IMPLEMENTATION OF SVM USING SEQUENTIAL MINIMAL OPTIMIZATION FOR POWER TRANSFORMER FAULT ANALYSIS USING DGA
Journal Title: INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY - Year 2013, Vol 10, Issue 5
Abstract
Reliable operations of power transformers are necessary for effective transmission and distribution of power supply. During normal functions of the power transformer, distinct types of faults occurs due to insulation failure, oil aging products, overheating of windings, etc., affect the continuity of power supply thus leading to serious economic losses. To avoid interruptions in the power supply, various software fault diagnosis approaches are developed to detect faults in the power transformer and eliminate the impacts. SVM and SVM-SMO are the software fault diagnostic techniques developed in this paper for the continuous monitoring and analysis of faults in the power transformer. The SVM algorithm is faster, conceptually simple and easy to implement with better scaling properties for few training samples. The performances of SVM for large training samples are complex, subtle and difficult to implement. In order to obtain better fault diagnosis of large training data, SVM is optimized with SMO technique to achieve high interpretation accuracy in fault analysis of power transformer. The proposed methods use Dissolved Gas-in-oil Analysis (DGA) data set obtained from 500 KV main transformers of Pingguo Substation in South China Electric Power Company. DGA is an important tool for diagnosis and detection of incipient faults in the power transformers. The Gas Chromatograph (GC) is one of the traditional methods of DGA, utilized to choose the most appropriate gas signatures dissolved in transformer oil to detect types of faults in the transformer. The simulations are carried out in MATLAB software with an Intel core 3 processor with speed of 3 GHZ and 2 GB RAM PC. The results obtained by optimized SVM and SVM-SMO are compared with the existing SVM classification techniques. The test results indicate that the SVM-SMO approach significantly improve the classification accuracy and computational time for power transformer fault classification.
Authors and Affiliations
V. Gomathy, Dr. S. Sumathi
Keywords
Related Articles
- EP ID EP650248
- DOI 10.24297/ijct.v10i5.4153
- Views 87
- Downloads 0
Skin Color detection Using Stepwise Neural Network and Color Mapping Co-occurrence Matrix
Skin color has been proven to be a useful and robust cue for face detection, human tracking, image content filtering, pornographic filtering, etc. Most of skin classification researches are focused on using pixel-based...
ANALYSIS OF MODIFIED COSH WINDOW FUNCTION AND PERFORMANCE EVALUATION OF THE FIR FILTER DESIGNED USING WINDOWING TECHNIQUES
Filters are used in electronic circuits to remove the unwanted frequency components from desired signals. A digital filter basically provide high attenuation to the unwanted ones and offer very low or ideally zero attenu...
On the Cloud Web services: A Review
Cloud Computing is a constantly evolving IT generic term used to refer to various IT solutions and services provided via the internet. Precisely, cloud computing can be said to be  the delivery of scalable IT resource...
Algorithm of Ontology Similarity Measure Based on Similarity Kernel Learning
Ontology, as a structured conceptual model of knowledge representation and storage, has widely been used in biomedical and pharmaceutical research. The nature of the ontology application is to get the similarity between...
A New Similarity Measure for User-based Collaborative Filtering in Recommender Systems
Collaborative filtering is a popular approach in recommender Systems that helps users in identifying the items they may like in a wagon of items. Finding similarity among users with the available item ratings so as to pr...