配电云平台的决策级数据融合及其并行化

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Abstract With the continuous development of sensor technology, the number of sensors included in the power distribution master station is increasing. The power distribution cloud platform can receive massive amounts of data. In order to improve the utilization rate of data and speed up data processing in the cloud platform, this paper proposes a decision-level data fusion method on the distribution cloud platform and its parallelization scheme. By calculating the influence of the sensors, it is possible to determine the degree that each sensor in the sensor network reflects a certain item, thereby deciding whether to transmit the data to the application layer in real time. At the same time, the improved weight-based D-S theory is used for further data fusion at the application layer, and the entire process uses Spark for parallel computing. On the premise of ensuring the integrity of data transmission, the data transmission and fusion method proposed in this paper can greatly improve the decision-making efficiency of the application layer. Especially for events that require real-time judgment, this method can enable the distribution cloud platform to make decisions in real time and efficiently.

Key words： data fusion weight D-S theory power distribution cloud platform parallel (Spark)

Received: 09 October 2020

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	Articles by authors
	WANG Ke
	ZHAO Ruifeng
	LI Bo
	LI Shiming

Cite this article:

WANG Ke,ZHAO Ruifeng,LI Bo等. Decision level data fusion and parallelization of power distribution cloud latform[J]. Electrical Engineering, 2021, 22(7): 89-94.

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http://dqjs.cesmedia.cn/EN/Y2021/V22/I7/89

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