汽輪機(jī)滑壓運行初壓智能優(yōu)化方法的研究
發(fā)布時間：2018-07-20 11:54
【摘要】：近年來,社會電力用電結(jié)構(gòu)已發(fā)生了較大的變化,電網(wǎng)負(fù)荷晝夜峰谷差越來越大。大量超臨界汽輪機(jī)組被要求深度調(diào)峰,機(jī)組利用小時數(shù)逐年降低,低負(fù)荷運行時間普遍增加,熱經(jīng)濟(jì)性大大降低。同時,隨著我國經(jīng)濟(jì)、能源和環(huán)保形勢的發(fā)展,火電機(jī)組節(jié)能降耗已成為企業(yè)生存運行的客觀需要。因此,如何提高機(jī)組在低負(fù)荷階段的運行經(jīng)濟(jì)性成為一個亟待解決的問題。要確保汽輪機(jī)變工況運行時仍能保持最佳狀態(tài),就必須對汽輪機(jī)的運行初壓進(jìn)行優(yōu)化,以降低機(jī)組的熱耗率。群智能優(yōu)化技術(shù)是人們受生物進(jìn)化或自然現(xiàn)象啟發(fā)而提出的新方法,能很好的處理復(fù)雜系統(tǒng)的建模和優(yōu)化問題。針對傳統(tǒng)方法很難描述超臨界汽輪機(jī)的復(fù)雜非線性、多工況等熱力特性模型,不易實現(xiàn)機(jī)組初壓優(yōu)化的不足,本文對人工智能領(lǐng)域中混合蛙跳算法(shuffled frog leaping algorithm,SFLA)、最小二乘支持向量機(jī)(least squares support vector machine,LSSVM)及基于聚類的多模型建模技術(shù)進(jìn)行重點研究,并將它們應(yīng)用于機(jī)組初壓優(yōu)化,以實現(xiàn)機(jī)組的經(jīng)濟(jì)運行。主要研究內(nèi)容如下:首先,針對典型混合蛙跳算法尋優(yōu)能力不足的問題,提出了一種改進(jìn)SFLA算法(mixed search SFLA,MS-SFLA)。通過引入了混沌反學(xué)習(xí)策略、非線性自適應(yīng)慣性權(quán)值以及一個新的局部擾動策略以提高算法優(yōu)化能力。通過13個基準(zhǔn)測試函數(shù)的仿真測試,驗證了改進(jìn)的混合蛙跳算法具有較好的優(yōu)化性能�；谠撍惴▽ψ钚《酥С窒蛄繖C(jī)回歸算法超參數(shù)進(jìn)行優(yōu)化,數(shù)值仿真實驗驗證了該算法建模時的有效性。然后,研究了模糊C均值聚類算法在數(shù)據(jù)聚類劃分中的應(yīng)用。為了改善模糊C均值聚類對噪聲和孤立點的魯棒性,提出采用基于RBF核函數(shù)的模糊C均值算法。同時,為了解決諸如聚類精度受數(shù)據(jù)分布影響、對初始聚類中心敏感、易陷入局部最優(yōu)以及難以確定最優(yōu)聚類數(shù)的不足,提出一種新的基于G-K算法的雙層聚類算法,熱耗率多模型建模仿真試驗驗證了該算法的可行性。另外,針對單模型難以精確描述具有復(fù)雜非線性特性汽輪機(jī)熱耗率的問題,提出了一種基于雙層聚類算法和LSSVM融合的熱耗率多模型建模方法,并利用MS-SFLA算法進(jìn)行模型參數(shù)的選擇。隨后,將其應(yīng)用到某600MW超臨界汽輪機(jī)熱耗率的建模,仿真實驗證明該多模型建模方法能高精度的預(yù)測機(jī)組的熱耗率,具有良好的泛化能力。最后,在建立好的熱耗率多模型的基礎(chǔ)上,利用MS-SFLA算法在給定負(fù)荷的可行運行初壓范圍內(nèi),以熱耗率最低為優(yōu)化目標(biāo)來確定汽輪機(jī)變工況運行時的最優(yōu)運行初壓。將得到的最優(yōu)運行初壓作為汽輪機(jī)自動運行時主蒸汽壓力的設(shè)定值,能達(dá)到機(jī)組優(yōu)化運行的目的,并據(jù)此給出優(yōu)化后的滑壓運行曲線,該曲線具有更為實際的指導(dǎo)意義。
[Abstract]:In recent years, great changes have taken place in the structure of social electric power consumption, and the difference between day and night peak and valley of power grid load is increasing. A large number of supercritical steam turbine units are required to deep peak shaving, the number of operating hours of the units is reduced year by year, the running time of low load is generally increased, and the thermal economy is greatly reduced. At the same time, with the development of economy, energy and environmental protection in our country, energy saving and consumption reduction of thermal power units has become the objective need for enterprises to survive and run. Therefore, how to improve the operating economy of units at low load stage becomes an urgent problem to be solved. In order to ensure that the steam turbine can maintain the best condition in the off-condition operation, it is necessary to optimize the operation initial pressure of the turbine in order to reduce the heat consumption rate of the unit. Swarm intelligence optimization is a new method inspired by biological evolution or natural phenomena. It can deal with the modeling and optimization problems of complex systems well. The traditional method is difficult to describe the complex nonlinear, multi-condition thermodynamic characteristic model of supercritical steam turbine, and it is not easy to realize the initial pressure optimization of the unit. In this paper, the hybrid leapfrog algorithm (shuffled frog leaping algorithm), least square support vector machine (least squares support vector machine) and multi-model modeling technology based on clustering in artificial intelligence field are studied, and they are applied to initial pressure optimization. In order to achieve the economic operation of the unit. The main contents are as follows: firstly, an improved SFLA algorithm, (mixed search SFLA-MS-SFLA, is proposed to solve the problem of poor optimization ability of the typical hybrid leapfrog algorithm. Chaotic inverse learning strategy, nonlinear adaptive inertia weight and a new local perturbation strategy are introduced to improve the optimization ability of the algorithm. The simulation results of 13 benchmark functions show that the improved hybrid leapfrog algorithm has better performance. Based on this algorithm, the super-parameters of the least squares support vector machine regression algorithm are optimized, and the effectiveness of the algorithm is verified by numerical simulation. Then, the application of fuzzy C-means clustering algorithm in data clustering is studied. In order to improve the robustness of fuzzy C-means clustering to noise and outliers, a fuzzy C-means algorithm based on RBF kernel function is proposed. At the same time, in order to solve the problem that clustering accuracy is affected by data distribution, sensitive to the initial clustering center, easy to fall into local optimum and difficult to determine the optimal clustering number, a new two-layer clustering algorithm based on G-K algorithm is proposed. The feasibility of the algorithm is verified by heat consumption rate multi-model modeling and simulation. In addition, aiming at the problem that it is difficult to accurately describe the heat consumption rate of steam turbine with complex nonlinear characteristics by single model, a multi-model modeling method of heat consumption rate based on two-layer clustering algorithm and LSSVM fusion is proposed. MS-SFLA algorithm is used to select the model parameters. Then, it is applied to the modeling of heat consumption rate of a 600MW supercritical steam turbine. The simulation results show that the multi-model modeling method can predict the heat consumption rate of the unit with high accuracy and has a good generalization ability. Finally, on the basis of establishing a good multi-model of heat consumption rate, MS-SFLA algorithm is used to determine the optimal initial operating pressure of steam turbine in off-condition operation with the minimum heat consumption rate as the optimization objective within the feasible initial operating pressure range of a given load. The optimal operation initial pressure is regarded as the set value of the main steam pressure during the automatic operation of the steam turbine, which can achieve the purpose of the optimal operation of the unit, and based on this, the sliding pressure operation curve after the optimization is given, which has more practical guiding significance.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TP18;TM621
本文編號：2133432
[Abstract]:In recent years, great changes have taken place in the structure of social electric power consumption, and the difference between day and night peak and valley of power grid load is increasing. A large number of supercritical steam turbine units are required to deep peak shaving, the number of operating hours of the units is reduced year by year, the running time of low load is generally increased, and the thermal economy is greatly reduced. At the same time, with the development of economy, energy and environmental protection in our country, energy saving and consumption reduction of thermal power units has become the objective need for enterprises to survive and run. Therefore, how to improve the operating economy of units at low load stage becomes an urgent problem to be solved. In order to ensure that the steam turbine can maintain the best condition in the off-condition operation, it is necessary to optimize the operation initial pressure of the turbine in order to reduce the heat consumption rate of the unit. Swarm intelligence optimization is a new method inspired by biological evolution or natural phenomena. It can deal with the modeling and optimization problems of complex systems well. The traditional method is difficult to describe the complex nonlinear, multi-condition thermodynamic characteristic model of supercritical steam turbine, and it is not easy to realize the initial pressure optimization of the unit. In this paper, the hybrid leapfrog algorithm (shuffled frog leaping algorithm), least square support vector machine (least squares support vector machine) and multi-model modeling technology based on clustering in artificial intelligence field are studied, and they are applied to initial pressure optimization. In order to achieve the economic operation of the unit. The main contents are as follows: firstly, an improved SFLA algorithm, (mixed search SFLA-MS-SFLA, is proposed to solve the problem of poor optimization ability of the typical hybrid leapfrog algorithm. Chaotic inverse learning strategy, nonlinear adaptive inertia weight and a new local perturbation strategy are introduced to improve the optimization ability of the algorithm. The simulation results of 13 benchmark functions show that the improved hybrid leapfrog algorithm has better performance. Based on this algorithm, the super-parameters of the least squares support vector machine regression algorithm are optimized, and the effectiveness of the algorithm is verified by numerical simulation. Then, the application of fuzzy C-means clustering algorithm in data clustering is studied. In order to improve the robustness of fuzzy C-means clustering to noise and outliers, a fuzzy C-means algorithm based on RBF kernel function is proposed. At the same time, in order to solve the problem that clustering accuracy is affected by data distribution, sensitive to the initial clustering center, easy to fall into local optimum and difficult to determine the optimal clustering number, a new two-layer clustering algorithm based on G-K algorithm is proposed. The feasibility of the algorithm is verified by heat consumption rate multi-model modeling and simulation. In addition, aiming at the problem that it is difficult to accurately describe the heat consumption rate of steam turbine with complex nonlinear characteristics by single model, a multi-model modeling method of heat consumption rate based on two-layer clustering algorithm and LSSVM fusion is proposed. MS-SFLA algorithm is used to select the model parameters. Then, it is applied to the modeling of heat consumption rate of a 600MW supercritical steam turbine. The simulation results show that the multi-model modeling method can predict the heat consumption rate of the unit with high accuracy and has a good generalization ability. Finally, on the basis of establishing a good multi-model of heat consumption rate, MS-SFLA algorithm is used to determine the optimal initial operating pressure of steam turbine in off-condition operation with the minimum heat consumption rate as the optimization objective within the feasible initial operating pressure range of a given load. The optimal operation initial pressure is regarded as the set value of the main steam pressure during the automatic operation of the steam turbine, which can achieve the purpose of the optimal operation of the unit, and based on this, the sliding pressure operation curve after the optimization is given, which has more practical guiding significance.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TP18;TM621
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