Development of K- Means Based SVM Regression (KSVMR) Technique for Boiler Flue Gas Estimation

Ayyagari, Ramakalyan; Sivakumar, A.; Kannan, K.

doi:10.15676/ijeei.2014.6.2.10

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…SVM used to forecast electric load along with other algorithm such as Fuzzy Time Series and Global Harmony Search [12]. A computational model was developed to estimate mass concentration of boiler flue gas in [13]. Another study implemented SVM to classify the results of the simulation in defining synchronization capability limits of permanentmagnet motor [14].…”

Section: Introductionmentioning

confidence: 99%

Power Transformer Paper Insulation Assessment based on Oil Measurement Data using SVM-Classifier

Prasojo¹,

Suwarno²

2018

IJEEI

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Oil immersed paper insulation condition is a crucial aspect of power transformer's life condition diagnostics. The measurements testing database collected over the years made it possible for researchers to implement classification analysis to in-service power transformer. In order to generate a reliable model, more studies related to machine learning implementation to power transformer assessment need to be done. In this article, the objective of the study is to develop reliable new approach in transformer oil-immersed paper insulation condition assessment based on SVM-classifier model using its oil measurements. The measurements data (dielectric characteristics, dissolved gas analysis, and furanic compounds) of 149 transformers with primary voltage of 150 kV had been gathered and analyzed. The algorithm employed for developing classification model is Support Vector Machine (SVM). The model had been trained and tested using different datasets. Several different models have been created and the best has been chosen, resulting in 90.63% accuracy in predicting the oil-immersed paper insulation condition. Further implementation was executed to classify oil-paper condition of 19 transformers which Furan data is not available. The classification results were combined, reviewed, and compared to conventional assessment methods and standards. The comparation confirmed that the model developed has the ability to do classification of current oil-paper condition for the transformer population observed, based on Dissolved Gasses and Dielectric Characteristics.

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Section: Introductionmentioning

confidence: 99%

Power Transformer Paper Insulation Assessment based on Oil Measurement Data using SVM-Classifier

Prasojo¹,

Suwarno²

2018

IJEEI

View full text Add to dashboard Cite

show abstract

“…SVM used in [10] to forecast electric load along with other algorithm such as Fuzzy Time Series and Global Harmony Search. A computational model was developed to estimate mass concentration of boiler flue gas in [11]. Study in [12] implemented SVM to classify the results of the simulation in defining synchronization capability limits of permanent-magnet motor.…”

Section: Introductionmentioning

confidence: 99%

Power Transformer Insulation Assessment Based on Oil-Paper Measurement Data Using SVM-Classifier

Suwarno¹,

Prasojo²

2018

Preprint

View full text Add to dashboard Cite

Oil immersed paper insulation condition is a crucial aspect of power transformer’s life condition diagnostic. The measurement testing database collected over the years made it possible for researchers to implement classification analysis to in-service power transformer. This article presents classification analysis of transformer oil-immersed paper insulation condition. The measurements data (dielectric characteristics, dissolved gas analysis, and furanic compounds) of 149 transformers with primary voltage of 150 kV had been gathered and analyzed. The algorithm used for developing classification model is Support Vector Machine (SVM). The model has been trained and tested using different datasets. Different models have been created and the best chosen, resulting in 90.63% accuracy in predicting the oil-immersed paper insulation condition. Further implementation was executed to classify oil-paper condition of 19 Transformers which Furan data is not available. The classification results combined, reviewed, and compared to conventional assessment methods and standards, confirming that the model developed has the ability to do classification of current oil-paper condition based on Dissolved Gasses and Dielectric Characteristics.

show abstract

Development of K- Means Based SVM Regression (KSVMR) Technique for Boiler Flue Gas Estimation

Cited by 2 publications

References 22 publications

Power Transformer Paper Insulation Assessment based on Oil Measurement Data using SVM-Classifier

Power Transformer Paper Insulation Assessment based on Oil Measurement Data using SVM-Classifier

Power Transformer Insulation Assessment Based on Oil-Paper Measurement Data Using SVM-Classifier

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