Rotating speed optimizing method, device and equipment and readable storage medium

Abstract

The invention discloses a rotating speed optimization method, device and equipment and a readable storage medium, and the method comprises the steps: obtaining a full characteristic curve data set of to-be-optimized pumped storage equipment, the full characteristic curve data set comprises a plurality of first working condition point data of the pumped storage equipment, and the first working condition point data comprises a plurality of operation parameters; determining a plurality of second working condition point data meeting working condition requirements based on the full characteristic curve data set; performing optimization processing by taking the second working condition point data as particles in a particle swarm algorithm to obtain target working condition point data with optimal efficiency; and based on the operation parameters in the target working condition point data, the target rotating speed of the pumped storage equipment is determined. The rotating speed optimization accuracy of the pumped storage equipment can be effectively improved.

Description

Technical Field

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

[0002] The rotational speed of a variable-speed pumped-storage unit can be adjusted within a certain range, which brings additional high efficiency and flexibility to the unit. The adjustable range of the rotational speed is mainly determined according to the technical characteristics on the power side, while the rotational speed of the pump-turbine on the hydraulic side needs to be specifically determined. In related technologies, the rotational speed is determined from a pre-set database using the look-up table function provided in simulation software to control the operation of the pumped-storage unit. However, the rotational speed determined in this way is not necessarily optimal, and the accuracy of rotational speed optimization needs to be improved. Summary of the Invention

[0003] Embodiments of the present invention provide a rotational speed optimization method, device, equipment and readable storage medium, aiming to effectively improve the accuracy of rotational speed optimization of pumped-storage equipment.

[0004] In a first aspect, an embodiment of the present invention provides a rotational speed optimization method, including: Obtaining a full characteristic curve data set of a pumped-storage device to be optimized, where the full characteristic curve data set includes a plurality of first operating point data of the pumped-storage device, and the first operating point data includes various operating parameters; Determining a plurality of second operating point data that meet the operating conditions based on the full characteristic curve data set; Using the second operating point data as particles in a particle swarm algorithm for optimization processing to obtain target operating point data with the optimal efficiency; Determining the target rotational speed of the pumped-storage device based on the operating parameters in the target operating point data.

[0005] Optionally, the step of using the second operating point data as particles in a particle swarm algorithm for optimization processing to obtain target operating point data with the optimal efficiency includes: Generating particles corresponding to each second operating point data based on the particle swarm algorithm; Adjusting the positions of the particles to update the second operating point data corresponding to the particles; Determining the individual optimal fitness and individual optimal position of the particles after adjustment according to the efficiency of the updated second operating point data; In the case of not meeting the preset optimization condition, updating the global optimal position of the particle swarm according to the individual optimal fitness and individual optimal position of the particles after adjustment, and re-executing the step of adjusting the positions of the particles to update the second operating point data corresponding to the particles based on the updated global optimal position of the particle swarm and the individual optimal position of the particles after adjustment until the preset optimization condition is met; Under the condition of meeting the preset optimization conditions, determine the target operating condition point data according to the individual optimal position of the particle corresponding to the latest optimal fitness of the particle swarm.

[0006] Optionally, determining the adjusted individual optimal fitness and individual optimal position of the particle according to the efficiency of the updated second operating condition point data includes: Obtain the historical individual optimal fitness of the particle before adjustment; When the efficiency of the updated second operating condition point data corresponding to the particle is greater than the historical individual optimal fitness, use the efficiency of the updated second operating condition 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 condition point data corresponding to the particle is less than the historical individual optimal fitness, use the historical individual optimal fitness as the adjusted individual optimal fitness of the particle, and use the historical individual optimal position of the particle before adjustment as the adjusted individual optimal position of the particle.

[0007] Optionally, the preset optimization condition includes that the adjusted individual optimal fitness of a preset number of particles is less than or equal to the optimal fitness of the particle swarm before adjustment.

[0008] Optionally, the operating condition requirements include actual power requirements and actual head requirements. Determining a plurality of second operating condition point data that meet the operating condition requirements based on the full characteristic curve data set includes: Determine the target unit power of the pumped storage device according to the actual power requirement and the actual head requirement; Screen out the first operating condition point data that meets the target unit power from the full characteristic curve data set as the second operating condition point data.

[0009] Optionally, obtaining the full characteristic curve data set of the pumped storage device to be optimized, where the full characteristic curve data set includes a plurality of first operating condition point data of the pumped storage device, includes: Obtain a plurality of initial operating condition point data of the pumped storage device; Based on the plurality of initial operating condition point data, perform interpolation and fitting to obtain the full characteristic curve data set.

[0010] Optionally, obtaining the plurality of initial operating condition point data of the pumped storage device includes: Collect the unit speed, unit flow rate, and unit torque of the pumped storage device under at least two operating conditions; Determine the unit power according to the unit speed, the unit flow rate, and the unit torque; Determine the unit efficiency of the pumped-storage device according to the unit power and the unit flow rate; Obtain the initial operating condition point data corresponding to the operating condition according to the unit speed, the unit flow rate, the unit torque, the unit power, and the unit efficiency.

[0011] In a second aspect, an embodiment of the present invention provides a rotational speed optimization device, which includes: An acquisition module, configured to acquire a full characteristic curve data set of a pumped-storage device to be optimized, where the full characteristic curve data set includes a plurality of first operating condition point data of the pumped-storage device, and the first operating condition point data includes various operating parameters; A screening module, configured to determine a plurality of second operating condition point data that meet the condition requirements based on the full characteristic curve data set; An optimization module, configured to perform optimization processing using the second operating condition point data as particles in a particle swarm algorithm to obtain target operating condition point data with the optimal efficiency; A determination module, configured to determine the target rotational speed of the pumped-storage device based on the operating parameters in the target operating condition point data.

[0012] In a third aspect, an embodiment of the present invention further provides a rotational speed optimization device, including a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the processor is caused to execute the steps of any one of the rotational speed optimization methods provided by the embodiments of the present invention.

[0013] 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 cause the electronic device to execute the steps of any one of the rotational speed optimization methods provided by the embodiments of the present invention.

[0014] The present invention obtains a full characteristic curve data set of a pumped-storage device to be optimized. The full characteristic curve data set includes a plurality of first operating point data of the pumped-storage device, and the first operating point data includes various operating parameters. Based on the full characteristic curve data set, a plurality of second operating point data that meet the operating conditions are determined. Using the second operating point data as particles in the particle swarm optimization algorithm for optimization processing, the target operating point data with the optimal efficiency is obtained. Based on the operating parameters in the target operating point data, the target speed of the pumped-storage device is determined. In this way, first, a plurality of second operating point data that meet the operating conditions are screened out from the full characteristic curve data set of the pumped-storage device to be optimized. Based on the second operating point data and through the particle swarm optimization algorithm for optimization processing, the target operating point data with the highest efficiency can be quickly and accurately found. Furthermore, the optimal speed with the highest efficiency and meeting the operating conditions of the pumped-storage device can be accurately determined as the target speed, which can improve the accuracy of the speed optimization of the pumped-storage device. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those skilled in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0016] Figure 1 It is a schematic flowchart of an embodiment of the speed optimization method provided in the embodiment of the present invention; Figure 2 It is a schematic optimization flowchart provided in the embodiment of the present invention; Figure 3 It is a schematic diagram of the relationship between data sets involved in the embodiment of the present invention; Figure 4 It is a schematic structural diagram of the speed optimization device provided in the embodiment of the present invention; Figure 5 It is a schematic structural diagram of the speed optimization device provided in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention. At the same time, in the description of the embodiments of the present invention, terms such as "first" and "second" are only used for descriptive distinction and cannot be understood as indicating or implying relative importance. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more features. In the description of the embodiments of the present invention, "a plurality of" means two or more, unless otherwise specifically defined.

[0018] The embodiments of the present invention provide a rotational speed optimization method, device, equipment and readable storage medium.

[0019] Specifically, this embodiment will be described from the perspective of the rotational speed optimization device, which can be specifically integrated in the pumped storage equipment, that is, the rotational speed optimization method in the embodiments of the present invention can be executed by the pumped storage equipment.

[0020] The following will be described in detail with reference to the accompanying drawings. In this embodiment, the execution subject is taken as the pumped storage device as an example. It should be noted that the description order of the following embodiments does not limit the preferred order of the embodiments. Although the logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order from that shown in the drawings.

[0021] Compared with the traditional fixed-speed pumped storage unit, the power-side technology of the variable-speed pumped storage unit has been innovated, making its rotational speed adjustable within a certain range. This is the technical advantage of the variable-speed pumped storage unit, bringing additional high efficiency and flexibility to the unit.

[0022] Since the adjustable range of the rotational speed is mainly determined according to the technical characteristics of the power side, the rotational speed of the pump-turbine on the hydraulic side of the variable-speed pumped storage unit needs to determine a suitable operating rotational speed according to specific working conditions requirements to ensure that the hydraulic side matches the power-side characteristics and fully utilizes the advantage of adjustable rotational speed. In actual engineering applications, in the power generation working condition, its pump-turbine operates in the turbine mode. The power grid puts forward power requirements for the unit. Considering the head of the pumped storage equipment corresponding to the power station at this time, there may be multiple unit operating points that meet the working condition requirements. At this time, a reasonable optimization strategy is needed to determine the optimal rotational speed of the unit.

[0023] 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.

[0024] 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.

[0025] 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: 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; In this embodiment, the rotational speed of the pumped-storage device is adjustable. The pumped-storage device may be a variable-speed pumped-storage unit, and the variable-speed pumped-storage unit includes a pump-turbine. Specifically, the adjustable parameter is the rotational speed of the pump-turbine. Obtain the full characteristic curve dataset of the pumped-storage device whose rotational speed is to be optimized. The full characteristic curve dataset can reflect the operating characteristics of the pumped-storage device or the pump-turbine in the pumped-storage device. The full characteristic curve dataset includes a plurality of first operating point data of the pumped-storage device. The first operating point data can characterize the operating conditions of the pumped-storage device or the pump-turbine in the pumped-storage device under a certain operating condition. The first operating point data may include various operating parameters under its corresponding operating condition to more comprehensively characterize its corresponding operating condition. The operating parameter may be a unit operating parameter, specifically including at least one of unit rotational speed, unit flow rate, unit torque, unit power, and unit efficiency.

[0026] Step S20: Determine a plurality of second operating point data that meet the operating condition requirements based on the full characteristic curve dataset; In this embodiment, obtain the operating condition requirements of the pumped-storage device. The operating condition requirements are set based on the operating requirements of the pumped-storage device and the corresponding environmental conditions of the pumped-storage device. Screen according to the operating condition requirements in the full characteristic curve dataset, and it will be found that there are a plurality of first operating point data in the dataset that meet the operating condition requirements, so as to obtain a plurality of second operating point data that meet the operating condition requirements.

[0027] Step S30: Use the second operating point data as particles in the particle swarm optimization algorithm to perform optimization processing to obtain the target operating point data with the optimal efficiency; In this embodiment, after obtaining a plurality of second operating point data that meet the operating condition requirements, optimization is performed with the optimal efficiency as the standard. Considering the characteristics of the rotational speed optimization process of the pumped-storage device, which has multiple variables and requires interpolation from discrete operating point data, and fully considering the control accuracy of the pumped-storage device and the accuracy of interpolation optimization, this embodiment introduces the particle swarm optimization algorithm. The particle swarm optimization 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 cooperation among individuals. In the particle swarm optimization algorithm, the potential solution of each optimization problem can be imagined as a bird in the search space, which is called a "particle". All particles have a fitness determined by the function to be optimized. Each particle also has a velocity that determines the direction and distance of its flight. The particles follow the current optimal particle to search in the solution space.

[0028] The goal of using the particle swarm optimization algorithm is to find the rotational speed value corresponding to the optimal efficiency according to the working condition requirements. By simulating the foraging behavior of a bird flock, iterative search is carried out within the range given by the database to find the optimal target working condition point data. Using the above second working condition point data as particles in the particle swarm algorithm for optimization processing, each particle represents a potential selectable working condition. By continuously updating the position and velocity of the particles, the target working condition point data that optimizes the objective function (efficiency) is finally found to characterize the optimal working condition of the pumped-storage equipment to be optimized.

[0029] Step S40, based on the operating parameters in the target working condition point data, determine the target rotational speed of the pumped-storage equipment.

[0030] In this embodiment, the target working condition point data also includes operating parameters under various optimal working conditions. Generally, the types of operating parameters included in the target working condition point data are the same as those in the first working condition point data and the second working condition point data. The operating parameters included in the target working condition point data can be at least one of unit rotational speed, unit flow rate, unit torque, unit power, and unit efficiency.

[0031] In some embodiments, if the operating parameters in the target working condition point data include rotational speed, the rotational speed can be directly obtained from its operating parameters as the target rotational speed of the pumped-storage equipment.

[0032] In some embodiments, the operating parameters in the target working condition point data do not include rotational speed but unit parameters. Then, it is necessary to calculate the actual rotational speed of the real machine operation based on the operating parameters in the target working condition point data as the target rotational speed.

[0033] The target rotational speed is the optimal rotational speed with the highest efficiency after optimization and meeting the public requirements. After determining the target rotational speed, controlling the operation of the pumped-storage equipment based on the target rotational speed can adjust the pump-turbine in the pumped-storage equipment to the target rotational speed, so that the pumped-storage equipment is in the operating condition that meets the working condition requirements and has the highest efficiency.

[0034] In the technical solution disclosed in this embodiment, for the full characteristic curve data set of the pumped-storage equipment to be optimized, first screen out multiple second working condition point data that meet the working condition requirements. Based on the second working condition point data and through the particle swarm algorithm for optimization processing, the target working condition point data with the highest efficiency can be quickly and accurately found, and then the optimal rotational speed with the highest efficiency and meeting the working condition requirements of the pumped-storage equipment can be accurately determined as the target rotational speed, which can improve the accuracy of rotational speed optimization of the pumped-storage equipment. Operating the pumped-storage equipment at a more accurate optimal rotational speed can make full use of its advantageous characteristic of adjustable rotational speed, making the pumped-storage equipment operate more efficiently.

[0035] In this embodiment, using the second operating condition point data as particles in the particle swarm optimization algorithm to perform optimization processing to obtain the target operating condition point data with the optimal efficiency includes: Generating particles corresponding to each second operating condition point data based on the particle swarm optimization algorithm; Adjusting the positions of the particles to update the second operating condition point data corresponding to the particles; Determining the individual optimal fitness and individual optimal position of the particles according to the efficiency of the updated second operating condition point data; When the preset optimization condition is not satisfied, updating the global optimal position of the particle swarm according to the adjusted individual optimal fitness and individual optimal position of the particles, and re - executing the step of adjusting the positions of the particles to update the second operating condition point data corresponding to the particles based on the updated global optimal position of the particle swarm and the adjusted individual optimal position of the particles until the preset optimization condition is met; When the preset optimization condition is satisfied, determining the target operating condition point data according to the particle corresponding to the latest global optimal fitness of the particles.

[0036] In one embodiment, the goal of the particle swarm optimization algorithm is to select a particle X = [x1, x2, ……, xn] to meet the efficiency requirement f(X). Inputting each second operating condition data into the particle swarm optimization algorithm to generate particles X corresponding to each second operating condition point data.

[0037] The position of particle X refers to the variable in the particle, which has n dimensions, where n refers to the number of factors that have not been fully solved in a problem. f(X) is called the 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, the fitness of the particle is evaluated by the efficiency of the operating condition point data corresponding to the particle.

[0038] Iteratively adjusting the positions of each particle to iteratively adjust the second operating condition point data corresponding to the particles. The particle velocity of the particle after t iterations can be expressed as , and the position can be expressed as . The formula for adjusting the particle position is as follows: (1) (2) Where represents the update of the velocity of the i - th particle in the d - dimensional space at the (t + 1)-th iteration. c1 is the self - learning factor, c2 is the global learning factor, r1 and r2 represent random numbers uniformly distributed in (0, 1), represents the individual optimal position of the i - th particle in the d - dimensional space at the t - th iteration, It represents the global optimal position of the $i$-th particle in the $d$-dimensional space after $t$ iterations. It can be understood that formula (1) represents the update of the particle velocity, and formula (2) represents the update of the particle position, where in formula (1), is the inertia weight, which determines the stability of the change of the particle velocity.

[0039] The particle positions are adjusted through the particle swarm algorithm, so that the data of the second operating point corresponding to the particles can be updated. Before meeting the preset optimization conditions, the positions of the particles are iteratively adjusted to update the data of the second operating point corresponding to the particles, so as to obtain a new operating point, and the efficiency of the updated data of the second operating point is calculated based on the efficiency formula and the updated data of the second operating point corresponding to the particles, to characterize the efficiency of the operating point corresponding to the adjusted particle position, and further determine the individual optimal fitness and individual optimal position after the particle position is adjusted. After each iteration, it is judged whether the preset optimization conditions set based on the rotational speed optimization principle are met. If the preset optimization conditions are not met, the individual optimal fitnesses of the adjusted particles can be compared, the particle with the largest adjusted individual optimal fitness is determined, and the individual optimal position after the adjustment of this particle is updated to the global optimal position of the particle swarm, and the adjusted individual optimal fitness of this particle is used as the global optimal fitness of the particle swarm.

[0040] The global optimal position of the particle swarm can characterize the operating point with higher efficiency found in the current iteration round, and the global optimal fitness of the particle swarm can characterize the efficiency of the operating point found in the current iteration round. Then, based on the updated global optimal position of the particle swarm and the individual optimal position after the particle adjustment, the positions of the particles are readjusted, so that the particle swarm gradually moves towards a better solution, to accurately update the data of the second operating point corresponding to the particles, and re-update the individual optimal fitness and individual optimal position of the particles. Until the preset optimization conditions are met. The preset optimization conditions are pre-set conditions, which characterize that the global optimal fitness of the current iteratively updated particle swarm reaches the global maximum after iteration, and the operating point corresponding to the global optimal position of the particle swarm is the globally optimal operating point found after iteration.

[0041] When the preset optimization conditions are met, it means that the global optimal fitness of the particle swarm reaches the global maximum after iteration, and the operating point corresponding to the global optimal position of the particle swarm is the globally optimal operating point found after iteration. Based on the global optimal position of the particle swarm, the globally optimal operating point data can be determined as the target operating point data, which can characterize the operating point with the optimal efficiency of the pumped-storage equipment to be optimized and meeting the operating conditions requirements.

[0042] In this way, by updating the individual optimal position and the global optimal position through the particle swarm algorithm, the globally optimal target operating point data can be iteratively found, which ensures the comprehensiveness and reliability of the rotational speed optimization, is not easy to miss the operating conditions with the optimal efficiency, and thus further improves the accuracy of the rotational speed optimization.

[0043] In one embodiment, determining the individual optimal fitness and the individual optimal position of the particle after adjustment according to the efficiency of the updated second operating condition point data includes: Obtain the historical individual optimal fitness of the particle before adjustment; When the efficiency of the updated second operating condition point data corresponding to the particle is greater than the historical individual optimal fitness, use the efficiency of the updated second operating condition point data as the individual optimal fitness of the particle after adjustment, and use the position of the particle after adjustment as the individual optimal position of the particle after adjustment; When the efficiency of the updated second operating condition point data corresponding to the particle is less than the historical individual optimal fitness, use the historical individual optimal fitness as the individual optimal fitness of the particle after adjustment, and use the historical individual optimal position of the particle before adjustment as the individual optimal position of the particle after adjustment.

[0044] In this embodiment, after adjusting the position of the particle in each round, for each particle at the adjusted position, the operating condition point data corresponding to the adjusted position of the particle can be obtained and used as the updated second operating condition point data corresponding to the particle, and the efficiency of the updated second operating condition point data can evaluate the fitness of the particle after the position adjustment.

[0045] Obtain the historical individual optimal fitness of the particle before the position adjustment. This historical individual optimal fitness is also the optimal efficiency of the historical operating condition point data of the particle, which can represent the optimal operating condition point and the optimal efficiency found by the particle before adjustment.

[0046] For each particle at the adjusted position, compare the efficiency of the updated second operating condition point data corresponding to it with the historical individual optimal fitness before adjustment. When the efficiency of the updated second operating condition point data is greater than the historical individual optimal fitness, it is considered that the adjusted position is better for the particle. Therefore, use the efficiency of the updated second operating condition point data corresponding to it as the individual optimal fitness of the particle after adjustment to update the individual optimal fitness of the particle, and use the position of the particle after adjustment as the individual optimal position of the particle. On the contrary, when the efficiency of the updated second operating condition point data is less than the historical individual optimal fitness, it is considered that the historical individual optimal position before adjustment is better for the particle. Therefore, still use the historical individual optimal position as the individual optimal fitness of the particle after adjustment, essentially without updating the individual optimal fitness, and use the individual optimal position of the particle before adjustment as the individual optimal position of the particle after adjustment.

[0047] In this way, by comparing the efficiency after the adjustment of each particle position with its historical individual optimal fitness, particles can participate in the optimization process with a higher-efficiency individual optimal fitness and individual optimal position, further ensuring the comprehensiveness and reliability of the rotational speed optimization and further improving the accuracy of the rotational speed optimization.

[0048] 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 swarm before adjustment.

[0049] In this embodiment, in each iteration, the individual optimal fitness of the particles after adjustment is calculated and compared with the optimal fitness of the particle swarm before adjustment, and the number of particles whose individual fitness after adjustment is less than or equal to the optimal fitness of the particle swarm before adjustment is counted. If there are a preset number of particles whose individual fitness after adjustment is less than or equal to the optimal fitness of the particle swarm before adjustment, it can be determined that the preset optimization condition is satisfied and the iteration process can be ended. Generally, the preset number is equal to the total number of particles, so as to ensure finding a more accurate optimal solution. In this way, by setting the individual fitness of a preset number of particles after adjustment to be less than or equal to the optimal fitness of the particle swarm before adjustment as the optimization condition, it can be ensured that while finding the optimal solution, there is no need to update the optimal position of the particle swarm according to the individual optimal fitness and individual optimal position of the adjusted particles, quickly ending the process and saving computing resources.

[0050] Based on the above embodiments, as Figure 2 shown, the optimization process using the particle swarm algorithm in this embodiment may include the following steps: (1) Initialize the particle swarm. Based on the particle swarm algorithm, in the solution space of the problem, a group of particles is randomly generated. Each particle represents a potential operating point and corresponds 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.

[0051] (2) Evaluate the efficiency of the operating point. For each particle, the efficiency corresponding to each particle currently can be determined based on the efficiency of the second operating point data corresponding to the particle as the initial individual optimal fitness of the particle.

[0052] (3) Update the individual optimal and the group optimal: 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 current position of the particle to the individual optimal position. At the same time, compare the individual optimal fitness of all particles, find the optimal value among them as the optimal fitness of the particle swarm, and update the adjusted individual optimal position of the particle corresponding to the optimal value to the optimal position of the particle swarm.

[0053] (4) Update speed and position: According to the individual optimal position and the global optimal position of each particle, as well as a certain random factor, adjust the speed and position of the particle so that the particle swarm gradually moves towards a better solution.

[0054] (5) Iterative optimization: Repeat the above steps of evaluating the individual optimal fitness, updating the individual optimal position and the global optimal fitness, global optimal position of the particle swarm, and updating the speed and position until the preset optimization condition is met.

[0055] (6) Output result: After the iteration ends, output the global optimal position of the particle swarm, that is, the global optimal position of the particle swarm for rotational speed optimization obtained based on the particle swarm algorithm, corresponding to the optimal target operating condition point data, and convert it to obtain the optimal target rotational speed of the pumped-storage equipment.

[0056] In one embodiment, the operating condition requirements include actual power requirements and actual head requirements. Based on the full characteristic curve data set, determining multiple second operating condition point data that meet the operating condition requirements includes: Determine the target unit power of the pumped-storage equipment according to the actual power requirement and the actual head requirement; From the full characteristic curve data set, screen out the first operating condition point data that meets the target unit power as the second operating condition point data.

[0057] Optionally, based on any of the above embodiments, in another embodiment of the rotational speed optimization method of the present invention, the operating condition requirements include actual power requirements and actual head requirements, and the step S20 further includes: S21. Determine the target unit power of the pumped-storage equipment according to the actual power requirement and the actual head requirement; In this embodiment, the operating condition requirements refer to the requirements set according to the actual operation of the pumped-storage equipment, which may include the actual power requirement and the actual head requirement set for the pumped-storage equipment to be optimized. The actual power requirement refers to the power threshold that the pumped-storage equipment needs to reach during power generation, and usually needs to be determined comprehensively according to factors such as the demand of the power system, the installed capacity of the power station, the head characteristics, the engineering layout characteristics, and the design and manufacturing capabilities. The actual head requirement is the actual head threshold set for the pumped-storage equipment.

[0058] According to formula (3), the target unit power corresponding to the actual power requirement and the actual head requirement can be calculated: (3) Wherein, P is the power threshold corresponding to the actual power requirement, H is the head threshold corresponding to the actual head requirement, Dis the diameter of the pump-turbine of the pumped storage equipment, and the calculated P 11 can be used as the target unit power.

[0059] S22. From the full characteristic curve data set, filter out the first operating point data that meets the target unit power as the second operating point data.

[0060] In this embodiment, after determining the target unit power, filter out the first operating point data corresponding to the unit power that meets the target unit power from the full characteristic curve data set as the second operating point data.

[0061] In this way, before optimization, filter out the data set of the second operating point data that meets the requirements of the target unit power. At this time, only need to give an optimization strategy based on the optimal efficiency, and can quickly and accurately filter out the target operating point data with the highest efficiency from the data set that meets the operating conditions requirements, thus further improving the optimization efficiency and accuracy.

[0062] In one embodiment, obtaining the full characteristic curve data set of the pumped storage equipment to be optimized, the full characteristic curve data set includes multiple first operating point data of the pumped storage equipment, including: Obtain multiple initial operating point data of the pumped storage equipment; Based on the multiple initial operating point data, perform interpolation and fitting to obtain the full characteristic curve data set.

[0063] In this embodiment, obtain multiple initial operating point data of the pumped storage equipment. The initial operating point data can be data actually collected and calculated during the operation of the pumped storage equipment. Perform difference and fitting on the multiple initial operating point data of the pumped storage equipment, and a rich amount of data can be obtained to obtain the full characteristic curve data set, which provides a data basis for optimization and can further improve the accuracy of optimization.

[0064] In one embodiment, obtaining the multiple initial operating point data of the pumped storage equipment includes: Collect the unit speed, unit flow rate, and unit torque of the pumped storage equipment under at least two operating conditions; Determine the unit power according to the unit speed, the unit flow rate, and the unit torque; Determine the unit efficiency of the pumped storage equipment according to the unit power and the unit flow rate; Obtain the initial operating point data corresponding to the operating condition according to the unit speed, the unit flow rate, the unit torque, the unit power, and the unit efficiency.

[0065] In this embodiment, the unit speed, unit flow rate, and unit torque of the pumped-storage equipment are collected under at least two operating conditions to obtain a set of discrete data sets n 11 ,Q 11 ,M 11 . From the following calculation formulas of unit power and unit efficiency, a new discrete data set can be obtained n 11 ,Q 11 ,M 11 ,P 11 , η T : (4) (5) Wherein, n 11 is the unit speed, Q 11 is the unit flow rate, M 11 is the unit torque, P 11 is the unit torque, η T is the unit power.

[0066] Based on the above formulas, in this embodiment, the unit speed, unit flow rate, and unit torque of the pumped-storage equipment are collected under at least two operating conditions. For each operating condition, the unit power of the pumped-storage equipment under this operating condition is determined according to the unit speed, unit flow rate, and unit torque under this operating condition, and the unit efficiency of the pumped-storage equipment under this operating condition is determined according to the calculated unit power and unit flow rate; according to the unit speed, unit flow rate, unit torque, unit power, and unit efficiency under each operating condition, the initial operating condition point data under the corresponding operating condition is composed, and finally the initial operating condition point data under at least two operating conditions is obtained.

[0067] By collecting the unit speed, unit flow rate, and unit torque of the pumped-storage equipment under at least two operating conditions, rich and accurate discrete data can be constructed to obtain the initial operating condition point data of the pumped-storage equipment, so as to interpolate and fit an accurate full characteristic curve data set for optimization, further improving the accuracy of optimization.

[0068] In the above embodiment, such as Figure 3As shown, by processing the collected data of the pumped-storage equipment, a data set including unit speed, unit flow rate, 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 rate, unit torque, and unit power through interpolation and fitting, which is represented by the full characteristic curve data set. According to specific working condition requirements, the unit working condition point data that meet the working condition requirements are screened out from the full characteristic curve data set for optimization, and the unit working condition point data with the highest efficiency is obtained. Finally, the optimal operating speed and other data are obtained to guide the operation of the pumped-storage equipment.

[0069] In this way, data processing is directly carried out based on the full characteristic curve data set, screening is performed and then optimization is carried out, and finally the conversion from unit working conditions to actual machine operation parameters is carried out. Decoupling interpolation, algorithm optimization, and actual machine working condition conversion is beneficial to reducing errors in the speed optimization process.

[0070] Applying the particle swarm optimization algorithm to speed optimization improves the accuracy of optimization from the discrete full characteristic curve data set and improves the efficiency of the speed optimization process. The iterative process of updating the individual and global optimal positions in the particle swarm optimization algorithm ensures the comprehensiveness and reliability of speed optimization and is not easy to miss the operating condition point with the optimal efficiency.

[0071] Compared with the related technology, a speed optimization method for variable-speed pumped-storage equipment based on the particle swarm optimization algorithm is proposed. Based on the above embodiments, the speed optimization of variable-speed pumped-storage equipment for power generation work can be carried out. In this embodiment, the following beneficial effects are achieved: First, a complete set of standardized processes for speed optimization of variable-speed pumped-storage equipment is established; Second, the particle swarm optimization algorithm adopted has the advantages of few parameters, easy implementation, fast search speed, fast convergence speed, and few adjusted parameters. Applying the particle swarm optimization algorithm to the speed optimization process improves the accuracy of optimization from the discrete four-quadrant curve and improves the optimization efficiency and accuracy.

[0072] In actual engineering applications, speed optimization is of great significance for variable-speed pumped-storage equipment. Using the speed optimization algorithm provided in this embodiment can reduce the errors brought by full characteristic interpolation. Accurately and fully utilize the speed adjustability, improve the hydraulic characteristics and efficiency of variable-speed pumped-storage equipment, provide technical support and guarantee for the commissioning of variable-speed pumped-storage equipment, and better promote the application and development of variable-speed pumped-storage units.

[0073] This embodiment also provides a speed optimization device, which can also be specifically integrated in a speed optimization device. The speed optimization device can be a terminal device, a pumped-storage device, etc. For example, as Figure 4 shown, the speed optimization device can include: An acquisition module 1001, configured to acquire a full characteristic curve data set of a pumped storage device to be optimized, where the full characteristic curve data set includes a plurality of first operating point data of the pumped storage device, and the first operating point data includes various operating parameters; A screening module 1002, configured to determine a plurality of second operating point data that meet the operating conditions requirements based on the full characteristic curve data set; An optimization module 1003, configured to perform optimization processing on the second operating point data as particles in a particle swarm algorithm to obtain target operating point data with the optimal efficiency; A determination module 1004, configured to determine a target rotational speed of the pumped storage device based on the operating parameters in the target operating point data.

[0074] Optionally, the optimization module 1003 is further configured to: Generate particles corresponding to each second operating point data based on the particle swarm algorithm; Adjust the positions of the particles to update the second operating point data corresponding to the particles; Determine the individual optimal fitness and individual optimal position of the particles after adjustment according to the efficiency of the updated second operating point data; In the case where the preset optimization condition is not satisfied, update the global optimal position of the particle swarm according to the individual optimal fitness and individual optimal position of the particles after adjustment, and re-execute the step of adjusting the positions of the particles to update the second operating point data corresponding to the particles based on the updated global optimal position of the particle swarm and the individual optimal position of the particles after adjustment until the preset optimization condition is met; In the case where the preset optimization condition is satisfied, determine the target operating point data according to the individual optimal position of the particle corresponding to the latest global optimal fitness of the particle swarm.

[0075] Optionally, the optimization module 1003 is further configured to: Obtain the historical individual optimal fitness of the particles before adjustment; In the case where the efficiency of the updated second operating point data corresponding to the particles is greater than the historical individual optimal fitness, use the efficiency of the updated second operating point data as the individual optimal fitness of the particles after adjustment, and use the position of the particles after adjustment as the individual optimal position of the particles after adjustment; In the case where the efficiency of the updated second operating point data corresponding to the particles is less than the historical individual optimal fitness, use the historical individual optimal fitness as the individual optimal fitness of the particles after adjustment, and use the historical individual optimal position of the particles before adjustment as the individual optimal position of the particles after adjustment.

[0076] 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 swarm before adjustment.

[0077] Optionally, the operating condition requirements include actual power requirements and actual head requirements. The screening module 1002 is further configured to: Determine the target unit power of the pumped-storage device according to the actual power requirement and the actual head requirement; From the full characteristic curve dataset, screen out the first operating condition point data that meets the target unit power as the second operating condition point data.

[0078] Optionally, the acquisition module 1001 is further configured to: Obtain a plurality of initial operating condition point data of the pumped-storage device; Interpolate and fit based on the plurality of initial operating condition point data to obtain the full characteristic curve dataset.

[0079] Optionally, the acquisition module 1001 is further configured to: Collect the unit speed, unit flow rate, and unit torque of the pumped-storage device under at least two operating conditions; Determine the unit power according to the unit speed, the unit flow rate, and the unit torque; Determine the unit efficiency of the pumped-storage device according to the unit power and the unit flow rate; Obtain the initial operating condition point data corresponding to the operating condition according to the unit speed, the unit flow rate, the unit torque, the unit power, and the unit efficiency.

[0080] In this embodiment, a full characteristic curve dataset of the pumped-storage device to be optimized is obtained. The full characteristic curve dataset includes a plurality of first operating condition point data of the pumped-storage device, and the first operating condition point data includes various operating parameters. Based on the full characteristic curve dataset, a plurality of second operating condition point data that meet the operating condition requirements are determined. The second operating condition point data is used as particles in the particle swarm algorithm for optimization to obtain the target operating condition point data with the optimal efficiency. Based on the operating parameters in the target operating condition point data, the target speed of the pumped-storage device is determined. In this way, for the full characteristic curve dataset of the pumped-storage device to be optimized, a plurality of second operating condition point data that meet the operating condition requirements are first screened out, and optimization is performed based on the second operating condition point data through the particle swarm algorithm, so that the target operating condition point data with the highest efficiency can be quickly and accurately found, and then the optimal speed with the highest efficiency and meeting the operating condition requirements of the pumped-storage device can be accurately determined as the target speed, which can improve the accuracy of the speed optimization of the pumped-storage device.

[0081] For the specific implementation of each of the above operations, reference may be made to the previous embodiments and will not be elaborated herein.

[0082] As Figure 5 shown, Figure 5 FIG. is a schematic structural diagram of a rotational speed optimization device provided by an embodiment of the present invention. The rotational 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 on the memory 1102 and executable on the processor. Among them, the processor 1101 is electrically connected to the memory 1102. Those skilled in the art can understand that the structural diagram of the rotational speed optimization device shown in the figure does not constitute a limitation on the rotational speed optimization device, and may include more or fewer components than shown, or combine certain components, or different component arrangements.

[0083] The processor 1101 is the control center of the rotational speed optimization device 1100, connects various parts of the entire rotational speed optimization device 1100 through various interfaces and lines, and executes various functions and processes data of the rotational speed optimization device 1100 by running or loading software programs and / or units stored in the memory 1102, and calling data stored in the memory 1102, so as to perform overall monitoring of the rotational speed optimization device 1100. The processor 1101 may be a central processing unit (CPU), a graphics processing unit (GPU), a network processor (NP), etc., and may implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention.

[0084] In the embodiment of the present invention, the processor 1101 in the rotational speed optimization device 1100 will load instructions corresponding to the processes of one or more application programs into the memory 1102 according to the following steps, and the processor 1101 will run the application programs stored in the memory 1102 to implement various functions, such as: Obtain a full characteristic curve data set of the rotational speed optimization device to be optimized, where the full characteristic curve data set includes a plurality of first operating point data of the rotational speed optimization device, and the first operating point data includes various operating parameters; Determine a plurality of second operating point data that meet the operating conditions requirements based on the full characteristic curve data set; Use the second operating point data as particles in the particle swarm optimization algorithm to perform optimization processing to obtain target operating point data with the optimal efficiency; Determine the target rotational speed of the rotational speed optimization device based on the operating parameters in the target operating point data.

[0085] For the specific implementation of each of the above operations, reference may be made to the previous embodiments and will not be elaborated herein.

[0086] Optionally, asFigure 5 As shown, the rotational speed optimization device 1100 further includes: a touch display 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 display screen 1103, the radio frequency circuit 1104, the audio circuit 1105, the input unit 1106, and the power supply 1107 respectively. Those skilled in the art can understand that Figure 5 the structure of the rotational speed optimization device shown in [the figure] does not constitute a limitation on the rotational speed optimization device, and it may include more or fewer components than those shown, or combine some components, or have different component arrangements.

[0087] The touch display screen 1103 can be used to display a graphical user interface and receive operation instructions generated by a user acting on the graphical user interface. The touch display screen 1103 may include a display panel and a touch panel. Among them, the display panel can be used to display information input by the user or information provided to the user, as well as various graphical user interfaces of the rotational speed optimization device. These graphical user interfaces can be composed of graphics, text, icons, videos, and 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), etc. The touch panel can be used to collect touch operations of the user on or near it (such as operations of the user using a finger, a stylus, or any suitable object or accessory on or near the touch panel), and generate corresponding operation instructions, and the operation instructions execute corresponding programs. Optionally, the touch panel can include two parts: a touch detection device and a touch controller. Among them, the touch detection device detects the touch position of the user and detects the signal brought 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 and execute commands sent by the processor 1101. The touch panel can cover the display panel. After the touch panel detects a touch operation on or near it, it transmits it to the processor 1101 to determine the type of touch event. Subsequently, the processor 1101 provides a corresponding visual output on the display panel according to the type of touch event. In the embodiment of the present invention, the touch panel and the display panel can be integrated into the touch display screen 1103 to implement input and output functions. However, in some embodiments, the touch panel and the touch panel can be implemented as two independent components to implement input and output functions. That is, the touch display screen 1103 can also be used as a part of the input unit 1106 to implement the input function.

[0088] The radio frequency circuit 1104 can be used to receive and transmit radio frequency signals to establish wireless communication with a network device or other rotational speed optimization devices through wireless communication, and to receive and transmit signals between the network device or other rotational speed optimization devices.

[0089] The audio circuit 1105 can be used to provide an audio interface between the user and the rotational speed optimization device through a speaker and a microphone. The audio circuit 1105 can transmit the electrical signal converted from the received audio data to the speaker, which converts it into a sound signal for output; on the other hand, the microphone converts the collected sound signal into an electrical signal, which is received by the audio circuit 1105 and then converted into audio data. After the audio data is output to the processor 1101 for processing, it is sent through the radio frequency circuit 1104 to, for example, another rotational speed optimization device, or the audio data is output to the memory 1102 for further processing. The audio circuit 1105 may also include an earphone jack to provide communication between a peripheral earphone and the rotational speed optimization device.

[0090] The input unit 1106 can be used to receive input digital, character information or user characteristic information (such as fingerprint, iris, facial information, etc.), and to generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function controls.

[0091] The power supply 1107 is used to supply power to each component of the rotational speed optimization device 1100. Optionally, the power supply 1107 can be logically connected to the processor 1101 through a power management system, so as to implement functions such as management of charging, discharging, and power consumption management through the power management system. The power supply 1107 can also include any components such as one or more DC or AC power supplies, a recharge system, a power failure detection circuit, a power converter or inverter, and a power status indicator.

[0092] Although Figure 5 not shown in the figure, the rotational speed optimization device 1100 may also include a camera, a sensor, a Wi-Fi module, a Bluetooth module, etc., which will not be elaborated here.

[0093] In the above embodiments, the descriptions of each embodiment have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0094] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed through instructions, or through instructions to control related hardware. The instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0095] To this end, an embodiment of the present invention provides a computer-readable storage medium, which stores multiple computer programs that can be loaded by a processor to execute any one of the rotational speed optimization methods provided by the embodiments of the present invention. The computer program can execute the following steps of the rotational speed optimization method: Obtain a full characteristic curve data set of the rotational speed optimization device to be optimized, where the full characteristic curve data set includes multiple first operating point data of the rotational speed optimization device, and the first operating point data includes various operating parameters; Based on the full characteristic curve data set, determine multiple second operating point data that meet the operating conditions; Use the second operating point data as particles in the particle swarm optimization algorithm for optimization processing to obtain the target operating point data with the optimal efficiency; Based on the operating parameters in the target operating point data, determine the target rotational speed of the rotational speed optimization device.

[0096] For the specific implementation of each of the above operations, reference can be made to the previous embodiments, and details are not described herein again.

[0097] Among them, the computer-readable storage medium may include: read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, etc.

[0098] Since the computer program stored in the computer-readable storage medium can execute any one of the rotational speed optimization methods provided by the embodiments of the present invention, the beneficial effects achievable by any one of the rotational speed optimization methods provided by the embodiments of the present invention can be realized. For details, reference can be made to the previous embodiments, and details are not described herein again.

[0099] In the above embodiments of the rotational speed optimization device, computer-readable storage medium, rotational speed optimization device, and computer program product, the descriptions of each embodiment have their own focuses. For parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes and the beneficial effects that can be brought by the above-described rotational speed optimization device, computer-readable storage medium, computer program product, rotational speed optimization device, and their corresponding units can refer to the description of the rotational speed optimization method in the above embodiments, and details are not described herein again.

[0100] The above has introduced in detail a rotational speed optimization method, a rotational speed optimization device, a rotational speed optimization equipment, a computer-readable storage medium, and a computer program product provided by embodiments of the present invention. Specific examples are used in this article to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; 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 manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A rotational speed optimization method, characterized in that, The rotational speed optimization method includes: Obtaining a full characteristic curve dataset of the pumped-storage equipment to be optimized, where the full characteristic curve dataset includes multiple first operating point data of the pumped-storage equipment, and the first operating point data includes various operating parameters; Determining multiple second operating point data that meet the operating conditions requirements based on the full characteristic curve dataset; Using the second operating point data as particles in the particle swarm optimization algorithm for optimization processing to obtain the target operating point data with the optimal efficiency; Determining the target rotational speed of the pumped-storage equipment based on the operating parameters in the target operating point data.

2. The rotational speed optimization method according to claim 1, wherein The step of using the second operating point data as particles in the particle swarm optimization algorithm for optimization processing to obtain the target operating point data with the optimal efficiency includes: Generating particles corresponding to each second operating point data based on the particle swarm optimization algorithm; Adjusting the positions of the particles to update the second operating point data corresponding to the particles; Determining the individual optimal fitness and individual optimal position of the particles after adjustment according to the efficiency of the updated second operating point data; In the case where the preset optimization condition is not satisfied, updating the global optimal position of the particle swarm according to the individual optimal fitness and individual optimal position of the particles after adjustment, and re-executing the step of adjusting the positions of the particles to update the second operating point data corresponding to the particles based on the updated global optimal position of the particle swarm and the individual optimal position of the particles after adjustment until the preset optimization condition is met; In the case where the preset optimization condition is satisfied, determining the target operating point data according to the individual optimal position of the particle corresponding to the latest global optimal fitness of the particles.

3. The rotational speed optimization method according to claim 2, characterized in that, The step of determining the individual optimal fitness and individual optimal position of the particles after adjustment according to the efficiency of the updated second operating point data includes: Obtaining the historical individual optimal fitness of the particles before adjustment; In the case where the efficiency of the updated second operating point data corresponding to the particles is greater than the historical individual optimal fitness, using the efficiency of the updated second operating point data as the individual optimal fitness of the particles after adjustment, and using the position of the particles after adjustment as the individual optimal position of the particles after adjustment; In the case where the efficiency of the updated second operating point data corresponding to the particles is less than the historical individual optimal fitness, using the historical individual optimal fitness as the individual optimal fitness of the particles after adjustment, and using the historical individual optimal position of the particles before adjustment as the individual optimal position of the particles after adjustment.

4. The rotational speed optimization method according to claim 2, 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 global optimal fitness of the particles before adjustment.

5. The rotational speed optimization method according to claim 1, wherein The operating conditions requirements include actual power requirements and actual head requirements. Determining multiple second operating point data that meet the operating conditions requirements based on the full characteristic curve dataset includes: Determining the target unit power of the pumped-storage equipment according to the actual power requirements and the actual head requirements; Selecting the first operating point data that meets the target unit power from the full characteristic curve dataset as the second operating point data.

6. The rotational speed optimization method according to claim 1, wherein Obtaining a full characteristic curve dataset of the pumped-storage equipment to be optimized, where the full characteristic curve dataset includes multiple first operating point data of the pumped-storage equipment, including: Obtaining multiple initial operating point data of the pumped-storage equipment; Based on the multiple initial operating point data, performing interpolation and fitting to obtain the full characteristic curve dataset.

7. The rotational speed optimization method according to claim 6, characterized in that The obtaining of the multiple initial operating point data of the pumped-storage equipment includes: Collecting the unit speed, unit flow rate, and unit torque of the pumped-storage equipment under at least two operating conditions; Determining the unit power according to the unit speed, the unit flow rate, and the unit torque; Determining the unit efficiency of the pumped-storage equipment according to the unit power and the unit flow rate; Obtaining the initial operating point data corresponding to the operating condition according to the unit speed, the unit flow rate, the unit torque, the unit power, and the unit efficiency.

8. A rotational speed optimization device, characterized in that The speed optimization device includes: An obtaining module, configured to obtain a full characteristic curve dataset of the pumped-storage equipment to be optimized, where the full characteristic curve dataset includes multiple first operating point data of the pumped-storage equipment, and the first operating point data includes multiple operating parameters; A screening module, configured to determine multiple second operating point data that meet the condition requirements based on the full characteristic curve dataset; An optimization module, configured to perform optimization processing on the second operating point data as particles in a particle swarm algorithm to obtain target operating point data with the optimal efficiency; A determination module, configured to determine the target speed of the pumped-storage equipment based on the operating parameters in the target operating point data.

9. A rotational speed optimization device, characterized in that, Including a processor and a memory, where the memory stores a computer program, and when the computer program is executed by the processor, the processor is caused to execute the steps of the speed optimization method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a computer program, and when the computer program runs on an electronic device, the computer program is used to cause the electronic device to execute the steps of the speed optimization method according to any one of claims 1-7.

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