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

Vladimir Mikhailovich Levin ◽  
Ammar Abdulazez Yahya ◽  
Diana A. Boyarova

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.

2021 ◽  
Vol 2131 (5) ◽  
pp. 052049
V Z Manusov ◽  
M R Otuzbaev ◽  
M A Scherbinina ◽  
G V Ivanov

Abstract Assessment of the current technical condition is an important task, so the state of electrical equipment depends on its further operability. Thanks to modern computing devices, it is possible to implement actively artificial intelligence and computer-assisted learning methods that allow achieving high efficiency in data processing. A study was conducted and an algorithm for diagnosing the technical condition based on an artificial neural network was developed. A model based on a multilayer perceptron is proposed, which allows evaluating the technical condition of a high-voltage power transformer. The result of the technical diagnostics of the model is the assignment of the condition to one of the five classes, proposed by the guidelines presented by the International Council on Large Electrical Systems. The methodology is presented on the example of a 250 MVA transformer with a certain defect history, which allowed us to show the reliability and validity of the obtained results. It is shown that the use of the proposed model makes it possible to achieve accuracy in determining the technical condition of 0.95. The introduction of this model into an automated monitoring and diagnostics system will allow assessing the technical condition of electrical equipment in real time with sufficient accuracy.

Algorithms ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 319
Diego A. Zaldivar ◽  
Andres A. Romero ◽  
Sergio R. Rivera

In every electric power system, power transformers (PT) play a critical role. Under ideal circumstances, PT should receive the utmost care to maintain the highest operative condition during their lifetime. Through the years, different approaches have been developed to assess the condition and the inherent risk during the operation of PT. However, most proposed methodologies tend to analyze PT as individuals and not as a fleet. A fleet assessment helps the asset manager make sound decisions regarding the maintenance scheduling for groups of PT with similar conditions. This paper proposes a new methodology to assess the risk of PT fleets, considering the technical condition and the strategic importance of the units. First, the state of the units was evaluated using a health index (HI) with a fuzzy logic algorithm. Then, the strategic importance of each unit was assessed using a weighting technique to obtain the importance index (II). Finally, the analyzed units with similar HI and II were arranged into a set of clusters using the k-means clustering technique. A fleet of 19 PTs was used to validate the proposed method. The obtained results are also provided to demonstrate the viability and feasibility of the assessment model.

2021 ◽  
Gints Poišs

A power transformer is a key unit in the transmission system, and its cut-off can impact both consumers and the general stability of the system. Therefore, it is an important tool for processing the operational and technical condition data to quantify them as the technical condition index (TCI). Based on the technical condition of the power transformer, the TCI enables objective and reasoned decisions on the future investments related to replacement or repairs of transformers. Thus, by using the TCI, the service life of the transformer can be safely extended, since the identified risks have been recognized and are being followed-up. The TCI method is useful for a power transformer park, because it allows easy identification of transformers that require most attention. A crucial precondition for this method is data availability, diversity, and regularity or frequency of data collection. These features (preconditions) may vary in different power transmission systems, and it creates the necessity for a tailored approach. The present Doctoral Thesis studies the diagnostic methods used in the transmission system in Latvia and the results thereof. Thus, it takes advantage of an already existing data set and flow to develop a TCI-based complex of algorithms for determining the risk level of high-power transformers in acceptable risk conditions.

2021 ◽  
Vol 2091 (1) ◽  
pp. 012064
A P Khlebtsov ◽  
A N Shilin ◽  
A V Rybakov ◽  
A Yu Klyucharev

Abstract In this paper, an expert information system for assessing the technical condition of a power transformer is developed. The system will work on the basis of the fuzzy logic device, and provide operational information about the state of the power transformer. The paper uses fuzzy inference algorithms. The R programming language is used to write a program that uses fuzzy logic. We analyzed the data of chromatographic analysis of gases dissolved in oil, as well as the data of thermal imaging images, identifying the most heated points in power transformers. A database of fuzzy logic rules has been formed. Several examples of defuzzification of the results obtained by the center of gravity method are given. As a result of the program, a three-dimensional graph was obtained that characterizes the surface of the fuzzy output. The developed software package allows you to detect defects in working electrical equipment at an early stage of their development, which not only prevents a sudden shutdown of production as a result of an accident, but also significantly reduces the cost of repairing equipment and increases its service life

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.

2019 ◽  
Vol 26 (11) ◽  
pp. 1946-1959 ◽  
Le Minh Tu Phan ◽  
Lemma Teshome Tufa ◽  
Hwa-Jung Kim ◽  
Jaebeom Lee ◽  
Tae Jung Park

Background:Tuberculosis (TB), one of the leading causes of death worldwide, is difficult to diagnose based only on signs and symptoms. Methods for TB detection are continuously being researched to design novel effective clinical tools for the diagnosis of TB.Objective:This article reviews the methods to diagnose TB at the latent and active stages and to recognize prospective TB diagnostic methods based on nanomaterials.Methods:The current methods for TB diagnosis were reviewed by evaluating their advantages and disadvantages. Furthermore, the trends in TB detection using nanomaterials were discussed regarding their performance capacity for clinical diagnostic applications.Results:Current methods such as microscopy, culture, and tuberculin skin test are still being employed to diagnose TB, however, a highly sensitive point of care tool without false results is still needed. The utilization of nanomaterials to detect the specific TB biomarkers with high sensitivity and specificity can provide a possible strategy to rapidly diagnose TB. Although it is challenging for nanodiagnostic platforms to be assessed in clinical trials, active TB diagnosis using nanomaterials is highly expected to achieve clinical significance for regular application. In addition, aspects and future directions in developing the high-efficiency tools to diagnose active TB using advanced nanomaterials are expounded.Conclusion:This review suggests that nanomaterials have high potential as rapid, costeffective tools to enhance the diagnostic sensitivity and specificity for the accurate diagnosis, treatment, and prevention of TB. Hence, portable nanobiosensors can be alternative effective tests to be exploited globally after clinical trial execution.

Arunesh Kumar Singh ◽  
Abhinav Saxena ◽  
Nathuni Roy ◽  
Umakanta Choudhury

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.

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