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基于特征融合与深度学习的非侵入式负荷辨识算法
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智能电网教育部重点实验室天津大学

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Non-invasive Load Identification Algorithm Based on Feature Fusion and Deep Learning
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Key Laboratory of the Ministry of Educationon Smart Power Grids TianjinUniversity Tianjin China

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    摘要:

    针对使用单一设备特征进行负荷辨识存在的局限性,提出了一种基于特征融合与深度学习的非侵入式负荷辨识算法。通过分析设备的高频采样数据提取了V-I轨迹图像特征与功率数值特征。利用人工神经网络的高级特征提取能力实现了V-I轨迹图像特征与功率数值特征的融合。以复合特征作为设备新的特征训练BP神经网络进行非侵入式负荷辨识。最后使用了PLAID数据集对算法辨识效果进行了验证,并对比了不同分类算法对特征融合的有效性与负荷辨识能力。结果表明本算法利用了不同特征之间的互补性,克服了使用V-I轨迹特征无法反映设备功率大小的缺点,从而提高了V-I轨迹特征的负荷辨识能力,并且在嵌入式设备中的运算速度为毫秒级。

    Abstract:

    Aiming at the limitation of using single equipment features for load identification, we proposed a non-invasive load identification algorithm based on feature fusion and deep learning. Firstly, The V-I trajectory image features and power numerical features are extracted by analyzing the high frequency sampling data of the equipment. Then the fusion of V-I track image features and power numerical features is realized by using the advanced feature extraction ability of ANN. Finally, the BP neural network is trained to identify equipment by using fusion feature as the new feature of the equipment. We used PLAID data set to verify the identification performance of the algorithm, and compared the performance of different classification algorithms for feature fusion and load identification ability. The results show that the proposed algorithm makes use of the complementarity of different features, overcomes the disadvantage that V-I track features cannot reflect the power of the equipment, and improves the load identification ability of V-I track features. In embedded devices, the computing speed of proposed algorithms can reach the millisecond level.

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王守相,郭陆阳,陈海文,等.基于特征融合与深度学习的非侵入式负荷辨识算法[J].电力系统自动化. DOI:10.7500/AEPS20190625010.

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  • 收稿日期:2019-06-25
  • 最后修改日期:2019-10-29
  • 录用日期:2019-08-14
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