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June 2, 2026International Journal of Information Quality0 citationsOpen Access

Terminal fault processing in power system based on reinforcement learning and mobile edge computing

JYJing YangQSQiang SongQFQingqing Fu

Key Points

  • This research aims to enhance fault processing in power systems by integrating reinforcement learning with mobile edge computing.
  • Developed a framework combining reinforcement learning and mobile edge computing for power system management.
  • Tested the framework's efficiency in processing system fault data.
  • Analyzed the performance metrics to evaluate the improvements in fault processing speed.
  • Achieved a 30% reduction in fault processing time compared to traditional methods (p<0.01).
  • Improved overall system reliability by 25% with the new framework (p=0.005).
  • Demonstrated compatibility with existing power system infrastructures.

Abstract

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

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Cite This Study

Yang et al. (2026) studied this question.

synapsesocial.com/papers/6a1e730830b38c64201b6458https://doi.org/10.1504/ijiq.2026.10078910
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