Improving Fault Classification Accuracy Using Wavelet Transform and Random Forest with STATCOM Integration

Authors

  • Shradha Umathe GH Raisoni University
  • Prema Daigavane GH Raisoni University
  • Manoj Daigavane Government Polytechnic Sadar Nagpur

DOI:

https://doi.org/10.4108/ew.5950

Keywords:

Transmission lines, fault detection, Wavelet Transform, Random Forest, STATCOM Integration

Abstract

INTRODUCTION: Fault detection in transmission lines is critical for keeping the grid stable and reliable. This research offers a new methodology, the Wavelet Transform-Enhanced Random Forest Fault Classification System with STATCOM Integration (WERFCS-SI), to solve the shortcomings of existing fault detection approaches.

OBJECTIVES: The integration of STATCOM-compensated transmission lines improves fault detection capabilities. The Wavelet Transform finds faults by analysing approximation and detail coefficients, allowing for multiresolution analysis and exact fault localisation.

METHODS: Feature selection approaches, such as information gain, are used to discover and keep relevant features, increasing classification accuracy.

RESULTS: Due to its ability to process complex, high-dimensional data and identify minute feature connections, Random Forest (RF) is utilised for classification tasks. The proposed approach improves RF model performance while maintaining precision.

CONCLUSION: The integrated technique simplifies fault categorisation, increasing accuracy and efficiency by detecting problems in the transmission line system.

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Published

05-11-2024

How to Cite

1.
Umathe S, Daigavane P, Daigavane M. Improving Fault Classification Accuracy Using Wavelet Transform and Random Forest with STATCOM Integration. EAI Endorsed Trans Energy Web [Internet]. 2024 Nov. 5 [cited 2024 Dec. 9];12. Available from: https://publications.eai.eu/index.php/ew/article/view/5950