A review on prognosis and diagnosis of transformer oil quality using intelligent techniques based on dissolved gas analysis

Taneja, Manjeet Singh; Pandey, Kamlesh; Sehrawat, Sumeet

doi:10.1109/iicpe.2016.8079419

Cited by 9 publications

(3 citation statements)

References 22 publications

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“…This ratio technique correlates the main gases to faults types and try to identify four different types of faults that also include winding failure (corona) (PD), overheating of oil cellulose paper and arcing. However, key gas ratio method has several drawbacks, the diagnoses are not accurate enough, the diagnoses may be inconclusive if some of gases are not found (Taneja et al, 2016).…”

Section: Key Gas Ratio Methodsmentioning

confidence: 99%

“…As it mainly depends on human expert experience, therefore there is always probability of irregularity and uncertainty in interpreting the acquired data. 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%

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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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Section: Key Gas Ratio Methodsmentioning

confidence: 99%

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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“…The authors point out that the development of new algorithms is necessary to improve diagnostic accuracy. Taneja et al (2016) review and summarize both traditional methods of DGA and DGA-based intelligence techniques and hope that future research in this area will not be limited to one diagnostic method. Ge et al (2018) review the application of improved DGA methods in transformer fault diagnosis in terms of AI algorithms combined with DGA techniques, improvement of traditional DGA techniques, and statistical methods for DGA interpretation.…”

mentioning

confidence: 99%

Fault diagnosis of transformer using artificial intelligence: A review

et al. 2022

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Transformer is one of the important components of the power system, capable of transmitting and distributing the electricity generated by renewable energy sources. Dissolved Gas Analysis (DGA) is one of the effective techniques to diagnose early faults in oil-immersed transformers. It correlates the concentration and ratio of dissolved gases with transformer faults. Researchers have proposed many methods for fault diagnosis, such as double ratio method, Rogers method, Duval triangle method, etc., but all of them have some problems. Based on the strong data mining capability and good robustness of AI techniques, many researchers introduced AI techniques to mine the features of DGA data. According to the characteristics and scale of DGA data, researchers select appropriate AI techniques or make appropriate improvements to AI techniques to improve diagnostic performance. This paper presents a systematic review of the literature on the application of artificial intelligence techniques for DGA-based diagnosis and for solving intractable problems in early transformer fault diagnosis, which include neural networks, clustering, support vector machines, etc. In addition to reviewing the applications of these intelligent techniques, the diagnostic thinking proposed in this literature, such as the introduction of temporal parameters for comprehensive analysis of DGA data and the extraction of optimal features for DGA data, is also reviewed. Finally, this paper summarizes and prospects the artificial intelligence techniques applied by researchers in transformer fault diagnosis.

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