PD pattern analysis during induced test of large power transformers

Abstract-Liberalization of the energy market has put increasing pressure on both electricity producers and distributors for lower costs. Since maintenance is a major expense account, such companies will be inclined to reduce maintenance budgets. At the same time, increased liability for non-delivered-energy increases the costs of sudden failure of a component. Transformers are such a component; they are often an essential link in the distribution network.In order to reconcile both decreasing maintenance spending and reliable service, condition-based maintenance (CBM) is often proposed. The basis of a successful application of CBM lies in obtaining information on transformers, so that, on the one hand, a critical condition will be noted early enough to take measures and, on the other hand, so that only minimal maintenance is being applied to transformers still in good condition. This paper will review a series of often mentioned techniques in order to assess what the value of this technique will be for CBM and whether it can be used for condition monitoring.

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“…10, and [48]). These HV apparatus are not discharge free and in general a discharge level 500 pC is tolerated.…”

Section: H Partial Dischargementioning

confidence: 96%

Monitoring and diagnostic of transformer solid insulation

Bolhuis

Gulski

Smit

2002

IEEE Trans. Power Delivery

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“…Most of the research was done by Fromm [16][17][18], Morshuis et al [19] and Gulski et al [20]. Fromm has built a model with parameters including discharge magnitude q i , time of discharge interval t i , and instantaneous voltage across the test object, which describes the stochastic discharge process at dc voltage.…”

Section: Introductionmentioning

confidence: 99%

Remote Detection and Recognition of Electrostatic Discharge From HVDC Transmission Lines

Zhang¹,

Liu²,

Liu³

2014

PIER C

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Abstract-To remotely detect corona discharge from High-Voltage Direct Current (HVDC) transmission lines, a detecting system combining detecting platform and data progressing system is designed. Detecting platform is developed resorting to the principle of differential noise reduction, which can fulfill narrow-band detection breaking away interference from broadcasting and easily catch the electrostatic discharge signal. To get rid of interference from spark discharge, a data progressing system containing feature extractions, clustering and recognition technologies is developed. Clustering is realized by extracting five discharge features, including peak factor, form factor, skewness, kurtosis and mean square error. The unsupervised clustering Fuzzy C-Means (FCM) method is used to achieve fast separation for electrostatic discharges and provide training set for pattern recognition. Pattern recognition resorts to Support Vector Machine (SVM) method. For comparison, Back Propagation (BP) and Learning Vector Quantization (LVQ) approaches are taken to test the recognition ability. The results show that SVM recognizer with a recognition rate of 97.5% achieves higher performance than BP and LVQ methods. It can be concluded that the detecting system can be an interesting alternative for electrostatic discharge detection.

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“…PD behavior can be represented in various ways. Because of the randomization of PD activity, one of the most popular representations is the 3-D (ĳ-Q-N) distribution [2], [3], i.e., the PD pattern is described using a pulse count N versus pulse height Q and phase angle ĳ diagram. Previous experimental results have adequately demonstrated that ĳ-Q-N distributions are strongly dependent upon the PD source.…”

Section: Introductionmentioning

confidence: 99%

Pattern recognition for partial discharge diagnosis of power transformer

Chen

Liu

et al. 2010

2010 International Conference on Machine Learning and Cybernetics

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This paper presents a novel pattern recognition approach based on a four-layer artificial neural network (ANN) for the partial discharge (PD) diagnosis of power transformer. A precious PD detector is used to measure 3-D (ĳ-Q-N) signals and PD-fingerprints of four experimental models in a shielded laboratory. This work has established a database containing 160 sets of 3-D patterns and PD-fingerprints. The database is used as the training data to train a back-propagation neural network.The training-accomplished neural network can be a good diagnosis system for the practical insulation diagnosis of epoxy-resin power transformers. The proposed approach is successfully applied to practical power transformers field experiments. Experimental results demonstrated that the proposed pattern recognition approach has high recognition rate.

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PD pattern analysis during induced test of large power transformers

Cited by 14 publications

References 3 publications

Monitoring and diagnostic of transformer solid insulation

Monitoring and diagnostic of transformer solid insulation

Remote Detection and Recognition of Electrostatic Discharge From HVDC Transmission Lines

Pattern recognition for partial discharge diagnosis of power transformer

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