Advances in DGA based condition monitoring of transformers: A review

Wani, Shufali Ashraf; Rana, Ankur Singh; Sohail, Shiraz; Rahman, Obaidur; Parveen, Shaheen; Khan, Shakeb A.

doi:10.1016/j.rser.2021.111347

Cited by 47 publications

(14 citation statements)

References 96 publications

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“…The power transformer is one of the most essential pieces of hinge equipment of an electrical power system [1]. There are still a considerable number of large and mediumsized transformers around the world that use transformer oil as a cooling and insulating medium [2].…”

Section: Introductionmentioning

confidence: 99%

Adsorption and Sensing Properties of Dissolved Gas in Oil on Cr-Doped InN Monolayer: A Density Functional Theory Study

Qian

Jin²,

Wang³

et al. 2022

Chemosensors

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Dissolved gas analysis (DGA) is recognized as one of the most reliable methods in transformer fault diagnosis technology. In this paper, three characteristic gases of transformer oil (CO, C2H4, and CH4) were used in conjunction with a Cr-decorated InN monolayer according to first principle calculations. The adsorption performance of Cr–InN for these three gases were studied from several perspectives such as adsorption structures, adsorption energy, electron density, density of state, and band gap structure. The results revealed that the Cr–InN monolayer had good adsorption performance with CO and C2H4, while the band gap of the monolayer slightly changed after the adsorption of CO and C2H4. Additionally, the adsorption property of the Cr–InN monolayer on CH4 was acceptable and a significant response was simultaneously generated. This paper provides the first insights regarding the possibility of Cr-doped InN monolayers for the detection of gases dissolved in oil.

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Section: Introductionmentioning

confidence: 99%

Adsorption and Sensing Properties of Dissolved Gas in Oil on Cr-Doped InN Monolayer: A Density Functional Theory Study

Qian

Jin²,

Wang³

et al. 2022

Chemosensors

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“…With the installation of a dissolved gas measurement system, these measurements can be taken more frequently, and online monitoring has been enabled. Dissolved gas analysis (DGA) is the premier diagnostic approach to monitoring and detecting faults within oilimmersed transformers (Wani et al, 2021). This technical brief expands on the work previously published in (Agarwal, Lybeck, Pham, Rusaw, & Bickford, 2015), which demonstrated the Chendong model and the Institute of Electrical and Electronics Engineers (IEEE) thermal life consumption model on plant data, with simulated drift to represent primary winding insulation degradation, as part of the Electric Power Research Institute's Fleet-wide Prognostic and Health Management Suite software (Electric Power Research Institute (EPRI), 2012).…”

Section: Introductionmentioning

confidence: 99%

“…This research expands on that work by enabling broader usage, incorporating (IEEE, 2019) to enable prognostic models, and then testing the models on actual plant data. Additionally, Wani et al provided an excellent review of state-of-the-art nonlinear techniques for DGAbased transformer fault diagnosis, but failed to mention linear techniques such as autoregressive integrated moving average (ARIMA), which is covered in this brief (Wani et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Transformer Health Monitoring Using Dissolved Gas Analysis

Walker

Rashdan²,

Agarwal³

2022

IJPHM

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As integral components of any power plant, transformers sup-ply the generated electricity to the grid. However, the trans-former’s cellulose-based paper insulation and the mineral oilin which it is immersed break down over time under stan-dard operating conditions—or more rapidly due to potentialfaults within the system. This technical brief exhibits a col-lection of diagnostic and prognostic techniques that utilitiescan adopted in lieu of labor-intense periodic preventive main-tenance routines. Furthermore, prognostic models have beenincorporated using the latest version of the Institute of Elec-trical and Electronics Engineers (IEEE) standard (IEEE StdC57.104TM-2019) for dissolved gas analysis (DGA), thusexpanding it to include estimation of the time to maintenance.Overall, four different methodologies are explained, each ofwhich aid in determining a transformer’s state of health. Thesemethodologies include the Chendong model, the IEEE C57.91-2011 thermal life consumption model, a diagnostic model forDGA, and a prognostic model for DGA that uses an autore-gressive integrated moving average (ARIMA) model. An ad-ditional improvement for estimating missing system parame-ters from monitoring data (i.e., a tool for parameter estimationutilizing Powell’s method) is presented, enabling the IEEEthermal life consumption model to benefit not only the col-laborating power plant, but also the power industry at large.

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“…Such a hazardous event lowers the reliability of the power system and incurs financial damages. Hence, it is crucial to enhance power transformer monitoring and troubleshooting (Wani et al, 2021;Xie et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Untitled

2022

JST

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In Malaysia, on-site technical personnel manually inspect power transformers. Some vital condition indicators, such as oil and winding temperatures, are not monitored in real-time. This condition can be hazardous if the transformer gets overheated. Overheating can cause mechanical deformation and insulation degradation if not monitored regularly. Thus, an online monitoring system that meets industry standards is needed to enhance power transformer monitoring and troubleshooting. In this research, the Internet of Things (IoT) based data acquisition (DAQ) system was deployed for real-time oil temperature monitoring and inspection to detect incipient faults in power transformers early. This IoT-based DAQ system was connected to the substation remote terminal unit (RTU) to update real-time data on each power transformer. The long-range (LoRa) technology is proposed for the system to transmit temperature, current, and voltage from the power transformers. The data transmission from the oil temperature indicator (OTI), network server, and database was monitored and compared. It is observed that the temperature data was transferred from the network server to the database without any transmission delay. The average deviation from the two experiments was 0.006 and 0.003, respectively, compared to the manual reading from the OTI scale meter with a digital reading by the proposed DAQ system. For testing purposes, the alert

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Advances in DGA based condition monitoring of transformers: A review

Cited by 47 publications

References 96 publications

Adsorption and Sensing Properties of Dissolved Gas in Oil on Cr-Doped InN Monolayer: A Density Functional Theory Study

Adsorption and Sensing Properties of Dissolved Gas in Oil on Cr-Doped InN Monolayer: A Density Functional Theory Study

Transformer Health Monitoring Using Dissolved Gas Analysis

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