Identification of Internal Failure in Power Transformers Using Fuzzy Logic Through The Dissolved Gas Analysis in Mineral Insulating Oil

2020 ◽  
Author(s):  
Renzo Luigui Zarsosa Pacori ◽  
Josmell Henry Alva Alcantara
Keyword(s):  
Fuzzy Logic ◽  
Gas Analysis ◽  
Power Transformers ◽  
Dissolved Gas ◽  
Insulating Oil ◽  
Dissolved Gas Analysis

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

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 1009 ◽  
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.

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

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.

scholarly journals 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 ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 4017 ◽  
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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