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

Abstract

The invention relates to the technical field of black start, and discloses a black start simulation test method, device and equipment, a storage medium and a program product, and the black start simulation test method comprises the steps: carrying out black start simulation according to a first simulation parameter, and obtaining first simulation operation data; determining a first evaluation index according to the first simulation operation data and the second simulation operation data; generating a third simulation parameter according to the first evaluation index, the first simulation parameter, a preset step length and the second simulation parameter; updating the first simulation parameter by using the third simulation parameter and returning to the black-start simulation step until the black-start simulation of the preset batch is completed; wherein the second simulation parameter represents an initialization parameter required by black-start simulation of a previous batch corresponding to the current batch. According to the method, the simulation parameters used for black-start simulation are adaptively generated, the batch of simulation tests is reduced on the premise that the simulation effect is not affected, and the efficiency of black-start simulation tests is improved.

Description

Technical Field

[0001] The present invention 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 Art

[0002] With the large-scale grid connection of new energy sources, the power system faces a greater risk of power outages. Black start is the first step to restore power supply after a power outage. 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 to the started units and establish stable voltage and frequency during power outages. Therefore, it is necessary to simulate and test the black start to improve the operating performance of the 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 intervals are set for the initial parameters of each batch, and the initial parameters of each batch are increased or decreased according to the intervals. For example, for the initial parameter wind speed value, the interval value of the wind speed value is set at an equal interval of 2m / s, and the wind speed value of each batch is increased according to the interval value of 2m / s, that is, the wind speed value of the first batch is 2m / s, the wind speed value of the second batch is 4m / s, the wind speed value of the third batch is 6m / s, etc. However, this method can only complete multiple batches of black start simulations through equal interval settings. When setting the interval value of the equal interval, it is usually set manually according to experience. Therefore, there are many subjective influencing factors when setting the interval value of the equal interval, and the initial parameters are not adjusted according to the results of the simulation test, resulting in low efficiency of the simulation test. Summary of the invention

[0004] In view of this, the present invention 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 the first aspect, the present invention provides a black start simulation test method, comprising: performing a black start simulation according to a first simulation parameter to obtain a first simulation running data, wherein the first simulation parameter represents the initialization parameter used for the current batch of black start simulations; the first simulation running data represents the running data of the wind storage system obtained according to the black start simulation of the current batch; determining a first evaluation index according to the first simulation running data and the second simulation running data; wherein the second simulation running data represents the 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 a third simulation parameter according to the first evaluation index, the first simulation parameter, a preset step size and the second simulation parameter; updating the first simulation parameter using the third simulation parameter and returning to the step of black start simulation until the preset batch of black start simulations 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.

[0006] The present invention first performs a black start simulation according to a first simulation parameter used in a current batch of black start simulations to obtain first simulation operation data of a wind storage system, and determines a first evaluation index by using the first simulation operation data and second simulation operation data of the wind storage system obtained according to a previous batch of black start simulations. The present invention 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 status of the black start simulation. Whether the black start simulation operation is stable can be determined according to the operation status of the black start simulation, so as to facilitate real-time discovery of problems occurring during the operation of the black start simulation. A third simulation parameter is generated according to the first evaluation index, the first simulation parameter, a preset step size and the second simulation parameter. The present invention processes the current simulation parameter, the simulation parameter of the previous batch, the first evaluation index and the preset step size to obtain the simulation parameter of the next batch, so as to realize adaptive generation of the simulation parameter of the next batch. The present invention integrates multiple influencing factors to obtain the third simulation parameter, and uses the third simulation parameter to update the first simulation parameter and return to the step of black start simulation until the preset batch of black start simulations is completed, thereby improving the efficiency of the black start simulation test. Compared with the related art in which simulation parameters are set at equal intervals, the present invention adaptively generates simulation parameters used in each batch of black start simulation according to multiple influencing factors, reduces the batches of simulation tests without affecting the simulation effect, and improves the efficiency of black start simulation tests.

[0007] In an optional embodiment, a first evaluation index is determined based on first simulation operation data and second simulation operation data, including: obtaining multiple evaluation index data based on the difference 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; and the first evaluation index is composed of multiple evaluation index data.

[0008] The present invention subtracts multiple first operating indicators in the first simulation operating data from multiple second operating indicators in the second simulation operating data to obtain multiple evaluation indicator data, and compares the first operating indicator with the second operating indicator to obtain the fluctuation value of the operating indicator of the current black start simulation process and the previous batch of black start simulation processes. The first evaluation indicator composed of multiple evaluation indicator data is used to evaluate the operation status of the black start simulation.

[0009] In an optional embodiment, a third simulation parameter is generated according to a first evaluation index, a first simulation parameter, a preset step size and a second simulation parameter, including: obtaining a parameter error value according to a difference between the first simulation parameter and the second simulation parameter; obtaining multiple step size coefficients according to ratios of multiple evaluation index data to the parameter error value; selecting a target step size coefficient from the multiple step size coefficients; and adding the first simulation parameter to the product of the target step size coefficient and the preset step size to obtain the third simulation parameter.

[0010] The present invention obtains a parameter error value according to the difference between a first simulation parameter and a second simulation parameter; obtains a plurality of step length coefficients according to the ratios of a plurality of evaluation index data to the parameter error value; selects a target step length coefficient from the plurality of step length coefficients, and the step length coefficient is determined by the difference between the first simulation parameter and the second simulation parameter and the change rate between the plurality of evaluation indexes.

[0011] In an optional implementation, selecting a target step coefficient from a plurality of step coefficients includes: taking absolute values ​​of the plurality of step 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 coefficient.

[0012] In an optional embodiment, the first simulation parameter includes a battery parameter, and a black start simulation is performed according to the first simulation parameter to obtain first simulation operation data, including: charging simulation of the DC capacitor of the black start fan according to the battery parameter until the black start fan operates at a preset power; determining whether the speed of the black start fan reaches a preset value within a preset time period; if the 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 for power generation to obtain first simulation operation data; if the speed of the black start fan does not reach the preset value within the preset time period, adjusting the speed of the black start fan until the 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 for power generation to obtain first simulation operation data.

[0013] In an optional embodiment, the operation mode of the black-start wind turbine is set to a maximum power point tracking mode for power generation, including: if the first power generation power of the black-start wind turbine is greater than the second power generation power required by the load, controlling the battery to perform a charging simulation; if the first power generation power of the black-start wind turbine is less than or equal to the second power generation power required by the load, controlling the battery to perform a discharging simulation.

[0014] In the second aspect, the present invention provides a black start simulation test device, including: a wind storage system black start simulation module, which is used to perform a black start simulation according to a first simulation parameter to obtain a first simulation operation data, wherein the first simulation parameter represents the initialization parameter used for the current batch of black start simulation; the first simulation operation data represents the operation data of the wind storage system obtained according to the black start simulation of the current batch; a wind storage system black start performance evaluation module, which is used to determine a first evaluation index according to 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 according to the black start simulation of the previous batch corresponding to the current batch; the first evaluation index is used to evaluate the operation status of the black start simulation; a wind storage system black start simulation parameter initialization module, which is used to generate a third simulation parameter according to the first evaluation index, the first simulation parameter, a preset step size and the second simulation parameter; the step of updating the first simulation parameter using the third simulation parameter and returning to the black start simulation until the preset batch of black start simulation 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.

[0015] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the black start simulation test method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0016] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the black start simulation test method of the first aspect or any corresponding embodiment thereof.

[0017] In a fifth aspect, the present invention provides a computer program product, including computer instructions, where the computer instructions are used to enable a computer to execute the black start simulation test method of the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the related technologies, the drawings required for use in the specific embodiments or the related technical descriptions will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 is a flow chart of a black start simulation test method according to an embodiment of the present invention;

[0020] Figure 2 is a schematic flow chart of another black start simulation test method according to an embodiment of the present invention;

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

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

[0023] Figure 5 is a flow chart of another black start simulation test method according to an embodiment of the present invention;

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

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

[0026] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0027] Black start means that when the entire power system is completely blacked out and cannot operate normally, the generator sets with self-starting capability in the power system are started to drive the generator sets without self-starting capability, gradually expanding the recovery scope of the power system and ultimately achieving the recovery and power supply of the entire power system.

[0028] In an embodiment of the present invention, in the black start simulation process, in order to improve the starting capability of the power system, it is considered to use the wind storage power generation system as a black start power source to drive the generator set without self-starting capability. At present, a real-time simulator is used to perform simulation tests on black starts, verify the operating performance of the power system under different conditions, and analyze the changing rules of the operating performance with the initial parameters. When the black start is simulated under different conditions, the initial parameters are set at equal intervals, that is, the intervals are set for the initial parameters of each batch, and the initial parameters of each batch are increased or decreased according to the intervals. There are many subjective influencing factors when setting the interval values ​​of the equal intervals. The initial parameters are not adjusted according to the results of the simulation test, resulting in low efficiency of the simulation test.

[0029] The embodiment of the present invention provides a black start simulation test method, which improves the efficiency of the black start simulation test by adaptively generating simulation parameters used in the black start simulation.

[0030] According to an embodiment of the present invention, an embodiment of a black start simulation test method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a 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 this embodiment, a black start simulation test method is provided, which can be used for computer equipment. Figure 1 is a flow chart of a black start simulation test method according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0032] Step S101, perform black start simulation according to first simulation parameters to obtain first simulation operation data, wherein the first simulation parameters represent initialization parameters used in the current batch of black start simulations; the first simulation operation data represent operation data of the wind storage system obtained according to the current batch of black start simulations.

[0033] The first simulation parameters include initialization parameters required for black start simulation, such as wind speed value and energy storage capacity value. After the first simulation parameters are generated, they are stored in a database, and when black start simulation needs to be performed on the first simulation parameters, the first simulation parameters are obtained from the database.

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

[0035] In some optional embodiments, a real-time simulation machine is used to perform a simulation test on the black start. Exemplarily, the real-time simulation machine can be a real-time digital simulator (Real-Time Digital Simulation, RTDS) or a real-time digital simulation laboratory platform (Real Time Labratory, RT-LAB). The first simulation parameters are input into the real-time simulation machine to perform a simulation test on the black start, and the first simulation operation data is output.

[0036] In some optional embodiments, the first simulation operation data includes multiple first operation indicators. By way of example, the multiple first operation indicators 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, etc.

[0037] Step S102, 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 according to the black start simulation of the previous batch corresponding to the current batch; the first evaluation index is used to evaluate the operation status of the black start simulation.

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

[0039] In some optional implementations, the first evaluation index is used to evaluate the operation status of the black start simulation. Exemplarily, the operation status of the black start simulation may include: stable operation, unstable operation, stable operation under certain conditions, etc.

[0040] In some optional implementations, determining the first evaluation index according to the first simulation operation data and the second simulation operation data includes:

[0041] Based on the difference between multiple first operating indicators in the first simulation operating data and multiple second operating indicators in the second simulation operating data, multiple evaluation indicator data are obtained; wherein the multiple first operating indicators represent multiple indicators obtained from the current batch of black start simulations for judging the stability of the black start simulation; the multiple second operating indicators represent multiple indicators obtained from the previous batch of black start simulations for judging the stability of the black start simulation; the first evaluation indicator is composed of multiple evaluation indicator data.

[0042] The second simulation operation data includes a plurality of second operation indicators, and illustratively, the plurality of first operation indicators include: historical wind turbine power generation, historical energy storage charging power, historical energy storage power generation, historical DC bus voltage, historical AC bus voltage, and historical AC bus frequency, etc. The plurality of evaluation indicator data include: wind turbine power generation difference, energy storage charging power difference, energy storage power generation difference, DC bus voltage difference, AC bus voltage difference, and AC bus frequency difference, etc.

[0043] In some optional implementations, each first operating indicator and each second operating indicator takes the average value of 10 operating indicators after a preset operating time, so as to reduce the impact of fluctuations in simulation data.

[0044] Exemplarily, the calculation formula for the wind turbine power difference is:

[0045]

[0046] Where ΔP 1 Indicates the power difference of the wind turbine. Indicates the real-time wind turbine power generation. Indicates the historical wind turbine power generation.

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

[0048]

[0049] Where ΔP 2 Indicates the energy storage charging power difference, Indicates the real-time energy storage charging power, Indicates the historical energy storage charging power.

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

[0051]

[0052] Where ΔP 3 Represents the energy storage power difference, Indicates the real-time energy storage power generation, Indicates the historical energy storage power generation.

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

[0054]

[0055] Among them, ΔU 1 represents the DC bus voltage difference, Indicates the real-time DC bus voltage, Indicates the historical DC bus voltage.

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

[0057]

[0058] Among them, ΔU 2 Indicates the AC bus voltage difference, Indicates the real-time AC bus voltage, Indicates the historical AC bus voltage.

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

[0060]

[0061] Where, ΔF 1 Indicates the AC bus frequency difference, Indicates the real-time AC bus frequency, Indicates the historical AC bus frequency.

[0062] Step S103, generating a third simulation parameter according to the first evaluation index, the first simulation parameter, the preset step size and the second simulation parameter; using the third simulation parameter to update the first simulation parameter and returning to the black start simulation step until the preset batch of black start simulations 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; and the third simulation parameter represents the initialization parameter required for the black start simulation of the next batch corresponding to the current batch.

[0063] Among them, the preset step size can be customized according to actual conditions.

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

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

[0066]

[0067] Among them, k 1 represents the first step length coefficient, ΔP 1 Indicates the wind turbine power difference, S now represents the first simulation parameter, S last represents the second simulation parameter.

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

[0069]

[0070] Among them, k 2 represents the second step size coefficient, ΔP 2 Represents the energy storage charging power difference, S now represents the first simulation parameter, S last represents the second simulation parameter.

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

[0072]

[0073] Among them, k 3 represents the third step coefficient, ΔP 3 Represents the energy storage 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] Among them, k 4 represents the fourth step coefficient, ΔU 1 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 coefficient is:

[0078]

[0079] Among them, k 5 represents the fifth step coefficient, ΔU 2 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 coefficient is:

[0081]

[0082] Among them, k 6 represents the sixth step coefficient, ΔF 1 Indicates the AC bus frequency difference, S now represents the first simulation parameter, S last represents the second simulation parameter.

[0083] Further, selecting a target step length coefficient from a plurality of step length coefficients includes: 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, we take k 1 , k 2 , k 3 , k 4 , k 5 and k 6 The absolute value of is obtained, and six absolute value results are obtained. The smallest absolute value result is selected as the target step coefficient among the six absolute value results.

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

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

[0087] Among them, S next represents the third simulation parameter, S now represents the first simulation parameter, k s represents the target step size coefficient, M s Indicates the preset step size.

[0088] In some optional implementations, the preset batches may be automatically generated according to the black start simulation scale, or may be customized according to actual conditions.

[0089] The black start simulation test method provided in the present embodiment first performs a black start simulation according to the first simulation parameter used in the current batch of black start simulations to obtain first simulation operation data of the wind storage system, and determines a first evaluation index by using the first simulation operation data and second simulation operation data of the wind storage system obtained according to the previous batch of black start simulations. The embodiment of the present invention 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 status of the black start simulation. Whether the black start simulation operation is stable can be determined according to the operation status of the black start simulation, so as to facilitate real-time discovery of problems occurring during the operation of the black start simulation. A third simulation parameter is generated according to the first evaluation index, the first simulation parameter, the preset step size and the second simulation parameter. The embodiment of the present invention processes the current simulation parameter, the simulation parameter of the previous batch, the first evaluation index and the preset step size to obtain the simulation parameter of the next batch, so as to realize adaptive generation of the simulation parameter of the next batch. The embodiment of the present invention integrates multiple influencing factors to obtain the third simulation parameter, uses the third simulation parameter to update the first simulation parameter and returns to the step of black start simulation until the preset batch of black start simulations is completed, thereby improving the efficiency of the black start simulation test. Compared with the related art in which simulation parameters are set at equal intervals, the embodiment of the present invention adaptively generates simulation parameters used in each batch of black start simulation according to multiple influencing factors, reduces the batches of simulation tests without affecting the simulation effect, and improves the efficiency of black start simulation tests.

[0090] In this embodiment, a black start simulation test method is provided, which can be used for computer equipment. Figure 2 FIG. 4 is a flow chart of another black start simulation test method according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0091] Step S201, perform black start simulation according to first simulation parameters to obtain first simulation operation data, wherein the first simulation parameters represent initialization parameters used in the current batch of black start simulations; the first simulation operation data represent operation data of the wind storage system obtained according to the current batch of black start simulations.

[0092] Furthermore, the above step S201 includes:

[0093] Step S2011, charging simulation is performed on the DC capacitor of the black start wind turbine according to the battery parameters until the black start wind turbine operates at a preset power.

[0094] Among them, the preset power can be a low power, and the specific value can be set according to actual conditions.

[0095] In some optional implementations, the wind power generation system is used as a black start power source to provide stable power for the started units, such as Figure 3 As shown, the wind energy storage system structure diagram includes multiple wind turbines, multiple batteries, multiple energy storage converters, multiple energy storage transformers and multiple AC loads. For example, Figure 1 The invention comprises two fans: a first fan and a second fan; a battery: a first battery; a plurality of energy storage converters; two energy storage transformers: T1 and T2; a plurality of AC loads: L1, L2 and L3; wherein the energy storage converters are connected via a direct current bus (DC bus), and the energy storage transformers and the AC loads are connected via an AC bus.

[0096] like Figure 4 The figure shows a schematic diagram of 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 operate in a low power operation mode. After the wind turbine speed stabilizes, the black start wind turbine operates in the maximum power point tracking (MPPT) mode to start stable power generation. When the power generation power of the black start wind turbine is less than the power of the load, the black start battery discharges. When the power generation power of the wind turbine is greater than the power of the load, the black start battery charges.

[0097] Step S2012, determining whether the rotation speed of the black start fan reaches a preset value within a preset time period.

[0098] The preset value indicates a preset speed value that enables the black start fan to run stably, and the specific value can be set according to actual conditions. The preset time period can be 5 minutes.

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

[0100] When the speed of the black start wind turbine reaches the preset value within the preset time period, it indicates that the speed of the black start wind turbine is stable. The function of the Maximum Power Point Tracking (MPPT) mode is to achieve maximum power output under different environmental conditions and improve the efficiency of multi-generation.

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

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

[0103] Step S202, 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 according to 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. Figure 1 Step S102 of the illustrated embodiment will not be described in detail here.

[0104] Step S203, generating a third simulation parameter according to the first evaluation index, the first simulation parameter, the preset step size and the second simulation parameter; using the third simulation parameter to update the first simulation parameter and return to the step of black start simulation until the preset batch of black start simulation 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. For details, please refer to Figure 1 Step S103 of the illustrated embodiment will not be described in detail 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 batches of simulation tests without affecting the simulation effect, and improves the efficiency of the black start simulation test.

[0106] In this embodiment, a black start simulation test method is provided, which can be used for computer equipment. Figure 5 FIG. 4 is a flow chart of another black start simulation test method according to an embodiment of the present invention. Figure 5 As shown, the process includes the following steps:

[0107] When the simulation is started, the wind storage system black start simulation parameter initialization module provides the wind storage system black start simulation module with the first batch of pre-set initialization parameters; the wind storage system black start simulation module performs the first batch of simulations according to the initialization parameters to obtain simulation data, and provides the simulation status data to the wind storage system black start performance evaluation module for calculating the evaluation index results, and 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 the evaluation index results and evaluation data to the wind storage system black start simulation parameter initialization module and the wind storage system black start simulation visualization module respectively; the wind storage system black start simulation parameter initialization module calculates the initialization parameters of the next batch according to the evaluation index results, and then proceeds in sequence, continuously cycles, and completes the simulation of all batches.

[0108] In this embodiment, a black start simulation test device is also provided, which is used to implement the above embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

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

[0110] The wind-storage system black start simulation module 601 is used to perform black start simulation according to first simulation parameters to obtain first simulation operation data, wherein the first simulation parameters represent the initialization parameters used for the current batch of black start simulations; the first simulation operation data represent the operation data of the wind-storage system obtained according to the current batch of black start simulations.

[0111] The wind-storage system black start performance evaluation module 602 is used to determine a first evaluation index based on first simulation operation data and second simulation operation data; wherein the second simulation operation data represents the operation 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 operation status of the black start simulation.

[0112] The wind-storage system black start simulation parameter initialization module 603 is used to generate a third simulation parameter based on the first evaluation index, the first simulation parameter, the preset step size and the second simulation parameter; use the third simulation parameter to update the first simulation parameter and return to the black start simulation step until a preset batch of black start simulations 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; and the third simulation parameter represents the initialization parameter 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] An evaluation index data determination unit is used to obtain multiple evaluation index data based on the difference between multiple first operating indicators in the first simulation operation data and multiple second operating indicators in the second simulation operation data; wherein the multiple first operating indicators represent multiple indicators obtained from the current batch of black start simulations for judging the stability of the black start simulation; and the multiple second operating indicators represent multiple indicators obtained from the previous batch of black start simulations for judging the stability of the black start simulation.

[0115] The first evaluation index determination unit is used to form a first evaluation index from a plurality of 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 determining unit is used to obtain the parameter error value according to the difference between the first simulation parameter and the second simulation parameter.

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

[0119] The target step length coefficient determination unit is used to select a target step length coefficient from a plurality of step length coefficients.

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

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

[0122] The absolute value result determination subunit is used to take absolute values ​​of multiple step coefficients to obtain multiple absolute value results.

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

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

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

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

[0127] The first operation unit is used to set the operation mode of the black-start wind turbine to a maximum power point tracking mode for power generation according to the rotation speed of the black-start wind turbine reaching a preset value within a preset time period, so as to obtain first simulation operation data.

[0128] The second operating unit is used to adjust the speed of the black-start fan according to the fact that the speed of the black-start fan fails to reach the preset value within the preset time period, until the speed of the black-start fan reaches the preset value within the preset time period, and set the operating mode of the black-start fan to the maximum power point tracking mode for power generation, so as to obtain the first simulation operation data.

[0129] Specifically, the first operating unit includes:

[0130] The charging simulation subunit is used to control the battery to perform charging simulation according to the first power generation of the black start wind turbine being 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 according to the first power generation power of the black start wind turbine being less than or equal to the second power generation power required by the load.

[0132] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

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

[0134] The embodiment of the present invention also provides a computer device having the above Figure 6 The black start simulation test setup is shown.

[0135] See also Figure 7 , Figure 7 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 7As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 7 A processor 10 is taken as an example.

[0136] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0137] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0138] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

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

[0140] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0141] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0142] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

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

Claims

1. A black start simulation test method, characterized in that: The method comprises: Performing a black start simulation according to first simulation parameters to obtain first simulation operation data, wherein the first simulation parameters represent initialization parameters used in the current batch of black start simulations; the first simulation operation data represent operation data of the wind-storage system obtained according to the current batch of black start simulations; Determine a first evaluation index according to 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 according to 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; According to the first evaluation index, the first simulation parameter, the preset step size and the second simulation parameter, a third simulation parameter is generated; the first simulation parameter is updated by using the third simulation parameter and the step of returning to the black start simulation is performed until a preset batch of black start simulations 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; and the third simulation parameter represents the initialization parameter required for the black start simulation of the next batch corresponding to the current batch.

2. The method according to claim 1, characterized in that Determining a first evaluation index according to the first simulation operation data and the second simulation operation data includes: A plurality of evaluation index data are obtained according to the difference between a plurality of first operation indexes in the first simulation operation data and a plurality of second operation indexes in the second simulation operation data; wherein the plurality of first operation indexes represent a plurality of indexes obtained from the black start simulation of the current batch for judging the stability of the black start simulation; and the plurality of second operation indexes represent a plurality of indexes obtained from the black start simulation of the previous batch for judging the stability of the black start simulation; The first evaluation index is composed of the plurality of evaluation index data.

3. The method according to claim 2, characterized in that The step of generating a third simulation parameter according to the first evaluation index, the first simulation parameter, a preset step size and the second simulation parameter comprises: Obtaining a parameter error value according to a difference between the first simulation parameter and the second simulation parameter; Obtaining a plurality of step coefficients according to the ratios of the plurality of evaluation index data to the parameter error values ​​respectively; Selecting a target step length coefficient from among the plurality of step length coefficients; The first simulation parameter is added to the product of the target step coefficient and the preset step length to obtain the third simulation parameter.

4. The method according to claim 3, characterized in that The step of selecting a target step coefficient from the plurality of step coefficients comprises: Taking absolute values ​​of the multiple step size coefficients to obtain multiple absolute value results; The smallest absolute value result is selected from the multiple absolute value results as the target step coefficient.

5. The method according to any one of claims 1 to 4, characterized in that The first simulation parameter includes a battery parameter, and performing a black start simulation according to the first simulation parameter to obtain first simulation operation data includes: Performing charging simulation on the DC capacitor of the black start wind turbine according to the battery parameters until the black start wind turbine operates at a preset power; Determining whether the speed of the black start fan reaches a preset value within a preset time period; If the speed of the black start wind turbine reaches the preset value within the preset time period, the operation mode of the black start wind turbine is set to the maximum power point tracking mode to generate electricity, and the first simulation operation data is obtained; If the speed of the black-start fan does not reach the preset value within the 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 the preset time period, and the operation mode of the black-start fan is set to the maximum power point tracking mode to generate electricity, so as to obtain the first simulation operation data.

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

7. A black start simulation test device, characterized in that: The device comprises: A wind-storage system black start simulation module, used to perform a black start simulation according to a first simulation parameter to obtain first simulation operation data, wherein the first simulation parameter represents an initialization parameter used in a current batch of black start simulations; the first simulation operation data represents operation data of the wind-storage system obtained according to the current batch of black start simulations; A wind-storage system black start performance evaluation module, 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 according to 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; A wind-storage system black start simulation parameter initialization module is used to generate a third simulation parameter based on the first evaluation index, the first simulation parameter, a preset step size and the second simulation parameter; use the third simulation parameter to update the first simulation parameter and return to the black start simulation step until a preset batch of black start simulations 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; and the third simulation parameter represents the initialization parameter required for the black start simulation of the next batch corresponding to the current batch.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the black start simulation test method according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the black start simulation test method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions, wherein the computer instructions are used to enable a computer to execute the black start simulation test method according to any one of claims 1 to 6.

Images