基于改进PSO优化RBF神经网络线损计算与分析  被引量:8

Calculation and Analysis of Optimized RBF Neural Network Line Loss Based on Improved PSO

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作  者:何立强 赵允 于景亮 HE Liqiang;ZHAO Yun;YU Jingliang(State Grid Dandong Power Supply Company,Dandong,Liaoning 118000,China)

机构地区:[1]国网丹东供电公司,辽宁丹东118000

出  处:《东北电力技术》2020年第4期55-59,共5页Northeast Electric Power Technology

摘  要:为了准确计算配电网线路损耗,进行窃电位置的判断,提出改进粒子群算法优化RBF神经网络的计算和分析模型。以机器学习为切入点,通过数据驱动的方式,利用改进粒子群算法优化RBF神经网络重要参数,分别构建了相关线损计算和分析模型,基于IEEE13节点配电网络参数,实现理论线损计算和窃电位置判断。通过Matlab仿真验证上述模型的准确性和可靠性。In order to accurately calculate the line loss of the distribution network and to determine the location of power theft,an improved particle swarm optimization algorithm is proposed to optimize the calculation and analysis model of the RBF neural network.With machine learning as the starting point,the data-driven approach is used to optimize the important parameters of the RBF neural network by using improved particle swarm optimization algorithms.The relevant line loss calculation and analysis models are constructed.Based on the parameters of the IEEE13 node distribution network,the theoretical line loss calculation and location judgment of power theft are realized.Through Matlab simulation,it verifies the accuracy and reliability of the above model.

关 键 词:粒子群算法 人工神经网络算法 线损计算 窃电分析 

分 类 号:TP18[自动化与计算机技术—控制理论与控制工程]

 

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