Power Grid Cloud Resource Status Data Compression Method Based on Deep-Learning

Title:Power Grid Cloud Resource Status Data Compression Method Based on Deep-Learning

Volume: 14 Issue: 3

Author(s): Weixuan Liang, Youchan Zhu*Guoliang Li

Affiliation:

• School of Control and Computer Engineering, North China Electric Power University, Baoding,China

Keywords: Power cloud resources, data compression, self-adaptive differential evolution, multilevel sparse automatic encoder, deep-learning, two networks.

Abstract:

Background: As the "three-type two-net, world-class" strategy is proposed, the key issues to be addressed are that the number of cloud resources in power grid continues to grow and there is a large amount of data to be filed every day. The long-term preservation of data, using backup data for the operation and maintenance, fault recovery, fault drill and tracking of cloud platform are essential. The traditional compression algorithm faces severe challenges.

Methods: In this case, this paper proposes the deep-learning method for data compression. First, a more accurate and complete grid cloud resource status data is gathered through data cleaning, correction, and standardization, the preprocessed data is then compressed by SaDE-MSAE.

Results: Experiments show that the SaDE-MSAE method can compress data faster. The data compression ratio based on neural network is basically between 45% and 60%, which is relatively stable and stronger than the traditional compression algorithm.

Conclusion: The paper can compress the data quickly and efficiently in a large amount of power data. Improve the speed and accuracy of the algorithm while ensuring that the data is correct and complete, and improve the compression time and efficiency through the neural network. It gives better compression schemes and cloud resource data grid.

Cite this article as:

Liang Weixuan , Zhu Youchan *, Li Guoliang , Power Grid Cloud Resource Status Data Compression Method Based on Deep-Learning, Recent Advances in Computer Science and Communications 2021; 14 (3) . https://dx.doi.org/10.2174/2213275912666190710155318