Robust Condition Assessment of Electrical Equipment with One Class Support Vector Machines Based on the Measurement of Partial Discharges

Abstract

This paper presents a system for the detection of partial discharges (PD) in industrial applications based on One Class Support Vector Machines (OCSVM). The study stresses the detection of Partial Discharges (PD) as they represent a major source of information related to degradation in the equipment. PD measurement is a widely extended technique for condition monitoring of electrical machines and power cables to avoid catastrophic failures and the consequent blackouts. One of the most important keystones in the interpretation of partial discharges is their separation from other signals considered as not-PD especially in low SNR measurements. In this sense, the OCSVM is an interesting alternative to binary SVMs since it does not need a training set with examples of all the output classes correctly labelled. On the contrary, the OCSVM learns a model of the signals acquired when the equipment is in PD-free mode, defined as a state where no degradation mechanism is active, so one only needs to make sure that the training signals were recorded under this setting. These default mode signals are easier to characterize and acquire in industrial environments than PD and lead to more robust detectors that practically do not need domain adaptation to perform in scenarios prone to different types of PD. In fact, the experimental results show that the performance of the OCSVM is comparable to that achieved by a binary SVM trained using both noise and PD pulses. Finally, the method is successfully applied to a more realistic scenario involving the detection of PD in a damaged distribution power cable.

Artificial Generation of Partial Discharge Sources Through an Algorithm Based on Deep Convolutional Generative Adversarial Networks

“…This problem has gradually increased due to a higher use of systems based on power electronics, such as switched-mode power supplies, frequency inverters, rectifiers, inverters or other electrical-electronic devices capable of generating some type of similar switching [17], [23]. Likewise, in many measurement processes it is very common to find simultaneous presence of multiple PD, which causes the PRPD measured in any real equipment or test object to be of complex interpretation since certain less harmful sources with greater amplitudes can hide the presence of more critical sources, such as internal PD, (whose presence can indicate accelerated deterioration of equipment insulation) [9], [13], [18], [24]- [29]. In addition, certain types of discharges, such as corona PD, usually do not have a significant influence on the life expectancy of insulation systems.…”

“…Another widely used procedure for identifying PD sources is the application of algorithms based on machine learning techniques [12]- [18], [26], [28], [31]. Recent advances in the computing field meant significant improvements in data storage and processing capacities, allowing these techniques to be widely used in stochastic applications, where the signals obtained cannot be easily identified by conventional mathematical techniques or by a simple visual inspection of the spectral or temporal content of the signal.…”

“…The results obtained so far have shown that intelligent ML-based techniques can be an adequate solution when identifying different PD types. However, many aspects remain to be addressed and investigated, such as the decrease in the success rate of these algorithms when, after training, the PD signals show temporary or spectral changes, product of some variation in the test object or measurement circuit [9], [12], [13], [18].…”

The Use of Artificial Intelligence as a Tool Supporting Sustainable Development Local Policy

“…Its application primarily solves problems of linear and non-linearly separable data classification [52][53][54], detects and identifies failures and errors in various systems [55,56], and assesses risk in various branches of the economy [57,58]. In this paper, the range of special zone development in the spa areas was determined by means of the SVM technique.…”

Recognition and Classification of Incipient Cable Failures Based on Variational Mode Decomposition and a Convolutional Neural Network

“…Fault classification methods can be categorized into machine learning algorithms and deep learning algorithms. Machine learning algorithms, including decision tree (DT), support vector machine (SVM), K-nearest neighbor (KNN), and artificial neural network (ANN), have been widely used in power systems [19][20][21][22][23]. Recently, researchers have conducted studies of traditional algorithms for different problems and further improved the classification accuracy [24][25][26].…”