Model-data combined driven power system real-time operation risk assessment method
Through the combined model-data method, the Monte Carlo method and the optimal load cutting and wind and light-blind optimal flow method are used to form a real-time grid operation scenario, and feature extraction and real-time risk assessment are combined with the convolutional neural network and support vector machine model, which solves the problem of difficulty in dealing with new energy uncertainty and volatility in traditional power systems, and achieves a more accurate assessment of the operation safety and stability of power systems.
Patent Information
- Application Number
- CN202411795595.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-09
- Publication Date
- 2025-03-18
- Estimated Expiration
- 2044-12-09
AI Technical Summary
The risk assessment methods of traditional power systems are difficult to effectively deal with the uncertainty and volatility brought about by new energy. Especially in the case of large-scale access to new energy, how to comprehensively and accurately evaluate the safety and stability of the system has become a key issue that needs to be solved urgently.
The model-data joint drive method is adopted to form a real-time grid operation scenario that takes into account multi-source uncertainty through the Monte Carlo method, and risk calculation is performed by combining the optimal load cutting and wind and light abandonment optimal flow methods, and features are extracted using convolutional neural network and support vector machine model for real-time risk assessment.
This method can more accurately evaluate the operational safety and stability of the power system, improve risk identification and early warning capabilities, and take into account the economic and safety of the power system. It has important theoretical significance and extensive application prospects.