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.

Power Transformer Fault Diagnosis using DGA and Artificial Intelligence

2020 ◽  
Vol 13 (4) ◽  
pp. 579-587
Author(s):  
Seyed Javad Tabatabaei Shahrabad ◽  
Vahid Ghods ◽  
Mohammad Tolou Askari
Keyword(s):  
Artificial Intelligence ◽  
Decision Tree ◽  
Power Transformer ◽  
Gas Analysis ◽  
Power Transformers ◽  
Detection Methods ◽  
Support Vector ◽  
Dissolved Gas ◽  
Dissolved Gas Analysis ◽  
Internal Faults

Background: Power transformers are one of the most applicable electricity network devices which transmit output power of the generator to the network through increasing voltage and decreasing current. Due to high cost of such devices and cost of disconnecting device upon failure, disconnection and failure of the transformer should be avoided as much as possible. Objective: In addition, in order to increase reliability and reduce maintenance costs, such devices should be monitored constantly. Internal faults ionize and warm up oil and as a result, gases like carbon dioxide, methane, ethane, ethylene and acetylene are produced. Various methods have been proposed for diagnosing fault in power transformers where one of the most well-known methods is dissolved gas analysis (DGA). DGA in oil is one of the effective tools for diagnosing initial faults in transformers. Methods: Common fault detection methods using oil-dissolved gas analysis include Dornemburge, Duval’s triangle, IEC/IEEE standard, key gases and Rogers. In recent years, artificial intelligence like genetic algorithm, fuzzy logic and neural networks have been used to detect faults using DGA. In this paper, support vector machine (SVM) and decision tree are used to detect internal faults in power transformers. Results: By evaluation of the proposed methods, total accuracies of classifiers using SVM and decision tree were 90% and 97.5%, respectively. Conclusion: Decision tree shows better performance and it is suggested as a proper method for obtaining promising results.

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 Power Transformer Fault Severity Estimation Based on Dissolved Gas Analysis and Energy of Fault Formation Technique

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Edwell T. Mharakurwa ◽  
G. N. Nyakoe ◽  
A. O. Akumu
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Total Energy ◽  
Power Transformer ◽  
Gas Analysis ◽  
Dissolved Gas ◽  
Dissolved Gas Analysis ◽  
Fault Severity ◽  
Incipient Fault ◽  
Transformer Maintenance

Decision making on transformer insulation condition based on the evaluated incipient faults and aging stresses has been the norm for many asset managers. Despite being the extensively applied methodology in power transformer incipient fault detection, solely dissolved gas analysis (DGA) techniques cannot quantify the detected fault severity. Fault severity is the core property in transformer maintenance rankings. This paper presents a fuzzy logic methodology in determining transformer faults and severity through use of energy of fault formation of the evolved gasses during transformer faulting event. Additionally, the energy of fault formation is a temperature-dependent factor for all the associated evolved gases. Instead of using the energy-weighted DGA, the calculated total energy of related incipient fault is used for severity determination. Severity of faults detected by fuzzy logic-based key gas method is evaluated through the use of collected data from several in-service and faulty transformers. DGA results of oil samples drawn from transformers of different specifications and age are used to validate the model. Model results show that correctly detecting fault type and its severity determination based on total energy released during faults can enhance decision-making in prioritizing maintenance of faulty transformers.

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