Abstract
Heterogeneous characteristics of a big data system for intelligent power distribution and utilization have already become more and more prominent, which brings new challenges for the traditional data analysis technologies and restricts the comprehensive management of distribution network assets. In order to solve the problem that heterogeneous data resources of power distribution systems are difficult to be effectively utilized, a novel generative adversarial networks (GANs) based heterogeneous data integration method for intelligent power distribution and utilization is proposed. In the proposed method, GANs theory is introduced to expand the distribution of completed data samples. Then, a so-called peak clustering algorithm is proposed to realize the finite open coverage of the expanded sample space, and repair those incomplete samples to eliminate the heterogeneous characteristics. Finally, in order to realize the integration of the heterogeneous data for intelligent power distribution and utilization, the well-trained discriminator model of GANs is employed to check the restored data samples. The simulation experiments verified the validity and stability of the proposed heterogeneous data integration method, which provides a novel perspective for the further data quality management of power distribution systems.
Highlights
With the rapid development of smart grid and sensing technology, China’s power user side data showed high complexity and redundancy
In order to solve the problem that heterogeneous data resources for intelligent power distribution and utilization are difficult to be effectively utilized in the small sample environment, a novel heterogeneous data integration technology that is based on generative adversarial networks (GANs-HDI) is proposed
GANs, this method constructs new sample set expanded by the generative model of GANs, this method constructs a peak clustering a peakto clustering model realize the finite open coverage of the restored sample space, and repair model realize the finitetoopen coverage of the restored sample space, and repair those incomplete those incomplete samples with entropy function
Summary
With the rapid development of smart grid and sensing technology, China’s power user side data showed high complexity and redundancy. Li and Socher introduced deep learning theory to fulfill the incomplete data restoration and integration tasks, respectively [17,18] These researches do not take the small sample environment of IPDU data into account, so it is difficult to be directly applied in the actual projects, and the integration of heterogeneous data in distribution network is not quite satisfying. In order to solve the problem that heterogeneous data resources for intelligent power distribution and utilization are difficult to be effectively utilized in the small sample environment, a novel heterogeneous data integration technology that is based on generative adversarial networks (GANs-HDI) is proposed In this proposed GANs-HDI method, the sample space expansion is realized by employing the generator of Goodfellow and et al.’s GANs [19,20], according to the targeted samples with all of the measurement indexes complete. This proposed heterogeneous data integration method is helpful to realize the efficient integration of heterogeneous data, and provides a novel perspective for the further data quality management in power companies
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