基于改进神经网络和能量守恒法的热电联产机组发电量计算

Power generation calculation for cogeneration units based on improved neural network and energy conservation method

针对地方热电厂供热量和热电联产机组“以热定电”原则下发电量难以准确确定的问题,提出了一种基于改进神经网络和能量守恒法的热电联产机组发电量计算方法。首先根据经验公式和尝试法确定BP神经网络(BPNN)最佳隐含层神经元数。然后采用自适应遗传算法(AGA)对BPNN的初始权值和阈值进行优化,以提升网络的全局搜索能力,构建起AGA-BPNN供热量预测模型,实现对供热量的准确预测。最后通过能量守恒法推导得到“以热定电”原则下的热电联产机组发电量最大值和最小值表达式,可准确计算在给定的民用供热量和工业供热量下机组发电量范围。本文方法可为调度部门合理制定热电厂发电量计划提供可靠的参考依据。

It is difficult to accurately determine the heating capacity of local thermal power plants and the generation capacity of cogeneration units under the principle of"determine power by heat".To solve this problem,a calculation method of the generation capacity of cogeneration units based on improved neural network and energy conservation method is proposed.Firstly,the optimal number of neurons in hidden layer of BP neural network(BPNN)is determined according to empirical formula and trial method.Then,self-adaptive genetic algorithm(AGA)is used to optimize the initial weights and thresholds of the BPNN,thus to enhance the global search ability of the network,and build up the AGA-BPNN heat supply prediction model to achieve accurate prediction of heat supply.Finally the expression of the maximum and minimum generation capacity of cogeneration units based on the principle of"determine power by heat"is derived by energy conservation method,which can accurately calculate the range of generation capacity of cogeneration units under given residential heating and industrial heating.The above method can provide a reliable reference for the dispatching department to formulate a reasonable power generation plan for thermal power plants.