A Study on the Effectiveness of Partial Discharge Models for Various Electrical Machines’ Insulation Materials

Author:

Verginadis Dimosthenis1ORCID,Iakovidis Tryfon1,Karlis Athanasios1ORCID,Danikas Michael1,Antonino-Daviu Jose-Alfonso2ORCID

Affiliation:

1. Department of Electrical & Computer Engineering, Democritus University of Thrace (DUTh), 671 00 Xanthi, Greece

2. Instituto Tecnologico de la Energia, Universitat Politècnica de València, 46022 Valencia, Spain

Abstract

A vital component of electrical machines (EMs), which plays the most significant role in their reliable and proper operation, is their insulation system. Synchronous generators (SGs) are the most commonly used EMs in energy production and industry. Epoxy resin and mica are the predominant insulation materials for the SGs’ windings because their characteristics and properties are suitable for extending the lifetime of the insulation. Partial discharges (PDs) are both a symptom of insulation degradation, as they cause serious problems for insulation, and a means to identify possible insulation faults with offline and/or online PD tests and measurements. A comparison of three different equivalent circuit models of PDs occurring in different insulation materials (epoxy resin, mica, and a combination of these two) is presented in this paper. Different applied voltages and/or various geometries of voids are the factors investigated through simulations. The number of PDs, PD activity, and flashover voltages are examined in order to evaluate which of the aforementioned materials has the best reaction against PD activity.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Control and Optimization,Mechanical Engineering,Computer Science (miscellaneous),Control and Systems Engineering

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Development of a Flexible Rogowski Coil Sensor for Partial Discharge Detection in Power Cables;2023 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET);2023-09-12

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