华电技术

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基于纵横交叉算法优化神经网络的电缆谐波损耗智能评估

陈德   

  1. 广东工业大学自动化学院
  • 收稿日期:2021-03-04 修回日期:2021-03-31 发布日期:2021-05-18
  • 通讯作者: 陈德

Intelligent Evaluation of Cable Harmonic Loss Based on Crossover Algorithm to Optimize Neural Network

De CHEN   

  1. School of Automation, Guangdong University of Technology
  • Received:2021-03-04 Revised:2021-03-31 Published:2021-05-18
  • Contact: De CHEN

摘要: 国内外对于电缆线路谐波损耗的研究主要是通过电磁物理分析进行计算,等值参数的修正多依赖经验公式,精度方面有所欠缺。为准确评估电缆线路的谐波损耗,提出一种基于纵横交叉算法优化BP神经网络的损耗智能评估模型。谐波影响下的电缆线路普遍是谐波次数多样、各次含有率不定,训练样本的影响因素众多,使用传统的BP算法存在收敛速度慢、容易陷入局部最优等不足。为了克服这些缺点,使用搜索能力更强的纵横交叉算法优化BP神经网络,得到基于CSO- BP神经网络的电缆线路谐波损耗智能评估模型。将该模型的损耗评估值、传统BP模型评估值以及物理公式法计算值进行对比,仿真结果表明,本文提出的电缆谐波损耗智能评估模型得出的结果与实际值更为接近,具有较高的准确性和稳定性。

关键词: 电缆, 损耗, 谐波, BP神经网络, 纵横交叉算法

Abstract: The research on the harmonic loss of cable lines at home and abroad is mainly calculated through electromagnetic physical analysis. The correction of equivalent parameters mostly relies on empirical formulas, and the accuracy is lacking. In order to accurately evaluate the harmonic loss of the cable line, an intelligent loss evaluation model based on the vertical and horizontal crossover algorithm to optimize the BP neural network is proposed. Cable lines under the influence of harmonics generally have various harmonic orders, variable content of each order, and many influencing factors of training samples. The use of traditional BP algorithm has the disadvantages of slow convergence and easy to fall into local optimum. In order to overcome these shortcomings, the BP neural network is optimized by the cross-over algorithm with stronger search ability, and the intelligent evaluation model of the cable line harmonic loss based on the CSO-BP neural network is obtained. The loss evaluation value of the model, the traditional BP model evaluation value and the calculation value of the physical formula method are compared. The simulation results show that the result of the cable harmonic loss intelligent evaluation model proposed in this paper is closer to the actual value and has a higher value. Accuracy and stability.

Key words: Cable, Loss, Harmonic, BP neural network, CSO algorithm