correct fault diagnosis is especially important. Researchers have studied many methods of transformer fault diagnosis [1,2]. The Dissolved Gas Analysis (DGA) is an important method for oil-immersed transformer fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

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A novel two‐stage Dissolved Gas Analysis fault diagnosis system based semi‐supervised learning

et al. 2022

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Dissolved Gas Analysis (DGA) is an important method for oil‐immersed transformer fault diagnosis. However, collecting labelled DGA data is difficult because the determination of the transformer fault is time‐consuming and expensive in the transformer substation, but DGA data without labels is easier to obtain. Therefore, the paper proposed a semi‐supervised two‐stage diagnostic system based DGA by using less labelled samples. The two‐stage system includes a novel semi‐supervised feature selection based Genetic Algorithm (GA) and Support Vector Machine (SVM) model (SSL‐FS‐GASVM) for selecting optimal features and a novel semi‐supervised transformer fault diagnosis model based improved Artificial Fish Swarm Algorithm (AFSA) and SVM (SSL‐IAFSA‐SVM) for optimising the SVM parameter. Finally, the performances of SSL‐FS‐GASVM and SSL‐IAFSA‐SVM models are tested and compared with traditional supervised diagnostic models combined with other optimisation methods, respectively. The results show that the proposed two‐stage system works in optimising features and parameters and has strong robustness in solving small sample classification problems.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel two‐stage Dissolved Gas Analysis fault diagnosis system based semi‐supervised learning

et al. 2022

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“…With the rapid development of the smart grid, advanced electric field measurement technology is increasingly required, which is one of the most significant segments to ensure the normal operation of power equipment [1,2]. The amplitude and frequency composition of an electric field becomes more complex accompanied by the introduction of a large number of power electronic devices [3].…”

Section: Introductionmentioning

confidence: 99%

Optimising the electroluminescence performance of ZnS: Cu/epoxy composites via exploring the relationship between electroluminescence intensity and filler intrinsic attributes

Liu

Chen

et al. 2022

High Voltage

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Electroluminescent composites have considerable potential for applications in photoelectric display, electric field measurement and other fields due to their advantages of simple preparation and uniform luminescence. However, high voltage required by the operation of electroluminescent composites hinders their applications in the electric domain, and research on the relationship between electroluminescence principle of composites and filler intrinsic attributes is insufficient. In this paper, the effects of electric field strength, filler mass fraction and voltage frequency on the electroluminescence intensity of ZnS: Cu/epoxy composites are studied, which are majorly used as the luminescent layer of electroluminescent devices. To enhance the electroluminescence intensity of composites, high-temperature sintered BaTiO 3 and zinc oxide whiskers (ZnOw) particles are introduced in the base matrix. The finite element method is applied to verify that the matrix of high dielectric constant is in favour of improving the electroluminescence intensity of composites. The band structure of the ZnS: Cu particle is calculated based on the density functional theory + U method to analyse the intrinsic relationship between electroluminescence and particle electronic structure. This paper provides a basis for the research of high electroluminescence intensity devices and will be conducive to better understanding on the relationship between electroluminescence principle and filler intrinsic attributes.

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“…With the development of VOCs detection technology, some scholars use various types of sensors for multi-component gas detection [13] in many fields, such as purified essential oil detection in commercial plant products [14], alcoholic beverages and perfume quality control [15], bakery food product classification [16], and coffee processing [17]. However, in the field of DGA real-time detection, it is necessary to use the high anti-interference, fast response time and high-performance detection method that does not consume the test samples [18]. Therefore, optical detection technology can effectively satisfy the above requirements.…”

Section: Introductionmentioning

confidence: 99%

Development of an Online Detection Setup for Dissolved Gas in Transformer Insulating Oil

Chen

Wang

et al. 2021

Applied Sciences

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The type and concentration of dissolved gases in transformer insulating oil are used to assess transformer conditions. In this paper, an online detection setup for measuring the concentration of multicomponent gases dissolved in transformer insulating oil is developed, which consists of an oil-gas separation system and an optical system for acquiring the transformer status in real time. The oil-gas separation system uses low pressure, constant temperature, and low-frequency stirring as working conditions for degassing large-volume oil samples based on modified headspace degassing. The optical system uses tunable diode laser absorption spectroscopy (TDLAS) to determine the gas concentration. Six target gases (methane, ethylene, ethane, acetylene, carbon monoxide, and carbon dioxide) were detected by three near-infrared lasers (1569, 1684, and 1532 nm). The stability of the optical system was improved by the common optical path formed by time-division multiplexing (TDM) technology. The calibration experiments show that the second harmonics and the concentrations of the six gases are linear. A comparison experiment with gas chromatography (GC) demonstrates that the error of acetylene reaches the nL/L level, while the other gases reach the μL/L level. The data conforms to the power industry testing standards, and the state of the transformer is analyzed by the detected six characteristic gases. The setup provides an effective basis for the online detection of dissolved gas in transformer insulating oil.

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Optical sensors for power transformer monitoring: A review

Cited by 74 publications

References 111 publications

A novel two‐stage Dissolved Gas Analysis fault diagnosis system based semi‐supervised learning

A novel two‐stage Dissolved Gas Analysis fault diagnosis system based semi‐supervised learning

Optimising the electroluminescence performance of ZnS: Cu/epoxy composites via exploring the relationship between electroluminescence intensity and filler intrinsic attributes

Development of an Online Detection Setup for Dissolved Gas in Transformer Insulating Oil

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