Primary frequency modulation method and device of generator set and terminal equipment

Abstract

The invention relates to a primary frequency modulation method and device of a generator set and terminal equipment. The method comprises the following steps: acquiring a frequency deviation of a power grid corresponding to the generator set; under the condition that the frequency deviation is in a preset range, optimizing control parameters in a pre-constructed model prediction controller by using a genetic algorithm to obtain an optimized model prediction controller; obtaining the water supply offset quantity and the condensation water throttling quantity at a plurality of time points output by the optimized model prediction controller; and primary frequency modulation is carried out on the generator set based on the water supply offset amount and the condensation water throttling amount at the multiple time points. Through application of the method and the device, the problems of relatively slow response speed and relatively low accuracy of the primary frequency modulation of the generator set in the related technology are solved, and the technical effect of improving the response speed and accuracy of the primary frequency modulation of the generator set is realized.

Description

Technical Field

[0001] The present application belongs to the field of power grid control technology, and in particular relates to a primary frequency regulation method, device and terminal equipment of a generator set. Background Art

[0002] The grid frequency reflects the 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 increases; when the power generation is less than the power load, the frequency decreases. The grid frequency is an important indicator for measuring the quality of electric energy 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.

[0003] Thermal power generating units are the main units that transmit electricity to the power grid. Their primary frequency regulation system directly controls the turbine valve through the rapid operation of the digital electro-hydraulic control system of the turbine, changes the unit load, and quickly stabilizes 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, no effective solution has been proposed for the problem of slow response speed and low accuracy of primary frequency regulation of generator sets in related technologies. Summary of the invention

[0005] The embodiments of the present application provide a primary frequency regulation method, apparatus, terminal device and computer program product for a generator set, so as to at least solve the problem in the related art that the primary frequency regulation of the generator set has a slow response speed and low accuracy.

[0006] In the first aspect, an embodiment of the present application provides a primary frequency regulation method for a generator set, comprising: obtaining the frequency deviation of the power grid corresponding to the generator set; when the frequency deviation is within a preset range, using a genetic algorithm to optimize the control parameters in a pre-built model predictive controller to obtain an optimized model predictive controller; obtaining the water supply bias and condensate water saving amount at multiple time points output by the optimized model predictive controller; and performing a primary frequency regulation on the generator set based on the water supply bias and condensate water saving amount at multiple time points.

[0007] In some embodiments, based on the water supply bias and condensate throttling amount at multiple time points, frequency modulation of the generator set includes: obtaining a time period in 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-built sliding mode controller to obtain a control rate output by the sliding mode controller; correcting the water supply bias and condensate throttling amount at multiple time points based on the control rate to obtain the corrected water supply bias and condensate throttling amount at multiple time points; and frequency modulation of the generator set based on the corrected water supply bias and condensate throttling amount at multiple time points.

[0008] In some embodiments, based on the corrected feed water bias and condensate throttling at multiple time points, frequency modulation of the generator set includes: at each time point, controlling the boiler feed water amount in the generator set to increase based on the feed water bias corresponding to each time point, and controlling the opening of the deaerator water supply valve in the generator set to decrease based on the condensate throttling corresponding to the time point.

[0009] In some embodiments, a genetic algorithm is used to optimize the control parameters in a pre-built model predictive controller to obtain an optimized model predictive controller, including: obtaining a target optimization function of the model predictive controller; using a genetic algorithm to optimize the control parameters in the target optimization function to obtain target control parameters; bringing the target control parameters into the target optimization function to obtain the optimized model predictive controller.

[0010] In some embodiments, a genetic algorithm is used to optimize the control parameters in the target optimization function to obtain the target control parameters, including: generating an initial population, wherein the initial population includes multiple groups of initial control parameters; using a genetic algorithm to calculate the current fitness function of each group of initial control parameters in the initial population; based on the current fitness function of each group of initial control parameters, selecting, crossover and mutating each group of initial control parameters to obtain the next population; based on 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.

[0011] In some embodiments, after the generator set is frequency-modulated once based on the feed water bias and condensate saving amount at multiple time points, the method further includes: during the frequency modulation of the generator set, monitoring the boiler operating parameters of the generator set; inputting the boiler operating 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 generator set, the method further includes: when the frequency deviation is not within a 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 of the turbine throttle of the boiler in the generator set.

[0013] In the second aspect, an embodiment of the present application provides a primary frequency regulation device for a generator set, comprising: an acquisition module, used to obtain the frequency deviation of the power grid corresponding to the generator set; an optimization module, used to use a genetic algorithm to optimize the control parameters in a pre-built model predictive controller when the frequency deviation is within a preset range, so as to obtain an optimized model predictive controller; an output module, used to obtain the water supply bias and condensate water saving amount at multiple time points output by the optimized model predictive controller; a frequency regulation module, used to perform a primary frequency regulation on the generator set based on the water supply bias and condensate water saving amount 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, wherein when the processor executes the computer program, the primary frequency regulation method of a generator set according to any one of the first aspects described above is implemented.

[0015] In a fourth aspect, an embodiment of the present application provides a computer program product, including a computer program, and when the computer program is executed, the primary frequency regulation method of the generator set described in any one of the above-mentioned first aspects is executed.

[0016] Compared with the related art, the primary frequency regulation method, device, terminal equipment and computer program product of the generator set provided in the embodiment of the present application determine whether the generator set is in a large frequency difference condition by judging whether the frequency deviation of the power grid is within a preset range; when the generator set is in a large frequency difference condition, the control parameters in the model predictive controller are optimized using a genetic algorithm to obtain an optimized model predictive controller; the optimized model predictive controller is used to predict the control variables (including feed water bias and condensate water saving amount) at multiple time points; finally, the generator set is subjected to a primary frequency regulation based on the feed water bias and condensate water saving amount at multiple time points. In this way, the model predictive controller is optimized using a genetic algorithm, so that the optimized model predictive controller can accurately predict the optimal control variables and improve the response speed and accuracy of the primary frequency regulation. Through the present application, the problem of slow response speed and low accuracy of the primary frequency regulation of the generator set in the related art is solved, and the technical effect of improving the response speed and accuracy of the primary frequency regulation of the generator 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 readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 is a flow chart of a primary frequency regulation method for a generator set according to an embodiment of the present application; Figure 2 is a flow chart of a primary frequency regulation method of a generator set according to another embodiment of the present application; Figure 3 is a structural schematic diagram of a primary frequency regulation device of a generator set according to an embodiment of the present application; Figure 4 It is a structural diagram of a terminal device according to an embodiment of the present application. DETAILED DESCRIPTION

[0020] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may 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 prevent unnecessary details from obstructing the description of the present application.

[0021] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of 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 combinations thereof.

[0022] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0023] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0024] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0025] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0026] The grid frequency reflects the 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 increases; when the power generation is less than the power load, the frequency decreases. The grid frequency is an important indicator for measuring the quality of electric energy 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.

[0027] Among them, primary frequency regulation means that when the grid frequency exceeds a certain range, the change of the grid frequency will cause the generator sets participating in the primary frequency regulation in the grid to automatically and quickly increase or decrease the load in a short period of time, and use the heat storage of the generator sets to quickly respond to the change of the grid frequency, so that the grid frequency will tend to balance and stabilize again. Primary frequency regulation is an important means to prevent large fluctuations in grid frequency and maintain grid frequency stability.

[0028] Thermal power generating units are the main units that transmit electricity to the power grid. Their primary frequency regulation system directly controls the turbine valve through the rapid action of the digital electro-hydraulic control system of the turbine, changes the unit load, and quickly stabilizes 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, resulting in the inability to quickly smooth out the frequency fluctuations of the power grid, making it difficult to meet the needs of modern power grids for efficient frequency regulation. In particular, with the large-scale grid connection of new energy sources and the increase in power load fluctuations, the complexity of power grid frequency fluctuations has significantly increased, which has put forward higher requirements on the response speed, regulation amplitude 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 and excessive main steam pressure. The existing frequency regulation control method lacks accurate modeling and optimization of the system dynamic characteristics, cannot fully utilize the regulation potential of the generator set, and has poor robustness when facing external disturbances and model uncertainties.

[0029] Currently, no effective solution has been proposed for the problem of slow response speed and low accuracy of primary frequency regulation of generator sets in related technologies.

[0030] In view of this, the embodiment of the present application provides a primary frequency regulation method for a generator set, which determines whether the generator set is in a large frequency difference condition by judging whether the frequency deviation of the power grid is within a preset range; when the generator set is in a large frequency difference condition, a 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 bias and condensate water saving amount) at multiple time points; finally, based on the feed water bias and condensate water saving amount at multiple time points, the generator set is subjected to a primary frequency regulation. In this way, the model predictive controller is optimized using a genetic algorithm, so that the optimized model predictive controller can accurately predict the optimal control variables and improve the response speed and accuracy of the primary frequency regulation. Through this application, the problem of slow response speed and low accuracy of the primary frequency regulation of the generator set in the related art is solved, and the technical effect of improving the response speed and accuracy of the primary frequency regulation of the generator set is achieved.

[0031] The following will be combined Figure 1 For an explanation of the primary frequency modulation method of a generator set provided by an embodiment of the present application, see Figure 1 , Figure 1 is a flow chart of a primary frequency regulation method for a generator set according to an embodiment of the present application, such as Figure 1 As shown, the method includes: Step S101, obtaining the frequency deviation of the power grid corresponding to the generator set.

[0032] In this embodiment, the generator set may be an ultra-supercritical thermal power generator set (also called a thermal power generation set), or may be other types of thermal power generation sets.

[0033] In one embodiment, the frequency deviation of the power grid can be obtained by establishing a mathematical model of power grid frequency response. The mathematical model of power grid frequency response can be expressed as: ∆f=(P gen -P load ) / M 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.

[0034] In this embodiment, when ∆f appears and ∆f is greater than a certain level (that is, the generator set faces a large frequency difference condition), the boiler feed water flow and condensate flow rate can be adjusted to balance P gen With P load , thereby stabilizing the grid frequency.

[0035] Step S102, when the frequency deviation is within a preset range, a genetic algorithm is used to optimize control parameters in a pre-built model predictive controller to obtain an optimized model predictive controller.

[0036] In this embodiment, the preset range can be set according to the actual operating condition of the generator set, and the preset range can be set according to the historical operating 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 operating 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 operating condition of the generator set is not a large frequency difference condition.

[0037] 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, the current operating condition is determined to be a large frequency difference condition. It should be noted that the preset range can be set according to the type of generator set, historical operating data, and actual operating conditions, and this application does not limit this.

[0038] 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 the problems of slow response speed and insufficient regulation amplitude. The primary frequency regulation method of the generator set provided in the embodiment of the present application combines the model predictive control (MPC) with the genetic algorithm (GA) for the purpose of improving the timeliness and accuracy of the frequency regulation response, and 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 time points in the future, as well as the corresponding control variables (including feed water bias and condensate saving amount).

[0039] In one embodiment, a genetic algorithm is used to optimize control parameters in a pre-built model predictive controller to obtain an optimized model predictive controller, which includes: obtaining a target optimization function of the model predictive controller; using a genetic algorithm to optimize the control parameters in the target optimization function to obtain target control parameters; bringing the target control parameters into the target optimization function to obtain the optimized model predictive controller.

[0040] In this embodiment, the objective optimization function of the model predictive controller can be expressed as:

[0041] Among them, J is the target optimization function, N is the prediction time domain, λ is the weight parameter, u(i) is the control variable at the i-th moment, including the water supply bias ∆Q w and condensate saving flow ∆Q c By solving the objective optimization function, the optimal control sequence u(k) can be obtained for real-time adjustment of the unit operating parameters, wherein the control sequence u(k) includes control variables at multiple time points.

[0042] In this embodiment, a genetic algorithm may 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.

[0043] Specifically, the steps of optimizing the control parameters in the target optimization function using the genetic algorithm may include: Step 1: Generate an initial population, wherein the initial population includes multiple groups of initial control parameters.

[0044] In this embodiment, the control parameters N and λ may be used as genes of individuals and encoded with real numbers, and a set of initial control parameter N and λ combinations may be randomly generated and used as the initial population.

[0045] Step 2: Using a genetic algorithm, calculate the current fitness function of each set of initial control parameters in the initial population.

[0046] In this embodiment, the fitness function can be expressed as:

[0047] Among them, 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 energy consumption and frequency deviation.

[0048] Each group of initial control parameters in the initial population can be brought into the fitness function respectively, the fitness value corresponding to each group of initial control parameters can be calculated, and the groups of initial control parameters can be sorted according to the size of the fitness value.

[0049] It should be noted that, although the fitness function shown above does not directly involve the control parameter λ, 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 the control parameter N, λ or other control parameters to describe each individual.

[0050] During the iteration of the genetic algorithm, these individuals will be evaluated and selected through the fitness function, so the change of the control parameter λ will also be reflected in the change of 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 energy consumption, but since the genetic 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 fitness.

[0051] Step 3: According to the current fitness function of each group of initial control parameters, selection, crossover and mutation operations are performed on each group of initial control parameters to obtain the next population.

[0052] In this embodiment, after sorting each group of initial control parameters according to the size of the fitness value, a proportional selection operator or a bidding selection operator can be used to select initial control parameters with high fitness from each group of initial control parameters to form an intermediate population; then, a pair of initial control parameters can be sequentially extracted from the intermediate population for crossover operation to generate a new pair of initial control parameters; and, each gene position of the pair of new initial control parameters can be mutated with a preset mutation probability, and the mutated control parameters can be placed in the next population to increase the diversity of individuals; the above operation is repeated until the number of individuals in the next population is the same as that in the initial population.

[0053] Step 4: Calculate the next fitness function based on the next population and perform iterative evolution until the preset termination condition is reached, the iteration stops, and the target control parameters are output.

[0054] In this embodiment, after obtaining the next population, iterative evolution can be performed according to the above steps 2 and 3, and the above steps are repeated until the fitness function converges or the maximum number of iterations is reached. The control parameters in the model predictive controller are globally searched through the genetic algorithm to find the control parameter combination that can minimize the fitness function and use it as the target control parameter. Placing the target control parameter 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 modulation of the generator set.

[0055] Step S103, obtaining the water supply bias and condensate saving amount at multiple time points output by the optimized model predictive controller.

[0056] Step S104: frequency-modulate the generator set based on the feed water bias and condensate saving amount at multiple time points.

[0057] In this embodiment, after obtaining the feedwater bias and condensate throttling at multiple time points, when the current time reaches a time point, the boiler feedwater in the generator set can be controlled to increase or decrease based on the feedwater bias corresponding to the time point, and the deaerator water valve opening in the generator set can be controlled to decrease or increase based on the condensate throttling corresponding to the time point, and the traditional primary frequency modulation mode is multi-optimized by adjusting the boiler feedwater and the deaerator water valve opening to make it suitable for large frequency difference conditions. The deaerator includes a water valve and a water outlet valve corresponding to the water valve.

[0058] For example, when the current time point reaches time point t1, the water supply bias ∆Q corresponding to time point t1 can be w and condensate saving flow ∆Q c The primary frequency modulation control signal is sent to the control system of the generator set, which can be based on the feedwater bias ∆Q w Increase boiler feed water, increase main steam pressure, increase steam work capacity, and improve primary frequency regulation response capability of generator sets; the control system can be based on condensate saving flow ∆Q c To reduce the opening of the deaerator upper water valve to throttle the condensate, thereby reducing the condensate flow, reducing the low-pressure steam extraction of the turbine, increasing the amount of steam doing work in the low-pressure section of the turbine, and utilizing the energy storage of the deaerator to further improve the primary frequency regulation response capability of the generator set.

[0059] 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 in the embodiment of the present application can optimize the frequency modulation strategy in real time based on the model prediction controller optimized by the genetic algorithm, provide a more accurate frequency response, and in the case of large frequency fluctuations, the primary frequency modulation has a faster response speed and a larger adjustment range, which can effectively meet the frequency modulation needs of the grid.

[0060] In addition, in the power grid, especially under complex working conditions such as large unit tripping and DC system failure, the power grid frequency will fluctuate greatly. The primary frequency regulation method of the generator set provided in the embodiment 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 provide an efficient frequency regulation response strategy, which is conducive to the recovery and stabilization of the power grid frequency, reduces the impact of frequency fluctuations on the safe operation of the power system, enhances the power grid's ability to respond to emergencies, and improves the safety and stability of the entire power system.

[0061] In addition, with the large-scale grid connection of new energy (such as wind energy, photovoltaics, etc.), the complexity of grid frequency fluctuations increases. The primary frequency regulation method of the generator set provided in the embodiment of the present application optimizes the model predictive controller through a genetic algorithm, so that the optimized model predictive controller has adaptive frequency regulation capability, which can help the generator set to better participate in the grid frequency regulation when the new energy is connected, and provide stable frequency regulation support. At the same time, the frequency regulation coordination between the generator set and the new energy can also be optimized to improve the overall frequency regulation efficiency of the power grid.

[0062] In this embodiment, if the generator set is an ultra-supercritical thermal power unit, due to the limited heat storage capacity of the boiler of the ultra-supercritical thermal power unit and insufficient adjustable margin, the traditional primary frequency regulation method is difficult to meet the frequency regulation requirements under large frequency difference conditions. The primary frequency regulation method of the generator set provided in the embodiment 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, and performs a primary frequency regulation on the generator set based on the control variables at the multiple time points, which can effectively solve the problem of slow response speed and insufficient adjustment range of the primary frequency regulation of the ultra-supercritical thermal power unit under large frequency difference conditions, thereby improving the load regulation capability of the generator set and enhancing the frequency regulation response speed of the generator set under complex conditions.

[0063] In one embodiment, based on the water supply bias and condensate throttling amount at multiple time points, frequency modulation of the generator set includes: obtaining a time period in 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-built sliding mode controller to obtain a control rate output by the sliding mode controller; correcting the water supply bias and condensate throttling amount at multiple time points based on the control rate to obtain the corrected water supply bias and condensate throttling amount at multiple time points; and frequency modulation of the generator set based on the corrected water supply bias and condensate throttling amount at multiple time points.

[0064] In this embodiment, based on the corrected feed water bias and condensate throttling amount at multiple time points, the generator set is frequency modulated once, including: at each time point, the boiler feed water amount in the generator set is controlled to increase based on the feed water bias amount corresponding to each time point, and the opening of the deaerator water supply valve in the generator set is controlled to decrease based on the condensate throttling amount corresponding to the time point.

[0065] In this embodiment, the primary frequency regulation can be finely controlled at multiple time scales. The duration of the large frequency difference condition of the generator set can be determined by obtaining the time period in which the frequency deviation is within the preset range. When the duration is greater than the 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) can be used to enhance the robustness of the primary frequency regulation method of the generator set provided in the embodiment of the present application under model uncertainty and external disturbances, thereby ensuring 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.

[0066] Specifically, the sliding surface can be designed as:

[0067] Where s is the sliding surface, c1 and c2 are the sliding surface coefficients, and d∆f / dt is the rate of change of the frequency deviation.

[0068] The control rate of the sliding mode controller output can be expressed as: u(t)=-k*sgn(s) Among them, 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, which can be expressed as:

[0069] The control rate of the sliding mode controller output can be adjusted by adjusting the sliding mode gain k and the sliding mode surface coefficients c1 and c2, thereby optimizing the robustness of the primary frequency regulation method of the generator set.

[0070] In this embodiment, a multi-time scale control mechanism is introduced into the primary frequency modulation method provided in the embodiment of the present application, which can perform frequency modulation responses for short-term and long-term frequency fluctuations respectively.

[0071] Specifically, when the duration of the large frequency difference condition is less than the first preset threshold, the generator set can be frequency-regulated only by the control variables at multiple time points output by the model predictive controller optimized by the genetic algorithm, and the high-gain SMC strategy can be adopted to quickly suppress the frequency deviation; and 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, and the MPC strategy can be adopted for the frequency change with a long duration to smooth the control process and reduce energy consumption, so that the primary frequency regulation capability of the generator set can be optimized, the robustness of the primary frequency regulation method of the generator set under complex working conditions can be improved, and the deterioration of the grid frequency caused by the complexity of the working conditions can be suppressed; and the primary frequency regulation method of the generator set provided in the embodiment of the present application can not only improve the accuracy and speed of the frequency regulation response, but also optimize the frequency regulation strategy of the generator set through a multi-time scale control mechanism, thereby reducing the energy consumption of the generator set during the frequent adjustment process and improving the operating efficiency of the generator set. Under the premise of not affecting the stability of the power grid, the operating load regulation of the generator set is optimized, thereby achieving the dual optimization of the economy and energy efficiency of the generator set.

[0072] The following will be combined Figure 2 Another embodiment of the present invention provides a method for primary frequency modulation of a generator set. Figure 2 , Figure 2 is a flow chart of a primary frequency modulation method for a generator set according to another embodiment of the present application. Figure 2 As shown, the method includes: Step S201, obtaining the frequency deviation of the power grid.

[0073] Step S202, determine whether the frequency deviation is within a preset range. If yes, proceed to step S203; if no, proceed to step S210.

[0074] Step S203: Use a genetic algorithm to optimize the control parameters in the model predictive controller.

[0075] Step S204: Use the optimized model predictive controller to predict control variables at multiple time points.

[0076] Step S205: frequency-modulate the generator set based on the control variables at multiple time points.

[0077] Step S206, during a frequency modulation process of the generator set, monitoring the boiler operating parameters of the generator set.

[0078] Step S207, inputting the boiler operating parameters into the Bayesian network, and obtaining the safety assessment result of the boiler in the generator set output by the Bayesian network.

[0079] Step S208, determining whether the security assessment result is lower than a second preset threshold.

[0080] Step S209: Generate a security control strategy.

[0081] Step S210, performing primary frequency regulation based on boiler energy storage on the generator set. The primary frequency regulation based on boiler energy storage includes: adjusting the opening of the turbine throttle of the boiler in the generator set.

[0082] In this embodiment, a real-time status monitoring system can be integrated in a frequency modulation process, and a Bayesian network can be pre-constructed to perform real-time safety assessment on the boiler operating parameters of the frequency modulation process to ensure that all boiler operating parameters are within a safe range during the frequency modulation process, thereby preventing equipment damage or safety accidents caused by excessive frequency modulation.

[0083] Specifically, the Bayesian network model can be designed as:

[0084] Among them, X i are the boiler operating parameters that need to be monitored, including boiler water level, main steam pressure, etc.; P(Safe) is the prior probability of system safety.

[0085] When P(Safe|X1, X2, ... , Xn) is lower than a second preset threshold (the 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, and when P(Safe|X1, X2, ... , Xn) is lower than 0.85, a safety control strategy can be generated, and the safety control strategy can include measures to ensure equipment safety, such as shutting down the steam turbine and reducing power.

[0086] In addition, an adaptive Kalman filter (AKF) can be used to update various control parameters of the model predictive controller, so that the model predictive controller can be used in different working conditions.

[0087] Specifically, the state estimation equation can be designed as:

[0088] Among them, X k is the state estimate, K k is the adaptively adjusted Kalman gain.

[0089] In this embodiment, K can be adjusted according to the real-time operating data of the generator set during the primary frequency modulation process. k The estimation error is minimized and the prediction accuracy of the model predictive controller is improved. In this way, the accuracy of the prediction of the model predictive controller can be further improved by introducing the AKF, and the adaptive ability of the primary frequency regulation method of the generator set provided in the embodiment of the present application can be enhanced.

[0090] In one embodiment, a control system can be designed to implement the primary frequency modulation method of the generator set provided in the embodiment of the present application. The control system may include a controller, a sensor, and an actuator; wherein the controller may include a high-performance industrial computer for running a control algorithm to execute the primary frequency modulation method of the generator set provided in the embodiment of the present application; the sensor is used to collect parameters such as the frequency deviation of the power grid, the boiler water level, and the main steam pressure; the actuator is used to receive the primary frequency modulation control signal sent by the controller. The actuator can increase the boiler feed water based on the feed water offset ∆Qw in the primary frequency modulation control signal, and reduce the deaerator upper water valve opening based on the condensate throttling ∆Qc in the primary frequency modulation control signal.

[0091] Specifically, the controller may include a control algorithm module, a data processing module, and a human-machine interface. 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; and the human-machine interface is used to provide functions such as parameter setting, status monitoring, and alarm prompts.

[0092] Corresponding to the primary frequency regulation method of the generator set described in the above embodiment, Figure 3 A structural schematic diagram of a primary frequency regulation device of a generator set according to an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0093] See also Figure 3The primary frequency regulation device 3 of the generator set includes: an acquisition module 30, which is used to obtain the frequency deviation of the power grid corresponding to the generator set; an optimization module 31, which is used to optimize the control parameters in the pre-built model prediction controller using a genetic algorithm when the frequency deviation is within a preset range, so as to obtain an optimized model prediction controller; an output module 32, which is used to obtain the water supply bias and condensate water saving amount at multiple time points output by the optimized model prediction controller; a frequency regulation module 33, which is used to perform primary frequency regulation on the generator set based on the water supply bias and condensate water saving amount at multiple time points.

[0094] In one embodiment, the frequency modulation module 33 is also used to obtain a time period when the frequency deviation is within a preset range; when the time period is greater than a first preset threshold, the rate of change of the frequency deviation is obtained based on the frequency deviation and the time period; the frequency deviation and the rate of change of the frequency deviation are input into a pre-built sliding mode controller to obtain a control rate output by the sliding mode controller; based on the control rate, the water supply bias and the condensate water saving amount at multiple time points are corrected to obtain the corrected water supply bias and condensate water saving amount at multiple time points; based on the corrected water supply bias and condensate water saving amount at multiple time points, the generator set is frequency modulated once.

[0095] In one embodiment, the frequency regulation module 33 is also used to control the increase of boiler feed water in the generator set based on the feed water bias corresponding to each time point at each time point, and to control the decrease of the opening of the deaerator water supply valve in the generator set based on the condensate throttling corresponding to the time point.

[0096] In one embodiment, the optimization module 31 is also used to obtain 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; bring the target control parameters into the target optimization function to obtain an optimized model predictive controller.

[0097] In one embodiment, the optimization module 31 is also used to generate an initial population, wherein the initial population includes multiple groups of initial control parameters; a genetic algorithm is used to calculate the current fitness function of each group of initial control parameters in the initial population; based on the current fitness function of each group of initial control parameters, each group of initial control parameters is selected, crossed and mutated to obtain the next population; based on the next population, the next fitness function is calculated, and iterative evolution is performed until a preset termination condition is reached, the iteration stops, and the target control parameters are output.

[0098] In one embodiment, the primary frequency regulation device 3 of the generator set also includes a safety assessment module, which is used to monitor the boiler operating parameters of the generator set during the primary frequency regulation process of the generator set; input the boiler operating 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; when the safety assessment result is lower than a second preset threshold, generate a safety control strategy.

[0099] In one embodiment, the frequency regulation module 33 is also used 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 of the turbine throttle of the boiler in the generator set.

[0100] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0101] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0102] Figure 4 is a schematic diagram of the structure of a terminal device according to an embodiment of the present application. Figure 4 As shown, the terminal device 4 includes: at least one processor 40 ( Figure 4 (only one is shown) 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 the primary frequency regulation method embodiment of any of the above-mentioned generator sets are implemented.

[0103] The terminal device 4 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal device 4 may include but is not limited to a processor 40 and a memory 41. It is understood by those skilled in the art that Figure 4 It is only an example of the terminal device 4 and does not constitute a limitation on the terminal device 4. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, etc.

[0104] The processor 40 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0105] In some embodiments, the memory 41 may be an internal storage unit of the terminal device 4, such as a 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 an internal storage unit and an external storage device of the terminal device 4. The memory 41 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as program codes of a computer program 42. The memory 41 may also be used to temporarily store data that has been output or is to be output.

[0106] The embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps in the embodiment of the primary frequency regulation method for each of the above-mentioned generator sets can be implemented.

[0107] An embodiment of the present application provides a computer program product. When the computer program product is run on a mobile terminal, the mobile terminal implements the steps in the embodiment of the primary frequency regulation method for each generator set described above.

[0108] The present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the terminal device, a recording medium, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), an electric carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0109] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0110] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0111] In the embodiments provided in the present application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are only schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0112] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0113] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A primary frequency modulation method for a generator set, characterized in that: include: Obtain the frequency deviation of the power grid corresponding to the generator set; When the frequency deviation is within a preset range, a genetic algorithm is used to optimize control parameters in a pre-built model predictive controller to obtain an optimized model predictive controller; Obtaining the water supply bias and condensate saving amount at multiple time points output by the optimized model predictive controller; Based on the water supply bias and condensate throttling at the multiple time points, the generator set is frequency-regulated once.

2. The method according to claim 1, characterized in that Based on the water supply bias and condensate throttling amount at the multiple time points, frequency regulation of the generator set includes: Obtaining a time period in which the frequency deviation is within a preset range; When the time period is greater than a first preset threshold, acquiring a change rate 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-built sliding mode controller to obtain a control rate output by the sliding mode controller; Correcting the water supply bias and condensate saving amount at the multiple time points based on the control rate to obtain the corrected water supply bias and condensate saving amount at the multiple time points; Based on the corrected water supply bias and condensate throttling at multiple time points, the generator set is frequency-regulated once.

3. The method according to claim 2, characterized in that Based on the corrected feed water bias and condensate throttling at multiple time points, frequency regulation of the generator set includes: At each of the time points, the boiler feed water amount in the generator set is controlled to increase based on the feed water bias amount corresponding to each of the time points, and the opening of the deaerator water supply valve in the generator set is controlled to decrease based on the condensate throttling amount corresponding to the time point.

4. The method according to any one of claims 1 to 3, characterized in that The control parameters in the pre-built model predictive controller are optimized using a genetic algorithm. The optimized model predictive controller includes: Obtaining a 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; The target control parameters are brought into the target optimization function to obtain an optimized model predictive controller.

5. The method according to claim 4, characterized in that The control parameters in the target optimization function are optimized using the genetic algorithm, and the target control parameters obtained include: generating an initial population, wherein the initial population includes a plurality of groups of initial control parameters; Using the genetic algorithm, calculate the current fitness function of each group of the initial control parameters in the initial population; According to the current fitness function of each group of the initial control parameters, selection, crossover and mutation operations are performed on each group of the initial control parameters to obtain the next population; According to the next population, the next fitness function is calculated and iterative evolution is performed until a preset termination condition is reached, the iteration stops, and the target control parameter is output.

6. The method according to any one of claims 1 to 3, characterized in that After the generator set is frequency-regulated once based on the feed water bias and condensate throttling at the multiple time points, the method further includes: During a frequency modulation process of the generator set, monitoring the boiler operating parameters of the generator set; Inputting the boiler operating 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; When the security assessment result is lower than a second preset threshold, a security control strategy is generated.

7. 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, the generator set is subjected to primary frequency regulation based on boiler energy storage; wherein the primary frequency regulation based on boiler energy storage includes: adjusting the opening of the turbine damper of the boiler in the generator set.

8. A primary frequency modulation device for a generator set, characterized in that: include: An acquisition module, used to acquire the frequency deviation of the power grid corresponding to the generator set; An optimization module, used for optimizing the control parameters in the pre-built model predictive controller using a genetic algorithm when the frequency deviation is within a preset range, to obtain an optimized model predictive controller; An output module, used to obtain the feed water bias and condensate saving amount at multiple time points output by the optimized model predictive controller; The frequency modulation module is used to perform a frequency modulation on the generator set based on the water supply bias and condensate water saving amount at the multiple time points.

9. 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, the primary frequency regulation method of the generator set as described in any one of claims 1 to 7 is implemented.

10. A computer program product, characterized in that It comprises a computer program, and when the computer program is executed, the primary frequency regulation method of the generator set as claimed in any one of claims 1 to 7 is executed.

Images