不平衡数据下基于PSO-BP算法的输电线路弧垂预测

针对架空输电线路弧垂在计算过程中易受测量数据(温度、风速、档距等参数)影响的问题,提出了基于数据预处理的PSO-BP神经网络弧垂预测模型.对收集数据中部分样本缺失的情况,使用合成少数过采样技术(SMOTE)对不平衡样本进行合成;构建PSO-BP神经网络用于弧垂预测,使用不同工况条件的数据训练网络,实现弧垂预测的目的,并将网络的性能与传统的BP神经网络性能进行对比.实验结果表明,与传统BP神经网络模型相比,本文提出的模型进行弧垂值预测后所得的误差绝对值显著降低.本文提出的模型可以加快训练速度、提高预测精度.

Transmission line sag prediction based on PSO-BP neural network with unbalanced data

A BP (Back-Propagation) neural network model optimized by PSO (Particle Swarm Optimization) and based on data preprocessing is proposed for sag prediction of overhead transmission lines, in order to solve the susceptibility of sag computation to measured data of temperature, wind speed, span and other parameters.For the missing data in collected database, the Synthetic Minority Oversampling Technique (SMOTE) was used to synthesize unbalanced samples.The proposed PSO-BP neural network was trained and tested by data obtained in different working environments.Experiments were carried out to verify the effectiveness of the proposed approach.The results showed that, compared with traditional BP neural network, the proposed model has a significant reduction in the relative error of sag prediction, and can accelerate the training speed as well as improve the sag prediction accuracy.