Recognition of low-temperature overheating in power transformers by dissolved gas analysis

Shutenko, Оleg; Kulyk, Oleksii

doi:10.1007/s00202-021-01465-5

Cited by 10 publications

(10 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Practical implementation of this approach makes it possible to eliminate most of the described problems and increase the reliability of faulttype recognition. This is partly demonstrated in the works of the authors [24][25][26][27].…”

Section: Introductionmentioning

confidence: 81%

“…At the first stage of research, the values of diagnostic criteria for faults of different types were analysed. Partially the results of this analysis are given in [23–27]. The analysis resulted in 96 arrays with DGA results with close values of gas ratios, percentage gas content and gas‐to‐gas ratios with maximum gas content.…”

Section: Proposed Methods For Transformers Faults Diagnosticmentioning

confidence: 99%

“…The results of analysis of values of diagnostic criteria in equipment with faults of different types given in [23–27] showed that for the same type of fault there can be several reference data arrays, which significantly differ between the content of gases. For example, as shown in [27] for thermal fault of 150 to 300°C, 16 reference arrays are formed depending on the hot spot temperature, with methane and ethane having the maximum content. This circumstance, on the one hand, allows a more precise and detailed estimation of the intensity and degree of fault danger, but, on the other hand, it significantly increases the number of possible calculations.…”

Section: Proposed Methods For Transformers Faults Diagnosticmentioning

confidence: 99%

“…The results of analysis of values of diagnostic criteria in equipment with faults of different types given in [23][24][25][26][27] showed that for the same type of fault there can be several reference data arrays, which significantly differ between the content of gases. For example, as shown in [27] for thermal fault of 150 to 300 • C, 16 reference arrays are formed depending on the hot spot temperature, with methane and ethane having the maximum content. This circumstance, on the one hand, allows a more precise and detailed estimation of the intensity and degree of fault danger, but, on the other hand, it significantly increases the number of possible calculations.…”

Section: Recognition Proceduresmentioning

confidence: 99%

See 3 more Smart Citations

Method of fault‐type recognition based on the dissolved gas analysis using a set of diagnostic criteria

Shutenko,

Kulyk

2023

IET Generation Trans & Dist

View full text Add to dashboard Cite

This paper proposes a method for recognising the fault type using a set of diagnostic criteria. Existing approaches to the interpretation of dissolved gas analysis (DGA) results usually rely on single diagnostic criteria. The proposed method solves the problems of incomplete use of diagnostic information and discrepancies in the norms and criteria for recognising the same fault inherent in the existing methods. The feature of the method is the analysis of the compliance of the DGA results of the diagnosed equipment with the ones of the equipment with a confirmed diagnosis from the fault database. This approach outperforms the existing methods in the number of faults recognised, allowing a wider range of faults to be detected, including combined faults. In addition, the method provides an opportunity to identify possible causes and localise the fault. This feature is unique and provides a significant advantage when interpreting DGA results. The proposed method demonstrates significantly higher accuracy (100%) in recognising various fault types, including combined faults, in comparison to existing methods (about 50%). The proposed method has a 60% success rate in determining the cause and location of a fault, but this figure depends on the database with faults of various types used.

show abstract

Section: Introductionmentioning

confidence: 81%

Section: Proposed Methods For Transformers Faults Diagnosticmentioning

confidence: 99%

Section: Proposed Methods For Transformers Faults Diagnosticmentioning

confidence: 99%

Section: Recognition Proceduresmentioning

confidence: 99%

See 2 more Smart Citations

Method of fault‐type recognition based on the dissolved gas analysis using a set of diagnostic criteria

Shutenko,

Kulyk

2023

IET Generation Trans & Dist

View full text Add to dashboard Cite

show abstract

“…Local overheating at temperatures lower than 300 • C (also known as low-temperature overheating) is a common flaw in high-voltage power transformers. The appearance of such flaws does not result in immediate transformer damage, but it does accelerate the aging processes of the insulation and shortens its service life [30]. This paper addresses five labels or inspections of the overheating of the power transformer: the true false, thermal fault label, basically describing only if there is an existence of thermal fault or not, and in the second dataset, there are four levels of overhearing (high, middle, middle-low and low temperature overheating, giving more exact levels of temperatures).…”

Section: Temperature-overheatingmentioning

confidence: 99%

An AI-Layered with Multi-Agent Systems Architecture for Prognostics Health Management of Smart Transformers: A Novel Approach for Smart Grid-Ready Energy Management Systems

et al. 2022

View full text Add to dashboard Cite

After the massive integration of distributed energy resources, energy storage systems and the charging stations of electric vehicles, it has become very difficult to implement an efficient grid energy management system regarding the unmanageable behavior of the power flow within the grid, which can cause many critical problems in different grid stages, typically in the substations, such as failures, blackouts, and power transformer explosions. However, the current digital transition toward Energy 4.0 in Smart Grids allows the integration of smart solutions to substations by integrating smart sensors and implementing new control and monitoring techniques. This paper is proposing a hybrid artificial intelligence multilayer for power transformers, integrating different diagnostic algorithms, Health Index, and life-loss estimation approaches. After gathering different datasets, this paper presents an exhaustive algorithm comparative study to select the best fit models. This developed architecture for prognostic (PHM) health management is a hybrid interaction between evolutionary support vector machine, random forest, k-nearest neighbor, and linear regression-based models connected to an online monitoring system of the power transformer; these interactions are calculating the important key performance indicators which are related to alarms and a smart energy management system that gives decisions on the load management, the power factor control, and the maintenance schedule planning.

show abstract

Recognition of combined defects with high-temperature overheating based on the dissolved gas analysis

Shutenko

Kulyk

2022

Sādhanā

View full text Add to dashboard Cite

Recognition of low-temperature overheating in power transformers by dissolved gas analysis

Cited by 10 publications

References 30 publications

Method of fault‐type recognition based on the dissolved gas analysis using a set of diagnostic criteria

Method of fault‐type recognition based on the dissolved gas analysis using a set of diagnostic criteria

An AI-Layered with Multi-Agent Systems Architecture for Prognostics Health Management of Smart Transformers: A Novel Approach for Smart Grid-Ready Energy Management Systems

Recognition of combined defects with high-temperature overheating based on the dissolved gas analysis

Contact Info

Product

Resources

About