Calculation of torque and speed of induction machines under rotor winding faults

Based on the multi-loop method, the rotating torque and speed of theinduction machine are analyzed. The fluctuating components of the torque and speed caused by rotorwinding faults are studied. The models for calculating the fluctuating components are put forward.Simulation and computation results show that the rotor winding faults will cause electromagnetictorque and rotating speed to fluctuate; and fluctuating frequencies are the same and their magnitudewill increase with the rise of the severity of the faults. The load inertia affects the torque andspeed fluctuation, with the increase of inertia, the fluctuation of the torque will rise, while thecorresponding speed fluctuation will obviously decline.

A New Method of Wind Turbine Bearing Fault Diagnosis Based on Multi-Masking Empirical Mode Decomposition and Fuzzy C-Means Clustering

Based on Multi-Masking Empirical Mode Decomposition (MMEMD) and fuzzy c-means (FCM) clustering, a new method of wind turbine bearing fault diagnosis FCM-MMEMD is proposed, which can determine the fault accurately and timely. First, FCM clustering is employed to classify the data into different clusters, which helps to estimate whether there is a fault and how many fault types there are. If fault signals exist, the fault vibration signals are then demodulated and decomposed into different frequency bands by MMEMD in order to be analyzed further. In order to overcome the mode mixing defect of empirical mode decomposition (EMD), a novel method called MMEMD is proposed. It is an improvement to masking empirical mode decomposition (MEMD). By adding multi-masking signals to the signals to be decomposed in different levels, it can restrain low-frequency components from mixing in highfrequency components effectively in the sifting process and then suppress the mode mixing. It has the advantages of easy implementation and strong ability of suppressing modal mixing. The fault type is determined by Hilbert envelope finally. The results of simulation signal decomposition showed the high performance of MMEMD. Experiments of bearing fault diagnosis in wind turbine bearing fault diagnosis proved the validity and high accuracy of the new method.

GENERATOR VIBRATION FAULT DIAGNOSIS METHOD BASED ON ROTOR VIBRATION AND STATOR WINDING PARALLEL BRANCHES CIRCULATING CURRENT CHARACTERISTICS

Rotor vibration characteristics are first analyzed, which are that the rotor vibration of fundamental frequency will increase due to rotor winding inter-turn short circuit fault, air-gap dynamic eccentricity fault, or imbalance fault, and the vibration of the second frequency will increase when the air-gap static eccentricity fault occurs. Next, the characteristics of the stator winding parallel branches circulating current are analyzed, which are that the second harmonics circulating current will increase when the rotor winding inter-turn short circuit fault occurs, and the fundamental circulating current will increase when the air-gap eccentricity fault occurs, neither being strongly affected by the imbalance fault. Considering the differences of the rotor vibration and circulating current characteristics caused by different rotor faults, a method of generator vibration fault diagnosis, based on rotor vibration and circulating current characteristics, is developed. Finally, the rotor vibration and circulating current of a type SDF-9 generator is measured in the laboratory to verify the theoretical analysis presented above.

Automatic Fault Prediction of Wind Turbine Main Bearing Based on SCADA Data and Artificial Neural Network

As the demand for wind energy continues to grow at exponential rate, reducing operation and maintenance (O & M) costs and improving reliability have become top priorities in wind turbine maintenance strategies. Prediction of wind turbine failures before they reach a catastrophic stage is critical to reduce the O & M cost due to unnecessary scheduled maintenance. A SCADA-data based condition monitoring system, which takes advantage of data already collected at the wind turbine controller, is a cost-effective way to monitor wind turbines for early warning of failures. This article proposes a methodology of fault prediction and automatically generating warning and alarm for wind turbine main bearings based on stored SCADA data using Artificial Neural Network (ANN). The ANN model of turbine main bearing normal behavior is established and then the deviation between estimated and actual values of the parameter is calculated. Furthermore, a method has been developed to generate early warning and alarm and avoid false warnings and alarms based on the deviation. In this way, wind farm operators are able to have enough time to plan maintenance, and thus, unanticipated downtime can be avoided and O & M costs can be reduced.

Fault Ride-Through Study of Wind Turbines

The installation of wind energy has increased rapidly around the world. The grid codes about the wind energy require wind turbine (WT) has the ability of fault (or low voltage) ride-through (FRT). To study the FRT operation of the wind farms, three methods were discussed. First, the rotor short current of doubly-fed induction generator (DFIG) was limited by introducing a rotor side protection circuit. Second, the voltage of DC bus was limited by a DC energy absorb circuit. Third, STATCOM was used to increase the low level voltages of the wind farm. Simulation under MATLAB was studied and the corresponding results were given and discussed. The methods proposed in this paper can limit the rotor short current and the DC voltage of the DFIG WT to some degree, but the voltage support to the power system during the fault largely depend on the installation place of STATCOM.

Investigation on Frequent Wind Power Off-Grid Fault

With the commissioning of the 750-kV Hexi power transmission and transformation project, the first stage of the 10-GW class Jiuquan Wind Power Base project was completed and put into operation this year. However, disconnections involving some wind turbines took place quite a few times in Jiuquan recently, which have caused significant impacts on the power grid and drawn extensive attentions both domestically and abroad. Take the typical faults in Jiuquan for examples, the basic situations are presented and the causes of the fault on February 24 th are analyzed. Then the corresponding solutions are put forward afterwards.

Operation of offshore wind farms connected with DRU-HVDC transmission systems with special consideration of faults

The diode rectifier unit(DRU)-based high-voltage DC(DRU-HVDC) system is a promising solution for offshore wind energy transmission thanks to its compact design, high efficiency, and strong reliability. Herein we investigate the feasibility of the DRU-HVDC system considering onshore and offshore AC grid faults, DC cable faults, and internal DRU faults. To ensure safe operation during the faults, the wind turbine(WT) converters are designed to operate in either current-limiting or voltage-limiting mode to limit potential excessive overcurrent or overvoltage. Strategies for providing fault currents using WT converters during offshore AC faults to enable offshore overcurrent and differential fault protection are investigated. The DRU-HVDC system is robust against various faults, and it can automatically restore power transmission after fault isolation. Simulation results confirm the system performance under various fault conditions.

基于频率响应与图像特征提取的动车组变压器绕组状态诊断方法研究

动车组变压器是保障高速铁路稳定运行的核心设备,频率响应法是目前检测变压器绕组状态的有效方法。为提升车载变压器绕组状态诊断的准确性,结合暂态信号与频率响应法提出基于频率响应与图像特征提取的动车组变压器绕组状态诊断方法。搭建试验车载变压器绕组故障模拟平台,获取不同故障类型和故障位置的频响曲线,利用类Gram矩阵结合幅频和相频曲线信息,再利用密度分层法转换为伪彩色图,提取对应的灰度共生矩阵和灰度差分矩阵特征值,根据鹈鹕优化支持向量机方法对绕组故障进行诊断。试验结果表明:车载变压器绕组故障发生时,伪彩色图能够反映出故障信息,有利于图像分析和特征提取,采用基于频率响应与图像特征提取的动车组变压器绕组状态诊断方法能够识别车载变压器绕组的典型故障类型和位置。

考虑风电机组故障电压穿越特性的连锁故障关键线路辨识

新能源的接入提高了电网连锁故障发生的概率,给故障传播中关键线路的辨识增加了难度。为此,建立了考虑风电机组故障电压穿越特性和线路可靠性的连锁故障仿真模型,从线路失负荷严重程度、失负荷不均匀性和结构脆弱性3个角度建立了线路综合风险指标评价集。基于状态故障网络计及线路失负荷程度和线路失负荷不均匀性,分别定义了失负荷风险指标和不均匀风险指标;基于电气介数提出了考虑分布式新能源接入的脆弱结构指标;同时,考虑电网的网络结构和状态转移特性,采用模糊熵权法定义综合风险指标,以衡量线路开断的综合影响。通过IEEE 39节点和IEEE 118节点系统算例验证所提方法用于关键线路辨识的有效性,且针对关键线路的缓解措施能显著降低大停电风险。

Stator Winding Turn Faults Diagnosis for Induction Motor by Immune Memory Dynamic Clonal Strategy Algorithm

Quick detection of a small initial fault is important for an induction motor to prevent a consequent large fault.The mathematical model with basic motor equations among voltages,currents,and fluxes is analyzed and the motor model equations are described.The fault related features are extracted.An immune memory dynamic clonal strategy(IMDCS)system is applied to detecting the stator faults of induction motor.Four features are obtained from the induction motor,and then these features are given to the IMDCS system.After the motor condition has been learned by the IMDCS system,the memory set obtained in the training stage can be used to detect any fault.The proposed method is experimentally implemented on the induction motor,and the experimental results show the applicability and effectiveness of the proposed method to the diagnosis of stator winding turn faults in induction motors.

基于AM和CNN的多级特征融合的风力发电机轴承故障诊断方法

提出一种基于注意力机制的多级特征融合卷积神经网络(A2ML2F-CNN)故障诊断方法。该方法将原始电流和振动信号作为输入,首先使用基于注意力卷积神经网络(AMCNN)模块分别进行数据信号特征提取,并进行一级特征融合连接。在此基础上,再次分别采用注意力机制一维卷积神经网(AM1DCNN)和二维卷积神经网络(2DCNN)提取相关信息,并进行二级特征融合,以此来解决单传感器数据故障信息不足及互补特征难以提取的问题,最后采用全连接层和Softmax层进行分类,得到诊断结果。为验证所提方法的故障诊断效果,通过帕德伯恩数据集进行实验验证,并将其与CNN、LSTM、SVM等方法的诊断精度进行对比,相较于上述方法,该文方法的诊断准确率分别提高1.8、3.2和4.8个百分点,验证了所提方法的有效性。

基于电压阻尼振荡的变压器故障绕组识别方法

变压器绕组振荡波能够有效地反映绕组状态变化,实际应用中发现振荡波末期存在不规律振荡行为,影响了基于振荡波的变压器绕组故障诊断准确性。该文针对此问题,首先,搭建变压器绕组故障模拟平台,获取轴向移位、局部翘曲、饼间短路和匝间短路四种故障下绕组振荡波数据;其次,通过定义的能量衰减因子,提出了一种基于电压阻尼振荡的变压器绕组振荡波有效波段动态选取方法;再次,基于确定的有效波段,提出了基于二值化的Tamura纹理特征的特征参数提取方法,并结合波形特征关联度(FCD),提出了用于故障类型(轴向移位、局部翘曲、饼间短路和匝间短路)、区域、程度诊断的特征参数组合并分析了其分布规律;最后,基于特征参数组合的分布规律通过实际变压器进行了应用分析。结果表明,动态选取出的波段干扰信息少、衰减振荡规律性明显且包含丰富的特征信息,可实现对故障类型、故障程度和故障区域的识别分类。

改进MFO-LSTM网络的风电机组齿轮箱故障预警研究

风电机组齿轮箱在数据采集与监控系统(SCADA)的帮助下,通过监控齿轮箱油温是否超过阈值实现故障报警,其判断精度不高且问题发现不及时,因此使用长短期记忆网络模型(LSTM)融合SCADA数据实现对齿轮箱油温状态的预测。用齿轮箱正常运行状态下的数据训练LSTM模型,计算油温预测值与真实值之间的残差,根据正态分布的原则设置残差的上下预警阈值,用来对齿轮箱故障进行预警。为简化训练模型的复杂度,在SCADA数据中选用与齿轮箱油温相关性较为密切的参数作为LSTM模型的输入项。为降低因LSTM模型超参数设置不当造成的预测准确度表现不佳,提出改进飞蛾火焰算法(MFO)与LSTM的组合模型,在保留MFO算法强大的全局搜索能力的同时,使其避免陷入局部搜索的陷阱,通过改进MFO对LSTM模型参数进行迭代优化,最终构建合适的模型。最后通过某风电机组SCADA数据验证该方法能够有效预警齿轮箱的故障,并且与其他方法相比准确度更高,预警更及时,迭代效果更好。

基于QM-DBSCAN与BiLSTM的风电机组异常工况预警研究

提出一种基于四分位(QM)-具有噪声的基于密度聚类法(DBSCAN)与双向长短期记忆网络(BiLSTM)的风电机组故障预警方法。首先,针对风速-功率图中限功率点难以清洗完全的问题,提出利用QM与DBSCAN联合来对建模运行数据进行预处理;其次,通过分析风电机组运行原理,并结合轻量梯度提升机(LightGBM)特征选择法确定风电机组正常工况预测模型的输入输出参数,并基于BiLSTM建立了高精度的风电机组正常性能预测模型;之后,利用滑窗算法构建了风电机组状态性能评价指标,并通过统计学区间估计法确定指标阈值;最后,采用风电机组真实故障数据,开展风电机组异常工况预警实验,验证了方法的有效性。

基于MTF-ResNet-ViT的风电机组精细级联故障预警

提出一种基于MTF-ResNet-ViT的风电机组(WT)精细级联故障预警方法。第1级将SCADA数据转换为马尔可夫转移场图像,利用残差网络提取故障特征,实现WT大部件状态监测和故障预警,并对故障代码数据进行标签与扩充。第2级将标签后数据灰度图像化后,利用视觉变换器建立故障代码预警模型,实现精细故障代码预警。实验结果表明,该方法可以有效标签和扩充故障代码数据,实现精细故障代码早期预警。

基于多层级时空图神经网络的风电机组在线异常检测

在风电场运营中,准确及时的故障检测是降低风电机组运行维护成本的关键。然而,现有检测方法未充分挖掘功能单元间的潜在时空关联,限制了检测准确性的提升。文中提出了一种基于多层级时空图神经网络的风电机组在线异常检测方法,以提高故障检测的准确性。该方法依据风电机组物理结构,将其功能单元划分为多个子图,从而构筑了一个多层级的时空图神经网络,通过图注意力机制和多头注意力机制全方位地分析风电机组各传感器节点与功能单元之间的关联强度。同时,针对数据采集与监控(SCADA)系统数据的时间关联,设计了动态图神经网络和时间注意力机制,使正常行为预测模型捕捉了SCADA系统数据的时间关联特性,实现了空间和时间特性的有效融合。最后,基于中国上海某风电场的实际数据验证了所提方法的显著有效性。

变转速下基于改进多阶概率方法的风电齿轮箱故障诊断研究

阶次跟踪是一种有效的解决变转速故障诊断问题的方法,其关键前提是存在转速信号作为参考。然而,由于强背景噪声和弱谐波关系的影响,现有转速估计方法的准确性和自适应性有待进一步提高。因此,提出一种融合多传感器信号的改进多阶概率方法(MOPA)用以估计瞬时转速。首先,依据不同传感器信号的基频统一性和主导分量差异性,通过时频图瞬时切片归一化融合的方式,构建具有强谐波关系的时频图;其次,为消除时变工况下时频图中横纵方向上的间歇恒频和短时宽频背景噪声,提出滑动消噪方法;最后,基于处理后的时频图执行MOPA,实现瞬时转速自动估计,结合阶次跟踪解决风电齿轮箱变转速故障诊断问题。经实测数据验证,改进MOPA估计的瞬时频率的准确性和自适应性均优于对方法,平均绝对百分比误差为0.56%,均小于对比方法的15.73%、13.99%和1.21%。结合阶次分析诊断了变转速下风电齿轮箱异常。

基于MTF-Swin Transformer的风机齿轮箱故障诊断

针对风机齿轮箱实际工况复杂多变及含有强噪声,传统故障诊断方法对风机齿轮箱故障诊断识别准确率较低的问题,文章提出了MTF-Swin Transformer风机齿轮箱故障诊断模型。首先,采用马尔科夫变迁场(MTF)图形编码方法将原始一维振动时序信号转化为具有关联时间信息的二维特征图谱;然后,将特征图谱作为Swin Transformer模型的输入,基于自注意力机制进行自动特征提取;最后,实现对不同故障类型的分类。仿真结果表明,该方法对齿轮箱故障诊断准确率达到了99.48%,证明了该方法的有效性和优越性。

不对称短路故障下永磁直驱风电机组并网控制策略研究

电网侧发生不对称故障时,永磁直驱风电机组并网电流会发生三相不平衡的问题。针对这一问题,提出一种基于自适应陷波器的网侧换流器控制策略。该控制策略基于解决机组并网电流二倍频分量的思想,在电网侧发生不对称短路故障时,将进入逆变器的dq坐标系下的二倍频电压分量去除,从而使得网侧换流器输出的电流达到三相平衡。本文首先分析了电网侧发生不对称短路故障后永磁直驱风力发电机组的动态响应;然后建立了机侧和网侧换流器的模型,并分别采用了双闭环矢量控制技术和基于自适应陷波器的控制策略;最后进行仿真分析,验证了改进后的控制策略相较于传统控制策略,有效解决了不对称短路故障下并网电流三相不平衡问题。

基于改进DDAE的风电场集电线单相接地故障测距

为解决风电场混合接线的集电线短路后难以精确定位的问题,提出基于改进深度去噪自编码网络的故障测距方法。分析集电线故障零序电流可知,暂态电流值、稳态电流幅值、稳态电流相位与故障距离呈现强非线性关系,借助深度学习挖掘这一复杂关系以实现集电线精确定位。在深度自编码框架上添加距离回归输出端口,采用联合训练以提升定位网络的准确性、抗噪性和鲁棒性。其过程为:借助PSCAD/EMTDC搭建集电线模型,将给定时窗内故障零序电流序列和对应距离作为故障样本,仿真不同情况故障生成样本集;在训练集上训练改进深度自编码网络,得到最优网络用于精确测定故障距离。借助各测点零序电流幅值关系可先确定故障区域,将故障信号送入已训练好的网络即可确定故障所在精确位置。文中方法对集电线多分支、混合短线路有着良好的适应能力,定位性能明显优于传统机器学习算法,且受过渡电阻、采样率、噪音、故障相位角影响较小。