Dissolved gas analysis of transformers using fuzzy logic approach

Aghaei, Jamshid; Gholami, Ahmad; Shayanfar, Heidar Ali; Dezhamkhooy, A.

doi:10.1002/etep.343

Cited by 17 publications

(10 citation statements)

References 10 publications

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“…This can not only gain the running state of the transformer timely, based on which latent faults can be detected and tracked, but also diagnose fault automatically due to the expert system so as for operating crew, fault can be handled rapidly. Using on-line monitoring device can improve the management level of substation operation and lay a foundation of transition from preventive maintenance system to predictive maintenance system [28]. …”

Section: The Methods For Dissolved Gases Detection In Oil-insulated Tmentioning

confidence: 99%

Experimental Analysis of Modified CNTs-Based Gas Sensor

Tang¹,

Zhang²,

Xiao³

et al. 2017

Electrochemical Sensors Technology

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As a significant equipment in power system, the operation condition of transformers directly determines the safety of power system. Therefore, it has been an indispensable measure to detect and analyze the dissolved gases in transformers, aiming to estimate the early potential faults in oil-insulated transformers. In this chapter, the adsorption processes between modified carbon nanotubes (CNTs) (CNTs-OH, Ni-CNTs) and dissolved gases in transformers oil including C 2 H 2 , C 2 H 4 , C 2 H 6 , CH 4 , CO, and H 2 have been simulated based on the first principle theory. Meanwhile, the density of states (DOS), adsorption energy, charge transfer amount, and adsorption distance of adsorption process between CNTs and dissolved gases were calculated. Moreover, two kinds of sensors, mixed acid-modified CNTs and NiCl 2 -modified CNTs, are prepared to conduct the dissolved gases response experiment. Then, the gas response mechanisms were investigated. Finally, the results between response experiment and theoretical calculation were compared, reflecting a good coherence with each other. The CNTs gas sensors possess a relatively high sensitivity and fine linearity, and could be employed in dissolved gas analysis equipment in transformer.

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Section: The Methods For Dissolved Gases Detection In Oil-insulated Tmentioning

confidence: 99%

Experimental Analysis of Modified CNTs-Based Gas Sensor

Tang¹,

Zhang²,

Xiao³

et al. 2017

Electrochemical Sensors Technology

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“…It is also utilized in detection and verification of 20 transformer faults and the results show that this proposed diagnostic tool is very useful to both expert and novice engineers in DGA result interpretation. In addition, Aghaei et al [153] used three fuzzy methods for specifying the internal faults of transformer through the ratio method of oil-immersed gases. The results show that the proposed methods are effective enough in the diagnosis of transformers internal faults.…”

Section: Fuzzy Theory In Dga-based Transformer Fault Diagnosis: a Surveymentioning

confidence: 99%

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

Cheng

2018

Energies

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Abstract: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.

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“…., K, a unique coding c k = [ y (1) m ,. Let us first consider a K classes with a set of N two-class classifiers.…”

Section: Multiclass Svm-based Classifiersmentioning

confidence: 99%

A multiclass SVM-based classifier for transformer fault diagnosis using a particle swarm optimizer with time-varying acceleration coefficients

Liao

Zheng

Grzybowski

et al. 2011

Int. Trans. Electr. Energ. Syst.

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SUMMARY A multiclass support vector machine (SVM) classifier based upon particle swarm optimization (PSO) with time‐varying acceleration coefficients for fault diagnosis of power transformers is proposed in this paper. The one‐against‐one combination scheme is adopted to extend SVM for settling the multiclass classification problem. The algorithm of PSO with time‐varying acceleration coefficients (PSO‐TVAC) is employed to optimize the parameters for SVM. The results show that the convergence of the PSO‐TVAC algorithm is relatively faster and much more precise than that of the classical PSO. Moreover, compared with other reference diagnosis approaches, the results demonstrate the improved classification accuracy based upon the proposed approach and show it can be used as an effective tool for fault diagnosis of power transformers. Copyright © 2011 John Wiley & Sons, Ltd.

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Dissolved gas analysis of transformers using fuzzy logic approach

Cited by 17 publications

References 10 publications

Experimental Analysis of Modified CNTs-Based Gas Sensor

Experimental Analysis of Modified CNTs-Based Gas Sensor

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

A multiclass SVM-based classifier for transformer fault diagnosis using a particle swarm optimizer with time-varying acceleration coefficients

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