项目名称： 大功率风力发电机多层复合绝缘在重复脉冲电压下树枝化击穿的机理与绝缘缺陷的诊断方法

项目编号： No.51477133

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 电工技术

项目作者： 刘学忠

作者单位： 西安交通大学

项目金额： 88万元

中文摘要： 大功率风力发电机绝缘系统具有多种材料复合和承受重复脉冲电压的特点，其绝缘过早电击穿破坏与绝缘缺陷及其引发的电树枝化密切相关。本项目首先剖析现有制造的和发生故障的风力发电机绝缘缺陷，实验室模拟试验研究多层材料复合绝缘体系在重复脉冲电压下的电树枝化的致因及破坏通道的微观形态，并应用元胞自动机方法数值模拟电树枝的微观演化过程及规律，对比研究不同绝缘材料和结构、电极形式、缺陷类型、脉冲电压水平和重复率等因素对复合绝缘体系中电树枝引发和生长的影响，研究基于外部激励和电磁感应实现匝间绝缘缺陷诊断和检测的方法，设计适宜于风力发电机绕组缺陷放电等信号的非接触式电磁感应传感器；应用小波分析对检测的缺陷特征信号进行提取分析，并采用人工神经网络进行模式识别，以实现风力发电机典型绝缘缺陷的有效诊断、辨识和定位。研究成果将丰富复合电介质的电树枝击穿理论，对提高风力发电机的品质，保证其运行安全及可靠性有重要的意义。

中文关键词： 风力发电机；复合绝缘系统；绝缘缺陷；重复脉冲电压；电树枝

英文摘要： The insulation systems of large capacity wind turbine generator are usually designed to be a multi-layer structure combining variety of materials，which withstand repetitive impulse voltage during operation. The premature electrical breakdown and damage is closely related to the electrical treeing phenomenon initiated by insulation defects. In this research project, insulation defects in newly manufactured and failed windings of wind turbine generator will be analysed and summarized. A series of laboratory tests on modeled specimens will be carried out in order to investigate the causes and microscopic morphology of the electrical tree in multi-layer composite insulation system under repetitive impulse voltage. Numerical simulation of electrical tree propagation will be executed with combined Cellular Automata and Finite Element approach. A comparative study will be started aiming to find out how different factors, such as insulation materials and structures, electrode forms, types of defects, voltage levels and recurring frequencies of the applied repetitive impulse, will influence the electrical tree initiation and growth in multi-layer composite insulation system. To realize the effective diagnosis, identification and location of typical inter-turn insulation defects, a set of method based on external impulse excitation and electromagnetic induction is to be developed and established. To be specific, a kind of non-contact electromagnetic induction sensor suitable for discharge detection due to insulation defects in wind turbine generator coil should be designed, wavelet analysis of the obtained signal is to be applied for feature extraction and the pattern recognition will be fulfilled by artificial neural networks. The research achievement of this project will enrich the electrical tree breakdown theory in composite dielectric under repetitive impulse voltage and make a difference on improving the quality of wind turbine generators, and ensuring the safety and reliability of their operation.

英文关键词： wind turbine generator;composite insulation system;insulation defect;repetitive impulse voltage;electrical treeing