High-Penetration New Energy Power System Outage Loss Uncertainty Analysis-Oriented Ultra-Short-Term Wind Speed Prediction Based on Physics-Informed Neural Network Considering Different Maintenance Assemblies

In high-penetration wind power systems, outage loss uncertainty analysis is fundamental to maintenance scheduling, and its accuracy critically depends on real-time wind power generation, which is dominated by ultra-short-term wind speed fluctuations. Accurate wind speed prediction is therefore essential for reliable outage loss evaluation and subsequent maintenance decision-making. Dense turbine layouts in wind farms lead to strong wake effects, resulting in complex physical attenuation and spatiotemporal correlations in wind speed between upstream and downstream turbines. Leveraging upstream turbine information can therefore enhance the accuracy of downstream wind speed forecasting. However, existing approaches that incorporate neighboring information, such as graph neural networks, rely primarily on data-driven learning and do not explicitly account for the physical mechanisms of wake attenuation, which limits their predictive performance. To address these challenges, a physics-informed ultra-short-term wind speed forecasting method is proposed which integrates an LSTM network for temporal feature extraction with the Jensen wake model through a weighted loss function within a PINN framework. Wake relationships are first identified based on wind direction and turbine layout, and the Jensen wake model is employed to characterize downstream wind speed attenuation. The weighted loss jointly optimizes data-driven and physics-based objectives, enabling the model to coordinate temporal pattern learning with wake-related physical interactions while adhering to wake decay physics. Moreover, the proposed approach accounts for topology-sensitive power flow variations under high-penetration renewable systems, where outage losses are strongly influenced by real-time wind power and wake-effect uncertainties. Case studies demonstrate that, compared with a conventional LSTM model, the proposed method reduces the normalized mean absolute error and the normalized root mean square error by 14.3% and 13.5%, respectively, indicating improved forecasting accuracy and potential for more reliable system outage analysis.