Health evaluation of power transformer using deep learning neural network

Xing, Zhikai; He, Yigang; Chen, Jianfei; Xiao, Wang; Du, Bolun

doi:10.1016/j.epsr.2022.109016

Cited by 9 publications

(4 citation statements)

References 35 publications

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“…In the realm of assessing the effectiveness of the APA training, this neural network model has identified four pivotal variables gleaned from the fsQCA that are integral to predicting the APA training effectiveness. The neural network model exhibits a commendable level of prediction accuracy, as already mentioned by the preceding scholars [ 83 ]. The analysis of the artificial neural network reveals varying levels of importance for distinct factors in predicting the effectiveness of the training program.…”

Section: Discussionmentioning

confidence: 73%

Enhancing post-training evaluation of annual performance agreement training: A fusion of fsQCA and artificial neural network approach

Islam,

Abdul Kader Jilani,

Karim

2024

PLoS ONE

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This study aims to enhance the post-training evaluation of the annual performance agreement (APA) training organized by the Bangladesh Public Administration Training Centre (BPATC), the apex training institute for civil servants. Utilizing fuzzy-set qualitative comparative analysis (fsQCA) and artificial neural network (ANN) techniques within Kirkpatrick’s four-stage model framework, data were collected from a self-administered questionnaire survey of 71 in-service civil servants who participated in the APA training program. This study employs an asymmetric, non-linear model analyzed through a configurational approach and ANN to explore interrelationships among the four Kirkpatrick levels namely, reaction, learning, behavior, and results. Findings indicate that trainees were satisfied across all levels, identifying a non-linear relationship among these levels in post-training evaluation process. The research highlights that "learning skills" are most significant in the APA post-training evaluation, followed by behavior, results, and reaction. Theoretically, this research advances Kirkpatrick’s model and adds to the literature on public service post-training evaluation. Practically, it recommends prioritizing strategies that address cognitive barriers to enhance training effectiveness. This study’s innovative approach lies in its concurrent use of fsQCA and ANN methods to analyze the success or failure of APA-related trainees, offering alternative pathways to desired outcomes and contrasting traditional quantitative methods that provide a single solution. The findings have practical implications for public service training institutions and bureaucratic policymakers involved in capacity development, guiding the creation of more effective in-service training courses for public officials. The methodology and analysis can be applied in other contexts, allowing bureaucratic policymakers to replicate these findings in their learning institutes to identify unique configurations that lead to successful or unsuccessful training outcomes, adopt effective strategies, and avoid detrimental ones.

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Section: Discussionmentioning

confidence: 73%

Enhancing post-training evaluation of annual performance agreement training: A fusion of fsQCA and artificial neural network approach

Islam,

Abdul Kader Jilani,

Karim

2024

PLoS ONE

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“…These methods make full use of the advantages of machine learning and artificial intelligence. The application of linear regression, support vector machine (SVM) [16][17][18], support vector data description [19][20][21], neural network, and deep learning theory [22][23][24] has strongly promoted the development of health index evaluation research.…”

Section: Introductionmentioning

confidence: 99%

A Health Status Assessment Method of Fluid Loop System Based on Hierarchical Multi-Information Fusion

Li,

Zhang,

Fang

et al. 2024

Mathematical Problems in Engineering

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As a kind of thermal control device, fluid loop systems must operate with the demands of high safety, high reliability, and long life. In order to accurately assess the health status of fluid loop systems, a hierarchical multi-information fusion (HMIF) method is proposed in this paper. Considering that fluid loop systems generally have distinct structural hierarchies, the health evaluation process in this method is divided into three levels, which are the indicator level, the component level, and the system level. In the evaluation process, the health indices are, respectively, constructed to quantify the health status at the three levels. At the indicator level, One-Class support vector machine algorithm is used to obtain the distribution space of each state monitoring indicator under a normal state. The indicator-level health indices are evaluated by calculating the ratio of the data located in the distribution space to the overall data. At the component level, a fuzzy theory is used to calculate the health indices of the component level. Health indices at the indicator level are first converted to membership degree by membership degree function. Then, the evaluation fusion strategy is used to deduce the membership degree of the component level. The health indices at the component level are obtained from the mapping relationship between the membership degree and the health index. At the system level, an adaptive weight adjustment strategy is proposed to fuze all component-level health indices. Taking a practical fluid loop system as an example, health indices at the three levels evaluated by the HMIF method are compared with the actual status. The results indicate that the proposed method can correctly judge the health state of the system and provide a reference for the maintenance and fault diagnosis of fluid loop systems.

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“…A total of 335 case data sets from 12 transformers located in a substation in Wuhan, China, and 42 case data sets collected from reference sources were used. An accuracy of 95.18 ± 0.81 was obtained when the DLNN method was used in the determination of transformer health status [13]. Li et al proposed a fault diagnosis model for transformers that contains adaptive synthetic oversampling, the reconstructed data method, and an improved deep coupled dense convolutional neural network.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Condition Monitoring System for Power Transformer Fault Diagnosis

2023

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The important parts of a transformer, such as the core, windings, and insulation materials, are in the oil-filled tank. It is difficult to detect faults in these materials in a closed area. Dissolved Gas Analysis (DGA)-based fault diagnosis methods predict a fault that may occur in the transformer and take the necessary precautions before the fault grows. Although these fault diagnosis methods have an accuracy of over 95%, their validity is controversial since limited data are used in the studies. The success rates and reliability of fault diagnosis methods in transformers, one of the most important pieces of power systems equipment, should be increased. In this study, a hybrid fault diagnosis system is designed using DGA-based methods and Fuzzy Logic. A mathematical approach and support vector machines (SVMs) were used as decision-making methods in the hybrid fault diagnosis systems. The results of tests performed with 317 real fault data sets relating to transformers showed accuracy of 95.58% using a mathematical approach and 96.23% using SVMs.

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Health evaluation of power transformer using deep learning neural network

Cited by 9 publications

References 35 publications

Enhancing post-training evaluation of annual performance agreement training: A fusion of fsQCA and artificial neural network approach

Enhancing post-training evaluation of annual performance agreement training: A fusion of fsQCA and artificial neural network approach

A Health Status Assessment Method of Fluid Loop System Based on Hierarchical Multi-Information Fusion

Hybrid Condition Monitoring System for Power Transformer Fault Diagnosis

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