發(fā)布時間：2018-12-06 16:11

【摘要】：火電機組在運行過程中不僅產(chǎn)生大量的歷史數(shù)據(jù),同時這些邊界參數(shù)還與熱耗率之間存在復雜的非線性關系。針對某電廠的實時數(shù)據(jù),首先利用敏感性分析,從大量的機組運行參數(shù)中篩選出對機組能耗影響較大的重要參數(shù):負荷、循環(huán)水入口溫度、主蒸汽溫度、再熱蒸汽溫度、主蒸汽壓力、循環(huán)水流量。然后,對BP和RBF神經(jīng)網(wǎng)絡在熱耗率與機組邊界參數(shù)的應用進行了對比分析。訓練和預測結果表明,BP和RBF神經(jīng)網(wǎng)絡都能對此進行分析研究,但RBF比BP神經(jīng)網(wǎng)絡的訓練和預測的相對誤差較小些,可以更準確地對機組熱耗進行預測。為今后的可控參數(shù)優(yōu)化提供了有效的模型,具有一定指導意義。
[Abstract]:Not only a large amount of historical data are generated during the operation of thermal power units, but also there is a complex nonlinear relationship between these boundary parameters and the heat consumption rate. According to the real-time data of a power plant, firstly, by using sensitivity analysis, the important parameters which have great influence on unit energy consumption are selected from a large number of unit operating parameters: load, inlet temperature of circulating water, main steam temperature, reheat steam temperature, etc. Main steam pressure, circulating water flow. Then, the application of BP neural network and RBF neural network in heat consumption rate and unit boundary parameters are compared and analyzed. The results of training and prediction show that both BP and RBF neural networks can analyze and study this problem, but the relative error of training and prediction of RBF neural network is smaller than that of BP neural network, so it is more accurate to predict unit heat consumption. It provides an effective model for the optimization of controllable parameters in the future and has certain guiding significance.
【作者單位】： 華北電力大學能源動力與機械工程學院;國電懷安熱電有限公司;
【基金】：中央高�；究蒲袠I(yè)務費專項基金資助項目(12NQ40)
【分類號】：TM621

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