A cost effective artificial intelligence based transformer insulation health index

Alqudsi, Alhaytham Y.; El-Hag, Ayman H.

doi:10.1109/catcon.2017.8280194

Cited by 9 publications

(6 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ki is identified in accordance with the importance of the test (DGA, OQ, and DP) in evaluating the PTHI state. The weighting factor of DGAF is 10 because its effect on evaluating the PTHI state is higher than that of OQF (8) and DPF (6). DGAF is the main factor that indicates faulty (poor) and non-faulty (good and fair) states.…”

Section: Depolarization Factormentioning

confidence: 99%

“…An analysis was conducted using the results of oil tests, such as water content, breakdown voltage, dissipation factor, furan, H2, CH4, C2H2, C2H6, and C2H4. The ANN approach was applied to predict the PTHI state in [8][9][10]. In [8], an ANN was proposed for PTHI prediction by using a feature-based exhaustive method to eliminate the least significant tests.…”

Section: Introductionmentioning

confidence: 99%

“…The ANN approach was applied to predict the PTHI state in [8][9][10]. In [8], an ANN was proposed for PTHI prediction by using a feature-based exhaustive method to eliminate the least significant tests. The constructed model was based on DGA, OQ, and FA.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparative Study of Full and Reduced Feature Scenarios for Health Index Computation of Power Transformers

Ghoneim

Taha

2020

IEEE Access

View full text Add to dashboard Cite

Power transformer health index (PTHI) computation is performed based on the results of different tests, such as dissolved gas analysis (DGA), oil quality (OQ) evaluation, and depolarization factor (DP) testing. In this study, PTHI computation is performed using 631 dataset samples from Malaysia and 730 samples from the Gulf Region. A new model is proposed to predict the PTHI state by adopting intelligent classification methods (e.g., decision tree, support vector machine, k-nearest neighbor, and ensemble methods). The model is built via two-stage data processing. The first stage separates the test results into three modules that represent DGA, OQ, and DP factor codes. In the second stage, the output of the three modules is processed to predict the PTHI state. The four classification methods are applied to the proposed model, and the prediction accuracy of the PTHI state is determined. Results indicate that the proposed model has superior classification accuracy for each AI method compared with recent work. Furthermore, feature reductions are applied to minimize the testing time, effort, and costs. The reduced-feature models reveal the effectiveness of the adopted feature reduction technique. A slight difference in accuracy is observed between the full-and reduced-feature scenarios. Thus, the reduced-feature scenario is considered to decrease the effort and time of the computation process and the experimental cost. The proposed model is validated against uncertain noise in features of up to ±20%.

show abstract

Section: Depolarization Factormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparative Study of Full and Reduced Feature Scenarios for Health Index Computation of Power Transformers

Ghoneim

Taha

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In a previous study [15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31], the parameters that were used in their equations or algorithms have different weights. These weight differences, equations and algorithms between studies are discussed in [32], and this review assumes that the subjective judgement of the weighting factor leads to different values of the final HI.…”

Section: Health Index Of the Power Transformermentioning

confidence: 99%

“…The results of these tests are used to prevent incipient failures and to evaluate the preventive maintenance processes, such as the replacement or recovery of transformer oil [36]. Even the use of these tests are different and have different weights when calculating the HI, depending on the study [15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31].…”

Section: Health Index Of the Power Transformermentioning

confidence: 99%

Dissolved Gas Analysis Equipment for Online Monitoring of Transformer Oil: A Review

Bustamante

Canteli

Arroyo

et al. 2019

Sensors

117

View full text Add to dashboard Cite

Power transformers are the most important assets of electric power substations. The reliability in the operation of electric power transmission and distribution is due to the correct operation and maintenance of power transformers. The parameters that are most used to assess the health status of power transformers are dissolved gas analysis (DGA), oil quality analysis (OQA) and content of furfuraldehydes (FFA) in oil. The parameter that currently allows for simple online monitoring in an energized transformer is the DGA. Although most of the DGA continues to be done in the laboratory, the trend is online DGA monitoring, since it allows for detection or diagnosis of the faults throughout the life of the power transformers. This study presents a review of the main DGA monitors, single- or multi-gas, their most important specifications, accuracy, repeatability and measurement range, the types of installation, valve or closed loop, and number of analogue inputs and outputs. This review shows the differences between the main existing DGA monitors and aims to help in the selection of the most suitable DGA monitoring approach according to the needs of each case.

show abstract

Development of a Multiple Regression Model for Early Diagnosis of Transformer Oil Condition

Shutenko

Ponomarenko

2022

Arab J Sci Eng

View full text Add to dashboard Cite

A cost effective artificial intelligence based transformer insulation health index

Cited by 9 publications

References 6 publications

Comparative Study of Full and Reduced Feature Scenarios for Health Index Computation of Power Transformers

Comparative Study of Full and Reduced Feature Scenarios for Health Index Computation of Power Transformers

Dissolved Gas Analysis Equipment for Online Monitoring of Transformer Oil: A Review

Development of a Multiple Regression Model for Early Diagnosis of Transformer Oil Condition

Contact Info

Product

Resources

About