Fault Diagnosis of Power Transformers Using Computational Intelligence: A Review

Sun, Huo-Ching; Huang, Yann-Chang; Huang, Chao-Ming

doi:10.1016/j.egypro.2011.12.1080

Cited by 58 publications

(26 citation statements)

References 38 publications

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“…Also, in the works [23][24][25] of the authors H. Suna, R. M. Velasquez; J.W.M. Lara; Dong Ling; Yao-Yu Xu; Yu Liang; Yuan Li; Ning Liuand Quan and Jun Zhang were reviewed methods of intelligent diagnostics of transformers that use fuzzy logic and in the future can be applied to improve diagnostic systems and other power equipment.…”

Section: Discussion Of the Results Of Transformation Ratios Of Ees Trmentioning

confidence: 99%

Determination of Optimal Transformation Ratios of Power System Transformers in Conditions of Incomplete Information Regarding the Values of Diagnostic Parameters

Demianovych¹,

Evgeniovich²,

Oleksandrivna³

2020

Fuzzy Logic

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On the base of damage rate analysis of power transformers and methods of electrical energy system (EES) modes control the necessity of using the results of online diagnostics of LTC transformers not only for determinations of the expending of further operation or equipment repair but also for calculation of optimal transformation coefficients (with account of the suggested RRCT) for their application in the process of modes control has been proved. Improved method of determination of control action, realized by the LTC transformers by means of comparative analysis of the results calculation of EES modes with quasi resistances of the circuit branches. Such peculiarity of the suggested method of determination of control actions by LTC transformers, as the account of RRCT, in the process of EES mode control provides such advantages as reduction of the damage rate of the equipment, reduction of active power losses in EES. Due to the peculiarities of the method of determination of control actions by LTC transformers, with the account of their technical state, perspectives of the development and introduction in EES modern microprocessor-based systems of automatic control of transformers LTC open.

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Section: Discussion Of the Results Of Transformation Ratios Of Ees Trmentioning

confidence: 99%

Determination of Optimal Transformation Ratios of Power System Transformers in Conditions of Incomplete Information Regarding the Values of Diagnostic Parameters

Demianovych¹,

Evgeniovich²,

Oleksandrivna³

2020

Fuzzy Logic

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“…There is need for more advanced techniques based on artificial intelligence (AI) (Taneja et al, 2016;Perez et al, 1994). Fuzzy interface, wavelet network, genetic algorithm, artificial neural network are some of the examples for AI system where the trained data provides quick and accurate result as compare to classical one or conventional methods (Su et al, 2000;Khan et al, 2015;Wang, 2003;Ahmed et al, 2013;Fei and Zhang, 2009;Li et al, 2016;Shintemirov et al, 2009;Sun et al, 2012a).…”

Section: Artificial Intelligence Techniquesmentioning

confidence: 99%

A review on fault detection and condition monitoring of power transformer

Aslam¹

2019

Int. j. adv. appl. sci.

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Real-time monitoring of transformers ensures equipment safety and guarantees the necessary intervention in precise time thereby reducing the risk of non-schedule energy blackouts. Many utilities monitor the condition of the components that make up a power transformer and use this information to minimize interruption and prolong life. Currently, routine and diagnostic tests are used to monitor conditions and assess the aging and defects of the core, windings, bushings and power transformer tape changers. To accurately assess the remaining life and probability of failure, methods have been developed to correlate the results of different routine and diagnostic tests. There are several electrical and chemical (diagnostic) techniques available for condition monitoring of power transformer. This paper reviews real time techniques used for condition-based monitoring of power transformer.

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“…The analysis technique of dissolved gas is based on traditional (conventional) methods of faults diagnosis such as, Key Gas Method, Dornenburg Ratio Method, Rogers Ratio Method, Method nomograph, IEC Ratio Method, Duval Triangle Method, and CIGRE Method [2]. Recently results accuracy and remove the inherent uncertainty in the diagnosis and data reliability (Fuzzy Logic (FL), Artificial Neural Networks (ANN), Support Vector Machine (SVM), Evolutionary Algorithms and Expert System) [3].…”

Section: Introductionmentioning

confidence: 99%

Transformer fault diagnosis using dissolved gas analysis technology and Bayesian networks

Lakehal

Ghemari

Saad

2015

2015 4th International Conference on Systems and Control (ICSC)

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Bayesian model is developed for transformer faults diagnosis using dissolved gas in oil analysis. DGA (Dissolved Gas Analysis) is the traditional and conventional transformer fault diagnosis method, which mainly depends on the experience of operators and of the percentages of dissolved gases. In addition, the only measurement of the gases percentage is not sufficient to evaluate the equipment health. There are several cases where the proportions of dissolved gases remain trapped in the transformer. Regarding this uncertainty and in order to make decisions in a certain environment, the model developed in this study represents a powerful tool for decision making. In addition, one traditional method of DGA does not enable the diagnosis of all faults, for example the Rogers Ratio Method diagnose five faults only, but using the proposed Bayesian network (BN) it is possible to diagnose all faults from the same model. To illustrate the advantages of Bayesian methods in transformer fault diagnosis, a study of power station main transformer is conducted and the results are analyzed and discussed.

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Fault Diagnosis of Power Transformers Using Computational Intelligence: A Review

Cited by 58 publications

References 38 publications

Determination of Optimal Transformation Ratios of Power System Transformers in Conditions of Incomplete Information Regarding the Values of Diagnostic Parameters

Determination of Optimal Transformation Ratios of Power System Transformers in Conditions of Incomplete Information Regarding the Values of Diagnostic Parameters

A review on fault detection and condition monitoring of power transformer

Transformer fault diagnosis using dissolved gas analysis technology and Bayesian networks

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