基于改进神经网络算法的电力系统经济调度

为解决新能源接入大电网带来的能源损耗问题，进行常规能源发电机组的快速经济调度是一种有效措施。针对存在多种常规能源的电力系统，建立了考虑传输损耗的电力系统经济调度问题模型，基于增广的Lagrange-Hopfield 神经网络优化算法，引入乘子神经元传输速率参数，提出了改进的增广Lagrange-Hopfield 神经网络算法。从理论上证明了该算法的收敛性，以及收敛的快速性。以某一项目为例，选取不同的传输速率参数，验证了改进的增广Lagrange-Hopfield 神经网络算法受传输速率参数的影响，并且工程可行，收敛快速。

Economic Dispatch of Power System Based on Improved Neural Network Algorithm

In order to solve the energy loss problem of the new energy connected to the large power grid，economic dispath is an effective method to realize fast economic dispatch of generating units with conventional energy． Considering the existence of power systems with multiple energy sources，an economic dispatch model of power systems with transmission loss is established． Based on the extended Lagrange-Hopfield neural network optimization algorithm，the multiplier neuron transmission rate parameter is introduced，and the improved extended Lagrange-Hopfield neural network algorithm is proposed． The convergence of the algorithm is proved theoretically，and the algorithm has fast convergence speed． Taking a project as an example， different transmission rate parameters are selected to verify that the property of the improved augmented Lagrange-Hopfield neural network algorithm is affected by transmission rate parameters． The method is feasible in engineering application and can converge rapidly．