Separation of radio-frequency signals triggered by valve switching in a HVDC converter by supervised machine learning methods

Matteri, Andrea; Palo, M.; Ogliari, Emanuele; Schuberl, Benjamin; Wei, Jianguo; Gu, Kai; Liu, Weilin

doi:10.1109/eeeic/icpseurope51590.2021.9584660

Cited by 3 publications

(4 citation statements)

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“…In a previous study [31], we showed that training a classification ANN model on the entire waveform as an input can lead to satisfying results. However, the original waveform of each signal has 5120 samples thus the training and classification become highly demanding for even highend machines.…”

Section: A Onset Detectionmentioning

confidence: 94%

“…Artificial Neural Networks have been tested with good results with classification methodologies that focus mostly on pattern recognition within the PRPD [26], [27] and its statistical features [28], [29]. Approaches using the time-domain recording to classify individual pulses have also been made with the pulse statistical and waveform features [30], [31] or using various dimensionality reduction techniques, such as PCA [32]. In [33], [34] combined approaches have also been tested.…”

Section: Machine Learning In Pd Detectionmentioning

confidence: 99%

“…We conducted a detailed measurement campaign with physical sources of PD pulses and interference. The same acquisition system has been used to record pulses in other case studied including real-life situations [22], [31], [45], [46].…”

Section: Measurementmentioning

confidence: 99%

“…In the selection process of the model best fitted for the filtering task, we studied 5 typical classification models that are widely available in open-source packages: Support Vector Machine Classifier (SVC), Random Forest Ensemble Classifier (RFC), K-Nearest Neighbors Classifier (KNN), Gradient Boosting Classifier (GBC), and an Artificial Neural Network Classifier (ANN). The preliminary hypothesis (according to previously performed studies in [31]) assumed the best performance for ANN, therefore the training procedure was focused on achieving the best possible parameter tuning for this classifier. Also for this reason, in the next sections of the paper, the detailed simulation results are going to be shown only for ANN.…”

Section: B Model Selection and Trainingmentioning

confidence: 99%

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General Machine Learning-Based Approach to Pulse Classification for Separation of Partial Discharges and Interference

Ogliari,

Sakwa,

Wei

et al. 2023

IEEE Sensors J.

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This paper describes a complete approach to filtering Partial Discharge (PD) pulses from interference in High Voltage electrical equipment using supervised Machine Learning (ML) techniques. The PD signals are registered in Ultra High Frequency radiation band with a multisensor acquisition system composed of 4 antennae. The proposed methodology focuses on the implementation ML algorithms and proposes a novel in this field approach to the onset detection of incoming signals. The goal was to achieve high accuracy of filtering with reasonably low compilation times of the ML classifier. That would allow to use the model on edge sensor devices. In the paper, different models and training variants of the ML framework are tested. The presented results are based on a robust measurement campaign performed in laboratories of GEIRI Europe. The methodology is validated through tests on 3 separate test scenarios. Each represents a different complexity of the problem with an increasing number of active sources. The results show high potential for utilization of the ANN and other classifiers for PD filtering problems as the accuracy achieves the desired threshold of 80% for most of the tested variants. The methodology is a step forward toward a fully online PD and interference filter.

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Section: A Onset Detectionmentioning

confidence: 94%