Review of the fault diagnosis technology for new power systems (PartⅡ): Machine learning and large language model techniques

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.

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.

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.

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.