Abstract : This paper addresses the problem of poor superheated steam temperature regulation characteristics and frequent superheater steam temperature overshoot in thermal power plant boilers. Based on fuzzy control theory, an improved main steam temperature control loop is proposed, and a fuzzy logic-based PID controller for main steam temperature is developed. Combining the advantages of fuzzy logic control and traditional cascade PID control, a hybrid fuzzy PID control system for main steam temperature is designed. Simulation experiments were conducted on MATLAB, and the results show that, compared with the traditional cascade PID control system, the fuzzy cascade control of main steam temperature has better dynamic regulation quality and stronger robustness.
Keywords : Serial pole control, fuzzy PID control, Matlab
Main Steam Temperature Fuzzy Controller of Thermoelectric Power Station Boiler
Abstract: To solve the problems of thermal power plants, such as poor regulation performance of superheated steam temperature and overshooting of superheated stem temperature, the control circuit for the main steam temperature was improved based on the fuzzy control theory. It proposed one kind of fuzzy logic main vapor temperature PID controller, together with the union fuzzy logic control and traditional cascade PID controls the respective merit, and design a mixed type fuzzy host vapor temperature PID control system; and it did the simulation test in Matlab. Finally indicated that compares with the traditional cascade PID control system, the main vapor temperature fuzzy cascade control has good dynamic regulation quality and strong robustness．
Key words: fuzzy logic control; cascaded control; MATLAB
introduction
Superheated steam temperature is one of the important indicators of boiler operation quality in thermal power plants. Excessively high or low superheated steam temperatures will significantly affect the safety and economy of the power plant.
The necessity of improving boiler main steam temperature control: Theoretically, higher main steam temperature leads to better unit economy but lower safety; conversely, lower main steam temperature leads to lower unit economy but higher safety. However, during the operation of thermal power units, it has been found that operators frequently adjust the main steam temperature, resulting in high labor intensity, especially when the unit load disturbance is significant, easily leading to overheating. In such cases, the superheated steam temperature exhibits typical complex systems characterized by nonlinearity, large inertia, time-varying parameters, and uncertainties. Conventional unit control schemes, combined with feedforward compensation, variable parameter, and cascade control strategies, are no longer sufficient to meet the requirements of such complex control systems. Automatic desuperheating water cannot guarantee long-term normal operation and can only be used to maintain stable production processes. Complex operating conditions require extensive manual operation and monitoring by operators, resulting in significant deviations of unit operating parameters from economic indicators, severely impacting the unit's economic efficiency and safe, reliable operation. In particular, the Class I desuperheating water regulation exhibits poor regulation characteristics and slow response speed, frequently shutting down automatically due to large temperature deviations, affecting main steam temperature control. Therefore, advanced control strategies are needed to optimize the control loop. Conventional PID controllers are based on precise mathematical models of the controlled object, while fuzzy control does not require a precise model of the controlled object and has strong adaptability. Fuzzy control mainly imitates human control experience rather than relying entirely on the model of the controlled object. Therefore, fuzzy control can approximately reflect human control behavior without establishing a mathematical model of the object, and has strong robustness. This paper proposes a fuzzy controller for boiler main steam temperature.
1. Mathematical model of the controlled object
By conducting disturbance tests on the unit under different loads, data on the desuperheating water was collected, and the test curves were analyzed. Using the T.S. fuzzy model identification method, the test curves were fitted to approximate the generalized steam temperature object model under different loads. Analysis of the steam temperature object model showed that the time constant and order decreased with decreasing load. Cascade control loops were built in MATLAB using 50% and 100% steam temperature object models. PID parameters were obtained through...
After the criteria were determined, simulations were performed. However, in practice, due to variations in coal type and load conditions, the control requirements for main steam temperature cannot be fully met. Considering the nonlinear and variable parameter adjustment capabilities of the fuzzy controller, a combined fuzzy controller and PID controller approach was adopted for the main steam temperature control loop. This paper establishes a mathematical model of the superheated steam temperature of a 600MW once-through boiler. See Table 1.
Table 1 Dynamic characteristics of steam temperature on water jet disturbance under specific loads
2. Design of Fuzzy PID Composite Controller
A fuzzy PID system organically combines traditional cascade PID control with a fuzzy controller. Specifically, it uses a fuzzy controller as the main loop controller in the traditional cascade PID control scheme, while the secondary loop controller remains unchanged, thus establishing a hybrid fuzzy PID system. This system mainly consists of an outer loop and an inner loop. The inner loop (secondary loop) controller still uses PID control to overcome internal disturbances and eliminate internal static deviations. The outer loop (main loop) controller uses a fuzzy controller to fully utilize the dynamic adjustment characteristics of fuzzy control to overcome the influence of external disturbances on the system.
2.1 System Structure Principle
The actuator of the main steam temperature fuzzy PID control system consists of a primary desuperheater and a secondary desuperheater. The primary desuperheater performs coarse adjustment, while the secondary desuperheater performs fine adjustment. The fuzzy controller converts the main steam temperature deviation and the rate of change of deviation into fuzzy quantities. After processing by fuzzy control rules, a fuzzy output is obtained. Finally, the fuzzy variables are defuzzified to obtain precise values, which serve as the reference input signal for the secondary controller PID2. The structure of the fuzzy control system mainly consists of two parts: the fuzzy controller and the controlled object, as shown in Figure 1. The input r, after correction of the main steam flow rate, is the system setpoint (precise quantity). e = r - y is the system error, ec = de / dt is the error rate of change (precise quantity), and y is the system output (precise quantity). The error e and the error rate of change ec are input signals to the fuzzy controller. U is the opening degree of the output valve, which serves as the output of the fuzzy controller and is defuzzified to control the controlled object.
Figure 1. Structure diagram of fuzzy control system
2.2 Design of Fuzzy Controller
The key to designing a fuzzy controller is obtaining the fuzzy control rules. Through statistical analysis of field data, the maximum deviation of the screen outlet temperature, the maximum rate of change of the screen outlet temperature, and the maximum change in valve opening are calculated, and this data is set as the universe of discourse for the linguistic variables. Based on the universe of discourse and subsets of the universe of discourse, trigonometric functions are selected to determine the membership functions, as shown in Figures 2-4.
Membership function of Figure 2e | Membership functions of Figure 3ec |
Membership function of u in Figure 4 |
The fuzzy rules are shown in Table 2 below.
Table 2 Fuzzy Rule Table
3. Simulation experiments in Matlab
3.1 Simulink Simulation Comparison of Main Temperature Control System
The main steam temperature control system discussed in this paper adopts a cascade control system, where the main loop uses a fuzzy controller and the secondary loop uses a PID controller. The simulation block diagram of the fuzzy PID cascade control system is shown in Figure 5. In the Simulink simulation interface, the designed fuzzy controller is added to the entire control loop, and after tuning the parameters, the simulation of the control loop can be achieved.
Figure 5 Simulation model of fuzzy PID series control | Figure 7. Matlab simulation graph of 100% load. |
Figure 6. Curves of fuzzy control and PID control | Figure 8. Matlab simulation graph at 50% load |
As can be seen from Figure 6, compared with cascaded PID control, fuzzy control has a shorter transition time, reaches stability faster, has no overshoot, and has better dynamic adjustment quality.
3.2 Simulation graphs of the controlled object under different loads
To verify the control effect of the designed FC on the main steam temperature, this paper uses the dynamic characteristics of the superheated steam temperature of a 600MW once-through boiler under two load conditions (100% and 50%) for simulation. The performance of the fuzzy control system is compared with that of the traditional cascade PID control system to analyze the theoretical and engineering feasibility of this control system. The simulation results are shown in Figures 7 and 8.
4. Conclusion
A fuzzy control strategy is proposed and applied to the main steam temperature control system of a thermal power plant boiler. Compared with cascade PID control, the fuzzy controller utilizes a control strategy generated by an inference engine to achieve fast, overshoot-free control of the system. Simulation results demonstrate the effectiveness and superiority of this proposed solution.
References
[1] Chen Gang . Optimization of fuzzy control for main steam temperature in thermal power plants [J] . East China Electric Power, 2008, 36(8): 88-91
[2] Chen Jingtong, Li Zhongshu . Research on fuzzy control system for main steam temperature of boiler in thermal power plant [J] . Journal of Shenyang Normal University, 2010 , 28(4) : 510-513
