Abstract
Missing values are prevalent in real-world datasets and they may reduce predictive performance of a learning algorithm. Dissolved Gas Analysis (DGA), one of the most deployable methods for detecting and predicting incipient faults in power transformers is one of the casualties. Thus, this paper proposes filling-in the missing values found in a DGA dataset using the k-nearest neighbor imputation method with two different distance metrics: Euclidean and Cityblock. Thereafter, using these imputed datasets as inputs, this study applies Support Vector Machine (SVM) to built models which are used to classify transformer faults. Experimental results are provided to show the effectiveness of the proposed approach.
Highlights
Power transformers are used for transmitting and distributing electricity from plant to customer in utility companies worldwide
Dissolved Gas Analysis (DGA) is a method that detects and predict faults found in oil-filled transformers by: a) analyzing the concentration of certain gases dissolved in the insulating oil, and their gassing rates, and gas ratios, b) identification of fault using diagnostic tools such as Key Gas [1], IEC ratios, Rogers ratios [1], Dornenburg ratios [1] and Duval Triangle [2]
The first DGA dataset is obtained from a local Malaysian utility company which manage various transformers located throughout Malaysia, whilst IECDB10 [6] is the second
Summary
Power transformers are used for transmitting and distributing electricity from plant to customer in utility companies worldwide. It is important to always ensure good operating condition of power transformers to provide reliable and continuous supply of electrical power, a necessity in this modern world Acting on this fact, utility companies have implemented various condition assessment and maintenance measures, and Dissolved Gas Analysis (DGA) is one of them. DGA is a method that detects and predict faults found in oil-filled transformers by: a) analyzing the concentration of certain gases dissolved in the insulating oil, and their gassing rates, and gas ratios, b) identification of fault using diagnostic tools such as Key Gas [1], IEC ratios, Rogers ratios [1], Dornenburg ratios [1] and Duval Triangle [2]. These tools can give different analysis results for the same dissolved gas record, and it is difficult for engineers to conclude a final assessment when faced with so much diverse information [4]
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