Improving Fault Classification Accuracy Using Wavelet Transform and Random Forest with STATCOM Integration
Year: 2024
Type: article
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.
DOI: 10.4108/ew.5950
Institution: G H Raisoni University
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4404080573