Analysis of artificial intelligence expert systems for power transformer condition monitoring and diagnostics

Žarković, Mileta; Stojković, Zlatan

doi:10.1016/j.epsr.2017.04.025

Cited by 82 publications

(43 citation statements)

References 25 publications

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“…In addition, EPS generally has a poor learning ability. (2) Fuzzy theory is difficult to determine appropriate membership function between the input and output variables [28]. (3) SVM is essentially a two-classification algorithm, which makes it difficult to construct a learning machine, select kernel functions, and determine parameters in multi-classification problems.…”

Section: Introductionmentioning

confidence: 99%

A Novel Expertise-Guided Machine Learning Model for Internal Fault State Diagnosis of Power Transformers

Zhang

2019

Sustainability

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The fault diagnosis of power transformers is of great significance to improve the reliability of power systems. This paper proposes a novel fault diagnosis method called the expertise-guided machine learning (EGML) model where a genetic algorithm (GA) and a mind evolutionary algorithm (MEA) are used as optimization algorithms. Thereby, two types of EGML models are generated, that is, the GA-EGML model and the MEA-EGML model. In the EGML model, knowledge function replaces the cost function of traditional artificial intelligence algorithms, which can provide additional information for each individual and bring some corrections to the prediction results. To investigate the application potentials of the proposed models in power transformer fault diagnosis, real dissolved gases data are utilized to evaluate the diagnosis performance of the proposed models. Results indicate that the performance of the EGML model outperforms the traditional back propagation neural network (BPNN) model and all other models participating in the comparison. Both the GA-EGML model and MEA-EGML model can be used to diagnose the faults of a power transformer, and the latter is better. In addition, to further investigate the robustness of the proposed models for different data, four scenarios are simulated. Empirical results show that the accuracies of all models decrease in the other three scenarios compared to the baseline scenario, especially in scenario 2. However, the proposed models decline less than the traditional models in scenario 2 and scenario 4, and obtain satisfactory accuracy in all scenarios.

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Section: Introductionmentioning

confidence: 99%

A Novel Expertise-Guided Machine Learning Model for Internal Fault State Diagnosis of Power Transformers

Zhang

2019

Sustainability

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“…In 2017, Zarkovic et al [28] suggested that the main problem in the diagnosis of power transformer that the exact interpretation of failure detection is tough. They have presented method based on artificial intelligence to overcome this problem.…”

Section: Related Workmentioning

confidence: 99%

Transformer failure diagnosis by different rule extraction method: a review

Tamrakar¹,

Reddy²

2018

IJATEE

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“…The test results have proved the efficacy and reliability of the proposed technique. Žarković and Stojković [102] also presented a methodology for power transformer condition monitoring and diagnostics based on the analysis of AI expert systems. The possibility of the presented monitoring methodology is to assist the operator's engineers in decision making about urgency of intervention and type of maintenance of power transformer.…”

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confidence: 99%

Dissolved Gas Analysis Principle Based Intelligent Approaches to Fault Diagnosis and Decision Making of Large Oil-Immersed Power Transformers: A Survey

Cheng¹,

Chen²

2018

Preprint

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Compared with conventional methods in fault diagnosis of power transformers, which have defects such as imperfect encoding and too absolute encoding boundary, this paper systematically reveals various intelligent approaches applied in fault diagnosing and decision making of large oil-immersed power transformers based on dissolved gas analysis (DGA), including expert system (EPS), artificial neural network (ANN), fuzzy theory, rough sets theory (RST), grey system theory (GST), swarm intelligence (SI) algorithms, data mining technology, machine learning (ML), and other intelligent diagnosis tools, and summarizes existing problems and solutions. From this survey, it is found that a single intelligent approach for fault diagnosis can only reflect operation status of the transformer in one certain aspect, causing some shortcomings in various degrees cannot be revealed effectively. Combined with the current research status in this field, the problems that must be addressed in DGA-based transformer fault diagnosis are identified, and the prospects for future development trends and research directions are outlined. This contribution presents a detailed and systematic survey on various intelligent approaches to faults diagnosing and decisions making of the power transformer, in which their merits and demerits are thoroughly investigated, as well as their improvement schemes and future development trends are proposed. Moreover, this paper concludes that a variety of intelligent algorithms should be combined for mutual complementation to form a hybrid fault diagnosis network, such that avoiding these algorithms falling into a local optimum. Moreover, it is necessary to improve the detection instruments so as to acquire reasonable characteristic gas data samples. The research summary, empirical generalization and analysis of predicament in this paper provide some thoughts and suggestions for the research of complex power grid in the new environment, as well as references and guidance for researchers to choose optimal approach to achieve DGA-based fault diagnosis and decision of the large oil-immersed power transformers in preventive electrical tests.

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Analysis of artificial intelligence expert systems for power transformer condition monitoring and diagnostics

Cited by 82 publications

References 25 publications

A Novel Expertise-Guided Machine Learning Model for Internal Fault State Diagnosis of Power Transformers

A Novel Expertise-Guided Machine Learning Model for Internal Fault State Diagnosis of Power Transformers

Transformer failure diagnosis by different rule extraction method: a review

Dissolved Gas Analysis Principle Based Intelligent Approaches to Fault Diagnosis and Decision Making of Large Oil-Immersed Power Transformers: A Survey

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