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

Shradha Umathe; Prema Daigavane; Manoj Daigavane

doi:10.4108/ew.5950

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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