Black start simulation test method, device, equipment, storage medium and program product

Abstract

The present application relates to the technical field of black start, and discloses a black start simulation test method, device, equipment, storage medium and program product, the black start simulation test method comprises the following steps: black start simulation is carried out according to first simulation parameters, and first simulation running data is obtained; first evaluation indexes are determined according to the first simulation running data and second simulation running data; the third simulation parameters are generated according to the first evaluation indexes, the first simulation parameters, a preset step and the second simulation parameters; the first simulation parameters are updated by using the third simulation parameters, and the step of black start simulation is returned until the preset batch of black start simulation is completed; wherein the second simulation parameters represent the initialization parameters required by the last batch of black start simulation corresponding to the current batch.The simulation parameters used in the black start simulation are adaptively generated, the batches of simulation test are reduced under the premise of not affecting the simulation effect, and the efficiency of the black start simulation test is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of black start, and in particular to a black start simulation test method, device, equipment, storage medium and program product. BACKGROUND

[0002] With large-scale grid connection of new energy, the power system faces greater power failure risks. Black start is the first step to restore power supply after power failure of the power system. In order to restore power supply as soon as possible, more reliable black start power sources need to be added. In order to improve the starting capability of the power system, the wind storage power generation system is considered as a black start power source. The black start power source needs to provide stable power for the started unit and establish stable voltage and frequency during power failure. Therefore, simulation test of black start needs to be performed to improve the operation performance of black start in actual conditions.

[0003] In the related art, when the black start is simulated and tested under different conditions, the initial parameters are set at equal intervals, that is, the interval of each batch of initial parameters is set. Each batch of initial parameters is increased or decreased according to the interval. For example, for the initial parameter of wind speed value, the equal interval interval value of wind speed value is set to 2 m / s. Each batch of wind speed value is increased according to the interval value of 2 m / s, that is, the wind speed value of the first batch is 2 m / s, the wind speed value of the second batch is 4 m / s, the wind speed value of the third batch is 6 m / s, and so on. However, this method can only complete multiple batches of black start simulation through equal interval setting. When the equal interval interval value is set, it is usually set artificially according to experience. Therefore, there are many subjective factors in setting the equal interval interval value. The initial parameters are not adjusted according to the simulation test results, resulting in low efficiency of simulation test. SUMMARY

[0004] Therefore, the present application provides a black start simulation test method, device, equipment, storage medium and program product to solve the problem of low efficiency of black start simulation test.

[0005] In a first aspect, the present application provides a black start simulation test method, comprising: performing black start simulation according to first simulation parameters to obtain first simulation running data, wherein the first simulation parameters represent initialization parameters used by the current batch of black start simulation; the first simulation running data represents running data of a wind storage system obtained according to the current batch of black start simulation; determining a first evaluation index according to the first simulation running data and second simulation running data, wherein the second simulation running data represents running data of the wind storage system obtained according to the black start simulation of the previous batch corresponding to the current batch; the first evaluation index is used to evaluate the running condition of the black start simulation; generating third simulation parameters according to the first evaluation index, the first simulation parameters, a preset step size and second simulation parameters; updating the first simulation parameters by using the third simulation parameters and returning to the step of black start simulation until the black start simulation of the preset batch is completed, wherein the second simulation parameters represent initialization parameters required by the black start simulation of the previous batch corresponding to the current batch; and the third simulation parameters represent initialization parameters required by the black start simulation of the next batch corresponding to the current batch.

[0006] The present application first performs black start simulation according to the first simulation parameters used by the current batch of black start simulation to obtain the first simulation running data of the wind storage system, and determines the first evaluation index by processing the simulation running data of the current batch and the simulation running data of the previous batch according to the second simulation running data of the wind storage system obtained by the black start simulation of the previous batch. The first evaluation index obtained by the present application is used to evaluate the running condition of the black start simulation, and the running condition of the black start simulation can be determined according to the running condition of the black start simulation. The problems occurring in the running process of the black start simulation can be found in real time. The third simulation parameters are generated according to the first evaluation index, the first simulation parameters, a preset step size and second simulation parameters. The present application processes the current simulation parameters, the simulation parameters of the previous batch, the first evaluation index and the preset step size to obtain the simulation parameters of the next batch, realizes the adaptive generation of the simulation parameters of the next batch, and obtains the third simulation parameters by fusing various influencing factors. The first simulation parameters are updated by using the third simulation parameters and returned to the step of black start simulation until the black start simulation of the preset batch is completed, thereby improving the efficiency of the black start simulation test. Compared with the equal interval setting of simulation parameters in the related art, the simulation parameters used by each batch of black start simulation are adaptively generated according to various influencing factors, the batches of simulation test are reduced under the premise of not affecting the simulation effect, and the efficiency of the black start simulation test is improved.

[0007] In an optional implementation, the first evaluation index is determined according to the first simulation running data and the second simulation running data, and the method comprises the following steps: obtaining a plurality of evaluation index data according to differences between a plurality of first running indexes in the first simulation running data and a plurality of second running indexes in the second simulation running data, wherein the plurality of first running indexes represent a plurality of indexes obtained by the current batch of black-start simulation and used for judging stability of the black-start simulation, and the plurality of second running indexes represent a plurality of indexes obtained by the previous batch of black-start simulation and used for judging stability of the black-start simulation; and the first evaluation index is composed of the plurality of evaluation index data.

[0008] The plurality of evaluation index data is obtained by subtracting the plurality of second running indexes in the second simulation running data from the plurality of first running indexes in the first simulation running data, and the first evaluation index is composed of the plurality of evaluation index data, so that the fluctuation value of the running indexes of the current black-start simulation process and the previous batch of black-start simulation process can be obtained by comparing the first running indexes with the second running indexes, and the running situation of the black-start simulation can be evaluated by the first evaluation index.

[0009] In an optional implementation, the third simulation parameter is generated according to the first evaluation index, the first simulation parameter, the preset step length and the second simulation parameter, and the method comprises the following steps: obtaining a parameter error value according to a difference between the first simulation parameter and the second simulation parameter; obtaining a plurality of step length coefficients according to ratios of the plurality of evaluation index data to the parameter error value; selecting a target step length coefficient from the plurality of step length coefficients; and adding the first simulation parameter to a product of the target step length coefficient and the preset step length to obtain the third simulation parameter.

[0010] The parameter error value is obtained according to the difference between the first simulation parameter and the second simulation parameter, the plurality of step length coefficients are obtained according to the ratios of the plurality of evaluation index data to the parameter error value, and the target step length coefficient is selected from the plurality of step length coefficients, so that the step length coefficient is determined by the difference between the first simulation parameter and the second simulation parameter and the change rates between the plurality of evaluation indexes.

[0011] In an optional implementation, the target step length coefficient is selected from the plurality of step length coefficients, and the method comprises the following steps: taking absolute values of the plurality of step length coefficients to obtain a plurality of absolute value results; and selecting the smallest absolute value result from the plurality of absolute value results as the target step length coefficient.

[0012] In an optional implementation, the first simulation parameter comprises a battery parameter, the black start simulation is performed according to the first simulation parameter, and first simulation running data is obtained, comprising: performing charging simulation on a direct-current capacitor of the black start fan according to the battery parameter until the black start fan operates at a preset power; determining whether the rotating speed of the black start fan reaches a preset value within a preset time period; if the rotating speed of the black start fan reaches the preset value within the preset time period, setting the operation mode of the black start fan to a maximum power point tracking mode to generate power, and obtaining the first simulation running data; if the rotating speed of the black start fan does not reach the preset value within the preset time period, adjusting the rotating speed of the black start fan until the rotating speed of the black start fan reaches the preset value within the preset time period, setting the operation mode of the black start fan to the maximum power point tracking mode to generate power, and obtaining the first simulation running data.

[0013] In an optional implementation, setting the operation mode of the black start fan to the maximum power point tracking mode to generate power comprises: if the first power generated by the black start fan is greater than the second power required by the load, controlling the battery to perform charging simulation; if the first power generated by the black start fan is less than or equal to the second power required by the load, controlling the battery to perform discharging simulation.

[0014] In a second aspect, the present application provides a black start simulation test device, comprising: a wind storage system black start simulation module, configured to perform black start simulation according to first simulation parameters to obtain first simulation running data, wherein the first simulation parameters represent initialization parameters used in the current batch of black start simulation; the first simulation running data represents running data of the wind storage system obtained according to the current batch of black start simulation; a wind storage system black start performance evaluation module, configured to determine a first evaluation index according to the first simulation running data and second simulation running data; wherein the second simulation running data represents running data of the wind storage system obtained according to the black start simulation of the previous batch corresponding to the current batch; the first evaluation index is used to evaluate the running situation of the black start simulation; a wind storage system black start simulation parameter initialization module, configured to generate third simulation parameters according to the first evaluation index, the first simulation parameters, a preset step length and second simulation parameters; update the first simulation parameters by using the third simulation parameters and return to the step of black start simulation until the preset batch of black start simulation is completed; wherein the second simulation parameters represent initialization parameters required in the black start simulation of the previous batch corresponding to the current batch; the third simulation parameters represent initialization parameters required in the black start simulation of the next batch corresponding to the current batch.

[0015] In a third aspect, the present application provides a computer device, comprising a memory and a processor, which are connected with each other in communication, the memory stores computer instructions, and the processor executes the black start simulation test method of the first aspect or any of the corresponding embodiments by executing the computer instructions.

[0016] In a fourth aspect, the present application provides a computer readable storage medium, which stores computer instructions for making a computer execute the black start simulation test method of the first aspect or any of the corresponding embodiments.

[0017] In a fifth aspect, the present application provides a computer program product, which comprises computer instructions for making a computer execute the black start simulation test method of the first aspect or any of the corresponding embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the specific embodiments or the related art, the drawings needed to be used in the specific embodiments or the related art description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0019] Figure 1 Fig. 1 is a flowchart of a black start simulation test method according to an embodiment of the present application;

[0020] Figure 2 Fig. 2 is a flowchart of another black start simulation test method according to an embodiment of the present application;

[0021] Figure 3 Fig. 3 is a schematic diagram of a wind storage system structure according to an embodiment of the present application;

[0022] Figure 4 Fig. 4 is a schematic diagram of a wind storage system black start process according to an embodiment of the present application;

[0023] Figure 5 Fig. 5 is a flowchart of still another black start simulation test method according to an embodiment of the present application;

[0024] Figure 6 Fig. 6 is a structural block diagram of a black start simulation test device according to an embodiment of the present application;

[0025] Figure 7 Fig. 7 is a schematic diagram of a hardware structure of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of the present application.

[0027] Black start refers to that when the entire power system is in a full black state and cannot operate normally, a generator set with self-starting capability in the power system is started to drive a generator set without self-starting capability, so as to gradually expand the recovery range of the power system, and finally realize the recovery and power supply of the entire power system.

[0028] In the embodiments of the present application, in the black start simulation process, in order to improve the starting capability of the power system, the wind storage power generation system is considered as a black start power source to drive the generator set without self-starting capability. At present, the real-time simulator is applied to simulate and test the black start, verify the operation performance of the power system under different conditions, analyze the change law of the operation performance with the initial parameters, and set the initial parameters at equal intervals when simulating and testing the black start under different conditions, that is, set intervals for each batch of initial parameters, and each batch of initial parameters is increased or decreased according to the intervals. When the interval values of the equal intervals are set, there are more subjective influencing factors, the initial parameters are not adjusted according to the simulation test results, and the efficiency of the simulation test is low.

[0029] The embodiments of the present application provide a black start simulation test method, which generates simulation parameters used for black start simulation adaptively, so as to improve the efficiency of the black start simulation test.

[0030] According to the embodiments of the present application, a black start simulation test method embodiment is provided. It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a group of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0031] In the embodiments of the present application, a black start simulation test method is provided, which can be used for a computer device, Figure 1 The flowchart of the black start simulation test method according to the embodiments of the present application is shown in FIG. 1, which includes the following steps: Figure 1

[0032] ​In step S101, black start simulation is performed according to first simulation parameters to obtain first simulation running data, wherein the first simulation parameters represent initialization parameters used in the current batch of black start simulation; and the first simulation running data represents running data of the wind storage system obtained according to the current batch of black start simulation.

[0033] The first simulation parameters include wind speed values and energy storage capacity values, and other initialization parameters required for black start simulation. After the first simulation parameters are generated, they are stored in a database, and when black start simulation is performed on the first simulation parameters, the first simulation parameters are obtained from the database.

[0034] In some optional embodiments, the first batch of simulation parameters can be set in advance. For example, the wind speed value can be set to 2 m / s, and the energy storage capacity value can be set to 10%.

[0035] In some optional embodiments, a real-time simulator is applied to perform simulation testing on black start. For example, the real-time simulator can be a real-time digital simulator (RTDS) or a real-time laboratory (RT-LAB). The first simulation parameters are input into the real-time simulator to perform simulation testing on black start, and the first simulation running data is output.

[0036] In some optional embodiments, the first simulation running data includes a plurality of first running indexes. For example, the plurality of first running indexes include real-time wind turbine power generation, real-time energy storage charging power, real-time energy storage power generation, real-time DC bus voltage, real-time AC bus voltage, and real-time AC bus frequency.

[0037] In step S102, a first evaluation index is determined according to the first simulation running data and second simulation running data. The second simulation running data represents running data of the wind storage system obtained according to black start simulation of a previous batch corresponding to the current batch. The first evaluation index is used to evaluate the running situation of the black start simulation.

[0038] The second simulation running data is stored in a database, and is obtained when needed.

[0039] In some optional embodiments, the first evaluation index is used to evaluate the running situation of the black start simulation. For example, the running situation of the black start simulation can include stable running, unstable running, stable running under certain conditions, and the like.

[0040] In some optional embodiments, the first evaluation index is determined according to the first simulation running data and the second simulation running data, including:

[0041] According to the difference between the plurality of first operation indexes in the first simulation running data and the plurality of second operation indexes in the second simulation running data, a plurality of evaluation index data is obtained; wherein the plurality of first operation indexes represent a plurality of indexes obtained by the current batch of black start simulation for judging the stability of the black start simulation; the plurality of second operation indexes represent a plurality of indexes obtained by the last batch of black start simulation for judging the stability of the black start simulation; the first evaluation index is composed of the plurality of evaluation index data.

[0042] The second simulation running data includes a plurality of second operation indexes, and the plurality of first operation indexes include, for example, historical wind turbine power, historical energy storage charging power, historical energy storage power, historical DC bus voltage, historical AC bus voltage, and historical AC bus frequency.

[0043] In some optional embodiments, each first operation index and each second operation index take the average value of 10 operation indexes after a preset operation time, so as to reduce the influence of simulation data fluctuation.

[0044] For example, the calculation formula of the wind turbine power difference is:

[0045]

[0046] Wherein, ΔP1 represents the wind turbine power difference, represents the real-time wind turbine power, represents the historical wind turbine power.

[0047] The calculation formula of the energy storage charging power difference is:

[0048]

[0049] Wherein, ΔP2 represents the energy storage charging power difference, represents the real-time energy storage charging power, represents the historical energy storage charging power.

[0050] The calculation formula of the energy storage power difference is:

[0051]

[0052] Wherein, ΔP3 represents the energy storage power difference, represents the real-time energy storage power, represents the historical energy storage power.

[0053] The calculation formula of the DC bus voltage difference is:

[0054]

[0055] wherein ΔU1 represents a DC bus voltage difference, represents a real-time DC bus voltage, represents a historical DC bus voltage.

[0056] The calculation formula of the AC bus voltage difference is:

[0057]

[0058] wherein ΔU2 represents an AC bus voltage difference, represents a real-time AC bus voltage, represents a historical AC bus voltage.

[0059] The calculation formula of the AC bus frequency difference is:

[0060]

[0061] wherein ΔF1 represents an AC bus frequency difference, represents a real-time AC bus frequency, represents a historical AC bus frequency.

[0062] In step S103, a third simulation parameter is generated according to the first evaluation index, the first simulation parameter, a preset step length, and a second simulation parameter; the first simulation parameter is updated by using the third simulation parameter, and the step of black-start simulation is returned until a preset batch of black-start simulation is completed; wherein the second simulation parameter represents an initialization parameter required by a previous batch of black-start simulation corresponding to a current batch; and the third simulation parameter represents an initialization parameter required by a next batch of black-start simulation corresponding to the current batch.

[0063] The preset step length can be customized according to actual conditions.

[0064] In some optional embodiments, the third simulation parameter is generated according to the first evaluation index, the first simulation parameter, the preset step length, and the second simulation parameter, including: obtaining a parameter error value according to a difference between the first simulation parameter and the second simulation parameter; obtaining a plurality of step length coefficients according to ratios of a plurality of evaluation index data to the parameter error value; selecting a target step length coefficient from the plurality of step length coefficients; and adding the first simulation parameter to a product of the target step length coefficient and the preset step length to obtain the third simulation parameter.

[0065] Exemplarily, the calculation formula of the first step length coefficient is:

[0066]

[0067] Wherein, k1 indicates the first step length coefficient, ΔP1 indicates the wind power generation power difference, S now represents the first simulation parameter, S last represents the second simulation parameter.

[0068] The calculation formula of the second step length coefficient is:

[0069]

[0070] Wherein, k2 indicates the second step length coefficient, ΔP2 indicates the energy storage charging power difference, S now represents the first simulation parameter, S last represents the second simulation parameter.

[0071] The calculation formula of the third step length coefficient is:

[0072]

[0073] Wherein, k3 indicates the third step length coefficient, ΔP3 indicates the energy storage power generation power difference, S now represents the first simulation parameter, S last represents the second simulation parameter.

[0074] The calculation formula of the fourth step length coefficient is:

[0075]

[0076] Wherein, k4 indicates the fourth step length coefficient, ΔU1 indicates the DC bus voltage difference, S now represents the first simulation parameter, S last represents the second simulation parameter.

[0077] The calculation formula of the fifth step length coefficient is:

[0078]

[0079] Wherein, k5 indicates the fifth step length coefficient, ΔU2 indicates the AC bus voltage difference, S now represents the first simulation parameter, S last represents the second simulation parameter.

[0080] The calculation formula of the sixth step length coefficient is:

[0081]

[0082] Wherein, k6 indicates the sixth step length coefficient, ΔF1 indicates the AC bus frequency difference, S now represents the first simulation parameter, S last represents the second simulation parameter.

[0083] Further, the target step length coefficient is selected from the plurality of step length coefficients, including: taking absolute values of the plurality of step length coefficients to obtain a plurality of absolute value results; and selecting a minimum absolute value result from the plurality of absolute value results as the target step length coefficient.

[0084] For example, the absolute values of k1, k2, k3, k4, k5 and k6 are taken respectively to obtain six absolute value results, and a minimum absolute value result is selected from the six absolute value results as the target step length coefficient.

[0085] In some optional embodiments, the first simulation parameter is added to a product of the target step length coefficient and a preset step length to obtain a third simulation parameter, and a calculation formula of the third simulation parameter is:

[0086] S next = S now + k s *M s ,

[0087] wherein, S next represents the third simulation parameter, S now represents the first simulation parameter, k s represents the target step length coefficient, and M s represents the preset step length.

[0088] In some optional embodiments, the preset batch can be automatically generated according to the black start simulation scale, or can be customized according to actual conditions.

[0089] The black-start simulation testing method provided in this embodiment first performs a black-start simulation based on the first simulation parameters used in the current batch of black-start simulations to obtain the first simulation operation data of the wind-storage system. Using the first simulation operation data and the second simulation operation data of the wind-storage system obtained from the previous batch of black-start simulations, a first evaluation index is determined. This embodiment processes the simulation operation data of the current batch and the simulation operation data of the previous batch to obtain the first evaluation index, which is used to evaluate the operation of the black-start simulation. Based on the operation of the black-start simulation, it can be determined whether the black-start simulation is stable, facilitating the real-time detection of problems that occur during the black-start simulation operation. Based on the first evaluation index, the first simulation parameters, the preset step size, and the second simulation parameters, a third simulation parameter is generated. This embodiment processes the current simulation parameters, the simulation parameters of the previous batch, the first evaluation index, and the preset step size to obtain the simulation parameters for the next batch, achieving adaptive generation of simulation parameters for the next batch. This embodiment integrates multiple influencing factors to obtain the third simulation parameter. The third simulation parameter is used to update the first simulation parameter and return to the black-start simulation step, until the preset batch of black-start simulations is completed, improving the efficiency of black-start simulation testing. Compared with the related technologies that set simulation parameters at equal intervals, the embodiments of the present invention adaptively generate simulation parameters for each batch of black-start simulations based on multiple influencing factors, thereby reducing the number of simulation test batches and improving the efficiency of black-start simulation testing without affecting the simulation effect.

[0090] This embodiment provides a black-start simulation testing method that can be used in computer devices. Figure 2 This is a flowchart of another black-start simulation test method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps:

[0091] Step S201: Perform a black start simulation based on the first simulation parameters to obtain the first simulation running data. The first simulation parameters represent the initialization parameters used in the black start simulation of the current batch; the first simulation running data represent the operating data of the wind-storage system obtained based on the black start simulation of the current batch.

[0092] Further, step S201 above includes:

[0093] Step S2011: Charge the DC capacitor of the black start fan according to the battery parameters until the black start fan runs at the preset power.

[0094] The preset power can be low power, and the specific value can be set according to the actual situation.

[0095] In some alternative implementations, the wind-storage power generation system is used as a black-start power source to provide stable power to the started-up unit, such as...Figure 3 As shown in the schematic diagram of the wind-storage system, it includes multiple wind turbines, multiple batteries, multiple energy storage converters, multiple energy storage transformers, and multiple AC loads. For example, Figure 1 It includes two wind turbines: the first wind turbine and the second wind turbine; one battery: the first battery; multiple energy storage converters; two energy storage transformers: T1 and T2; and multiple AC loads: L1, L2, and L3. The energy storage converters are connected to each other via a direct current bus (DC bus), and the energy storage transformers are connected to the AC loads via an AC bus.

[0096] like Figure 4 The diagram shows the black start process of the wind-storage system. During the black start process, the battery charges the DC capacitor of the black start wind turbine. After charging is completed, the black start wind turbine is set to run in low-power operation mode. After the wind turbine speed stabilizes, the black start wind turbine starts to generate electricity stably in maximum power point tracking (MPPT) mode. When the power generation of the black start wind turbine is less than the power of the load, the black start battery discharges. When the power generation of the wind turbine is greater than the power of the load, the black start battery charges.

[0097] Step S2012: Determine whether the speed of the black start fan reaches the preset value within the preset time period.

[0098] The preset value represents the pre-defined rotational speed at which the black start fan will operate stably. The specific value can be set according to actual conditions. The preset time period can be 5 minutes.

[0099] Step S2013: If the speed of the black-start fan reaches the preset value within a preset time period, the operating mode of the black-start fan is set to the maximum power point tracking mode for power generation, and the first simulation operation data is obtained.

[0100] Specifically, when the rotational speed of the black-start wind turbine reaches a preset value within a preset time period, it indicates that the black-start wind turbine's rotational speed is stable. The maximum power point tracking (MPPT) mode aims to achieve maximum power output under different environmental conditions, thereby improving power generation efficiency.

[0101] In some optional implementations, the black-start wind turbine is set to operate in maximum power point tracking mode for power generation, including: if the first power generation of the black-start wind turbine is greater than the second power generation required by the load, the battery is controlled to perform charging simulation; if the first power generation of the black-start wind turbine is less than or equal to the second power generation required by the load, the battery is controlled to perform discharging simulation.

[0102] Step S2014: If the speed of the black start fan does not reach the preset value within the preset time period, adjust the speed of the black start fan until the speed of the black start fan reaches the preset value within the preset time period, set the operating mode of the black start fan to the maximum power point tracking mode for power generation, and obtain the first simulation operation data.

[0103] Step S202: Determine the first evaluation index based on the first simulation operation data and the second simulation operation data; wherein, the second simulation operation data represents the operation data of the wind-storage system obtained from the black-start simulation of the previous batch corresponding to the current batch; the first evaluation index is used to evaluate the operation of the black-start simulation. For details, please refer to... Figure 1 Step S102 of the illustrated embodiment will not be described again here.

[0104] Step S203: Based on the first evaluation index, the first simulation parameters, the preset step size, and the second simulation parameters, generate the third simulation parameters; update the first simulation parameters using the third simulation parameters and return to the black-start simulation step, until the preset batch of black-start simulations is completed; wherein, the second simulation parameters represent the initialization parameters required for the black-start simulation of the previous batch corresponding to the current batch; the third simulation parameters represent the initialization parameters required for the black-start simulation of the next batch corresponding to the current batch. For details, please refer to [link to details]. Figure 1 Step S103 of the illustrated embodiment will not be described again here.

[0105] The black-start simulation test method provided in this embodiment adaptively obtains the initial simulation parameters of the wind-storage system, reduces the number of simulation test batches without affecting the simulation effect, and improves the efficiency of black-start simulation test.

[0106] This embodiment provides a black-start simulation testing method that can be used in computer devices. Figure 5 This is a flowchart of another black-start simulation test method according to an embodiment of the present invention, such as... Figure 5 As shown, the process includes the following steps:

[0107] During simulation startup, the wind-storage system black-start simulation parameter initialization module provides the first batch of pre-set initialization parameters to the wind-storage system black-start simulation module. The wind-storage system black-start simulation module performs the first batch of simulations based on these initialization parameters, obtains simulation data, and provides the simulation status data to the wind-storage system black-start performance evaluation module for calculating evaluation index results. It also provides the simulation data to the wind-storage system black-start simulation visualization module for visualization display. The wind-storage system black-start performance evaluation module provides evaluation index results and evaluation data to both the wind-storage system black-start simulation parameter initialization module and the wind-storage system black-start simulation visualization module. The wind-storage system black-start simulation parameter initialization module calculates the initialization parameters for the next batch based on the evaluation index results, and this process is repeated continuously until all batches of simulations are completed.

[0108] This embodiment also provides a black-start simulation testing device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0109] This embodiment provides a black-start simulation test device, such as... Figure 6 As shown, it includes:

[0110] The wind-storage system black-start simulation module 601 is used to perform black-start simulation based on the first simulation parameters to obtain the first simulation running data. The first simulation parameters represent the initialization parameters used in the current batch of black-start simulations, and the first simulation running data represent the running data of the wind-storage system obtained based on the black-start simulations of the current batch.

[0111] The wind-storage system black-start performance evaluation module 602 is used to determine a first evaluation index based on the first simulation operation data and the second simulation operation data; wherein, the second simulation operation data represents the operation data of the wind-storage system obtained from the black-start simulation of the previous batch corresponding to the current batch; the first evaluation index is used to evaluate the operation of the black-start simulation.

[0112] The wind-storage system black-start simulation parameter initialization module 603 is used to generate third simulation parameters based on the first evaluation index, the first simulation parameters, the preset step size, and the second simulation parameters; update the first simulation parameters using the third simulation parameters and return to the black-start simulation steps until the black-start simulation of the preset batch is completed; wherein, the second simulation parameters represent the initialization parameters required for the black-start simulation of the previous batch corresponding to the current batch; and the third simulation parameters represent the initialization parameters required for the black-start simulation of the next batch corresponding to the current batch.

[0113] In some optional implementations, the wind-storage system black-start performance evaluation module 602 includes:

[0114] The evaluation index data determination unit is used to obtain multiple evaluation index data based on the differences between multiple first operation indicators in the first simulation operation data and multiple second operation indicators in the second simulation operation data; wherein, the multiple first operation indicators represent multiple indicators obtained from the current batch of black start simulations for judging the stability of black start simulations; and the multiple second operation indicators represent multiple indicators obtained from the previous batch of black start simulations for judging the stability of black start simulations.

[0115] The first evaluation index determination unit is used to compose the first evaluation index from multiple evaluation index data.

[0116] In some optional implementations, the wind-storage system black-start simulation parameter initialization module 603 includes:

[0117] The parameter error value determination unit is used to obtain the parameter error value based on the difference between the first simulation parameter and the second simulation parameter.

[0118] The step size coefficient determination unit is used to obtain multiple step size coefficients based on the ratios of multiple evaluation index data to parameter error values.

[0119] The target step size coefficient determination unit is used to select the target step size coefficient from multiple step size coefficients.

[0120] The third simulation parameter determination unit is used to add the product of the target step size coefficient and the preset step size to the first simulation parameters to obtain the third simulation parameters.

[0121] Specifically, the target step size coefficient determination unit mentioned above includes:

[0122] The absolute value result determines the sub-unit, which is used to take the absolute value of multiple step size coefficients to obtain multiple absolute value results.

[0123] The target step size coefficient determination sub-unit is used to select the smallest absolute value result from multiple absolute value results as the target step size coefficient.

[0124] In some alternative implementations, the wind-storage system black-start simulation module 601 includes:

[0125] The charging simulation unit is used to simulate charging the DC capacitor of the black start fan according to the battery parameters until the black start fan runs at the preset power.

[0126] The judgment unit is used to determine whether the speed of the black start fan reaches the preset value within a preset time period.

[0127] The first operating unit is used to set the operating mode of the black-start fan to maximum power point tracking mode for power generation based on the fact that the speed of the black-start fan reaches a preset value within a preset time period, thereby obtaining the first simulation operating data.

[0128] The second operating unit is used to adjust the speed of the black start fan if the speed of the black start fan fails to reach the preset value within a preset time period, until the speed of the black start fan reaches the preset value within a preset time period, and then set the operating mode of the black start fan to the maximum power point tracking mode for power generation, thereby obtaining the first simulation operating data.

[0129] Specifically, the aforementioned first operating unit includes:

[0130] The charging simulation subunit is used to control the battery to perform charging simulation based on the fact that the first power generation of the black-start fan is greater than the second power generation required by the load.

[0131] The discharge simulation subunit is used to control the battery to perform discharge simulation based on the fact that the first power generation of the black start fan is less than or equal to the second power generation required by the load.

[0132] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0133] In this embodiment, the black-start simulation test device is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0134] This invention also provides a computer device having the above-described features. Figure 6 The black-start simulation test device shown.

[0135] Please see Figure 7 , Figure 7 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 7As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 7 Take a processor 10 as an example.

[0136] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0137] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0138] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0139] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0140] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0141] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0142] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0143] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A black-start simulation test method, characterized in that, The method includes: A black-start simulation is performed based on the first simulation parameters to obtain the first simulation running data. Here, the first simulation parameters represent the initialization parameters used in the current batch of black-start simulations; the first simulation running data represents the operating data of the wind-storage system obtained based on the black-start simulations of the current batch. A first evaluation index is determined based on the first simulation operation data and the second simulation operation data; wherein, the second simulation operation data represents the operation data of the wind-storage system obtained from the black-start simulation of the previous batch corresponding to the current batch; the first evaluation index is used to evaluate the operation of the black-start simulation. Based on the first evaluation index, the first simulation parameters, the preset step size, and the second simulation parameters, a third simulation parameter is generated; the first simulation parameter is updated using the third simulation parameter, and the black start simulation is returned, until the black start simulation of the preset batch is completed; wherein, the second simulation parameter represents the initialization parameter required for the black start simulation of the previous batch corresponding to the current batch; the third simulation parameter represents the initialization parameter required for the black start simulation of the next batch corresponding to the current batch. The step of determining the first evaluation index based on the first simulation data and the second simulation data includes: Multiple evaluation index data are obtained based on the differences between multiple first operation indicators in the first simulation operation data and multiple second operation indicators in the second simulation operation data; wherein, the multiple first operation indicators represent multiple indicators obtained from the current batch of black start simulations for judging the stability of black start simulations; the multiple second operation indicators represent multiple indicators obtained from the previous batch of black start simulations for judging the stability of black start simulations; the first evaluation index is composed of the multiple evaluation index data.

2. The method according to claim 1, characterized in that, The step of generating the third simulation parameters based on the first evaluation index, the first simulation parameters, the preset step size, and the second simulation parameters includes: The parameter error value is obtained based on the difference between the first simulation parameter and the second simulation parameter; Based on the ratios of the multiple evaluation index data to the parameter error values, multiple step size coefficients are obtained; Select the target step size coefficient from the plurality of step size coefficients; The third simulation parameter is obtained by adding the product of the target step size coefficient and the preset step size to the first simulation parameter.

3. The method according to claim 2, characterized in that, The step of selecting the target step size coefficient from the plurality of step size coefficients includes: Taking the absolute value of the multiple step size coefficients yields multiple absolute value results; The smallest absolute value among the multiple absolute value results is selected as the target step size coefficient.

4. The method according to any one of claims 1 to 3, characterized in that, The first simulation parameters include battery parameters. The step of performing a black-start simulation based on the first simulation parameters to obtain the first simulation running data includes: The DC capacitor of the black start fan is charged according to the battery parameters until the black start fan operates at the preset power. Determine whether the rotational speed of the black start fan reaches a preset value within a preset time period; If the rotational speed of the black-start fan reaches the preset value within a preset time period, the operating mode of the black-start fan is set to maximum power point tracking mode for power generation, and the first simulation operation data is obtained. If the speed of the black-start fan does not reach the preset value within a preset time period, the speed of the black-start fan is adjusted until the speed of the black-start fan reaches the preset value within a preset time period. Then, the operating mode of the black-start fan is set to maximum power point tracking mode for power generation, and the first simulation operation data is obtained.

5. The method according to claim 4, characterized in that, Setting the black-start wind turbine to maximum power point tracking mode for power generation includes: If the first power generation of the black-start fan is greater than the second power generation required by the load, the battery is controlled to perform a charging simulation. If the first power generation of the black-start fan is less than or equal to the second power generation required by the load, the battery is controlled to perform a discharge simulation.

6. A black-start simulation test device, characterized in that, The device includes: The wind-storage system black-start simulation module is used to perform black-start simulation based on first simulation parameters to obtain first simulation operation data. Here, the first simulation parameters represent the initialization parameters used in the current batch of black-start simulations; the first simulation operation data represents the operation data of the wind-storage system obtained based on the black-start simulations of the current batch. The wind-storage system black-start performance evaluation module is used to determine a first evaluation index based on the first simulation operation data and the second simulation operation data; wherein, the second simulation operation data represents the operation data of the wind-storage system obtained based on the black-start simulation of the previous batch corresponding to the current batch; the first evaluation index is used to evaluate the operation of the black-start simulation. The black-start simulation parameter initialization module for the wind-storage system is used to generate third simulation parameters based on the first evaluation index, the first simulation parameters, the preset step size, and the second simulation parameters; update the first simulation parameters using the third simulation parameters and return to the black-start simulation, until the black-start simulation of a preset batch is completed; wherein, the second simulation parameters represent the initialization parameters required for the black-start simulation of the previous batch corresponding to the current batch; and the third simulation parameters represent the initialization parameters required for the black-start simulation of the next batch corresponding to the current batch. The black-start performance evaluation module for the wind-storage system includes: The evaluation index data determination unit is used to obtain multiple evaluation index data based on the differences between multiple first operation indicators in the first simulation operation data and multiple second operation indicators in the second simulation operation data; wherein, the multiple first operation indicators represent multiple indicators obtained from the current batch of black start simulations for judging the stability of black start simulations; and the multiple second operation indicators represent multiple indicators obtained from the previous batch of black start simulations for judging the stability of black start simulations. The first evaluation index determination unit is used to compose the first evaluation index from multiple evaluation index data.

7. A computer device, characterized in that, include: A memory and a processor are interconnected, the memory stores computer instructions, and the processor executes the black-start simulation test method according to any one of claims 1 to 5 by executing the computer instructions.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the black-start simulation test method according to any one of claims 1 to 5.

9. A computer program product, characterized in that, It includes computer instructions for causing a computer to execute the black-start simulation test method according to any one of claims 1 to 5.

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