Information Operation and Maintenance Optimization Strategy for Power Industry Based on Artificial Intelligence

Hao Wang, Xiangcong Zhang, Bingjie Wang, Lei Wang, Feifei Zhang

2025

Abstract

In this study, an AI-based information operation and maintenance management system for the power industry was designed to improve the efficiency and reliability of information operation and maintenance in the power industry. The system mainly includes multiple layers, such as data acquisition layer, data processing and analysis layer, decision support layer, execution and control layer, user interface and reporting layer, etc. In this system, it uses integrated smart sensors and technologies such as RTUs and PMUs to collect high-quality real-time data. In addition, it also uses big data platforms and machine learning algorithms to carry out data processing and analysis, and at the same time, optimizes power generation dispatch and grid load management. Based on the multiple strategies provided by the system, fault isolation and system recovery operations can be automatically controlled, improving the overall response speed and accuracy of the power grid. With this system, the equipment failure rate can be greatly reduced, and the power resource allocation can be optimized, and at the same time, the power operation and maintenance efficiency can be improved.

Paper Citation

in Harvard Style

Wang H., Zhang X., Wang B., Wang L. and Zhang F. (2025). Information Operation and Maintenance Optimization Strategy for Power Industry Based on Artificial Intelligence. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 537-541. DOI: 10.5220/0013548600004664

in Bibtex Style

@conference{incoft25,
author={Hao Wang and Xiangcong Zhang and Bingjie Wang and Lei Wang and Feifei Zhang},
title={Information Operation and Maintenance Optimization Strategy for Power Industry Based on Artificial Intelligence},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT},
year={2025},
pages={537-541},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013548600004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT
TI - Information Operation and Maintenance Optimization Strategy for Power Industry Based on Artificial Intelligence
SN - 978-989-758-763-4
AU - Wang H.
AU - Zhang X.
AU - Wang B.
AU - Wang L.
AU - Zhang F.
PY - 2025
SP - 537
EP - 541
DO - 10.5220/0013548600004664
PB - SciTePress