Dissolved gas analysis evaluation in electric power transformers using conventional methods a review

Power transformers are the most important assets of electric power substations. The reliability in the operation of electric power transmission and distribution is due to the correct operation and maintenance of power transformers. The parameters that are most used to assess the health status of power transformers are dissolved gas analysis (DGA), oil quality analysis (OQA) and content of furfuraldehydes (FFA) in oil. The parameter that currently allows for simple online monitoring in an energized transformer is the DGA. Although most of the DGA continues to be done in the laboratory, the trend is online DGA monitoring, since it allows for detection or diagnosis of the faults throughout the life of the power transformers. This study presents a review of the main DGA monitors, single- or multi-gas, their most important specifications, accuracy, repeatability and measurement range, the types of installation, valve or closed loop, and number of analogue inputs and outputs. This review shows the differences between the main existing DGA monitors and aims to help in the selection of the most suitable DGA monitoring approach according to the needs of each case.

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Dissolved Gas Analysis of Insulating Oil in Electric Power Transformers: A Case Study Using SDAE-LSTM

Mathematical Problems in Engineering ◽

10.1155/2020/2420456 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Zhao Luo ◽

Zhiyuan Zhang ◽

Xu Yan ◽

Jinghui Qin ◽

Zhendong Zhu ◽

...

Keyword(s):

Fault Diagnosis ◽

Electric Power ◽

Recognition Accuracy ◽

Power Transformer ◽

Gas Analysis ◽

Parameter Analysis ◽

Power Transformers ◽

Dissolved Gas ◽

Insulating Oil ◽

Dissolved Gas Analysis

Dissolved gas analysis (DGA) is the most important tool for fault diagnosis in electric power transformers. To improve accuracy of diagnosis, this paper proposed a new model (SDAE-LSTM) to identify the dissolved gases in the insulating oil of power transformers and perform parameter analysis. The performance evaluation is attained by the case studies in terms of recognition accuracy, precision ratio, and recall ratio. Experiment results show that the SDAE-LSTM model performs better than other models under different input conditions. As evidenced from the analyses, the proposed model achieves considerable results of recognition accuracy (95.86%), precision ratio (95.79%), and recall ratio (97.51%). It can be confirmed that the SDAE-LSTM model using the dissolved gas in the power transformer for fault diagnosis and analysis has great research prospect.

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A Modified Dissolved Gas Analysis Technique as a Diagnostic Tool for Faults in Power Transformers

2019 21st International Middle East Power Systems Conference (MEPCON) ◽

10.1109/mepcon47431.2019.9008209 ◽

2019 ◽

Author(s):

Sayed A.Ward ◽

Adel ELFaraskoury ◽

Mohamed Badwi ◽

Shimaa Adel Ibrahim

Keyword(s):

Diagnostic Tool ◽

Gas Analysis ◽

Power Transformers ◽

Dissolved Gas ◽

Dissolved Gas Analysis ◽

Analysis Technique

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A Novel Fault Diagnosis Method for Power Transformer Based on Dissolved Gas Analysis Using Hypersphere Multiclass Support Vector Machine and Improved D–S Evidence Theory

Energies ◽

10.3390/en12204017 ◽

2019 ◽

Vol 12 (20) ◽

pp. 4017 ◽

Cited By ~ 7

Author(s):

Haikun Shang ◽

Junyan Xu ◽

Zitao Zheng ◽

Bing Qi ◽

Liwei Zhang

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Evidence Theory ◽

Gas Analysis ◽

Power Transformers ◽

Support Vector ◽

Dissolved Gas ◽

Dissolved Gas Analysis ◽

Diagnosis Method ◽

Multiclass Support Vector Machine

Power transformers are important equipment in power systems and their reliability directly concerns the safety of power networks. Dissolved gas analysis (DGA) has shown great potential for detecting the incipient fault of oil-filled power transformers. In order to solve the misdiagnosis problems of traditional fault diagnosis approaches, a novel fault diagnosis method based on hypersphere multiclass support vector machine (HMSVM) and Dempster–Shafer (D–S) Evidence Theory (DET) is proposed. Firstly, proper gas dissolved in oil is selected as the fault characteristic of power transformers. Secondly, HMSVM is employed to diagnose transformer fault with selected characteristics. Then, particle swarm optimization (PSO) is utilized for parameter optimization. Finally, DET is introduced to fuse three different fault diagnosis methods together, including HMSVM, hybrid immune algorithm (HIA), and kernel extreme learning machine (KELM). To avoid the high conflict between different evidences, in this paper, a weight coefficient is introduced for the correction of fusion results. Results indicate that the fault diagnosis based on HMSVM has the highest probability to identify transformer faults among three artificial intelligent approaches. In addition, the improved D–S evidence theory (IDET) combines the advantages of each diagnosis method and promotes fault diagnosis accuracy.

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A Fuzzy Logic Model for Power Transformer Faults’ Severity Determination Based on Gas Level, Gas Rate, and Dissolved Gas Analysis Interpretation

Energies ◽

10.3390/en13041009 ◽

2020 ◽

Vol 13 (4) ◽

pp. 1009 ◽

Cited By ~ 4

Author(s):

Rahman Azis Prasojo ◽

Harry Gumilang ◽

Suwarno ◽

Nur Ulfa Maulidevi ◽

Bambang Anggoro Soedjarno

Keyword(s):

Fuzzy Logic ◽

Power Transformer ◽

Local Population ◽

Gas Analysis ◽

Power Transformers ◽

Support Vector ◽

Dissolved Gas ◽

New Approach ◽

Dissolved Gas Analysis ◽

Novel Approach

In determining the severity of power transformer faults, several approaches have been previously proposed; however, most published studies do not accommodate gas level, gas rate, and Dissolved Gas Analysis (DGA) interpretation in a single approach. To increase the reliability of the faults’ severity assessment of power transformers, a novel approach in the form of fuzzy logic has been proposed as a new solution to determine faults’ severity using the combination of gas level, gas rate, and DGA interpretation from the Duval Pentagon Method (DPM). A four-level typical concentration and rate were established based on the local population. To simplify the assessment of hundreds of power transformer data, a Support Vector Machine (SVM)-based DPM with high agreements to the graphical DPM has been developed. The proposed approach has been implemented to 448 power transformers and further implementation was done to evaluate faults’ severity of power transformers from historical DGA data. This new approach yields in high agreement with the previous methods, but with better sensitivity due to the incorporation of gas level, gas rate, and DGA interpretation results in one approach.

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