Intelligent Power Grid Design Review via Active Perception-Enabled Multimodal Large Language Models

TL;DR

This paper introduces a three-stage framework using multimodal large language models for intelligent review of power grid drawings, improving error detection accuracy and reliability by mimicking expert analysis.

Contribution

The paper presents a novel prompt-driven approach leveraging pre-trained multimodal models for holistic and detailed power grid drawing review, addressing high-resolution processing challenges.

Findings

Enhanced defect discovery accuracy

Improved reliability in review judgments

Effective semantic understanding of complex drawings

Abstract

The intelligent review of power grid engineering design drawings is crucial for power system safety. However, current automated systems struggle with ultra-high-resolution drawings due to high computational demands, information loss, and a lack of holistic semantic understanding for design error identification. This paper proposes a novel three-stage framework for intelligent power grid drawing review, driven by pre-trained Multimodal Large Language Models (MLLMs) through advanced prompt engineering. Mimicking the human expert review process, the first stage leverages an MLLM for global semantic understanding to intelligently propose domain-specific semantic regions from a low-resolution overview. The second stage then performs high-resolution, fine-grained recognition within these proposed regions, acquiring detailed information with associated confidence scores. In the final stage, a comprehensive decision-making module integrates these confidence-aware results to accurately diagnose design errors and provide a reliability assessment. Preliminary results on real-world power grid drawings demonstrate our approach significantly enhances MLLM's ability to grasp macroscopic semantic information and pinpoint design errors, showing improved defect discovery accuracy and greater reliability in review judgments compared to traditional passive MLLM inference. This research offers a novel, prompt-driven paradigm for intelligent and reliable power grid drawing review.