Expert System Based on ESTA and Guide for Condition Evaluation of Transformers

Cui, Qi; Cui, Shu Ting; Guan, Zeyuan; Sun, Wen

doi:10.4028/www.scientific.net/amr.347-353.306

Cited by 2 publications

(2 citation statements)

References 1 publication

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“…Computational techniques have also been utilized to address these challenges. DGA has been conducted utilizing wavelet networks (WN) 35 , 36 , expert systems (ES) 37 , adaptive neuro-fuzzy inference system (ANFIS) 38 , artificial immune networks (AIN) 39 , support vector machines (SVM) 40 , and fuzzy logic (FL) 41 . Although the results for LOL of transformers presented in this work are decent, it is essential to promote the proposed approach to enhance LOL prediction accuracy to provide dielectric testing facilities and transformer manufacturers with reliable alternatives to existing techniques.…”

Section: Introductionmentioning

confidence: 99%

Application of back propagation neural network in complex diagnostics and forecasting loss of life of cellulose paper insulation in oil-immersed transformers

Ngwenyama,

Gitau

2024

Sci Rep

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Oil-immersed transformers are expensive equipment in the electrical system, and their failure would lead to widespread blackouts and catastrophic economic losses. In this work, an elaborate diagnostic approach is proposed to evaluate twenty-six different transformers in-service to determine their operative status as per the IEC 60599:2022 standard and CIGRE brochure. The approach integrates dissolved gas analysis (DGA), transformer oil integrity analysis, visual inspections, and two Back Propagation Neural Network (BPNN) algorithms to predict the loss of life (LOL) of the transformers through condition monitoring of the cellulose paper. The first BPNN algorithm proposed is based on forecasting the degree of polymerization (DP) using 2-Furaldehyde (2FAL) concentration measured from oil samples using DGA, and the second BPNN algorithm proposed is based on forecasting transformer LOL using the 2FAL and DP data obtained from the first BPNN algorithm. The first algorithm produced a correlation coefficient of 0.970 when the DP was predicted using the 2FAL measured in oil and the second algorithm produced a correlation coefficient of 0.999 when the LOL was predicted using the 2FAL and DP output data obtained from the first algorithm. The results show that the BPNN can be utilized to forecast the DP and LOL of transformers in-service. Lastly, the results are used for hazard analysis and lifespan prediction based on the health index (HI) for each transformer to predict the expected years of service.

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Section: Introductionmentioning

confidence: 99%

Application of back propagation neural network in complex diagnostics and forecasting loss of life of cellulose paper insulation in oil-immersed transformers

Ngwenyama,

Gitau

2024

Sci Rep

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“…SMA is a kind of unique smart material which has shape memory effect, superelasticity, high damping, strain is sensitive to resistance, etc, has attracted considerable attention and becomes a research hotspot. Research and application of SMA in civil engineering has achieved fast development [1][2][3][4][5][6]. Early researches made a variety of energy dissipation devices with SMA.…”

Section: Introductionmentioning

confidence: 99%

Tensile Test of Shape Memory Alloys and Numerical Simulation

Guan

Zhang

Cui

2013

AMR

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Shape memory alloy (SMA) as an effective alternative to steel has received much attention. The constitutive model of SMA has raped developed for nearly 10 years, such as Boyd and Lagoudas model, Auricchio model, etc. However, a number of constitutive models were more complex, and not easily applied to engineering. In order to study SMA concrete pullout test using numerical simulation software, the tensile property of superelastic SMA was studied in this paper, different units (SOLID185 and LINK8) were simulated at the same time. Through the comparative analysis of results of different constitutive models, an easier used constitutive model of SMA was obtained. Combining with the numerical case, it shows that link8 is more convenient and accurate to simulates SMA.

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Expert System Based on ESTA and Guide for Condition Evaluation of Transformers

Cited by 2 publications

References 1 publication

Application of back propagation neural network in complex diagnostics and forecasting loss of life of cellulose paper insulation in oil-immersed transformers

Application of back propagation neural network in complex diagnostics and forecasting loss of life of cellulose paper insulation in oil-immersed transformers

Tensile Test of Shape Memory Alloys and Numerical Simulation

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