Feature Selection for Effective Health Index Diagnoses of Power Transformers

Benhmed, Kamel; Mooman, Abdelniser; Younes, Abdunnaser; Shaban, Khaled; El-Hag, Ayman H.

doi:10.1109/tpwrd.2017.2762920

Cited by 57 publications

(29 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A fuzzy-logic based model was also incorporated in [20], where the HI class condition (three classes) was predicted using the technical oil test features as inputs. In other attempts, such as [21], a general study was conducted using different conventional feature selection methods on oil test features for predicting the HI condition with different ML techniques.…”

Section: Novel Methods For Hi Computationmentioning

confidence: 99%

Application of Machine Learning in Transformer Health Index Prediction

Alqudsi

El-Hag

2019

Energies

Self Cite

View full text Add to dashboard Cite

The presented paper aims to establish a strong basis for utilizing machine learning (ML) towards the prediction of the overall insulation health condition of medium voltage distribution transformers based on their oil test results. To validate the presented approach, the ML algorithms were tested on two databases of more than 1000 medium voltage transformer oil samples of ratings in the order of tens of MVA. The oil test results were acquired from in-service transformers (during oil sampling time) of two different utility companies in the gulf region. The illustrated procedure aimed to mimic a realistic scenario of how the utility would benefit from the use of different ML tools towards understanding the insulation health index of their transformers. This objective was achieved using two procedural steps. In the first step, three different data training and testing scenarios were used with several pattern recognition tools for classifying the transformer health condition based on the full set of input test features. In the second step, the same pattern recognition tools were used along with the three training/testing scenarios for a reduced number of test features. Also, a previously developed reduced model was the basis to reduce the needed number of tests for transformer health index calculations. It was found that reducing the number of tests did not influence the accuracy of the ML prediction models, which is considered as a significant advantage in terms of transformer asset management (TAM) cost reduction. current health condition, and all related financial costs (maintenance, operation, and failure) would result in an economic risk management plan with adequate subsequent decisions.

show abstract

Section: Novel Methods For Hi Computationmentioning

confidence: 99%

Application of Machine Learning in Transformer Health Index Prediction

Alqudsi

El-Hag

2019

Energies

Self Cite

View full text Add to dashboard Cite

show abstract

“…MRMR ranks the importance features of the classification problem. The main objective of MRMR is to minimize the redundancy of a feature set and maximize the relevance of a feature set to classification output c by using mutual information I, as follows [18,19]:…”

Section: B Reduced-feature Scenariomentioning

confidence: 99%

“…However, the model was applied in 90 transformers only, which may not be enough to provide useful insights into the accuracy of the method. In [19], a feature reduction model was developed based on different reduced-feature approaches. The results showed that water content, breakdown voltage, furan, and acidity are the most important features in predicting the PTHI state.…”

Section: Introductionmentioning

confidence: 99%

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

“…To solve these uncertainties, some fuzzy logic, Bayesian network, intelligent algorithm, and other methods based on electrical, chemical, physical, and other test parameters are developed and applied to the condition assessment of power transformers [15,[17][18][19][20]. In order to reduce the assessment complexities of power transformers, some methods are developed to extract the most influential assessment factors based on feature selection and classification techniques [21]. These methods take into account the meaning behind the test value and achieve good results, but they can only provide a single value for diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Multifactorial condition assessment for power transformers

Zhou

2020

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

Feature Selection for Effective Health Index Diagnoses of Power Transformers

Cited by 57 publications

References 9 publications

Application of Machine Learning in Transformer Health Index Prediction

Application of Machine Learning in Transformer Health Index Prediction

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

Multifactorial condition assessment for power transformers

Contact Info

Product

Resources

About