Protection scheme of power transformer based on time–frequency analysis and KSIR-SSVM

The aim of this paper is to extend a hybrid protection plan for Power Transformer (PT) based on MRA-KSIR-SSVM. This paper offers a new scheme for protection of power transformers to distinguish internal faults from inrush currents. Some significant characteristics of differential currents in the real PT operating circumstances are extracted. Multi Resolution Analysis (MRA) is used as Time–Frequency Analysis (TFA) for decomposition of Contingency Transient Signals (CTSs), and the feature reduction is done by Kernel Sliced Inverse Regression (KSIR). Smooth Supported Vector Machine (SSVM) is utilized for classification. Integration KSIR and SSVM is tackled effectively and fast technique for accurate differentiation of the faulted and unfaulted conditions. The Particle Swarm Optimization (PSO) is used to obtain optimal parameters of the classifier. The proposed structure for Power Transformer Protection (PTP) provides a high operating accuracy for internal faults and inrush currents even in noisy conditions. The efficacy of the proposed scheme is tested by means of numerous inrush and internal fault currents. The achieved results are utilized to verify the suitability and the ability of the proposed scheme to make a distinction inrush current from internal fault. The assessment results illustrate that the proposed scheme presents an enhancement of distinguished inrush current from internal fault over the method to be compared without Dimension Reduction (DR).

Keywords:

Transformer Protection Scheme , Multi Resolution Analysis (MRA) , Kernel Sliced Inverse Regression (KSIR) , Smooth Supported Vector Machine (SSVM)