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Related Experiment Videos

A multi-source heterogeneous massive operation and maintenance data collection method for substations based on

Jing Wang1, Yongbo Zhou1, Ning Liu2

  • 1Digital Business Unit of State Grid Gansu Electric Power Company, Lanzhou, 730050, Gansu, China.

Scientific Reports
|May 18, 2026
PubMed
Summary

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This study introduces an AI-powered cloud-edge method for collecting substation data, improving real-time fault detection and grid stability. The approach optimizes data aggregation, ensuring efficient and secure operation and maintenance data collection.

Area of Science:

  • Electrical Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Real-time operation and maintenance (O&M) data collection is crucial for power grid safety and stability.
  • Challenges exist in achieving both real-time and secure data acquisition in power grids.
  • Substations generate multi-source heterogeneous massive O&M data, requiring efficient collection methods.

Purpose of the Study:

  • To propose a novel cloud-edge collaboration and artificial intelligence-based method for collecting multi-source heterogeneous massive O&M data in substations.
  • To ensure timely identification and resolution of potential faults for safe and stable power grid operation.
  • To address the persistent challenge of achieving both real-time and secure data acquisition.

Main Methods:

Keywords:
Artificial intelligenceCloud-edge collaborationMulti-source heterogeneous dataOperation and maintenance data collectionParticle swarm optimizationSubstation

Related Experiment Videos

  • A cloud-edge collaboration architecture is employed, with data gathered at the terminal layer and aggregated at the edge layer.
  • An artificial intelligence-based Particle Swarm Optimization (PSO) algorithm is utilized in the cloud layer for task offloading decisions.
  • The PSO algorithm aims to minimize total delay and total energy consumption for efficient data aggregation.

    • Main Results:

    • The proposed method demonstrates stable data transmission with small fluctuation amplitudes in uplink and downlink communication links.
    • Maximum latency was recorded at approximately 3.4s (uplink) and 2.4s (downlink).
    • Data integrity ranged between 94% and 98%, with resource utilization between 5% and 9%.

    Conclusions:

    • The developed cloud-edge and AI-based method effectively collects massive O&M data from substations.
    • The approach ensures efficient data aggregation, minimizing delay and energy consumption.
    • The method's practical effectiveness is confirmed by stable data transmission, acceptable latency, and high data integrity.