基于NWPSO-BP神经网络的异常用电行为检测算法

用户的异常用电行为会导致电力负荷异常增长,给电力公司造成巨大损失,同时也易造成输电线路故障,从而危害公共安全,然而传统人工检查异常用电行为的检测效率低下且精度较低。针对上述问题,首先分析了异常用电用户存在的行为特征,然后选择用于训练模型的最佳特征指标,最后提出了基于改进的非线性权重调整粒子群优化算法优化BP神经网络(NWPSO-BP)的异常用电行为检测算法。结果表明,与现有的异常用电行为检测方法相比,所提算法具有更高的精度,且误差收敛速度更快。

Detection Algorithm of Abnormal Electrical Behavior Based on NWPSOBP Neural Network

The user''s abnormal power consumption behavior will lead to abnormal growth of power load,causing huge losses to the power company,and also causing failure of the transmission line,thereby endangering public safety (such as fire,electric shock,etc.),and the conventional manual inspection is inefficient and has low precision.Aiming at the above problems,this paper first analyzes the behavior characteristics of abnormal power users,then selects the best feature indicators for training models,and finally,an improved nonlinear abnormal electricity behavior detection algorithm based on improved nonlinear weight adjustment particle swarm optimization algorithm BP neural network (NWPSO-BP) is established.The experimental results show that the proposed model has higher error convergence speed is compared with the existing abnormal electricity behavior detection.