全球能源互联网2026，Vol.9Issue(1)：101-111,11.DOI:10.19705/j.cnki.issn2096-5125.20250306
PowerVLM:基于Federated Learning与模型剪枝的电力视觉语言大模型
PowerVLM:A Vision-language Large Model for Power Systems Enhanced by Federated Learning and Model Pruning
摘要
Abstract
The rapid evolution of smart grids has produced massive volumes of multimodal,heterogeneous power-system data,posing new challenges for AI models in complex electric-field perception.Meanwhile,the sensitivity of industry data and stringent privacy-preservation requirements further restrict the cross-scenario transferability of general-purpose models in the power domain.To address these issues,we propose a federated-learning and model-pruning framework for a power-domain vision-language large model.Specifically,we introduce PowerVLM,a class-guided vision-language model that incorporates a novel class-guided enhancement module to strengthen its comprehension and question-answering capabilities on power-related image-text pairs.A reinforcement-learning-driven federated-training strategy is adopted to mitigate domain gaps while strictly preserving data privacy.Finally,an information-resolution-based pruning algorithm is designed to enable efficient fine-tuning with significantly reduced trainable parameters.Extensive experiments on three representative power scenarios—substation inspection,transmission-line inspection,and operation safety supervision—demonstrate that our method achieves superior performance on all key metrics(METEOR,BLEU,and CIDEr)in multimodal power-domain question-answering tasks,offering a new technical paradigm and practical support for intelligent perception in power systems.关键词
智能电网/人工智能/视觉语言大模型/Federated Learning/模型剪枝Key words
smart grid/artificial intelligence/vision-language large model/federated learning/model pruning分类
信息技术与安全科学引用本文复制引用
欧阳旭东,雒鹏鑫,何绍洋,崔艺林,张中超,闫云凤..PowerVLM:基于Federated Learning与模型剪枝的电力视觉语言大模型[J].全球能源互联网,2026,9(1):101-111,11.基金项目
广东电网有限公司科技项目(GDKJXM20230471). Technology Project of Guangdong Power Grid Co.,Ltd.(GDKJXM20230471). （GDKJXM20230471）
