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Appl. Sci. 2018, 8(1), 93; doi:10.3390/app8010093

Generative Adversarial Networks Based Heterogeneous Data Integration and Its Application for Intelligent Power Distribution and Utilization

1
Beijing Key Laboratory of Distribution Transformer Energy-Saving Technology, China Electric Power Research Institute, Beijing 100192, China
2
Department of Engineering Physics, Tsinghua University, Beijing 100084, China
*
Author to whom correspondence should be addressed.
Received: 31 October 2017 / Revised: 5 January 2018 / Accepted: 7 January 2018 / Published: 11 January 2018
(This article belongs to the Section Computer Science and Electrical Engineering)
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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. View Full-Text
Keywords: intelligent power distribution and utilization; heterogeneous data integration; generative adversarial networks; peak clustering; finite open coverage intelligent power distribution and utilization; heterogeneous data integration; generative adversarial networks; peak clustering; finite open coverage
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Tan, Y.; Liu, W.; Su, J.; Bai, X. Generative Adversarial Networks Based Heterogeneous Data Integration and Its Application for Intelligent Power Distribution and Utilization. Appl. Sci. 2018, 8, 93.

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