Multi-variable State Controller for Steam Generator
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Solution Overview
Problem
Current control methods for thermal power plants are inefficient and costly due to the need for manual parameterization of decoupling networks and the difficulty in handling nonlinearities and load dependencies in multi-variable controllers, which complicates the simultaneous control of multiple state variables in steam generators.
Innovation Solution
Implementing a multi-variable state controller using a linear quadratic regulator (LQR) that accounts for physical couplings between control loops, combined with a scalable steam generator model and an extended Kalman filter observer to estimate states and disturbances, allowing for simultaneous control of steam temperature, pressure, and enthalpy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a multi-variable controller is used to control multiple state variables simultaneously, then the control accuracy and stability improve, but the device complexity and difficulty of handling nonlinearities increase
Solution Approach 1:
The multi-variable control problem is segmented into multiple single-variable control loops, each with its own controller. This allows the complex multi-variable system to be managed through simpler, independent control modules that can be designed and tuned separately, reducing overall system complexity while maintaining control effectiveness.
Solution Approach 2:
Multiple single-variable controllers are merged into a coordinated multi-variable control system where the controllers work together to manage interactions between state variables. This combining approach enables the system to achieve the benefits of multi-variable control (improved accuracy and stability) while using the simpler building blocks of single-variable control.
2Reliability
If manual parameterization of decoupling networks is performed, then the control of coupled state variables improves, but the loss of time and operational costs increase
Solution Approach 1:
The control system performs self-parameterization automatically without requiring manual intervention. The decoupling network parameters are determined through automated algorithms that analyze system characteristics and configure the controllers accordingly, eliminating the time-consuming manual parameterization process while maintaining effective control of coupled state variables.
Solution Approach 2:
The system prepares and configures controller parameters in advance through automated procedures before actual operation begins. This preliminary automated parameterization eliminates the need for time-consuming manual adjustments during operation, allowing the system to quickly adapt to different operating conditions.
3Device complexity
If single-variable controllers are used for each control task, then the device complexity is reduced, but the ability to handle physical couplings between control loops deteriorates
Solution Approach 1:
Decoupling networks are introduced as intermediary elements between single-variable controllers and the controlled process. These intermediaries actively compensate for physical couplings between control loops by introducing opposite-sign signals that cancel out unwanted interactions, allowing simple single-variable controllers to effectively manage a coupled multi-variable system.
Data Source
AI summary
A device for closed-loop control of a plurality of state variables of a steam generator of a thermal power plant is provided. In order to achieve stable and exact closed-loop control of the plurality of state variables, a multi-variable control/controller controls the plurality of state variables and uses a linear quadratic controller for this multi-variable control/controller.