Abstract

Surface discharge may cause irreversible damage to turn-to-ground insulation in valve windings of converter transformer, where withstand with AC-DC combined voltage. This paper analyzes the phenomenon and characteristics of surface discharge on oil-impregnated pressboard (OIP) under AC-DC combined voltage, and develops a discharge state recognition method. The cylinder-plate discharge model was used to simulate surface discharge. The results showed that discharge development and OIP failure were significantly accelerated by white marks on OIP which were essentially gaseous channels. The discharge characteristics before and after white mark occurrence were both dominated by AC component because of its bigger contribution to electrical field distribution (EFD), and the DC component had obvious effect on accelerating OIP failure. A set of features representing discharge state after white mark occurrence was selected out by the entropy weight method (EWM), based on which the discharge process was classified into three states (stable, fast development and pre-breakdown state) by fuzzy C means clustering method (FCM). A support vector machine (SVM) classifier to recognize discharge state was trained and showed a good performance, whose average assessment accuracy was up to 91.98%. Moreover, the ratio between negative and positive discharge numbers could be used as an auxiliary indicator of pre-breakdown state.

Highlights

  • The phenomenon of surface discharge on oil-impregnated pressboard (OIP) under pulsating DC voltage was observed and deeply analysed by combined camera observation, partial discharge (PD) measurement, dissolved gas analysis (DGA) and electrical field distribution (EFD) simulation, the discharge state recognition method were constructed by using fuzzy C means clustering method (FCM) and support vector machine (SVM)

  • The following conclusions can be drawn from the above results: (1) Because OIP is a porous medium, it is found that white mark which is essentially gaseous channel occurred on OIP during surface discharge

  • The discharge development and OIP failure were significantly accelerated after white mark occurrence both under pulsating DC and AC voltage, and this is because the maximum electric field intensity (EFI) was significantly enhanced after white mark occurred

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Summary

INTRODUCTION

As a main type of partial discharge (PD), develops along the OIP surface and inside OIP that causes irretrievable damage to insulation performance of oil-pressboard insulation.[1,2,3,4] As one of the common faults in transformers, surface discharge is more complex and dangerous in converter transformers than in AC transformers, considering the complicated AC voltage stresses, DC and harmonic voltage withstood by turn-to-ground insulation in valve windings, where the voltage combined by AC and DC is called AC-DC combined voltage.[5,6] adequate knowledge to understand the surface discharge on OIP and development of a diagnostic method to recognize the discharge state under AC-DC combined voltage are of significance importance for converter transformers’ operation safety. Few studies about discharge state recognition were carried out under AC voltage, let alone under AC-DC combined voltage. As for surface discharge on OIP, there are some studies on its properties under AC voltage. Reference 10 reported that OIP aging has little influence but the oil aging had great influence on surface discharge under AC voltage. References 3 and 4 reported the development of surface discharge was significantly influenced by moisture in OIP. The paper presents the phenomenon analysis and methods to recognize the discharge state for better interpretation and diagnosis of surface discharge on OIP under AC-DC combined voltage. The discharge phenomenon was observed by combined camera and PD measurement; the phenomenon was analysed by dissolved gas analysis (DGA) and EFD simulation. The extraction and selection of PD features were achieved, and a discharge state recognition method was constructed by combined FCM and SVM

Experimental setup
Discharge phenomenon
Modified step-up stress method
Reason for white mark occurrence and its content
PRPD patterns after white mark occurrence
EFD before and after white mark occurrence
Feature extraction and selection
State classification by FCM
Discharge state recognition
Findings
CONCLUSION
Full Text
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