复杂工况下风力发电机组关键部件故障分析与诊断研究

发布时间：2018-03-24 07:11

本文选题：风力发电机　切入点：复杂工况　出处：《沈阳工业大学》2014年博士论文

【摘要】：近年来，全球风电产业迅猛发展，然而风力发电机组的维护成本一直居高不下，这严重制约了风电产业的健康发展。风力发电机工作环境极为复杂，受风速波动、负载变化等影响，其振动信号具有非平稳、非线性、时变等特点。传统的故障诊断方法，极少考虑风力发电机的复杂工况条件对其动态特征的影响，针对这一问题，本文提出基于状态变化过程和多传感器融合的复杂工况下风力发电机组关键部件故障定量分析与诊断方法。针对风力发电机组关键部件故障定量分析与诊断问题，提出一种基于Hilbert-Huang变换和信息熵的故障定量分析与诊断方法——Hilbert空间特征熵方法，首先应用Hilbert-Huang变换方法对信号时频空间进行划分，进而对得到的信号在瞬时时频空间上的能量分布矩阵做奇异值分解，最后定义了信号在瞬时时频划分下的Hilbert空间特征熵。此外，为提高时频空间划分的精度，提出了一种改进Hilbert-Huang变换端点效应问题的自适应算法。为验证该算法，利用转子实验台设计不平衡-碰摩、松动-碰摩两种常见的转子碰摩耦合故障实验，采集了不同转速下转子故障信号，，应用Hilbert空间特征熵分析测试数据，用故障信号熵值随转速变化的熵值曲线来描述转子故障的程度和类型，实现了对转子碰摩耦合故障的定量分析与诊断。针对复杂工况下的风力发电机组关键部件故障诊断问题，首先对不同工况下风力发电机传动系统进行了振动分析。分析了风力发电机控制策略对其振动的影响，进而对不同风速、不同负载条件下风力发电机轴承振动信号进行了分析，从时域、频域、时频域、Hilbert空间特征熵等多个角度对不同工况下风力发电机轴承的振动信号特征进行了研究，总结了风力发电机振动信号随风速、负载变化的规律。在此基础上，分别对风力发电机中的轴承故障和齿轮箱故障的程度和状态问题进行了研究。给出了考虑风速影响的轴承振动模型，从时域及时频域的角度，对不同风速下，正常轴承和故障轴承振动信号进行了比对分析。应用Hilbert空间特征熵对不同风速下的轴承振动信号进行分析，通过比较正常轴承与故障轴承振动信号Hilbert空间特征熵值随风速变化的曲线，可以直观的判断出轴承故障。进而应用Hilbert空间特征熵方法对轴承故障前一个月的在线监测数据进行分析，结果表明，该法能有效的定量描述轴承故障程度变化的过程，并能根据其熵值突变的时间点，较早的发现风力发电机轴承故障。给出了风力发电机齿轮箱中各级传动啮合频率及各齿轮特征频率的计算方法，应用啮合频率分析方法对齿轮箱正常信号及故障信号进行了分析，结果表明，该方法虽然能有效分析出齿轮箱故障原因，但无法反映故障的程度，且诊断的结果不直观，其过程也较为繁琐。为更全面的反映齿轮箱的运行状态，应用Hilbert空间特征熵方法对齿轮箱多测点、多转速、多故障状态下的振动信号进行融合分析。从而得到了齿轮箱振动信号Hilbert空间特征熵值随测点位置、转速变化的熵值平面，通过对比正常齿轮箱与故障齿轮箱的熵值平面，可以直观的诊断出齿轮箱故障。通过对比连续离线测试获得的故障齿轮箱熵值平面，表明通过该方法可以定量描述齿轮箱故障程度和状态的变化。
[Abstract]:In recent years, rapid development of global wind power industry, but the maintenance cost of the wind turbine has been high, which seriously restricts the healthy development of wind power industry. The wind turbine working environment is very complex, affected by the fluctuation of wind speed, the load changes, the vibration signal is non-stationary, nonlinear, time-varying characteristics of fault. The traditional diagnostic methods, rarely take into account the influence of complex working conditions of wind turbines on the dynamic characteristics, in order to solve this problem, this paper based on the complicated working state change process and multi sensor fusion under the key components of wind turbine fault diagnosis and quantitative analysis method.
Aiming at the problem of quantitative analysis and fault diagnosis of the key components of wind turbine, proposed a Hilbert spatial entropy method based on quantitative analysis and fault diagnosis method of Hilbert-Huang transform and information entropy, the first application of Hilbert-Huang transform method of signal in time-frequency space division, and the energy distribution of the signal matrix in the instantaneous time-frequency space the singular value decomposition, finally defines the spatial features of Hilbert signal in time and frequency division instantaneous entropy condition. In addition, in order to improve the time-frequency space division accuracy, proposed an improved Hilbert-Huang transform to the end effect problem of adaptive algorithm. In order to validate the algorithm, using the design of rotor experimental platform of unbalance rubbing, loosening two kinds of rubbing rotor rubbing coupling faults of rotor fault signal acquisition experiment, different speed, entropy feature of the application of Hilbert spatial analysis test number According to the entropy value curve of the fault signal entropy and the speed change, it describes the degree and type of the rotor fault, and realizes the quantitative analysis and diagnosis of the rotor rub impact coupling fault.
The problem of fault diagnosis for the key components of wind turbine under complicated working conditions, the different conditions of wind turbine drive system was analyzed. The vibration analysis of wind turbine control strategy influence on its vibration, and the different wind speed, wind turbine bearing vibration signal under different load conditions are analyzed from time domain, frequency domain, time the frequency domain characteristic of vibration signals of multi angle Hilbert space characteristic entropy on different working conditions of wind turbine bearings were studied, summarized the wind turbine vibration signal of wind speed and load changes.
On this basis, the degree and status of fault bearing fault and gear box of wind turbine are studied. Given the bearing vibration model considering the influence of wind speed, time from time domain frequency domain, the different wind speed, normal bearing and bearing fault vibration signals were analyzed. The bearing vibration signal entropy the application of Hilbert spatial characteristics under different wind speeds were analyzed by comparing the normal bearing and the fault bearing vibration signal Hilbert spatial entropy change with wind speed curve, we can judge the bearing fault. Then using Hilbert spatial entropy method of on-line monitoring data a month before the bearing fault is analyzed, the results show that method can describe the change process of bearing fault degree effective quantitative, and according to the entropy point in the time, found that the wind turbine bearing earlier The fault is given. Calculation method of meshing frequency levels of transmission gearbox of wind turbine and the characteristic frequency of the gear, using the meshing frequency analysis method, the gear box of normal signals and fault signals. The results show that the method can effectively analyze the reasons for the failure of gear box, but can not reflect the degree of fault diagnosis, and the result is not intuitive, the process is more complicated. In order to reflect the operation state of gear box is more comprehensive, the application of Hilbert spatial entropy method for multi point measurement, the multi speed gear box, vibration signal of multi fault state of fusion analysis. To obtain the vibration signal of the gear box with the Hilbert space characteristic entropy measurement locations entropy plane speed changes, through the entropy plane compared with normal gear box and gearbox fault, can diagnose the fault of gearbox. By comparing the offline continuous test by The entropy value plane of the fault gear box is obtained, which shows that this method can quantitatively describe the change of the gear box fault degree and state.

【学位授予单位】：沈阳工业大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：TM315

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