本文關鍵詞：基于高斯過程的風電機組部件建模與監(jiān)測研究　出處：《華北電力大學》2015年碩士論文　論文類型：學位論文

  更多相關文章： 風力發(fā)電 高斯過程 高斯優(yōu)化改進 齒輪箱溫度 塔架振動

【摘要】：風力發(fā)電是新能源發(fā)電的新興力量。經(jīng)過近幾年的迅猛發(fā)展,我國風電產(chǎn)業(yè)正處在由粗放型發(fā)展向精密型發(fā)展的階段。在發(fā)展速度放緩的過程中,解決發(fā)展初期遺留下來的技術問題成為風電制造企業(yè)關注的焦點。其中,風電機組的狀態(tài)監(jiān)測是亟需解決的關鍵點之一。本文采用高斯過程(Gaussian process,GP)進行建模分析,由于高斯過程建模既能提取運行數(shù)據(jù)的隨機分布規(guī)律,又能有效的分離測量噪聲,適合風電機組大數(shù)據(jù)樣本的建模工作。同時風電機組部件監(jiān)測是通過建模和分析殘差方式實現(xiàn)的,因此提高建模精度對監(jiān)測分析的意義重大。論文的主要研究內(nèi)容如下：1、由于風電機組建模數(shù)據(jù)集較大,協(xié)方差矩陣維數(shù)較高,直接求解高斯過程協(xié)方差矩陣逆存在一定的困難。為此采用Cholesky分解法避免矩陣求逆可能存在的矩陣病態(tài),同時采用緩存矩陣解決矩陣求逆重復計算的問題,從而保證高斯過程建模的快速性和準確性。2、風電機組具有強隨機性和間歇性工作的特點,對象工況復雜多變,高斯過程優(yōu)化最優(yōu)解可能不是全局最優(yōu)解,為此提出信賴域高斯過程回歸方法進行監(jiān)測研究。同時信賴域的優(yōu)化算法中包括二階導數(shù)信息,為避免運算量較大造成計算量過大的問題,簡化海森矩陣的計算,提高建模效率,加速二階優(yōu)化過程。3、將以上高斯過程改進方法應用于兩個監(jiān)測對象,即齒輪箱溫度和塔架振動。通過研究監(jiān)測對象的運行特征,提取與監(jiān)測對象相關的變量集,構建相應的高斯模型。將殘差結果與其他建模方法進行對比,驗證了高斯過程建模的高效穩(wěn)健。同時通過塔架振動的狀態(tài)監(jiān)測分析,監(jiān)測出塔架故障所在,表明高斯過程建模分析能夠?qū)崟r監(jiān)測塔架故障。
[Abstract]:Wind power is a new force of new energy power generation. With the rapid development in recent years, China's wind power industry is in from extensive development to precision development stage. In the process of slowing down the speed of development, to solve the technical problems of the legacy of the early development of the wind power manufacturing enterprises has become the focus of attention. Among them, the wind turbine state monitoring is one of the key points that need to be solved. This paper uses the Gauss process (Gaussian process, GP) are modeled and analyzed by Gauss process modeling can extract the random distribution of data, and can separate the effective measurement noise, modeling of wind turbine for large data samples. At the same time, wind power the monitoring unit components is achieved through the modeling and analysis of residual method, thus to improve the modeling accuracy of monitoring and analysis of great significance. The main contents of this thesis are as follows: 1, the wind turbine modeling The data set is large, the dimension of the covariance matrix is higher, there are some difficulties in solving inverse Gauss process covariance matrix. Using Cholesky decomposition method to avoid inverse matrix ill conditioned matrix may exist, and the use of cache matrix to solve the inverse matrix problem of repetitive calculation, so as to ensure the accuracy and speed of.2 Gauss process modeling, characteristics of wind power the unit has strong randomness and intermittent work, object condition is complicated, the optimal solution may not be the optimal solution of Gauss process, the trust region Gauss process regression monitoring research. At the same time trust includes two derivative information optimization domain, to avoid a large amount of computation caused by the large amount of calculation. Simplified calculation of Hessian matrix, improve the modeling efficiency, accelerate the optimization process of order two.3, the Gauss process improvement method is applied to the two monitoring The object that the gear box temperature and vibration of the tower. By running the characteristics of monitoring objects, monitoring object extraction and related variables, construct the Gauss model. The residual results and other modeling methods are compared and verified in an efficient and robust modeling of the Gauss process. At the same time through monitoring the tower vibration analysis, monitoring tower the fault that Gauss, the process modeling and real-time monitoring of tower failure.

【學位授予單位】：華北電力大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TM315

【參考文獻】

相關期刊論文 前1條

1 熊志化,黃國宏,邵惠鶴;基于高斯過程和支持向量機的軟測量建模比較及應用研究[J];信息與控制;2004年06期

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本文編號：1399227