【摘要】：中長(zhǎng)期徑流預(yù)報(bào)方法一直是國(guó)內(nèi)外研究的熱點(diǎn)和難點(diǎn),從傳統(tǒng)的成因分析方法、水文統(tǒng)計(jì)法、時(shí)間序列分析方法等,發(fā)展到現(xiàn)代的人工神經(jīng)網(wǎng)絡(luò)、小波理論、灰色系統(tǒng)和混沌理論等,各方法因其機(jī)理與適用環(huán)境不同而各具優(yōu)勢(shì)。另外,隨著水電站在電網(wǎng)系統(tǒng)的作用日益顯著,以及水電站在電網(wǎng)系統(tǒng)的調(diào)度與運(yùn)行日益復(fù)雜,繼續(xù)深入研究中長(zhǎng)期徑流預(yù)報(bào)方法、補(bǔ)充和完善相關(guān)理論與方法,以合理、有效地提高中長(zhǎng)期徑流預(yù)報(bào)的精度,并在此基礎(chǔ)上形成指導(dǎo)水庫運(yùn)行的調(diào)度策略,具有重要的理論意義和應(yīng)用前景。本文主要完成如下兩部分工作：(1)采用一元線性回歸模型模擬貝葉斯分析的先驗(yàn)分布和似然函數(shù),建立了基于小波-BP神經(jīng)網(wǎng)絡(luò)的貝葉斯概率組合預(yù)測(cè)模型,將其應(yīng)用于老撾Namngum水庫月徑流量預(yù)測(cè)中。該模型有效提高了預(yù)測(cè)精度；此外,同時(shí)相對(duì)于確定性水文預(yù)報(bào)方法而言,組合預(yù)測(cè)模型可定量地、以分布函數(shù)形式描述水文預(yù)報(bào)的不確定度,為后續(xù)水庫調(diào)度提供了更多、更全面的信息。(2)以Namngum水電站為研究實(shí)例,以組合預(yù)報(bào)結(jié)果為依據(jù),建立以發(fā)電量最大為目標(biāo)函數(shù)的優(yōu)化調(diào)度模型,并采用POA算法進(jìn)行求解；將調(diào)度結(jié)果同現(xiàn)有運(yùn)行方式下的結(jié)果進(jìn)行對(duì)比,結(jié)果表明,應(yīng)用WA-BP-BY模型預(yù)報(bào)結(jié)果可在原有基礎(chǔ)上進(jìn)一步提高Namngum水電站水庫的發(fā)電效益,可為今后水電站水庫發(fā)電計(jì)劃制定提供參考依據(jù)。
[Abstract]:Long-term runoff forecasting method has been a hot and difficult point in domestic and international research. From traditional cause analysis method, hydrological statistics method, time series analysis method and so on, it has developed to modern artificial neural network, wavelet theory, etc. The grey system and chaos theory have their own advantages because of their different mechanism and applicable environment. In addition, with the increasingly significant role of hydropower stations in the power network system, and the increasingly complex operation and operation of hydropower stations in the grid system, the long-term runoff forecasting methods are further studied to supplement and improve the relevant theories and methods in order to be reasonable. It is of great theoretical significance and application prospect to improve the precision of medium and long term runoff forecasting and to form the dispatching strategy to guide reservoir operation on this basis. The main work of this paper is as follows: (1) A Bayesian probability combination prediction model based on wavelet BP neural network is established by using a linear regression model to simulate the prior distribution and likelihood function of Bayesian analysis. It is applied to forecast monthly runoff of Namngum reservoir in Laos. In addition, compared with the deterministic hydrological forecasting method, the combined forecasting model can quantitatively describe the uncertainty of hydrological forecast in the form of distribution function, which provides more for the subsequent reservoir operation. (2) taking the Namngum hydropower station as an example, based on the combined forecast results, the optimal dispatching model with the maximum generating capacity as the objective function is established and solved by using the POA algorithm; By comparing the operation results with those under the existing operation mode, the results show that the application of WA-BP-BY model forecast results can further improve the power generation efficiency of the Namngum hydropower station reservoir on the basis of the original prediction results. It can provide reference basis for future hydropower station reservoir power generation plan formulation.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TV697.1;TV124

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