Detecting partial discharge by AI approach
This project investigates the efficacy of Artificial Intelligence (AI) models, including Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and Support Vector Machines (SVMs), for detecting Partial Discharge (PD) in electrical systems using waveform data. Key to our ap...
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| Format: | Final Year Project |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/176921 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | This project investigates the efficacy of Artificial Intelligence (AI) models, including
Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and
Support Vector Machines (SVMs), for detecting Partial Discharge (PD) in electrical systems
using waveform data. Key to our approach was cleaning the dataset through denoising,
standardization, and advanced feature extraction techniques like Fast Fourier Transform (FFT)
and Continuous Wavelet Transform (CWT). Our results highlight the trade-offs between the
models in terms of processing speed and temporal analysis capabilities. We also explored
model deployment on portable devices, identifying significant challenges related to
computational resource constraints. Future work will focus on data augmentation to simulate
real-world signal characteristics and algorithmic improvements to optimize model performance
for practical PD detection applications. |
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