Open AccessIdentification of internal fault against external abnormalities in power transformer using hierarchical ensemble extreme learning machine technique
Author(s) -
Raichura Maulik B.,
Chothani Nilesh G.,
Patel Dharmesh D.
Publication year - 2020
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2019.0102
Subject(s) - extreme learning machine , support vector machine , computer science , artificial intelligence , transformer , classifier (uml) , pattern recognition (psychology) , inrush current , feature extraction , artificial neural network , machine learning , engineering , voltage , electrical engineering
Various unwanted phenomena that are taken place in the transformer may occasionally mal‐operate selected fault classification based protective schemes. Hence, it is necessary to discriminate internal fault from external abnormal conditions for unit protection of power transformer. This study presents a new hierarchical ensemble extreme learning machine (HE‐ELM) based classifier technique to identify faults in & out of transformer. The component extreme learning machine (ELM) is structured hierarchically to improve its fault data classification accuracy. The developed algorithm is evaluated by simulating multiple disorders on 100 MVA, 132/220 kV transformer with the help of PSCAD software. DWT is used to extract features from acquired current signals from transformer. The feature vector formed after extraction process is fed to the HE‐ELM algorithm for data classification. The fault discrimination accuracy of HE‐ELM technique is 99.91%. This shows its effectiveness with respect to other classifier techniques. Moreover, the developed algorithm is successfully tested on hardware prototype in laboratory environment under various inrush and fault conditions using Cortex M4 microcontroller (STM32F407) with maximum identification time of 27 ms. The proposed HE‐ELM technique is compared with existing support vector machine, probabilistic neural network and ELM techniques for identical fault data. Results demonstrate that HE‐ELM outperforms than existing schemes in cross‐domain recognition task.