一种发电系统的多变量动态协调控制方法及系统
By employing a multivariable dynamic coordinated control method, data from the solar-thermal storage-ORC power generation system is collected and analyzed in real time. Adaptive control and performance prediction models are used to dynamically adjust the flow rate and rotational speed, solving the problem of stable power supply under complex operating conditions and achieving efficient and stable operation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HANGZHOU XUANCAI TECHNOLOGY CO LTD
- Filing Date
- 2026-04-14
- Publication Date
- 2026-07-17
AI Technical Summary
Existing control methods for solar-thermal storage-ORC power generation systems are difficult to adapt to complex and ever-changing actual operating conditions, leading to performance degradation when the system is under partial load. The nonlinear mapping relationships between subsystems are complex, and a single control strategy cannot take into account the operating requirements of each subsystem. This results in frequent and significant fluctuations in parameters such as pump flow rate and expander speed, affecting equipment lifespan and power supply stability.
A multivariable dynamic coordinated control method is adopted. By collecting system operation data and external demand data in real time, and using adaptive control strategies and pre-trained performance prediction models, combined with multi-objective optimization algorithms and closed-loop feedback, the flow setpoints of the solar collector circuit and thermal storage system, as well as the flow rate and speed of the ORC evaporator, are dynamically adjusted to achieve coordinated and optimized operation of the system.
It effectively mitigates the impact of solar energy fluctuations on system operation, ensures stable energy supply under different seasons and weather conditions, improves the system's energy utilization rate and operational safety, and adapts to the unattended operation requirements of distributed energy supply scenarios.
Smart Images
Figure CN122044277B_ABST