A generative artificial intelligence framework for automated data quality rule generation in power grid systems
Year: 2026
Type: article
Abstract: In the era of industrial digitalization, high-quality data is critical for optimizing operations and decision-making, yet inconsistent, fragmented datasets from heterogeneous sources often undermine its utility. Traditional data governance approaches, reliant on manual rule creation and rigid workflows, struggle to address scalability and adaptability challenges in dynamic environments like power grid systems. This study proposes a new framework leveraging generative AI to automate SQL rule generation for anomaly detection, integrating domain-specific constraints and reinforcement learning to iteratively refine rules. By training Qwen2 on preprocessed grid data, the methodology transforms raw sensor logs and metadata into standardized, actionable rules, while embedding compliance checks to align with industry standards. Experimental validation demonstrates that the proposed model outperforms mainstream techniques (e.g., LSTM, GRU, CNN) by 2.3–3.6% in accuracy and F1-score, achieving 88.5% precision in detecting voltage anomalies. Ablation studies are conducted to highlight the importance of incorporating physical features in the model performance.
Source: Discover Computing
Authors: Wenxiang Yang, Li Tang, Guanyu Zhang
Institution: Yunnan Power Grid Co., Ltd. (China)
Language: English
Topic: Smart Grid and Power Systems
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,520
OpenAlex ID: W7128676362