UHF signals generated by partial discharges due to corona activity, surface discharge and particle movement in transformer oil were acquired and used to develop a classification model. Time-Frequency transformation of UHF signal emitted by partial discharge source was used for classification of the discharges using quadratic support vector machine (SVM) learning tool. The method was validated by simulating the field condition, by studying the classification model in an oil filled closed tank along with the pressboard along the path of UHF signal, as observed in real system. © 2018 IEEE.

Ramanujam Sarathi

Department of Electrical Engineering

Classification (of information)

Information Systems

Information use

OIL FILLED TRANSFORMERS

Support vector machines

SURFACE DISCHARGES

UHF devices

CLASSIFICATION MODELS

CORONA ACTIVITIES

Field conditions

LEARNING TOOL

PARTIAL DISCHARGE SOURCES

PARTICLE MOVEMENT

Real systems

TIME-FREQUENCY TRANSFORMATION

Partial discharges

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Partial Discharge Source Classification using Time-Frequency Transformation

2018 13th International Conference on Industrial and Information Systems, ICIIS 2018 - Proceedings

Ultra-high-frequency signals are generated due to particle movement, floating conductors, corona, and surface discharges in gas-insulated switchgear. The ultra-high-frequency signal generated due to particle movement is independent of operating pressure and applied voltage. The bandwidth of the ultra-high-frequency signal formed due to corona and surface discharges vary with applied voltage. In a ternary plot, each type of discharge has a unique location. If a variety of discharge occurs simultaneously, it is difficult to classify the type of discharges through the ternary plot. It is demonstrated that the type of discharge can be classified efficiently by adopting a multi-class support vector machine. © 2011 Copyright Taylor and Francis Group, LLC.

Electric Power Components and Systems

Identification of partial discharges in gas-insulated switchgear by ultra-high-frequency technique and classification by adopting multi-class support vector machines

One of the major causes of failure of converter transformer is the incipient discharges caused due to deterioration of insulation. The insulation in the converter transformers is stressed not only by AC voltages but also with remnant DC voltages. The Acoustic Emission (AE) Technique can identify such incipient discharges in the converter transformer. In the present work, experimental studies were carried out by simulating various incipient discharges such as corona discharge, partial discharge (PD) and surface discharge in an insulation test cell under AC and DC voltages by using AE Technique. The bandwidth of the AE sensor used in the present study is of wide band type and the bandwidth lies in the range of 100 kHz-1 MHz. It is observed that AE signals produced due to different incipient discharges are having different dominant frequency range, amplitude, rise time, number of pulses and energy. To classify the discharges an innovative methodology is adopted by constructing the ternary plot from the FFT output of AE signal generated by the incipient discharges. © 2010 IEEE.

Proceedings of the 2010 Annual IEEE India Conference: Green Energy, Computing and Communication, INDICON 2010

Classification of incipient discharges in transformer insulation using acoustic emission signatures

Transformer Engineering

Transformer Fundamentals

2016 International Conference on Condition Monitoring and Diagnosis (CMD)

UHF signal model parameters identification and reconstruction for partial discharge in substation

IEEE Transactions on Power Delivery

Comparison of Known PD Signals With the Developed and Commercial HFCT Sensors

Journal	Data powered by Typeset2018 13th International Conference on Industrial and Information Systems, ICIIS 2018 - Proceedings
Publisher	Data powered by TypesetInstitute of Electrical and Electronics Engineers Inc.
Open Access	No