Incipient Equipment Failure Assessment and Avoidance through Robust Detection Technqiue

Conference paper (2019)

Authors

R. Bhandia Intelligent Electrical Power Grids - EEMCS
M. Cvetkovic Intelligent Electrical Power Grids - EEMCS
Jose de Jesus Chavez Intelligent Electrical Power Grids - EEMCS
P. Palensky Intelligent Electrical Power Grids - EEMCS
Research Group
Intelligent Electrical Power Grids (EEMCS) (TU Delft)
Copyright: © 2019 R. Bhandia, M. Cvetkovic, Jose de Jesus Chavez, P. Palensky
DOI
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https://doi.org/10.1049/cp.2018.1877
Condition Based Maintenance Distortion Detection Equipment Failure Power System Reliability Waveform Analytics
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Published Date

2019

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Electrical Sustainable Energy

Research Group

Intelligent Electrical Power Grids

Abstract

Key contributor to normal power grid operations is optimal working of the various power grid equipment/apparatus. Nonoptimal operation of any of this equipment causes power quality problems which can pose great risk to the stability of the grid. Damaged or partially damaged equipment leaves characteristic signatures in the form of current and voltage waveform distortions. Detecting and localizing such signal distortions would contribute to grid reliability as the damaged equipment could be replaced in time before it can cause further damage. This paper proposes a Distortion Detection Technique (DDT) based on second-difference approach. This distortion detection technique has very low memory requirements and can be easily implemented on decentralized systems. The paper investigates the performance of this technique and evaluates it with case studies involving different kind of equipment failures simulated on Real Time Digital Simulator (RTDS).