Transform Waveforms into Signature Vectors for General-purpose Incipient Fault Detection

Yang Weng, Qiushi Cui, Muhao Guo

Research output: Contribution to journalArticlepeer-review

Abstract

Power system equipment presents special signatures at the incipient stage of faults. As more renewables are integrated into the systems, these signatures are harder to detect. If faults are detected at an early stage, economical losses and power outages can be avoided in modern power grids. Many researchers and power engineers have proposed a series of signature-specific methods for one type of equipment's waveform abnormality. However, conventional methods are not designed to identify multiple types of incipient faults (IFs) signatures at the same time. Therefore, we develop a general-purpose IF detection method that detects waveform abnormality stemming from multiple types of devices. To avoid the computational burden of the general-purpose IF detection method, we embed the abnormality signatures into a vector and develop a pre-training model (PTM) for machine understanding. In the PTM, signal "words," "sentences," and "dictionaries" are designed and proposed. Through the comparison with a machine learning classifier and a simple probabilistic language model, the results show a superior detection performance and reveal that the training radius is highly related to the size of abnormal waveforms.

Original languageEnglish (US)
JournalIEEE Transactions on Power Delivery
DOIs
StateAccepted/In press - 2022
Externally publishedYes

Keywords

  • Databases
  • Dictionaries
  • Distribution networks
  • distribution system protection
  • Feature extraction
  • Harmonic analysis
  • Incipient fault
  • pre-training model
  • representation learning
  • Training
  • Transient analysis

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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