风电机组变工况变桨系统异常状态在线识别  

On-line Abnormal State Identification of Pitch System Based on Transitional Mode for Wind Turbine

作　　者：王爽心[1] 郭婷婷 李蒙 WANG Shuangxin;GUO Tingting;LI Meng(School of Mechanical,Electronic and Control Engineering,Beijing Jiaotong University,Haidian District,Beijing 100044,China)

机构地区：[1]北京交通大学机械与电子控制工程学院

出　　处：《中国电机工程学报》2019年第17期5144-5152,共9页Proceedings of the CSEE

基　　金：国家自然科学基金项目(50776005,51577008)~~

摘　　要：风电机组的变桨系统因风速随机波动而经常在不同工况下切换,且其子系统间存在的强耦合性使其故障难以实时检测和精准定位,因此,实际运行中的数据采集与监视控制系统对变桨故障的误报率较高。针对上述问题,提出变工况变桨系统异常状态在线监测与识别系统。由于变桨系统特征参量在不同工况下对其状态的贡献率不同,基于熵优化邻域粗糙集(entropy-optimized neighborhood rough set,ENRS)对不同工况下的特征参量进行约简建模,提出全工况变桨系统状态特征参量挖掘策略。以其约简数据集作为输入样本,提出以小世界粒子群(small-world particle swarm optimized,SWPSO)优化的熵加权学习向量量化(learning vector quantization,LVQ)为基础模型的SWPSO-熵加权LVQ多模型状态监测器,实现异常状态的精准定位。最后,基于实际风场数据对上述模型进行训练,仿真与测试结果表明,基于ENRS的SWPSO-熵加权LVQ模型能实时并准确地反映变桨系统在变工况下的异常状态模式识别。Pitch system of wind turbine is often switched under different operating modes due to random fluctuation of wind speed and strong coupling between subsystems, which makes it difficult to detect and locate faults precisely in real time. Therefore, supervisory control and data acquisition(SCADA) system in practice running has a high false alarm rate for pitch system. Accounting for these problems, this paper proposed an abnormal status monitoring and recognition system online. First, because the contribution rate of characters to state is discrepant under different modes, the paper put forward reduction model based on the entropy-optimized neighborhood rough set(ENRS) under different operating modes, and a mining strategy of state characteristic parameters of pitch system in all operating modes. Then, the multi-model state monitor based on a small-world particle swarm optimized entropy weighted learning vector quantization(SWPSOEntropy LVQ) with reduction data set as input sample is constructed to realize locate faults precisely. Finally, above models were trained the based on actual wind field data. Simulations and test results indicate that the SWPSO-Entropy weighed LVQ based on ENRS can accurately and real-time reflect the abnormal state pattern recognition of the pitch system under transitional operating modes.

关 键 词：风电机组 变桨距系统 异常状态识别 熵优化邻域粗糙集模型 变工况 

分 类 号：TM315[电气工程—电机]