基于改进BP神经网络的低压配电台区智能电能表误差状态评估模型

针对配电网台区中智能电能表误差估计问题,基于粒子群优化BP神经网络提出智能电能表误差估计方法。所提方法从数据搜集和数据预测、预处理建立智能电能表误差估计模型;针对传统BP神经网络隐含层节点数制定的局限性,提出采用粒子群优化算法对隐含层节点数进行优化,并采用优化得到的隐含层节点数构建BP神经网络结构对训练样本数据进行训练,基于训练得到的BP神经网络对测试样本数据进行计算得到智能电能表误差数据。针对某地区典型配电网台区中智能电网运行误差估计问题,采用所建立的方法进行智能电能表运行误差的评估。仿真算例表明,所建立的模型能够有效评估智能电能表运行误差,相比于传统的评估方法,其评估准确性有显著提升。

Estimation method of operation error of intelligent meter based on particle swarm optimization BP neural network

Aiming at the error estimation of smart meters in the substation area of distribution network, an error estimation method of smart meters was proposed based on particle swarm optimized BP neural network. Firstly, this method established the error estimation model of intelligent electricity meter from data collection and data prediction preprocessing; secondly, aiming at the limitation of the traditional BP neural network hidden layer node number, it proposes to optimize the hidden layer node number by particle swarm optimization algorithm, and constructs the BP neural network structure based on the optimized hidden layer node number to train the training sample data, then the obtained BP neural network calculates the test sample data to get the error data of intelligent meter. Aiming at the problem of smart grid operation error estimation in a typical distribution network area, the method established in this paper is used to evaluate the operation error of smart meters. The simulation example shows that the established model can effectively evaluate the operation error of intelligent watt hour meter, effectively improve the evaluation accuracy, and provide a basis for the discrimination of leakage and theft.