【作者】 李强；

【导师】 张宇献；

【作者基本信息】 沈阳工业大学 ， 工程硕士（专业学位）， 2020， 硕士

【摘要】 在当今能源危机以及全球温室效应的影响条件下,传统的火力发电消耗化石资源并且造成一定的环境污染问题。风力发电具有清洁且可再生等特点,在新能源发电领域受到各国的关注。近些年来我国对风电不断加大研究与投入,风电机组的装机容量不断提高。随着各国对于风电机组的装机数量不断提高,维护风电机组的稳定运行,提高风电机组的可靠性,制定安全有效的维护计划显得尤为重要,因而需要对于风电机组的关键部件进行异常检测研究。本文基于风电机组状态采集与监测系统(Supervisory Control And Data Acquisition,SCADA)数据,实现风电机组变桨系统的异常检测研究。该文主要针对以下几部分内容进行研究:1)基于对双馈型风力发电机组的基本构成以及运行原理的掌握,重点阐述和分析双馈型风力发电机组变桨系统的故障形式以及故障原因。进一步对所监测参数的特点进行分析,采用ReliefF算法实现风机变桨系统特征选择。2)考虑变桨系统运行工况复杂,监测变量间具有较强非线性,且SCADA系统的数据信息动态更新等问题,提出基于在线贯序极限学习机建立多参数状态监测模型。针对在线贯序极限学习机的输入权值与偏置一般取随机数且OS-ELM的训练效果受初始值影响很大的问题,采用量子进化算法优化极限学习机的超参数集,提高模型的预测精度。3)采用基于量子进化算法优化的在线序贯极限学习机建立的变桨系统多参数状态监测模型,训练得到正常状态下变桨系统的残差集,采用马氏距离函数对模型残差信号进行计算,在利用马氏距离的Weibull分布确定的异常检测阈值,当模型得到的实际马氏距离值超过该阈值时则产生异常报警。更多还原

【Abstract】 Under the influence of the current energy crisis and the global greenhouse effect,traditional thermal power generation which consumes fossil resources has causesd certain environmental pollution problems.Wind power generation with the characteristics of clean and renewable has attracted the attention of various countries in the field of new energy power generation.In recent years,China has been continuously increasing the scientific research and investment in wind power.With the increasing number of installed wind turbines in the world,the stablility and reliability of wind turbines,as well as the formulation of a safe and effective maintenance plan are particularly important,which requires anomaly monitoring and research on the key components of wind turbines.This thesis uses the data of the Supervisory Control And Data Acquisition(SCADA)of the wind turbine to realize the abnormal monitoring research of the wind turbine pitch system.This thesis focuses on the following parts:1)Based on mastering the basic structure and operating principles of doubly-fed wind turbines,the focus is on elaborating and analyzing the failure modes and causes of doubly-fed wind turbine pitch systems.Further,this thesis analyzes the characteristics of the monitored parameters and uses the ReliefF algorithm to realize the feature selection of the fan pitch system.2)Considering the complicated operating conditions of the pitch system,the strong nonlinearity among the monitoring variables,and the dynamic update of the data information of the SCADA system,a multi-parameter state monitoring model based on an online sequential limit learning machine was proposed.Aiming at the problem that the input weights and offsets of the online sequential extreme learning machine are generally random numbers,and the training effect of OS-ELM is greatly affected by the initial value,the quantum evolution algorithm is used to optimize the extreme parameter set of the extreme learning machine and improve the model Prediction accuracy.3)The multi-parameter state monitoring model of the pitch system based on the online sequential limit learning machine optimized by the quantum evolution algorithm was established.The residual set of the pitch system under the normal state was trained,and the residual signal of the model is calculated by using the Mahalanobis distance function.The anomaly detection threshold is determined by the Weibull distribution of Mahalanobis distance.when the actual Mahalanobis distance value obtained by the model exceeds the threshold,an anomaly alarm is generated.更多还原