Support Vector Machine-Based Fault Diagnosis of Power Transformer Using k Nearest-Neighbor Imputed DGA Dataset
نویسندگان
چکیده
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.
منابع مشابه
Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation
Early detection of power transformer fault is important because it can reduce the maintenance cost of the transformer and it can ensure continuous electricity supply in power systems. Dissolved Gas Analysis (DGA) technique is commonly used to identify oil-filled power transformer fault type but utilisation of artificial intelligence method with optimisation methods has shown convincing results....
متن کاملOptimization of LS-SVM Parameters Using Genetic Algorithm to Improve DGA Based Fault Classification of Transformer- A Review
This paper gives the comparison of different methodologies for the fault classification in transformer based on Dissolved Gas Analysis. This paper describes the Dissolved Gas Analysis based fault classification of Transformer using Least Square Support Vector Machine. The parameters of Support Vector Machine are optimized using Genetic Algorithm. Failure of a large power transformer not only re...
متن کاملClassification of transformer faults using frequency response analysis based on cross-correlation technique and support vector machine
One of the most important methods for transformers fault diagnosis (especially mechanical defects) is the frequency response analysis (FRA) method. The most important step in the FRA diagnostic process is to differentiate the faults and classify them in different classes. This paper uses the intelligent support vector machine (SVM) method to classify transformer faults. For this purpose, two gr...
متن کاملLS-SVM Parameter Optimization Using Genetic Algorithm To Improve Fault Classification Of Power Transformer
The LS-SVM (least square support vector machines) is applied to solve the practical problems of small samples and non-linear prediction better and it is suitable for the DGA in power transformers. The selection of the parameters, impact on the result of the diagnosis greatly, so it is necessary to optimize these parameters. The parameters of Support Vector Machine are optimized using GA (Geneti...
متن کاملOptimal Feature Selection for SVM Based Fault Diagnosis in Power Transformers
Power transformer is one of the most vital equipment in an electrical system and its failure results in huge economic losses. Amongst the various data driven techniques available in literature for diagnosing faults in a power transformer, Support Vector Machine (SVM) is one of the most promising. In this context, SVMs have typically been implemented using all the gaseous species available from ...
متن کامل