摘要
发电机是风电机组的关键部件,其可靠性直接影响机组的运行状态。将混合核函数支持向量机用于发电机正常工作情况下的温度模型训练及预测。首先筛选合适的样本数据,利用相关性计算选择与发电机驱动端轴承温度关联度最高的几个参数,建立发电机正常工作的温度区间。当发电机运行发生异常时,其动态特性偏离正常工作状态,导致混合核函数支持向量机预测的温度值与实际值残差发生变化,通过动态计算滑动窗口内残差的分布特性,设定合理的报警逻辑及阈值,实现机组异常状态的预警。研究结果表明,该方法对于风电机组的故障预警、远程诊断具有重要的指导意义。
The operation state of a wind turbine will be affected by its generator,a key component of a wind turbine.The hybrid kernel-support vector machine(HK-SVM)was used to train the temperature model of a generator under normal working condition to make prediction.Firstly,appropriate sample data were screened,and the parameters with the highest correlationship with the generator bearing temperature at driving end were selected from them by using correlation calculation.Then,temperature range for a generator at normal working condition was established.When the generator ran abnormally,its dynamic characteristics deviated from that under normal working state,resulting in residual difference between the actual temperature and the predicted temperature obtained by HK-SVM.The residual distribution characteristics in the sliding window were calculated dynamically,and a reasonable alarm logic and a threshold were set to realize the early warning for the abnormal state of generators.The results show that this method is of great guiding significance for fault early warning and remote diagnosis of wind turbine.
作者
曹力
潘巧波
王明宇
马东
CAO Li;PAN Qiaobo;WANG Mingyu;MA Dong(Huadian Electric Power Research Institute Company Limited,Hangzhou 310030,China)
出处
《华电技术》
CAS
2020年第5期43-49,共7页
HUADIAN TECHNOLOGY
基金
中国华电集团公司科技项目(CHDKJ19-02-208)
华电电力科学研究院重点科技项目(CHDERKJ-01-01)。
关键词
风电机组
支持向量机
混合核函数
风力发电机
温度预警
wind turbine unit
support vector machine
hybrid kernel function
wind turbine generator
temperature warning
