【作者】 武桦；

【导师】 罗兴锜； 冯建军；

【作者基本信息】 西安理工大学 ， 水利水电工程， 2017， 博士

【摘要】 水力发电机组是水电站的核心设备,在水电站及整个电力系统中都扮演着至关重要的角色,其主绝缘性能的好坏会极大地影响机组运行的稳定性和安全性,决定整个系统的安全可靠运行。局部放电(partial discharge,PD)作为主绝缘体老化的前兆信息和主要诱因,可以有效地表征水力发电机组的主绝缘健康状况,因此研究水力发电机组局部放电在线监测及故障诊断技术对掌握机组设备的绝缘劣化程度具有重要意义。本文以某水力发电机组为例,对机组主绝缘系统的老化机理、局部放电故障诊断与故障评估进行了研究。水电机组局部放电信号中通常含有大量的噪声信号,传统的小波变换方法信号检测方法在一定的程度上能够抑制噪声的干扰,但存在的误判及漏判的问题,为此本文引入相关空域法理论,并将分位数的概念运用到小波变换的各分解层上多尺度阈值的设定,提出了一种基于相关概率小波变换的信号检测方法,从而地有效抑制信号中存在的噪声干扰。由于局部放电信号具有较强的非线性和时变性,而且在检测时会受到多个干扰信号的叠加,本文将流形学习理论引入局部放电信号的处理中,提出了基于时频流形的局部放电信号特征提取方法,有效地去除背景噪声,解决了局放信号频带分散、去噪困难等问题。将发电机组的局部放电现象划分为三大典型的绝缘缺陷,并以局部放电信号的时/频特征参数为输入向量,构建了基于SVM-KNN算法的发电机组局放故障诊断模型,并将其应用于实际水电厂的发电机局部放电故障诊断中,提高了故障诊断精度。根据水力发电机组的实际运行情况和放电能量,将发电机局部放电故障等级划分为:正常、异常、预警等3个状态等级。选取正、负半周放电脉冲数N+和N-,正负最大脉冲幅度umax+和umax-及等值累计放电量Qc等5个参数来描述局放故障程度,建立了发电机组局放故障模糊综合评价模型,实现对机组局放故障严重程度的模糊评估,为大型发电机组的状态检修提供技术指导。以上研究结果表明,本文在水力发电机局部放电特征提取、故障模式识别以及故障严重程度评估等方面取得的成果具有重要的学术意义和工程应用价值。更多还原

【Abstract】 Hydropower unit is a core equipment of the hydropower station,which plays an important role in the station and the whole power system.Whose safety and reliability are closely related to the operation condition of the whole system.Furthermore,in the process of actual operation,power transformer is unavoidable to subject to such outside factor influence as electricity,machinery and heat,and so forth further causing its winding insulation deterioration to produce partial discharge(PD)phenomena,threatening the safety of operation of the whole system.Therefore,it is essential to monitor the insulation conditionand provide a proper maintenance action for in-service hydropower units.In this paper,taking hydropower units as an example,the aging mechanism of the main insulation system,partial discharge fault diagnosis and fault evaluation are studied.A large number of noise signals are usually contained in the partial discharge signal of the hydroelectric unit.The traditional wavelet transform method can suppress the noise interference to a certain extent,but there is a problem of misjudgement and leakage.Therefore,this paper introduces the theory of correlation space domain method,and applies the concept of quantile to each of the wavelet transform.A signal detection method based on the correlation probability wavelet transform is proposed to effectively suppress the noise interference in the signal.PD signal is of strong nonlinearity and time variation.And in the in situ detection,it is frequently subject to the overlap of many interference signals.Which leads great significant to signals feature extraction and fault diagnosis.Therefore,how to accurately extract signals feature is the key to PD faults identification.So this paper introduces manifold learning theory to partial discharge signals processing,and suggests a novel method for the PD signal feature extraction based on manifold learning theory.This method can effectively remove the background noise,solved the partial discharge signal frequency dispersion,denoising difficulties and other issues.The hydropower units partial discharge phenomenon is divided into three typical insulation defects.Then fault diagnosis system of hydropower units based on SVM-KNN algorithm is established,taking the partial discharge signals of time-frequency characteristic parameters as input vectors.The result indicates that this method is able to identify and classify different partial discharge faults.According to the practical operation of hydropower units and discharge energy,the generator partial discharge fault degree is divided into three state level,normal,abnormal and warning.N+and N-,umax+ and umax-,Qc are selected to describe fault degree.And the fuzzy comprehensive evaluation model is established,which can realize the fuzzy evaluation of the severity of the unit.Above all,it shows that methods of hydropower units partial discharge feature extraction,the fault pattern recognition and fault evaluation proposed in this paper are obtained has important academic significance and engineering application value.更多还原