基于BP神经网络群的中压配电网电压降落估算

摘要

The paper analyzes the factors influencing the voltage drop of the rural middle-voltage power network. Considering the self-learning ability, associative memory and approximating nonlinear mapping of neural network, a new method to estimate voltage drop of middle voltage distribution network is proposed based on BP neural network group. As the large number of sample data of voltage estimation and the complexity of classification are easy to lead to difficult network convergence, the BP network group structure is proposed. According to the specific requirements, samples are classified and input to each BP subnet to complete the single training as well as parallel training and tests. According to impact factors of the voltage drop and the actual parameters of distribution network, the neural network input and output characteristics are identified. The samples are classified according to the load distribution type, thus the complexity of BP network training is reduced and the training efficiency is improved. The simulation results of actual examples show that the proposed method is effective and feasible for estimation of voltage drop.%对影响农村中压电网电压降落的因素进行了分析，利用神经网络具有自学习、联想记忆功能以及逼近任意非线性映射的能力，提出了基于BP神经网络群的中压电网电压降落估算方法。为解决由于样本多、分类空间复杂而易导致网络不容易收敛的问题，采用分层的BP网络群结构，将样本分类，由各BP子网进行单类样本训练，完成对样本的并行训练及测试。该方法依据电压降落影响因素及实际电网结构参数，确定神经网络输入输出特征量；按照线路负荷分布类型将样本分类，减小了BP网络训练复杂度；根据样本误差和误差变化调整学习率和冲量因子，提高了BP网络学习效率。实际算例结果验证了所提出方法的有效性和可行性。