Power-Q: A Hybrid Screener-Analyst Framework Using LLM Agents for Real-Time Grid Monitoring and Diagnostics

This paper presents Power-Q, a hybrid multi-agent framework that combines rule-based anomaly screening with Large Language Model (LLM)-driven analytical agents to deliver explainable, near real-time supervisory insights into power quality disturbances. The architecture integrates decentralized screening agents dedicated to solar, wind, and load domains with LLM-based Analyst Agents that perform structured root cause interpretation, generate live event summaries, and support natural language interaction with operators. The framework was evaluated on a simulated five-bus microgrid using parametric fault injection and real-time data streaming. Results show that Power-Q achieves sub-200 ms anomaly detection latency, produces live summaries within approximately 2 seconds, and generates complete diagnostic reports in under 4 seconds. The Supervisor Agent demonstrated alignment with key reporting elements defined in IEEE standards, producing interpretable and structured diagnostic outputs across the evaluated scenarios. The Ticker Agent achieved complete event coverage with clear, operator-readable summaries, while the Query Agent enabled effective human-in-the-loop interaction. Overall, Power-Q provides a software-centric, operator-focused framework designed to support scalable deployment in principle, enhancing situational awareness and decision support in smart grid environments.