Partial discharge spectral characterization in HF, VHF and UHF bands using particle swarm optimization

1Department of Electrical Engineering, Universidad Carlos III de Madrid, Leganés, 28911 Madrid, Spain
2Department of Electrical Engineering, Universidad Técnica Federico Santa María, 8940000 Santiago de Chile, Chile
3Department of Signal Processing and Communications, Universidad Carlos III de Madrid, Leganés, 28911 Madrid, Spain
*Author to whom correspondence should be addressed.

Robles, G.; Fresno, J.M.; Martínez-Tarifa, J.M.; Ardila-Rey, J.A.; Parrado-Hernández, E. Partial Discharge Spectral Characterization in HF, VHF and UHF Bands Using Particle Swarm Optimization. Sensors 2018, 18, 746.

  • 2017 Impact Factor: 2.475
  • 16/62 (Q2) in ‘Instruments & Instrumentation’
  • Journal Impact Factor Percentile: 74.59

This paper is Open Access and can be downloaded here.

https://doi.org/10.3390/s18030746

http://www.mdpi.com/1424-8220/18/3/746

Abstract— The measurement of partial discharge (PD) signals in the radio-frequency (RF) range has gained popularity among utilities and specialized monitoring companies in recent years. Unfortunately, in most of the occasions the data are hidden by noise and coupled interferences that hinder their interpretation and renders them useless especially in acquisition systems in the UHF band where the signals of interest are weak. This paper is focused on a method that uses a selective spectral signal characterization to feature each signal, type of partial discharge or interferences/noise, with the power contained in the most representative frequency bands. The technique can be considered as a dimensionality reduction problem where all the energy information contained in the frequency components is condensed in a reduced number of UHF or HF/VHF bands. In general, dimensionality reduction methods make the interpretation of results a difficult task because the inherent physical nature of the signal is lost in the process. The proposed selective spectral characterization is a preprocessing tool that facilitates further main processing. The starting point is a clustering of signals that could form the core of a partial discharge monitoring system. Therefore, the dimensionality reduction technique should find out the best frequency bands to enhance the affinity between signals in the same cluster and the differences between signals in different clusters. This is done maximizing the minimum Mahalanobis distance between clusters using particle swarm optimization (PSO). The tool is tested with three sets of experimental signals to demonstrate its capabilities in separating noise and partial discharges with low signal-to-noise ratio and separating different types of partial discharges measured in the UHF and HF/VHF bands.

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: grobles@ing.uc3m.es
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.

https://doi.org/10.3390/en11030486

http://www.mdpi.com/1424-8220/17/11/2666

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.