Wavelet-alienation-neural-based protection scheme for STATCOM compensated transmission line

Bhuvnesh Rathore, Om Prakash Mahela, Baseem Khan, Hassan Haes Alhelou, Pierluigi Siano

Research output: Contribution to journalArticleResearchpeer-review

31 Citations (Scopus)

Abstract

The custom power devices play important role for enhancing the power transfer capacity of transmission system. However, these devices introduce challenges of under reach or over reach, in the protection of transmission system. This article introduces a novel, protection algorithm based on wavelet-alienation-neural technique for STATCOM-compensated transmission system. For detecting and classifying faults, approximate coefficients are computed from the postfault quarter cycle current waveforms. Fault index, which is summation of alienation coefficients (computed by approximate coefficients) of both the buses, is computed and compared with the threshold magnitude for detecting and classifying the different faults. For the determination of fault location, artificial neural network is applied, with input as three-phase approximate coefficients, evaluated from the voltage and current signals over a time duration of a quarter cycle. Robustness of the developed scheme has been validated for various faults at different locations with varying fault impedances and angles of fault incidence.

Original languageEnglish
Pages (from-to)2557-2565
Number of pages9
JournalIEEE Transactions on Industrial Informatics
Volume17
Issue number4
DOIs
Publication statusPublished - Apr 2021
Externally publishedYes

Keywords

  • Alienation coefficients
  • artificial neural network (ANN)
  • fault detection and classification
  • static compensator (statcom)
  • wavelet transform (WT)

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