A transformer condition recognition method based on dissolved gas analysis features selection and multiple models fusion

Han, Xiaohui; Huang, Song; Zhang, Xu; Zhu, Yuman; An, Guoqing; Du, Zhenbin

doi:10.1016/j.engappai.2023.106518

Cited by 4 publications

(4 citation statements)

References 21 publications

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“…Several transformers had undergone maintenance over their existence. The cooling system designs of transformers ranged from Oil Natural Air Natural (ONAN); Oil Natural Air Forced (ONAF); Oil Forced Air Forced (OFAF); Oil Natural Water Forced (ONWF); and Oil Forced Water Forced (OFWF) 72 , 73 . Table 5 contains technical information on the transformer group under consideration 72 .…”

Section: Methods Used To Investigate Transformers In-servicementioning

confidence: 99%

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Application of back propagation neural network in complex diagnostics and forecasting loss of life of cellulose paper insulation in oil-immersed transformers

Ngwenyama,

Gitau

2024

Sci Rep

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Oil-immersed transformers are expensive equipment in the electrical system, and their failure would lead to widespread blackouts and catastrophic economic losses. In this work, an elaborate diagnostic approach is proposed to evaluate twenty-six different transformers in-service to determine their operative status as per the IEC 60599:2022 standard and CIGRE brochure. The approach integrates dissolved gas analysis (DGA), transformer oil integrity analysis, visual inspections, and two Back Propagation Neural Network (BPNN) algorithms to predict the loss of life (LOL) of the transformers through condition monitoring of the cellulose paper. The first BPNN algorithm proposed is based on forecasting the degree of polymerization (DP) using 2-Furaldehyde (2FAL) concentration measured from oil samples using DGA, and the second BPNN algorithm proposed is based on forecasting transformer LOL using the 2FAL and DP data obtained from the first BPNN algorithm. The first algorithm produced a correlation coefficient of 0.970 when the DP was predicted using the 2FAL measured in oil and the second algorithm produced a correlation coefficient of 0.999 when the LOL was predicted using the 2FAL and DP output data obtained from the first algorithm. The results show that the BPNN can be utilized to forecast the DP and LOL of transformers in-service. Lastly, the results are used for hazard analysis and lifespan prediction based on the health index (HI) for each transformer to predict the expected years of service.

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Section: Methods Used To Investigate Transformers In-servicementioning

confidence: 99%

“…Several studies have used the BPNN approach 60 , 72 to anticipate numerous transformer states, such as the diagnosis of incipient defects using DGA 111 . In this work, BPNN algorithms are presented to forecast the remaining DP utilizing 2FAL concentration.…”

Section: Methods Used To Investigate Transformers In-servicementioning

confidence: 99%

Application of back propagation neural network in complex diagnostics and forecasting loss of life of cellulose paper insulation in oil-immersed transformers

Ngwenyama,

Gitau

2024

Sci Rep

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“…DGA is a technique for detecting and forecasting problems in OITs by (i) determining the levels of various gases contained in the insulation oil, as well as respective gas rates and gas proportions, (ii) fault detection utilizing diagnosis instruments such as KG 20 , 21 , IEC ratios 22 , Rogers ratios 23 , Doernenburg ratios 24 and Duval triangle 23 . Nevertheless, these instruments have certain flaws.…”

Section: Introductionmentioning

confidence: 99%

Discernment of transformer oil stray gassing anomalies using machine learning classification techniques

Ngwenyama,

Gitau

2024

Sci Rep

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This work examines the application of machine learning (ML) algorithms to evaluate dissolved gas analysis (DGA) data to quickly identify incipient faults in oil-immersed transformers (OITs). Transformers are pivotal equipment in the transmission and distribution of electrical power. The failure of a particular unit during service may interrupt a massive number of consumers and disrupt commercial activities in that area. Therefore, several monitoring techniques are proposed to ensure that the unit maintains an adequate level of functionality in addition to an extended useful lifespan. DGA is a technique commonly employed for monitoring the state of OITs. The understanding of DGA samples is conversely unsatisfactory from the perspective of evaluating incipient faults and relies mainly on the proficiency of test engineers. In the current work, a multi-classification model that is centered on ML algorithms is demonstrated to have a logical, precise, and perfect understanding of DGA. The proposed model is used to analyze 138 transformer oil (TO) samples that exhibited different stray gassing characteristics in various South African substations. The proposed model combines the design of four ML classifiers and enhances diagnosis accuracy and trust between the transformer manufacturer and power utility. Furthermore, case reports on transformer failure analysis using the proposed model, IEC 60599:2022, and Eskom (Specification—Ref: 240-75661431) standards are presented. In addition, a comparison analysis is conducted in this work against the conventional DGA approaches to validate the proposed model. The proposed model demonstrates the highest degree of accuracy of 87.7%, which was produced by Bagged Trees, followed by Fine KNN with 86.2%, and the third in rank is Quadratic SVM with 84.1%.

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“…In addition, multi-model fusion is also the research direction of many scholars. Xiaohui Han et al [25] proposed a multi-model fusion transformer state identification method combining the vector classifier (SVC) model, the plain Bayesian classifier (NBC) model, and the backpropagation neural network (BPNN) model. Mingwei Zhong et al [3] proposed a hybrid model based on the Hierarchical Attention Network (HATT) and Recursive Long Short-Term Memory Network (RLSTM), which effectively eliminates the time lag problem when predicting the results of the DGA sequence.…”

Section: Introductionmentioning

confidence: 99%

Transformer Fault Diagnosis Method Based on TimesNet and Informer

Zhang,

Yang,

Zheng

2024

Actuators

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Since the traditional transformer fault diagnosis method based on dissolved gas analysis (DGA) is challenging to meet today’s engineering needs, this paper proposes a multi-model fusion transformer fault diagnosis method based on TimesNet and Informer. First, the original TimesNet structure is improved by adding the MCA module to the Inception structure of the original TimesBlock to reduce the model complexity and computational burden; second, the MUSE attention mechanism is introduced into the original TimesNet to act as a bridge, so that associations can be carried out effectively among the local features, thus enhancing the modeling capability of the model; finally, when constructing the feature module, the TimesNet and Informer multilevel parallel feature extraction modules are introduced, making full use of the local features of the convolution and the global correlation of the attention mechanism module for feature summarization, so that the model learns more time-series information. To verify the effectiveness of the proposed method, the model is trained and tested on the public DGA dataset, and the model is compared and experimented with classical models such as Informer and Transformer. The experimental results show that the model has a strong learning ability for transformer fault data and has an advantage in accuracy compared with other models, which can provide a reference for transformer fault diagnosis.

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A transformer condition recognition method based on dissolved gas analysis features selection and multiple models fusion

Cited by 4 publications

References 21 publications

Application of back propagation neural network in complex diagnostics and forecasting loss of life of cellulose paper insulation in oil-immersed transformers

Application of back propagation neural network in complex diagnostics and forecasting loss of life of cellulose paper insulation in oil-immersed transformers

Discernment of transformer oil stray gassing anomalies using machine learning classification techniques

Transformer Fault Diagnosis Method Based on TimesNet and Informer

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