Classification and Location of Transformer Winding Deformations using Genetic Algorithm and Support Vector Machine

Title:Classification and Location of Transformer Winding Deformations using Genetic Algorithm and Support Vector Machine

Volume: 14 Issue: 8

Author(s): Zhenhua Li*, Junjie Cheng and A. Abu-Siada

Affiliation:

• College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002,China

Keywords: Power transformers, winding deformation, fault diagnosis, sinusoidal sweep signal, mathematical index, support vector machine, genetic algorithm.

Abstract:

Background: Winding deformation is one of the most common faults an operating power transformer experiences over its operational life. Thus, it is essential to detect and rectify such faults at early stages to avoid potential catastrophic consequences to the transformer. At present, methods published in the literature for transformer winding fault diagnosis are mainly focused on identifying fault type and quantifying its extent without giving much attention to the identification of fault location.

Methods: This paper presents a method based on a genetic algorithm and support vector machine (GA-SVM) to improve the faults’ classification of power transformers in terms of type and location. In this regard, a sinusoidal sweep signal in the frequency range of 600 kHz to 1MHz is applied to one terminal of the transformer winding.

A mathematical index of the induced current at the head and end of the transformer winding under various fault conditions is used to extract unique features that are fed to a Support Vector Machine (SVM) model for training. Parameters of the SVM model are optimized using a Genetic Algorithm (GA).

Results: The effectiveness of mathematical indicators to extract fault type characteristics and the proposed fault classification model for fault diagnosis is demonstrated through extensive simulation analysis for various transformer winding faults at different locations.

Conclusion: The proposed model can effectively identify different fault types and determine their location within the transformer winding, and the diagnostic rates of the fault type and fault location are 100% and 90%, respectively.

Cite this article as:

Li Zhenhua *, Cheng Junjie and Abu-Siada A. , Classification and Location of Transformer Winding Deformations using Genetic Algorithm and Support Vector Machine, Recent Advances in Electrical & Electronic Engineering 2021; 14 (8) . https://dx.doi.org/10.2174/2352096514666211026142216