Application of Artificial Intelligence (AI) in Power Transformer Fault Diagnosis

Qiping, Yang; Meng-qun, Li; Mu, Xueyun; Wang, Jun

doi:10.1109/aici.2009.497

Cited by 6 publications

(4 citation statements)

References 1 publication

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“…By applying more accurate information processing models and more efficient intelligent analysis algorithms, intelligent diagnosis technology deeply mines the value of data, constantly improving the economy and accuracy of power system and line diagnosis. Literature [11] also points out that introducing artificial intelligence technology into power system fault diagnosis is undoubtedly a major breakthrough, which can not only provide massive data for people but also make correct judgments on power system faults in the shortest possible time. Therefore, systematic research on power system fault diagnosis is particularly important.…”

Section: ) the Development Of Artificial Intelligence Technology Prom...mentioning

confidence: 99%

Research on intelligent algorithm-based power system fault prediction and diagnosis technology

2024

jeeem

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Fault prediction and diagnosis technology in the power system is an important application field of intelligent algorithms. Intelligent algorithms play a key role in fault prediction and diagnosis technology in the power system, aiming to improve the accuracy and efficiency of fault detection. This article reviews the current development status of intelligent algorithms in fault prediction and diagnosis technology in the power system, summarizes several problems and corresponding countermeasures of several commonly used intelligent algorithms in fault diagnosis applications. Finally, the development trend of intelligent algorithms is discussed: by focusing on data quality and integrating multisource data, optimizing the selection and parameter tuning of algorithms and models, as well as combining multiple algorithms and models, the effectiveness and accuracy of fault prediction and diagnosis in the power system can be improved, enhancing the stability and reliability of the power system.

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Section: ) the Development Of Artificial Intelligence Technology Prom...mentioning

confidence: 99%

Research on intelligent algorithm-based power system fault prediction and diagnosis technology

2024

jeeem

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“…It is well identified that power transformers certainly suffer the impacts of short-circuit electromagnetic forces in deal [27]. It states that AI based system is very convenient tools for power transformer early hidden faults accomplishes the opportunity and accurateness of prime analysis [28]- [31]. 2).…”

Section: Energymentioning

confidence: 99%

Optimal Design of Power Transformer with Advance Core Material using ANSYS Technique

Urooj

Singh

Amir

et al. 2020

EJECE

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Due to indulge behavior of core materials and losses that leads to degradation of transformer efficiency. This research paper evaluating the optimal performance of transformer with designing of a newly core material namely as Mo.Me6. A possibility of increment in the efficiency for designed proposed model of transformer is observable. Also, a comparative analytical study based on the different designs of core type transformer with various core materials. The design and evaluation are performed on the Maxwell ANSYS electronic desktop platform, which provides an optimized design of transformer for practical applications. Whether it is a power transformer having lower frequency applications use in electronic circuits such as rectifying circuits or higher frequency applications power distribution circuit, etc. All transformer designs require performance evaluation in the working field and better efficiency to optimize the overall three-phase type core transformer. In this paper, proposed Mo.Me6 material with improved physical properties have been present of transformer efficiency and optimal design of core. The criteria include the changes in some effective parameters of the core, bringing out advancement in the working efficiency of the transformer. There is an opportunity to have a choice of desired material as designed particularly for the customer need making it will be more reliable and more suitable for various changeable conditions. For more efficient and effective working of transformer, this paper suggests core design with ANSYS techniques to achieve mentioned high efficiency with lower losses.

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“…Dissolved gas analysis (DGA) is the study of key dissolved gases in transformer oil and is helpful for determining the condition and identifying underlying faults in power transformers. (5)(6)(7) DGA techniques can be used to help monitor the condition of the transformer and give possible explanations for faults and precautions to take upon discovering a fault. Faults in transformers are classified into four main types: a thermal fault of the oil, a thermal fault of the cellulose, a partial discharge, and arcing.…”

Section: Introductionmentioning

confidence: 99%

Automated Dissolved Gas Analysis Report Generator for Monitoring Condition of High-voltage Transformers

Chanchotisatien¹,

Phumpho²

2021

Sensors and Materials

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Dissolved gas analysis (DGA) is a commonly used method that allows pending or occurring faults within transformers to be determined. DGA has been widely used for many years. There are various DGA techniques, including the evaluation of individual and total dissolved combustible gas (TDCG) concentrations, the Doernenburg method, the Rogers method, and Duval's triangle. To facilitate the process of monitoring the condition of transformers and reduce potential human error, we discuss how to create a user-friendly system to monitor and evaluate the condition of high-voltage transformers. The system receives key gas values, which are extracted from oil samples inside transformers obtained by gas chromatography (GC), and is capable of automatically generating downloadable reports of specified transformers and gives insight on any faults found. It also visualizes the changes in extracted key gas values of transformers over time. To develop this system, the front end of the application was made with HTML and CSS, and the back end was made with JavaScript with MySQL as a database combined with Microsoft SharePoint. The automatic downloadable report generator was made with Power Automate. This system assists the monitoring of transformer conditions.

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Application of Artificial Intelligence (AI) in Power Transformer Fault Diagnosis

Cited by 6 publications

References 1 publication

Research on intelligent algorithm-based power system fault prediction and diagnosis technology

Research on intelligent algorithm-based power system fault prediction and diagnosis technology

Optimal Design of Power Transformer with Advance Core Material using ANSYS Technique

Automated Dissolved Gas Analysis Report Generator for Monitoring Condition of High-voltage Transformers

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