Interpretation of DGA for transformer fault diagnosis with complementary SaE-ELM and arctangent transform

Li, Shuaibing; Wu, Guangning; Gao, Bo; Hao, Changjin; Xin, Dongli; Yin, Xu-Cheng

doi:10.1109/tdei.2015.005410

Cited by 93 publications

(58 citation statements)

References 29 publications

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“…To compare the proposed model with other artificial intelligent (AI)-based models published in the literature, DGA results in [38] have been re-assessed using the proposed GEP model. In [38], 10 DGA samples are assessed using four AI-based models namely; artificial neural network (ANN), support vector machine (SVM), extreme learning machine (ELM) and self-adaptive evolutionary extreme learning machine (SaE-ELM).…”

Section: Comparison With Other Ai-based Modelsmentioning

confidence: 99%

Improved Consistent Interpretation Approach of Fault Type within Power Transformers Using Dissolved Gas Analysis and Gene Expression Programming

Abu-Siada

2019

Energies

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Dissolved gas analysis (DGA) of transformer oil is considered to be the utmost reliable condition monitoring technique currently used to detect incipient faults within power transformers. While the measurement accuracy has become relatively high since the development of various off-line and on-line measuring sensors, interpretation techniques of DGA results still depend on the level of personnel expertise more than analytical formulation. Therefore, various interpretation techniques may lead to different conclusions for the same oil sample. Moreover, ratio-based interpretation techniques may fail in interpreting DGA data in case of multiple fault conditions and when the oil sample comprises insignificant amount of the gases used in the specified ratios. This paper introduces an improved approach to overcome the limitations of conventional DGA interpretation techniques, automate and standardize the DGA interpretation process. The approach is built based on incorporating all conventional DGA interpretation techniques in one expert system to identify the fault type in a more consistent and reliable way. Gene Expression Programming is employed to establish this expert system. Results show that the proposed approach provides more reliable results than using individual conventional methods that are currently adopted by industry practice worldwide.

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Section: Comparison With Other Ai-based Modelsmentioning

confidence: 99%

Improved Consistent Interpretation Approach of Fault Type within Power Transformers Using Dissolved Gas Analysis and Gene Expression Programming

Abu-Siada

2019

Energies

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“…More references on the applications of ELM in machine fault diagnostics were provided in Refs. [39][40][41][42].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

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Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few comprehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition monitoring and fault diagnosis. The recent research and development of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are discussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mechanism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suitable method for a specific situation and pointing out potential research directions.

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“…More recently, Frequency Domain Spectroscopy (FDS) [11][12][13], Polarization and Depolarization Current (PDC) [14][15][16] and Recovery Voltage Measurement (RVM) [17], which are based on dielectric response, have become a hotspot for scholars to study because of easy operation and no need for sampling. Compared with PDC and RVM, FDS has the advantages of rich insulation information and strong anti-interference ability, so it has great potential in oil-paper insulation condition evaluation [18].…”

Section: Introductionmentioning

confidence: 99%

A New Method for Evaluating Moisture Content and Aging Degree of Transformer Oil-Paper Insulation Based on Frequency Domain Spectroscopy

Xia

Gao

et al. 2017

Energies

Self Cite

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Abstract:The condition of oil-paper insulation is closely related to the life expectancy of a transformer. The accurate results of oil-paper have not been obtained due to the impact of influencing factors. Therefore, in order to improve the evaluation accuracy of oil-paper insulation, in this paper, oil-paper samples which were prepared with different aging and moisture content were analyzed by frequency domain spectroscopy (FDS). Results show that when the moisture content is less than 2%, the range of 10 1~1 0 3 Hz is mainly affected by moisture and aging has little effect. However, with the increase of moisture content, the effect of aging degree on this band became increasingly prominent. S m , which represents the integral value from 10 −1 to 10 −3 Hz, and S DP , which represents the integral value from 10 1 to 10 3 Hz, were extracted as characteristic parameters of moisture content and aging degree respectively. Compensation factors γ which represents the influence ratio of moisture on S DP and φ which represents the influence ratio of aging on S m were introduced to compensate for the influence of moisture content and aging degree on characteristics respectively. Then, a new method was proposed to evaluate the condition of oil-paper based on compensation factors. Through this method, the influence in characteristics can be eliminated by the obtained actual compensation factors, thus distinguishing the internal influence between moisture content and aging degree on FDS. Finally, this method was verified by field test.

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Interpretation of DGA for transformer fault diagnosis with complementary SaE-ELM and arctangent transform

Cited by 93 publications

References 29 publications

Improved Consistent Interpretation Approach of Fault Type within Power Transformers Using Dissolved Gas Analysis and Gene Expression Programming

Improved Consistent Interpretation Approach of Fault Type within Power Transformers Using Dissolved Gas Analysis and Gene Expression Programming

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

A New Method for Evaluating Moisture Content and Aging Degree of Transformer Oil-Paper Insulation Based on Frequency Domain Spectroscopy

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