Water turbine generator set operation optimization method based on digital twinning

Abstract

The water turbine generator set operation optimization method based on digital twinning comprises the steps that basic data of a water turbine generator set are collected, and a corresponding water turbine generator set digital twinning model is constructed according to the basic data; based on the digital twin model of the water turbine generator set, generating a corresponding operation scheme of the water turbine generator set by using a swarm intelligence optimization algorithm; the operation scheme of the water turbine generator set is applied to the water turbine generator set, real-time operation data and real-time demand information are collected, and the digital twin model of the water turbine generator set is updated according to the real-time operation data and the real-time demand information; the hydraulic turbine generator can be operated according to user requirements through the digital twin model of the hydraulic turbine generator set, and intelligent control over the hydraulic turbine generator set is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of water turbine generator sets, and in particular to a water turbine generator set operation optimization method based on digital twins. Background Art

[0002] With the vigorous development of hydropower, large and medium-sized hydropower stations have been built and put into operation one after another. The proportion of hydropower in electricity supply is increasing, and hydropower is an important part of the global energy strategy. The turbine generator set is the core equipment of the hydropower station. In the existing technology, the way of starting and stopping the turbine generator set selected by relevant personnel based on their own experience does not achieve the optimal operation of the turbine generator set. Summary of the invention

[0003] In response to the above-mentioned problems, the present application provides a method for optimizing the operation of a hydro-turbine generator set based on digital twins, which solves the technical problem in the prior art that the method of starting and stopping the hydro-turbine generator set selected by relevant personnel relying on their own experience does not achieve the optimal operation of the hydro-turbine generator set. The optimal operation of the hydro-turbine generator set can be achieved through a digital twin model of the hydro-turbine generator set.

[0004] The embodiment of the present application provides a method for optimizing the operation of a hydraulic turbine generator set based on digital twins, including:

[0005] Collect the basic data of the turbine generator set and build the corresponding digital twin model of the turbine generator set based on the basic data;

[0006] Based on the digital twin model of the hydro-turbine generator set, a swarm intelligence optimization algorithm is used to generate the corresponding hydro-turbine generator set operation plan;

[0007] Apply the turbine generator set operation plan to the turbine generator set, collect real-time operation data and real-time demand information, and update the digital twin model of the turbine generator set based on the real-time operation data and real-time demand information.

[0008] In some embodiments, the basic data includes facility parameters of the hydro-turbine generator set, historical user demand information, and historical operation data.

[0009] In some embodiments, the collecting of basic data of the water turbine generator set and building a corresponding digital twin model of the water turbine generator set according to the basic data include:

[0010] According to the facility parameters of the turbine generator set, a corresponding physical model is constructed;

[0011] Based on the physical model of the turbine generator set and the historical operation data, the corresponding behavior model is constructed;

[0012] Based on the physical model and behavioral model, use simulation tools to simulate the model and build the corresponding simulation model;

[0013] The user demand information and operation data of the turbine generator set are taken as monitoring objects, and the historical user demand information and historical operation data are input into the simulation model to build the corresponding digital twin model of the turbine generator set.

[0014] In some embodiments, the facility parameters include the size, shape, material properties, and mechanical structure of the turbine generator set;

[0015] The behavior model includes the operation control strategy of the turbine generator set and the turbine operation mode, as well as the user power consumption mode under various demand conditions;

[0016] The historical operation data includes the historical power output, historical speed, historical torque, and historical efficiency of the turbine generator set under different operating conditions, and the historical user demand information includes the historical electricity demand and historical electricity demand time of the turbine generator set under different operating conditions.

[0017] In some embodiments, the digital twin model of the hydro-turbine generator set is used to generate a corresponding hydro-turbine generator set operation plan using a swarm intelligence optimization algorithm, including:

[0018] Based on the digital twin model of the hydro-turbine generator set, define the hydro-turbine generator set operation sub-model;

[0019] According to the characteristics of the turbine generator unit operation sub-model, the specific optimization parameters of the swarm intelligence optimization algorithm are set;

[0020] According to the specific optimization parameters, the swarm intelligent optimization algorithm is used to generate the initial operation plan of the turbine generator set;

[0021] According to the characteristics of the hydro-turbine generator set operation sub-model, the feasibility of the initial hydro-turbine generator set operation plan is checked to obtain the final hydro-turbine generator set operation plan.

[0022] In some embodiments, the turbine generator set operation sub-model includes turbine basic equations, generator basic equations, governor control equations, turbine and generator coupling equations, fluid dynamics equations, mechanical dynamics equations, circuit equations, excitation system equations, cooling system equations and state equations.

[0023] In some embodiments, the specific optimization parameters of the swarm intelligence optimization algorithm are set according to the characteristics of the hydro-turbine generator set operation sub-model, including:

[0024] According to the characteristics of the generator set operation sub-model, the I FWA optimization algorithm is selected as the swarm intelligent optimization algorithm for the operation of the turbine generator set;

[0025] Define the objective function of the I FWA optimization algorithm, and set the fitness function of the I FWA optimization algorithm according to the objective function; set specific optimization parameters of the I FWA optimization algorithm.

[0026] In some embodiments, the generating of an initial operation plan of the hydro-turbine generator set using a swarm intelligence optimization algorithm according to specific optimization parameters includes:

[0027] The operation scheme of the turbine generator set is used as the position of the I FWA individual, and the I FWA population is initialized according to the specific optimization parameters of the I FWA optimization algorithm to obtain the initialized I FWA population;

[0028] According to the specific optimization parameters of the I FWA optimization algorithm, the initialized I FWA population is exploded to obtain an updated I FWA population;

[0029] Using the Gaussian mutation algorithm, the initialized I FWA population is subjected to Gaussian mutation to generate a Gaussian mutated I FWA population;

[0030] Using a dynamic reverse learning algorithm, dynamically reverse learning is performed on the initialized I FWA population to generate a dynamically reversed I FWA population;

[0031] Use the fitness function of the I FWA optimization algorithm to calculate the fitness values ​​of all I FWA individuals in the updated I FWA population, the Gaussian mutation I FWA population, and the dynamic reverse I FWA population, and take the I FWA individual with the minimum fitness value as the optimal individual;

[0032] If the number of iterations reaches the threshold or the fitness value of the optimal individual meets the requirements, the optimal solution corresponding to the current optimal individual is output to obtain the initial operation plan of the turbine generator set.

[0033] In some embodiments, the feasibility check of the initial operation plan of the water turbine generator set is performed according to the characteristics of the water turbine generator set operation sub-model to obtain the final operation plan of the water turbine generator set, including:

[0034] According to the characteristics of the hydro-turbine generator set operation sub-model, the constraints of the hydro-turbine generator set operation are determined;

[0035] According to the constraints of the operation of the turbine generator set, the feasibility check is performed on the initial operation plan of the turbine generator set to obtain the corresponding feasibility check result;

[0036] If the initial operation plan of the turbine generator set meets the constraint conditions, the feasibility check result is passed, and the final operation plan of the turbine generator set is obtained. Otherwise, the feasibility check result is failed, the initial operation plan of the turbine generator set is adjusted, and the previous step is returned.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] By collecting the basic data of the hydro-turbine generator set and building the corresponding digital twin model of the hydro-turbine generator set based on the basic data; based on the digital twin model of the hydro-turbine generator set, using the swarm intelligence optimization algorithm, generate the corresponding operation plan of the hydro-turbine generator set; applying the operation plan of the hydro-turbine generator set to the hydro-turbine generator set, collecting real-time operation data and real-time demand information, and updating the digital twin model of the hydro-turbine generator set based on the real-time operation data and real-time demand information; the digital twin model of the hydro-turbine generator set can achieve the optimal operation of the hydro-turbine generator set. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The embodiments of the present invention are further described below with reference to the accompanying drawings:

[0040] Figure 1 A schematic diagram of the implementation flow of the method for optimizing the operation of a hydraulic turbine generator set based on digital twins provided in an embodiment of the present application;

[0041] Figure 2 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0043] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0044] If similar descriptions of "first\second\third" appear in the application documents, the following instructions will be added. In the following description, the terms "first\second\third" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0046] Based on the problems existing in the related art, the embodiment of the present application provides a method for optimizing the operation of a hydraulic turbine generator set based on digital twins, and the execution subject of the optimization method can be an electronic device. The electronic device can be various types of terminals such as a laptop, a tablet computer, a desktop computer, a set-top box, a mobile device (for example, a mobile phone, a portable music player, a personal digital assistant, a dedicated messaging device, a portable gaming device), and can also be implemented as a server. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms.

[0047] In some embodiments, the functions implemented by the optimization method provided in the embodiments of the present application can be implemented by calling program codes by a processor of an electronic device, wherein the program codes can be stored in a computer storage medium.

[0048] The embodiment of the present application provides a method for optimizing the operation of a hydraulic turbine generator set based on digital twins. Figure 1 A schematic diagram of the implementation flow of the method for optimizing the operation of a hydraulic turbine generator set based on digital twins provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, including:

[0049] Step S1: Collect basic data of the hydro-turbine generator set, and build a corresponding digital twin model of the hydro-turbine generator set based on the basic data;

[0050] In some embodiments, the basic data includes facility parameters of the hydro-turbine generator set, historical user demand information, and historical operation data.

[0051] In some embodiments, the step S1 includes:

[0052] Step S11: constructing a corresponding physical model according to the facility parameters of the hydro-turbine generator set; the facility parameters include the size, shape, material properties and mechanical structure of the hydro-turbine generator set;

[0053] Step S12: Based on the physical model of the hydro-turbine generator set and according to the historical operation data, a corresponding behavior model is constructed; the behavior model includes the operation control strategy and the operation mode of the hydro-turbine generator set, and the user power consumption mode under various demand conditions; the historical operation data includes the historical power output, historical speed, historical torque, and historical efficiency of the hydro-turbine generator set under different working conditions, and the historical user demand information includes the historical power demand and historical power demand time of the hydro-turbine generator set under different working conditions;

[0054] Step S13: Based on the physical model and the behavior model, use a simulation tool to perform model simulation and build a corresponding simulation model;

[0055] Step S14: user demand information and operation data of the hydro-turbine generator set are taken as monitoring objects, historical user demand information and historical operation data are input into the simulation model, and a corresponding digital twin model of the hydro-turbine generator set is constructed.

[0056] In an embodiment of the present application, the digital twin model of the hydro-turbine generator set is an advanced simulation model, which represents a virtual mapping of the actual hydro-turbine generator set. Through the digital twin model of the hydro-turbine generator set, the real-time status of the hydro-turbine generator set can be simulated, the future status can be predicted, and the impact of different hydro-turbine generator set operation schemes on the performance of the hydro-turbine generator set can be evaluated.

[0057] Step S2: Based on the digital twin model of the hydro-turbine generator set, a swarm intelligence optimization algorithm is used to generate a corresponding hydro-turbine generator set operation plan;

[0058] In some embodiments, step S2 includes

[0059] Step S21: Based on the digital twin model of the hydraulic turbine generator set, define a hydraulic turbine generator set operation sub-model;

[0060] In some embodiments, the turbine generator set operation sub-model includes turbine basic equations, generator basic equations, governor control equations, turbine and generator coupling equations, fluid dynamics equations, mechanical dynamics equations, circuit equations, excitation system equations, cooling system equations and state equations.

[0061] The basic equations of the turbine include the water flow dynamic equation and the turbine characteristic equation:

[0062] The formula of the water flow dynamic equation is: Q = A·v; where Q is the flow rate, A is the flow area, and v is the flow velocity;

[0063] The formula of the turbine characteristic equation is: P m =ρ·g·Q·H·η m ; Among them, P m is the mechanical power output of the turbine, ρ is the density of water, g is the acceleration of gravity, H is the water head, η m is the turbine efficiency;

[0064] The basic equations of the generator include the law of electromagnetic induction and the electromagnetic torque equation:

[0065] The formula for the law of electromagnetic induction is: Among them, E is the induced electromotive force, K is the motor constant, is the magnetic flux, n is the rotation speed;

[0066] The formula of the electromagnetic torque equation is: T e is the electromagnetic torque, P e is the electric power, ω is the angular velocity;

[0067] The governor control equation includes the guide vane opening adjustment equation:

[0068] The guide vane opening adjustment equation is: δ=∫(P ref , P m ), where δ is the guide vane opening, P ref Reference power, P m Actual mechanical power;

[0069] The turbine and generator coupling equations include the coupling torque equation:

[0070] The formula of the coupling torque equation is: T m =T e ; Among them, T m The mechanical torque provided to the turbine, R e is the electromagnetic torque of the generator;

[0071] The equations of fluid dynamics include the energy loss equation:

[0072] The formula of energy loss equation is: H loss =∫(Q,v,D,η f );H loss is the energy loss, D is the turbine diameter, η f Fluid efficiency;

[0073] The mechanical dynamics equations include the shaft dynamics equations:

[0074] The formula of the rotating shaft dynamic equation is: Where J is the moment of inertia, is the angular velocity;

[0075] The circuit equations include the generator circuit equation:

[0076] The formula for the generator circuit equation is: Where V is the generator output voltage, R a is the armature resistance, I a Armature current, L a Armature inductance;

[0077] The excitation system equation includes the excitation regulation equation: E f =K f ·(V ref -V); where E f is the excitation voltage, K f is the excitation regulator gain, V ref is the reference voltage, V is the actual output voltage;

[0078] The cooling system equations include the thermodynamic equilibrium equations:

[0079] The formula for the thermodynamic equilibrium equation is: Q c =m·c p ·(T out -T in ), where Q c is the cooling heat, m is the coolant mass flow rate, c p is the specific heat capacity of the coolant, T out is the outlet temperature, T in is the inlet temperature;

[0080] The state equation includes the operating state equation:

[0081] The formula of the operating state equation is: S = ∫ (P m , P e ,ω,T m , T e ), where S is the operating status of the turbine generator set.

[0082] Step S22: setting specific optimization parameters of the swarm intelligence optimization algorithm according to the characteristics of the hydro-turbine generator set operation sub-model;

[0083] In some embodiments, the step S22 includes:

[0084] Step S221: According to the characteristics of the generator set operation sub-model, the I FWA optimization algorithm is selected as the swarm intelligent optimization algorithm for the operation of the turbine generator set;

[0085] Step S222: define the objective function of the I FWA optimization algorithm, and set the fitness function of the I FWA optimization algorithm according to the objective function; set specific optimization parameters of the I FWA optimization algorithm.

[0086] Step S23: Generate an initial operation plan of the hydro-turbine generator set using a swarm intelligence optimization algorithm according to specific optimization parameters;

[0087] In the embodiment of the present application, the I FWA optimization algorithm is inspired by the explosion and diffusion process of fireworks. The algorithm has the characteristics of simple parameter setting, wide search range and fast convergence speed, and has been successfully applied to various optimization problems. In the generation of operation plans for hydro-turbine generator sets, the I FWA optimization algorithm can effectively find the optimal solution, improve the efficiency and safety of operation, help optimize the operation plans of hydro-turbine generator sets, and achieve the optimal operation of hydro-turbine generator sets.

[0088] In some embodiments, the step S23 includes:

[0089] Step S231: taking the operation plan of the turbine generator set as the position of the I FWA individual, and initializing the I FWA population according to the specific optimization parameters of the I FWA optimization algorithm to obtain the initialized I FWA population;

[0090] Step S232: according to the specific optimization parameters of the I FWA optimization algorithm, the initialized I FWA population is subjected to fireworks explosion to obtain an updated I FWA population;

[0091] Step S233: using a Gaussian mutation algorithm to perform Gaussian mutation on the initialized I FWA population to generate a Gaussian mutated I FWA population;

[0092] Step S234: using a dynamic reverse learning algorithm, dynamically reverse learning is performed on the initialized I FWA population to generate a dynamically reversed I FWA population;

[0093] Step S235: using the fitness function of the I FWA optimization algorithm, calculate the fitness values ​​of all I FWA individuals in the updated I FWA population, the Gaussian mutated I FWA population, and the dynamically reversed I FWA population, and take the I FWA individual with the minimum fitness value as the optimal individual;

[0094] Step S236: If the number of iterations reaches the threshold or the fitness value of the optimal individual meets the requirement, the optimal solution corresponding to the current optimal individual is output to obtain an initial operation plan of the hydro-turbine generator set.

[0095] In the embodiment of the present application, the I FWA optimization algorithm is a meta-heuristic algorithm that can directly search for the optimal solution in the solution space of the problem without explicitly constructing a turbine generator set operation plan generation model. However, in order for the I FWA optimization algorithm to effectively search the solution space, specific information of the turbine generator set operation problem needs to be input into the algorithm, and this information usually comes from the digital twin model; although the I FWA optimization algorithm itself does not require an explicit turbine generator set operation plan generation model, it requires information provided by the digital twin model to define the search space and evaluate the quality of potential solutions; the digital twin model provides an accurate and reliable simulation environment for the swarm intelligence algorithm, so that the algorithm can perform effective optimization search in this environment, and finally obtain the optimal turbine generator set operation plan; the intelligent operation of the turbine generator set can be realized.

[0096] Step S24: According to the characteristics of the hydro-turbine generator set operation sub-model, a feasibility check is performed on the initial hydro-turbine generator set operation plan to obtain a final hydro-turbine generator set operation plan.

[0097] In some embodiments, the step S24 includes:

[0098] Step S241: determining the constraints of the operation of the hydro-turbine generator set according to the characteristics of the hydro-turbine generator set operation sub-model;

[0099] Step S242: performing a feasibility check on the initial operation plan of the water turbine generator set according to the constraint conditions of the operation of the water turbine generator set, and obtaining a corresponding feasibility check result;

[0100] Step S243: If the initial operation plan of the hydro-turbine generator set meets the constraint conditions, the feasibility check result is passed, and the final operation plan of the hydro-turbine generator set is obtained; otherwise, the feasibility check result is failed, the initial operation plan of the hydro-turbine generator set is adjusted, and the process returns to the previous step.

[0101] Step S3: Apply the operation plan of the hydro-turbine generator set to the hydro-turbine generator set, collect real-time operation data and real-time demand information, and update the digital twin model of the hydro-turbine generator set according to the real-time operation data and real-time demand information.

[0102] In an embodiment of the present application, a digital twin model of a hydro-turbine generator set is applied to the hydro-turbine generator set, and the digital twin model of the hydro-turbine generator set is regularly maintained and updated to adapt to changes in the hydro-turbine generator set and new operating requirements.

[0103] In summary, by collecting the basic data of the hydro-turbine generator set, and based on the basic data, building the corresponding digital twin model of the hydro-turbine generator set; based on the digital twin model of the hydro-turbine generator set, using the swarm intelligence optimization algorithm, generating the corresponding operation plan of the hydro-turbine generator set; applying the operation plan of the hydro-turbine generator set to the hydro-turbine generator set, collecting real-time operation data and real-time demand information, and updating the digital twin model of the hydro-turbine generator set according to the real-time operation data and real-time demand information; the digital twin model of the hydro-turbine generator set can achieve the optimal operation of the hydro-turbine generator set.

[0104] It should be noted that in the embodiment of the present application, if the above-mentioned optimization method is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM, ReadOnlyMemory), a disk or an optical disk. In this way, the embodiment of the present application is not limited to any specific combination of hardware and software.

[0105] Accordingly, an embodiment of the present application provides a storage medium on which a computer program is stored, characterized in that when the computer program is executed by a processor, the steps in the optimization method provided in the above embodiment are implemented.

[0106] An embodiment of the present application provides an electronic device; Figure 2 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the electronic device 400 includes: a processor 401, at least one communication bus 402, a user interface 403, at least one external communication interface 404, and a memory 405. The communication bus 402 is configured to realize the connection and communication between these components. The user interface 403 may include a display screen, and the external communication interface 404 may include a standard wired interface and a wireless interface. The processor 401 is configured to execute the program of the optimization method stored in the memory to implement the steps in the optimization method provided in the above embodiment.

[0107] It should be noted here that the description of the above storage medium and electronic device embodiments is similar to the description of the above method embodiments, and has similar beneficial effects as the method embodiments. For technical details not disclosed in the storage medium and device embodiments of this application, please refer to the description of the method embodiments of this application for understanding.

[0108] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present application, the size of the sequence number of the above-mentioned processes does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The above-mentioned sequence numbers of the embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments.

[0109] It should be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, object or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, object or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, object or device including the element.

[0110] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the 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 can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0111] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; 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 present embodiment.

[0112] In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be a separate unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0113] A person skilled in the art can understand that all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read On ly Memory), disks or optical disks, etc. Various media that can store program codes.

[0114] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application can essentially or in other words, the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a controller to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0115] The above is only an implementation method of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for optimizing the operation of a hydraulic turbine generator set based on digital twins, characterized in that: include: Collect the basic data of the turbine generator set and build the corresponding digital twin model of the turbine generator set based on the basic data; Based on the digital twin model of the hydro-turbine generator set, a swarm intelligence optimization algorithm is used to generate the corresponding hydro-turbine generator set operation plan; Apply the turbine generator set operation plan to the turbine generator set, collect real-time operation data and real-time demand information, and update the digital twin model of the turbine generator set based on the real-time operation data and real-time demand information.

2. The method according to claim 1, characterized in that The basic data include facility parameters of the turbine generator set, historical user demand information and historical operation data.

3. The method according to claim 2, characterized in that The basic data of the water turbine generator set is collected, and a corresponding digital twin model of the water turbine generator set is constructed according to the basic data, including: According to the facility parameters of the turbine generator set, a corresponding physical model is constructed; Based on the physical model of the turbine generator set and the historical operation data, the corresponding behavior model is constructed; Based on the physical model and behavioral model, use simulation tools to simulate the model and build the corresponding simulation model; The user demand information and operation data of the turbine generator set are taken as monitoring objects, and the historical user demand information and historical operation data are input into the simulation model to build the corresponding digital twin model of the turbine generator set.

4. The method according to claim 3, characterized in that The facility parameters include the size, shape, material properties and mechanical structure of the turbine generator set; The behavior model includes the operation control strategy of the turbine generator set and the turbine operation mode, as well as the user power consumption mode under various demand conditions; The historical operation data includes the historical power output, historical speed, historical torque, and historical efficiency of the turbine generator set under different operating conditions, and the historical user demand information includes the historical electricity demand and historical electricity demand time of the turbine generator set under different operating conditions.

5. The method according to claim 4, characterized in that Based on the digital twin model of the hydro-turbine generator set, a swarm intelligence optimization algorithm is used to generate a corresponding hydro-turbine generator set operation plan, including Based on the digital twin model of the hydro-turbine generator set, define the hydro-turbine generator set operation sub-model; According to the characteristics of the turbine generator unit operation sub-model, the specific optimization parameters of the swarm intelligence optimization algorithm are set; According to the specific optimization parameters, the swarm intelligent optimization algorithm is used to generate the initial operation plan of the turbine generator set; According to the characteristics of the hydro-turbine generator set operation sub-model, the feasibility of the initial hydro-turbine generator set operation plan is checked to obtain the final hydro-turbine generator set operation plan.

6. The method according to claim 5, characterized in that The turbine generator set operation submodel includes turbine basic equations, generator basic equations, governor control equations, turbine and generator coupling equations, fluid dynamics equations, mechanical dynamics equations, circuit equations, excitation system equations, cooling system equations and state equations.

7. The method according to claim 5, characterized in that According to the characteristics of the turbine generator set operation sub-model, the specific optimization parameters of the swarm intelligence optimization algorithm are set, including: According to the characteristics of the generator set operation sub-model, the I FWA optimization algorithm is selected as the swarm intelligent optimization algorithm for the operation of the turbine generator set; Define the objective function of the I FWA optimization algorithm, and set the fitness function of the I FWA optimization algorithm according to the objective function; set specific optimization parameters of the I FWA optimization algorithm.

8. The method according to claim 7, characterized in that The method of generating an initial operation plan of the hydro-turbine generator set by using a swarm intelligent optimization algorithm according to specific optimization parameters includes: The operation scheme of the turbine generator set is used as the position of the I FWA individual, and the I FWA population is initialized according to the specific optimization parameters of the I FWA optimization algorithm to obtain the initialized I FWA population; According to the specific optimization parameters of the I FWA optimization algorithm, the initialized I FWA population is exploded to obtain an updated I FWA population; Using the Gaussian mutation algorithm, the initialized I FWA population is subjected to Gaussian mutation to generate a Gaussian mutated I FWA population; Using the dynamic reverse learning algorithm, the initialized I FWA population is dynamically reversed to generate a dynamically reversed IFWA population; Use the fitness function of the I FWA optimization algorithm to calculate the fitness values ​​of all I FWA individuals in the updated I FWA population, the Gaussian mutation I FWA population, and the dynamic reverse I FWA population, and take the I FWA individual with the minimum fitness value as the optimal individual; If the number of iterations reaches the threshold or the fitness value of the optimal individual meets the requirements, the optimal solution corresponding to the current optimal individual is output to obtain the initial operation plan of the turbine generator set.

9. The method according to claim 5, characterized in that According to the characteristics of the water turbine generator set operation sub-model, the feasibility check is performed on the initial water turbine generator set operation plan to obtain the final water turbine generator set operation plan, including: According to the characteristics of the hydro-turbine generator set operation sub-model, the constraints of the hydro-turbine generator set operation are determined; According to the constraints of the operation of the turbine generator set, the feasibility check is performed on the initial operation plan of the turbine generator set to obtain the corresponding feasibility check result; If the initial operation plan of the turbine generator set meets the constraint conditions, the feasibility check result is passed, and the final operation plan of the turbine generator set is obtained. Otherwise, the feasibility check result is failed, the initial operation plan of the turbine generator set is adjusted, and the previous step is returned.

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