Robust condition assessment of electrical equipment with One Class SVM based on the measurement of partial discharges

1Department of Signal Processing and Communications, Universidad Carlos III de Madrid, Avda. Universidad, 30, Leganés, Madrid 28911, Spain
2Department of Electrical Engineering, Universidad Carlos III de Madrid, Avda. Universidad, 30, Leganés, Madrid 28911, Spain; Emails:
3Department of Electrical Engineering, Federico Santa María Technical University, 8940000 Santiago de Chile, Chile


Parrado-Hernández, E.; Robles, G.; Ardila-Rey, J.A.; Martínez-Tarifa, J.M. Robust Condition Assessment of Electrical Equipment with One Class Support Vector Machines Based on the Measurement of Partial Discharges. Energies 2018, 11, 486.

  • 2017 Impact Factor: 2.676
  • 48/97 (Q2) in ‘Energy & Fuels’
  • Journal Impact Factor Percentile: 51.031

This paper is Open Access and can be downloaded here.

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.