Deep Learning for High Voltage Condition Monitoring
Authors:   , Salimy Alireza 
Published: September 2024

High voltage (HV) assets that generate and make electricity readily available for commercial use require ongoing maintenance and diagnosis. If regular maintenance does not occur on these assets, serious faults may occur, incurring substantial costs and severe losses. Currently, fault diagnosis is performed manually by experts, carrying this out using various signals such as vibration, audio, or electromagnetic interference (EMI). These signals are analysed diferently, with each method attempting to extract fault characteristics. The methods experts use include observing the time-series signal as one-dimensional signals, listening to the signals produced as audio waveforms, and looking at the signals in the frequency domain. The downfalls of this manual approach come from the reliance on experts to perform diagnosis. If these experts are not available, the diagnosis cannot be made. The manual nature of this process is also time consuming and does not allow for round-the-clock continuous monitoring of systems, allowing for quick responses when faults are detected. To overcome the shortcomings of current best practice in condition monitoring in this feld, developing a low-cost, continuous, and autonomous system is proposed and explored in this project. Creating such a system would require the application of methods in Digital Signal Processing and Computer Science. Using these methods on data observed from HV assets in real-world settings will allow desired systems to emerge. This thesis will outline the proposed strategies to tackle the fault classifcation task. These will comprise Signal Processing methods to extract features in the time-frequency (TF) domain and machine learning (ML) techniques to further extract features from the TF transformed data, using them to provide a fault classifcation, combining the methods as mentioned earlier to provide a confdent and efcient system classifying faults with desirable and sufcient performances and characteristics. Developing such a system will reduce the dependence on experts, allowing moreemployees to carry out fault diagnosis to an acceptable level. This thesis will outline the varying faults and issues found in the literature review. It is essential to note that the fault signal patterns vary over time and site location and are corrupted with noise and other interference. These variations found in the signals could prove challenging for methods regarding feature extraction. Such unwanted characteristics should be considered while dealing with the data and the problem task at hand. Techniques such as denoising and, in some cases, re-introducing noise to observe the confdence of a system are considered throughout the research. The automated systems proposed use various Signal Processing feature extraction methods combined with deep learning (DL) classifcation algorithms, providing systems with desirable fault classifcation capability and accuracy, solving issues encountered when considering the development of such systems. The thesis presents three novel approaches to address the problems faced when considering the production of an autonomous condition monitoring system. The use of the Constant-Q transform (CQT) as a feature extraction method to feed to a capsule network (CapsNet) based DL classifer to maintain accuracy and overcome the complexity currently found in the state-ofthe-art DL classifer used for EMI classifcation. A further system is developed to overcome the non-stationary nature and noise contamination in EMI signals, the proposed system implements a Stockwell (S) transform for feature extraction and uses a deep residual shrinkage Network (DRSN) architecture to classify data in varying noise contamination levels. A final system is developed to address the challenge of signal instances containing single or multiple faults, the proposed system uses the short-time Fourier Transform (STFT) for feature extraction with these features being fed to a multi label graph convolutional network (ML-GCN) DL classifier with the ability to classify signals in an efficient and scalable manner.