Optimization method, device and equipment for rotating speed and readable storage medium

Abstract

The application discloses a rotating speed optimization method, device and equipment and a readable storage medium. Full characteristic curve data set of a pumped storage power station to be optimized is acquired, the full characteristic curve data set comprises a plurality of first working condition point data of the pumped storage power station, and a plurality of operating parameters are included in the first working condition point data. A plurality of second working condition point data meeting working condition requirements are determined based on the full characteristic curve data set. The second working condition point data are used as particles in a particle swarm optimization algorithm to perform optimization processing, so that target working condition point data with optimal efficiency are obtained. The target rotating speed of the pumped storage power station is determined based on operating parameters in the target working condition point data. The application can effectively improve the accuracy of the rotating speed optimization of the pumped storage power station.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage equipment, and in particular to a rotational speed optimization method, device, equipment and readable storage medium. Background Art

[0002] The speed of a variable-speed pumped-storage unit is adjustable within a certain range, providing additional efficiency and flexibility. The adjustable speed range is primarily determined by the technical characteristics of the power supply, while the speed of the pump-turbine on the hydraulic side requires specific determination. Related technologies use the simulation software's built-in table lookup function to determine the speed from a pre-set database to control the operation of the pumped-storage unit. However, this speed determination is not necessarily optimal, and the accuracy of speed optimization needs to be improved. Summary of the Invention

[0003] The embodiments of the present invention provide a speed optimization method, device, equipment and readable storage medium, which are intended to effectively improve the speed optimization accuracy of pumped storage equipment.

[0004] In a first aspect, an embodiment of the present invention provides a method for optimizing a rotational speed, comprising:

[0005] Acquire a full characteristic curve data set of a pumped storage device to be optimized, wherein the full characteristic curve data set includes a plurality of first operating point data of the pumped storage device, wherein the first operating point data includes a plurality of operating parameters;

[0006] Determining a plurality of second operating point data that meet the operating condition requirements based on the full characteristic curve data set;

[0007] The second operating point data is used as particles in a particle swarm algorithm to perform optimization processing to obtain the target operating point data with the best efficiency;

[0008] The target rotational speed of the pumped-storage equipment is determined based on the operating parameters in the target operating point data.

[0009] Optionally, the step of performing optimization processing using the second operating point data as particles in a particle swarm algorithm to obtain target operating point data with optimal efficiency includes:

[0010] Based on the particle swarm algorithm, generating particles corresponding to the data of each second operating point;

[0011] Adjusting the position of the particle to update the second operating point data corresponding to the particle;

[0012] Determining the adjusted individual optimal fitness and individual optimal position of the particle according to the efficiency of the updated second operating point data;

[0013] In the case where the preset optimization condition is not met, the optimal position of the particle group is updated according to the adjusted individual optimal fitness and individual optimal position of the particle, and the step of adjusting the position of the particle is re-executed based on the updated optimal position of the particle group and the adjusted individual optimal position of the particle to update the second operating point data corresponding to the particle until the preset optimization condition position is met;

[0014] When the preset optimization conditions are met, the target operating point data is determined according to the individual optimal position of the particle corresponding to the latest optimal fitness of the particle group.

[0015] Optionally, determining the adjusted individual optimal fitness and individual optimal position of the particle according to the efficiency of the updated second operating point data includes:

[0016] Obtaining the historical individual optimal fitness of the particle before adjustment;

[0017] When the efficiency of the updated second operating point data corresponding to the particle is greater than the historical individual optimal fitness, the efficiency of the updated second operating point data is used as the adjusted individual optimal fitness of the particle, and the adjusted position of the particle is used as the adjusted individual optimal position of the particle;

[0018] When the efficiency of the updated second operating point data corresponding to the particle is less than the historical individual optimal fitness, the historical individual optimal fitness is used as the individual optimal fitness of the particle after adjustment, and the historical individual optimal position of the particle before adjustment is used as the individual optimal position of the particle after adjustment.

[0019] Optionally, the preset optimization condition includes that the individual optimal fitness of a preset number of particles after adjustment is less than or equal to the optimal fitness of the particle group before adjustment.

[0020] Optionally, the operating condition requirement includes an actual power requirement and an actual water head requirement, and determining a plurality of second operating point data meeting the operating condition requirement based on the full characteristic curve data set includes:

[0021] determining a target unit power of the pumped-storage equipment according to the actual power requirement and the actual water head requirement;

[0022] From the full characteristic curve data set, first operating point data that meets the target unit power is screened out as the second operating point data.

[0023] Optionally, the step of obtaining a full characteristic curve data set of the pumped-storage equipment to be optimized, wherein the full characteristic curve data set includes a plurality of first operating point data of the pumped-storage equipment, includes:

[0024] Acquiring multiple initial operating point data of the pumped storage equipment;

[0025] Interpolation and fitting are performed based on the multiple initial operating point data to obtain the full characteristic curve data set.

[0026] Optionally, the obtaining of a plurality of initial operating point data of the pumped storage equipment includes:

[0027] collecting unit speed, unit flow rate, and unit torque of the pumped storage equipment under at least two operating conditions;

[0028] Determining unit power according to the unit speed, the unit flow rate, and the unit torque;

[0029] determining a unit efficiency of the pumped-storage equipment according to the unit power and the unit flow rate;

[0030] Initial operating point data corresponding to the operating condition is obtained according to the unit speed, the unit flow, the unit torque, the unit power and the unit efficiency.

[0031] In a second aspect, an embodiment of the present invention provides a speed optimization device, the speed optimization device comprising:

[0032] an acquisition module, configured to acquire a full characteristic curve data set of the pumped-storage equipment to be optimized, wherein the full characteristic curve data set includes a plurality of first operating point data of the pumped-storage equipment, wherein the first operating point data includes a plurality of operating parameters;

[0033] a screening module, configured to determine a plurality of second operating point data meeting the operating condition requirements based on the full characteristic curve data set;

[0034] An optimization module, configured to perform optimization processing using the second operating point data as particles in a particle swarm algorithm to obtain target operating point data with the best efficiency;

[0035] A determination module is used to determine the target rotational speed of the pumped-storage equipment based on the operating parameters in the target operating point data.

[0036] In a third aspect, an embodiment of the present invention further provides a speed optimization device, comprising a processor and a memory, wherein the memory stores a computer program. When the computer program is executed by the processor, the processor executes the steps of any speed optimization method provided in the embodiment of the present invention.

[0037] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, which includes a computer program. When the computer program runs on an electronic device, the computer program is used to enable the electronic device to execute the steps of any speed optimization method provided by the embodiment of the present invention.

[0038] The present invention obtains a full characteristic curve data set for a pumped-storage device to be optimized, the full characteristic curve data set comprising multiple first operating point data for the pumped-storage device, the first operating point data comprising multiple operating parameters; determines multiple second operating point data that meet operating requirements based on the full characteristic curve data set; uses the second operating point data as particles in a particle swarm algorithm for optimization processing to obtain target operating point data with the highest efficiency; and determines the target speed of the pumped-storage device based on the operating parameters in the target operating point data. Thus, by first screening multiple second operating point data that meet operating requirements for the full characteristic curve data set for the pumped-storage device to be optimized, and then performing optimization processing using the particle swarm algorithm based on the second operating point data, the target operating point data with the highest efficiency can be quickly and accurately found, thereby accurately determining the optimal speed of the pumped-storage device with the highest efficiency and that meets the operating requirements as the target speed, thereby improving the accuracy of speed optimization for the pumped-storage device. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 This is a flow chart of an embodiment of a method for optimizing rotational speed provided in an embodiment of the present invention;

[0041] Figure 2 This is a schematic diagram of an optimization process provided in an embodiment of the present invention;

[0042] Figure 3 is a schematic diagram of the relationship between data sets involved in the embodiments of the present invention;

[0043] Figure 4 1 is a schematic structural diagram of a rotation speed optimization device provided in an embodiment of the present invention;

[0044] Figure 5 It is a structural schematic diagram of the speed optimization device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention. At the same time, in the description of the embodiments of the present invention, the terms "first", "second", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more features. In the description of the embodiments of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0046] Embodiments of the present invention provide a rotation speed optimization method, apparatus, device, and readable storage medium.

[0047] Specifically, this embodiment will be described from the perspective of a speed optimization device, which can be integrated into a pumped storage device. That is, the speed optimization method of the embodiment of the present invention can be executed by the pumped storage device.

[0048] The following detailed description is provided in conjunction with the accompanying drawings. This embodiment uses a pumped-storage system as an example. It should be noted that the order in which the following embodiments are described does not limit the preferred order of the embodiments. Although the flowcharts illustrate a logical order, in some cases, the steps shown or described may be performed in a different order than that shown in the accompanying drawings.

[0049] Compared with traditional fixed-speed pumped storage units, the power side technology of variable-speed pumped storage units has been innovated, making their speed adjustable within a certain range. This is the technical advantage of variable-speed pumped storage units, which brings additional efficiency and flexibility to the units.

[0050] Because the adjustable speed range is primarily determined by the technical characteristics of the power supply, the pump-turbine speed of the variable-speed pumped-storage unit's hydraulic side must be appropriately determined based on specific operating conditions. This ensures the hydraulic side aligns with the power supply characteristics and fully utilizes the adjustable speed. In actual engineering applications, the pump-turbine operates in turbine mode under power generation conditions. The power grid's power generation requirements for the unit, combined with the head of the pumped-storage unit corresponding to the power station, may result in multiple unit operating points that meet these requirements. Therefore, a rational optimization strategy is necessary to determine the optimal unit speed.

[0051] Some related technologies propose strategies for optimizing the speed of variable-speed pumped-storage units. Based on the full characteristic curve of the pump-turbine, an interpolation method is used to derive the corresponding relationship between speed, power, head, and efficiency. For each head and power combination within the turbine's operating range, the optimal speed corresponding to the operating condition is determined based on the principle of maximum efficiency. Subsequently, using the simulation software's built-in table lookup function, based on the actual operating conditions, an interpolation is performed within the established power, head, and corresponding database to ultimately determine the optimal speed that meets the operating conditions.

[0052] The above-mentioned technology establishes the speed optimization principle of the variable-speed pumped storage unit, clarifies the optimization ideas, and proposes that the speed optimization process is mainly based on the full characteristic curve, and adopts a method that combines calculation and interpolation methods to obtain the optimal speed of the unit according to the specific operating conditions. However, when establishing a database based on the full characteristic curve, the speed optimization method uses calculation and interpolation methods to process the data. In the optimization process, only the table lookup function provided by the software is used, and no scientific optimization algorithm is combined. Multiple interpolations will also affect the accuracy of the final optimal speed. Therefore, although the speed optimization principle of the variable-speed pumped storage unit in the relevant technology is clear, there is no systematic, scientific, and precision-guaranteed interpolation process and method for the optimization process of ultimately obtaining the optimal speed from a discrete full characteristic curve data set. Therefore, we urgently need to develop a speed optimization method that requires minimal interpolation and combines existing mature optimization algorithms. This method addresses the multivariable nature of the speed optimization process for variable-speed pumped storage units, requiring interpolation from discrete data. Furthermore, it combines the control accuracy of the governor opening and speed to establish a speed optimization method with a clear data processing scheme, a reasonable interpolation method, and reliable optimization algorithm accuracy. This method can be used in practical projects to guide the speed optimization of the pump-turbine of a variable-speed pumped storage unit, fully utilizing the advantageous speed adjustability and improving the efficiency of the variable-speed unit.

[0053] In order to solve the above problems, the present invention discloses a method for optimizing the rotation speed. Figure 1 The specific process of the speed optimization method can be as follows: Step S10 to Step S40, wherein:

[0054] Step S10, obtaining a full characteristic curve data set of the pumped storage equipment to be optimized, wherein the full characteristic curve data set includes a plurality of first operating point data of the pumped storage equipment, wherein the first operating point data includes a plurality of operating parameters;

[0055] In this embodiment, the speed of the pumped-storage equipment is adjustable. The pumped-storage equipment may be a variable-speed pumped-storage unit, which includes a pump-turbine. Specifically, the speed of the pump-turbine is adjustable. A full characteristic curve dataset is obtained for the pumped-storage equipment whose speed is to be optimized. The full characteristic curve dataset can reflect the operating characteristics of the pumped-storage equipment or the pump-turbine in the pumped-storage equipment. The full characteristic curve dataset includes multiple first operating point data for the pumped-storage equipment. The first operating point data can characterize the operating conditions of the pumped-storage equipment or the pump-turbine in the pumped-storage equipment under a certain operating condition. The first operating point data can include multiple operating parameters under the corresponding operating condition to more comprehensively characterize the corresponding operating condition. The operating parameter can be a unit operating parameter, which can specifically include at least one of unit speed, unit flow, unit torque, unit power, and unit efficiency.

[0056] Step S20, determining a plurality of second operating point data that meet the operating condition requirements based on the full characteristic curve data set;

[0057] In this embodiment, the operating condition requirements of the pumped-storage equipment are obtained. The operating condition requirements are set based on the operating requirements of the pumped-storage equipment and the environmental conditions corresponding to the pumped-storage equipment. According to the operating condition requirements, the full characteristic curve data set is screened, and it is found that there are multiple first operating point data in the data set that meet the operating condition requirements, thereby obtaining multiple second operating point data that meet the operating condition requirements.

[0058] Step S30, using the second operating point data as particles in a particle swarm algorithm to perform optimization processing to obtain target operating point data with the best efficiency;

[0059] In this embodiment, after obtaining multiple second operating point data that meet the operating condition requirements, optimization is performed based on the optimal efficiency. Considering that the speed optimization process of the pumped storage equipment has multiple variables and requires interpolation from discrete operating point data, and fully considering the accuracy of pumped storage equipment control and interpolation optimization, this embodiment introduces a particle swarm algorithm. The particle swarm algorithm is a stochastic optimization algorithm based on swarm intelligence. This method simulates the foraging behavior of a flock of birds and searches for the optimal solution through information sharing and collaboration between individuals. In the particle swarm algorithm, each potential solution to the optimization problem can be imagined as a bird in the search space, called a "particle." All particles have a fitness determined by the function being optimized. Each particle also has a speed that determines the direction and distance of their flight. Particles search the solution space following the current optimal particle.

[0060] The goal of the particle swarm optimization algorithm is to find the speed value that achieves optimal efficiency based on the required operating conditions. By simulating the foraging behavior of a flock of birds, the algorithm iteratively searches for the optimal target operating point within a database range. The second operating point data is used as particles in the particle swarm optimization process, with each particle representing a potential alternative operating condition. By continuously updating the particle's position and velocity, the algorithm ultimately finds the target operating point that optimizes the objective function (efficiency), representing the optimal operating condition for the pumped storage system being optimized.

[0061] Step S40: determining a target rotational speed of the pumped storage equipment based on the operating parameters in the target operating point data.

[0062] In this embodiment, the target operating point data also includes operating parameters under various optimal operating conditions. Generally, the target operating point data includes the same types of operating parameters as the first operating point data and the second operating point data. The operating parameters included in the target operating point data may be at least one of unit speed, unit flow, unit torque, unit power, and unit efficiency.

[0063] In some embodiments, the operating parameters in the target operating point data include a rotational speed, and the rotational speed can be directly obtained from the operating parameters as the target rotational speed of the pumped storage equipment.

[0064] In some embodiments, the operating parameters in the target operating point data do not include the rotational speed but are unit parameters. In this case, the actual rotational speed of the real machine needs to be calculated based on the operating parameters in the target operating point data as the target rotational speed.

[0065] The target speed is the optimal speed with the highest efficiency and meeting the public requirements after optimization. After determining the target speed, the operation of the pumped storage equipment is controlled based on the target speed. The pump turbine in the pumped storage equipment can be adjusted to the target speed, so that the pumped storage equipment is in an operating condition that meets the operating requirements and has the highest efficiency.

[0066] In the technical solution disclosed in this embodiment, a plurality of second operating point data that meet the operating conditions are first screened out for the full characteristic curve data set of the pumped-storage equipment to be optimized. Based on the second operating point data and through the particle swarm algorithm, the target operating point data with the highest efficiency can be quickly and accurately found, and then the optimal speed of the pumped-storage equipment with the highest efficiency and meeting the operating conditions is accurately determined as the target speed. This can improve the accuracy of the speed optimization of the pumped-storage equipment, operate the pumped-storage equipment at a more accurate optimal speed, and make full use of its advantageous characteristic of adjustable speed, so that the pumped-storage equipment can operate more efficiently.

[0067] In this embodiment, the second operating point data is used as particles in the particle swarm algorithm to perform optimization processing to obtain the target operating point data with the best efficiency, including:

[0068] Based on the particle swarm algorithm, generating particles corresponding to the data of each second operating point;

[0069] Adjusting the position of the particle to update the second operating point data corresponding to the particle;

[0070] determining the individual optimal fitness and the individual optimal position of the particle according to the efficiency of the updated second operating point data;

[0071] In the case where the preset optimization condition is not met, the optimal position of the particle group is updated according to the adjusted individual optimal fitness and individual optimal position of the particle, and the step of adjusting the position of the particle is re-executed based on the updated optimal position of the particle group and the adjusted individual optimal position of the particle to update the second operating point data corresponding to the particle until the preset optimization condition position is met;

[0072] When the preset optimization conditions are met, the target operating point data is determined according to the latest particle swarm optimal fitness corresponding particles.

[0073] In one embodiment, the goal of the particle swarm algorithm is to select a particle X = [x1, x2, ..., xn] to meet the efficiency requirement f(X). Each second operating condition data is input into the particle swarm algorithm to generate a particle X corresponding to each second operating condition point data.

[0074] The position of a particle X refers to the variable within that particle and has n dimensions, where n refers to the number of factors not fully resolved in a problem. f(X) is called fitness, which is an estimate of the quality of X and its ability to achieve a specific goal or meet specific conditions. In this embodiment, particle fitness is evaluated by the particle's efficiency in responding to operating point data.

[0075] Iteratively adjust the position of each particle to iteratively adjust the second operating point data corresponding to the particle. The particle velocity of the particle after t iterations can be expressed as , the position can be expressed as The formula for adjusting the particle position is as follows:

[0076] (1)

[0077] (2)

[0078] in Indicates the update of the velocity of the i-th particle in the d-dimensional space at the t+1th iteration, c1 is the self-learning factor, c2 is the global learning factor, r1 and r2 are random numbers uniformly distributed in (0,1), represents the individual optimal position of the i-th particle in the d-dimensional space after t iterations, It is understood that formula (1) represents the update of particle velocity, and formula (2) represents the update of particle position, where formula (1) represents the update of particle velocity, and formula (2) represents the update of particle position. is the inertia weight, which determines the stability of particle velocity changes.

[0079] The particle position is adjusted by the particle swarm algorithm, so that the second operating point data corresponding to the particle can be updated. Before the preset optimization conditions are met, the particle position is iteratively adjusted to update the second operating point data corresponding to the particle to obtain a new operating point. The efficiency of the updated second operating point data is calculated based on the efficiency formula and the second operating point data corresponding to the updated particle to characterize the efficiency of the corresponding operating point after the particle position is adjusted, and the individual optimal fitness and individual optimal position of the adjusted particle position are further determined. After each round of iteration, it is determined whether the preset optimization conditions set based on the speed optimization principle are met. If the preset optimization conditions are not met, the adjusted individual optimal fitness of each particle can be compared to determine the particle with the largest adjusted individual optimal fitness. The adjusted individual optimal position of the particle is then updated to the optimal position of the particle swarm, and the adjusted individual optimal fitness of the particle is used as the optimal fitness of the particle swarm.

[0080] The optimal position of the particle swarm can represent the operating point with higher efficiency found in the current iteration round, and the optimal fitness of the particle swarm can represent the efficiency of the operating point found in the current iteration round. Then, based on the updated optimal position of the particle swarm and the adjusted individual optimal position of the particle, the position of the particle is readjusted so that the particle swarm gradually moves towards a better solution, so as to accurately update the second operating point data corresponding to the particle, and re-update the individual optimal fitness and individual optimal position of the particle. Until the preset optimization condition is met. The preset optimization condition is a pre-set condition, which represents that the optimal fitness of the particle swarm updated in the current iteration reaches the global highest after iteration, and the operating point corresponding to the optimal position of the particle swarm is the globally optimal operating point found after iteration.

[0081] When the preset optimization conditions are met, the particle swarm's optimal fitness reaches a global maximum after iteration, and the particle swarm's optimal position corresponds to the globally optimal operating point found after iteration. Based on the particle swarm's optimal position, the globally optimal operating point data can be determined. This serves as the target operating point data, representing the operating point of the pumped storage equipment to be optimized that has the highest efficiency and meets the operating requirements.

[0082] In this way, by updating the individual optimal position and the group optimal position through the particle swarm algorithm, the global optimal target operating point data can be iteratively found, ensuring the comprehensiveness and reliability of speed optimization, and not easily missing the operating point with the best efficiency, thereby further improving the accuracy of speed optimization.

[0083] In one embodiment, determining the adjusted individual optimal fitness and individual optimal position of the particle according to the efficiency of the updated second operating point data includes:

[0084] Obtaining the historical individual optimal fitness of the particle before adjustment;

[0085] When the efficiency of the updated second operating point data corresponding to the particle is greater than the historical individual optimal fitness, the efficiency of the updated second operating point data is used as the adjusted individual optimal fitness of the particle, and the adjusted position of the particle is used as the adjusted individual optimal position of the particle;

[0086] When the efficiency of the updated second operating point data corresponding to the particle is less than the historical individual optimal fitness, the historical individual optimal fitness is used as the individual optimal fitness of the particle after adjustment, and the historical individual optimal position of the particle before adjustment is used as the individual optimal position of the particle after adjustment.

[0087] In this embodiment, after each round of adjusting the position of the particle, the operating point data corresponding to the adjusted position of each particle can be obtained and used as the updated second operating point data corresponding to the particle. The efficiency of the updated second operating point data can be used to evaluate the fitness of the particle after the position is adjusted.

[0088] The historical individual optimal fitness of the particle before position adjustment is obtained. The historical individual optimal fitness is also the optimal efficiency of the particle's historical operating point data, which can represent the optimal operating point and optimal efficiency found before the particle is adjusted.

[0089] For each particle with an adjusted position, the efficiency of the corresponding updated second operating point data is compared with the historical individual optimal fitness before adjustment. If the efficiency of the updated second operating point data is greater than the historical individual optimal fitness, the adjusted position is considered to be more optimal for the particle. Therefore, the efficiency of the corresponding updated second operating point data is used as the particle's adjusted individual optimal fitness, and the particle's individual optimal fitness is updated. The particle's adjusted position is then used as the particle's individual optimal position. Conversely, if the efficiency of the updated second operating point data is less than the historical individual optimal fitness, the particle's historical individual optimal position before adjustment is considered to be more optimal. Therefore, the historical individual optimal position is still used as the particle's adjusted individual optimal fitness, and the individual optimal fitness is not updated. The particle's individual optimal position before adjustment is used as the particle's adjusted individual optimal position.

[0090] In this way, by comparing the efficiency of each particle after position adjustment with its historical individual optimal fitness, the particles can participate in the optimization process with more efficient individual optimal fitness and individual optimal position, further ensuring the comprehensiveness and reliability of speed optimization and further improving the accuracy of speed optimization.

[0091] In one embodiment, the preset optimization condition includes that the individual optimal fitness of a preset number of particles after adjustment is less than or equal to the optimal fitness of the particle group before adjustment.

[0092] In this embodiment, in each iteration, the calculated optimal fitness of the adjusted individual particles is compared with the optimal fitness of the swarm particles before adjustment. The number of particles whose adjusted individual fitness is less than or equal to the optimal fitness of the swarm particles before adjustment is counted. If a preset number of particles have an adjusted individual fitness less than or equal to the optimal fitness of the swarm particles before adjustment, it is determined that the preset optimization condition is met and the iterative process can be terminated. Generally, the preset number is equal to the total number of particles, thereby ensuring that a more accurate optimal solution is found. By setting the optimized condition that the adjusted individual fitness of a preset number of particles is less than or equal to the optimal fitness of the swarm particles before adjustment, it is ensured that the optimal solution is found without having to update the optimal position of the swarm particles based on the adjusted individual fitness and individual optimal position of the particles, thus quickly terminating the process and saving computing resources.

[0093] Based on the above embodiments, Figure 2 As shown, the optimization process using the particle swarm algorithm in this embodiment may include the following steps:

[0094] (1) Initialize the particle swarm. Based on the particle swarm algorithm, a group of particles are randomly generated in the solution space of the problem. Each particle represents a potential operating point, corresponding to a second operating point data. Each particle has a position and a velocity vector, where the position vector represents the current operating point selection scheme, and the velocity vector determines the search direction and step size of the particle in the solution space.

[0095] (2) Evaluate the efficiency of the operating point. For each particle, the efficiency of the particle at the second operating point can be determined based on the efficiency of the particle's corresponding data, which serves as the particle's initial individual optimal fitness.

[0096] (3) Update individual and group optimality: Compare the current individual optimal fitness of each particle with its historical individual optimal fitness. If the current individual optimal fitness is better, update the particle's current position to the individual optimal position. At the same time, compare the individual optimal fitness of all particles, find the optimal value as the particle group optimal fitness, and update the adjusted individual optimal position of the particle corresponding to the optimal value as the particle group optimal position.

[0097] (4) Update speed and position: According to the individual optimal position of each particle and the optimal position of the particle group, as well as a certain random factor, adjust the speed and position of the particle so that the particle group gradually moves towards a better solution.

[0098] (5) Iterative optimization: Repeat the above steps of evaluating the optimal fitness of individuals, updating the optimal position of individuals and the optimal fitness of particle groups, the optimal position of particle groups, and updating the speed and position until the preset optimization conditions are met.

[0099] (6) Output results: After the iteration is completed, the optimal position of the particle swarm is output, that is, the optimal position of the particle swarm obtained by the particle swarm algorithm for speed optimization corresponds to the optimal target operating point data, and the optimal target speed of the pumped storage equipment is obtained after conversion.

[0100] In one embodiment, the operating condition requirement includes an actual power requirement and an actual water head requirement, and determining a plurality of second operating point data that meet the operating condition requirement based on the full characteristic curve data set includes:

[0101] determining a target unit power of the pumped-storage equipment according to the actual power requirement and the actual water head requirement;

[0102] From the full characteristic curve data set, first operating point data that meets the target unit power is screened out as the second operating point data.

[0103] Optionally, based on any of the above embodiments, in another embodiment of the speed optimization method of the present invention, the operating condition requirements include actual power requirements and actual water head requirements, and step S20 further includes:

[0104] S21. Determine a target unit power of the pumped-storage equipment according to the actual power requirement and the actual water head requirement;

[0105] In this embodiment, the operating condition requirements refer to the actual requirements set for the operation of the pumped-storage equipment, and may include the actual power requirements and actual hydraulic head requirements set for the pumped-storage equipment to be optimized. The actual power requirement refers to the power threshold that the pumped-storage equipment must reach during power generation, and is generally determined based on factors such as power system requirements, the installed capacity of the power station, hydraulic head characteristics, project layout characteristics, and design and manufacturing capabilities. The actual hydraulic head requirement is the actual hydraulic head threshold set for the pumped-storage equipment.

[0106] According to formula (3), the target unit power corresponding to the actual power requirement and the actual head requirement can be calculated:

[0107] (3)

[0108] in, P is the power threshold corresponding to the actual power requirement, H is the head threshold corresponding to the actual head requirement, D is the diameter of the pump turbine of the pumped storage equipment, calculated P 11 Can be used as target unit power.

[0109] S22 . Filtering out first operating point data that meets the target unit power from the full characteristic curve data set as the second operating point data.

[0110] In this embodiment, after the target unit power is determined, first operating point data whose corresponding unit power meets the target unit power is screened from the full characteristic curve data set as second operating point data.

[0111] In this way, before the optimization is performed, a data set of the second operating point data that meets the target unit power requirements is screened out. At this time, only an optimization strategy based on the optimal efficiency standard needs to be given, which can quickly and accurately screen out the target operating point data with the highest efficiency from the data set that meets the operating requirements, thereby further improving the optimization efficiency and accuracy.

[0112] In one embodiment, the step of obtaining a full characteristic curve dataset of the pumped-storage equipment to be optimized, wherein the full characteristic curve dataset includes a plurality of first operating point data of the pumped-storage equipment, includes:

[0113] Acquiring multiple initial operating point data of the pumped storage equipment;

[0114] Interpolation and fitting are performed based on the multiple initial operating point data to obtain the full characteristic curve data set.

[0115] In this embodiment, multiple initial operating point data of the pumped-storage equipment are obtained. The initial operating point data can be data actually collected and calculated during the operation of the pumped-storage equipment. By performing interpolation and fitting on the multiple initial operating point data of the pumped-storage equipment, a rich amount of data can be obtained, and a full characteristic curve data set can be obtained, which provides a data basis for optimization and can further improve the accuracy of optimization.

[0116] In one embodiment, the obtaining of a plurality of initial operating point data of the pumped storage equipment includes:

[0117] collecting unit speed, unit flow rate, and unit torque of the pumped storage equipment under at least two operating conditions;

[0118] Determining unit power according to the unit speed, the unit flow rate, and the unit torque;

[0119] determining a unit efficiency of the pumped-storage equipment according to the unit power and the unit flow rate;

[0120] Initial operating point data corresponding to the operating condition is obtained according to the unit speed, the unit flow, the unit torque, the unit power and the unit efficiency.

[0121] In this embodiment, the unit speed, unit flow and unit torque of the pumped storage equipment under at least two operating conditions are collected to obtain a set of discrete data sets [ n 11 ,Q 11 ,M 11 ]. The new discrete data set can be obtained by the following calculation formulas for unit power and unit efficiency [ n 11 ,Q 11 ,M 11 ,P 11 ,η T ]:

[0122] (4)

[0123] (5)

[0124] in, n11 is the unit speed, Q 11 is the unit flow rate, M 11 is the unit moment, P 11 is the unit moment, η T is the unit power.

[0125] Based on the above formula, this embodiment collects unit speed, unit flow rate, and unit torque of the pumped-storage equipment under at least two operating conditions. For each operating condition, the unit power of the pumped-storage equipment under that operating condition is determined based on the unit speed, unit flow rate, and unit torque under that operating condition. The unit efficiency of the pumped-storage equipment under that operating condition is determined based on the calculated unit power and unit flow rate. Initial operating point data for each operating condition is assembled based on the unit speed, unit flow rate, unit torque, unit power, and unit efficiency under each operating condition, ultimately obtaining initial operating point data for at least two operating conditions.

[0126] By collecting the unit speed, unit flow and unit torque of the pumped storage equipment under at least two operating conditions, rich and accurate discrete data can be constructed to obtain multiple initial operating point data of the pumped storage equipment, thereby interpolating and fitting an accurate full-characteristic curve data set for optimization, further improving the accuracy of optimization.

[0127] In the above embodiments, Figure 3 As shown in the figure, by processing the collected data of the pumped storage equipment, a data set including unit speed, unit flow, unit torque, unit power, and unit efficiency is established. The second part is to establish the relationship between unit efficiency and unit speed, unit flow, unit torque, and unit power through interpolation and fitting, and express it as a full characteristic curve data set. Based on the specific operating conditions, the unit operating point data that meets the operating conditions is selected from the full characteristic curve data set and optimized to obtain the unit operating point data with the highest efficiency. Ultimately, the optimal operating speed and other data are obtained to guide the operation of the pumped storage equipment.

[0128] This allows direct data processing based on the full characteristic curve dataset, followed by screening and optimization, and ultimately the conversion of unit operating conditions to real machine operating parameters. Decoupling interpolation, algorithm optimization, and real machine operating condition conversion helps reduce errors during the speed optimization process.

[0129] Applying the particle swarm algorithm to speed optimization improves the accuracy and efficiency of the speed optimization process from a discrete full-characteristic curve dataset. The iterative process of updating individual and group optimal positions ensures the comprehensiveness and reliability of the speed optimization process, making it less likely to miss the most efficient operating point.

[0130] Compared with related technologies, a method for optimizing the speed of variable-speed pumped storage equipment based on a particle swarm algorithm is proposed. Based on the above embodiment, the speed optimization of variable-speed pumped storage equipment for power generation can be performed. In this embodiment, the following beneficial effects are achieved:

[0131] First, a complete set of standardized processes for optimizing the speed of variable-speed pumped storage equipment was established;

[0132] Second, the particle swarm algorithm used has the advantages of few parameters, easy implementation, fast search speed, fast convergence speed and few adjustment parameters. The particle swarm algorithm is used in the speed optimization process to improve the accuracy of optimization from discrete four-quadrant curves, and improve the optimization efficiency and accuracy.

[0133] In practical engineering applications, speed optimization is crucial for variable-speed pumped-storage equipment. The speed optimization algorithm provided in this embodiment can reduce errors introduced by full-characteristic interpolation. This algorithm accurately and fully utilizes speed adjustability, improving the hydraulic characteristics and efficiency of variable-speed pumped-storage equipment. This provides technical support and assurance for its commissioning, further promoting the application and development of variable-speed pumped-storage units.

[0134] This embodiment also provides a speed optimization device, which can also be integrated into a speed optimization device, which can be a terminal device, a pumped storage device, etc. Figure 4 As shown, the speed optimization device may include:

[0135] An acquisition module 1001 is configured to acquire a full characteristic curve data set of a pumped storage device to be optimized, wherein the full characteristic curve data set includes a plurality of first operating point data of the pumped storage device, wherein the first operating point data includes a plurality of operating parameters;

[0136] A screening module 1002 is configured to determine a plurality of second operating point data that meet the operating condition requirements based on the full characteristic curve data set;

[0137] An optimization module 1003 is configured to perform optimization processing using the second operating point data as particles in a particle swarm algorithm to obtain target operating point data with the best efficiency;

[0138] The determination module 1004 is configured to determine a target rotational speed of the pumped-storage equipment based on the operating parameters in the target operating point data.

[0139] Optionally, the optimization module 1003 is further configured to:

[0140] Based on the particle swarm algorithm, generating particles corresponding to the data of each second operating point;

[0141] Adjusting the position of the particle to update the second operating point data corresponding to the particle;

[0142] Determining the adjusted individual optimal fitness and individual optimal position of the particle according to the efficiency of the updated second operating point data;

[0143] In the case where the preset optimization condition is not met, the optimal position of the particle group is updated according to the adjusted individual optimal fitness and individual optimal position of the particle, and the step of adjusting the position of the particle is re-executed based on the updated optimal position of the particle group and the adjusted individual optimal position of the particle to update the second operating point data corresponding to the particle until the preset optimization condition position is met;

[0144] When the preset optimization conditions are met, the target operating point data is determined according to the individual optimal position of the particle corresponding to the latest optimal fitness of the particle group.

[0145] Optionally, the optimization module 1003 is further configured to:

[0146] Obtaining the historical individual optimal fitness of the particle before adjustment;

[0147] When the efficiency of the updated second operating point data corresponding to the particle is greater than the historical individual optimal fitness, the efficiency of the updated second operating point data is used as the adjusted individual optimal fitness of the particle, and the adjusted position of the particle is used as the adjusted individual optimal position of the particle;

[0148] When the efficiency of the updated second operating point data corresponding to the particle is less than the historical individual optimal fitness, the historical individual optimal fitness is used as the individual optimal fitness of the particle after adjustment, and the historical individual optimal position of the particle before adjustment is used as the individual optimal position of the particle after adjustment.

[0149] Optionally, the preset optimization condition includes that the individual optimal fitness of a preset number of particles after adjustment is less than or equal to the optimal fitness of the particle group before adjustment.

[0150] Optionally, the operating condition requirement includes an actual power requirement and an actual water head requirement. The screening module 1002 is further configured to:

[0151] determining a target unit power of the pumped-storage equipment according to the actual power requirement and the actual water head requirement;

[0152] From the full characteristic curve data set, first operating point data that meets the target unit power is screened out as the second operating point data.

[0153] Optionally, the acquisition module 1001 is further configured to:

[0154] Acquiring multiple initial operating point data of the pumped storage equipment;

[0155] Interpolation and fitting are performed based on the multiple initial operating point data to obtain the full characteristic curve data set.

[0156] Optionally, the acquisition module 1001 is further configured to:

[0157] collecting unit speed, unit flow rate, and unit torque of the pumped storage equipment under at least two operating conditions;

[0158] Determining unit power according to the unit speed, the unit flow rate, and the unit torque;

[0159] determining a unit efficiency of the pumped-storage equipment according to the unit power and the unit flow rate;

[0160] Initial operating point data corresponding to the operating condition is obtained according to the unit speed, the unit flow, the unit torque, the unit power and the unit efficiency.

[0161] This embodiment obtains a full characteristic curve data set for a pumped-storage device to be optimized, the full characteristic curve data set comprising multiple first operating point data for the pumped-storage device, the first operating point data comprising multiple operating parameters; determines multiple second operating point data that meet operating requirements based on the full characteristic curve data set; uses the second operating point data as particles in a particle swarm algorithm for optimization processing to obtain target operating point data with the highest efficiency; and determines the target speed of the pumped-storage device based on the operating parameters in the target operating point data. In this way, by first screening multiple second operating point data that meet operating requirements for the full characteristic curve data set for the pumped-storage device to be optimized, and then performing optimization processing using the particle swarm algorithm based on the second operating point data, the target operating point data with the highest efficiency can be quickly and accurately found, and then the optimal speed of the pumped-storage device with the highest efficiency and that meets the operating requirements can be accurately determined as the target speed, thereby improving the accuracy of speed optimization for the pumped-storage device.

[0162] The specific implementation of the above operations can be found in the previous embodiments and will not be repeated here.

[0163] like Figure 5 As shown, Figure 5A schematic diagram of the structure of a speed optimization device provided in an embodiment of the present invention. The speed optimization device 1100 includes a processor 1101 having one or more processing cores, a memory 1102 having one or more computer-readable storage media, and a computer program stored in the memory 1102 and executable on the processor. The processor 1101 is electrically connected to the memory 1102. Those skilled in the art will appreciate that the speed optimization device structure shown in the figure does not limit the speed optimization device, and may include more or fewer components than shown, or combine certain components, or arrange the components differently.

[0164] Processor 1101 is the control center of speed optimization device 1100. It connects various components of speed optimization device 1100 using various interfaces and circuits. By running or loading software programs and / or units stored in memory 1102 and accessing data stored in memory 1102, it executes various functions of speed optimization device 1100 and processes data, thereby providing overall monitoring of speed optimization device 1100. Processor 1101 can be a CPU, a graphics processor (GPU), a network processor (NP), etc., and can implement or execute the various methods, steps, and logic blocks disclosed in the embodiments of the present invention.

[0165] In an embodiment of the present invention, the processor 1101 in the speed optimization device 1100 loads instructions corresponding to one or more application processes into the memory 1102 according to the following steps, and the processor 1101 runs the application stored in the memory 1102 to implement various functions, such as:

[0166] Acquire a full characteristic curve data set of a speed optimization device to be optimized, wherein the full characteristic curve data set includes a plurality of first operating point data of the speed optimization device, wherein the first operating point data includes a plurality of operating parameters;

[0167] Determining a plurality of second operating point data that meet the operating condition requirements based on the full characteristic curve data set;

[0168] The second operating point data is used as particles in a particle swarm algorithm to perform optimization processing to obtain the target operating point data with the best efficiency;

[0169] The target speed of the speed optimization device is determined based on the operating parameters in the target operating point data.

[0170] The specific implementation of the above operations can be found in the previous embodiments and will not be repeated here.

[0171] Optional, such as Figure 5As shown, the speed optimization device 1100 further includes: a touch screen 1103, a radio frequency circuit 1104, an audio circuit 1105, an input unit 1106, and a power supply 1107. Among them, the processor 1101 is electrically connected to the touch screen 1103, the radio frequency circuit 1104, the audio circuit 1105, the input unit 1106, and the power supply 1107 respectively. It can be understood by those skilled in the art that Figure 5 The structure of the speed optimizing device shown in the figure does not constitute a limitation to the speed optimizing device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0172] The touchscreen display 1103 can be used to display a graphical user interface (GUI) and receive operational instructions generated by a user acting on the GUI. The touchscreen display 1103 can include a display panel and a touch panel. The display panel can be used to display information input by the user or provided to the user, as well as various graphical user interfaces (GUIs) of the speed optimization device. These GUIs can be composed of graphics, text, icons, videos, or any combination thereof. Optionally, the display panel can be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), or the like. The touch panel can be used to collect user touch operations on or near the touch panel (e.g., operations performed by a user using a finger, stylus, or any other suitable object or accessory on or near the touch panel), generate corresponding operational instructions, and execute corresponding programs in response to the operational instructions. Optionally, the touch panel can include two components: a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch direction, detects the signal caused by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into touch point coordinates, and then sends it to the processor 1101, and can receive commands sent by the processor 1101 and execute them. The touch panel can cover the display panel. When the touch panel detects a touch operation on or near it, it is transmitted to the processor 1101 to determine the type of touch event. Then the processor 1101 provides a corresponding visual output on the display panel according to the type of touch event. In an embodiment of the present invention, the touch panel and the display panel can be integrated into the touch display screen 1103 to realize input and output functions. However, in some embodiments, the touch panel and the touch panel can be used as two independent components to realize input and output functions. That is, the touch display screen 1103 can also be used as part of the input unit 1106 to realize the input function.

[0173] The RF circuit 1104 may be used to transmit and receive RF signals, thereby establishing wireless communication with a network device or other speed optimization device through wireless communication, and transmitting and receiving signals between the network device or other speed optimization device.

[0174] Audio circuit 1105 can be used to provide an audio interface between the user and the speed optimization device via a speaker and microphone. Audio circuit 1105 can convert received audio data into electrical signals and transmit them to the speaker, which then converts them into sound signals for output. The microphone, on the other hand, converts collected sound signals into electrical signals, which are then received by audio circuit 1105 and converted into audio data. The audio data is then output to processor 1101 for processing, and then transmitted via RF circuit 1104 to, for example, another speed optimization device. Alternatively, the audio data can be output to memory 1102 for further processing. Audio circuit 1105 may also include an earphone jack to provide communication between an external headset and the speed optimization device.

[0175] The input unit 1106 may be configured to receive input numbers, character information, or user feature information (such as fingerprint, iris, or facial information), and generate keyboard, mouse, joystick, optical, or trackball signal input related to user settings and function control.

[0176] Power supply 1107 is used to power various components of speed optimization device 1100. Optionally, power supply 1107 can be logically connected to processor 1101 via a power management system, thereby enabling the power management system to manage charging, discharging, and power consumption. Power supply 1107 can also include one or more DC or AC power supplies, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and other arbitrary components.

[0177] although Figure 5 Not shown in the figure, the speed optimization device 1100 may also include a camera, a sensor, a wireless fidelity module, a Bluetooth module, etc., which will not be described in detail here.

[0178] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0179] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be accomplished by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0180] To this end, an embodiment of the present invention provides a computer-readable storage medium storing a plurality of computer programs, which can be loaded by a processor to execute any of the speed optimization methods provided in the embodiments of the present invention. The computer program can execute the following steps of the speed optimization method:

[0181] Acquire a full characteristic curve data set of a speed optimization device to be optimized, wherein the full characteristic curve data set includes a plurality of first operating point data of the speed optimization device, wherein the first operating point data includes a plurality of operating parameters;

[0182] Determining a plurality of second operating point data that meet the operating condition requirements based on the full characteristic curve data set;

[0183] The second operating point data is used as particles in a particle swarm algorithm to perform optimization processing to obtain the target operating point data with the best efficiency;

[0184] The target speed of the speed optimization device is determined based on the operating parameters in the target operating point data.

[0185] The specific implementation of the above operations can be found in the previous embodiments and will not be repeated here.

[0186] The computer-readable storage medium may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0187] Since the computer program stored in the computer-readable storage medium can execute any of the speed optimization methods provided in the embodiments of the present invention, the beneficial effects that can be achieved by any of the speed optimization methods provided in the embodiments of the present invention can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0188] In the above-described embodiments of the speed optimization device, computer-readable storage medium, speed optimization apparatus, and computer program product, the descriptions of each embodiment have different focuses. For portions not described in detail in a particular embodiment, reference can be made to the relevant descriptions of other embodiments. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes and beneficial effects of the speed optimization device, computer-readable storage medium, computer program product, speed optimization apparatus, and their corresponding units described above can be referred to in the description of the speed optimization method in the above embodiments, and will not be further elaborated upon here.

[0189] The above is a detailed introduction to a speed optimization method, a speed optimization device, a speed optimization equipment, a computer-readable storage medium and a computer program product provided in an embodiment of the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for optimizing speed, characterized in that: The speed optimization method comprises: Acquire multiple initial operating point data of the pumped storage equipment to be optimized; performing interpolation and fitting based on the multiple initial operating point data to obtain a full characteristic curve data set, the full characteristic curve data set including multiple first operating point data of the pumped storage equipment, the first operating point data including multiple operating parameters, the operating parameters being unit operating parameters, including unit speed, unit flow, unit torque, unit power, and unit efficiency; Obtaining an operating condition requirement of the pumped storage equipment, screening the full characteristic curve data set according to the operating condition requirement, and obtaining a plurality of first operating condition point data meeting the operating condition requirement as second operating condition data; Based on the particle swarm algorithm, particles corresponding to the data of each second operating point are generated; Adjusting the position of the particle to update the second operating point data corresponding to the particle; Obtaining the historical individual optimal fitness of the particle before adjustment; When the efficiency of the updated second operating point data corresponding to the particle is greater than the historical individual optimal fitness, the efficiency of the updated second operating point data is used as the adjusted individual optimal fitness of the particle, and the adjusted position of the particle is used as the adjusted individual optimal position of the particle; When the efficiency of the updated second operating point data corresponding to the particle is less than the historical individual optimal fitness, the historical individual optimal fitness is used as the individual optimal fitness of the particle after adjustment, and the historical individual optimal position of the particle before adjustment is used as the individual optimal position of the particle after adjustment; If the preset optimization condition is not met, updating the optimal position of the particle group according to the adjusted individual optimal fitness and individual optimal position of the particle, and re-performing the step of adjusting the position of the particle based on the updated optimal position of the particle group and the adjusted individual optimal position of the particle to update the second operating point data corresponding to the particle until the preset optimization condition is met; Under the condition of meeting the preset optimization conditions, the target operating point data is determined according to the individual optimal position of the particle corresponding to the latest optimal fitness of the particle group; Determining a target rotational speed of the pumped-storage equipment based on the operating parameters in the target operating point data includes: If the operating parameters in the target operating point data include a rotational speed, obtaining the rotational speed from the target operating point data as a target rotational speed of the pumped-storage equipment; If the operating parameters in the target operating point data do not include the rotational speed, the actual rotational speed of the actual machine is calculated based on the operating parameters in the target operating point data as the target rotational speed.

2. The speed optimization method according to claim 1, characterized in that: The preset optimization condition includes that the individual optimal fitness of a preset number of particles after adjustment is less than or equal to the optimal fitness of the particle group before adjustment.

3. The speed optimization method according to claim 1, wherein: The operating condition requirements include actual power requirements and actual water head requirements, and a plurality of second operating point data meeting the operating condition requirements are determined based on the full characteristic curve data set, including: determining a target unit power of the pumped-storage equipment according to the actual power requirement and the actual water head requirement; From the full characteristic curve data set, first operating point data that meets the target unit power is screened out as the second operating point data.

4. The speed optimization method according to claim 1, wherein: The obtaining of a plurality of initial operating point data of the pumped storage equipment comprises: collecting unit speed, unit flow rate, and unit torque of the pumped storage equipment under at least two operating conditions; Determining unit power according to the unit speed, the unit flow rate, and the unit torque; determining a unit efficiency of the pumped-storage equipment according to the unit power and the unit flow rate; Initial operating point data corresponding to the operating condition is obtained according to the unit speed, the unit flow, the unit torque, the unit power and the unit efficiency.

5. A speed optimization device, characterized in that: The speed optimization device comprises: An acquisition module, used to obtain multiple initial operating point data of the pumped storage equipment to be optimized; performing interpolation and fitting based on the multiple initial operating point data to obtain a full characteristic curve data set, the full characteristic curve data set including multiple first operating point data of the pumped storage equipment, the first operating point data including multiple operating parameters, the operating parameters being unit operating parameters, including unit speed, unit flow, unit torque, unit power, and unit efficiency; a screening module, configured to obtain the operating condition requirements of the pumped storage equipment, and screen the full characteristic curve data set according to the operating condition requirements to obtain a plurality of first operating condition point data meeting the operating condition requirements as second operating condition data; The optimization module is used to generate particles corresponding to each second operating point data based on the particle swarm algorithm; adjust the position of the particle to update the second operating point data corresponding to the particle; obtain the historical individual optimal fitness of the particle before adjustment; when the efficiency of the updated second operating point data corresponding to the particle is greater than the historical individual optimal fitness, use the efficiency of the updated second operating point data as the adjusted individual optimal fitness of the particle, and use the adjusted position of the particle as the adjusted individual optimal position of the particle; when the efficiency of the updated second operating point data corresponding to the particle is less than the historical individual optimal fitness, use the historical individual optimal fitness The particle swarm is updated based on the individual optimal fitness and individual optimal position of the particle after adjustment, and the particle swarm is updated based on the individual optimal fitness and individual optimal position of the particle after adjustment, and the particle swarm is updated based on the updated individual optimal position of the particle after adjustment, so as to update the second operating point data corresponding to the particle, until the preset optimization condition is met; if the preset optimization condition is met, the target operating point data is determined based on the individual optimal position of the particle corresponding to the latest optimal fitness of the particle swarm; A determination module is used to determine the target speed of the pumped-storage equipment based on the operating parameters in the target operating point data, including: if the operating parameters in the target operating point data include the speed, obtaining the speed from the target operating point data as the target speed of the pumped-storage equipment; if the operating parameters in the target operating point data do not include the speed, calculating the actual speed of the actual machine operation based on the operating parameters in the target operating point data as the target speed.

6. A speed optimization device, characterized in that: The invention comprises a processor and a memory, wherein the memory stores a computer program. When the computer program is executed by the processor, the processor executes the steps of the speed optimization method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a computer program. When the computer program is run on an electronic device, the computer program is used to enable the electronic device to execute the steps of the speed optimization method according to any one of claims 1 to 4.

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