Primary frequency regulation method, device and terminal equipment for generator set

Abstract

The present application relates to a primary frequency regulation method, device, and terminal device for a generating set. The method includes: obtaining the frequency deviation of the power grid corresponding to the generating set; when the frequency deviation is within a preset range, using a genetic algorithm to optimize the control parameters in a pre-constructed model predictive controller to obtain an optimized model predictive controller; obtaining the feedwater offset and condensate throttle flow at multiple time points output by the optimized model predictive controller; and performing primary frequency regulation on the generating set based on the feedwater offset and condensate throttle flow at multiple time points. Through the present application, the problem that the primary frequency regulation response speed of the generating set in the related art is slow and the accuracy is low is solved, and the technical effect of improving the response speed and accuracy of the primary frequency regulation of the generating set is achieved.

Description

Technical Field

[0001] This application belongs to the technical field of power grid control, and particularly relates to a primary frequency regulation method, device, and terminal device for a generator set. Background Technique

[0002] The power grid frequency reflects the relationship between power balance on the power generation side and the power consumption side. When the power generation is equal to the power load, the power grid frequency remains stable; when the power generation is greater than the power load, the frequency rises; when the power generation is less than the power load, the frequency drops. The power grid frequency is an important indicator to measure the power quality and is closely related to the safety and reliability of the power grid. Therefore, maintaining frequency stability is a key task for power system operation.

[0003] The thermal power generator set is the main unit for delivering electric energy to the power grid. Its primary frequency regulation system quickly acts through the steam turbine digital electro-hydraulic control system to directly control the steam turbine throttle valve, change the unit load, and quickly stabilize the power grid frequency. However, this primary frequency regulation method has problems such as slow response speed and insufficient regulation accuracy when facing large frequency differences and complex working conditions.

[0004] Currently, in response to the problem of slow primary frequency regulation response speed and low accuracy for generator sets in related technologies, no effective solution has been proposed. Summary of the Invention

[0005] Embodiments of this application provide a primary frequency regulation method, device, terminal device, and computer program product for a generator set to at least solve the problem of slow primary frequency regulation response speed and low accuracy for generator sets in related technologies.

[0006] In a first aspect, embodiments of this application provide a primary frequency regulation method for a generator set, including: obtaining the frequency deviation of the power grid corresponding to the generator set; in the case where the frequency deviation is within a preset range, using a genetic algorithm to optimize the control parameters in a pre-constructed model predictive controller to obtain an optimized model predictive controller; obtaining the feedwater offset and condensate flow rate at multiple time points output by the optimized model predictive controller; and performing primary frequency regulation on the generator set based on the feedwater offset and condensate flow rate at multiple time points.

[0007] In some embodiments, primary frequency regulation of a generator set based on the feedwater offset and condensate throttle flow rate at multiple time points includes: obtaining a time period during which the frequency deviation is within a preset range; when the time period is greater than a first preset threshold, obtaining the change rate of the frequency deviation based on the frequency deviation and the time period; inputting the frequency deviation and the change rate of the frequency deviation into a pre-constructed sliding mode controller to obtain a control rate output by the sliding mode controller; correcting the feedwater offset and condensate throttle flow rate at multiple time points based on the control rate to obtain the corrected feedwater offset and condensate throttle flow rate at multiple time points; and performing primary frequency regulation on the generator set based on the corrected feedwater offset and condensate throttle flow rate at multiple time points.

[0008] In some embodiments, primary frequency regulation of a generator set based on the corrected feedwater offset and condensate throttle flow rate at multiple time points includes: at each time point, controlling an increase in the boiler feedwater volume in the generator set based on the feedwater offset corresponding to each time point, and controlling a decrease in the opening of the deaerator water inlet valve in the generator set based on the condensate throttle flow rate corresponding to the time point.

[0009] In some embodiments, optimizing the control parameters in a pre-constructed model predictive controller using a genetic algorithm to obtain an optimized model predictive controller includes: obtaining the target optimization function of the model predictive controller; using the genetic algorithm to optimize the control parameters in the target optimization function to obtain target control parameters; and substituting the target control parameters into the target optimization function to obtain the optimized model predictive controller.

[0010] In some embodiments, using the genetic algorithm to optimize the control parameters in the target optimization function to obtain target control parameters includes: generating an initial population, where the initial population includes multiple groups of initial control parameters; using the genetic algorithm to calculate the current fitness function of each group of initial control parameters in the initial population; performing selection, crossover, and mutation operations on each group of initial control parameters according to the current fitness function of each group of initial control parameters to obtain the next population; calculating the next fitness function according to the next population and performing iterative evolution until a preset termination condition is reached, at which point the iteration stops and the target control parameters are output.

[0011] In some embodiments, after performing primary frequency regulation on the generator set based on the feedwater offset and condensate throttle flow rate at multiple time points, the method further includes: during the primary frequency regulation of the generator set, monitoring the boiler operation parameters of the generator set; inputting the boiler operation parameters into a pre-constructed Bayesian network to obtain a safety assessment result of the boiler in the generator set output by the Bayesian network; and generating a safety control strategy when the safety assessment result is lower than a second preset threshold.

[0012] In some embodiments, after obtaining the frequency deviation of the power grid corresponding to the generating set, the method further includes: when the frequency deviation is not within the preset range, performing primary frequency regulation on the generating set based on boiler energy storage; wherein, the primary frequency regulation based on boiler energy storage includes: adjusting the opening of the steam turbine control valve of the boiler in the generating set.

[0013] In a second aspect, an embodiment of the present application provides a primary frequency regulation device for a generating set, including: an acquisition module, configured to acquire the frequency deviation of the power grid corresponding to the generating set; an optimization module, configured to, when the frequency deviation is within the preset range, use a genetic algorithm to optimize the control parameters in a pre-constructed model predictive controller to obtain an optimized model predictive controller; an output module, configured to acquire the feedwater offset and condensate flow rate at multiple time points output by the optimized model predictive controller; and a frequency regulation module, configured to perform primary frequency regulation on the generating set based on the feedwater offset and condensate flow rate at multiple time points.

[0014] In a third aspect, an embodiment of the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the primary frequency regulation method of the generating set according to any one of the above first aspects is implemented.

[0015] In a fourth aspect, an embodiment of the present application provides a computer program product, including a computer program. When the computer program is run, the primary frequency regulation method of the generating set according to any one of the above first aspects is executed.

[0016] Compared with the related art, the primary frequency regulation method, device, terminal device, and computer program product for a generating set provided by the embodiments of the present application determine whether the generating set is in a large frequency difference condition by determining whether the frequency deviation of the power grid is within the preset range; when the generating set is in a large frequency difference condition, use a genetic algorithm to optimize the control parameters in the model predictive controller to obtain an optimized model predictive controller; use the optimized model predictive controller to predict the control variables (including the feedwater offset and condensate flow rate) at multiple time points; and finally perform primary frequency regulation on the generating set based on the feedwater offset and condensate flow rate at multiple time points. In this way, using a genetic algorithm to optimize the model predictive controller can enable the optimized model predictive controller to accurately predict the optimal control variables, improving the response speed and accuracy of primary frequency regulation. Through the present application, the problem of slow response speed and low accuracy of primary frequency regulation of the generating set in the related art is solved, and the technical effect of improving the response speed and accuracy of primary frequency regulation of the generating set is achieved.

[0017] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more concise and understandable. Description of the Drawings

[0018] To more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1 is a flowchart of a primary frequency regulation method for a generator set according to an embodiment of the present application;

[0020] Figure 2 is a flowchart of a primary frequency regulation method for a generator set according to another embodiment of the present application;

[0021] Figure 3 is a schematic structural diagram of a primary frequency regulation device for a generator set according to an embodiment of the present application;

[0022] Figure 4 is a schematic structural diagram of a terminal device according to an embodiment of the present application. Detailed Embodiments

[0023] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0024] It should be understood that when used in the specification and appended claims of the present application, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0025] It should also be understood that the term " / and / " as used in the specification and appended claims of the present application refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0026] As used in the specification and appended claims of this application, the term "if" may be construed, depending on the context, as "when", "once", "in response to determining", or "in response to detecting". Similarly, the phrase "if determined" or "if [the described condition or event] is detected" may be construed, depending on the context, to mean "once determined", "in response to determining", "once [the described condition or event] is detected", or "in response to detecting [the described condition or event]".

[0027] In addition, in the description of the specification and appended claims of this application, the terms "first", "second", "third", etc. are used only for distinguishing descriptions and cannot be construed as indicating or implying relative importance.

[0028] The reference to "one embodiment" or "some embodiments" etc. described in the specification of this application means that a specific feature, structure, or characteristic described in connection with that embodiment is included in one or more embodiments of this application. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this specification do not necessarily all refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "comprising", "including", "having", and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0029] The grid frequency reflects the relationship of power balance between the power generation side and the power consumption side. When the power generation is equal to the power load, the grid frequency remains stable; when the power generation is greater than the power load, the frequency rises; when the power generation is less than the power load, the frequency drops. The grid frequency is an important indicator for measuring the power quality and is closely related to the safety and reliability of the power grid. Therefore, maintaining frequency stability is a key task for the operation of the power system. According to the different adjustment ranges and capabilities, frequency regulation can be divided into primary frequency regulation and secondary frequency regulation.

[0030] Among them, primary frequency regulation refers to that when the grid frequency exceeds a certain range, the change in the grid frequency will cause the generator sets participating in primary frequency regulation in the power grid to automatically and rapidly increase or decrease the load within a short period of time, and utilize the heat storage of the generator sets to quickly respond to the change in the grid frequency, so that the grid frequency tends to be balanced and stable again. Primary frequency regulation is an important means to prevent large fluctuations in the grid frequency and maintain the stability of the grid frequency.

[0031] A thermal power generating unit is the main unit for transmitting electric energy to the power grid. Its primary frequency regulation system quickly acts through the turbine digital electro-hydraulic control system to directly control the turbine throttle, change the unit load, and quickly stabilize the power grid frequency. However, when facing large frequency differences and complex working conditions, this primary frequency regulation method has problems such as slow response speed and insufficient regulation accuracy, resulting in the inability to quickly suppress the frequency fluctuations of the power grid and being difficult to meet the requirements of modern power grids for efficient frequency regulation. Especially with the large-scale grid connection of new energy and the increase in the fluctuation of electricity consumption load, the complexity of the power grid frequency fluctuations has increased significantly, which puts higher requirements on the response speed, regulation range, and adaptive ability of primary frequency regulation; at the same time, excessive frequency adjustment may cause equipment safety risks, such as abnormal boiler water level, too high main steam pressure, etc. The existing frequency regulation control methods lack accurate modeling and optimization of the system dynamic characteristics, cannot fully utilize the regulation potential of the generating unit, and have poor robustness when facing external disturbances and model uncertainties.

[0032] Currently, for the problem of slow primary frequency regulation response speed and low accuracy of the generating unit in the related technology, no effective solution has been proposed.

[0033] In view of this, the embodiments of the present application provide a primary frequency regulation method for a generating unit. By judging whether the frequency deviation of the power grid is within a preset range, it is determined whether the generating unit is in a large frequency difference working condition; in the case where the generating unit is in a large frequency difference working condition, the genetic algorithm is used to optimize the control parameters in the model predictive controller to obtain an optimized model predictive controller; the optimized model predictive controller is used to predict the control variables (including feed water offset and condensate flow rate) at multiple time points; finally, based on the feed water offset and condensate flow rate at multiple time points, primary frequency regulation is performed on the generating unit. In this way, using the genetic algorithm to optimize the model predictive controller can enable the optimized model predictive controller to accurately predict the optimal control variables, improving the response speed and accuracy of primary frequency regulation. Through the present application, the problem of slow primary frequency regulation response speed and low accuracy of the generating unit in the related technology is solved, and the technical effect of improving the response speed and accuracy of primary frequency regulation of the generating unit is achieved.

[0034] The following will be combined with Figure 1 A primary frequency regulation method for a generating unit provided by an embodiment of the present application will be described. Please refer to Figure 1 , Figure 1 is a flowchart of a primary frequency regulation method for a generating unit according to an embodiment of the present application. As shown in Figure 1 shown, the method includes:

[0035] Step S101, obtaining the frequency deviation of the power grid corresponding to the generating unit.

[0036] In this embodiment, the generator set can be an ultra (ultra)-critical thermal power generator set (also known as a thermal power unit), or it can be other types of thermal power units.

[0037] In one embodiment, the frequency deviation of the power grid can be obtained by establishing a mathematical model of the power grid frequency response, and the mathematical model of the power grid frequency response can be expressed as:

[0038] ∆f=(P gen -P load ) / M

[0039] where ∆f is the frequency deviation, P gen is the power generation, P load is the load power, and M is the system inertia constant.

[0040] In this embodiment, when ∆f appears and ∆f is greater than a certain degree (that is, the generator set faces a large frequency difference condition), the boiler feed water volume and condensate flow rate can be adjusted to balance P gen and P load , thereby stabilizing the power grid frequency.

[0041] Step S102, when the frequency deviation is within a preset range, use the genetic algorithm to optimize the control parameters in the pre-constructed model predictive controller to obtain an optimized model predictive controller.

[0042] In this embodiment, the preset range can be set according to the actual working conditions of the generator set, and the preset range can be set according to the historical operation data of the generator set, so as to determine the following logic: when the frequency deviation falls within the preset range, it is determined that the current working condition of the generator set is a large frequency difference condition; when the frequency deviation does not fall within the preset range, it is determined that the current working condition of the generator set is not a large frequency difference condition.

[0043] For example, the preset range can be set to be greater than or equal to 0.1 Hz. If the frequency deviation is greater than or equal to 0.1 Hz, it is determined that the current working condition is a large frequency difference condition. It should be noted that the preset range can be set according to the type, historical operation data, and actual working conditions of the generator set, and this application does not limit this.

[0044] In this embodiment, when the generator set faces a large frequency difference condition, that is, when the frequency deviation is within a preset range, the traditional primary frequency regulation method has problems such as slow response speed and insufficient adjustment amplitude. For the purpose of improving the timeliness and accuracy of frequency regulation response, the primary frequency regulation method of the generator set provided in this application combines a model predictive control (MPC) controller with a genetic algorithm (GA), uses the genetic algorithm to optimize the control parameters in the model predictive controller, and uses the optimized model predictive controller to predict the frequency deviation at multiple future time points and the corresponding control variables (including the feed water offset and the condensate flow rate).

[0045] In one embodiment, using the genetic algorithm to optimize the control parameters in the pre-constructed model predictive controller to obtain the optimized model predictive controller includes: obtaining the target optimization function of the model predictive controller; using the genetic algorithm to optimize the control parameters in the target optimization function to obtain the target control parameters; substituting the target control parameters into the target optimization function to obtain the optimized model predictive controller.

[0046] In this embodiment, the target optimization function of the model predictive controller can be expressed as:

[0047]

[0048] where J is the target optimization function, N is the prediction horizon, λ is the weight parameter, u(i) is the control variable at the i-th moment, including the feed water offset ΔQ w and the condensate flow rate ΔQ c . By solving this target optimization function, the optimal control sequence u(k) can be obtained for real-time adjustment of the unit operation parameters, where the control sequence u(k) includes control variables at multiple time points.

[0049] In this embodiment, the genetic algorithm can be used to optimize the control parameters N and λ in the target optimization function, thereby improving the prediction accuracy of the model predictive controller.

[0050] Specifically, the optimization steps of using the genetic algorithm for the control parameters in the target optimization function can include:

[0051] Step 1, generate an initial population, where the initial population includes multiple groups of initial control parameters.

[0052] In this embodiment, the control parameters N and λ can be used as the genes of an individual, and real number coding can be adopted. A combination of a group of initial control parameters N and λ can be randomly generated and used as the initial population.

[0053] Step 2: Use the genetic algorithm to calculate the current fitness function of each group of initial control parameters in the initial population.

[0054] In this embodiment, the fitness function can be expressed as:

[0055]

[0056] where f GA is the fitness value, E consumption(i) is the energy consumption of the boiler adjusted by u(i) at the i-th moment, and γ is the weight parameter between the energy consumption and the frequency deviation.

[0057] Each group of initial control parameters in the initial population can be substituted into the fitness function respectively to calculate the fitness value corresponding to each group of initial control parameters, and the groups of initial control parameters can be sorted according to the magnitude of the fitness value.

[0058] It should be noted that although the control parameter λ is not directly involved in the fitness function shown above, the genetic algorithm can still optimize λ as one of the genes of the population individuals. Specifically, the genetic algorithm can use a gene encoding containing control parameters N, λ or other control parameters to describe each individual.

[0059] In the iterative process of the genetic algorithm, these individuals will be evaluated and selected through the fitness function. Therefore, the change of the control parameter λ will also be reflected in the change of the fitness, and the control parameter λ can be optimized through subsequent selection, crossover and mutation operations. In this way, the fitness function can only calculate the frequency deviation and the energy consumption. However, since the gene encoding of the individuals in the population contains different combinations of control parameters (including N, λ or other control parameters), the genetic algorithm can still indirectly optimize these control parameters through the fitness.

[0060] Step 3: According to the current fitness function of each group of initial control parameters, perform selection, crossover and mutation operations on each group of initial control parameters to obtain the next population.

[0061] In this embodiment, after sorting each group of initial control parameters according to the magnitude of the fitness value, a proportional selection operator or a tournament selection operator can be used to select the initial control parameters with high fitness from each group of initial control parameters to form an intermediate population; subsequently, a pair of initial control parameters can be sequentially extracted from the intermediate population for crossover operation to generate a pair of new initial control parameters; and, each gene bit of the pair of new initial control parameters can be mutated with a preset mutation probability, and the mutated control parameters can be placed into the next population to increase the diversity of individuals; repeat the above operations until the number of individuals in the next population is the same as that in the initial population.

[0062] Step 4: Calculate the next fitness function based on the next population, and perform iterative evolution until a preset termination condition is reached. Then the iteration stops, and the target control parameters are output.

[0063] In this embodiment, after obtaining the next population, iterative evolution can be performed according to Steps 2 and 3 above. Repeat the above steps until the fitness function converges or the maximum number of iterations is reached. Through the genetic algorithm, a global search is performed on the control parameters in the model predictive controller to find the combination of control parameters that can minimize the fitness function, and use it as the target control parameters. Placing the target control parameters into the model predictive controller can optimize the control performance and prediction accuracy of the model predictive controller, thereby improving the response speed and accuracy of the primary frequency regulation of the generator set.

[0064] Step S103: Obtain the feedwater offset and condensate flow rate at multiple time points output by the optimized model predictive controller.

[0065] Step S104: Based on the feedwater offset and condensate flow rate at multiple time points, perform primary frequency regulation on the generator set.

[0066] In this embodiment, after obtaining the feedwater offset and condensate flow rate at multiple time points, when the current time reaches a time point, the feedwater flow rate in the generator set can be controlled to increase or decrease based on the feedwater offset corresponding to this time point, and the opening of the deaerator water inlet valve in the generator set can be controlled to decrease or increase based on the condensate flow rate corresponding to this time point. By adjusting the feedwater flow rate and the opening of the deaerator water inlet valve, a multi - element optimization of the traditional primary frequency regulation mode is carried out to make it applicable to large frequency difference conditions. Among them, the deaerator includes a water inlet valve and a water outlet valve corresponding to the water inlet valve.

[0067] For example, when the current time point reaches time point t1, the feedwater offset ∆Q w corresponding to time point t1 and the condensate flow rate ∆Q c can be used as the primary frequency regulation control signal and sent to the control system of the generator set. The control system can increase the feedwater flow rate in the boiler based on the feedwater offset ∆Q w to increase the main steam pressure, increase the work - doing ability of the steam, and enhance the primary frequency regulation response ability of the generator set; the control system can reduce the opening of the deaerator water inlet valve based on the condensate flow rate ∆Q c to throttle the condensate, thereby reducing the condensate flow rate, reducing the low - pressure extraction steam volume of the steam turbine, increasing the steam volume for work in the low - pressure section of the steam turbine, and using the energy storage of the deaerator to further enhance the primary frequency regulation response ability of the generator set.

[0068] During the operation of the generator set, when the grid frequency fluctuates, frequency modulation control is an important means to ensure the stability of the grid. Through the above steps S101 to S104, the primary frequency modulation method of the generator set provided by the embodiments of the present application based on a model predictive controller optimized by a genetic algorithm can optimize the frequency modulation strategy in real time, provide a more accurate frequency response, and in the case of large fluctuations in frequency, the response speed of primary frequency modulation is faster and the adjustment range is larger, which can effectively meet the frequency modulation requirements of the grid.

[0069] In addition, in the power grid, especially under complex working conditions such as large unit tripping and DC system failures, the grid frequency will fluctuate greatly. The primary frequency modulation method of the generator set provided by the embodiments of the present application optimizes the model predictive controller through a genetic algorithm. The optimized model predictive controller can quickly adapt to these complex working conditions and can provide an efficient frequency modulation response strategy, which helps to restore and stabilize the grid frequency, reduce the impact of frequency fluctuations on the safe operation of the power system, enhance the power grid's ability to respond to emergencies, and improve the safety and stability of the entire power system.

[0070] In addition, with the large-scale grid connection of new energy sources (such as wind energy, photovoltaics, etc.), the complexity of grid frequency fluctuations increases. The primary frequency modulation method of the generator set provided by the embodiments of the present application optimizes the model predictive controller through a genetic algorithm, enabling the optimized model predictive controller to have an adaptive frequency modulation ability. This can help the generator set better participate in grid frequency modulation when new energy is connected, providing stable frequency modulation support. At the same time, it can also optimize the frequency modulation cooperation between the generator set and new energy sources, improving the overall frequency modulation efficiency of the power grid.

[0071] In this embodiment, if the generator set is a super (ultra)-critical thermal power unit, due to the limited boiler heat storage and insufficient adjustable margin of the super (ultra)-critical thermal power unit, the traditional primary frequency modulation method is difficult to meet the frequency modulation requirements under large frequency difference conditions. The primary frequency modulation method of the generator set provided by the embodiments of the present application optimizes the model predictive controller through a genetic algorithm, and uses the optimized model predictive controller to predict the control variables at multiple time points. Based on the control variables at these multiple time points, the generator set is subjected to primary frequency modulation, which can effectively solve the problems of slow response speed and insufficient adjustment range of the primary frequency modulation of the super (ultra)-critical thermal power unit under large frequency difference conditions, thereby improving the load regulation ability of the generator set and enhancing the frequency modulation response speed of the generator set under complex working conditions.

[0072] In one embodiment, primary frequency regulation of a generator set based on the feedwater offset and condensate flow rate at multiple time points includes: obtaining a time period during which the frequency deviation is within a preset range; when the time period is greater than a first preset threshold, obtaining the rate of change of the frequency deviation based on the frequency deviation and the time period; inputting the frequency deviation and the rate of change of the frequency deviation into a pre-constructed sliding mode controller to obtain a control rate output by the sliding mode controller; correcting the feedwater offset and condensate flow rate at multiple time points based on the control rate to obtain the corrected feedwater offset and condensate flow rate at multiple time points; and performing primary frequency regulation on the generator set based on the corrected feedwater offset and condensate flow rate at multiple time points.

[0073] In this embodiment, primary frequency regulation of the generator set based on the corrected feedwater offset and condensate flow rate at multiple time points includes: at each time point, controlling an increase in the boiler feedwater volume in the generator set based on the feedwater offset corresponding to each time point, and controlling a decrease in the opening degree of the deaerator water supply valve in the generator set based on the condensate flow rate corresponding to the time point.

[0074] In this embodiment, fine control of primary frequency regulation can be performed on a multi-time scale. The duration of the large frequency difference condition of the generator set can be determined by obtaining the time period during which the frequency deviation is within a preset range. When the duration is greater than a first preset threshold (the first preset threshold can be set to 1 minute, 2 minutes, 5 minutes, 10 minutes, etc.), a sliding mode control (SMC) controller can be used to enhance the robustness of the primary frequency regulation method of the generator set provided in this embodiment under model uncertainty and external disturbances, so as to ensure that the primary frequency regulation method of the generator set can still maintain a stable frequency regulation response speed and frequency regulation accuracy under complex conditions.

[0075] Specifically, the sliding mode surface can be designed as:

[0076]

[0077] where s is the sliding mode surface, c1 and c2 are sliding mode surface coefficients, and d∆f / dt is the rate of change of the frequency deviation.

[0078] The control rate output by the sliding mode controller can be expressed as:

[0079] u(t)=-k*sgn(s)

[0080] where k is the sliding mode gain, which is used to determine the response speed and chattering degree of the system; sgn(s) is the sign function and can be expressed as:

[0081]

[0082] The control rate output by the sliding mode controller can be adjusted by adjusting the sliding mode gain \(k\), the sliding mode surface coefficients \(c_1\) and \(c_2\), so as to optimize the robustness of the primary frequency modulation method for the generator set.

[0083] In this embodiment, a multi-time scale control mechanism is introduced into the primary frequency modulation method provided in this application, and frequency modulation responses can be made respectively for short-term and long-term frequency fluctuations.

[0084] Specifically, when the duration of the large frequency difference condition is less than the first preset threshold, the generator set can be subjected to primary frequency modulation only by the control variables at multiple time points output by the model predictive controller optimized based on the genetic algorithm, and a high-gain SMC strategy is adopted to quickly suppress the frequency deviation; while when the duration of the large frequency difference condition is greater than the first preset threshold, the control variables at multiple time points output by the optimized model predictive controller can be corrected by the control rate output by the sliding mode controller. For the frequency change with a long duration, the MPC strategy is adopted to smooth the control process and reduce the energy consumption. The primary frequency modulation ability of the generator set can be optimized, the robustness of the primary frequency modulation method of the generator set under complex conditions can be improved, and the deterioration of the grid frequency caused by the complexity of the conditions can be suppressed; moreover, the primary frequency modulation method of the generator set provided in this application embodiment can not only improve the accuracy and speed of the frequency modulation response, but also optimize the frequency modulation strategy of the generator set through the multi-time scale control mechanism, thereby reducing the energy consumption during the frequent adjustment process of the generator set and improving the operation efficiency of the generator set. On the premise of not affecting the grid stability, the operation load regulation of the generator set is optimized, so as to realize the dual optimization of the economy and energy efficiency of the generator set.

[0085] Next, Figure 2 the primary frequency modulation method of the generator set provided in another embodiment of this application will be described. Please refer to Figure 2 , Figure 2 which is a flowchart of the primary frequency modulation method of the generator set according to another embodiment of this application. As Figure 2 shown, the method includes:

[0086] Step S201, obtain the frequency deviation of the power grid.

[0087] Step S202, determine whether the frequency deviation is within a preset range. If yes, go to step S203; if no, go to step S210.

[0088] Step S203, optimize the control parameters in the model predictive controller using the genetic algorithm.

[0089] Step S204, use the optimized model predictive controller to predict the control variables at multiple time points.

[0090] Step S205: Based on the control variables at multiple time points, perform primary frequency regulation on the generator set.

[0091] Step S206: During the primary frequency regulation of the generator set, monitor the boiler operation parameters of the generator set.

[0092] Step S207: Input the boiler operation parameters into the Bayesian network to obtain the safety assessment result of the boiler in the generator set output by the Bayesian network.

[0093] Step S208: Determine whether the safety assessment result is lower than the second preset threshold.

[0094] Step S209: Generate a safety control strategy.

[0095] Step S210: Perform primary frequency regulation on the generator set based on boiler energy storage. Among them, the primary frequency regulation based on boiler energy storage includes: adjusting the opening of the steam turbine control valve of the boiler in the generator set.

[0096] In this embodiment, a real-time status monitoring system can be integrated during primary frequency regulation. A Bayesian network can be pre-constructed, and the real-time safety assessment of the boiler operation parameters during primary frequency regulation can be carried out through the Bayesian network to ensure that all boiler operation parameters during primary frequency regulation are within the safe range and prevent equipment damage or safety accidents caused by excessive frequency regulation.

[0097] Specifically, the Bayesian network model can be designed as:

[0098]

[0099] Among them, X i is the boiler operation parameter to be monitored, including boiler water level, main steam pressure, etc.; P(Safe) is the prior probability of system safety.

[0100] When P(Safe|X1, X2,..., Xn) is lower than the second preset threshold (this second preset threshold can be set to 0.6, 0.8, 0.9, etc.), a safety control strategy can be generated. For example, the second preset threshold can be set to 0.85. When P(Safe|X1, X2,..., Xn) is lower than 0.85, a safety control strategy can be generated, and this safety control strategy can include measures to ensure equipment safety such as shutting down the steam turbine and reducing power.

[0101] In addition, an Adaptive Kalman Filter (AKF) can also be used to update the control parameters of the model predictive controller, so that the model predictive controller can be used in different working conditions.

[0102] Specifically, the state estimation equation can be designed as follows:

[0103]

[0104] where X k is the state estimation value, and K k is the Kalman gain adjusted adaptively.

[0105] In this embodiment, during the primary frequency regulation process, based on the real-time operation data of the generator set, K k can be adjusted to minimize the estimation error and improve the prediction accuracy of the model predictive controller. In this way, by introducing AKF, the prediction accuracy of the model predictive controller can be further improved, and at the same time, the adaptive ability of the primary frequency regulation method of the generator set provided in the embodiments of the present application can be enhanced.

[0106] In one embodiment, a control and regulation system can be designed to implement the primary frequency regulation method of the generator set provided in the embodiments of the present application. The control and regulation system can include a controller, sensors, and actuators; among them, the controller can include a high-performance industrial computer for running control algorithms and implementing the primary frequency regulation method of the generator set provided in the embodiments of the present application; the sensors are used to collect parameters such as the frequency deviation of the power grid, the boiler water level, and the main steam pressure; the actuators are used to receive the primary frequency regulation control signal sent by the controller. The actuator can increase the boiler feed water volume based on the feed water offset ΔQw in the primary frequency regulation control signal, and reduce the opening of the deaerator water inlet valve based on the condensate flow rate ΔQc in the primary frequency regulation control signal.

[0107] Specifically, the controller can include a control algorithm module, a data processing module, and a human-machine interface. Among them, the control algorithm module is used to implement algorithms such as MPC, GA, SMC, and AKF; the data processing module is used for data acquisition, preprocessing, storage, and analysis; the human-machine interface is used to provide functions such as parameter setting, status monitoring, and alarm prompting.

[0108] Corresponding to the primary frequency regulation method of the generator set described in the above embodiments, Figure 3 FIG. shows a schematic structural diagram of a primary frequency regulation device of a generator set according to an embodiment of the present application. For the sake of convenience of description, only the parts related to the embodiments of the present application are shown.

[0109] Please refer to Figure 3, the primary frequency modulation device 3 of the generator set includes: an acquisition module 30, configured to acquire the frequency deviation of the power grid corresponding to the generator set; an optimization module 31, configured to, when the frequency deviation is within a preset range, use a genetic algorithm to optimize the control parameters in a pre-constructed model predictive controller to obtain an optimized model predictive controller; an output module 32, configured to acquire the feed water offsets and condensate throttle flows at multiple time points output by the optimized model predictive controller; and a frequency modulation module 33, configured to perform primary frequency modulation on the generator set based on the feed water offsets and condensate throttle flows at multiple time points.

[0110] In one embodiment, the frequency modulation module 33 is further configured to acquire the time period during which the frequency deviation is within the preset range; when the time period is greater than a first preset threshold, acquire the change rate of the frequency deviation based on the frequency deviation and the time period; input the frequency deviation and the change rate of the frequency deviation into a pre-constructed sliding mode controller to obtain the control rate output by the sliding mode controller; correct the feed water offsets and condensate throttle flows at multiple time points based on the control rate to obtain the corrected feed water offsets and condensate throttle flows at multiple time points; and perform primary frequency modulation on the generator set based on the corrected feed water offsets and condensate throttle flows at multiple time points.

[0111] In one embodiment, the frequency modulation module 33 is further configured to, at each time point, control the increase of the boiler feed water volume in the generator set based on the feed water offset corresponding to each time point, and control the reduction of the opening degree of the deaerator water inlet valve in the generator set based on the condensate throttle flow corresponding to the time point.

[0112] In one embodiment, the optimization module 31 is further configured to acquire the target optimization function of the model predictive controller; use a genetic algorithm to optimize the control parameters in the target optimization function to obtain target control parameters; and substitute the target control parameters into the target optimization function to obtain an optimized model predictive controller.

[0113] In one embodiment, the optimization module 31 is further configured to generate an initial population, where the initial population includes multiple groups of initial control parameters; use a genetic algorithm to calculate the current fitness function of each group of initial control parameters in the initial population; perform selection, crossover, and mutation operations on each group of initial control parameters according to the current fitness functions of each group of initial control parameters to obtain the next population; calculate the next fitness function according to the next population, and perform iterative evolution until a preset termination condition is reached, at which point the iteration stops and the target control parameters are output.

[0114] In one embodiment, the primary frequency regulation device 3 of the generator set further includes a safety assessment module, which is configured to monitor the boiler operation parameters of the generator set during the primary frequency regulation process of the generator set; input the boiler operation parameters into a pre-constructed Bayesian network to obtain the safety assessment result of the boiler in the generator set output by the Bayesian network; and generate a safety control strategy when the safety assessment result is lower than a second preset threshold.

[0115] In one embodiment, the frequency modulation module 33 is further configured to perform primary frequency regulation based on boiler energy storage on the generator set when the frequency deviation is not within a preset range; wherein, the primary frequency regulation based on boiler energy storage includes: adjusting the opening degree of the steam turbine throttle valve of the boiler in the generator set.

[0116] It should be noted that for the information interaction, execution process, etc. between the above-mentioned device / units, since they are based on the same concept as the method embodiments of the present application, their specific functions and the technical effects brought, for details, please refer to the method embodiment part, and will not be elaborated here.

[0117] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the above-mentioned division of each functional unit and module is used as an example. In actual applications, the above-mentioned functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of the present application. The specific working process of the units and modules in the above system can refer to the corresponding process in the foregoing method embodiments and will not be elaborated here.

[0118] Figure 4 is a schematic structural diagram of a terminal device according to an embodiment of the present application. As Figure 4 shown, the terminal device 4 includes: at least one processor 40 ( Figure 4 only one is shown in the figure), a processor, a memory 41, and a computer program 42 stored in the memory 41 and executable on at least one processor 40. When the processor 40 executes the computer program 42, the steps in any of the foregoing method embodiments of the primary frequency regulation method of the generator set are implemented.

[0119] The terminal device 4 may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal device 4 may include, but is not limited to, a processor 40 and a memory 41. Those skilled in the art can understand that Figure 4 merely examples of the terminal device 4, which do not constitute a limitation on the terminal device 4, may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, it may also include input / output devices, network access devices, etc.

[0120] The processor 40 may be a central processing unit (CPU), and the processor 40 may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0121] In some embodiments, the memory 41 may be an internal storage unit of the terminal device 4, such as the hard disk or memory of the terminal device 4. In other embodiments, the memory 41 may also be an external storage device of the terminal device 4, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal device 4. In other embodiments, the memory 41 may also include both the internal storage unit and the external storage device of the terminal device 4. The memory 41 is used to store an operating system, application programs, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program 42. The memory 41 may also be used to temporarily store data that has been output or will be output.

[0122] The embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps in the above-mentioned embodiments of the primary frequency regulation method of each generator set can be implemented.

[0123] The embodiment of the present application provides a computer program product. When the computer program product runs on a mobile terminal, the mobile terminal is caused to execute the steps in the above-mentioned embodiments of the primary frequency regulation method of each generator set.

[0124] The implementation of all or part of the processes in the method of the above embodiments in this application can be completed by instructing relevant hardware through a computer program. This computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disc, etc.

[0125] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0126] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed in this document can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0127] In the embodiments provided in this application, it should be understood that the disclosed device / network device and method can be implemented in other ways. For example, the device / network device embodiments described above are only illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of devices or units can be in electrical, mechanical or other forms.

[0128] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0129] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included within the protection scope of the present application.

Claims

1. A primary frequency regulation method for a generator set, characterized in that, Including: Obtaining the frequency deviation of the power grid corresponding to the generating unit; When the frequency deviation is within a preset range, using a genetic algorithm to optimize the control parameters in a pre-constructed model predictive controller to obtain an optimized model predictive controller; Obtaining the feedwater offset and condensate flow rate at multiple time points output by the optimized model predictive controller; Based on the feedwater offset and condensate flow rate at the multiple time points, performing primary frequency regulation on the generating unit; Among them, using a genetic algorithm to optimize the control parameters in a pre-constructed model predictive controller to obtain an optimized model predictive controller includes: Obtaining the target optimization function of the model predictive controller; where, the target optimization function is expressed as: ； Where, J is the target optimization function, N is the prediction horizon, λ is the weight parameter, u(i) is the control variable at the i-th moment, and the control sequence u(k) includes control variables at multiple time points; Using the genetic algorithm to optimize the control parameters N and λ in the target optimization function to obtain the target control parameters; Substituting the target control parameters into the target optimization function to obtain an optimized model predictive controller; and Based on the feedwater offset and condensate flow rate at the multiple time points, performing primary frequency regulation on the generating unit includes: Obtaining the time period when the frequency deviation is within the preset range; When the time period is greater than a first preset threshold, obtaining the change rate of the frequency deviation based on the frequency deviation and the time period; Inputting the frequency deviation and the change rate of the frequency deviation into a pre-constructed sliding mode controller to obtain the control rate output by the sliding mode controller; Based on the control rate, correcting the feedwater offset and condensate flow rate at the multiple time points to obtain the corrected feedwater offset and condensate flow rate at the multiple time points; Based on the corrected feedwater offset and condensate flow rate at the multiple time points, performing primary frequency regulation on the generating unit.

2. The method according to claim 1, characterized in that, Based on the corrected feedwater offset and condensate flow rate at the multiple time points, performing primary frequency regulation on the generating unit includes: At each of the time points, controlling the increase of the boiler feed water volume in the generating unit based on the feedwater offset corresponding to each time point, and controlling the reduction of the opening of the deaerator water supply valve in the generating unit based on the condensate flow rate corresponding to the time point.

3. The method according to claim 1, wherein Using the genetic algorithm to optimize the control parameters in the target optimization function to obtain the target control parameters includes: Generating an initial population, where the initial population includes multiple groups of initial control parameters; Adopting the genetic algorithm to calculate the current fitness function of each group of the initial control parameters in the initial population; According to the current fitness functions of each group of the initial control parameters, performing selection, crossover, and mutation operations on each group of the initial control parameters to obtain the next population; According to the next population, calculating the next fitness function and performing iterative evolution until a preset termination condition is reached, the iteration stops, and the target control parameters are output.

4. The method according to any one of claims 1 to 3, characterized in that, After performing primary frequency regulation on the generator set based on the feedwater offset and condensate flow rate at the multiple time points, the method further includes: During the primary frequency regulation process of the generator set, monitoring the boiler operation parameters of the generator set; Inputting the boiler operation parameters into a pre-constructed Bayesian network to obtain a safety evaluation result of the boiler in the generator set output by the Bayesian network; Generating a safety control strategy when the safety evaluation result is lower than a second preset threshold.

5. The method according to any one of claims 1 to 3, characterized in that, After obtaining the frequency deviation of the power grid corresponding to the generator set, the method further includes: When the frequency deviation is not within the preset range, performing primary frequency regulation on the generator set based on boiler energy storage; wherein, the primary frequency regulation based on boiler energy storage includes: adjusting the opening degree of the steam turbine control valve of the boiler in the generator set.

6. A primary frequency regulation device for a generator set, characterized in that, Including: An acquisition module, configured to acquire the frequency deviation of the power grid corresponding to the generator set; An optimization module, configured to use a genetic algorithm to optimize the control parameters in a pre-constructed model predictive controller to obtain an optimized model predictive controller when the frequency deviation is within the preset range; An output module, configured to acquire the feedwater offset and condensate flow rate at multiple time points output by the optimized model predictive controller; A frequency regulation module, configured to perform primary frequency regulation on the generator set based on the feedwater offset and condensate flow rate at the multiple time points; Wherein, using a genetic algorithm to optimize the control parameters in a pre-constructed model predictive controller to obtain an optimized model predictive controller includes: Obtaining the target optimization function of the model predictive controller; wherein, the target optimization function is expressed as: ； Where J is the target optimization function, N is the prediction horizon, λ is the weight parameter, u(i) is the control variable at the i-th moment, and the control sequence u(k) includes control variables at multiple time points; Using the genetic algorithm to optimize the control parameters N and λ in the target optimization function to obtain target control parameters; Substituting the target control parameters into the target optimization function to obtain an optimized model predictive controller; and Performing primary frequency regulation on the generator set based on the feedwater offset and condensate flow rate at the multiple time points includes: Obtaining the time period during which the frequency deviation is within the preset range; When the time period is greater than a first preset threshold, obtaining the change rate of the frequency deviation based on the frequency deviation and the time period; Inputting the frequency deviation and the change rate of the frequency deviation into a pre-constructed sliding mode controller to obtain a control rate output by the sliding mode controller; Correcting the feedwater offset and condensate flow rate at the multiple time points based on the control rate to obtain the corrected feedwater offset and condensate flow rate at the multiple time points; Performing primary frequency regulation on the generator set based on the corrected feedwater offset and condensate flow rate at the multiple time points.

7. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the primary frequency regulation method of the generator set according to any one of claims 1 to 5.

8. A computer program product, characterized in that, Comprising a computer program which, when run, causes the primary frequency regulation method of the generating set according to any one of claims 1 to 5 to be executed.

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