Signal analysis and feature generation for pattern identification of partial discharges in high-voltage equipment

David, Mathieu; Sánchez-Urán, Miguel Ángel; Álvarez, F.; Ortego, Javier; Garnacho, Fernando

doi:10.1016/j.epsr.2012.08.016

Cited by 17 publications

(7 citation statements)

References 27 publications

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“…In practice, a simple way of PD clustering is to determine PD sources located in different sites along a HV grid, by applying the transient traveling wave theory [5,6]. Therefore, the clustering capabilities of a PD analyser can be split into two different tests: the PD location test and the PD clustering test when various PD sources are located at the same site [7,8]. This last clustering concept is called in this paper PD clustering test.…”

Section: Introductionmentioning

confidence: 99%

Validation of a Qualification Procedure Applied to the Verification of Partial Discharge Analysers Used for HVDC or HVAC Networks

Vera,

Garnacho,

Klüss

et al. 2023

Applied Sciences

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The insulation condition of HVDC grids consisting of cable systems, GIS, and converters should be monitored by partial discharge (PD) analysers using artificial intelligence (AI) tools for efficient insulation diagnosis. Although there are many experiences of PD monitoring solutions developed for the supervision of the insulation condition of HVAC grids using PD analysers, there are no standardised requirements for their qualification available yet. The international technical specification TS IEC 62478 provides general rules for PD measurements using electromagnetic methods but does not define performance requirements for qualification tests. HVDC and HVAC PD analysers must be tested by unambiguous test procedures. This paper compiles experiences of using PD analysers with HFCT sensors in HVAC grids (cable systems, GIS, and AIS) to define a qualification procedure for HVAC systems. This procedure is applicable to HVDC grids (cable systems, GIS, AIS, and converters) because the particularities related to the insulation behaviour under HVDC voltage are also considered. Representative PD sources are discussed in HVAC and HVDC positive and negative polarity. The PD pulse trend of representative insulation defects in HVDC cable systems is quite different from that of HVAC grids. Special attention should be paid to the acquisition of PD signals in HVDC grids since few pulses appear in solid insulations, mainly during voltage changes (polarity reversals or surges), but rarely in continuous operation with constant direct voltage. A synthetic PD simulator has been developed to reproduce trains of PD pulses or noise signals, similar to those that can appear in the power network. A set of three functionality tests has been developed for qualification of the diagnostic capabilities of PD analysers working up to 30 MHz addressed to HVDC or HVAC grids: (1) PD recognition test, (2) PD clustering test, and (3) PD location test. This qualification procedure has been validated by means of a round-robin test performed by five research institutes (RISE, FFII, TUDelft, TAU, and UPM) using commercial and in-development AI PD recognition and clustering tools to demonstrate its robustness and applicability. Applying this qualification procedure, two PD methods for electrical detection and prevention of insulation defects have been approved, one for HVAC and the other for HVDC grids.

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Section: Introductionmentioning

confidence: 99%

Validation of a Qualification Procedure Applied to the Verification of Partial Discharge Analysers Used for HVDC or HVAC Networks

Vera,

Garnacho,

Klüss

et al. 2023

Applied Sciences

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“…One of the most challenging issues in classifying PD patterns according to the ageing state of the cable insulator is to extract informative features from PD measurements. Most of the existing researches on PD feature extraction is applied to PD pattern recognition for defect models classification in HV equipment [14,15,16,17,18,19,20,21] or PD-noise discrimination [3]. Therefore, feature extraction and selection techniques are not sufficiently investigated for ageing state recognition.…”

Section: Introductionmentioning

confidence: 99%

Feature extraction and ageing state recognition using partial discharges in cables under HVDC

Morette

Ditchi

Oussar

2020

Electric Power Systems Research

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PD detection is an effective way to evaluate the degradation state of cable insulation. The extraction and selection of relevant features from PD raw data have been mostly investigated to recognize the types of insulation defects in HV equipment. In this study, two different feature extraction methods combined with supervised classification techniques are implemented for ageing state recognition of a polyethylene-insulated cable under HVDC conditions. For this purpose, an original experimental setup is implemented. Experiments are performed on a long length 100 m coaxial cable subjected to high electric fields. PD events are detected by direct coupling and collected with a digitizing oscilloscope. Feature extraction based on PD pulse shape parameters represented in time domain as well as wavelet decomposition coefficients are used separately as input variables of Support Vector Machines classifiers (SVMs). A feature selection method is implemented to design optimized SVM classifiers that attribute an ageing state to the cable insulation. The classification performance achieved with both feature extraction methods are presented and compared. The results show satisfactory recognition rates of two ageing states of cable insulation, up to 100% with a small subset of variables, particularly when features are extracted from wavelet decomposition of PD experimental data.

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“…PD signals are linked to the transformer insulation system, high levels of these signals may lead to breakdown of the insulation [1]. In fact, PD analyses in power transformers has been widely applied for detecting and quantifying premature damage in insulation systems [2], therefore detection and location plays an important role. However, detection and location of PD in power transformer has been a complex task, since its random occurrence may produce a wide frequency spectrum from 10 kHz to 3 MHz [3].…”

Section: Introductionmentioning

confidence: 99%

Partial discharge location in power transformer windings using the wavelet Laplace function

Guillén

Idárraga-Ospina

Mombello

2014

Electric Power Systems Research

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Signal analysis and feature generation for pattern identification of partial discharges in high-voltage equipment

Cited by 17 publications

References 27 publications

Validation of a Qualification Procedure Applied to the Verification of Partial Discharge Analysers Used for HVDC or HVAC Networks

Validation of a Qualification Procedure Applied to the Verification of Partial Discharge Analysers Used for HVDC or HVAC Networks

Feature extraction and ageing state recognition using partial discharges in cables under HVDC

Partial discharge location in power transformer windings using the wavelet Laplace function

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