scholarly journals Practical experience condition diagnosis gained from oil-immersed power transformers voltage class 10 kV

2021 ◽  
Vol 2052 (1) ◽  
pp. 012033
Author(s):  
S Yu Petrova
Keyword(s):  
Decision Making ◽  
Practical Experience ◽  
Condition Assessment ◽  
Gas Analysis ◽  
Power Transformers ◽  
Ratio Method ◽  
Dissolved Gas ◽  
Maintenance Decision ◽  
Diagnosis Tool ◽  
Condition Diagnosis

Abstract Dissolved Gas Analysis (DGA) for oil samples has been the most widely used diagnosis tool for transformer condition assessment for many years. However, DGA use to oil-filled transformers with a voltage class up to 100 kV. The aim of this paper is to address the issue of DGA interpretation to oil-filled transformers with a voltage class of 10 kV. This paper will present DGA tests results from 57 power transformers and will propose a maintenance decision making procedure using the IEC 60599-2015 Ratio Method, IEEE Std C57.104-2008 include Dornenberg Ratio Method and Rogers Ratio Method, and Russian Std CTO 56947007-29.180.010.094-2011 and Russian Std RD 153-34.0-46.302-00.

scholarly journals Using the Analysis of the Gases Dissolved in Oil in Diagnosis of Transformer Bushings with Paper-Oil Insulation—A Case Study

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6713
Author(s):  
Tomasz Piotrowski ◽  
Pawel Rozga ◽  
Ryszard Kozak ◽  
Zbigniew Szymanski
Keyword(s):  
Positive Result ◽  
Gas Analysis ◽  
Power Transformers ◽  
Ratio Method ◽  
Dissolved Gas ◽  
Step Process ◽  
Lightning Impulse ◽  
Triangle Method ◽  
Inside Part

The article describes a case study when the voltage collapse during lightning impulse tests of new power transformers was noticed and when the repeated tests finished with a positive result. The step-by-step process of reaching the conclusion on the basis of dissolved gas analysis (DGA) as a key method of the investigations was presented. The considerations on the possible source of the analysis showed that the Duval triangle method, used in the analysis of the concentration of gases dissolved in oil samples taken from bushings, more reliably and unambiguously than the ratio method recommended in the IEC 60599 Standard, indicated a phenomenon which was identified in the insulation structure of bushings analyzed. Additionally, the results from DGA were found to be consistent with an internal inspection of bushings, which showed a visible trace of discharge on the inside part of the epoxy housing, as a result of the lightning induced breakdown.

scholarly journals Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey

2018 ◽  
Author(s):  
Lefeng Cheng ◽  
Tao Yu
Keyword(s):  
Decision Making ◽  
Fault Diagnosis ◽  
Future Development ◽  
Gas Analysis ◽  
Grey System Theory ◽  
Power Transformers ◽  
Dissolved Gas ◽  
Development Trends ◽  
Rough Sets Theory ◽  
Dissolved Gas Analysis

Compared with conventional methods of fault diagnosis for power transformers, which have defects such as imperfect encoding and too absolute encoding boundaries, this paper systematically discusses various intelligent approaches applied in fault diagnosis and decision making for large oil-immersed power transformers based on dissolved gas analysis (DGA), including expert system (EPS), artificial neural network (ANN), fuzzy theory, rough sets theory (RST), grey system theory (GST), swarm intelligence (SI) algorithms, data mining technology, machine learning (ML), and other intelligent diagnosis tools, and summarizes existing problems and solutions. From this survey, it is found that a single intelligent approach for fault diagnosis can only reflect operation status of the transformer in one particular aspect, causing various degrees of shortcomings that cannot be resolved effectively. Combined with the current research status in this field, the problems that must be addressed in DGA-based transformer fault diagnosis are identified, and the prospects for future development trends and research directions are outlined. This contribution presents a detailed and systematic survey on various intelligent approaches to faults diagnosing and decisions making of the power transformer, in which their merits and demerits are thoroughly investigated, as well as their improvement schemes and future development trends are proposed. Moreover, this paper concludes that a variety of intelligent algorithms should be combined for mutual complementation to form a hybrid fault diagnosis network, such that avoiding these algorithms falling into a local optimum. Moreover, it is necessary to improve the detection instruments so as to acquire reasonable characteristic gas data samples. The research summary, empirical generalization and analysis of predicament in this paper provide some thoughts and suggestions for the research of complex power grid in the new environment, as well as references and guidance for researchers to choose optimal approach to achieve DGA-based fault diagnosis and decision of the large oil-immersed power transformers in preventive electrical tests.

Condition assessment of power transformers based on dissolved gas analysis

2019 ◽  
Vol 13 (12) ◽  
pp. 2299-2310 ◽  
Author(s):  
Osama E. Gouda ◽  
Salah H. El-Hoshy ◽  
Hassan H. E.L.-Tamaly
Keyword(s):  
Condition Assessment ◽  
Gas Analysis ◽  
Power Transformers ◽  
Dissolved Gas ◽  
Dissolved Gas Analysis

Dissolved Gas Analysis Principle Based Intelligent Approaches to Fault Diagnosis and Decision Making of Large Oil-Immersed Power Transformers: A Survey

2018 ◽  
Author(s):  
Lefeng Cheng ◽  
Tao Yu
Keyword(s):  
Decision Making ◽  
Fault Diagnosis ◽  
Future Development ◽  
Gas Analysis ◽  
Grey System Theory ◽  
Power Transformers ◽  
Dissolved Gas ◽  
Development Trends ◽  
Rough Sets Theory ◽  
Dissolved Gas Analysis

Compared with conventional methods in fault diagnosis of power transformers, which have defects such as imperfect encoding and too absolute encoding boundary, this paper systematically reveals various intelligent approaches applied in fault diagnosing and decision making of large oil-immersed power transformers based on dissolved gas analysis (DGA), including expert system (EPS), artificial neural network (ANN), fuzzy theory, rough sets theory (RST), grey system theory (GST), swarm intelligence (SI) algorithms, data mining technology, machine learning (ML), and other intelligent diagnosis tools, and summarizes existing problems and solutions. From this survey, it is found that a single intelligent approach for fault diagnosis can only reflect operation status of the transformer in one certain aspect, causing some shortcomings in various degrees cannot be revealed effectively. Combined with the current research status in this field, the problems that must be addressed in DGA-based transformer fault diagnosis are identified, and the prospects for future development trends and research directions are outlined. This contribution presents a detailed and systematic survey on various intelligent approaches to faults diagnosing and decisions making of the power transformer, in which their merits and demerits are thoroughly investigated, as well as their improvement schemes and future development trends are proposed. Moreover, this paper concludes that a variety of intelligent algorithms should be combined for mutual complementation to form a hybrid fault diagnosis network, such that avoiding these algorithms falling into a local optimum. Moreover, it is necessary to improve the detection instruments so as to acquire reasonable characteristic gas data samples. The research summary, empirical generalization and analysis of predicament in this paper provide some thoughts and suggestions for the research of complex power grid in the new environment, as well as references and guidance for researchers to choose optimal approach to achieve DGA-based fault diagnosis and decision of the large oil-immersed power transformers in preventive electrical tests.

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.

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 A Novel Maintenance Decision Making Model of Power Transformers Based on Reliability and Economy Assessment

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 28778-28790 ◽  
Author(s):  
Manling Dong ◽  
Hanbo Zheng ◽  
Yiyi Zhang ◽  
Kuikui Shi ◽  
Shuai Yao ◽  
...  
Keyword(s):  
Decision Making ◽  
Power Transformers ◽  
Maintenance Decision ◽  
Decision Making Model

scholarly journals Research on Multi-Attribute Decision-Making in Condition-Based Maintenance for Power Transformers Based on Cloud and Kernel Vector Space Models

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 5948
Author(s):  
Renxi Gong ◽  
Siqiang Li ◽  
Weiyu Peng
Keyword(s):  
Decision Making ◽  
Vector Space ◽  
Assessment System ◽  
Condition Based Maintenance ◽  
Power Transformers ◽  
Maintenance Strategy ◽  
Cloud Models ◽  
Optimal Maintenance ◽  
Maintenance Decision ◽  
Vector Space Models

Decision-making for the condition-based maintenance (CBM) of power transformers is critical to their sustainable operation. Existing research exhibits significant shortcomings; neither group decision-making nor maintenance intention is considered, which does not satisfy the needs of smart grids. Thus, a multivariate assessment system, which includes the consideration of technology, cost-effectiveness, and security, should be created, taking into account current research findings. In order to address the uncertainty of maintenance strategy selection, this paper proposes a maintenance decision-making model composed of cloud and vector space models. The optimal maintenance strategy is selected in a multivariate assessment system. Cloud models allow for the expression of natural language evaluation information and are used to transform qualitative concepts into quantitative expressions. The subjective and objective weights of the evaluation index are derived from the analytic hierarchy process and the grey relational analysis method, respectively. The kernel vector space model is then used to select the best maintenance strategy through the close degree calculation. Finally, an optimal maintenance strategy is determined. A comparison and analysis of three different representative maintenance strategies resulted in the following findings: The proposed model is effective; it provides a new decision-making method for power transformer maintenance decision-making; it is simple, practical, and easy to combine with the traditional state assessment method, and thus should play a role in transformer fault diagnosis.

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