Hydroelectric generating set speed regulation parameter optimization method and system based on ultralow frequency damping

Abstract

The invention discloses a hydroelectric generating set speed regulation parameter optimization method and system based on ultralow frequency damping, and relates to the technical field of hydroelectric generating set optimization. A plurality of speed regulation parameters are acquired, and each speed regulation parameter is used as a particle to construct a corresponding initial particle swarm; performing frequency modulation performance evaluation on each speed regulation parameter by adopting a preset primary frequency modulation performance simulation model to obtain a plurality of frequency modulation performance indexes, performing ultra-low frequency damping evaluation on each speed regulation parameter by adopting a preset ultra-low frequency damping simulation model to obtain a plurality of damping torques, and performing frequency modulation performance evaluation on each speed regulation parameter on the basis of a preset objective function to obtain a plurality of frequency modulation performance indexes; and performing iterative optimization on the initial particle swarm according to each frequency modulation performance index and each damping torque to obtain a corresponding target speed regulation parameter. The technical problems that according to a traditional speed regulation parameter optimization method, collaborative optimization of two core indexes is difficult to achieve under the multi-focus single-machine operation condition, and the reliability of hydroelectric generating set operation is reduced are solved.

Description

Technical Field

[0001] This invention relates to the field of hydropower unit optimization technology, and in particular to a method and system for optimizing the speed regulation parameters of hydropower units based on ultra-low frequency damping. Background Technology

[0002] With the large-scale grid connection of new energy sources, the power system exhibits a multi-entity collaborative operation characteristic of "source, grid, load, and storage." Hydropower units, as the most flexible power source with optimal regulation performance in the power system, play a crucial role in ensuring the safe and stable operation of the power grid through their frequency regulation, peak shaving, and stability control capabilities. The hydropower governor of a hydropower unit, as a key device for achieving power regulation and frequency response, directly affects the unit's dynamic response speed, stability, and anti-interference capability through its speed regulation parameter configuration. Currently, the power system places increasingly stringent requirements on the dynamic performance of hydropower units. These units not only need rapid frequency tracking capabilities under normal operating conditions but also need to maintain stable operation under complex disturbance scenarios such as new energy power fluctuations and sudden load changes, avoiding problems such as overshoot, oscillation, or response delay. Therefore, optimizing speed regulation parameters to improve the dynamic regulation quality of hydropower units has become an important technical direction for ensuring the safe and stable operation of a high-proportion new energy power grid.

[0003] Currently, traditional speed regulation parameter optimization methods mostly focus on single-unit operating conditions, lacking consideration of the electromechanical coupling characteristics of the large power grid. Furthermore, they generally adopt the method of reducing speed regulation parameters to suppress ultra-low frequency oscillations, which often leads to a decline in primary frequency regulation performance. This makes it difficult to achieve coordinated optimization of the two core indicators and reduces the reliability of hydropower unit operation. Summary of the Invention

[0004] This invention provides a method and system for optimizing the speed regulation parameters of hydropower units based on ultra-low frequency damping. It solves the technical problems of traditional speed regulation parameter optimization methods, which often focus on single-unit operation conditions, lack consideration of the electromechanical coupling characteristics of the large grid, and generally use the method of reducing the speed regulation parameters to suppress ultra-low frequency oscillations, which often leads to a decrease in primary frequency regulation performance and makes it difficult to achieve synergistic optimization of the two core indicators, thus reducing the reliability of hydropower unit operation.

[0005] The first aspect of this invention provides a method for optimizing the speed regulation parameters of a hydropower unit based on ultra-low frequency damping, comprising:

[0006] Multiple speed control parameters are obtained, and each speed control parameter is used as a particle to construct the corresponding initial particle swarm.

[0007] The frequency modulation performance of each speed regulation parameter is evaluated using a preset primary frequency modulation performance simulation model, resulting in multiple frequency modulation performance indices.

[0008] A preset ultra-low frequency damping simulation model was used to evaluate the ultra-low frequency damping of each speed regulation parameter, and multiple damping torques were obtained.

[0009] Based on a preset objective function, the initial particle swarm is iteratively optimized according to each of the frequency modulation performance indices and each of the damping torques to obtain the corresponding target speed regulation parameters.

[0010] Optionally, the step of evaluating the frequency modulation performance of each speed regulation parameter using a preset primary frequency modulation performance simulation model to obtain multiple frequency modulation performance indices includes:

[0011] Each of the speed regulation parameters is input into a preset primary frequency regulation performance simulation model to obtain multiple target primary frequency regulation performance simulation models;

[0012] Each target primary frequency modulation performance simulation model was used to perform active large disturbance electromechanical transient simulation, and multiple first mechanical fluctuation curves were obtained.

[0013] The maximum power deviation and power adjustment of each of the first mechanical fluctuation curves are compared to obtain multiple overshoot values;

[0014] Based on a preset proportional coefficient, the overshoot corresponding to each of the first mechanical fluctuation curves is quantized and aggregated to obtain multiple frequency modulation performance indices.

[0015] Optionally, the step of quantizing and aggregating the overshoot corresponding to each of the first mechanical fluctuation curves based on a preset proportional coefficient to obtain multiple frequency modulation performance indices includes:

[0016] Each of the overshoot values ​​is multiplied by a preset proportional coefficient to obtain multiple first multiplication values;

[0017] The adjustment time of each of the first mechanical fluctuation curves is compared with the preset reference primary frequency modulation adjustment time to obtain multiple first ratios;

[0018] Each of the first multiplication values ​​is summed with its corresponding first ratio to obtain multiple frequency modulation performance indices.

[0019] Optionally, the step of using a preset ultra-low frequency damping simulation model to evaluate the ultra-low frequency damping of each speed regulation parameter to obtain multiple damping torques includes:

[0020] Each of the speed regulation parameters is input into a preset ultra-low frequency damping simulation model to obtain multiple target ultra-low frequency damping simulation models;

[0021] Each of the target ultra-low frequency damping simulation models was used to perform active large disturbance electromechanical transient simulation, and multiple second mechanical wave curves were obtained.

[0022] The damping torque corresponding to each of the second mechanical fluctuation curves is determined based on a preset damping torque function.

[0023] Optionally, the step of iteratively optimizing the initial particle swarm based on a preset objective function, according to each of the frequency modulation performance indices and each of the damping torques, to obtain the corresponding target speed regulation parameters includes:

[0024] Each damping torque and its corresponding frequency modulation performance index are input into a preset objective function to obtain multiple objective function values.

[0025] Determine whether the number of iterations of the initial particle swarm is greater than or equal to a preset iteration threshold;

[0026] When the number of iterations is less than the iteration threshold, the initial particle swarm is updated using each of the objective function values ​​to obtain a new initial particle swarm, and then the process jumps to the step of evaluating the frequency modulation performance of each speed regulation parameter using a preset primary frequency modulation performance simulation model to obtain multiple frequency modulation performance indices.

[0027] When the number of iterations is greater than or equal to the iteration threshold, the speed regulation parameter corresponding to the minimum value among the objective function values ​​is selected as the target speed regulation parameter.

[0028] Optionally, the objective function is specifically:

[0029]

[0030] in, The objective function value, As the first weighting coefficient, For frequency modulation performance index, This is the second weighting coefficient. For damping torque, This is the third weighting coefficient. As a stability index, For stability sensitivity.

[0031] The second aspect of this invention provides a speed regulation parameter optimization system for hydropower units based on ultra-low frequency damping, comprising:

[0032] The acquisition module is used to acquire multiple speed control parameters and use each speed control parameter as a particle to construct the corresponding initial particle swarm.

[0033] The first evaluation module is used to evaluate the frequency modulation performance of each speed regulation parameter using a preset primary frequency modulation performance simulation model, and obtain multiple frequency modulation performance indices.

[0034] The second evaluation module is used to evaluate the ultra-low frequency damping of each speed regulation parameter using a preset ultra-low frequency damping simulation model, and obtain multiple damping torques.

[0035] An optimization module is used to iteratively optimize the initial particle swarm based on a preset objective function, according to each of the frequency modulation performance indices and each of the damping torques, to obtain the corresponding target speed regulation parameters.

[0036] The third aspect of the present invention provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method for optimizing the speed regulation parameters of a hydropower unit based on ultra-low frequency damping as described above.

[0037] The fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed, it implements the above-described method for optimizing the speed regulation parameters of a hydropower unit based on ultra-low frequency damping.

[0038] The fifth aspect of the present invention provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein, when the program instructions are executed by a computer, the computer performs the above-described method for optimizing the speed regulation parameters of a hydropower unit based on ultra-low frequency damping.

[0039] As can be seen from the above technical solutions, the present invention has the following advantages:

[0040] This invention acquires multiple speed regulation parameters, constructs an initial particle swarm using each parameter as a particle, and evaluates the frequency regulation performance of each parameter using a pre-defined primary frequency regulation performance simulation model, obtaining multiple frequency regulation performance indices. Similarly, it evaluates the ultra-low frequency damping of each parameter using a pre-defined ultra-low frequency damping simulation model, obtaining multiple damping torques. Based on a pre-defined objective function, the initial particle swarm is iteratively optimized according to the frequency regulation performance indices and damping torques to obtain the corresponding target speed regulation parameters. This overcomes the technical problem of traditional speed regulation parameter optimization methods focusing on single-unit operating conditions, making it difficult to achieve coordinated optimization of the two core indicators and reducing the reliability of hydropower unit operation. Compared with traditional speed regulation parameter optimization methods, this invention evaluates the frequency regulation performance and ultra-low frequency damping of each speed regulation parameter by using a primary frequency regulation performance simulation model and an ultra-low frequency damping simulation model, respectively. This yields the frequency regulation performance index and damping torque corresponding to each speed regulation parameter. The frequency regulation performance index and damping torque are then used to synergistically optimize the speed regulation parameters, resulting in target speed regulation parameters that can suppress ultra-low frequency oscillations while ensuring primary frequency regulation performance, thus improving the reliability of hydropower unit operation. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. 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 effort.

[0042] Figure 1 This is a flowchart illustrating the steps of a method for optimizing the speed control parameters of a hydropower unit based on ultra-low frequency damping, as provided in Embodiment 1 of the present invention.

[0043] Figure 2 This is a flowchart illustrating the steps of a method for optimizing the speed control parameters of a hydropower unit based on ultra-low frequency damping, as provided in Embodiment 2 of the present invention.

[0044] Figure 3 This is a schematic diagram of the ultra-low frequency oscillation curve excited by electromechanical transient simulation of a large load fault, provided by the ultra-low frequency damping evaluation data in Embodiment 2 of the present invention.

[0045] Figure 4 This is a schematic diagram of the first mechanical fluctuation curve under different speed regulation parameters provided in Embodiment 2 of the present invention;

[0046] Figure 5 This is a schematic diagram of the second mechanical fluctuation curve under different speed regulation parameters provided in Embodiment 2 of the present invention;

[0047] Figure 6 This is a schematic diagram of the structure of a typical model of a hydroelectric speed regulator provided in Embodiment 2 of the present invention;

[0048] Figure 7 This is a schematic diagram of the primary frequency modulation power response curve provided in Embodiment 2 of the present invention;

[0049] Figure 8 This is a structural block diagram of a hydropower unit speed regulation parameter optimization system based on ultra-low frequency damping provided in Embodiment 3 of the present invention;

[0050] Figure 9 This is a structural block diagram of an electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0051] This invention provides a method and system for optimizing the speed regulation parameters of hydropower units based on ultra-low frequency damping. This method addresses the technical problem that traditional speed regulation parameter optimization methods often focus on single-unit operating conditions, lack consideration for the electromechanical coupling characteristics of the large power grid, and commonly use the method of reducing the speed regulation parameters to suppress ultra-low frequency oscillations, which often leads to a decrease in primary frequency regulation performance and makes it difficult to achieve synergistic optimization of the two core indicators, thus reducing the reliability of hydropower unit operation.

[0052] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. It should be noted that in the optional embodiments of the present invention, the object information and other related data involved require the permission or consent of the object when the embodiments of the present invention are applied to specific products or technologies, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. That is to say, if the embodiments of the present invention involve data related to the object, it needs to be obtained with the authorization and consent of the object, the authorization and consent of the relevant departments, and in compliance with the relevant laws, regulations, and standards of the country and region. If personal information is involved in the embodiments, the acquisition of all personal information requires the consent of the individual. If sensitive information is involved, the separate consent of the information subject is required, and the embodiments also need to be implemented with the authorization and consent of the object.

[0053] Please see Figure 1 , Figure 1 The flowchart illustrates the steps of a method for optimizing the speed control parameters of a hydropower unit based on ultra-low frequency damping, as provided in Embodiment 1 of the present invention.

[0054] This invention provides a method for optimizing the speed regulation parameters of hydropower units based on ultra-low frequency damping, comprising:

[0055] Step 101: Obtain multiple speed adjustment parameters, and use each speed adjustment parameter as a particle to construct the corresponding initial particle swarm.

[0056] Speed ​​control parameters refer to the PID parameters of the hydroelectric generator unit.

[0057] In this embodiment of the invention, multiple typical speed regulation parameters are obtained, and each speed regulation parameter is used to generate a corresponding initial particle swarm, wherein each particle in the initial particle swarm corresponds to a speed regulation parameter.

[0058] Step 102: Use a preset primary frequency modulation performance simulation model to evaluate the frequency modulation performance of each speed regulation parameter and obtain multiple frequency modulation performance indices.

[0059] The primary frequency regulation performance simulation model refers to a simulation model built based on the electromechanical transient characteristics of the power system to simulate the primary frequency regulation process of the unit under load disturbance. It includes core component models such as grid topology, generators, and hydroelectric governors, as well as fault settings. The fault settings of the primary frequency regulation performance simulation model are to cut off large loads and enable the primary frequency regulation function of thermal power. The time constant of the hydroelectric power governor's water hammer effect is a measured parameter.

[0060] Frequency modulation performance index refers to a quantitative indicator that reflects the performance of primary frequency modulation.

[0061] In this embodiment of the invention, each speed regulation parameter is input into a preset primary frequency regulation performance simulation model to obtain multiple target primary frequency regulation performance simulation models. Active power disturbance transient simulations are then performed using each target primary frequency regulation performance simulation model to obtain multiple first mechanical fluctuation curves. Based on a preset frequency regulation performance function, the frequency regulation performance index corresponding to each first mechanical fluctuation curve is determined.

[0062] It should be noted that the frequency modulation performance function is as follows:

[0063]

[0064] in, For frequency modulation performance index, This is the maximum power deviation value. For power regulation, To adjust the time, For overshoot, The primary frequency modulation adjustment time is used as the reference. This is the proportionality coefficient.

[0065] Step 103: Use the preset ultra-low frequency damping simulation model to evaluate the ultra-low frequency damping of each speed regulation parameter and obtain multiple damping torques.

[0066] The ultra-low frequency damping simulation model refers to a simulation model built based on the ultra-low frequency oscillation characteristics of the power system. It is used to simulate the ultra-low frequency oscillation process under grid disturbances and evaluate the damping effect of the unit. It includes specific fault settings and component parameter configurations, and can output oscillation-related data such as mechanical power and frequency. Among them, the fault settings of the ultra-low frequency damping simulation model are to cut off large loads and shut down the primary frequency regulation function of thermal power plants. The hydroelectric speed governor has a large water hammer effect time constant (a larger water hammer effect time constant makes it easier to induce ultra-low frequency oscillations).

[0067] Damping torque is a core physical quantity that measures the unit's contribution to the damping of ultra-low frequency oscillations. A negative value indicates that the unit provides negative damping (exacerbates oscillations), while a positive value indicates that it provides positive damping (suppresses oscillations). The larger the absolute value, the stronger the damping effect.

[0068] In this embodiment of the invention, various speed regulation parameters are input into a preset ultra-low frequency damping simulation model to obtain multiple target ultra-low frequency damping simulation models. Each target ultra-low frequency damping simulation model is used to perform a dynamic transient simulation of a large active disturbance, resulting in multiple second mechanical fluctuation curves. The damping torque corresponding to each second mechanical fluctuation curve is determined based on a preset damping torque function.

[0069] Step 104: Based on the preset objective function, the initial particle swarm is iteratively optimized according to each frequency regulation performance index and each damping torque to obtain the corresponding target speed regulation parameters.

[0070] In this embodiment of the invention, each damping torque and its corresponding frequency regulation performance index are input into a preset objective function to obtain multiple objective function values. It is then determined whether the number of iterations of the initial particle swarm is greater than or equal to a preset iteration threshold. If the number of iterations is less than the iteration threshold, the initial particle swarm is updated using each objective function value based on the particle swarm algorithm to obtain a new initial particle swarm, and the process jumps to step 102. If the number of iterations is greater than or equal to the iteration threshold, the speed regulation parameter corresponding to the minimum value among the objective function values ​​is selected as the target speed regulation parameter.

[0071] In this embodiment of the invention, multiple speed regulation parameters are acquired, and each speed regulation parameter is used as a particle to construct a corresponding initial particle swarm. A preset primary frequency regulation performance simulation model is used to evaluate the frequency regulation performance of each speed regulation parameter, resulting in multiple frequency regulation performance indices. A preset ultra-low frequency damping simulation model is used to evaluate the ultra-low frequency damping of each speed regulation parameter, resulting in multiple damping torques. Based on a preset objective function, the initial particle swarm is iteratively optimized according to each frequency regulation performance index and each damping torque to obtain the corresponding target speed regulation parameters. This overcomes the technical problem that traditional speed regulation parameter optimization methods often focus on single-unit operating conditions, making it difficult to achieve coordinated optimization of the two core indicators and reducing the reliability of hydropower unit operation. Compared with traditional speed regulation parameter optimization methods, this invention evaluates the frequency regulation performance and ultra-low frequency damping of each speed regulation parameter by using a primary frequency regulation performance simulation model and an ultra-low frequency damping simulation model, respectively. This yields the frequency regulation performance index and damping torque corresponding to each speed regulation parameter. The frequency regulation performance index and damping torque are then used to synergistically optimize the speed regulation parameters, resulting in target speed regulation parameters that can suppress ultra-low frequency oscillations while ensuring primary frequency regulation performance, thus improving the reliability of hydropower unit operation.

[0072] Please see Figure 2 , Figure 2 The flowchart illustrates the steps of a method for optimizing the speed control parameters of a hydropower unit based on ultra-low frequency damping, as provided in Embodiment 2 of the present invention.

[0073] This invention provides a method for optimizing the speed regulation parameters of hydropower units based on ultra-low frequency damping, comprising:

[0074] Step 201: Obtain multiple speed adjustment parameters, and use each speed adjustment parameter as a particle to construct the corresponding initial particle swarm.

[0075] In this embodiment of the invention, multiple speed regulation parameters are obtained, and each speed regulation parameter is used as the initial particle swarm for particle construction. For example, referring to Table 1, four speed regulation parameters are obtained, and each speed regulation parameter is used as the initial particle swarm for particle construction. The speed regulation parameters include the proportional coefficient KP1, the integral coefficient KI1, and the differential coefficient KD1.

[0076] Table 1

[0077]

[0078] Step 202: Use a preset primary frequency modulation performance simulation model to evaluate the frequency modulation performance of each speed regulation parameter and obtain multiple frequency modulation performance indices.

[0079] Further, step 202 includes the following sub-steps:

[0080] S11. Input each speed regulation parameter into the preset primary frequency regulation performance simulation model to obtain multiple target primary frequency regulation performance simulation models.

[0081] In this embodiment of the invention, each speed regulation parameter is input into the hydropower speed governor model in the preset primary frequency regulation performance simulation model for configuration, thereby obtaining multiple target primary frequency regulation performance simulation models.

[0082] It should be noted that the hydroelectric speed regulator model is as follows: Figure 6 As shown, KW is the gain of the measured value, KP1, KI1, and KD1 are the proportional coefficient, integral coefficient, and derivative coefficient of the speed governor, which are the speed regulation parameters. bp is the droop coefficient, KP2 is the proportional coefficient of the electro-hydraulic servo system, TW is the water hammer time constant, TR1 is the first time constant, T1V is the second time constant, T2 is the third time constant, and TOC is the fourth time constant. YPID is the guide vane opening.

[0083] S12. Using the primary frequency modulation performance simulation model of each target, the active large disturbance mechanical transient simulation is carried out to obtain multiple first mechanical fluctuation curves.

[0084] The first mechanical fluctuation curve refers to the curve obtained by electromechanical transient simulation of active power disturbance under the primary frequency regulation performance evaluation scenario, which reflects the change of unit mechanical power over time.

[0085] In the embodiments of the present invention, see Figure 4As shown, the active power large disturbance mechanical transient simulation was carried out using the primary frequency regulation performance simulation model of each target, and the mechanical power data of the unit at different time points of each target primary frequency regulation performance simulation model were recorded to obtain multiple first mechanical fluctuation curves.

[0086] It should be noted that active power disturbance electromechanical transient simulation refers to the simulation method of simulating the electromechanical transient process of a power system when a large active power disturbance (such as the shedding of a large load) occurs. It can reflect the dynamic changes of physical quantities such as unit power and system frequency after the disturbance.

[0087] S13. The maximum power deviation and power adjustment of each first mechanical fluctuation curve are processed by ratio to obtain multiple overshoot values.

[0088] It should be noted that, for reference Figure 7 The maximum power deviation value shown refers to the maximum difference between the actual mechanical power output of the unit and the preset power target value during the dynamic response process of a single frequency regulation.

[0089] It should be noted that, for reference Figure 7 As shown, the power regulation amount refers to the total amount of active power that the unit needs to adjust in order to restore the system frequency to the target value after a load disturbance occurs. That is, the difference between the target power value and the active power output of the unit before the disturbance.

[0090] Overshoot is a percentage indicator calculated by the ratio of the maximum power deviation to the power adjustment. It is used to quantify the severity of power fluctuations during a frequency modulation process. The smaller the overshoot, the smoother the frequency modulation process.

[0091] In this embodiment of the invention, the ratio between the maximum power deviation value and the power adjustment amount of each first mechanical fluctuation curve is calculated to obtain multiple overshoot values.

[0092] S14. Based on the preset proportional coefficient, the overshoot corresponding to each first mechanical fluctuation curve is quantized and aggregated to obtain multiple frequency modulation performance indices.

[0093] Furthermore, S14 includes the following sub-steps:

[0094] S141. Multiply each overshoot by a preset proportional coefficient to obtain multiple first multiplication values.

[0095] The proportional coefficient refers to the weight used to adjust the impact of overshoot on the overall performance index, and its value can be 1.

[0096] In this embodiment of the invention, the multiplication between each overshoot and a preset proportional coefficient is calculated to obtain multiple first multiplication values.

[0097] S142. The adjustment time of each first mechanical fluctuation curve is compared with the preset primary frequency modulation adjustment time to obtain multiple first ratios.

[0098] The reference primary frequency regulation adjustment time refers to the reference standard for primary frequency regulation adjustment time set according to the relevant management rules of the power industry, and can be taken as 30.

[0099] It should be noted that, for reference Figure 7 As shown, the settling time refers to the shortest time from the occurrence of the disturbance to the absolute value of the difference between the measured mechanical power value and the target value, which always does not exceed the allowable deviation. It is a key indicator for measuring the speed of frequency modulation response. The allowable deviation is set to 5% of the power adjustment amount.

[0100] In this embodiment of the invention, the ratio between the adjustment time of each first mechanical fluctuation curve and the preset reference primary frequency modulation adjustment time is calculated to obtain multiple first ratios.

[0101] S143. Each first multiplier value is summed with the corresponding first ratio value to obtain multiple frequency modulation performance indices.

[0102] In this embodiment of the invention, the sum of each first multiplier and the corresponding first ratio is calculated to obtain multiple frequency modulation performance indices.

[0103] In another embodiment, a preset primary frequency modulation performance simulation model is used to evaluate the frequency modulation performance of each speed regulation parameter in Table 1, and multiple frequency modulation performance indices, overshoot and settling time are obtained as shown in Table 2.

[0104] Table 2

[0105]

[0106] Step 203: Input each speed regulation parameter into the preset ultra-low frequency damping simulation model to obtain multiple target ultra-low frequency damping simulation models.

[0107] In this embodiment of the invention, each speed regulation parameter is input into the preset ultra-low frequency damping simulation model of the hydroelectric speed regulator model for configuration, thereby obtaining multiple target ultra-low frequency damping simulation models.

[0108] It is worth mentioning that in the ultra-low frequency damping simulation model, the time constant TW for the water hammer effect of hydropower units with a single unit capacity of less than 100MW is set to 3. The purpose is to induce the inherent ultra-low frequency oscillation of the power grid (i.e., as shown in the figure). Figure 3 As shown in the figure, the calculated oscillation frequency is approximately 0.04 Hz and the damping ratio is approximately 1%.

[0109] Step 204: Perform active large disturbance electromechanical transient simulation using the ultra-low frequency damping simulation model of each target to obtain multiple second mechanical fluctuation curves.

[0110] The second mechanical fluctuation curve refers to the curve obtained by electromechanical transient simulation of active large disturbance under the ultra-low frequency damping evaluation scenario, which reflects the change of unit mechanical power over time.

[0111] In the embodiments of the present invention, see Figure 5 As shown, the active large disturbance mechanical transient simulation was carried out using the ultra-low frequency damping simulation models of each target, and the mechanical power data of the unit at different time points of each target ultra-low frequency damping simulation model were recorded to obtain multiple second mechanical fluctuation curves.

[0112] Step 205: Determine the damping torque corresponding to each second mechanical fluctuation curve based on the preset damping torque function.

[0113] In this embodiment of the invention, the peak-to-peak value of mechanical power amplitude, peak-to-peak value of frequency amplitude, and phase difference between mechanical power and frequency corresponding to each second mechanical fluctuation curve are input into a preset damping torque function to obtain multiple damping torques.

[0114] It should be noted that the damping torque function is as follows:

[0115]

[0116] in, For damping torque, This represents the peak-to-peak value of the mechanical power amplitude. The frequency amplitude peak-to-peak value, This represents the mechanical power and the frequency phase difference.

[0117] It is worth mentioning that the other electromechanical models for the primary frequency regulation performance simulation model and the ultra-low frequency damping simulation model can be obtained from the PSD-ST transient stability program user manual of the Power System Analysis Program (PSD-BPA).

[0118] In another embodiment, a preset ultra-low frequency damping simulation model is used to evaluate the ultra-low frequency damping of each speed regulation parameter in Table 1, and multiple damping torques, multiple peak-to-peak values ​​of mechanical power amplitudes, and multiple phase differences between mechanical power and frequency are obtained as shown in Table 3.

[0119] Table 3

[0120]

[0121] Step 206: Based on the preset objective function, the initial particle swarm is iteratively optimized according to each frequency regulation performance index and each damping torque to obtain the corresponding target speed regulation parameters.

[0122] Furthermore, step 206 includes the following sub-steps:

[0123] S21. Input each damping torque and the corresponding frequency modulation performance index into the preset target function to obtain multiple target function values.

[0124] In this embodiment of the invention, each damping torque and its corresponding frequency modulation performance index are input into a preset target function to obtain multiple target function values.

[0125] It should be noted that the objective function is as follows:

[0126]

[0127] in, The objective function value, As the first weighting coefficient, For frequency modulation performance index, This is the second weighting coefficient. For damping torque, This is the third weighting coefficient. As a stability index, For stability sensitivity.

[0128] In another embodiment, let =2, =1, =2, input each damping torque and the corresponding frequency modulation performance index in Table 3 into the preset objective function to obtain multiple objective function values ​​as shown in Table 4.

[0129] Table 4

[0130]

[0131] S22. Determine whether the number of iterations of the initial particle swarm is greater than or equal to the preset iteration threshold.

[0132] The iteration threshold refers to the pre-set maximum number of particle swarm optimization iterations.

[0133] In this embodiment of the invention, it is determined that the number of iterations of the initial particle swarm has reached a preset iteration threshold.

[0134] S23. When the number of iterations is less than the iteration threshold, the initial particle swarm is updated using the values ​​of each objective function to obtain a new initial particle swarm. Then, the process jumps to the step of using a preset primary frequency modulation performance simulation model to evaluate the frequency modulation performance of each speed regulation parameter and obtain multiple frequency modulation performance indices.

[0135] In this embodiment of the invention, when the number of iterations is less than the iteration threshold, the initial particle swarm is updated based on the particle swarm algorithm using the values ​​of each objective function to obtain a new initial particle swarm, and then the process jumps to step 202.

[0136] In another embodiment, when the number of iterations is less than the iteration threshold, the minimum value of the objective function among the preset update function is selected as the optimal position of the swarm. The minimum value of the speed adjustment parameter corresponding to each particle is selected as its own optimal position. The optimal positions of each particle and the optimal position of the swarm are then input into the preset update function to obtain a new initial particle swarm. The process then jumps to step 202.

[0137] It should be noted that the update function is as follows:

[0138]

[0139] in, Let be the velocity of the i-th particle in the (k+1)-th iteration. For inertial weights, Let be the velocity of the i-th particle in the k-th iteration. For cognitive coefficient, The first random number, Let be the optimal position for the i-th particle. This represents the position (i.e., speed adjustment parameter) corresponding to the k-th iteration of the i-th particle. For social coefficient, The second random number, The optimal position for the group. The velocity coefficient corresponding to the (k+1)th iteration of the i-th particle. The velocity is the integral coefficient corresponding to the (k+1)th iteration of the i-th particle. The velocity is the differential coefficient corresponding to the (k+1)th iteration of the i-th particle. This represents the position of the i-th particle in the (k+1)-th iteration. As the first boundary, Let i be the index of the particle, representing the second boundary. This is a truncation function to ensure that the parameters are within a reasonable range for engineering applications.

[0140] S24. When the number of iterations is greater than or equal to the iteration threshold, the speed regulation parameter corresponding to the minimum value among the objective function values ​​is selected as the target speed regulation parameter.

[0141] In this embodiment of the invention, when the number of iterations reaches the iteration threshold, the speed regulation parameter corresponding to the minimum value among the various objective function values ​​is selected as the target speed regulation parameter.

[0142] In this embodiment of the invention, multiple speed regulation parameters are acquired, and each speed regulation parameter is used as a particle to construct a corresponding initial particle swarm. A preset primary frequency regulation performance simulation model is used to evaluate the frequency regulation performance of each speed regulation parameter, resulting in multiple frequency regulation performance indices. A preset ultra-low frequency damping simulation model is used to evaluate the ultra-low frequency damping of each speed regulation parameter, resulting in multiple damping torques. Based on a preset objective function, the initial particle swarm is iteratively optimized according to each frequency regulation performance index and each damping torque to obtain the corresponding target speed regulation parameters. This overcomes the technical problem that traditional speed regulation parameter optimization methods often focus on single-unit operating conditions, making it difficult to achieve coordinated optimization of the two core indicators and reducing the reliability of hydropower unit operation. Compared with traditional speed regulation parameter optimization methods, this invention evaluates the frequency regulation performance and ultra-low frequency damping of each speed regulation parameter by using a primary frequency regulation performance simulation model and an ultra-low frequency damping simulation model, respectively. This yields the frequency regulation performance index and damping torque corresponding to each speed regulation parameter. The frequency regulation performance index and damping torque are then used to synergistically optimize the speed regulation parameters, resulting in target speed regulation parameters that can suppress ultra-low frequency oscillations while ensuring primary frequency regulation performance, thus improving the reliability of hydropower unit operation.

[0143] Please see Figure 8 , Figure 8 This is a structural block diagram of a hydropower unit speed regulation parameter optimization system based on ultra-low frequency damping, provided in Embodiment 3 of the present invention.

[0144] This invention provides a speed regulation parameter optimization system for hydropower units based on ultra-low frequency damping, comprising:

[0145] The acquisition module 301 is used to acquire multiple speed regulation parameters and use each speed regulation parameter as a particle to construct the corresponding initial particle swarm.

[0146] The first evaluation module 302 is used to evaluate the frequency modulation performance of each speed regulation parameter using a preset primary frequency modulation performance simulation model, and obtain multiple frequency modulation performance indices.

[0147] The second evaluation module 303 is used to evaluate the ultra-low frequency damping of each speed regulation parameter using a preset ultra-low frequency damping simulation model, and obtain multiple damping torques.

[0148] The optimization module 304 is used to iteratively optimize the initial particle swarm based on a preset objective function, according to each frequency regulation performance index and each damping torque, to obtain the corresponding target speed regulation parameters.

[0149] Furthermore, the first evaluation module 302 includes:

[0150] The first simulation submodule is used to input each speed regulation parameter into a preset primary frequency regulation performance simulation model to obtain multiple target primary frequency regulation performance simulation models.

[0151] The active large disturbance mechanical transient simulation was carried out using the primary frequency modulation performance simulation model of each target, and multiple first mechanical fluctuation curves were obtained;

[0152] The first ratio submodule is used to perform ratio processing on the maximum power deviation value and power adjustment amount of each first mechanical fluctuation curve to obtain multiple overshoot values;

[0153] The quantization aggregation submodule is used to quantize and aggregate the overshoot corresponding to each first mechanical fluctuation curve based on a preset proportional coefficient, so as to obtain multiple frequency modulation performance indices.

[0154] Furthermore, the quantization aggregation submodule includes:

[0155] The multiplication unit is used to multiply each overshoot by a preset proportional coefficient to obtain multiple first multiplication values.

[0156] The ratio unit is used to perform ratio processing on the adjustment time of each first mechanical fluctuation curve and the preset reference primary frequency modulation adjustment time to obtain multiple first ratios;

[0157] The summation unit is used to sum each first multiplier with the corresponding first ratio to obtain multiple frequency modulation performance indices.

[0158] Furthermore, the second evaluation module 303 includes:

[0159] The second simulation submodule is used to input each speed regulation parameter into the preset ultra-low frequency damping simulation model to obtain multiple target ultra-low frequency damping simulation models.

[0160] Each target ultra-low frequency damping simulation model was used to perform active large disturbance electromechanical transient simulation, and multiple second mechanical wave curves were obtained.

[0161] The damping torque submodule is used to determine the damping torque corresponding to each second mechanical fluctuation curve based on a preset damping torque function.

[0162] Furthermore, module 304 is optimized, including:

[0163] The first optimization submodule is used to input each damping torque and the corresponding frequency modulation performance index into a preset objective function to obtain multiple objective function values;

[0164] The second optimization submodule is used to determine whether the number of iterations of the initial particle swarm is greater than or equal to the preset iteration threshold.

[0165] When the number of iterations is less than the iteration threshold, the initial particle swarm is updated using the values ​​of each objective function to obtain a new initial particle swarm, and then the process jumps to execute the step of evaluating the frequency modulation performance of each speed regulation parameter using a preset primary frequency modulation performance simulation model to obtain multiple frequency modulation performance indices.

[0166] When the number of iterations is greater than or equal to the iteration threshold, the speed regulation parameter corresponding to the minimum value among all objective function values ​​is selected as the target speed regulation parameter.

[0167] Furthermore, the objective function is specifically:

[0168]

[0169] in, The objective function value, As the first weighting coefficient, For frequency modulation performance index, This is the second weighting coefficient. For damping torque, This is the third weighting coefficient. As a stability index, For stability sensitivity.

[0170] Please see Figure 9 , Figure 9 This is a structural block diagram of an electronic device provided in Embodiment 4 of the present invention.

[0171] An electronic device according to an embodiment of the present invention includes: a memory 401 and a processor 402. The memory 401 stores a computer program. When the computer program is executed by the processor 402, the processor 402 executes the speed regulation parameter optimization method for hydropower units based on ultra-low frequency damping as described in any of the above embodiments.

[0172] Memory 401 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Memory 401 has storage space 403 for program code 413 for performing any of the method steps described above. For example, storage space 403 for program code may include individual program codes 413 for implementing the various steps in the methods described above. This program code may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When run by a computing processing device, this code causes the computing processing device to perform the various steps in the methods described above. This program code may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When this code is run by a computing device, it causes the computing device to perform the various steps in the method for optimizing the speed regulation parameters of hydropower units based on ultra-low frequency damping, as described above.

[0173] Embodiment 5 of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for optimizing the speed regulation parameters of a hydropower unit based on ultra-low frequency damping as described in any of the above embodiments.

[0174] Embodiment 6 of the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer executes the method for optimizing the speed regulation parameters of a hydropower unit based on ultra-low frequency damping as described in any of the above embodiments.

[0175] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0176] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0177] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0178] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0179] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0180] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the speed regulation parameters of a hydropower unit based on ultra-low frequency damping, characterized in that, include: Multiple speed control parameters are obtained, and each speed control parameter is used as a particle to construct the corresponding initial particle swarm. The frequency modulation performance of each speed regulation parameter is evaluated using a preset primary frequency modulation performance simulation model, resulting in multiple frequency modulation performance indices. A preset ultra-low frequency damping simulation model was used to evaluate the ultra-low frequency damping of each speed regulation parameter, and multiple damping torques were obtained. Based on a preset objective function, the initial particle swarm is iteratively optimized according to each of the frequency modulation performance indices and each of the damping torques to obtain the corresponding target speed regulation parameters.

2. The method for optimizing the speed regulation parameters of a hydropower unit based on ultra-low frequency damping according to claim 1, characterized in that, The step of evaluating the frequency modulation performance of each speed regulation parameter using a preset primary frequency modulation performance simulation model to obtain multiple frequency modulation performance indices includes: Each of the speed regulation parameters is input into a preset primary frequency regulation performance simulation model to obtain multiple target primary frequency regulation performance simulation models; Each target primary frequency modulation performance simulation model was used to perform active large disturbance electromechanical transient simulation, and multiple first mechanical fluctuation curves were obtained. The maximum power deviation and power adjustment of each of the first mechanical fluctuation curves are compared to obtain multiple overshoot values; Based on a preset proportional coefficient, the overshoot corresponding to each of the first mechanical fluctuation curves is quantized and aggregated to obtain multiple frequency modulation performance indices.

3. The method for optimizing the speed regulation parameters of a hydropower unit based on ultra-low frequency damping according to claim 2, characterized in that, The step of quantizing and aggregating the overshoot corresponding to each of the first mechanical fluctuation curves based on a preset proportional coefficient to obtain multiple frequency modulation performance indices includes: Each of the overshoot values ​​is multiplied by a preset proportional coefficient to obtain multiple first multiplication values; The adjustment time of each of the first mechanical fluctuation curves is compared with the preset reference primary frequency modulation adjustment time to obtain multiple first ratios; Each of the first multiplication values ​​is summed with its corresponding first ratio to obtain multiple frequency modulation performance indices.

4. The method for optimizing the speed regulation parameters of a hydropower unit based on ultra-low frequency damping according to claim 1, characterized in that, The step of using a preset ultra-low frequency damping simulation model to evaluate the ultra-low frequency damping of each speed regulation parameter and obtain multiple damping torques includes: Each of the speed regulation parameters is input into a preset ultra-low frequency damping simulation model to obtain multiple target ultra-low frequency damping simulation models; Each of the target ultra-low frequency damping simulation models was used to perform active large disturbance electromechanical transient simulation, and multiple second mechanical wave curves were obtained. The damping torque corresponding to each of the second mechanical fluctuation curves is determined based on a preset damping torque function.

5. The method for optimizing the speed regulation parameters of a hydropower unit based on ultra-low frequency damping according to claim 1, characterized in that, The step of iteratively optimizing the initial particle swarm based on a preset objective function, according to each of the frequency modulation performance indices and each of the damping torques, to obtain the corresponding target speed regulation parameters includes: Each damping torque and its corresponding frequency modulation performance index are input into a preset objective function to obtain multiple objective function values. Determine whether the number of iterations of the initial particle swarm is greater than or equal to a preset iteration threshold; When the number of iterations is less than the iteration threshold, the initial particle swarm is updated using each of the objective function values ​​to obtain a new initial particle swarm, and then the process jumps to the step of evaluating the frequency modulation performance of each speed regulation parameter using a preset primary frequency modulation performance simulation model to obtain multiple frequency modulation performance indices. When the number of iterations is greater than or equal to the iteration threshold, the speed regulation parameter corresponding to the minimum value among the objective function values ​​is selected as the target speed regulation parameter.

6. The method for optimizing the speed regulation parameters of a hydropower unit based on ultra-low frequency damping according to claim 1, characterized in that, The objective function is specifically: in, The objective function value, As the first weighting coefficient, For frequency modulation performance index, This is the second weighting coefficient. For damping torque, This is the third weighting coefficient. As a stability index, For stability sensitivity.

7. A speed regulation parameter optimization system for hydropower units based on ultra-low frequency damping, characterized in that, include: The acquisition module is used to acquire multiple speed control parameters and use each speed control parameter as a particle to construct the corresponding initial particle swarm. The first evaluation module is used to evaluate the frequency modulation performance of each speed regulation parameter using a preset primary frequency modulation performance simulation model, and obtain multiple frequency modulation performance indices. The second evaluation module is used to evaluate the ultra-low frequency damping of each speed regulation parameter using a preset ultra-low frequency damping simulation model, and obtain multiple damping torques. An optimization module is used to iteratively optimize the initial particle swarm based on a preset objective function, according to each of the frequency modulation performance indices and each of the damping torques, to obtain the corresponding target speed regulation parameters.

8. An electronic device, characterized in that, The device includes a memory and a processor. The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the method for optimizing the speed regulation parameters of a hydropower unit based on ultra-low frequency damping as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the method for optimizing the speed regulation parameters of hydropower units based on ultra-low frequency damping as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, wherein when the program instructions are executed by a computer, the computer performs the method for optimizing the speed regulation parameters of a hydropower unit based on ultra-low frequency damping as described in any one of claims 1-6.

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