Hybrid optimized artificial neural network using Latin hypercube sampling and Bayesian optimization for detection, classification and location of faults in transmission lines

Hybrid optimized artificial neural network using Latin hypercube sampling and Bayesian optimization for detection, classification and location of faults in transmission lines

This paper introduces a novel hybrid approach that integrates Latin hypercube sampling (LHS) and Bayesian optimization for optimizing artificial neural networks (ANNs) in fault detection, classification, and location for transmission lines. The proposed method advances the accuracy and efficiency of...

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Bibliographic Details
Main Authors: Abdul Yussif Seidu, Elvis Twumasi, Emmanuel Assuming Frimpong
Format: Article
Language:English
Published: AIMS Press 2024-11-01
Series:AIMS Electronics and Electrical Engineering
Subjects:
latin
hypercube
bayesian
optimization
transmission
artificial neural network
Online Access:https://www.aimspress.com/article/doi/10.3934/electreng.2024024
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https://www.aimspress.com/article/doi/10.3934/electreng.2024024

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