改进ASMDE算法和RBFNN的配电网线损计算

针对中压配电网结构复杂，运行数据不全，常规网损计算方法难以实施的问题，提出了一种配电网线损的实用计算方法。利用RBF神经网络的强拟合特性，映射配电线路的特征参量与线损之间复杂的非线性关系，记忆配电线路在结构参数和运行参数变化时线损的变化规律，建立了基于RBF神经网络的线损计算模型。采用改进的自适应二次变异差分进化（ASMDE）算法，对RBF神经网络的结构参数进行整体优化，克服了常规算法隐含层与输出层结构参数分开确定，输出层易陷入局部极小的缺点。实例仿真验证了所提方法的有效性和实用性。

Calculation of power loss in distribution systems based on improved ASMDE algorithm and RBFNN

In view of the problem that the structure of medium voltage distribution network is complex, operation data is incomplete, conventional power loss calculation methods are difficult to implement, a practical method of calculating power loss in distribution system is presented. By establishing the corresponding RBF Neural Network model, the method takes advantage of the strong regression ability of RBF Neural Network to map complex non-linear relation between power loss and feature parameters of distribution net, and memorizes the rule of power loss varying with distribution circuit structure and operation parameters. Adopting improved Adaptive Second Mutation Differential Evolution（ASMDE） algorithm to optimize integrally the structure parameters of RBF Neural Network, the method overcomes the shortcomings that conventional differential evolution algorithm is easy to fall into local optimum and the hidden layer and output layer structure parameters are determined separately. The simulation results prove the validity and practicability of the proposed method.