廠頂溢流式水電站廠房結(jié)構(gòu)振動響應(yīng)預(yù)測研究
發(fā)布時間：2018-07-17 14:55
【摘要】：水電站廠房結(jié)構(gòu)極其復(fù)雜，引起結(jié)構(gòu)振動的振源種類更是多種多樣，致使電站運(yùn)行過程中的廠房結(jié)構(gòu)振動問題非常普遍�？紤]到廠房結(jié)構(gòu)振動對儀器設(shè)備、工作人員健康以及建筑物運(yùn)行穩(wěn)定性和安全可靠性的影響，，利用較少的監(jiān)測數(shù)據(jù)達(dá)到全面掌握和控制水電站振動的目的成為新的研究課題。 本文運(yùn)用智能算法與神經(jīng)網(wǎng)絡(luò)混合的預(yù)測方法，不考慮結(jié)構(gòu)精確的數(shù)學(xué)和精準(zhǔn)的力學(xué)模型，而是依據(jù)尾水脈動和機(jī)組振動的觀測數(shù)據(jù)，非線性的映射出水電站結(jié)構(gòu)的振動特性，達(dá)到預(yù)測未知工況和未觀測部位的結(jié)構(gòu)振動響應(yīng)狀況的目的。 結(jié)合某廠頂溢流式水電站原型觀測實(shí)驗(yàn)，運(yùn)用果蠅算法優(yōu)化廣義回歸神經(jīng)網(wǎng)絡(luò)平滑參數(shù)P，構(gòu)建FOA-GRNN網(wǎng)絡(luò)模型。同時結(jié)合反向傳播神經(jīng)網(wǎng)絡(luò)（BP）、局部回歸神經(jīng)網(wǎng)絡(luò)（ELMAN）展開對比預(yù)測研究。最終得出：FOA-GRNN網(wǎng)絡(luò)在預(yù)測能力、學(xué)習(xí)速度上明顯優(yōu)于BP和ELMAN網(wǎng)絡(luò)。說明運(yùn)用FOA-GRNN神經(jīng)網(wǎng)絡(luò)預(yù)測廠房結(jié)構(gòu)振動響應(yīng)的可行性和優(yōu)越性。 為彌補(bǔ)基本粒子群優(yōu)化算法易陷入局部最優(yōu)、收斂性差的缺陷，提出了優(yōu)勝劣汰、步步選擇粒子群優(yōu)化算法—SSPSO，通過典型測試函數(shù)證明SSPSO具有很強(qiáng)的尋優(yōu)能力。并運(yùn)用SSPSO對廣義回歸神經(jīng)網(wǎng)絡(luò)平滑參數(shù)進(jìn)行優(yōu)化，充分利用SSPSO尋優(yōu)能力強(qiáng)及徑向基函數(shù)調(diào)整參數(shù)少的優(yōu)點(diǎn)，建立廠房結(jié)構(gòu)的振動響應(yīng)預(yù)測模型，展開廠壩結(jié)構(gòu)振動響應(yīng)預(yù)測研究。證明了：SSPSO算法的尋優(yōu)能力很強(qiáng)；基于SSPSO優(yōu)化的廣義回歸神經(jīng)網(wǎng)絡(luò)與其他網(wǎng)絡(luò)相比，在預(yù)測精度，收斂性能，泛化能力等各個方面得到了很大提升。 運(yùn)用粒子群優(yōu)化算法、遺傳算法和果蠅優(yōu)化算法分別對徑向基神經(jīng)網(wǎng)絡(luò)進(jìn)行優(yōu)化，建立最優(yōu)的PSO-RBF、GA-RBF、FOA-RBF網(wǎng)絡(luò)模型，展開泄流誘發(fā)廠房結(jié)構(gòu)振動響應(yīng)的預(yù)測研究。結(jié)果表明：PSO-RBF、GA-RBF和FOA-RBF預(yù)測效果均良好，適合運(yùn)用于泄流誘發(fā)水電站廠房結(jié)構(gòu)振動響應(yīng)預(yù)測研究中，其中FOA-RBF穩(wěn)定性及泛化能力最強(qiáng)。 綜上所述：智能算法與神經(jīng)網(wǎng)絡(luò)構(gòu)建的混合模型不僅易于理解、掌握，而且精度很高。非常適合于廠頂溢流式水電站廠房結(jié)構(gòu)的振動響應(yīng)的預(yù)測研究，為廠房結(jié)構(gòu)振動響應(yīng)預(yù)測提供了新的方法和思路，增強(qiáng)了廠房結(jié)構(gòu)的智能監(jiān)測水平。
[Abstract]:The structure of hydropower plant is extremely complex, and the types of vibration sources causing structural vibration are more and more varied, which makes the vibration problem of powerhouse structure very common in the process of operation of hydropower station. Considering the influence of structural vibration on instrument and equipment, staff health, operation stability and safety and reliability of buildings, it is a new research subject to make use of less monitoring data to master and control the vibration of hydropower station in an all-round way. In this paper, the hybrid forecasting method of intelligent algorithm and neural network is used. The accurate mathematical and mechanical model of the structure is not taken into account, but the vibration characteristics of the hydropower station structure are nonlinear mapped according to the observation data of the tail water pulsation and the vibration of the unit. The objective is to predict the vibration response of the structure under unknown working conditions and unobserved positions. Combined with the prototype observation experiment of a plant roof overflow hydropower station, the FOA-GRNN network model was constructed by using Drosophila algorithm to optimize the smoothing parameters of generalized regression neural network (PNN). At the same time, combining back propagation neural network (BP) and local regression neural network (ELMAN), a comparative prediction study was carried out. Finally, it is concluded that the prediction ability and learning speed of the FOA-GRNN network are obviously better than those of BP and ELMAN networks. The feasibility and superiority of using FOA-GRNN neural network to predict the vibration response of powerhouse structure are illustrated. In order to remedy the defect that the basic particle swarm optimization (PSO) is easy to fall into the local optimum and the convergence is poor, the survival of the fittest is proposed, and the particle swarm optimization (SSPSO) algorithm is selected step by step. It is proved by the typical test function that SSPSO has a strong searching ability. The smooth parameters of generalized regression neural network are optimized by using SSPSO, and the prediction model of vibration response of powerhouse structure is established by making full use of the advantages of strong searching ability of SSPSO and less adjustment parameters of radial basis function. Research on prediction of vibration response of plant dam structure is carried out. It is proved that the optimization ability of the SSPSO algorithm is very strong, and that the generalized regression neural network based on SSPSO optimization has been greatly improved in prediction accuracy, convergence performance, generalization ability and so on. Particle swarm optimization (PSO) algorithm, genetic algorithm (GA) and Drosophila optimization algorithm were used to optimize radial basis function neural network (RBNN), and the optimal PSO-RBFN GA-RBFFFOA-RBF neural network model was established to predict the vibration response of powerhouse structure induced by discharge. The results show that the prediction results of GA-RBF and FOA-RBF are good, and it is suitable for the prediction of vibration response of powerhouse structure induced by discharge. Among them, FOA-RBF has the strongest stability and generalization ability. To sum up, the hybrid model constructed by intelligent algorithm and neural network is not only easy to understand and master, but also has high accuracy. It is very suitable for predicting the vibration response of powerhouse structure of roof overflow hydropower station. It provides a new method and train of thought for predicting the vibration response of powerhouse structure and enhances the level of intelligent monitoring of powerhouse structure.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TV731.3;TV312
本文編號：2130002
[Abstract]:The structure of hydropower plant is extremely complex, and the types of vibration sources causing structural vibration are more and more varied, which makes the vibration problem of powerhouse structure very common in the process of operation of hydropower station. Considering the influence of structural vibration on instrument and equipment, staff health, operation stability and safety and reliability of buildings, it is a new research subject to make use of less monitoring data to master and control the vibration of hydropower station in an all-round way. In this paper, the hybrid forecasting method of intelligent algorithm and neural network is used. The accurate mathematical and mechanical model of the structure is not taken into account, but the vibration characteristics of the hydropower station structure are nonlinear mapped according to the observation data of the tail water pulsation and the vibration of the unit. The objective is to predict the vibration response of the structure under unknown working conditions and unobserved positions. Combined with the prototype observation experiment of a plant roof overflow hydropower station, the FOA-GRNN network model was constructed by using Drosophila algorithm to optimize the smoothing parameters of generalized regression neural network (PNN). At the same time, combining back propagation neural network (BP) and local regression neural network (ELMAN), a comparative prediction study was carried out. Finally, it is concluded that the prediction ability and learning speed of the FOA-GRNN network are obviously better than those of BP and ELMAN networks. The feasibility and superiority of using FOA-GRNN neural network to predict the vibration response of powerhouse structure are illustrated. In order to remedy the defect that the basic particle swarm optimization (PSO) is easy to fall into the local optimum and the convergence is poor, the survival of the fittest is proposed, and the particle swarm optimization (SSPSO) algorithm is selected step by step. It is proved by the typical test function that SSPSO has a strong searching ability. The smooth parameters of generalized regression neural network are optimized by using SSPSO, and the prediction model of vibration response of powerhouse structure is established by making full use of the advantages of strong searching ability of SSPSO and less adjustment parameters of radial basis function. Research on prediction of vibration response of plant dam structure is carried out. It is proved that the optimization ability of the SSPSO algorithm is very strong, and that the generalized regression neural network based on SSPSO optimization has been greatly improved in prediction accuracy, convergence performance, generalization ability and so on. Particle swarm optimization (PSO) algorithm, genetic algorithm (GA) and Drosophila optimization algorithm were used to optimize radial basis function neural network (RBNN), and the optimal PSO-RBFN GA-RBFFFOA-RBF neural network model was established to predict the vibration response of powerhouse structure induced by discharge. The results show that the prediction results of GA-RBF and FOA-RBF are good, and it is suitable for the prediction of vibration response of powerhouse structure induced by discharge. Among them, FOA-RBF has the strongest stability and generalization ability. To sum up, the hybrid model constructed by intelligent algorithm and neural network is not only easy to understand and master, but also has high accuracy. It is very suitable for predicting the vibration response of powerhouse structure of roof overflow hydropower station. It provides a new method and train of thought for predicting the vibration response of powerhouse structure and enhances the level of intelligent monitoring of powerhouse structure.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TV731.3;TV312
【參考文獻(xiàn)】
相關(guān)期刊論文 前10條
1 易曉梅;吳鵬;劉麗娟;戴丹;;基于PSO-RBF無線傳感器網(wǎng)絡(luò)入侵檢測技術(shù)研究[J];傳感器與微系統(tǒng);2011年09期
2 孫旭東;;燈泡貫流式水輪機(jī)水力振動的形成及其影響[J];電站系統(tǒng)工程;2006年05期
3 練繼建;何龍軍;王海軍;;基于PSO優(yōu)化LS-SVM算法的水電站廠房結(jié)構(gòu)振動響應(yīng)預(yù)測[J];中國工程科學(xué);2011年12期
4 杜永峰;郭劍虹;;基于RBF神經(jīng)網(wǎng)絡(luò)的結(jié)構(gòu)動力響應(yīng)預(yù)測[J];蘭州理工大學(xué)學(xué)報(bào);2006年02期
5 汪麗川;;淺析巖灘電廠廠房振動現(xiàn)象[J];廣西電力技術(shù);1996年01期
6 鄭啟富,陳德釗,俞歡軍;CGA-RBFN模型及其在丙烯產(chǎn)率預(yù)測中的應(yīng)用[J];高校化學(xué)工程學(xué)報(bào);2005年01期
7 王雪剛;鄒早建;;基于果蠅優(yōu)化算法的船舶操縱響應(yīng)模型的辨識[J];大連海事大學(xué)學(xué)報(bào);2012年03期
8 黃智宇;曹玉恒;;基于GA-RBF網(wǎng)絡(luò)的磷酸鐵鋰電池SOC預(yù)測研究[J];重慶郵電大學(xué)學(xué)報(bào)(自然科學(xué)版);2013年03期
9 王亞利;王宇平;;基于混合的GA-PSO神經(jīng)網(wǎng)絡(luò)算法[J];計(jì)算機(jī)工程與應(yīng)用;2007年02期
10 孫健,申瑞民,韓鵬;一種新穎的徑向基函數(shù)(RBF)網(wǎng)絡(luò)學(xué)習(xí)算法[J];計(jì)算機(jī)學(xué)報(bào);2003年11期
相關(guān)博士學(xué)位論文 前2條
1 劉小龍;細(xì)菌覓食優(yōu)化算法的改進(jìn)及應(yīng)用[D];華南理工大學(xué);2011年
2 孫俊;量子行為粒子群優(yōu)化算法研究[D];江南大學(xué);2009年
本文編號：2130002
本文鏈接：http://sikaile.net/kejilunwen/shuiwenshuili/2130002.html
最近更新
教材專著
