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基于CNN-LG模型的窃电行为检测方法研究
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Research on Detection Method of Electricity Theft Behavior Based on CNN-LG Model
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    摘要:

    针对当前电网单一学习器窃电检测方法准确率低、实时性差且无特征提取的问 题,提出一种基于卷积神经网络轻梯度提升机(CNN-LG)模型的窃电行为检测方法. 通过卷积 神经网络(CNN)提取用户用电数据电力特征,将提取特征输入以决策树为基学习器的轻梯度 提升机(LG)分类器对数据进行训练,据此建立基于卷积神经网络轻梯度提升机模型的窃电行 为检测方法 . 采用基于卷积神经网络轻梯度提升机模型对国家电网和爱尔兰智能能源径 (ISET)数据集分别进行窃电行为检测 . 实验结果表明,本文提出方法可快速准确实现电网中 各类窃电行为检测,相比于现有检测方法具有更高准确度、更优泛化性能和实时性.

    Abstract:

    Focusing on the problems of low accuracy, poor real-time performance, and no feature extraction in the current grid single learner power-theft detection method, a power-theft behavior detection method based on the Convolutional Neural Network-Light Gradient Boosting Machine (CNN-LG) model is proposed. First, the power fea? tures of user electricity data are extracted through the Convolutional Neural Network (CNN), and the extracted fea? tures are input into the Light Gradient Boosting Machine (LightGBM, LG) classifier based on the decision tree in or? der to train the data. On this basis, a detection method of electricity theft based on the CNN-LG model is estab? lished. Finally, the State Grid Corporation of China and Irish Smart Energy Trail(ISET)datasets are used to conduct experiments to verify the accuracy and effectiveness of the method proposed in this paper. The experimental results show that the method proposed in this paper can quickly and accurately realize the detection of various power theft behaviors in the power grid. Compared with the existing detection methods, it has higher accuracy, better generaliza? tion performance, and real-time performance.

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卿柏元 ,陈珏羽 ,李金瑾 ,蒋雯倩.基于CNN-LG模型的窃电行为检测方法研究[J].湖南大学学报:自然科学版,2022,49(8):138~148

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  • 在线发布日期: 2022-09-07
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