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Power system state monitoring big data query based on multilevel index

Author

Listed:
  • Zeyuan Zhou
  • Junrong Liu
  • Linyan Zhou

Abstract

The continuous operation of the power system generates a large amount of state data. By querying this data, the operation status of the power system can be judged, which is beneficial for improving the stability of the power system operation. Therefore, a multilevel index based big data query method for power system state monitoring is proposed. Firstly, density clustering algorithm is used to cluster the big data of power system status monitoring. Secondly, based on the clustering results, a distance sensitive hash algorithm is used to represent the mapping relationship of data points, and a multilevel index structure is constructed to complete the query of big data for power system status monitoring. The experimental results show that the proposed method reduces the response time of big data queries for power system status monitoring, improves query throughput and accuracy, and achieves a maximum query accuracy of 94.24%.

Suggested Citation

  • Zeyuan Zhou & Junrong Liu & Linyan Zhou, 2023. "Power system state monitoring big data query based on multilevel index," International Journal of Energy Technology and Policy, Inderscience Enterprises Ltd, vol. 18(3/4/5), pages 286-297.
    • Handle: RePEc:ids:ijetpo:v:18:y:2023:i:3/4/5:p:286-297
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