Equivalent modeling method, device and equipment for photovoltaic power generation unit and storage medium

Abstract

The invention provides a photovoltaic power generation unit equivalent modeling method and device, equipment and a storage medium. The method comprises the following steps: determining a grouping number of a current target photovoltaic power station and a grouping demarcation point of steady-state active power for grouping by acquiring a topological structure of the target photovoltaic power station and device parameters and steady-state active power parameters of each power generation unit; therefore, equivalent grouping of the power generation units in the target photovoltaic power station with the fixed grouping number can be achieved, corresponding grouping operation is executed according to the grouping number and the grouping demarcation points so as to generate target equivalent machines, then equivalent parameters and equivalent current collection line parameters of the target equivalent machines are obtained through calculation, and a target equivalent model is calculated; according to the method, the clustering of the target photovoltaic power station is completed, the problems of unstable clustering number and low physical interpretability of the clustering index caused by a clustering method in some embodiments are reduced, meanwhile, the method is simple and convenient to calculate, the data processing amount is reduced, and the equivalent modeling efficiency of the photovoltaic power station is improved.

Description

Technical Field

[0001] The present application relates to the field of renewable energy power generation, and in particular to a photovoltaic power generation unit equivalent modeling method, device, equipment and storage medium. Background Art

[0002] In the field of photovoltaic power generation, the grid connection of large-scale photovoltaic power generation units has significantly changed the fault characteristics of the power system. In order to analyze the impact of the transient characteristics of the photovoltaic power generation system on the safety and stability of the power grid, it is necessary to establish an accurate photovoltaic power station simulation model. However, a photovoltaic power station contains many photovoltaic power generation units. If a detailed model is used for each power generation unit, the established photovoltaic power station model will have an extremely high order, which seriously affects the simulation efficiency. Therefore, it is necessary to perform equivalent modeling on each power generation unit. Since the operating status of each power generation unit in a large photovoltaic power station is not the same, the single-machine equivalent method that aggregates all power generation units into one equivalent unit will bring about a large equivalent error. Therefore, in some feasible implementation methods, multiple equivalent units are often used to characterize the multi-machine equivalent method of the entire station. The core of this method is to determine a reasonable grouping strategy and divide power generation units with the same or similar responses into a group.

[0003] However, the current clustering strategy of the multi-machine equivalent method mostly adopts the clustering method, which makes the clustering process complicated and the number of clusters may not be fixed, which hinders the subsequent modeling application. Therefore, a photovoltaic power generation unit equivalent modeling method with a fixed number of clusters is needed to improve the modeling efficiency and simplify the model form. Summary of the invention

[0004] The purpose of the present application is to solve at least one of the above-mentioned technical defects, especially the technical defect that the efficiency of equivalent modeling of photovoltaic power generation units in the prior art is low.

[0005] In a first aspect, the present application provides a photovoltaic power generation unit equivalent modeling method, the method comprising:

[0006] Obtain the topological structure of the target photovoltaic power station and the device parameters and steady-state active power of each power generation unit;

[0007] The topological structure includes the number, layout and connection of collector lines of the power generation units in the target photovoltaic power station;

[0008] Determine the number of groups and the grouping demarcation points according to the topological structure and the steady-state active power of each of the power generation units;

[0009] According to the number of groups and the grouping demarcation point, a grouping operation is performed on each of the power generation units to generate a plurality of target equivalent machines;

[0010] According to the topological structure and the device parameters of each of the power generation units, the equivalent parameters and equivalent collector line parameters of each of the target equivalent machines are determined, and a target equivalent model is established.

[0011] As an optional implementation manner, determining the number of groups and the grouping demarcation point according to the topological structure and the steady-state active power of each of the power generation units includes:

[0012] Determining the operating state of each of the power generation units according to the topological structure and the steady-state active power of each of the power generation units;

[0013] The number of groups and the grouping demarcation point of the steady-state active power are determined according to the operating status of each of the power generation units.

[0014] As an optional implementation manner, the operating state includes high power output, medium power output and low power output, the number of groups includes three-machine groups, and the grouping demarcation points include 0.7pu and 0.35pu, where pu is a per-unit power value, which is used to indicate the rated output power of the corresponding power generation unit;

[0015] And, performing a grouping operation on each of the power generation units according to the grouping quantity and the grouping demarcation point to generate a plurality of target equivalent machines, including:

[0016] Determine the power generation unit whose steady-state active power is between 0.7pu-1pu as a high-power output unit, and group the high-power output units to generate first equal-value units;

[0017] Determine the power generation units whose steady-state active power is between 0.35pu and 0.7pu as medium power output units, and group the medium power output units to generate second equal-value units;

[0018] The power generation units whose steady-state active power is between 0pu and 0.35pu are determined as small power output units, and the small power output units are grouped to generate third equal value machines.

[0019] As an optional implementation, the method further includes:

[0020] Determine real-time weather data;

[0021] According to the real-time meteorological data, the determined number of clusters and the cluster demarcation point of the steady-state active power are optimized, and the optimized data are used as the number of clusters and the cluster demarcation point.

[0022] As an optional implementation manner, the method for determining the equivalent collector line parameters includes:

[0023] According to the topology structure, network loss is calculated;

[0024] The equivalent collection line parameters are determined according to the network loss.

[0025] As an optional implementation, the equivalent parameters include: equivalent light intensity, inverter equivalent capacity, filter inductance, filter resistance, filter capacitance, transformer equivalent capacity and equivalent impedance;

[0026] Determining the equivalent parameters of each target equivalent machine according to the topological structure and the device parameters of each power generation unit includes:

[0027] According to the first formula, determining the equivalent parameter of each target equivalent machine by a capacity weighted method;

[0028] Wherein, the first formula includes:

[0029]

[0030] Among them, G eq is the illumination intensity of the target equivalent machine, m is the number of power generation units in the current cluster, G i is the illumination intensity of each power generation unit in the cluster; S eq is the inverter capacity of the target equivalent machine, S PV is the inverter capacity of a single power generation unit, R f_eq is the filter resistance of the target equivalent machine, R f is the filter resistance of a single power generation unit, L f_eq is the filter inductance of the target equivalent machine, L f is the filter inductance of a single power generation unit, C f_eq is the filter capacitor of the target equivalent machine; C f is the filter capacitor of a single power generation unit, S T_eq is the transformer capacity of the target equivalent machine, S T is the transformer capacity of a single generating unit, Z T_eq is the transformer impedance of the target equivalent machine, Z T is the transformer impedance of a single power generation unit.

[0031] As an optional implementation, the method further includes:

[0032] determining at least one correction parameter among the equivalent parameters;

[0033] Determining a target optimization algorithm according to the topological structure;

[0034] Wherein, the target optimization algorithm includes a swarm optimization algorithm or a gradient optimization algorithm;

[0035] The correction parameters are optimized according to the target optimization algorithm, and the correction parameters are updated according to the optimization results.

[0036] In a second aspect, the present application provides a photovoltaic power generation unit equivalent modeling device, the device comprising:

[0037] An acquisition module is used to acquire the topological structure of the target photovoltaic power station and the device parameters and steady-state active power of each power generation unit;

[0038] The topological structure includes the number, layout and connection of collector lines of the power generation units in the target photovoltaic power station;

[0039] A processing module, used for determining the number of groups and the grouping demarcation point according to the topological structure and the steady-state active power of each of the power generation units;

[0040] The processing module is further used to perform a grouping operation on each of the power generation units according to the grouping quantity and the grouping demarcation point, and generate a plurality of target equivalent machines;

[0041] The processing module is further used to determine the equivalent parameters and equivalent collector line parameters of each target equivalent machine according to the topological structure and the device parameters of each power generation unit, and establish a target equivalent model.

[0042] In a third aspect, the present application provides a computer device comprising one or more processors and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the method described in the first aspect are performed.

[0043] In a fourth aspect, the present application provides a storage medium storing computer-readable instructions, which, when executed by one or more processors, causes the one or more processors to perform the steps of the method described in the first aspect.

[0044] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0045] Based on any of the above embodiments, the present application can determine the number of clusters of the current target photovoltaic power station and the clustering demarcation point of the reference indicator for clustering by obtaining the topological structure of the target photovoltaic power station and the device parameters of each power generation unit and the steady-state active power parameters. The reference indicator is the aforementioned steady-state active power, thereby achieving equivalent clustering of the power generation units in the target photovoltaic power station with a fixed number of clusters, performing corresponding clustering operations according to the number of clusters and the clustering demarcation point to generate a target equivalent machine, and then calculating and obtaining the equivalent parameters and equivalent collection line parameters of each target equivalent machine itself, and calculating the target equivalent model to complete the clustering of the target photovoltaic power station, thereby reducing the problems of unstable clustering number and low physical interpretability of clustering indicators caused by the clustering method in some implementation methods. At the same time, this method is simple to calculate, reduces the amount of data processing, and improves the efficiency of equivalent modeling of photovoltaic power stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0047] Figure 1 A schematic diagram of a process flow of a photovoltaic power generation unit equivalent modeling method provided in one embodiment of the present application;

[0048] Figure 2 This is a diagram of the internal structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0050] To help build new power systems, the proportion of photovoltaic power generation capacity continues to increase. The grid-connected large-scale photovoltaic power generation units have significantly changed the fault characteristics of the power system. In order to analyze the impact of the transient characteristics of the photovoltaic power generation system on the safety and stability of the power grid, it is necessary to establish an accurate photovoltaic power station simulation model. However, a photovoltaic power station contains many photovoltaic power generation units. If a detailed model is used for each power generation unit, the established photovoltaic power station model will have an extremely high order, which seriously affects the simulation efficiency. Therefore, it is necessary to perform equivalent modeling of photovoltaic power stations. Since the operating status of each power generation unit in a large photovoltaic power station is not the same, the single-machine equivalent method that aggregates all power generation units into one equivalent unit will bring large equivalent errors. Therefore, existing research focuses on the multi-machine equivalent method that uses multiple equivalent units to characterize the entire station. The core is to determine a reasonable grouping strategy to divide power generation units with the same or similar responses into a group.

[0051] For example, as a feasible implementation method, a large-scale photovoltaic clustering analysis method that considers low voltage ride-through characteristics derives the photovoltaic node voltage expression through the low voltage ride-through characteristics of photovoltaic power generation units and the influence of synchronous machines on node voltage, and uses hierarchical clustering method to cluster the synchronous machine participation factor matrix to complete the unit grouping.

[0052] For example, as a feasible implementation method, a distributed photovoltaic cluster multi-step grouping and equivalent modeling for transient analysis divides the photovoltaic units into several areas according to the electrical distance, uses the partition number, irradiance and inverter control parameters as grouping indicators, and adopts the K-means algorithm to realize photovoltaic unit grouping.

[0053] In general, the existing equivalence method of multiple machines in photovoltaic power stations focuses on exploring the characteristic quantities that characterize the operating status of photovoltaic power generation units as clustering indicators, and then uses clustering algorithms to perform clustering calculations. This type of method can achieve high equivalence accuracy, but the acquisition of clustering results requires the use of complex clustering algorithms, and the number of clusters is uncertain, which is not conducive to engineering applications.

[0054] In summary, the current clustering strategy of the multi-machine equivalent method mostly adopts the clustering method, which makes the clustering process complicated and the number of clusters may not be fixed, which hinders the subsequent modeling application. Therefore, a photovoltaic power generation unit equivalent modeling method with a fixed number of clusters is needed to improve the modeling efficiency and simplify the model form.

[0055] The technical concept of the present application is that by obtaining the topological structure of the target photovoltaic power station and the device parameters and steady-state active power parameters of each power generation unit, the number of clusters of the current target photovoltaic power station and the clustering demarcation point of the reference indicator used for clustering can be determined, and the reference indicator is the aforementioned steady-state active power. This can achieve equivalent clustering of power generation units in the target photovoltaic power station with a fixed number of clusters, and perform corresponding clustering operations according to the number of clusters and the clustering demarcation point to generate target equivalent machines, and then calculate and obtain the equivalent parameters and equivalent collection line parameters of each target equivalent machine itself, calculate the target equivalent model, and complete the clustering of the target photovoltaic power station, reducing the problems of unstable clustering number and low physical interpretability of clustering indicators caused by clustering methods in some implementation methods. At the same time, this method is simple to calculate, reduces the amount of data processing, and improves the efficiency of equivalent modeling of photovoltaic power stations.

[0056] See also Figure 1 , Figure 1 A schematic diagram of a process flow of a photovoltaic power generation unit equivalent modeling method provided in one embodiment of the present application, such as Figure 1 As shown, the method includes:

[0057] S101, obtaining the topological structure of the target photovoltaic power station and the device parameters and steady-state active power of each power generation unit;

[0058] The topological structure includes the number, layout and connection of collector lines of the power generation units in the target photovoltaic power station;

[0059] The device parameters and steady-state active power may include device parameters and electrical parameters. The device parameters are mainly used for the calculation of subsequent equivalent machine parameters, and the electrical parameters are mainly used for grouping.

[0060] In fact, in addition to steady-state active power, it is also feasible to use other electrical parameters as the basis for cluster modeling, but this application uses steady-state active power as the basis for division for example, mainly because this indicator is easy to obtain and can simplify the subsequent calculation process. According to the specific application scenario, the electrical parameters or electrical parameter combinations used in the clustering process can actually be flexibly adjusted in some scenarios.

[0061] S102, determining the number of groups and the grouping demarcation point according to the topological structure and the steady-state active power of each of the power generation units;

[0062] In the present application, three-machine grouping is used as an example for illustration. In actual application scenarios, the number of groups can actually be flexibly changed, and the corresponding grouping demarcation points can also be adjusted accordingly to correspond to the working status of each power generation unit. The correspondence between the number of groups and the grouping demarcation points and the working status can refer to the relevant content in other implementation methods.

[0063] S103, performing a grouping operation on each of the power generation units according to the grouping quantity and the grouping demarcation point, to generate a plurality of target equivalent machines;

[0064] After determining the feasible number of clusters in the current scenario and the cluster demarcation points corresponding to the clustering, the power generation units are grouped accordingly to generate target equivalent machines corresponding to each cluster.

[0065] S104, determining equivalent parameters and equivalent collector line parameters of each target equivalent machine according to the topological structure and device parameters of each power generation unit, and establishing a target equivalent model.

[0066] After generating the target equivalent machine, it is also necessary to generate equivalent collection line parameters according to the topological structure of the original photovoltaic power station, and by generating the device parameters of each power generation unit corresponding to the target equivalent machine, the equivalent parameters of the target equivalent machine are calculated according to the principle of capacity weighting, thereby establishing a target equivalent model. In addition, in addition to the above-mentioned mechanism modeling method, relevant means of machine learning can also be used for parameter calculation or parameter optimization, depending on the specific application scenario, which is not limited in this application.

[0067] The present application can determine the number of clusters of the current target photovoltaic power station and the clustering demarcation point of the reference index for clustering by obtaining the topological structure of the target photovoltaic power station and the device parameters and steady-state active power parameters of each power generation unit, wherein the reference index is the aforementioned steady-state active power. This can achieve equivalent clustering of power generation units in a target photovoltaic power station with a fixed number of clusters, and perform corresponding clustering operations according to the number of clusters and the clustering demarcation point to generate target equivalent machines. Then, the equivalent parameters and equivalent collector line parameters of each target equivalent machine are calculated and the target equivalent model is calculated to complete the clustering of the target photovoltaic power station, thereby reducing the problems of unstable clustering number and low physical interpretability of clustering indicators caused by clustering methods in some implementation methods. At the same time, the method is simple to calculate, reduces the amount of data processing, and improves the efficiency of equivalent modeling of photovoltaic power stations.

[0068] As an optional implementation manner, determining the number of groups and the grouping demarcation point according to the topological structure and the steady-state active power of each of the power generation units includes:

[0069] Determining the operating state of each of the power generation units according to the topological structure and the steady-state active power of each of the power generation units;

[0070] The number of groups and the grouping demarcation point of the steady-state active power are determined according to the operating status of each of the power generation units.

[0071] This implementation method can further determine the current operating status of each power generation unit through the topological structure and steady-state active power of each power generation unit in the photovoltaic power station, making the clustering index more interpretable in a physical sense. According to the operating status, the most suitable number of clusters and the clustering demarcation point based on the steady-state active power of the number of clusters can be determined, so that power generation units with similar operating status can be allocated to the same cluster, thereby improving the efficiency and effectiveness of the clustering.

[0072] As an optional implementation manner, the operating state includes high power output, medium power output and low power output, the number of groups includes three-machine groups, and the grouping demarcation points include 0.7pu and 0.35pu, where pu is a per-unit power value, which is used to indicate the rated output power of the corresponding power generation unit;

[0073] And, performing a grouping operation on each of the power generation units according to the grouping quantity and the grouping demarcation point to generate a plurality of target equivalent machines, including:

[0074] Determine the power generation unit whose steady-state active power is between 0.7pu-1pu as a high-power output unit, and group the high-power output units to generate first equal-value units;

[0075] Determine the power generation units whose steady-state active power is between 0.35pu and 0.7pu as medium power output units, and group the medium power output units to generate second equal-value units;

[0076] The power generation units whose steady-state active power is between 0pu and 0.35pu are determined as small power output units, and the small power output units are grouped to generate third equal value machines.

[0077] The grouping method of the present application and the correlation between the grouping method and the operating status of the power generation unit are explained using a practical application scenario:

[0078] In this application, each photovoltaic power generation unit is grouped according to its steady-state active power before the fault. Photovoltaic power generation units whose steady-state active power before the fault belongs to the large power output range (for example, 0.7pu~1p.u.) are classified into the first group. Photovoltaic units in this range usually have high power generation efficiency and output power when there is sufficient sunlight and good environmental conditions, and contribute more to the power of the power grid.

[0079] Photovoltaic power generation units whose steady-state active power before the fault belongs to the medium power output range (for example, 0.35pu~0.7pu) are classified as the second group. These units operate under normal lighting conditions, and their power generation performance and power output are relatively stable. They also account for a certain proportion of the overall power output of the photovoltaic power station.

[0080] Photovoltaic power generation units whose steady-state active power before the fault belongs to the small power output range (for example, 0p.u.~0.35pu) are classified as the third group. The photovoltaic units in this group may be affected by insufficient light, aging of some components or other factors, and the power output is low. However, in some special circumstances, such as low light periods or the initial start-up of the power station, they still play a certain role in the overall operation and power balance of the power station.

[0081] In addition, a dynamic power range adjustment mechanism can be introduced to dynamically adjust the boundary values ​​of each power output range based on real-time meteorological data (such as light intensity, temperature, cloud cover, etc.) and the actual operating status of the photovoltaic unit. For example, in the summer when the temperature is high and the light is strong, the upper limit of the high power output range can be appropriately increased to more accurately reflect the power output characteristics of the photovoltaic unit under different environmental conditions, thereby improving the accuracy and adaptability of the grouping.

[0082] For example, as an optional implementation, the method further includes:

[0083] Determine real-time weather data;

[0084] According to the real-time meteorological data, the determined number of clusters and the cluster demarcation point of the steady-state active power are optimized, and the optimized data are used as the number of clusters and the cluster demarcation point.

[0085] This implementation method optimizes the determined number of clusters and cluster demarcation points of steady-state active power by determining real-time meteorological data and related auxiliary data, such as temperature, humidity, light, time, region, date and other information, and dynamically adjusts the number of clusters and cluster demarcation points based on the actual operating environment, thereby improving the effectiveness of clustering.

[0086] This implementation method can further determine the current operating status of each power generation unit through the topological structure and steady-state active power of each power generation unit in the photovoltaic power station. The operating status can be further subdivided into high-power output, medium-power output and low-power output, and then used to indicate the operating environment and operating conditions of each power generation unit, so that the clustering indicators are more interpretable in a physical sense. Based on the number of clusters, the clustering demarcation points of steady-state active power are determined to be 0.7pu and 0.35pu, so that power generation units with similar operating status are allocated to the same cluster, and corresponding equivalent machines are generated, which improves the efficiency and effectiveness of clustering.

[0087] As an optional implementation manner, the method for determining the equivalent collector line parameters includes:

[0088] According to the topology structure, network loss is calculated;

[0089] The equivalent collection line parameters are determined according to the network loss.

[0090] This implementation adopts the principle of equal losses before and after grouping. First, the total network loss based on the original topology before grouping is calculated, and then the equivalent collection line parameters are determined based on the calculated network loss, thereby ensuring the effectiveness of grouping and improving the calculation efficiency of system parameters after grouping.

[0091] As an optional implementation, the equivalent parameters include: equivalent light intensity, inverter equivalent capacity, filter inductance, filter resistance, filter capacitance, transformer equivalent capacity and equivalent impedance;

[0092] Determining the equivalent parameters of each target equivalent machine according to the topological structure and the device parameters of each power generation unit includes:

[0093] According to the first formula, determining the equivalent parameter of each target equivalent machine by a capacity weighted method;

[0094] Wherein, the first formula includes:

[0095]

[0096] Among them, G eq is the illumination intensity of the target equivalent machine, m is the number of power generation units in the current cluster, G i is the illumination intensity of each power generation unit in the cluster; S eq is the inverter capacity of the target equivalent machine, S PV is the inverter capacity of a single power generation unit, R f_eq is the filter resistance of the target equivalent machine, R f is the filter resistance of a single power generation unit, L f_eq is the filter inductance of the target equivalent machine, L f is the filter inductance of a single power generation unit, C f_eq is the filter capacitor of the target equivalent machine; C f is the filter capacitor of a single power generation unit, S T_eq is the transformer capacity of the target equivalent machine, S T is the transformer capacity of a single generating unit, Z T_eq is the transformer impedance of the target equivalent machine, Z T is the transformer impedance of a single power generation unit.

[0097] This embodiment uses the capacity weighted method, based on the device parameters of each power generation unit itself, and through a series of calculation methods provided in the first formula, the equivalent parameters of the target equivalent machine can be obtained. This simplifies the calculation process of the equivalent parameters and ensures the validity of the equivalent parameters, thereby improving the efficiency and effectiveness of the grouping.

[0098] As an optional implementation, the method further includes:

[0099] determining at least one correction parameter among the equivalent parameters;

[0100] Determining a target optimization algorithm according to the topological structure;

[0101] Wherein, the target optimization algorithm includes a swarm optimization algorithm or a gradient optimization algorithm;

[0102] The correction parameters are optimized according to the target optimization algorithm, and the correction parameters are updated according to the optimization results.

[0103] After determining the equivalent parameters, this embodiment can also determine a feasible optimization algorithm according to the topological structure of the system, such as a swarm intelligence algorithm based on swarm behavior or a gradient optimization algorithm based on mathematical mechanisms, and then optimize the parameters to be corrected in the equivalent parameters, thereby adjusting the corresponding parameters of the equivalent machine, thereby further ensuring the validity of the equivalent parameters and improving the validity of the grouping results.

[0104] The embodiment of the present application also provides a photovoltaic power generation unit equivalent modeling device, the device comprising:

[0105] An acquisition module is used to acquire the topological structure of the target photovoltaic power station and the device parameters and steady-state active power of each power generation unit;

[0106] The topological structure includes the number, layout and connection of collector lines of the power generation units in the target photovoltaic power station;

[0107] A processing module, used for determining the number of groups and the grouping demarcation point according to the topological structure and the steady-state active power of each of the power generation units;

[0108] The processing module is further used to perform a grouping operation on each of the power generation units according to the grouping quantity and the grouping demarcation point, and generate a plurality of target equivalent machines;

[0109] The processing module is further used to determine the equivalent parameters and equivalent collector line parameters of each target equivalent machine according to the topological structure and the device parameters of each power generation unit, and establish a target equivalent model.

[0110] The present application can determine the number of clusters of the current target photovoltaic power station and the clustering demarcation point of the reference index for clustering by obtaining the topological structure of the target photovoltaic power station and the device parameters and steady-state active power parameters of each power generation unit, wherein the reference index is the aforementioned steady-state active power. This can achieve equivalent clustering of power generation units in a target photovoltaic power station with a fixed number of clusters, and perform corresponding clustering operations according to the number of clusters and the clustering demarcation point to generate target equivalent machines. Then, the equivalent parameters and equivalent collector line parameters of each target equivalent machine are calculated and the target equivalent model is calculated to complete the clustering of the target photovoltaic power station, thereby reducing the problems of unstable clustering number and low physical interpretability of clustering indicators caused by clustering methods in some implementation methods. At the same time, the method is simple to calculate, reduces the amount of data processing, and improves the efficiency of equivalent modeling of photovoltaic power stations.

[0111] As an optional implementation manner, the processing module determines the specific manner of determining the number of groups and the group demarcation points according to the topological structure and the steady-state active power of each of the power generation units, including:

[0112] Determining the operating state of each of the power generation units according to the topological structure and the steady-state active power of each of the power generation units;

[0113] The number of groups and the grouping demarcation point of the steady-state active power are determined according to the operating status of each of the power generation units.

[0114] This implementation method can further determine the current operating status of each power generation unit through the topological structure and steady-state active power of each power generation unit in the photovoltaic power station, making the clustering index more interpretable in a physical sense. According to the operating status, the most suitable number of clusters and the clustering demarcation point based on the steady-state active power of the number of clusters can be determined, so that power generation units with similar operating status can be allocated to the same cluster, thereby improving the efficiency and effectiveness of the clustering.

[0115] As an optional implementation manner, the operating state includes high power output, medium power output and low power output, the number of groups includes three-machine groups, and the grouping demarcation points include 0.7pu and 0.35pu, where pu is a per-unit power value, which is used to indicate the rated output power of the corresponding power generation unit;

[0116] And, the processing module performs a grouping operation on each of the power generation units according to the grouping quantity and the grouping demarcation point to generate a specific method of multiple target equivalent machines, including:

[0117] Determine the power generation unit whose steady-state active power is between 0.7pu-1pu as a high-power output unit, and group the high-power output units to generate first equal-value units;

[0118] Determine the power generation units whose steady-state active power is between 0.35pu and 0.7pu as medium power output units, and group the medium power output units to generate second equal-value units;

[0119] The power generation units whose steady-state active power is between 0pu and 0.35pu are determined as small power output units, and the small power output units are grouped to generate third equal value machines.

[0120] This implementation method can further determine the current operating status of each power generation unit through the topological structure and steady-state active power of each power generation unit in the photovoltaic power station. The operating status can be further subdivided into high-power output, medium-power output and low-power output, and then used to indicate the operating environment and operating conditions of each power generation unit, so that the clustering indicators are more interpretable in a physical sense. Based on the number of clusters, the clustering demarcation points of steady-state active power are determined to be 0.7pu and 0.35pu, so that power generation units with similar operating status are allocated to the same cluster, and corresponding equivalent machines are generated, which improves the efficiency and effectiveness of clustering.

[0121] As an optional implementation manner, the processing module is further used for:

[0122] Determine real-time weather data;

[0123] According to the real-time meteorological data, the determined number of clusters and the cluster demarcation point of the steady-state active power are optimized, and the optimized data are used as the number of clusters and the cluster demarcation point.

[0124] This implementation method optimizes the determined number of clusters and cluster demarcation points of steady-state active power by determining real-time meteorological data and related auxiliary data, such as temperature, humidity, light, time, region, date and other information, and dynamically adjusts the number of clusters and cluster demarcation points based on the actual operating environment, thereby improving the effectiveness of clustering.

[0125] As an optional implementation manner, the specific manner in which the processing module determines the equivalent collector line parameters includes:

[0126] According to the topology structure, network loss is calculated;

[0127] The equivalent collection line parameters are determined according to the network loss.

[0128] This implementation adopts the principle of equal losses before and after grouping. First, the total network loss based on the original topology before grouping is calculated, and then the equivalent collection line parameters are determined based on the calculated network loss, thereby ensuring the effectiveness of grouping and improving the calculation efficiency of system parameters after grouping.

[0129] As an optional implementation, the equivalent parameters include: equivalent light intensity, inverter equivalent capacity, filter inductance, filter resistance, filter capacitance, transformer equivalent capacity and equivalent impedance;

[0130] The specific manner in which the processing module determines the equivalent parameters of each of the target equivalent machines according to the topological structure and the device parameters of each of the power generation units includes:

[0131] According to the first formula, determining the equivalent parameter of each target equivalent machine by a capacity weighted method;

[0132] Wherein, the first formula includes:

[0133]

[0134] Among them, G eq is the illumination intensity of the target equivalent machine, m is the number of power generation units in the current cluster, G i is the illumination intensity of each power generation unit in the cluster; S eq is the inverter capacity of the target equivalent machine, S PV is the inverter capacity of a single power generation unit, R f_eq is the filter resistance of the target equivalent machine, R f is the filter resistance of a single power generation unit, L f_eq is the filter inductance of the target equivalent machine, L f is the filter inductance of a single power generation unit, C f_eq is the filter capacitor of the target equivalent machine; C f is the filter capacitor of a single power generation unit, S T_eq is the transformer capacity of the target equivalent machine, S T is the transformer capacity of a single generating unit, Z T_eq is the transformer impedance of the target equivalent machine, Z T is the transformer impedance of a single power generation unit.

[0135] This embodiment uses the capacity weighted method, based on the device parameters of each power generation unit itself, and through a series of calculation methods provided in the first formula, the equivalent parameters of the target equivalent machine can be obtained. This simplifies the calculation process of the equivalent parameters and ensures the validity of the equivalent parameters, thereby improving the efficiency and effectiveness of the grouping.

[0136] As an optional implementation manner, the processing module is further used for:

[0137] determining at least one correction parameter among the equivalent parameters;

[0138] Determining a target optimization algorithm according to the topological structure;

[0139] Wherein, the target optimization algorithm includes a swarm optimization algorithm or a gradient optimization algorithm;

[0140] The correction parameters are optimized according to the target optimization algorithm, and the correction parameters are updated according to the optimization results.

[0141] After determining the equivalent parameters, this embodiment can also determine a feasible optimization algorithm according to the topological structure of the system, such as a swarm intelligence algorithm based on swarm behavior or a gradient optimization algorithm based on mathematical mechanisms, and then optimize the parameters to be corrected in the equivalent parameters, thereby adjusting the corresponding parameters of the equivalent machine, thereby further ensuring the validity of the equivalent parameters and improving the validity of the grouping results.

[0142] It should be noted that it should be understood that the division of the various modules of the above device is only a division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated. And these modules can all be implemented in the form of software called by processing elements; they can also be all implemented in the form of hardware; some modules can also be implemented in the form of software called by processing elements, and some modules can be implemented in the form of hardware. For example, the processing module can be a separately established processing element, or it can be integrated in a chip of the above device. In addition, it can also be stored in the memory of the above device in the form of program code, and called and executed by a processing element of the above device. The function of the above-determined module. The implementation of other modules is similar. In addition, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each module above can be completed by an integrated logic circuit of hardware in the processor element or instructions in the form of software.

[0143] Indicatively, Figure 2 As shown, Figure 2 This is a schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. The computer device 300 may be provided as a server. Figure 2 The computer device 300 includes a processing component 302, which further includes one or more processors, and a memory resource represented by a memory 301 for storing instructions executable by the processing component 302, such as an application. The application stored in the memory 301 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 302 is configured to execute the instructions to perform the text recognition method of any of the above embodiments.

[0144] The computer device 300 may further include a power supply component 303 configured to perform power management of the computer device 300, a wired or wireless network interface 304 configured to connect the computer device 300 to a network, and an input / output (I / O) interface 305. The computer device 300 may operate based on an operating system stored in the memory 301, such as Windows Server TM, Mac OS X TM, Unix TM, Linux TM, Free BSD TM, or the like.

[0145] Those skilled in the art will understand that Figure 2 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0146] An embodiment of the present application provides a storage medium, in which computer-readable instructions are stored. When the computer-readable instructions are executed by one or more processors, the one or more processors execute a method provided in any embodiment.

[0147] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0148] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can refer to each other.

[0149] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A photovoltaic power generation unit equivalent modeling method, characterized in that: The method comprises: Obtain the topological structure of the target photovoltaic power station and the device parameters and steady-state active power of each power generation unit; The topological structure includes the number, layout and connection of collector lines of the power generation units in the target photovoltaic power station; Determine the number of groups and the grouping demarcation points according to the topological structure and the steady-state active power of each of the power generation units; According to the number of groups and the grouping demarcation point, a grouping operation is performed on each of the power generation units to generate a plurality of target equivalent machines; According to the topological structure and the device parameters of each of the power generation units, the equivalent parameters and equivalent collector line parameters of each of the target equivalent machines are determined, and a target equivalent model is established.

2. The method according to claim 1, characterized in that Determining the number of groups and the grouping demarcation points according to the topological structure and the steady-state active power of each of the power generation units includes: Determining the operating state of each of the power generation units according to the topological structure and the steady-state active power of each of the power generation units; The number of groups and the grouping demarcation point of the steady-state active power are determined according to the operating status of each of the power generation units.

3. The method according to claim 2, characterized in that The operating states include high power output, medium power output and low power output, the number of groups includes three-machine groups, and the grouping demarcation points include 0.7pu and 0.35pu, where pu is the per-unit power value, which is used to indicate the rated output power of the corresponding power generation unit; And, performing a grouping operation on each of the power generation units according to the grouping quantity and the grouping demarcation point to generate a plurality of target equivalent machines, including: Determine the power generation unit whose steady-state active power is between 0.7pu-1pu as a high-power output unit, and group the high-power output units to generate first equal-value units; Determine the power generation units whose steady-state active power is between 0.35pu and 0.7pu as medium power output units, and group the medium power output units to generate second equal-value units; The power generation units whose steady-state active power is between 0pu and 0.35pu are determined as small power output units, and the small power output units are grouped to generate third equal value machines.

4. The method according to claim 2, characterized in that: The method further comprises: Determine real-time weather data; According to the real-time meteorological data, the determined number of clusters and the cluster demarcation point of the steady-state active power are optimized, and the optimized data are used as the number of clusters and the cluster demarcation point.

5. The method according to claim 1, characterized in that The method for determining the equivalent collector line parameters includes: According to the topology structure, network loss is calculated; The equivalent collection line parameters are determined according to the network loss.

6. The method according to claim 1, characterized in that The equivalent parameters include: equivalent light intensity, inverter equivalent capacity, filter inductance, filter resistance, filter capacitance, transformer equivalent capacity and equivalent impedance; Determining the equivalent parameters of each target equivalent machine according to the topological structure and the device parameters of each power generation unit includes: According to the first formula, determining the equivalent parameter of each target equivalent machine by a capacity weighted method; Wherein, the first formula includes: Among them, G eq is the illumination intensity of the target equivalent machine, m is the number of power generation units in the current cluster, G i is the illumination intensity of each power generation unit in the cluster; S eq is the inverter capacity of the target equivalent machine, S PV is the inverter capacity of a single power generation unit, R f_eq is the filter resistance of the target equivalent machine, R f is the filter resistance of a single power generation unit, L f_eq is the filter inductance of the target equivalent machine, L f is the filter inductance of a single power generation unit, C f_eq is the filter capacitor of the target equivalent machine; C f is the filter capacitor of a single power generation unit, S T_eq is the transformer capacity of the target equivalent machine, S T is the transformer capacity of a single generating unit, Z T_eq is the transformer impedance of the target equivalent machine, Z T is the transformer impedance of a single power generation unit.

7. The method according to claim 6, characterized in that The method further comprises: determining at least one correction parameter among the equivalent parameters; Determining a target optimization algorithm according to the topological structure; Wherein, the target optimization algorithm includes a swarm optimization algorithm or a gradient optimization algorithm; The correction parameters are optimized according to the target optimization algorithm, and the correction parameters are updated according to the optimization results.

8. A photovoltaic power generation unit equivalent modeling device, characterized in that: The device comprises: An acquisition module is used to acquire the topological structure of the target photovoltaic power station and the device parameters and steady-state active power of each power generation unit; The topological structure includes the number, layout and connection of collector lines of the power generation units in the target photovoltaic power station; A processing module, used for determining the number of groups and the grouping demarcation point according to the topological structure and the steady-state active power of each of the power generation units; The processing module is further used to perform a grouping operation on each of the power generation units according to the grouping quantity and the grouping demarcation point, and generate a plurality of target equivalent machines; The processing module is also used to determine the equivalent parameters and equivalent collector line parameters of each target equivalent machine according to the topological structure and the device parameters of each power generation unit, and establish a target equivalent model.

9. A computer device, characterized in that: The method comprises one or more processors and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the method according to any one of claims 1 to 7 are performed.

10. A storage medium, characterized in that: The storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the method according to any one of claims 1 to 7.

Images