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基于遺傳算法和神經(jīng)網(wǎng)絡(luò)的泵站經(jīng)濟運行研究
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    摘要:

    以泵站總耗能最小為目標(biāo),， 建立了葉片可調(diào)節(jié)泵站站間和站內(nèi)經(jīng)濟運行優(yōu)化數(shù)學(xué)模型，采用遺傳算法應(yīng)用Matlab語言實現(xiàn)優(yōu)化計算,。對江都第4站1999年實際運行資料進(jìn)行優(yōu)化計算,，總消耗功率比經(jīng)驗操作可減小3.39%左右,。針對泵站的流量和揚程變化頻繁而一般的優(yōu)化計算方法速度較慢的問題,，以仿真優(yōu)化結(jié)果為樣本,，利用人工神經(jīng)網(wǎng)絡(luò)對相似工況進(jìn)行預(yù)測,，預(yù)測結(jié)果平均誤差為1.99%,。遺傳算法和神經(jīng)網(wǎng)絡(luò)聯(lián)合應(yīng)用,，求解精度和可靠性較高，是解決泵站優(yōu)化運行問題的有效方法,。

    Abstract:

    Aimed at the minimal energy consumption, the mathematical model for adjustable-blade pumps or pumping stations was established. Using the method of genetic algorithms and Matlab, the data of Jiangdu drainage and irrigation station were studied, the results showed that 3.39% energy could be save comparing with its actual energy consumed in the same year. Because the flowrate and head of pump station was changing frequently and the conventional optimal calculation method often takes long time, the author proposed artificial neural networks model to predict movement patterns of the Jiangdu first pumping station, the result of optimal operation is used as sample. By combination with the genetic algorithms and artificial neural networks, the satisfactory result can be obtained. The new method is not only simple and easy to implement, but also has high accuracy and reliability, it is an effective way for solving the optimization problem in pump station.

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鄢碧鵬,杜曉雷,劉超,成立.基于遺傳算法和神經(jīng)網(wǎng)絡(luò)的泵站經(jīng)濟運行研究[J].農(nóng)業(yè)機械學(xué)報,2007,38(1):80-82.[J]. Transactions of the Chinese Society for Agricultural Machinery,2007,38(1):80-82.

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