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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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
AIMS Press
2024-11-01
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| Series: | AIMS Electronics and Electrical Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/electreng.2024024 |
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