Diagnosis of Power Transformer Faults using Fuzzy Logic Techniques Based on IEC Ratio Method

2020 ◽  
Author(s):  
Omar M. Elmabrouk ◽  
Farag A. Masoud ◽  
Naji S. Abdelwanis
Keyword(s):  
Fuzzy Logic ◽  
Power Transformer ◽  
Ratio Method

scholarly journals Predicting the technical condition of the power transformer using fuzzy logic and dissolved gas analysis method

2022 ◽  
Vol 12 (2) ◽  
pp. 1139
Author(s):  
Vladimir Mikhailovich Levin ◽  
Ammar Abdulazez Yahya ◽  
Diana A. Boyarova
Keyword(s):  
Fuzzy Logic ◽  
High Efficiency ◽  
Power Transformer ◽  
Thermal Power ◽  
Technical Condition ◽  
Electric Power System ◽  
Diagnostic Methods ◽  
Digital Model ◽  
Vibration Monitoring ◽  
Ratio Method

Power transformers are one of the most important and complex parts of an electric power system. Maintenance is performed for this responsible part based on the technical condition of the transformer using a predictive approach. The technical condition of the power transformer can be diagnosed using a range of different diagnostic methods, for example, analysis of dissolved gases (DGA), partial discharge monitoring, vibration monitoring, and moisture monitoring. In this paper, the authors present a digital model for predicting the technical condition of a power transformer and determining the type of defect and its cause in the event of defect detection. The predictive digital model is developed using the programming environment in LabVIEW and is based on the fuzzy logic approach to the DGA method, interpreted by the key gas method and the Dornenburg ratio method. The developed digital model is verified on a set of 110 kV and 220 kV transformers of one of the sections of the distribution network and thermal power plant in the Russian Federation. The results obtained showed its high efficiency in predicting faults and the possibility of using it as an effective computing tool to facilitate the work of the operating personnel of power enterprises.

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.

Inter-turn fault stability enrichment and diagnostic analysis of power system network using wavelet transformation-based sample data control and fuzzy logic controller

2021 ◽  
pp. 014233122110070
Author(s):  
Arunesh Kumar Singh ◽  
Abhinav Saxena ◽  
Nathuni Roy ◽  
Umakanta Choudhury
Keyword(s):  
Fuzzy Logic ◽  
Power System ◽  
Wavelet Transformation ◽  
Fuzzy Logic Controller ◽  
Power Transformer ◽  
Harmonic Distortion ◽  
Inrush Current ◽  
Data Control ◽  
Sample Data ◽  
Z Transformation

In this paper, performance analysis of power system network is carried out by injecting the inter-turn fault at the power transformer. The injection of inter-turn fault generates the inrush current in the network. The power system network consists of transformer, current transformer, potential transformer, circuit breaker, isolator, resistance, inductance, loads, and generating source. The fault detection and termination related to inrush current has some drawbacks and limitations such as slow convergence rate, less stability and more distortion with the existing methods. These drawbacks motivate the researchers to overcome the drawbacks with new proposed methods using wavelet transformation with sample data control and fuzzy logic controller. The wavelet transformation is used to diagnose the fault type but contribute lesser for fault termination; due to that, sample data of different signals are collected at different frequencies. Further, the analysis of collected sample data is assessed by using Z-transformation and fuzzy logic controller for fault termination. The stability, total harmonic distortion and convergence rate of collected sample data among all three methods (wavelet transformation, Z-transformation and fuzzy logic controller) are compared for fault termination by using linear regression analysis. The complete performance of fault diagnosis along with fault termination has been analyzed on Simulink. It is observed that after fault injection at power transformer, fault recovers faster under fuzzy logic controller in comparison with Z-transformation followed by wavelet transformation due to higher stability, less total harmonic distortion and faster convergence.

scholarly journals Power transformer fault diagnosis system based on Internet of Things

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Guoshi Wang ◽  
Ying Liu ◽  
Xiaowen Chen ◽  
Qing Yan ◽  
Haibin Sui ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Internet Of Things ◽  
Power Transformer ◽  
Fault System ◽  
Ratio Method ◽  
Diagnosis System ◽  
Network Training ◽  
Fault Diagnosis System ◽  
Training Error ◽  
Transformer Fault

Abstract Transformer is the most important equipment in the power system. The research and development of fault diagnosis technology for Internet of Things equipment can effectively detect the operation status of equipment and eliminate hidden faults in time, which is conducive to reducing the incidence of accidents and improving people's life safety index. Objective To explore the utility of Internet of Things in power transformer fault diagnosis system. Methods A total of 30 groups of transformer fault samples were selected, and 10 groups were randomly selected for network training, and the rest samples were used for testing. The matter-element extension mathematical model of power transformer fault diagnosis was established, and the correlation function was improved according to the characteristics of three ratio method. Each group of power transformer was diagnosed for four months continuously, and the monitoring data and diagnosis were recorded and analyzed result. GPRS communication network is used to complete the communication between data acquisition terminal and monitoring terminal. According to the parameters of the database, the working state of the equipment is set, and various sensors are controlled by the instrument driver module to complete the diagnosis of transformer fault system. Results The detection success rate of the power transformer fault diagnosis system model established in this paper is as high as 95.6%, the training error is less than 0.0001, and it can correctly identify the fault types of the non training samples. It can be seen that the technical support of the Internet of Things is helpful to the upgrading and maintenance of the power transformer fault diagnosis system.

Research on Power Transformer Fault Forecast

2011 ◽  
Vol 204-210 ◽  
pp. 1553-1558
Author(s):  
Rui Rui Zheng ◽  
Ji Yin Zhao ◽  
Min Li ◽  
Bao Chun Wu
Keyword(s):  
Time Series ◽  
Fault Location ◽  
Power Transformer ◽  
Forecast Model ◽  
Ratio Method ◽  
Gray Model ◽  
Artificial Immune ◽  
Diagnosis Method ◽  
Type Data ◽  
Transformer Fault

To forecast power transformer fault, this paper proposed a integrated algorithm. Research found that discrete time series of power transformer dissolved gases concentration have 2 main types: the s type and the monotone increasing type. The gray verhulst model was chosen for forecasting the s type series, while the gray model predicted the monotone increasing type data. The two models combined a new integrated forecast model. The fault diagnosis method combines the improved three-ratio method and gray artificial immune algorithm, so it can diagnoses both single and multi power transformer faults, and give the fault location. Experiments show that the power transformer fault forecast algorithm is effective and reliable.

A Fuzzy Logic Proposal for Diagnosis Multiple Incipient Faults in a Power Transformer

2021 ◽  
pp. 187-198
Author(s):  
J. C. Fernández ◽  
L. B. Corrales ◽  
F. H. Hernández ◽  
I. F. Benítez ◽  
J. R. Núñez
Keyword(s):  
Fuzzy Logic ◽  
Power Transformer ◽  
Incipient Faults

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.

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