实验技术与管理2025，Vol.42Issue(12)：54-70,17.DOI:10.16791/j.cnki.sjg.2025.12.007
面向新型电力系统的故障诊断技术研究进展(二):机器学习和大语言模型技术
Review of the fault diagnosis technology for new power systems(PartⅡ):Machine learning and large language model techniques
摘要
Abstract
[Objective]Modern power systems are rapidly evolving,characterized by an exponential increase in equipment volume,highly complex network topologies,and continuously changing operating conditions.Traditional fault diagnosis methods,relying on expert knowledge and threshold-based judgment,now face technical bottlenecks,including limited real-time performance,high misjudgment rates,and difficulties in multimodal data fusion.Developing intelligent diagnostic technologies based on machine learning,artificial intelligence,big data mining,and multisource information fusion has therefore become essential for achieving accurate fault identification and rapid fault location.[Methods]Recent advances in machine learning and large language models(LLMs)have opened new paths for fault diagnosis.Deep-learning-based architectures can integrate time-series operational data with multisource monitoring signals,extract cross-modal fault correlation features,and analyze fault propagation paths under dynamic coupling relationships.Transfer learning and federated learning help mitigate data silos and improve cross-regional diagnostic generalization.Supervised learning technology enhances anomaly detection robustness in small-sample scenarios.Meanwhile,LLMs can incorporate domain knowledge graphs through knowledge distillation,perform semantic reasoning and multicriteria collaborative decision-making,and support the development of diagnostic systems that combine data-driven learning with knowledge-informed causal inference.[Results]This article systematically reviews recent research progress in machine learning and LLM-based fault diagnosis technologies for new-type power systems,compares the characteristics of representative approaches,summarizes the key challenges in their practical application,and finally,outlines future research directions.[Conclusion]The work provides theoretical insights and technical pathways for building efficient,robust,and interpretable intelligent fault diagnosis systems for new power systems through systematic technical analysis and sorting.关键词
新型电力系统/故障诊断/机器学习/大语言模型Key words
new power systems/fault diagnosis/machine learning/large language models分类
信息技术与安全科学引用本文复制引用
ZHANG Jianliang,ZHANG Xiaojie,MA Jian,YU Bingyang,HAN Tao,JI Ruisong..面向新型电力系统的故障诊断技术研究进展(二):机器学习和大语言模型技术[J].实验技术与管理,2025,42(12):54-70,17.基金项目
国网浙江省电力有限公司科技项目(5211JH250009) （5211JH250009）
浙江大学实验室技术研究项目(SYBJS202511) （SYBJS202511）
浙江省自然科学基金项目(LMS26E070005) （LMS26E070005）
