面向多源电力感知终端的异构多参量特征级融合：融合模式、融合框架与场景验证

doi:10.19595/j.cnki.1000-6753.tces.201094

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Abstract Fusion analysis of heterogeneous multi-parameter data generated by multi-source power sensing terminals is the key to effective power target sensing under the power Internet of Things. At present, however, multi-parameter fusion in power system is still dominated by homogeneous multi-parameter fusion and decision-level heterogeneous multi-parameter fusion, which can no longer meet the needs of deep fusion for heterogeneous multi-parameter under the power Internet of Things. To solve this problem, this paper proposes a universal fusion framework suitable for structured multiple time series and unstructured images, which can be used for descriptive, predictive or decision-making analysis of power things. Firstly, with the consideration of time and space characteristics, the time series are converted into a recurrence plot suitable for nonlinear chaotic systems, so that the time series and images have the same description space. Then convolutional neural networks are used to extract the features, following with weighted feature concatenate fusion, fully connection and target perception. Finally, taking transmission line icing level perception and insulator contamination degree perception as scenarios, the model is analyzed from the perspective of accuracy and fault tolerance, which verifies the universality of the proposed model.

Key words： Multi-source power sensing terminals power heterogeneous mutli-parameters feature level fusion feature assimilation multi-parameter recurrence plot

Received: 27 August 2020

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TM769

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	Wang Hongxia
	Wang Bo
	Dong Xuzhu
	Yao Liangzhong
	Zhang Ruifeng
	Ma Fuqi

Cite this article:

Wang Hongxia,Wang Bo,Dong Xuzhu等. Heterogeneous Multi-Parameter Feature-Level Fusion for Multi-Source Power Sensing Terminals: Fusion Mode, Fusion Framework and Application Scenarios[J]. Transactions of China Electrotechnical Society, 2021, 36(7): 1314-1323.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.201094 OR https://dgjsxb.ces-transaction.com/EN/Y2021/V36/I7/1314

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