【摘要】：變槳距系統(tǒng)是風(fēng)電機(jī)組控制系統(tǒng)的重要組成部分,其運(yùn)行狀態(tài)直接關(guān)系到風(fēng)電機(jī)組是否安全可靠運(yùn)行。風(fēng)電機(jī)組Supervisory Control and Data Acquisition(SCADA)系統(tǒng)具有數(shù)據(jù)采集與狀態(tài)監(jiān)測功能,能夠?qū)崟r(shí)監(jiān)控風(fēng)電機(jī)組變槳距系統(tǒng)的運(yùn)行狀態(tài)。然而,由于變槳距系統(tǒng)具有復(fù)雜的機(jī)電結(jié)構(gòu),導(dǎo)致變槳距系統(tǒng)的故障之間可能存在連鎖反應(yīng)或者相互影響,所以很多時(shí)候維修人員無法通過風(fēng)電機(jī)組SCADA系統(tǒng)及時(shí)準(zhǔn)確地判斷出引發(fā)變槳距系統(tǒng)故障的故障源。因此,開展變槳距系統(tǒng)故障診斷研究具有重要的學(xué)術(shù)意義和應(yīng)用價(jià)值。本文針對變槳距系統(tǒng)的相關(guān)參數(shù)多、非線性及精確建模困難等問題,從風(fēng)電機(jī)組SCADA系統(tǒng)監(jiān)測的風(fēng)電機(jī)組運(yùn)行數(shù)據(jù)出發(fā),利用基于核主元分析(Kernel Principal Component Analysis,KPCA)的數(shù)據(jù)挖掘方法,進(jìn)行風(fēng)電機(jī)組變槳距系統(tǒng)故障檢測與辨識(shí)的研究,實(shí)現(xiàn)了基于KPCA的風(fēng)電機(jī)組變槳距系統(tǒng)故障診斷,仿真結(jié)果驗(yàn)證了該方法的有效性。本文的主要研究工作如下:(1)本文對風(fēng)電機(jī)組變槳距系統(tǒng)典型故障的故障模式、故障原因以及故障間的關(guān)系進(jìn)行了分析。由分析結(jié)果得出,變槳距系統(tǒng)是一個(gè)故障率高、相關(guān)運(yùn)行參數(shù)多且相互耦合以及故障形式復(fù)雜的非線性系統(tǒng),為開展變槳距系統(tǒng)故障診斷研究奠定了理論基礎(chǔ)。(2)為了提高基于KPCA的變槳距系統(tǒng)故障診斷方法的快速性和準(zhǔn)確性,本文分析了風(fēng)電機(jī)組SCADA系統(tǒng)監(jiān)測到的與變槳距系統(tǒng)相關(guān)的運(yùn)行參數(shù),應(yīng)用Relief算法選擇出一些最具代表性、分類性能最好的變槳距系統(tǒng)故障特征變量,構(gòu)建了變槳距系統(tǒng)觀測向量。(3)核函數(shù)參數(shù)的最優(yōu)化對基于KPCA的變槳距系統(tǒng)故障診斷方法至關(guān)重要,本文應(yīng)用了基于粒子群優(yōu)化(Particle Swarm Optimization,PSO)的核函數(shù)參數(shù)尋優(yōu)方法,獲得了最優(yōu)核函數(shù)參數(shù);以風(fēng)電機(jī)組SCADA系統(tǒng)監(jiān)測的變槳距系統(tǒng)觀測向量運(yùn)行數(shù)據(jù)為基礎(chǔ),提出了基于KPCA的變槳距系統(tǒng)故障診斷方法,并進(jìn)行變槳距系統(tǒng)的故障檢測與辨識(shí),實(shí)現(xiàn)了變槳距系統(tǒng)的故障診斷;應(yīng)用風(fēng)電機(jī)組SCADA系統(tǒng)監(jiān)測到的故障信息開展了仿真研究,驗(yàn)證了基于KPCA的風(fēng)電機(jī)組變槳距系統(tǒng)故障診斷方法的有效性。
[Abstract]:Variable pitch system is an important part of wind turbine control system, and its running state is directly related to the safe and reliable operation of wind turbine. The Supervisory Control and Data Acquisition (SCADA) system of wind turbine has the functions of data acquisition and condition monitoring, and can monitor the running state of variable pitch system of wind turbine in real time. However, because of the complex electromechanical structure of the variable pitch system, there may be a chain reaction or interaction between the faults of the variable pitch system. So many times the maintainers can not accurately determine the fault source of the variable pitch system through the wind turbine SCADA system. Therefore, the research on fault diagnosis of variable pitch system has important academic significance and application value. In this paper, aiming at the problems of variable pitch system, such as many related parameters, nonlinear and accurate modeling, the data mining method based on kernel principal component analysis (Kernel Principal Component Analysis,KPCA) is used to solve the problem of wind turbine operation data monitored by SCADA system of wind turbine. The fault detection and identification of variable pitch system of wind turbine is studied, and the fault diagnosis of variable pitch system of wind turbine based on KPCA is realized. The simulation results verify the effectiveness of the method. The main work of this paper is as follows: (1) this paper analyzes the typical fault modes, fault causes and the relationship between the faults of wind turbine variable pitch system. From the analysis results, it is concluded that the variable pitch system is a nonlinear system with high failure rate, multiple related operating parameters, mutual coupling and complex fault forms. It lays a theoretical foundation for fault diagnosis of variable pitch system. (2) in order to improve the speed and accuracy of fault diagnosis method of variable pitch system based on KPCA, In this paper, the operating parameters related to the variable pitch system monitored by SCADA system of wind turbine are analyzed, and some of the most representative and best classification characteristic variables of variable pitch system are selected by using Relief algorithm. The observation vector of variable pitch system is constructed. (3) the optimization of kernel function parameters is very important to the fault diagnosis method of variable pitch system based on KPCA. The kernel function parameter optimization method based on particle swarm optimization (Particle Swarm Optimization,PSO) is applied in this paper. The optimal kernel function parameters are obtained. Based on the observation vector running data of variable pitch system monitored by SCADA system of wind turbine, the fault diagnosis method of variable pitch system based on KPCA is put forward, and the fault detection and identification of variable pitch system are carried out. The fault diagnosis of variable pitch system is realized. The fault information monitored by wind turbine SCADA system is simulated and studied, which verifies the effectiveness of the fault diagnosis method of wind turbine variable pitch system based on KPCA.
【學(xué)位授予單位】：沈陽工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM315

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