Zhijun Shen

Power documentation serves as the core guideline for the safe operation of power systems, and its precise retrieval is crucial for ensuring grid stability and safety. In this context, Retrieval-Augmented Generation (RAG) frameworks emerge as an effective technique by combining LLMs with natural language understanding capabilities and a retrieval-based model with traceability. However, existing Retrieval-Augmented Generation (RAG) frameworks face several main challenges for power-system documents: semantic drift caused by non-standardized industry terminology, increased semantic noise due to fixed-window segmentation, and knowledge conflicts in the multi-source retrieval context. To address these challenges, we propose a multi-path adaptive fusion retrieval framework based on small language models (SLMs). To map queries to standard terminology, our framework first constructs a common terminology repository and section-structure-aware index for the power industry while fully preserving the physical hierarchical logic from related documents. Subsequently, the SLM in our framework assigns prior weights based on query features and retrieved context, which contributes to adaptive fusion of retrieval paths through confidence assessment and consistency verification. With the help of the fusion process, our method effectively filters retrieval noise and resolves knowledge conflicts. Experimental results on real-world power-document datasets covering dispatch, energy storage and emergency response show that our framework achieves an average recall of 91%, outperforming DENSE and BM25 by 21% and 28% respectively. Compared with other methods, it yields the optimal BERTScore F1 (0.7798) and Rouge-1/2/L F1 (0.2430, 0.1588, 0.2098) and achieves the best results in the RAGAS framework evaluation, which significantly enhances the rigor and reliability of the question-answering system in the power engineering domain.

This paper addresses the challenges of scarce high-risk scenario samples in power grid operation and the difficulty of traditional methods to balance overall distribution rationality with specific feature requirements. A power grid scenario generation method based on a prior knowledge embedded conditional generative adversarial network is proposed. The method encodes operational risk features such as node overvoltage and line power flow overload as conditional variables. A feature-aware loss function is constructed to embed physical constraints into the training objective of generative adversarial networks. This approach achieves organic integration of data-driven learning and knowledge-driven guidance. Case studies demonstrate that the proposed method significantly improves the generation ratio of risk scenarios at designated locations and types while maintaining the reasonableness of overall data distribution. This provides data support with both physical interpretability and computational efficiency for power grid security analysis, risk assessment, and intelligent dispatching.