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In-context learning enhanced large language model for robust distribution system state estimation
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DOI：10.1016/j.apenergy.2025.127344.png)
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Abstract
En 中文
• An LLM-based DSSE framework incorporating massive zero-injection nodes is proposed. • Zero-injection nodes supplement insufficient real-time measurements to enhance distribution system observability. • QLoRA and in-context learning enables efficient adaptation across practical large-scale distribution systems. • Consistently superior MAE outperforms WLS and multiple data-driven baselines under noise, missing/bad data, and topology changes. • Validated on a Dominion Energy 2135-node unbalanced three-phase feeder.
Keywords:
Distribution state estimation
Robust estimation
Machine learning
Zero injections
Large language models
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