scholarly journals Power Transformer Fault Diagnosis using DGA based on Three Gas Ratio and Fuzzy Logic

2019 ◽  
Vol 8 (2) ◽  
pp. 95-100
Keyword(s):  
Fuzzy Logic ◽  
Power Transformer ◽  
Electrical Engineering ◽  
Gas Analysis ◽  
Ratio Method ◽  
Sharp Change ◽  
Multiple Faults ◽  
Engineering And Technology ◽  
College Of Engineering ◽  
Gas Ratio

For power system equipment with oil as insulating medium such as power transformer, Dissolved Gas Analysis (DGA) of oil is very helpful method in order to detect faults below oil level. Early detection of fault conditions in a transformer is possible if analysis of gases is done which gets evolved in it. Analysis of the specific value of every gas helps in diagnosing faults. Faults which can be identified by this method include disturbances like presence of corona discharge, partial discharges, arcing and increase in temperature. If correct preventive actions are initiated early for the diagnosis of gases produced, failure to equipment will get prevented. Even though many methods are researched for fault identification and analysis in power transformers, DGA is much superior in comparison with other techniques as it gives more helpful data about the condition of the transformers in running condition. Different techniques, like key gases and their ratio, and their analyzing them graphically are mainly used to understand DGA samples. For a transformer having multiple faults, above methods fail to diagnose. IEC standards are in use for DGA from many years and valuable experience gained over theses years around the world is in use to diagnose internal faults on transformers. IEC three gas ratio technique suggested by IEC is mainly preferred, but in some conditions it can’t correctly identify conditions like no suitable codes for diagnosis and multiple faults. The limitations of the traditional three gas ratio method are: with gas ratio is on the verge of crossing the coding boundary, there is a sharp change in the codes, but actually fuzzied boundary should be used. In this paper, codes "zero", "one", "two" are represented by fuzzy membership functions, then "AND" and "OR" conditions of three gas ratio method are coded into fuzzy logic based statements. MATLAB based scripts prove that the presented technique surely overcome the limitations of the traditional three gas ratio method, hence, it largely reduces the errors in diagnosis. In this paper, a method on the basis of fuzzy logic is explained which is able to identify many faults in oil insulated equipment. The presented diagnosis technique uses values of the ratios C2H4/C2H6, C2H2/C2H4 and CH4/H2 and the concentration of specific gases namely methane (CH4), hydrogen (H2), acetylene (C2H2), carbon monoxide (CO), ethylene (C2H4), carbon dioxide (CO2) and ethane (C2H6). Values of these three ratios reflect various patterns of faults inside the transformer. Fuzzy three ratio technique can also quantitatively indicate the likelihood of identified fault with more accuracy as compared to conventional three ratio method. Revised Manuscript Received on July 02, 2019. Nagesh Kalidas Bhosale, M.E. Student, Department of Electrical Engineering, M.S.S.’s College of Engineering and Technology, Jalna, MS, India. Prof. Chandra O. Reddy, H.O.D., Department of Electrical Engineering, M.S.S.’s College of Engineering and Technology, Jalna, MS, India. Prof. Pankaj Bhakre, Assistant Professor, Department of Electrical Engineering, M.S.S.’s College of Engineering and Technology, Jalna, MS, India. This tool will prove to be very useful to the engineers in DGA result interpretation.

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 Investigations on Incipient Fault Diagnosis of Power Transformer Using Neural Networks and Adaptive Neurofuzzy Inference System

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Nandkumar Wagh ◽  
D. M. Deshpande
Keyword(s):  
Distribution System ◽  
Power Transformer ◽  
Rbf Neural Network ◽  
Correct Diagnosis ◽  
Well Being ◽  
Gas Analysis ◽  
Inference System ◽  
Training Time ◽  
Multiple Faults ◽  
Adaptive Neurofuzzy Inference System

Continuity of power supply is of utmost importance to the consumers and is only possible by coordination and reliable operation of power system components. Power transformer is such a prime equipment of the transmission and distribution system and needs to be continuously monitored for its well-being. Since ratio methods cannot provide correct diagnosis due to the borderline problems and the probability of existence of multiple faults, artificial intelligence could be the best approach. Dissolved gas analysis (DGA) interpretation may provide an insight into the developing incipient faults and is adopted as the preliminary diagnosis tool. In the proposed work, a comparison of the diagnosis ability of backpropagation (BP), radial basis function (RBF) neural network, and adaptive neurofuzzy inference system (ANFIS) has been investigated and the diagnosis results in terms of error measure, accuracy, network training time, and number of iterations are presented.

scholarly journals DGA Method-Based ANFIS Expert System for Diagnosing Faults and Assessing Quality of Power Transformer Insulation Oil

2015 ◽  
Vol 10 (1) ◽  
pp. 13 ◽  
Author(s):  
Mohd Muhridza Bin Yaacob ◽  
Ahmed Raisan Hussein ◽  
Mohd Fauzi Bin Othman
Keyword(s):  
Expert System ◽  
Power Transformer ◽  
Oil Quality ◽  
Gas Analysis ◽  
Ratio Method ◽  
Economic Losses ◽  
Intelligent Software ◽  
Transformer Insulation ◽  
Insulation Oil

<p class="zhengwen"><span lang="EN-GB">Accurate fault diagnostics and assessment of electrical power transformer insulation oil for lifelong endurance are the key issues addressed in this research. The durability of a transformer is significantly determined by the quality of its insulation oil, which deteriorates over time due to temperature fluctuations and moisture content. Protecting transformers from potential failure through early and precise diagnosis of faults and through efficient assessment of oil quality during the actual conduct of the operation can avoid sizeable economic losses. The ANFIS Expert System that uses intelligent software plays an important role in this regard. The dissolved gas analysis (DGA) in oil is a reliable method for diagnosing faults and assessing insulation oil quality in transformers. The safeguarding teams of transformer power stations often suffer from the occurrence of sudden faults, which result in severe damages and heavy monetary loss. The oil in transformers must be appropriately treated to circumvent such failures. In this research, an ANFIS Expert System was used to diagnose faults and to assess the status and quality of insulation oil in power transformers. A suitable treatment was identified using the Rogers ratio method depending on the DGA in oil. The graphical user interface from the MATLAB environment was used and proven effective for fault diagnosis and oil quality evaluation. The training algorithm is capable of assessing oil quality according to the specifications of the IEEE standard C57-104 and the IEC standard 60599.</span></p>

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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