Novel prediction‐reliability based graphical DGA technique using multi‐layer perceptron network &amp; gas ratio combination algorithm

Chatterjee, Kingshuk; Dawn, Subham; Jadoun, Vinay Kumar; Jarial, R. K.

doi:10.1049/iet-smt.2018.5397

Cited by 20 publications

(13 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The study discovered that after k was set to 13 (k = 13), there was no further improvement in the accuracy. Chatterjee et al in [22] proposed a new DGA procedure which works by combining the gas ratio method with the Duval's Pentagon 1; the two techniques that are known to have a high prediction accuracy. In essence, the technique is a blend of the gains of the ratio and graphical methods.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Multinomial DGA Classifier for Incipient Fault Detection in Oil-Impregnated Power Transformers

2021

View full text Add to dashboard Cite

This study investigates the use of machine-learning approaches to interpret Dissolved Gas Analysis (DGA) data to find incipient faults early in oil-impregnated transformers. Transformers are critical pieces of equipment in transmitting and distributing electrical energy. The failure of a single unit disturbs a huge number of consumers and suppresses economic activities in the vicinity. Because of this, it is important that power utility companies accord high priority to condition monitoring of critical assets. The analysis of dissolved gases is a technique popularly used for monitoring the condition of transformers dipped in oil. The interpretation of DGA data is however inconclusive as far as the determination of incipient faults is concerned and depends largely on the expertise of technical personnel. To have a coherent, accurate, and clear interpretation of DGA, this study proposes a novel multinomial classification model christened KosaNet that is based on decision trees. Actual DGA data with 2912 entries was used to compute the performance of KosaNet against other algorithms with multiclass classification ability namely the decision tree, k-NN, Random Forest, Naïve Bayes, and Gradient Boost. Investigative results show that KosaNet demonstrated an improved DGA classification ability particularly when classifying multinomial data.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Fuzzy logic models however tend to perform poorly when subjected to new data. In [22] a multilayer perceptron (MLP) network is discussed with boundaries defined using a fuzzy class. The model is centered on the Duval Pentagon and gas ratio combination.…”

Section: Introductionmentioning

confidence: 99%

A Multinomial DGA Classifier for Incipient Fault Detection in Oil-Impregnated Power Transformers

2021

View full text Add to dashboard Cite

show abstract

“…In turn, the artificial intelligence-based diagnostic techniques are an effective tool for maintenance transformer scheduling [ 10 , 11 , 12 ]. Hence, graphical DGA techniques are easy to be applied, nonetheless they still have limited diagnostic accuracies for different transformer faults [ 13 , 14 ]. More recently, the artificial neural network (ANN) is considered the most extensively used method in the literature for not only DGA but also diverse practical applications [ 15 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Towards Precise Interpretation of Oil Transformers via Novel Combined Techniques Based on DGA and Partial Discharge Sensors

Ward

Elfaraskoury

Badawi

et al. 2021

Sensors

View full text Add to dashboard Cite

Power transformers are considered important and expensive items in electrical power networks. In this regard, the early discovery of potential faults in transformers considering datasets collected from diverse sensors can guarantee the continuous operation of electrical systems. Indeed, the discontinuity of these transformers is expensive and can lead to excessive economic losses for the power utilities. Dissolved gas analysis (DGA), as well as partial discharge (PD) tests considering different intelligent sensors for the measurement process, are used as diagnostic techniques for detecting the oil insulation level. This paper includes two parts; the first part is about the integration among the diagnosis results of recognized dissolved gas analysis techniques, in this part, the proposed techniques are classified into four techniques. The integration between the different DGA techniques not only improves the oil fault condition monitoring but also overcomes the individual weakness, and this positive feature is proved by using 532 samples from the Egyptian Electricity Transmission Company (EETC). The second part overview the experimental setup for (66/11.86 kV–40 MVA) power transformer which exists in the Egyptian Electricity Transmission Company (EETC), the first section in this part analyzes the dissolved gases concentricity for many samples, and the second section illustrates the measurement of PD particularly in this case study. The results demonstrate that precise interpretation of oil transformers can be provided to system operators, thanks to the combination of the most appropriate techniques.

show abstract

“…The major drawbacks of these methods are the facts that they need some specialist interpretation for the diagnostics without supporting decision making, they just use some gas ratios and they all have been constructed considering only IEC TC 10 database, which is a database that compiles a lot of power equipment post-mortem analysis, relating the failures with DGA tests performed on those assets. In order to overcome the problem of lack of expert personnel, some solutions use fuzzy logic as (ABU-SIADA et al, 2013), (KHAN et al, 2015 and (NOORI et al, 2017) and combinations of Neural Networks and other standard methods, like the method presented in (CHATTERJEE et al, 2019). However, these propositions do not contemplate other points like data reliability and continuous training.…”

Section: Introductionmentioning

confidence: 99%

Microgrid System Design Based on Model Based Systems Engineering: The Case Study in the Amazon Region

Postigo¹,

Silva²

2021

Engenharia Elétrica: Desenvolvimento E Inovação Tecnológica

View full text Add to dashboard Cite

Direitos para esta edição cedidos à Atena Editora pelos autores. Todo o conteúdo deste livro está licenciado sob uma Licença de Atribuição Creative Commons. Atribuição-Não-Comercial-NãoDerivativos 4.0 Internacional (CC BY-NC-ND 4.0).O conteúdo dos artigos e seus dados em sua forma, correção e confiabilidade são de responsabilidade exclusiva dos autores, inclusive não representam necessariamente a posição oficial da Atena Editora. Permitido o download da obra e o compartilhamento desde que sejam atribuídos créditos aos autores, mas sem a possibilidade de alterá-la de nenhuma forma ou utilizá-la para fins comerciais. Todos os manuscritos foram previamente submetidos à avaliação cega pelos pares, membros do Conselho Editorial desta Editora, tendo sido aprovados para a publicação com base em critérios de neutralidade e imparcialidade acadêmica.A Atena Editora é comprometida em garantir a integridade editorial em todas as etapas do processo de publicação, evitando plágio, dados ou resultados fraudulentos e impedindo que interesses financeiros comprometam os padrões éticos da publicação. Situações suspeitas de má conduta científica serão investigadas sob o mais alto padrão de rigor acadêmico e ético.

show abstract

Novel prediction‐reliability based graphical DGA technique using multi‐layer perceptron network & gas ratio combination algorithm

Cited by 20 publications

References 28 publications

A Multinomial DGA Classifier for Incipient Fault Detection in Oil-Impregnated Power Transformers

A Multinomial DGA Classifier for Incipient Fault Detection in Oil-Impregnated Power Transformers

Towards Precise Interpretation of Oil Transformers via Novel Combined Techniques Based on DGA and Partial Discharge Sensors

Microgrid System Design Based on Model Based Systems Engineering: The Case Study in the Amazon Region

Contact Info

Product

Resources

About