Development of a Quantum LSTM-Based Multi-Hazard Early Warning Framework for Climate-Resilient Smart Power Systems

Abstract

Modern power systems are increasingly threatened by extreme weather events such as cyclones, floods, lightning, heatwaves, heavy rainfall, wildfires and snowstorms, which can cause extensive damage to infrastructure, long-term power outages and significant economic losses . Most of the existing protection schemes work only after a fault has occurred . Thus they provide only limited proactive mitigation capability against weather induced failures . Recent AI-based Early Warning Systems (EWSs) have improved the prediction of weather hazards, but current ML and DL models are often designed for single-hazard prediction, require high computational resources, and show limited adaptability to rapidly changing environmental conditions. This paper proposes a Quantum Long Short-Term Memory (Q-LSTM)-Based Multi-Hazard Early Warning Framework for climate-resilient smart power systems to address these limitations. The proposed framework integrates meteorological observations, satellite images, Internet of Things (IoT) devices, Phasor Measurement Units (PMUs), SCADA measurements, and historical outage records into a single intelligent prediction framework. A cloud-assisted preprocessing module cleans, normalizes, and performs feature engineering and feature optimization on the data, and then feeds the processed information to the Quantum LSTM prediction engine. The proposed framework simultaneously predicts multiple weather hazards, estimates outage probability, performs dynamic risk assessment and generates intelligent early warning notifications for preventive grid protection and operational decision making. The experimental results show that the proposed Q-LSTM framework is superior to the traditional Random Forest, XGBoost and Deep Learning based weather outage prediction models with an Accuracy of 96.20%, Precision of 95.80%, Recall of 96.05% and F1-score of 95.92%. The proposed framework increases the prediction reliability and reduces the false alarms and missed outage events compared to the benchmark methods. The results demonstrate the efficacy of the proposed Q-LSTM architecture for smart weather hazard prediction and proactive power system protection, which can enhance grid resilience, operational reliability, and disaster preparedness for next-generation climate-adaptive smart grids.