A steam turbine power generation system control method and device based on random screening optimization algorithm

Abstract

The present invention discloses a control method and device for a steam turbine power generation system based on a random screening optimization algorithm. The control method includes: obtaining the setting range, number of iterations, random step size, and required number of control parameter groups for each control parameter used in performing active disturbance rejection control on the steam turbine power generation system; randomly selecting multiple random control parameter groups within the setting range of each control parameter based on the number of iterations and random step size; obtaining the transfer function of the steam turbine power generation system and analyzing the system stability margin corresponding to each random control parameter group based on the transfer function; determining the required number of excellent control parameter groups based on the system stability margin, and performing active disturbance rejection control on the steam turbine power generation system based on the excellent control parameter groups. By adopting the technical solution of the present invention, excellent control parameters can be quickly screened based on the system stability margin, thereby achieving rapid control of the stable operation of the power generation system.

Description

Technical Field

[0001] The present invention relates to the technical field of steam turbine power generation control, and in particular to a steam turbine power generation system control method and device based on a random screening optimization algorithm. Background Art

[0002] Power system load frequency is a crucial parameter in power system operation. Research and control of load frequency are crucial for the safe and economic operation of power systems. It can be used to determine whether a power system is operating stably, and the balance between supply and demand in a power system can be reflected through frequency stability. Abnormal frequency can deviate from the safe operation of the power system, resulting in extremely serious consequences for users. For example, unstable load frequency can cause some precision instruments to malfunction due to the unstable frequency. It can also deviate from normal operating conditions for power generation equipment, preventing it from operating at peak efficiency and impacting the economic operation of the power grid. When the frequency is too low, safe and stable power system operation cannot be guaranteed. Therefore, ensuring stable load frequency in power systems is a pressing need for national security and economic development. Research on load frequency issues in power systems with nonlinearities is extremely important and urgent.

[0003] With the rapid development of my country's power market, the interconnectivity between modern power grid regions has increased significantly. The power grid has now become a multi-regional interconnected power system consisting of multiple control areas. Power system stability control primarily involves two independent stability control issues: reactive power and voltage stability control; and active power and frequency stability control. The latter is known as load frequency control (LFC).

[0004] As modern power systems grow in size and complexity, the risk of widespread blackouts caused by system oscillations is also increasing. Numerous control methods have emerged to address load frequency issues in turbine generator control systems. However, limited research addresses load frequency issues in power systems with nonlinear factors, and these methods do not adequately address the inherent nonlinear characteristics of power systems. Many theoretical and practical challenges remain, requiring urgent resolution. For example, methods such as the fuzzy C-means clustering technique (FCM), the bacteria foraging optimization algorithm (BFOA), and the gravitational search algorithm (GSA) address the nonlinearities inherent in LFC systems by proposing various control strategies and performing parameter optimization. However, most of these methods employ direct approaches, which incorporate model nonlinearities into controller design and design or select controller parameters to prevent the closed-loop system from entering the unstable region. These approaches are limited to the system's stable region and may only achieve relatively conservative control performance. Furthermore, direct approaches rely on relatively precise models of nonlinearities, limiting their practical application.

[0005] Active disturbance rejection control (ADRC) is a combination of a new control structure and a new design concept. It can be applied to the interference rejection problem of nonlinear systems. During design, it is not necessary to know the complete model of the controlled object and the disturbance. Only the relative order and gain of the object need to be known, and the combination of the unknown dynamics of the system and the external disturbance is defined as a generalized disturbance. The core idea of ​​ADRC is to estimate the unknown generalized disturbance of the system through an extended state observer, and then use simple control to suppress it. This idea is similar to feedback linearization, but it is simpler in controller structure and can be applied to various nonlinear systems. In addition, this structure itself has integral behavior, and there is no need to add an additional integrator in the controller design.

[0006] Active disturbance rejection controllers (ADRCs), with their simple control structure and model-independent design, have been widely used in power generation systems, motors, electric vehicles, and pumped hydroelectric power plants. However, their control parameters are manually determined through personal experience, making tuning difficult, especially when considering nonlinear characteristics, and making it difficult to quickly achieve stable control. Summary of the Invention

[0007] The present invention provides a steam turbine power generation system control method and device based on a random screening optimization algorithm to solve the problem that when the existing steam turbine power generation system adopts active disturbance rejection control for load frequency control, the control parameters need to be manually adjusted, which affects the stable operation of the power generation system and cannot be quickly controlled.

[0008] To achieve the above-mentioned purpose, according to a first aspect of an embodiment of the present invention, a control method for a steam turbine power generation system based on a random screening optimization algorithm is provided, comprising: obtaining a setting interval range, number of iterations, random step size and required number of control parameter groups for performing active anti-disturbance control on the steam turbine power generation system; randomly selecting a plurality of random control parameter groups within the setting interval range of each control parameter according to the number of iterations and the random step size; obtaining a transfer function of the steam turbine power generation system, and analyzing the system stability margin corresponding to each random control parameter group based on the transfer function; determining an excellent control parameter group with the required number of groups according to the system stability margin, and performing active anti-disturbance control on the steam turbine power generation system according to the excellent control parameter group.

[0009] Optionally, analyzing the system stability margin corresponding to each group of control parameters based on the transfer function includes: obtaining the system Bode diagram corresponding to each random control parameter group based on the transfer function; and analyzing the system stability margin corresponding to each random control parameter group according to the Bode diagram.

[0010] Optionally, obtaining the transfer function of the steam turbine power generation system includes: linearizing a nonlinear link of the steam turbine power generation system; and obtaining the transfer function of the steam turbine power generation system after the linearization process.

[0011] Optionally, after obtaining the setting interval range of each control parameter, the steam turbine power generation system control method further includes: narrowing the setting interval range of each control parameter according to the system boundary stability condition.

[0012] Optionally, the control parameters include an observer bandwidth, a controller bandwidth, and an adaptive parameter of the active disturbance rejection control.

[0013] According to a second aspect of an embodiment of the present invention, a control device for a steam turbine power generation system based on a random screening optimization algorithm is provided, comprising: a parameter initialization module for obtaining a setting interval range, a number of iterations, a random step size, and a required number of control parameter groups for performing active anti-disturbance control on the steam turbine power generation system; a random screening module for randomly selecting a plurality of random control parameter groups within the setting interval range of each control parameter according to the number of iterations and the random step size; an analysis module for obtaining a transfer function of the steam turbine power generation system, and analyzing a system stability margin corresponding to each random control parameter group based on the transfer function; and a control module for determining an excellent control parameter group of the required number of groups according to the system stability margin, and performing active anti-disturbance control on the steam turbine power generation system according to the excellent control parameter group.

[0014] Optionally, when analyzing the system stability margin corresponding to each random control parameter group based on the transfer function, the analysis module is used to: obtain the system Bode diagram corresponding to each random control parameter group based on the transfer function; and analyze the system stability margin corresponding to each random control parameter group according to the Bode diagram.

[0015] Optionally, when obtaining the transfer function of the steam turbine power generation system, the analysis module is used to: perform linearization processing on the nonlinear link of the steam turbine power generation system; and obtain the transfer function of the linear steam turbine power generation system.

[0016] Optionally, after obtaining the setting interval range of each control parameter, the parameter initialization module is further configured to: reduce the setting interval range of each control parameter according to a system boundary stability condition.

[0017] Optionally, the control parameters include an observer bandwidth, a controller bandwidth, and an adaptive parameter of the active disturbance rejection control.

[0018] The steam turbine power generation system control method and device based on the random screening optimization algorithm of the embodiment of the present invention selects the control parameters of the active disturbance rejection control from a set range during the process of load frequency control of the steam turbine power generation system using active disturbance rejection control, analyzes the corresponding system stability margin, and screens the optimal control parameters according to the system stability margin. The optimal control parameters can be quickly determined by only setting the set range of the control parameters, thereby realizing rapid control of the stable operation of the power generation system. Compared with the method of manually adjusting the control parameters based on experience in the prior art, the control parameters can be screened more quickly and accurately, which facilitates the rapid control of the stable operation of the power generation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 A schematic structural diagram of a single-region power system according to an embodiment of the present invention;

[0021] Figure 2 A schematic structural diagram of a single-region power system with a speed regulator dead zone according to an embodiment of the present invention;

[0022] Figure 3 This is a flow chart of a steam turbine power generation system control method based on a random screening optimization algorithm according to a first embodiment of the present invention;

[0023] Figure 4 This is a flow chart of a random screening optimization algorithm according to the second embodiment of the present invention;

[0024] Figure 5 This is the Bode diagram of the nonlinear power system under the ROS algorithm according to the second embodiment of the present invention;

[0025] Figure 6 This is a step response diagram of a nonlinear power system according to the second embodiment of the present invention;

[0026] Figure 7 This is a step response diagram of a nonlinear power system under the ROS algorithm according to the second embodiment of the present invention;

[0027] Figure 8 This is a structural diagram of a steam turbine power generation system control device based on a random screening optimization algorithm according to the third embodiment of the present invention. DETAILED DESCRIPTION

[0028] To make the above-mentioned objects, features, and advantages of the present invention more readily apparent, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings. The following description sets forth numerous specific details to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways than those described herein, and those skilled in the art may make similar modifications without departing from the scope of the present invention. Therefore, the present invention is not limited to the basic embodiments disclosed below.

[0029] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0030] Those skilled in the art will understand that the terms "first" and "second" in the embodiments of the present invention are merely used to distinguish different steps, devices, or modules, and do not represent any specific technical meanings or necessarily indicate a logical order between them. Furthermore, the term "and / or" includes any and all combinations of one or more of the relevant listed items.

[0031] Before introducing a control method and system for a steam turbine power generation system based on a random screening optimization algorithm according to an embodiment of the present invention, the steam turbine power generation system and an active disturbance rejection control (ADRC) algorithm are first introduced.

[0032] First, if Figure 1 The single-area power system shown in the figure includes controllers, speed regulators, steam turbines, and power generation systems. is a load frequency controller, R is the unit descent characteristic, is the governor dynamics, is the turbine dynamics, It shows the load and generator dynamics. Obviously, the load frequency control (LFC) system consists of three parts: 1) Governor dynamics: ;2) Steam turbine dynamics: ;3) Generator dynamics: Therefore, the LFC problem is a disturbance rejection problem: using feedback Calming the subject And make Frequency change The impact is minimal.

[0033] Secondly, Figure 2 This paper shows a single-area power system model with a governor dead zone (GDZ). When the power system frequency fluctuates, the generator speed on the supply side changes accordingly. In practice, to suppress frequent governor changes, a dead zone is typically set for the generator's governor. This means that if the speed changes within this dead zone, the governor will not activate. Only when the speed changes exceed the generator's specified dead zone will the governor activate, changing the prime mover's valve opening, thereby adjusting the prime mover's power to balance load demand. Governor dead zone significantly impacts the performance of load-frequency control systems and can even cause system instability. Governor dead zone nonlinearity is common in load-frequency control (LFC) systems, affecting system dynamic performance and potentially causing instability.

[0034] And, in the active disturbance rejection control (ADRC) algorithm, it is assumed that the controlled system has the following model:

[0035]

[0036] in n is the order of ADRC, b is the gain of the series-integrator model, It is a combination of unknown system dynamics and external disturbances, which are assumed to be unknown in the ADRC algorithm design and are called generalized disturbances. In the ADRC algorithm framework, the central idea is to estimate the unknown generalized disturbances through an extended state observer (ESO). , as state feedback, thereby performing rapid suppression.

[0037] At present, when performing ADRC control on a steam turbine power generation system, it is usually only necessary to adjust the observer bandwidth. , controller bandwidth , adaptive parameters Parameters such as , can be used to perform control using the conventional ADRC algorithm, therefore, the technical solution of the embodiment of the present invention is based on this, in advance set the random screening optimization algorithm (RSO) in the ADRC control device, from the observer bandwidth , controller bandwidth , adaptive parameters Iterative calculations are performed within the setting range of parameters such as , and control parameters that can control the stable operation of the steam turbine power generation system are quickly screened out, thereby achieving rapid control of the stable operation of the steam turbine power generation system.

[0038] Example 1

[0039] Reference Figure 3 , shows a flow chart of a steam turbine power generation system control method based on a random screening optimization algorithm according to a first embodiment of the present invention. The control method can achieve rapid control of the stable operation of the system when the ADRC algorithm is used to perform LFC control on the steam turbine power generation system. The execution subject can be the control system of the steam turbine power generation system or an ADRC control device independently provided in the control system. The embodiment of the present invention is explained by taking the control system of the steam turbine power generation system as an example of the execution subject, but those skilled in the art should understand that in actual applications, any other device with corresponding data receiving and sending and processing functions can refer to this embodiment to execute the steam turbine power generation system control method based on a random screening optimization algorithm of the present invention.

[0040] A steam turbine power generation system control method based on a random screening optimization algorithm according to an embodiment of the present invention comprises the following steps:

[0041] Step S110 , obtaining the setting interval range, number of iterations, random step size and required number of control parameter groups of each control parameter for performing active disturbance rejection control on the steam turbine power generation system.

[0042] In practical applications, users can set the setting range of each control parameter based on the interactive device in the control system of the steam turbine power generation system, and set the initialization parameters of the RSO algorithm, including the number of iterations of the RSO algorithm, the random step size, and the required number of control parameter groups.

[0043] Step S120 : randomly selecting a plurality of random control parameter groups within a set interval range of each control parameter according to the number of iterations and the random step size.

[0044] Here, considering the time complexity of the algorithm, the number of iterations should not be set too high, for example, it can be set to 100 times.

[0045] Step S130 , obtaining a transfer function of the steam turbine power generation system, and analyzing the system stability margin corresponding to each random control parameter group based on the transfer function.

[0046] Among them, the transfer function obtained may include the transfer function of the speed regulator, steam turbine and other equipment in the system transfer function machine system. When obtaining the transfer function, first determine whether there is a nonlinear link in the steam turbine power generation system, that is, the above-mentioned speed regulator dead zone. If it exists, the nonlinear link of the steam turbine power generation system is linearized, and the transfer function of the steam turbine power generation system after linearization is obtained. Optionally, the system frequency, performance, quality and other related indicators of the system at a specific state point are analyzed in a digital manner, and the nonlinear system with the speed regulator dead zone is linearized to obtain a linear model of the corresponding state point. For example, the nonlinear input and output points are set, and the linearization function Linearize() in Matlab can be used to return the linear approximation of the nonlinear function at the equilibrium point.

[0047] Optionally, when determining the system stability margin, a Bode diagram calculation is performed on multiple random control parameter groups based on the obtained transfer function to obtain a system Bode diagram corresponding to each random control parameter group; and the system stability margin corresponding to each random control parameter group is analyzed based on the Bode diagram.

[0048] Step S140 , determining the required number of optimal control parameter groups according to the system stability margin, and performing active disturbance rejection control on the steam turbine power generation system according to the optimal control parameter groups.

[0049] According to an exemplary embodiment of the present invention, an excellent control parameter group is selected from multiple random control parameter groups using system stability margin as a measurement metric. This is used to perform ADRC control on a steam turbine power generation system, thereby ensuring stable system operation. Specifically, each random control parameter group can be encoded with its corresponding system stability margin. After traversing all random control parameter groups, they are sorted in descending order based on stability margin and mapped back to the original random control parameter groups. The parameter groups that rank highest and have the required number of groups are determined as the excellent control parameter groups.

[0050] The steam turbine power generation system control method based on the random screening optimization algorithm of the embodiment of the present invention obtains the interval range of the set control parameters, the number of required groups, and the screening parameters, randomly selects multiple control parameter groups within the set interval range, analyzes the system stability margin of the control parameter group based on the system transfer function, and determines the required excellent control parameter group according to the system stability margin, thereby completing the rapid screening of control parameters and realizing rapid control of the stable operation of the steam turbine power generation system. Compared with the method of manually adjusting the control parameters based on experience in the prior art, the control parameters can be screened more quickly and accurately, and the stable operation of the steam turbine power generation system can be quickly controlled.

[0051] Example 2

[0052] Reference Figure 4 , shows a schematic flow chart of a random screening optimization algorithm (RSO) according to the second embodiment of the present invention, the random screening optimization algorithm includes:

[0053] Step S210, determining the number of parameter groups and interval ranges;

[0054] Step S220, parameter preprocessing;

[0055] Step S230, setting algorithm parameters;

[0056] Steps S210-S230 are the initialization process for the RSO algorithm. Step S210 obtains the user-defined number of control parameter groups and the set ranges. Step S220 preprocesses the set ranges for the required control parameters, which may include narrowing the set ranges for each control parameter based on the system's boundary stability conditions. Specifically, one control parameter is selected, and the other control parameters are fixed until the selected control parameter reaches the stability boundary conditions. This determines the new range for the selected control parameter, i.e., the narrowed set range. Similarly, the narrowed determined ranges for each control parameter can be obtained in the same manner. Narrowing the control parameter ranges through step S220 allows for rapid convergence, selection of optimal parameters, and improved algorithm computational efficiency. Step S230 obtains RSO algorithm parameters set by the user, such as the number of iterations and random step size.

[0057] Step S240, random selection of parameters;

[0058] Step S250, verify whether the system is linear; if yes, execute step S270; if no, execute step S260;

[0059] Step S260, linearization processing;

[0060] Step S270 , arranging in descending order of system stability margin and mapping back to the original parameter group;

[0061] Step S280, verify whether the result is stable; if yes, execute step S290; if not, execute back to step S240;

[0062] Step S290: retain the excellent parameter group.

[0063] Steps S240-S290 are the main calculation process of the RSO algorithm. Step S240 randomly selects multiple control parameter groups within the set range of each control parameter based on the RSO algorithm parameters. Step S250 verifies whether the steam turbine power generation system has nonlinear links. If so, step S260 is executed to linearize the nonlinear links. If not, step S270 is executed to analyze the system stability margins of the multiple random control parameter groups based on the transfer function of the steam turbine power generation system and sort the multiple random control parameter groups in descending order based on the system stability margins. Step S280 verifies whether the top-ranked control parameter groups are stable. If stable, they are retained as excellent control parameter groups. If unstable, they are reselected. Step S290 obtains and retains the excellent control parameter groups with the highest system stability margin ranking and verified stability.

[0064] In an optional implementation, when executing step S240, taking 10 iterations as an example, three sets of control parameters are randomly generated as shown in Table 1. The three sets of parameters have no correlation and also show a certain degree of randomness in terms of system stability. Therefore, it is necessary to judge the pros and cons of the control parameters by analyzing the system stability.

[0065] surface Parameter Random Walk

[0066]

[0067] For example, conventional active disturbance rejection control is used for control, and the control parameters obtained by the RSO algorithm within the set range are At this time, the system stability of the set of parameters can be verified in step S280, and the system Bode diagram (such as Figure 5 As shown in ), the corresponding system stability margin can be obtained.

[0068] Further, Figure 6 and Figure 7 The system response curves without and with the RSO algorithm are shown respectively. In order to show the control effect of the RSO algorithm, t =1 second when a step signal is added , and the dead zone value is 0.1. The dashed line represents the system with a governor dead zone, while the solid line represents the system without a governor dead zone. By comparison, without the RSO algorithm, the response converges after 20 seconds, regardless of whether the system is linear or not. With the RSO algorithm, regardless of whether the system has a dead zone, the response converges quickly and effectively, reaching full convergence within 5 seconds. The system's overshoot is significantly reduced, and system stability is also improved.

[0069] Here, the embodiment of the present invention is described with the observer bandwidth , controller bandwidth , adaptive parameters As control parameters, the RSO algorithm is executed to quickly screen out an excellent parameter group. However, those skilled in the art should know that in other embodiments, the RSO algorithm can also be used to screen other control parameters.

[0070] Example 3

[0071] Reference Figure 8 A steam turbine power generation system control device based on a random screening optimization algorithm according to a third embodiment of the present invention includes: a parameter initialization module 310, used to obtain a setting range, a number of iterations, a random step size, and a required number of control parameter groups for performing active disturbance rejection control on the steam turbine power generation system; a random screening module 320, used to randomly select a plurality of random control parameter groups within the setting range of each control parameter according to the number of iterations and the random step size; an analysis module 330, used to obtain a transfer function of the steam turbine power generation system, and analyze the system stability margin corresponding to each random control parameter group based on the transfer function; and a control module 340, used to determine an excellent control parameter group with a required number of groups according to the system stability margin, and perform active disturbance rejection control on the steam turbine power generation system based on the excellent control parameter group.

[0072] Optionally, when analyzing the system stability margin corresponding to each random control parameter group based on the transfer function, the analysis module 330 is configured to: obtain the system Bode diagram corresponding to each random control parameter group based on the transfer function; and analyze the system stability margin corresponding to each random control parameter group according to the Bode diagram.

[0073] Optionally, when acquiring the transfer function of the steam turbine power generation system, the analysis module 330 is configured to: perform linearization processing on the nonlinear link of the steam turbine power generation system; and acquire the transfer function of the linear steam turbine power generation system.

[0074] Optionally, after obtaining the setting range of each control parameter, the parameter initialization module 310 is further configured to: reduce the setting range of each control parameter according to a system boundary stability condition.

[0075] Optionally, the control parameters include an observer bandwidth, a controller bandwidth, and an adaptive parameter of the active disturbance rejection control.

[0076] The steam turbine power generation system control device based on the random screening optimization algorithm of the third embodiment of the present invention can be used to execute the control method of the above-mentioned first embodiment and has corresponding beneficial effects, which will not be described in detail here.

[0077] In actual application scenarios, the steam turbine power generation system control device based on the random screening optimization algorithm of the embodiment of the present invention may also include other devices or components to realize the actual installation, application and other functions of the control system.

[0078] It should be pointed out that, according to the needs of implementation, the various components / steps described in the embodiments of the present invention can be split into more components / steps, or two or more components / steps or partial operations of components / steps can be combined into new components / steps to achieve the purpose of the embodiments of the present invention.

[0079] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A steam turbine power generation system control method based on random screening optimization algorithm, characterized in that: include: Obtaining the setting interval range, number of iterations, random step size and required number of control parameter groups of each control parameter for active disturbance rejection control of the steam turbine power generation system; A plurality of random control parameter groups are randomly selected within a set range of each control parameter according to the number of iterations and the random step size; a transfer function of the steam turbine power generation system is obtained, and a system stability margin corresponding to each random control parameter group is analyzed based on the transfer function; an optimal control parameter group having the required number of groups is determined according to the system stability margin, and active disturbance rejection control is performed on the steam turbine power generation system according to the optimal control parameter group; Analyzing the system stability margin corresponding to each group of control parameters based on the transfer function, including: obtaining a system Bode diagram corresponding to each random control parameter group based on the transfer function; and analyzing the system stability margin corresponding to each random control parameter group according to the Bode diagram; The obtaining of the transfer function of the steam turbine power generation system includes: performing linearization processing on the nonlinear link of the steam turbine power generation system; and obtaining the transfer function of the steam turbine power generation system after the linearization processing.

2. A steam turbine power generation system control method based on random screening optimization algorithm according to claim 1, characterized in that: After obtaining the setting range of each control parameter, the steam turbine power generation system control method further includes: Narrow the setting range of each control parameter according to the system boundary stability conditions.

3. A steam turbine power generation system control method based on a random screening optimization algorithm according to any one of claims 1 and 2, characterized in that: The control parameters include observer bandwidth, controller bandwidth, and adaptive parameters of active disturbance rejection control.

4. A steam turbine power generation system control device based on random screening optimization algorithm, characterized in that: include: a parameter initialization module for obtaining a setting range, number of iterations, random step size, and required number of control parameter groups for performing active disturbance rejection control on a steam turbine power generation system; a random screening module for randomly selecting a plurality of random control parameter groups within the setting range of each control parameter based on the number of iterations and the random step size; an analysis module for obtaining a transfer function of the steam turbine power generation system and analyzing the system stability margin corresponding to each random control parameter group based on the transfer function; and a control module for determining an optimal control parameter group of the required number of groups based on the system stability margin, and performing active disturbance rejection control on the steam turbine power generation system based on the optimal control parameter group; When analyzing the system stability margin corresponding to each random control parameter group based on the transfer function, the analysis module is used to: obtain the system Bode diagram corresponding to each random control parameter group based on the transfer function; and analyze the system stability margin corresponding to each random control parameter group according to the Bode diagram; When acquiring the transfer function of the steam turbine power generation system, the analysis module is used to: perform linearization processing on the nonlinear link of the steam turbine power generation system; and acquire the transfer function of the linear steam turbine power generation system.

5. The steam turbine power generation system control device based on random screening optimization algorithm according to claim 4, characterized in that: After obtaining the setting range of each control parameter, the parameter initialization module is further used to: Narrow the setting range of each parameter according to the system boundary stability conditions.

6. A steam turbine power generation system control device based on a random screening optimization algorithm according to any one of claims 4 and 5, characterized in that: The control parameters include observer bandwidth, controller bandwidth, and adaptive parameters of active disturbance rejection control.

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