李征,杨勇,杨智,何坚,蒋仕,孙林涛,朱泽继
LI Zheng,YANG Yong,YANG Zhi,HE Jian,JIANG Shi,SUN Lintao,ZHU Zeji

• 1:国网浙江省电力有限公司电力科学研究院
• 2:国网浙江省电力有限公司超高压分公司

摘要(Abstract)：

随着电力系统规模扩大与智能化需求增长，传统电力变压器运维方法因效率低、隐性故障难预测、维护成本高等问题，难以满足新型电力系统需求。对此，探讨了AI（人工智能）技术在变压器运维检修中的应用，系统梳理了专家系统、传统机器学习、深度学习及迁移学习等技术在巡检影像分析、状态评估、故障诊断、状态预测及运检策略优化中的进展，并结合典型案例，分析其技术优势与落地效果。同时，针对AI技术在实际应用中面临的技术挑战，如模型可解释性、小样本学习、数据失衡等问题，对未来研究方向进行了展望。上述研究可为推动变压器运维从“经验驱动”向“数据驱动”转型提供参考。
With the expansion of power systems and growing demands for intelligence, conventional operation and maintenance methods for power transformers, plagued by low efficiency, difficulties in predicting latent faults, and high maintenance costs, are increasingly inadequate to meet the requirements of modern power systems. This paper explores the application of artificial intelligence(AI) technology in transformer operation and maintenance. It systematically reviews advancements in expert systems, conventional machine learning, deep learning, and transfer learning technologies applied to inspection image analysis, condition assessment, fault diagnosis, state prediction, and maintenance strategy optimization. Case studies are used to illustrate technical advantages and implementation outcomes. Furthermore, the paper addresses technical challenges in practical AI applications, such as model interpretability, few-shot learning, and data imbalance, and suggests future research directions. The findings provide references for promoting the transition of transformer maintenance from “experience-driven” to “data-driven” paradigms.

关键词(KeyWords)： 电力变压器;人工智能;运维检修;状态监测;故障诊断
power transformer;artificial intelligence;operation and maintenance;condition monitoring;fault diagnosis

Abstract:

Keywords:

基金项目(Foundation): 国网浙江省电力有限公司科技项目（5211DS25000J）

作者(Author): 李征,杨勇,杨智,何坚,蒋仕,孙林涛,朱泽继
LI Zheng,YANG Yong,YANG Zhi,HE Jian,JIANG Shi,SUN Lintao,ZHU Zeji

DOI: 10.19585/j.zjdl.202605011

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