A Novel Power Transformer Fault Diagnosis Model Based on Harris-Hawks-Optimization Algorithm Optimized Kernel Extreme Learning Machine

Han, Xiaohui; Ma, Shifeng; Shi, Zhongqi; An, Guoqing; Du, Zhenbin; Zhao, Chunlin

doi:10.1007/s42835-022-01000-x

Cited by 13 publications

(6 citation statements)

References 30 publications

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“…This system was based on two versions of PSO approach (features selection and ensemble classifiers selection) and DS combination rule. The authors of [25] presented in their studies, an approach based on kernel extreme learning machine (KELM) optimized with the Harris hawks optimization (HHO) algorithm. The authors proposed in their research [27] a comparative study of an MLP network performance in power transformers diagnosis according to five different data sets.…”

Section: Comparison With Previous Work Resultsmentioning

confidence: 99%

“…This is due to the fact that the discriminator generalization strongly depends on the parameter space. Different representations have been exposed in the literature, such as: K-G [7,14,[21][22][23][24], IEC-R [14,25,26], Personalized Ratios [14,25], R-R [25,26], Logarithmic Data Transformation [23], G-P [23,26], DGA code [27], Standardized Data [23], D-R [26], D-T [25], etc. Also, several comparative studies between these descriptors [23][24][25][26] have been carried out, but each of these approaches has advantages and disadvantages [7]; it would be more interesting to take the benefits of each one through a combination.…”

Section: Study Motivations and Innovationsmentioning

confidence: 99%

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Using Generic Direct M-SVM Model Improved by Kohonen Map and Dempster–Shafer Theory to Enhance Power Transformers Diagnostic

Hendel,

Meghnefi,

Senoussaoui

et al. 2023

Sustainability

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Many power transformers throughout the world are nearing or have gone beyond their theoretical design life. Since these important assets represent approximately 60% of the cost of the substation, monitoring their condition is necessary. Condition monitoring helps in the decision to perform timely maintenance, to replace equipment or extend its life after evaluating if it is degraded. The challenge is to prolong its residual life as much as possible. Dissolved Gas Analysis (DGA) is a well-established strategy to warn of fault onset and to monitor the transformer’s status. This paper proposes a new intelligent system based on DGA; the aim being, on the one hand, to overcome the conventional method weaknesses; and, on the other hand, to improve the transformer diagnosis efficiency by using a four-step powerful artificial intelligence method. (1) Six descriptor sets were built and then improved by the proposed feature reduction approach. Indeed, these six sets are combined and presented to a Kohonen map (KSOM), to cluster the similar descriptors. An averaging process was then applied to the grouped data, to reduce feature dimensionality and to preserve the complete information. (2) For the first time, four direct Multiclass Support Vector Machines (M-SVM) were introduced on the Generic Model basis; each one received the KSOM outputs. (3) Dempster–Shafer fusion was applied to the nine membership probabilities returned by the four M-SVM, to improve the accuracy and to support decision making. (4) An output post-processing approach was suggested to overcome the contradictory evidence problem. The achieved AUROC and sensitivity average percentages of 98.78–95.19% (p-value < 0.001), respectively, highlight the remarkable proposed system performance, bringing a new insight to DGA analysis.

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Section: Comparison With Previous Work Resultsmentioning

confidence: 99%

Section: Study Motivations and Innovationsmentioning

confidence: 99%

Using Generic Direct M-SVM Model Improved by Kohonen Map and Dempster–Shafer Theory to Enhance Power Transformers Diagnostic

Hendel,

Meghnefi,

Senoussaoui

et al. 2023

Sustainability

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“…Concerning the faults of the above-mentioned conventional schemes, artificial intelligence (AI) schemes of PT fault analysis have attracted substantial consideration due to their superior flexibility and influential fault analysis presentation (e.g. expert system (EPS) 83 , fuzzy theory 84 , SVM 85 , extreme learning machine (ELM) 86 , as well as ANN 87 ). EPS remains a clever AI setup scheme linked with skilled knowledge, which can analyze faults more thoroughly, precisely, and instantly.…”

Section: Applicable Workmentioning

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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“…Of the above, the implementing of the effective diagnostic methods for the early and correct detection of power transformers' faults is essential for their reliability and a necessary practice for maintenance policy [3]. Several diagnostic methods have been proposed in the literature, such as partial discharge measurement, furans analysis, frequency response analysis, degree polymerization measurement, vibro-acoustic analysis, moisture analysis, and dissolved gas analysis (DGA) [4,5]. Among these techniques, DGA is one of the most widely used techniques.…”

Section: Introductionmentioning

confidence: 99%

Improved intelligent methods for power transformer fault diagnosis based on tree ensemble learning and multiple feature vector analysis

Hechifa,

Lakehal,

Nanfak

et al. 2023

Electr Eng

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This paper discusses the impact of the feature input vector on the performance of DGA-based intelligent power transformer fault diagnosis methods. For this purpose, 22 feature vectors from traditional diagnostic methods were used as feature input vectors for four tree-based ensemble algorithms, namely random forest (RF), tree ensemble (TE), gradient boosted tree (GBT), and extreme gradient tree (XGB). To build the proposed diagnostics models, 407 samples were used for training and testing. For validation and comparison with the existing methods of literature 89 samples were used. Based on the results obtained on the training and testing datasets, the best performance was achieved with feature vector 16, which consists of the gas ratios of Rogers' four ratios method and the three ratios technique. The test accuracies based on these vectors are 98.37, 96.75, 95.93, and 97.56% for the RF, TE, GBT, and XGB algorithms, respectively. Furthermore, the performance of the methods based on best input feature were evaluated and compared with other methods of literature such as Duval Triangle, modified Rogers' four ratios method, combined technique, three ratios technique, Gouda triangle, IEC 60599, NBR 7274, clustering, and key gases with gas ratio methods. On validating dataset, diagnostic accuracies of 92.13, 91.01, 89.89, and 91.01% were achieved by the RF, TE, GBT, and XGBoost models, respectively. These diagnostic accuracies are higher than 83.15 % of the clustering method and 82.02 % of combined technique which are the best existing methods. Even if the performance of DGA-based intelligent methods depends strongly on the shape of the feature vector used, this study provides scholars with a tool for choosing the feature vector to use when implementing these methods.

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A Novel Power Transformer Fault Diagnosis Model Based on Harris-Hawks-Optimization Algorithm Optimized Kernel Extreme Learning Machine

Cited by 13 publications

References 30 publications

Using Generic Direct M-SVM Model Improved by Kohonen Map and Dempster–Shafer Theory to Enhance Power Transformers Diagnostic

Using Generic Direct M-SVM Model Improved by Kohonen Map and Dempster–Shafer Theory to Enhance Power Transformers Diagnostic

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

Improved intelligent methods for power transformer fault diagnosis based on tree ensemble learning and multiple feature vector analysis

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