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On-line PD monitoring on Power System Components

Master Thesis Aalto University school of Science and Technology SungIn Cho

sungin cho

on 5 September 2011

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Transcript of On-line PD monitoring on Power System Components

On-line PD monitoring on
Power system Components Sung In Cho
Department of Electrical Engineering Supervised by Prof. Matti Lehtonen
Instructed by D.Sc. (Tech.) Petri Hyvönen
In collabolation with Doble Lemke Master of Science in Technology Holistic Review of Online PD Monitoring System Partial discharge, On-line PD monitoring, IEC 60270, IEC 62478
Pattern recognition, Feature extraction, Conventional method,
Unconventional method, PD sensor Index term: Abstract A holistic review of current on-line PD monitoring techniques of power system components (transformers, cables, and switchgears etc…) in order to understand recent future developments and trend in theory and in practice. Therefore this thesis is an intensive literature review of current on-line PD monitoring technology Table of Contents CHAPTER1 Introduction Motivation
Condition Based Maintenance
PD monitoing system in Power system CHAPTER2 PD measurement System Conventional method (IEC 60270) Unconventional method CHAPTER3 Sensing and Processing Sensors Visualization methods Festure extraction and de-noising methods Pattern recognition CHAPTER4 PD monitoring on power system components Transformer Cable Ratating machine GIS (Gas insulated System) WHY on-line PD • Trend of insulation condition in real time

• Monitoring while the system components are in operation

• In the real operating condition

• Location and possible fault types are available

• Can be applied to all kinds of High voltage equipment The trend of monitering techniques is... Condition Based Maintenance Should consider all stresses on equipment called TEAM stress On-line appplication for all kinds of system components PD monitoring system in the power system PD occur Transformer Cable Rotating Machine GIS Sensing Electrical Acoustical Chemical Optoical Feature
Extraction De-noising Analysis of PD signal Extracting
Feature vectors Pattern
Classification PD source and Location Faults type Conventional Method
Unconventional Method
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