Optimization method and device of cold end system, electronic equipment and storage medium

Abstract

The invention provides an optimization method and device for a cold end system, electronic equipment and a storage medium. The optimization method comprises the steps that external meteorological parameters of the cold end system and internal operation parameters of a steam turbine are obtained; determining a to-be-optimized parameter and an optimization target; the to-be-optimized parameters comprise the operation number and the operation frequency of cooling fluid equipment required by the cold end system; the optimization target is that the difference between the total operation power of the cold end system and the generated power of the steam turbine is maximum; inputting the external meteorological parameters and the to-be-optimized parameters into a pre-constructed first optimization model to obtain the total operation power of the cooling fluid equipment and the operation flow of the cooling fluid; inputting the external meteorological parameters, the internal operation parameters and the operation flow of the cooling fluid into a pre-constructed second optimization model to obtain the power generation power of the steam turbine; determining an optimization objective function of the cold end system according to the total operation power of the cooling fluid equipment and the power generation power of the steam turbine; the optimal solution of the optimization objective function is the value of the to-be-optimized parameter meeting the optimization objective.

Description

Technical Field

[0001] The present invention relates to the technical field of cold end design of thermal power plants, and in particular to an optimization method, device, electronic equipment and storage medium for a cold end system. Background Art

[0002] In the prior art, the cooling method for the cold end of the steam turbine in nuclear power plants generally adopts a seawater cooling system. Most circulating pumps are not equipped with frequency converters and can only rely on the number of running units, blade angles or dual-speed motors to perform rough flow regulation. This method is powerless when dealing with different working conditions and changes in the external environment, and it is difficult to achieve the optimal balance between cooling efficiency and energy consumption. In the field of thermal power generation, although some cooling fluid equipment has been equipped with frequency converters, its adjustment method mostly relies on the experience of operation and maintenance personnel, and lacks a real-time and accurate control mechanism. This results in the operation of cooling fluid equipment often failing to match external meteorological parameters and internal operating parameters of the steam turbine, and failing to maximize the difference between the steam turbine power generation and the total power of the cooling fluid equipment, thereby affecting power generation efficiency and operational safety.

[0003] In addition, as the thermal power generation unit increases in operating years, the equipment performance will gradually change, and the original cooling fluid equipment operation control strategy may gradually deviate from the optimal operating conditions. Therefore, it is necessary to regularly optimize the cooling fluid equipment operation control strategy based on the actual operating characteristics of the existing equipment to adapt to changes in equipment performance and fluctuations in the external environment. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems in the related art to a certain extent. To this end, the first object of the present invention is to propose an optimization method for a cold end system, by combining external meteorological parameters, parameters to be optimized of the cold end system and internal operating parameters of the steam turbine, an efficiency optimization objective function is established and solved, the optimization target of the cold end system is determined by finding the optimal solution of the function, and the operating efficiency of the cold end system is comprehensively optimized.

[0005] The second object of the present invention is to provide an optimization device for a cold end system.

[0006] A third objective of the present invention is to provide an electronic device.

[0007] A fourth object of the present invention is to provide a computer-readable storage medium.

[0008] To achieve the above-mentioned purpose, the first embodiment of the present invention proposes an optimization method for a cold end system, the method comprising obtaining external meteorological parameters of the cold end system and internal operating parameters of a steam turbine; determining parameters to be optimized and optimization targets; the parameters to be optimized include the number of operating units and operating frequency of cooling fluid equipment required by the cold end system; the optimization target is to maximize the difference between the total operating power of the cold end system and the power generation power of the steam turbine; inputting the external meteorological parameters and the parameters to be optimized into a pre-constructed first optimization model to obtain the total operating power of the cooling fluid equipment and the operating flow of the cooling fluid; inputting the external meteorological parameters, the internal operating parameters, and the operating flow of the cooling fluid into a pre-constructed second optimization model to obtain the power generation power of the steam turbine; determining the optimization objective function of the cold end system according to the total operating power of the cooling fluid equipment and the power generation power of the steam turbine; the optimal solution of the optimization objective function is the value of the parameters to be optimized that meets the optimization target. Therefore, the present invention establishes and solves an efficiency optimization objective function by combining external meteorological parameters, parameters to be optimized of the cold end system, and internal operating parameters of the steam turbine, and determines the optimization target of the cold end system by finding the optimal solution of the function, and comprehensively optimizes the operating efficiency of the cold end system.

[0009] In addition, the optimization method of the cold end system according to the above embodiment of the present invention may also have the following additional technical features:

[0010] According to some embodiments of the present invention, the first optimization model is obtained by modeling actual operating characteristics of the cooling fluid device through a machine learning algorithm based on historical operating data of the cooling fluid device.

[0011] According to some embodiments of the present invention, the first optimization model is modeled as follows:

[0012]

[0013] Where γ is the bulk density of the cooling fluid, in N / m 3 ; Q is the volume flow rate of the cooling fluid, in m 3 / s; H is the head of the cooling fluid equipment, in m; p is the total pressure of the cooling fluid equipment, in Pa; η is the efficiency of the cooling fluid equipment, in %; n is the operating frequency of the cooling fluid equipment; n0 is the rated frequency of the cooling fluid equipment; Q n H is the flow rate of the cooling fluid equipment at the operating frequency n; n is the head of the cooling fluid equipment at the operating frequency n; η n is the efficiency of the cooling fluid device at the operating frequency n; is the flow rate of the cooling fluid equipment at the rated frequency n0; It is the head of the cooling fluid equipment at the rated frequency n0; is the efficiency of the cooling fluid device at the rated frequency n0; p1 is the pressure at the inlet interface of the cooling fluid pipeline, in Pa; p2 is the pressure at the outlet interface of the cooling fluid pipeline, in Pa; H z is the height difference between the inlet and outlet interfaces of the cooling fluid pipeline, in meters; S is the impedance of the cooling fluid pipeline, in s 2 / m 2 ; i is the number of cooling fluid equipment in operation.

[0014] According to some embodiments of the present invention, the second optimization model includes a pre-built condenser heat exchange model and a turbine power output model, wherein the condenser heat exchange model is obtained by modeling the actual operating characteristics of the condenser based on the historical operating data of the condenser through a machine learning algorithm; the turbine power output model is obtained by modeling the actual operating characteristics of the turbine based on the historical operating data of the turbine through a machine learning algorithm.

[0015] According to some embodiments of the present invention, the condenser heat exchange model is modeled as follows:

[0016]

[0017] Among them, m f is the mass flow rate of exhaust steam, in kg / s; h f is the enthalpy of exhaust steam, in kJ / kg; h w is the enthalpy of condensed water, in kJ / kg; K is the total heat transfer coefficient of the condenser, in kW / (m 2 ℃); F is the heat exchange area of ​​the condenser, in m 2 ; Δt is the heat transfer temperature difference of the condenser, in °C; m l is the mass flow rate of the cooling fluid, in kg / s; c l is the specific heat capacity of the cooling fluid, in kJ / (kg℃); t in is the inlet temperature of the cooling fluid, in °C; t out is the outlet temperature of the cooling fluid, in °C; p f is the operating back pressure of the steam turbine, in kPa; t w is the temperature of condensed water, in °C; t f is the exhaust steam temperature, in °C.

[0018] According to some embodiments of the present invention, the steam turbine work output model is modeled as follows:

[0019] P = f(m z , p z ,h z , m zr , p zr,h zr , m f , p f ,h f )

[0020] Among them, m z is the flow rate of main steam, in kg / s; p z is the pressure of the main steam, in kPa; h z is the enthalpy of main steam, in kJ / kg; m zr is the flow rate of reheat steam, in kJ / s; p zr is the pressure of reheat steam, in kPa; h zr is the enthalpy of reheated steam, in kJ / kg.

[0021] According to some embodiments of the present invention, the method further comprises:

[0022] Inputting the external meteorological parameter, the main steam parameter, the reheat steam parameter and the operating flow into a pre-built second optimization model to obtain the operating back pressure of the steam turbine;

[0023] The upper limit and the lower limit of the operating back pressure of the steam turbine are determined according to the operating regulations of the steam turbine and the condenser and actual operating experience.

[0024] According to some embodiments of the present invention, the method further comprises:

[0025] The maximum and minimum values ​​of the number of operating cooling fluid devices required for the cold end system and the maximum and minimum values ​​of the operating frequency are determined according to the operating procedures of the steam turbine and the condenser and actual operating experience.

[0026] According to some embodiments of the present invention, the method further comprises:

[0027] The optimal solution of the optimization objective function is determined based on the upper and lower limits of the operating back pressure of the steam turbine, the maximum and minimum values ​​of the operating number of cooling fluid equipment required by the cold end system, and the maximum and minimum values ​​of the operating frequency.

[0028] According to the optimization method of the cold end system of the embodiment of the present invention, firstly, the external meteorological parameters of the cold end system that have a direct impact on the heat dissipation effect of the cold end system are obtained. At the same time, the parameters to be optimized that determine the operating state and operating efficiency of the cold end system are obtained. Then, according to the historical operating data of the equipment, the optimization model of the cold end system is obtained by the machine learning algorithm, and the total operating power and operating flow of the cold end system are obtained by the optimization model and related parameters. The total operating power reflects the electric energy or other forms of energy consumed by the cold end system during operation, and the operating flow reflects the cooling effect. Further, the internal operating parameters of the steam turbine that directly reflect the operating state and power generation capacity of the steam turbine are obtained, and the power generation power of the steam turbine is calculated by combining the external meteorological parameters, the operating flow and the internal operating parameters. Finally, according to the total operating power of the cold end system and the power generation power of the steam turbine, the efficiency optimization objective function of the cold end system is determined. The goal of the function is to maximize the difference between the power generation power of the steam turbine and the total operating power of the cold end system, that is, to maximize the energy utilization efficiency. By solving the optimal solution of the efficiency optimization objective function, the optimization target of the cold end system can be obtained, that is, the best combination of parameters to be optimized, so that the cold end system can achieve the optimization of operating efficiency while meeting the heat dissipation requirements. In the process of determining the efficiency optimization objective function, the present invention not only considers the influence of external meteorological parameters, but also combines the internal operating parameters of the cold end system and the steam turbine, and obtains the optimization model through the machine learning algorithm based on the historical operating data of the equipment, so as to more accurately reflect the actual situation and improve the optimization effect. At the same time, by solving the efficiency optimization objective function, the optimization target of the cold end system can be easily found, providing powerful guidance for actual operation.

[0029] To achieve the above object, a second embodiment of the present invention provides an optimization device for a cold end system, comprising:

[0030] An acquisition module is configured to acquire external meteorological parameters of the cold end system and internal operating parameters of the steam turbine;

[0031] A determination module is configured to determine parameters to be optimized and an optimization target; the parameters to be optimized include the number of operating units and the operating frequency of the cooling fluid equipment required by the cold end system; the optimization target is to maximize the difference between the total operating power of the cold end system and the power generation power of the steam turbine;

[0032] A first optimization module is configured to input the external meteorological parameters and the parameters to be optimized into a pre-built first optimization model to obtain the total operating power of the cooling fluid device and the operating flow rate of the cooling fluid;

[0033] A second optimization module is configured to input the external meteorological parameters, the internal operating parameters, and the operating flow rate of the cooling fluid into a pre-built second optimization model to obtain the power generation power of the steam turbine;

[0034] The third optimization module is configured to determine the optimization objective function of the cold end system according to the total operating power of the cooling fluid equipment and the power generation power of the turbine; wherein the optimal solution of the optimization objective function is the value of the parameter to be optimized that meets the optimization goal.

[0035] According to the optimization device of the cold end system of the embodiment of the present invention, the external meteorological parameters of the cold end system and the internal operating parameters of the steam turbine are obtained by the acquisition module; the parameters to be optimized and the optimization target are determined by the determination module; the parameters to be optimized include the number of operating units and the operating frequency of the cooling fluid equipment required by the cold end system; the optimization target is to maximize the difference between the total operating power of the cold end system and the power generation power of the steam turbine; the external meteorological parameters and the parameters to be optimized are input into the pre-built first optimization model through the first optimization module to obtain the total operating power of the cooling fluid equipment and the operating flow of the cooling fluid; the external meteorological parameters, the internal operating parameters, and the operating flow of the cooling fluid are input into the pre-built second optimization model through the second optimization module to obtain the power generation power of the steam turbine; the optimization objective function of the cold end system is determined according to the total operating power of the cooling fluid equipment and the power generation power of the steam turbine through the third optimization module; the optimal solution of the optimization objective function is the value of the parameters to be optimized that meets the optimization target. Therefore, the present invention establishes and solves an efficiency optimization objective function by combining the external meteorological parameters, the parameters to be optimized of the cold end system and the internal operating parameters of the steam turbine, determines the optimization target of the cold end system by finding the optimal solution of the function, and comprehensively optimizes the operating efficiency of the cold end system.

[0036] To achieve the above-mentioned purpose, the third aspect of the present invention proposes an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the cold end system optimization method as described above when executing the program.

[0037] To achieve the above-mentioned purpose, a fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the above-mentioned cold end system optimization method.

[0038] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 A schematic diagram of the framework structure of an optimization device for a cold end system provided in an embodiment of the present invention.

[0041] Figure 2 A schematic flow chart of a cold end system optimization method provided in an embodiment of the present invention.

[0042] Figure 3 A schematic diagram of an optimization device for a cold end system provided in an embodiment of the present invention.

[0043] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0045] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0046] As mentioned in the background technology section, the cold end system efficiency of the steam turbine plays a vital role in improving the overall efficiency of the power plant. The optimization of the cold end system efficiency directly affects the performance and operating economy of the steam turbine.

[0047] In the process of realizing the present invention, the applicant found that the seawater cooling system at the cold end of the steam turbine in domestic nuclear power plants has many deficiencies. At present, the cooling seawater flow rate is mainly adjusted by the number of running cooling circulation pumps, blade angles or dual-speed motors. This method lacks refined control capabilities and cannot achieve accurate and rapid adjustment of the cooling seawater flow rate. Even if the cooling fluid equipment of some thermal power generating units is equipped with a frequency converter, it is basically manually adjusted based on the experience of the operation and maintenance personnel, and lacks an intelligent system that automatically and in real time adjusts according to external meteorological parameters and internal operating parameters of the steam turbine. This results in low operating efficiency of the cooling fluid equipment and the inability to maximize the difference between the generated power and the total operating power of the cooling fluid equipment. In addition, as the operating years of the thermal power generating unit increase, the actual operating characteristics of the equipment will change. However, the current cooling fluid equipment operation control strategy is often based on the initial design parameters of the equipment or past operating experience, and may not be able to adapt to changes in equipment performance, causing the operation strategy to deviate from the optimal operating conditions, affecting the operating efficiency and stability of the entire system.

[0048] To this end, the first purpose of the present invention is to propose a cold-end system optimization method. By combining external meteorological parameters, the parameters to be optimized of the cold-end system and the internal operating parameters of the turbine, an efficiency optimization objective function is established and solved. By finding the optimal solution of the function, the optimization target of the cold-end system is determined, and the operating efficiency of the cold-end system is comprehensively optimized.

[0049] The technical solution of the present invention is further described in detail below through specific embodiments.

[0050] refer to Figure 1 , which is a schematic diagram of the framework structure of the optimization device of the cold end system provided in an embodiment of the present invention.

[0051] The cold end system framework provided by the present invention includes a cooling fluid system model, a condenser heat exchange model and a back pressure turbine output model. The present invention is based on the thermal engineering model of the cooling fluid system, condenser and turbine of the thermal power plant, and uses the historical operation data of the thermal power plant and the machine learning algorithm to model the actual operation characteristics of the above thermal power generation equipment, so as to obtain the cooling fluid system model, the condenser heat exchange model and the turbine output model. The cooling fluid system model is the first optimization model, and the joint model of the condenser heat exchange model and the turbine output model is the second optimization model. The external meteorological parameters and the parameters to be optimized are input into the cooling fluid system model, and the operation output of the cooling fluid equipment is calculated, which mainly includes the total operation power of the cooling fluid equipment and the operation flow of the cooling fluid. The external meteorological parameters, the internal operation parameters and the cooling fluid operation flow are input into the condenser heat exchange model and the turbine output model for joint solution, and the turbine operation back pressure and the turbine power generation are calculated. The calculated turbine power generation is subtracted from the total operation power of the cooling fluid equipment to obtain the efficiency optimization objective function of the present invention.

[0052] refer to Figure 2 , which is a flow chart of a cold end system optimization method provided in an embodiment of the present invention.

[0053] Step S201, obtaining external meteorological parameters of the cold end system and internal operating parameters of the steam turbine.

[0054] In the embodiment of the present invention, the cooling fluid equipment mainly includes hydraulic cooling equipment, which absorbs and removes heat by circulating cooling fluid to ensure the normal operation of other equipment such as condensers, etc. Its operating output (i.e., cooling effect) is affected by external meteorological parameters, the flow rate of cooling fluid, the number of cooling fluid equipment in operation, and the operating frequency.

[0055] The cooling fluid acts as a cooling medium, absorbing and removing heat through flow. The flow rate of the cooling fluid and the number of units in operation are adjustable to adjust the cooling effect. The function of the condenser is to condense the steam discharged from the turbine into water and release latent heat.

[0056] Step S202, determining the parameters to be optimized and the optimization target; the parameters to be optimized include the number of operating units and the operating frequency of the cooling fluid equipment required for the cold end system; the optimization target is to maximize the difference between the total operating power of the cold end system and the power generation power of the turbine.

[0057] Specifically, the number of operating units (discrete variables) and operating frequency (continuous variables) of the cooling fluid equipment in the cold end system are selected as optimization variables. The adjustment of these parameters will directly affect the cooling effect and energy consumption. The difference between the total operating power of the cold end system and the power generated by the steam turbine is maximized. This difference reflects the overall net power output capacity of the system and is a key indicator for evaluating system performance.

[0058] As an optional embodiment, the external meteorological parameters of the cold end system may include, but are not limited to, temperature, humidity, wind speed, etc., which have a direct impact on the heat dissipation effect of the cold end system. The parameters to be optimized may include, but are not limited to, the number of operating units, operating efficiency, cooling water flow, cooling water temperature, condenser pressure, etc. of the cooling fluid equipment required by the cold end system, which determine the operating state and operating efficiency of the cold end system. Among them, by adjusting the number of operating units of the cooling fluid equipment, the flow rate and circulation speed of the cooling fluid can be flexibly controlled according to actual needs, thereby ensuring the normal operation of key equipment such as the condenser. At the same time, optimizing operating efficiency parameters, such as improving the efficiency of the pump and reducing energy consumption, helps to reduce energy loss during the operation of the cooling fluid equipment and further improve the energy efficiency of the entire system. Among them, the number of operating units and operating efficiency of the cooling fluid equipment directly affect the total operating electrical power of the cooling fluid equipment, that is, the cooling energy consumption.

[0059] In practical applications, the adjustment of parameters to be optimized needs to take into account multiple factors, such as external meteorological conditions, unit load changes, equipment performance, etc. By establishing an accurate mathematical model and combining it with advanced optimization algorithms, real-time adjustment and optimization of these parameters can be achieved, thereby ensuring that the cold end system can maintain optimal performance and efficiency under various operating conditions.

[0060] Step S203: input the external meteorological parameters and the parameters to be optimized into a pre-built first optimization model to obtain the total operating power of the cooling fluid equipment and the operating flow rate of the cooling fluid.

[0061] As an optional embodiment, external meteorological parameters, operating number parameters and operating efficiency parameters can be input into a pre-built first optimization model to obtain the total operating power and the flow rate; wherein the first optimization model is based on the historical operating data of the cooling fluid equipment and is obtained by modeling the actual operating characteristics of the cooling fluid equipment through a machine learning algorithm.

[0062] In practice, parameter optimization in machine learning is an important step, which involves adjusting the model's hyperparameters to optimize the model's performance. Commonly used parameter optimization algorithms include grid search, which is the most direct parameter optimization method. It first defines a search range for each hyperparameter, and then evaluates the performance of each hyperparameter combination within this range through cross-validation. This method is simple and easy to understand, but it can be very time-consuming, especially when there are many hyperparameters or the search range for each parameter is large. Random search is an improved version of grid search. Unlike grid search, random search does not exhaustively search within the search range of each parameter, but randomly selects a part of parameter combinations for evaluation. This method is usually faster than grid search and may find better parameter combinations. Genetic algorithm is an optimization algorithm that simulates the principles of natural selection and genetics. It generates new parameter combinations through operations such as selection, crossover, and mutation, and iteratively optimizes based on the performance of these combinations. Genetic algorithms perform well in solving complex optimization problems, but may require long computing time.

[0063] In an embodiment of the present invention, it is preferred to use random search for modeling. First, it is necessary to collect historical operating data of cooling fluid equipment. The historical operating data of cooling fluid equipment is an important basis for evaluating equipment performance, optimizing operating parameters, and performing fault prediction and diagnosis. It usually includes the recorded values ​​of key parameters such as temperature, pressure, flow, and liquid level of the equipment at different time points. Further, the data is preprocessed, including data cleaning, normalization, etc., for subsequent algorithm processing. Further, according to the characteristics of historical operating data, the input and output of the model are defined, that is, external meteorological parameters, operating number parameters, and operating efficiency parameters are used as inputs, and total operating power and operating flow are used as outputs. Further, the parameters of the model are initialized, and these parameters can be random or set based on experience or prior knowledge. In the random search process, a set of model parameters are randomly generated as candidate solutions, and this set of parameters is input into the model, and the total operating power and operating flow predicted by the model are calculated. The difference between the model prediction results and the actual historical data is measured using appropriate evaluation indicators (such as mean square error, accuracy, etc.), and the evaluation results are recorded. It is determined whether to end the search according to the preset stop conditions (such as reaching the maximum number of iterations, the improvement of the evaluation index is lower than a certain threshold, etc.). If the search does not stop, the model parameters are updated according to a certain strategy (such as random selection, selection based on probability distribution, etc.) to generate new candidate solutions. Repeat the above model prediction and evaluation process. Continue to iterate until the stopping condition is met. During the random search process, multiple sets of model parameters and their corresponding evaluation results are recorded. The set of parameters with the best evaluation results is selected as the parameters of the final model. Use this set of parameters to build the final model, that is, the first optimization model, and verify and test it to ensure its effectiveness in practical applications. Apply the constructed model to the actual scenario, and predict the total operating power and operating flow based on the real-time collected external meteorological parameters, operating number parameters and operating efficiency parameters.

[0064] As an optional embodiment, the first optimization model is modeled as follows:

[0065]

[0066] Where N is the power consumption of the cold end system, γ is the bulk density of the cooling fluid, in N / m 3 ; Q is the volume flow rate of the cooling fluid, in m 3 / s; H is the head of the cooling fluid equipment, in m; p is the total pressure of the cooling fluid equipment, in Pa; η is the efficiency of the cooling fluid equipment, in %; n is the operating frequency of the cooling fluid equipment; n0 is the rated frequency of the cooling fluid equipment; Q n H is the flow rate of the cooling fluid equipment at the operating frequency n; n is the head of the cooling fluid equipment at the operating frequency n; η nis the efficiency of the cooling fluid device at the operating frequency n; is the flow rate of the cooling fluid equipment at the rated frequency n0; is the head of the cooling fluid equipment at the rated frequency n0; η n0 is the efficiency of the cooling fluid device at the rated frequency n0; p1 is the pressure at the inlet interface of the cooling fluid pipeline, in Pa; p2 is the pressure at the outlet interface of the cooling fluid pipeline, in Pa; H z is the height difference between the inlet and outlet interfaces of the cooling fluid pipeline, in meters; S is the impedance of the cooling fluid pipeline, in s 2 / m 2 ; i is the number of cooling fluid equipment in operation.

[0067] Among them, the bulk density of the cooling fluid, also known as density, is the mass of the cooling fluid per unit volume. It depends on the type and temperature of the cooling fluid. Different cooling fluids, such as water, oil or other liquids, have different bulk densities. At the same time, as the temperature changes, the bulk density of the fluid will also change, and the bulk density will usually decrease when the temperature rises. In practical applications, the bulk density of the cooling fluid is an important physical parameter that affects the flow characteristics, heat transfer performance of the cooling fluid, and the design and operation of the entire cooling system. Therefore, when designing and optimizing the cooling system, the present invention needs to accurately understand the bulk density of the cooling fluid used and consider its changes with temperature. The volume flow rate of the cooling fluid is a parameter used to measure the flow rate and flow rate of the fluid in a pipeline or container. The head of the cooling fluid equipment refers to the head required for the water pump to overcome a certain frictional resistance and transport water from a low place to a high place, usually in meters. The size of the head depends on many factors, including the type of water pump, speed, inlet pressure and outlet pressure, as well as the design and manufacturing quality of the water pump. The total pressure (TotalPressure) of a cooling fluid device refers to the total pressure generated by the device when it is working. It represents the total pressure to which the fluid is subjected when passing through the device. In the cooling fluid system, the total pressure is a key parameter that affects the performance of the device and the cooling effect. By adjusting the total pressure, the flow rate and flow rate of the fluid can be controlled, thereby optimizing the cooling process. The efficiency of a cooling fluid device generally refers to the ratio of the actual amount of work completed by the device when transferring heat or performing a cooling function to the theoretical amount of work that can be completed. For cooling fluid devices, efficiency is a key performance indicator that directly reflects the energy consumption, cooling capacity and operating cost of the device. The operating frequency of a cooling fluid device refers to the number of working cycles completed by the device per unit time, which is usually affected by many factors. These factors include but are not limited to power supply and load, temperature and humidity, operating time, internal structure, degree of wear of mechanical parts, and properties of the liquid medium. The rated frequency of a cooling fluid device refers to the standard operating frequency specified when the device is designed and manufactured. This frequency is usually determined based on the electrical design, mechanical structure and expected cooling requirements of the device, and is the frequency that the device should follow when operating normally. The pressure at the inlet and outlet interfaces of the cooling fluid pipeline is a result of a combination of multiple factors such as gravity, fluid movement, and pipeline geometric characteristics. When designing and using the cooling fluid pipeline, the present invention fully considers the impact of these factors on the fluid pressure to ensure the safe and stable operation of the pipeline. The height difference between the inlet interface and the outlet interface of the cooling fluid pipeline, that is, the vertical drop of the pipeline, has a significant impact on the flow and pressure distribution of the cooling fluid. The impedance of the cooling fluid pipeline refers to the total resistance encountered by the fluid when flowing in the pipeline, which determines the power and energy loss required for the fluid to pass through the pipeline.

[0068] Step S204: input the external meteorological parameters, the internal operating parameters, and the operating flow rate of the cooling fluid into a pre-built second optimization model to obtain the power generation of the steam turbine.

[0069] As an optional embodiment, the internal operating parameters of the steam turbine include but are not limited to main steam parameters, reheat steam parameters, unit output, regulating stage pressure, exhaust temperature, and vacuum conditions of the steam turbine.

[0070] As an optional embodiment, external meteorological parameters, main steam parameters, reheat steam parameters and operating flow rate can be input into a pre-constructed second optimization model to obtain the power generation power of the steam turbine; wherein the second optimization model includes a pre-constructed condenser heat exchange model and a steam turbine power output model, the condenser heat exchange model is obtained by modeling the actual operating characteristics of the condenser based on the historical operating data of the condenser through a machine learning algorithm; the steam turbine power output model is obtained by modeling the actual operating characteristics of the steam turbine based on the historical operating data of the steam turbine through a machine learning algorithm.

[0071] In specific implementation, main steam parameters mainly refer to the main pressure parameters in the steam system, which are crucial to the normal operation of the entire steam system. The main steam has high pressure and temperature, which can provide sufficient power to drive the turbine to rotate, and then drive the generator to generate electricity. The quality and stability of the main steam directly affect the operating efficiency, product quality and safety of the equipment.

[0072] In specific implementation, the reheat steam parameters refer to the steam parameters at the inlet of the reheat steam valve. During the operation of the steam turbine, after the steam is discharged from the high-pressure cylinder, it will be heated again. During this process, the temperature and pressure of the steam will be further increased. Such steam enters the low-pressure cylinder to continue working, which is called reheat steam. The optimization of reheat steam parameters helps to improve the efficiency of the steam turbine, and the energy consumed in the heating and condensation process can also be recovered and used, so it is also one of the important parameters in thermal power plants.

[0073] As an optional embodiment, the present invention preferably uses an automated machine learning algorithm (Automated Machine Learning, AutoML for short) to construct and optimize the power generation model and the turbine output model of the steam turbine. AutoML can automatically perform a series of tasks such as data preprocessing, feature selection, model selection, and hyperparameter tuning, thereby greatly simplifying the machine learning process and improving modeling efficiency.

[0074] Specifically, historical data including external meteorological parameters (such as temperature, humidity, wind speed, etc.), main steam parameters (such as pressure, temperature, flow, etc.), reheat steam parameters (such as pressure, temperature, etc.) and operating flow are first collected. Ensure that these data have a clear correspondence with the power generation of the steam turbine. Then, use data cleaning technology to remove outliers, missing values, etc., and perform data normalization or standardization so that subsequent models can better process the data. In the feature engineering stage, the AutoML algorithm can be used to automatically select and transform features. The algorithm automatically explores potential useful features in the data to improve the prediction performance of the model. Using historical operating data, the condenser heat exchange model and the turbine power output model are constructed by the AutoML algorithm, and the actual operating characteristics of the condenser and steam turbine are modeled based on machine learning algorithms (such as neural networks, decision trees, random forests, etc.). The AutoML algorithm will automatically select the most suitable algorithm and corresponding hyperparameters. Further, the condenser heat exchange model and the turbine power output model are integrated into a unified prediction framework, that is, the second optimization model is obtained. The AutoML algorithm will automatically adjust the weights and parameters of each model to optimize the overall prediction performance. When new external meteorological parameters, main steam parameters, reheat steam parameters, and operating flow data are input, these data will be fed into the optimized prediction model after preprocessing and feature engineering. The model will automatically calculate and output the predicted turbine power generation based on the input parameters. The prediction performance of the model is evaluated by comparing it with the actual power generation data. Based on the evaluation results, the model can be iteratively optimized to further improve the prediction accuracy.

[0075] As an optional embodiment, the condenser heat exchange model is modeled as follows:

[0076]

[0077] Among them, m f is the mass flow rate of exhaust steam, in kg / s; h f is the enthalpy of exhaust steam, in kJ / kg; h w is the enthalpy of condensed water, in kJ / kg; K is the total heat transfer coefficient of the condenser, in kW / (m 2 ℃); F is the heat exchange area of ​​the condenser, in m 2 ; Δt is the heat transfer temperature difference of the condenser, in °C; m l is the mass flow rate of the cooling fluid, in kg / s; c l is the specific heat capacity of the cooling fluid, in kJ / (kg℃); t in is the inlet temperature of the cooling fluid, in °C; t out is the outlet temperature of the cooling fluid, in °C; p f is the operating back pressure of the steam turbine, in kPa; tw is the temperature of condensed water, in °C; t f is the exhaust steam temperature, in °C.

[0078] Among them, the mass flow rate of exhaust steam refers to the mass flow rate of steam entering the condenser, that is, the mass flow rate of steam discharged from the low-pressure cylinder of the steam turbine after the steam turbine does work. After these steams complete the work process in the steam turbine, their pressure and temperature are relatively low, so they are called "exhaust steam". After the exhaust steam enters the condenser, it is condensed into water by the action of cooling water, and at the same time releases the latent heat of vaporization to maintain the vacuum in the condenser so that the steam turbine can continue to inhale fresh air and do work. The enthalpy of exhaust steam refers to the enthalpy of low-temperature water vapor discharged from the boiler. Enthalpy is a thermodynamic parameter that indicates the energy content of a substance in a certain state, including its internal energy and pressure potential energy. For the exhaust steam in the boiler, its enthalpy represents the energy contained in this part of the steam. The enthalpy of condensed water refers to the heat released when a unit mass of steam is cooled to condense into water under certain conditions, also known as the thermal enthalpy of steam condensation water. The total heat transfer coefficient of the condenser is an important parameter that comprehensively reflects the three parameters of the heat transfer coefficient, heat transfer area and convection thermal resistance inside and outside the condenser. It represents the efficiency of the condenser in the heat transfer process and is a key indicator for evaluating the performance of the condenser. The heat exchange area of ​​the condenser refers to the sum of the areas of all heat transfer elements that the cooling air contacts in the condenser, excluding the area of ​​the steam pipe and other pipes connected to the condenser. The heat transfer temperature difference of the condenser refers to the temperature difference between the cooling water and the steam in the condenser. The size of this temperature difference directly affects the heat transfer efficiency of the condenser. Generally speaking, the greater the temperature difference, the higher the heat transfer efficiency, but it also brings greater heat loss. Therefore, in the process of optimizing the design and operation of the condenser, the present invention considers minimizing the heat transfer temperature difference and reducing heat loss while ensuring the heat transfer efficiency. The mass flow rate of the cooling fluid refers to the mass of the cooling fluid passing through the effective cross-section of the closed pipe or open slot per unit time. The specific heat capacity of the cooling fluid represents the heat absorbed or released by the unit mass of the cooling fluid when the temperature increases or decreases by 1°C. The inlet temperature and outlet temperature of the cooling fluid directly reflect the cooling effect. The inlet temperature is the temperature of the cooling fluid when it enters the cooling system, while the outlet temperature is the temperature of the cooling fluid after it flows out of the cooling system. The difference between these two temperatures is usually used to evaluate the performance of the cooling system. The operating back pressure of the steam turbine refers to the pressure at the exhaust port of the steam turbine. The size of the back pressure affects the efficiency and performance of the steam turbine. At the same time, the back pressure is also closely related to the operation of the cooling system, because the cooling system needs to effectively reduce the exhaust temperature of the steam turbine to maintain appropriate back pressure. The temperature of the condensed water mainly depends on the performance of the condenser. In the condenser, the steam is cooled and condensed into water, and a large amount of heat is released in this process. The temperature of the condensed water is usually low to ensure that the steam can be fully condensed. The temperature of the exhaust steam is the temperature of the steam turbine exhaust, which reflects the residual heat energy of the steam after the steam turbine does work. The temperature of the exhaust steam is usually high because it still contains a certain amount of heat energy. This part of the heat energy can be recovered and utilized through the cooling system to improve the efficiency of the entire thermal system.

[0079] As an optional embodiment, the steam turbine power output model is modeled as follows:

[0080] P = f(m z , p z ,h z , m zr , p zr ,h zr , m f , p f ,h f )

[0081] Where P is the turbine output, m z is the flow rate of main steam, in kg / s; p z is the pressure of the main steam, in kPa; h z is the enthalpy of main steam, in kJ / kg; m zr is the flow rate of reheat steam, in kJ / s; p zr is the pressure of reheat steam, in kPa; h zr is the enthalpy of reheated steam, in kJ / kg.

[0082] Among them, the main steam flow in the steam boiler can be referred to as the main steam flow, which is divided into instantaneous flow and cumulative flow. Instantaneous flow is the instantaneous flow seen when observing, while cumulative flow is the sum of the flow of steam supplied to the outside within a certain period of time. The main steam flow is a key parameter for controlling the flow rate of steam in a system. It uses a humidity air fluid detector to control the equipment and quantifies the steam flow by detecting the humidity and temperature in the filter element. The main steam pressure is one of the steam state parameters, which refers to the main steam pressure at the outlet of the power plant boiler and the inlet of the steam turbine, usually expressed in "MPa", that is, megapascals. The enthalpy of the main steam refers to the heat absorbed by a unit mass of water when it is heated to an evaporation state under a certain pressure. The reheat steam flow refers to the total amount of steam that is reheated between the high, medium and low pressure cylinders of the steam turbine. The reheat steam pressure is formed after reheating, and its pressure is usually higher. Reheat steam is formed after the wet steam discharged from the high pressure cylinder of the steam turbine is reheated, and its temperature and pressure are both high. Therefore, the enthalpy of the reheat steam will also increase accordingly.

[0083] Step S205, determining the optimization objective function of the cold end system according to the total operating power of the cooling fluid equipment and the power generation power of the steam turbine; wherein the optimal solution of the optimization objective function is the value of the parameter to be optimized that meets the optimization target.

[0084] From the above content, it can be seen that in the thermal system, the cold end system usually refers to the condenser and related equipment connected to the steam turbine, whose main function is to condense the exhaust steam discharged from the steam turbine into water and recover heat at the same time. The efficiency of the cold end system is crucial to the performance of the entire thermal system. Therefore, determining the efficiency optimization objective function of the cold end system and solving its optimal solution are of great significance to improving the efficiency of the entire system. First, the total operating power of the cold end system usually includes the power of the cooling water pump, the heat dissipation power of the condenser, and the power consumption of other related equipment. These power consumptions reflect the energy required by the cold end system during operation. Secondly, the power generation power of the steam turbine is an important indicator for evaluating its performance, which represents the efficiency of the steam turbine in converting thermal energy into electrical energy. Next, the efficiency optimization objective function is constructed based on the total operating power of the cold end system and the power generation power of the steam turbine. The goal of this function is usually to maximize the efficiency of the cold end system, that is, to minimize the difference between the power generation power of the steam turbine and the power consumption of the cold end system, or to minimize the ratio of the power consumption of the cold end system to the power generation power of the steam turbine. By optimizing this function, we can find the optimal operating parameters of the cold-end system, such as the number of cooling fluid equipment required by the cold-end system and the operating efficiency parameters, so as to improve the efficiency of the cold-end system.

[0085] As an optional embodiment, the efficiency optimization objective function is obtained by minimizing the difference between the power generated by the steam turbine and the power consumption of the cold end system:

[0086] max(PN)

[0087] Among them, P is the turbine output mentioned above, and N is the power consumption of the cold end system.

[0088] In the process of steam turbine power generation, the power consumption of the cold end system is an important consideration. In order to maximize the efficiency of the steam turbine, we need to minimize the difference between the power generated by the steam turbine and the power consumption of the cold end system. In practical applications, this objective function also needs to consider other constraints, such as the maximum and minimum output power of the steam turbine, the performance limitations of the cold end system, etc.

[0089] As an optional embodiment, the objective function may be solved using a numerical optimization algorithm, such as a gradient descent method, a genetic algorithm, etc. The above algorithm can find the optimal solution that minimizes the objective function under the premise of satisfying the constraint conditions.

[0090] Particle Swarm Optimization (PSO) is an optimization algorithm based on swarm intelligence. It simulates the foraging behavior of bird flocks to find the optimal solution to the problem. When solving the efficiency optimization problem with the goal of minimizing the difference between the turbine power generation and the cold end system power consumption, the PSO algorithm can be applied to the objective function:

[0091] First, the particle swarm needs to be initialized, including setting the size of the particle swarm (i.e., the number of particles), randomly initializing the position (representing the solution to the optimization problem) and speed for each particle, and setting the individual optimal position (pBest) and global optimal position (gBest) of each particle.

[0092] Furthermore, the fitness value needs to be calculated, including: for each particle, the difference between the turbine power generation and the cold end system power consumption is calculated according to its position, that is, the value of the objective function. This value is used as the fitness value of the particle.

[0093] Furthermore, it is necessary to update the individual optimum and the global optimum, including: if the fitness value of the current particle is better than the fitness value of its individual optimal position, then the individual optimal position is updated to the current position. If the fitness value of the current particle is better than the fitness value of the global optimal position, then the global optimal position is updated to the current position.

[0094] Furthermore, it is necessary to update the particle speed and position, including: updating the particle speed and position according to the speed and position update formula of the PSO algorithm based on the particle's current speed, individual optimal position and global optimal position. Usually, the speed and position update involves some parameters, such as inertia weight, individual learning factor and social learning factor, which can be adjusted according to the characteristics of the efficiency optimization goal.

[0095] Finally, the global optimal position is output as the solution to the optimization problem, i.e., the best parameter configuration that minimizes the difference between the turbine power generation and the cold-end system power consumption.

[0096] It should be noted that in practical applications, the efficiency optimization of the cold-end system may be affected by many factors, such as ambient temperature, cooling water quality, equipment aging, etc. Therefore, when constructing and solving the efficiency optimization objective function, these factors need to be considered comprehensively to ensure the accuracy and practicality of the optimization results.

[0097] As an optional embodiment, external meteorological parameters, main steam parameters, reheat steam parameters and operating flow can be input into a pre-built second optimization model to obtain the operating back pressure of the steam turbine, and the upper and lower limits of the operating back pressure of the steam turbine are determined according to the operating procedures of the steam turbine and the condenser and the actual operating experience; wherein the safe operating limit parameters of the steam turbine are associated with the machine properties of the steam turbine. The operating back pressure is a key parameter in the operation of the steam turbine, which directly affects the efficiency and safety of the steam turbine. According to the calculated operating back pressure, combined with the machine properties of the steam turbine (such as design back pressure, maximum allowable back pressure, etc.), the safe operating limit parameters of the steam turbine can be determined. These parameters may include maximum / minimum load, maximum / minimum steam flow, maximum / minimum cooling water flow, etc., which together constitute the safe operating range of the steam turbine, and obtain the limiting parameters for safe operation (i.e., the upper and lower limits of the operating back pressure of the steam turbine).

[0098] The present invention can adjust the operating state of the steam turbine in real time according to environmental conditions and operating parameters by designing safe operating limit parameters, ensuring that it operates within a safe and efficient range. It can not only improve the operating efficiency of the steam turbine and reduce energy consumption, but also extend the service life of the steam turbine and reduce failure and maintenance costs.

[0099] As an optional embodiment, the maximum and minimum number of operating cooling fluid devices required for the cold end system and the maximum and minimum operating frequency may also be determined based on the operating procedures of the steam turbine and the condenser and actual operating experience.

[0100] Specifically, when optimizing the cold-end system, it is crucial to ensure that the operating parameters of the cooling fluid equipment (including the number of units in operation and the operating frequency) are in line with the operating procedures and actual operating experience of the turbine and condenser. These procedures and experience are usually based on factors such as the design capacity, safety restrictions, economy, and long-term operating stability of the equipment.

[0101] As a key component in the steam turbine system, the operating status of the condenser is directly related to the safety and efficiency of the entire system. Therefore, the machine properties of the condenser, such as design parameters, material properties, working conditions, etc., are crucial to determine its safe operating limit parameters.

[0102] First, the design parameters of the condenser, such as working pressure, temperature range, cooling water flow, etc., are the basis for determining its safe operating limits. These parameters are usually set based on the structure and material properties of the condenser to ensure that it can operate stably and efficiently under normal working conditions. Secondly, changes in the working conditions of the condenser will also affect its safe operating limit parameters. For example, factors such as ambient temperature, water quality, and cooling water flow may all have an impact on the operation of the condenser. Therefore, when determining the safe operating limit parameters of the condenser, these actual operating conditions need to be considered comprehensively. Finally, according to the machine properties and working conditions of the condenser, the corresponding safe operating limit parameters can be formulated, such as maximum / minimum working pressure, maximum / minimum temperature range, maximum / minimum cooling water flow, etc. These parameters will serve as guiding standards for the operation of the condenser to ensure that it operates within a safe and efficient range.

[0103] As an optional embodiment, based on the safe operation limit parameters of the steam turbine and the safe operation limit parameters of the condenser, the constraint conditions of the efficiency optimization objective function can be further determined. These constraint conditions ensure that the optimization process does not exceed the actual operating capacity of the equipment, thereby ensuring the safety and stability of the entire system.

[0104] As an optional embodiment, the optimal solution of the optimization objective function is determined based on the upper and lower limits of the turbine operating back pressure, the maximum and minimum numbers of operating cooling fluid devices required by the cold end system, and the maximum and minimum operating frequencies.

[0105] First, it is necessary to clarify the safe operating limit parameters of the steam turbine and condenser, which may include maximum / minimum load, maximum / minimum steam flow, maximum / minimum cooling water flow, maximum / minimum working pressure, maximum / minimum temperature range, etc. These parameters are determined based on the physical characteristics and design standards of the equipment, and they reflect the performance boundaries of the equipment under normal and abnormal working conditions.

[0106] Next, these safe operation limit parameters are converted into constraints for the efficiency optimization objective function. These constraints are usually expressed in the form of mathematical inequalities and are used to limit the range of values ​​of the optimization variables. For example, for the load of the steam turbine, we can set a constraint to ensure that the load does not exceed its maximum allowable value; for the cooling water flow of the condenser, we can also set a constraint to ensure that the flow is between the minimum and maximum allowable values.

[0107] When constructing constraints, it is also necessary to consider the mutual influence and constraints between different parameters. For example, there may be a positive correlation between the load and steam flow of the steam turbine, while there may be a negative correlation between the cooling water flow and temperature of the condenser. Therefore, when constructing constraints, it is necessary to comprehensively consider these relationships to ensure the rationality and effectiveness of the constraints.

[0108] Finally, these constraints are integrated into the efficiency optimization objective function to form a constrained optimization problem. By solving this optimization problem, the optimal solution that satisfies all constraints can be found, that is, the optimal solution of the efficiency optimization objective function. This optimal solution not only maximizes the operating efficiency of the turbine and condenser, but also ensures the safety and stability of the entire system.

[0109] From the above, it can be seen that the optimization method of the cold-end system provided by the present invention first obtains the external meteorological parameters and the parameters to be optimized of the cold-end system, and obtains the total operating power and operating flow of the cold-end system according to the external meteorological parameters and the parameters to be optimized; obtains the internal operating parameters of the steam turbine, and obtains the power generation power of the steam turbine according to the external meteorological parameters, the operating flow and the internal operating parameters; determines the efficiency optimization objective function of the cold-end system according to the total operating power of the cold-end system and the power generation power of the steam turbine; wherein the optimal solution of the efficiency optimization objective function is the optimization target of the cold-end system. Therefore, the present invention establishes and solves an efficiency optimization objective function by combining the external meteorological parameters, the parameters to be optimized of the cold-end system and the internal operating parameters of the steam turbine, determines the optimization target of the cold-end system by finding the optimal solution of the function, and comprehensively optimizes the operating efficiency of the cold-end system.

[0110] It should be noted that the method of the embodiment of the present invention can be performed by a single device, such as a computer or a server. The method of this embodiment can also be applied in a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present invention, and the multiple devices will interact with each other to complete the described method.

[0111] It should be noted that some embodiments of the present invention are described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0112] Based on the same inventive concept, corresponding to the method provided in any of the above embodiments, the present invention further provides an optimization device for a cold end system.

[0113] refer to Figure 3 , which is a schematic diagram of an optimization device for a cold end system provided in an embodiment of the present invention.

[0114] The system includes: an acquisition module 301 , a determination module 302 , a first optimization module 303 , a second optimization module 304 and a third optimization module 305 .

[0115] An acquisition module 301 is configured to acquire external meteorological parameters of the cold end system and internal operating parameters of the steam turbine;

[0116] The determination module 302 is configured to determine parameters to be optimized and optimization targets; the parameters to be optimized include the number of operating units and operating frequency of cooling fluid equipment required by the cold end system; the optimization target is to maximize the difference between the total operating power of the cold end system and the power generation power of the steam turbine;

[0117] The first optimization module 303 is configured to input the external meteorological parameters and the parameters to be optimized into a pre-built first optimization model to obtain the total operating power of the cooling fluid device and the operating flow rate of the cooling fluid;

[0118] The second optimization module 304 is configured to input the external meteorological parameters, the internal operating parameters, and the operating flow rate of the cooling fluid into a pre-built second optimization model to obtain the power generation of the steam turbine;

[0119] The third optimization module 305 is configured to determine the optimization objective function of the cold end system according to the total operating power of the cooling fluid equipment and the power generation power of the turbine; wherein the optimal solution of the optimization objective function is the value of the parameter to be optimized that meets the optimization target.

[0120] Optionally, the first optimization model is obtained by modeling actual operating characteristics of the cooling fluid equipment through a machine learning algorithm based on historical operating data of the cooling fluid equipment.

[0121] Optionally, the first optimization model is modeled as follows:

[0122]

[0123] Where γ is the bulk density of the cooling fluid, in N / m 3 ; Q is the volume flow rate of the cooling fluid, in m 3 / s; H is the head of the cooling fluid equipment, in m; p is the total pressure of the cooling fluid equipment, in Pa; η is the efficiency of the cooling fluid equipment, in %; n is the operating frequency of the cooling fluid equipment; n0 is the rated frequency of the cooling fluid equipment; Q n H is the flow rate of the cooling fluid equipment at the operating frequency n; n is the head of the cooling fluid equipment at the operating frequency n; η n is the efficiency of the cooling fluid device at the operating frequency n; is the flow rate of the cooling fluid equipment at the rated frequency n0; is the head of the cooling fluid equipment at the rated frequency n0; η n0 is the efficiency of the cooling fluid device at the rated frequency n0; p1 is the pressure at the inlet interface of the cooling fluid pipeline, in Pa; p2 is the pressure at the outlet interface of the cooling fluid pipeline, in Pa; H z is the height difference between the inlet and outlet interfaces of the cooling fluid pipeline, in meters; S is the impedance of the cooling fluid pipeline, in s 2 / m 2 ; i is the number of cooling fluid equipment in operation.

[0124] Optionally, the second optimization model includes a pre-built condenser heat exchange model and a turbine power output model, wherein the condenser heat exchange model is obtained by modeling the actual operating characteristics of the condenser based on the historical operating data of the condenser through a machine learning algorithm; the turbine power output model is obtained by modeling the actual operating characteristics of the turbine based on the historical operating data of the turbine through a machine learning algorithm.

[0125] Optionally, the condenser heat exchange model is modeled as follows:

[0126]

[0127] Among them, m f is the mass flow rate of exhaust steam, in kg / s; h f is the enthalpy of exhaust steam, in kJ / kg; h w is the enthalpy of condensed water, in kJ / kg; K is the total heat transfer coefficient of the condenser, in kW / (m 2 ℃); F is the heat exchange area of ​​the condenser, in m 2 ; Δt is the heat transfer temperature difference of the condenser, in °C; m l is the mass flow rate of the cooling fluid, in kg / s; c l is the specific heat capacity of the cooling fluid, in kJ / (kg℃); t in is the inlet temperature of the cooling fluid, in °C; t outis the outlet temperature of the cooling fluid, in °C; p f is the operating back pressure of the steam turbine, in kPa; t w is the temperature of condensed water, in °C; t f is the exhaust steam temperature, in °C.

[0128] Optionally, the steam turbine work output model is modeled as follows:

[0129] P = f(m z , p z ,h z , m zr , p zr ,h zr , m f , p f ,h f )

[0130] Among them, m z is the flow rate of main steam, in kg / s; p z is the pressure of the main steam, in kPa; h z is the enthalpy of main steam, in kJ / kg; m zr is the flow rate of reheat steam, in kJ / s; p zr is the pressure of reheat steam, in kPa; h zr is the enthalpy of reheated steam, in kJ / kg.

[0131] Optionally, the first optimization module 303 is further configured to:

[0132] Inputting the external meteorological parameter, the main steam parameter, the reheat steam parameter and the operating flow into a pre-built second optimization model to obtain the operating back pressure of the steam turbine;

[0133] The upper limit and the lower limit of the operating back pressure of the steam turbine are determined according to the operating regulations of the steam turbine and the condenser and actual operating experience.

[0134] Optionally, the first optimization module 303 is further configured to:

[0135] The maximum and minimum values ​​of the number of operating cooling fluid devices required for the cold end system and the maximum and minimum values ​​of the operating frequency are determined according to the operating procedures of the steam turbine and the condenser and actual operating experience.

[0136] Optionally, the first optimization module 303 is further configured to:

[0137] The optimal solution of the optimization objective function is determined based on the upper and lower limits of the turbine operating back pressure, the maximum and minimum numbers of operating cooling fluid equipment required by the cold end system, and the maximum and minimum operating frequencies.

[0138] According to the optimization device of the cold end system of the embodiment of the present invention, the external meteorological parameters of the cold end system and the internal operating parameters of the steam turbine are obtained by the acquisition module; the parameters to be optimized and the optimization target are determined by the determination module; the parameters to be optimized include the number of operating units and the operating frequency of the cooling fluid equipment required by the cold end system; the optimization target is to maximize the difference between the total operating power of the cold end system and the power generation power of the steam turbine; the external meteorological parameters and the parameters to be optimized are input into the pre-built first optimization model through the first optimization module to obtain the total operating power of the cooling fluid equipment and the operating flow of the cooling fluid; the external meteorological parameters, the internal operating parameters, and the operating flow of the cooling fluid are input into the pre-built second optimization model through the second optimization module to obtain the power generation power of the steam turbine; the optimization objective function of the cold end system is determined according to the total operating power of the cooling fluid equipment and the power generation power of the steam turbine through the third optimization module; the optimal solution of the optimization objective function is the value of the parameters to be optimized that meets the optimization target. Therefore, the present invention establishes and solves an efficiency optimization objective function by combining the external meteorological parameters, the parameters to be optimized of the cold end system and the internal operating parameters of the steam turbine, determines the optimization target of the cold end system by finding the optimal solution of the function, and comprehensively optimizes the operating efficiency of the cold end system.

[0139] For the convenience of description, the above system is described as being divided into various modules according to their functions. Of course, when implementing the present invention, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0140] The system of the above embodiment is used to implement the optimization method of the corresponding cold end system in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0141] Based on the same inventive concept, corresponding to the cold end system optimization method described in any of the above embodiments, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the cold end system optimization method described in any of the above embodiments when executing the program.

[0142] Figure 4A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 410, a memory 420, an input / output interface 430, a communication interface 440, and a bus 450. The processor 410, the memory 420, the input / output interface 430, and the communication interface 440 are connected to each other through the bus 450 in the device.

[0143] The processor 410 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0144] The memory 420 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 420 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 420 and are called and executed by the processor 410.

[0145] The input / output interface 430 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0146] The communication interface 440 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0147] The bus 450 comprises a pathway for transmitting information between the various components of the device (eg, the processor 410, the memory 420, the input / output interface 430, and the communication interface 440).

[0148] It should be noted that, although the above device only shows the processor 410, the memory 420, the input / output interface 430, the communication interface 440 and the bus 450, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0149] The electronic device of the above embodiment is used to implement the corresponding cold end system optimization method in any of the above embodiments, and has the beneficial effects of the corresponding cold end system optimization method embodiment, which will not be described in detail here.

[0150] Based on the same inventive concept, corresponding to the cold end system optimization method described in any of the above embodiments, the present invention also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the cold end system optimization method described in any of the above embodiments.

[0151] The above-mentioned non-transitory computer-readable storage medium can be any available medium or data storage device that can be accessed by a computer, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.), optical storage (such as CD, DVD, BD, HVD, etc.), and semiconductor storage (such as ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)), etc.

[0152] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the cold end system optimization method described in any embodiment in the above exemplary method part, and have the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0153] In addition, although the operations of the method of the present invention are described in a specific order in the accompanying drawings, this does not require or imply that the operations must be performed in this specific order, or that all the operations shown must be performed to achieve the desired results. On the contrary, the steps depicted in the flow chart can be performed in a different order. Additionally or alternatively, some steps can be omitted, multiple steps can be combined into one step, and / or one step can be decomposed into multiple steps.

[0154] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0155] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the embodiments of the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0156] Although the spirit and principle of the present invention have been described with reference to several specific embodiments, it should be understood that the present invention is not limited to the disclosed specific embodiments, and the division of various aspects does not mean that the features in these aspects cannot be combined to benefit, and this division is only for the convenience of expression. The present invention is intended to cover various modifications and equivalent arrangements included in the spirit and scope of the attached claims. The scope of the attached claims conforms to the broadest interpretation, thereby including all such modifications and equivalent structures and functions.

Claims

1. A cold end system optimization method, characterized in that: The method comprises: Obtain external meteorological parameters of the cold end system and internal operating parameters of the steam turbine; Determine the parameters to be optimized and the optimization target; the parameters to be optimized include the number of operating units and the operating frequency of the cooling fluid equipment required by the cold end system; the optimization target is to maximize the difference between the total operating power of the cold end system and the power generation power of the steam turbine; Inputting the external meteorological parameters and the parameters to be optimized into a pre-built first optimization model to obtain the total operating power of the cooling fluid device and the operating flow rate of the cooling fluid; Inputting the external meteorological parameters, the internal operating parameters, and the operating flow rate of the cooling fluid into a pre-built second optimization model to obtain the power generation power of the steam turbine; The optimization objective function of the cold end system is determined according to the total operating power of the cooling fluid equipment and the power generation power of the steam turbine; wherein the optimal solution of the optimization objective function is the value of the parameter to be optimized that meets the optimization target.

2. The cold end system optimization method according to claim 1, characterized in that: The first optimization model is obtained by modeling the actual operating characteristics of the cooling fluid equipment through a machine learning algorithm based on historical operating data of the cooling fluid equipment.

3. The cold end system optimization method according to claim 2, characterized in that: The first optimization model is modeled as follows: Where γ is the bulk density of the cooling fluid, in N / m 3 ; Q is the volume flow rate of the cooling fluid, in m 3 / s; H is the head of the cooling fluid equipment, in m; p is the total pressure of the cooling fluid equipment, in Pa; η is the efficiency of the cooling fluid equipment, in %; n is the operating frequency of the cooling fluid equipment; n0 is the rated frequency of the cooling fluid equipment; Q n H is the flow rate of the cooling fluid equipment at the operating frequency n; n is the head of the cooling fluid equipment at the operating frequency n; η n is the efficiency of the cooling fluid device at the operating frequency n; It is the flow rate of cooling fluid equipment at rated frequency n0; It is the head of the cooling fluid equipment at the rated frequency n0; is the efficiency of the cooling fluid device at the rated frequency n0; p1 is the pressure at the inlet interface of the cooling fluid pipeline, in Pa; p2 is the pressure at the outlet interface of the cooling fluid pipeline, in Pa; H z is the height difference between the inlet and outlet interfaces of the cooling fluid pipeline, in meters; S is the impedance of the cooling fluid pipeline, in s 2 / m 2 ; i is the number of cooling fluid equipment in operation.

4. The cold end system optimization method according to claim 1, characterized in that: The second optimization model includes a pre-built condenser heat exchange model and a turbine power output model. The condenser heat exchange model is obtained by modeling the actual operating characteristics of the condenser based on the historical operating data of the condenser through a machine learning algorithm; the turbine power output model is obtained by modeling the actual operating characteristics of the turbine based on the historical operating data of the turbine through a machine learning algorithm.

5. The cold end system optimization method according to claim 4, characterized in that: The condenser heat exchange model is modeled as follows: Among them, m f is the mass flow rate of exhaust steam, in kg / s; h f is the enthalpy of exhaust steam, in kJ / kg; h w is the enthalpy of condensed water, in kJ / kg; K is the total heat transfer coefficient of the condenser, in kW / (m 2 ℃); F is the heat exchange area of ​​the condenser, in m 2 ; Δt is the heat transfer temperature difference of the condenser, in °C; m l is the mass flow rate of the cooling fluid, in kg / s; c l is the specific heat capacity of the cooling fluid, in kJ / (kg℃); t in is the inlet temperature of the cooling fluid, in °C; t out is the outlet temperature of the cooling fluid, in °C; p f is the operating back pressure of the steam turbine, in kPa; t w is the temperature of condensed water, in °C; t f is the exhaust steam temperature, in °C.

6. The cold end system optimization method according to claim 4, characterized in that: The steam turbine output model is modeled as follows: P=f(m z ,p z ,h z ,m zr ,p zr ,h zr ,m f ,p f ,h f ) Among them, m z is the flow rate of main steam, in kg / s; p z is the pressure of the main steam, in kPa; h z is the enthalpy of main steam, in kJ / kg; m zr is the flow rate of reheat steam, in kJ / s; p zr is the pressure of reheat steam, in kPa; h zr is the enthalpy of reheated steam, in kJ / kg.

7. The cold end system optimization method according to claim 6, characterized in that: The method further comprises: Inputting the external meteorological parameter, the main steam parameter, the reheat steam parameter and the operating flow into a pre-built second optimization model to obtain the operating back pressure of the steam turbine; The upper limit and the lower limit of the operating back pressure of the steam turbine are determined according to the operating regulations of the steam turbine and the condenser and actual operating experience.

8. The cold end system optimization method according to claim 7, characterized in that: The method further comprises: The maximum and minimum values ​​of the number of operating cooling fluid devices required for the cold end system and the maximum and minimum values ​​of the operating frequency are determined according to the operating procedures of the steam turbine and the condenser and actual operating experience.

9. The cold end system optimization method according to any one of claims 7 or 8, characterized in that: The method further comprises: The optimal solution of the optimization objective function is determined according to the upper and lower limits of the turbine operating back pressure, the maximum and minimum values ​​of the operating number of cooling fluid equipment required by the cold end system, and the maximum and minimum values ​​of the operating frequency.

10. An optimization device for a cold end system, characterized in that: include: an acquisition module configured to acquire external meteorological parameters of the cold end system and internal operating parameters of the steam turbine; A determination module is configured to determine parameters to be optimized and an optimization target; the parameters to be optimized include the number of operating units and the operating frequency of the cooling fluid equipment required by the cold end system; the optimization target is to maximize the difference between the total operating power of the cold end system and the power generation power of the steam turbine; A first optimization module is configured to input the external meteorological parameters and the parameters to be optimized into a pre-built first optimization model to obtain the total operating power of the cooling fluid device and the operating flow rate of the cooling fluid; A second optimization module is configured to input the external meteorological parameters, the internal operating parameters, and the operating flow rate of the cooling fluid into a pre-built second optimization model to obtain the power generation power of the steam turbine; The third optimization module is configured to determine the optimization objective function of the cold end system according to the total operating power of the cooling fluid equipment and the power generation power of the turbine; wherein the optimal solution of the optimization objective function is the value of the parameter to be optimized that meets the optimization goal.

11. An electronic 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 program, the cold end system optimization method according to any one of claims 1 to 9 is implemented.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the cold end system optimization method according to any one of claims 1 to 9.

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