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

Abstract

The application provides a photovoltaic power generation unit equivalent modeling method, device and equipment and storage medium. By obtaining the topological structure of a target photovoltaic power station, the device parameters and the steady-state active power parameters of each power generation unit, the number of groups of the current target photovoltaic power station and the group division points of the steady-state active power used for grouping can be determined, so that the equivalent grouping of the power generation units in the target photovoltaic power station with a fixed number of groups can be realized. The corresponding grouping operation is performed according to the number of groups and the group division points to generate a target equivalent machine, and then the equivalent parameters and the equivalent collection line parameters of each target equivalent machine itself are calculated and obtained, the target equivalent model is calculated, and the grouping of the target photovoltaic power station is completed. The problem that the number of groups is unstable and the physical interpretability of the grouping index is not high caused by the clustering method in some embodiments is reduced. Meanwhile, the method is simple to calculate, the data processing amount is reduced, and the efficiency of the equivalent modeling of the photovoltaic power station is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of new energy power generation, and particularly to a photovoltaic power generation unit equivalent modeling method, device, equipment and storage medium. BACKGROUND

[0002] In the field of photovoltaic power generation, large-scale photovoltaic power generation units are connected to the grid, which significantly changes the fault characteristics of the power system. In order to analyze the influence of the transient characteristics of the photovoltaic power generation system on the safety and stability of the power grid, an accurate simulation model of the photovoltaic power station needs to be established. However, a photovoltaic power station contains numerous photovoltaic power generation units. If a detailed model is used for each power generation unit, the photovoltaic power station model established will have a very high order, which will seriously affect the simulation efficiency. Therefore, equivalent modeling of each power generation unit is needed. Since the operating states of each power generation unit in a large photovoltaic power station are not the same, the single-machine equivalent method of aggregating all power generation units into one equivalent unit will bring a large equivalent error. Therefore, in some feasible implementations, the multi-machine equivalent method is used to represent the entire station, and the core is to determine a reasonable grouping strategy to divide power generation units with the same or similar response into a group.

[0003] However, the current grouping strategy of the multi-machine equivalent method mostly uses clustering methods, which makes the grouping process complex and the number of groups may not be fixed, which hinders subsequent modeling applications. Therefore, a photovoltaic power generation unit equivalent modeling method with a fixed number of groups is needed to improve the efficiency of modeling and simplify the model form. SUMMARY

[0004] The present application aims to at least solve one of the above technical defects, particularly the technical defect of low efficiency of photovoltaic power generation unit equivalent modeling in the prior art.

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

[0006] obtaining the topological structure of a 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 collection line connection of the power generation units in the target photovoltaic power station.

[0008] determining the number of groups and group boundary points according to the topological structure and the steady-state active power of each power generation unit;

[0009] performing a grouping operation on each power generation unit according to the number of groups and the group boundary points to generate a plurality of target equivalent machines;

[0010] According to the topology and the device parameters of each of the power generation units, determine equivalent parameters and equivalent collection line parameters of each of the target equivalent machines, and establish a target equivalent model.

[0011] As an optional implementation, the determining of the number of groups and the group division points according to the topology and the steady-state active power of each of the power generation units comprises:

[0012] According to the topology and the steady-state active power of each of the power generation units, determine the operating state of each of the power generation units;

[0013] According to the operating state of each of the power generation units, determine the number of groups and the group division points of the steady-state active power.

[0014] As an optional implementation, the operating state comprises high-power output, medium-power output and low-power output, the number of groups comprises three-machine grouping, and the group division points comprise 0.7pu and 0.35pu, pu being a power unit, used to indicate the rated output power of the corresponding power generation unit;

[0015] And the performing of the grouping operation on each of the power generation units according to the number of groups and the group division points to generate a plurality of target equivalent machines comprises:

[0016] determine the power generation units whose steady-state active power is in the interval (0.7pu, 1pu] as high-power output units, and group each of the high-power output units to generate a first equivalent machine;

[0017] determine the power generation units whose steady-state active power is in the interval (0.35pu, 0.7pu] as medium-power output units, and group each of the medium-power output units to generate a second equivalent machine;

[0018] determine the power generation units whose steady-state active power is in the interval [0pu, 0.35pu] as low-power output units, and group each of the low-power output units to generate a third equivalent machine.

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

[0020] determine real-time weather data;

[0021] According to the real-time weather data, optimize the determined number of groups and the group division points of the steady-state active power, and use the optimized data as the number of groups and the group division points.

[0022] As an optional implementation, the determination of the equivalent collection line parameters comprises:

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

[0024] According to the network loss, the equivalent set line parameters are determined.

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

[0026] According to the topology structure and the device parameters of each power generation unit, the equivalent parameters of each target equivalent machine are determined, including:

[0027] According to a first formula, the equivalent parameters of each target equivalent machine are determined by a capacity weighting method;

[0028] The first formula includes:

[0029]

[0030] Wherein, G eq is the irradiance of the target equivalent machine, m is the number of the power generation units in the current group, G i is the irradiance of each power generation unit in the group; 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 capacitance of the target equivalent machine; C f is the filter capacitance 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 power generation 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] At least one correction parameter is determined in the equivalent parameters;

[0033] According to the topology structure, a target optimization algorithm is determined;

[0034] The target optimization algorithm includes a group optimization algorithm or a gradient optimization algorithm.

[0035] The correction parameter is optimized according to the target optimization algorithm, and the correction parameter is updated according to an optimization result.

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

[0037] An acquisition module is configured to acquire a topological structure of a target photovoltaic power station, and device parameters and steady-state active power of each power generation unit.

[0038] The topological structure includes the number, layout and collection line connection of the power generation units in the target photovoltaic power station.

[0039] A processing module is configured to determine a group number and a group demarcation point according to the topological structure and the steady-state active power of each power generation unit.

[0040] The processing module is further configured to perform a group operation on each power generation unit according to the group number and the group demarcation point, and generate a plurality of target equivalent machines.

[0041] The processing module is further configured to determine equivalent parameters and equivalent collection 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, which comprises one or more processors and a memory, and the memory stores computer readable instructions, and the computer readable instructions are executed by the one or more processors to perform the steps of the method according to the first aspect.

[0043] In a fourth aspect, the present application provides a storage medium, and the storage medium stores computer readable instructions, and the computer readable instructions are executed by one or more processors to make the one or more processors perform the steps of the method according to the first aspect.

[0044] From the above technical solutions, it can be seen that the embodiments of the present application have the following advantages:

[0045] Based on any of the above embodiments, the application can determine the number of groups of the target photovoltaic power station and the group boundary point of the reference index for grouping by acquiring the topological structure of the target photovoltaic power station, the device parameters of each power generation unit, and the steady-state active power parameters. The reference index is the aforementioned steady-state active power. Thus, the equivalent grouping of the power generation units in the target photovoltaic power station with a fixed number of groups can be realized. The corresponding grouping operation is performed according to the number of groups and the group boundary point to generate target equivalent machines. Then, the equivalent parameters and equivalent collection line parameters of each target equivalent machine itself are calculated and obtained. The target equivalent model is calculated to complete the grouping of the target photovoltaic power station. The problems of unstable number of groups and low physical interpretability of the grouping index caused by the clustering method in some embodiments are reduced. At the same time, the method is simple to calculate, reduces the data processing amount, and improves the efficiency of the equivalent modeling of the photovoltaic power station. BRIEF DESCRIPTION OF DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0047] Figure 1 The flowchart of the photovoltaic power generation unit equivalent modeling method provided by an embodiment of the present application is shown in the figure.

[0048] Figure 2 The internal structure diagram of the computer device provided by the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0049] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0050] To help build new power systems, the proportion of photovoltaic power generation installations continues to increase. The connection of large-scale photovoltaic power generation units to the grid significantly changes the fault characteristics of the power system. In order to analyze the influence of the transient characteristics of photovoltaic power generation systems on the safety and stability of the power grid, an accurate simulation model of photovoltaic power stations needs to be established. However, photovoltaic power stations contain numerous photovoltaic power generation units. If a detailed model is used for each power generation unit, the photovoltaic power station model established will have a very high order, severely affecting the simulation efficiency. Therefore, it is necessary to perform equivalent modeling of photovoltaic power stations. Since the operating states of each power generation unit in a large photovoltaic power station are not the same, the single-machine equivalent method, which aggregates all power generation units into one equivalent unit, will result in a large equivalent error. Therefore, existing research focuses on the multi-machine equivalent method, which uses multiple equivalent units to represent the entire station, and the core is to determine a reasonable grouping strategy to divide power generation units with the same or similar response into a group.

[0051] For example, as a feasible implementation, a large-scale photovoltaic clustering analysis method considering low penetration characteristics derives a photovoltaic node voltage expression through the low voltage ride-through characteristics of photovoltaic power generation units and the influence degree of synchronous machines on node voltage, applies hierarchical clustering to the synchronous machine participation factor matrix, and then completes the grouping of the units.

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

[0053] In general, existing photovoltaic power station multi-machine equivalent methods focus on exploring characteristic quantities representing the operating state of photovoltaic power generation units as grouping indicators, and then using clustering algorithms for grouping calculation. This type of method can achieve high equivalent accuracy, but the acquisition of grouping results requires the use of complex clustering algorithms, and the number of groups is uncertain, which is not conducive to engineering application.

[0054] In summary, the grouping strategy of the current multi-machine equivalent method mostly uses clustering methods, resulting in a complex grouping process and an uncertain number of groups, which hinders subsequent modeling applications. Therefore, a photovoltaic power generation unit equivalent modeling method with a fixed number of groups is needed to improve the efficiency of modeling and simplify the model form.

[0055] The technical concept of the present application is that by acquiring 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 cluster boundary points of the reference index used for clustering can be determined, and the reference index is the aforementioned steady-state active power. Thus, the equivalent clustering of the power generation units in the target photovoltaic power station with a fixed number of clusters can be realized. The corresponding clustering operation is performed according to the number of clusters and the cluster boundary points to generate target equivalent machines. Then, the equivalent parameters and equivalent collection line parameters of each target equivalent machine itself are calculated and obtained, and the target equivalent model is calculated to complete the clustering of the target photovoltaic power station. The problems of unstable number of clusters and low physical interpretability of clustering index caused by the clustering method in some embodiments are reduced. At the same time, the method is simple to calculate, reduces the data processing amount, and improves the efficiency of photovoltaic power station equivalent modeling.

[0056] Please refer to Figure 1 , Figure 1 The flowchart of the photovoltaic power generation unit equivalent modeling method provided by an embodiment of the present application is shown in FIG. 1. As shown in FIG. 1, the method comprises the following steps. Figure 1

[0057] S101, acquiring 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 comprises the number, layout and collection line connection of the power generation units in the target photovoltaic power station.

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

[0060] In fact, in addition to the steady-state active power, other electrical parameters can also be used as the basis for clustering modeling, but the present application uses the steady-state active power as an example for illustration. The main reason is that this index is easy to obtain and can simplify the subsequent calculation process. According to the specific application scenario, the electrical parameters or combinations of electrical parameters used in the clustering process can be flexibly adjusted in some scenarios.

[0061] S102, determining the number of clusters and the cluster boundary points according to the topological structure and the steady-state active power of each power generation unit.

[0062] In the present application, three-machine clustering is used as an example for illustration. In actual application scenarios, the number of clusters can actually be flexibly changed, and the corresponding cluster boundary points can also be adjusted accordingly. The corresponding relationship between the number of clusters and the cluster boundary points and the working state can be referred to the related content in other embodiments. ​

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

[0064] After determining the feasible grouping number in the current scenario and the grouping boundary point corresponding to the grouping operation, the grouping is performed on each power generation unit according to the grouping number and the grouping boundary point, to generate a plurality of target equivalent machines.

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

[0066] After generating the target equivalent machines, it is also necessary to generate equivalent collection line parameters according to the topological structure of the original photovoltaic power station, and to calculate the equivalent parameters of the target equivalent machines according to the device parameters of each of the power generation units corresponding to the target equivalent machines, according to the principle of capacity weighting, so as to establish a target equivalent model. In addition, in addition to the mechanism modeling method described above, a related means of machine learning can be used for parameter calculation or parameter optimization, which depends on the specific application scenario, and the present application does not limit this.

[0067] By obtaining the topological structure of the target photovoltaic power station and the device parameters and the steady-state active power parameters of each power generation unit, the present application can determine the grouping number of the current target photovoltaic power station and the grouping boundary point of the reference index for grouping. The reference index is the steady-state active power described above. Thus, the equivalent grouping of the power generation units in the target photovoltaic power station with a fixed grouping number can be realized. The corresponding grouping operation is performed according to the grouping number and the grouping boundary point to generate target equivalent machines, and then the equivalent parameters and equivalent collection line parameters of each target equivalent machine itself are calculated to calculate a target equivalent model, so as to complete the grouping of the target photovoltaic power station. This reduces the problem of unstable grouping number and low physical interpretability of the grouping index caused by the clustering method in some embodiments. At the same time, the method is simple to calculate, reduces the amount of data processing, and improves the efficiency of the equivalent modeling of the photovoltaic power station.

[0068] As an optional embodiment, the determination of the grouping number and the grouping boundary point according to the topological structure and the steady-state active power of each of the power generation units comprises:

[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] determining the grouping number and the grouping boundary point of the steady-state active power according to the operating state of each of the power generation units.

[0071] The embodiment can further determine the operation state of each power generation unit in the photovoltaic power station according to the topology structure and the steady-state active power of each power generation unit, so that the grouping index is more explainable in a physical sense, the most suitable grouping number can be determined according to the operation state, and the grouping demarcation point of the steady-state active power based on the grouping number is determined, so that the power generation units with similar operation states are allocated to the same group, and the efficiency and effectiveness of the grouping are improved.

[0072] As an optional embodiment, the operation state includes high-power output, medium-power output and low-power output, the grouping number includes three-machine grouping, and the grouping demarcation point includes 0.7pu and 0.35pu, pu being a power unit, used to indicate the rated output power of the corresponding power generation unit;

[0073] In addition, the grouping operation of each power generation unit is performed according to the grouping number and the grouping demarcation point, and a plurality of target equivalent machines are generated, including:

[0074] The power generation unit whose steady-state active power is in the interval (0.7pu, 1pu] is determined as a high-power output unit, and each high-power output unit is grouped to generate a first equivalent machine;

[0075] The power generation unit whose steady-state active power is in the interval (0.35pu, 0.7pu] is determined as a medium-power output unit, and each medium-power output unit is grouped to generate a second equivalent machine;

[0076] The power generation unit whose steady-state active power is in the interval [0pu, 0.35pu] is determined as a low-power output unit, and each low-power output unit is grouped to generate a third equivalent machine.

[0077] The grouping method of the application and the correlation between the grouping method and the operation state of the power generation unit are described in an actual application scenario:

[0078] In the application, each photovoltaic power generation unit is grouped according to its steady-state active power before failure. The photovoltaic power generation units whose steady-state active power before failure belongs to the high-power output range (for example, 0.7p.u.~1p.u.) are divided into the first group. The photovoltaic units in this range usually have high power generation efficiency and output power when the light is sufficient and the environmental conditions are good, and make a large contribution to the power grid.

[0079] The photovoltaic power generation units whose steady-state active power before failure belongs to the medium-power output range (for example, 0.35p.u.~0.7p.u.) are divided into the second group. These units operate under general lighting conditions, and their power generation performance and power output are relatively stable, and they also account for a certain proportion in the overall power output of the photovoltaic power station.

[0080] The photovoltaic power generation unit with steady-state active power belonging to the small power output range (for example, 0p.u.~0.35p.u.) before failure is classified into the third group. The photovoltaic unit in the third group may be affected by insufficient light, partial component aging or other factors, and the power output is low. However, in some special cases, such as a low-light period or an initial stage of starting the power station, the photovoltaic unit still has a certain effect on the overall operation and power balance of the power station.

[0081] In addition, a dynamic power range adjustment mechanism can be introduced. According to real-time meteorological data (such as light intensity, temperature, cloud coverage, etc.) and the actual operating state of the photovoltaic unit, the boundary values of each power output range are dynamically adjusted. For example, in summer with high temperature and strong light, the upper limit value of the large power output range is 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] determining real-time meteorological data;

[0084] According to the real-time meteorological data, the determined number of groups and the steady-state active power group boundary points are optimized, and the optimized data are used as the number of groups and the group boundary points.

[0085] The embodiment determines real-time meteorological data and related auxiliary data, such as temperature, humidity, light, time, region, date, etc., thereby optimizing the determined number of groups and the steady-state active power group boundary points. The number of groups and the group boundary points are dynamically adjusted based on the actual operating environment, and the effectiveness of the grouping is improved.

[0086] The embodiment can further determine the operating state of each power generation unit in the photovoltaic power station based on the topology structure and the steady-state active power of each power generation unit. The operating state can be further divided into large power output, medium power output and small power output, and is used to indicate the operating environment and the operating state of each power generation unit. The grouping index is more explainable in a physical sense. Based on the number of groups, the steady-state active power group boundary points are determined as 0.7pu and 0.35pu, thereby distributing the power generation units with similar operating states into the same group and generating the corresponding equivalent machine, and the efficiency and effectiveness of the grouping are improved.

[0087] As an optional implementation, the determination of the equivalent collection line parameters includes:

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

[0089] According to the network loss, the equivalent collection line parameters are determined.

[0090] The embodiment firstly calculates the total network loss based on the original topology structure before grouping according to the principle that the losses before and after grouping are equal, so that 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 embodiment, the equivalent parameters include: equivalent irradiance, equivalent capacity of inverter, filter inductance, filter resistance, filter capacitance, equivalent capacity of transformer and equivalent impedance;

[0092] The equivalent parameters of each target equivalent machine are determined according to the topology structure and the device parameters of each power generation unit, and the method comprises the steps of:

[0093] According to the first formula, the equivalent parameters of each target equivalent machine are determined by capacity weighting method;

[0094] The first formula comprises:

[0095]

[0096] Wherein, G eq is the irradiance of the target equivalent machine, m is the number of the power generation units in the current group, G i is the irradiance of each power generation unit in the group; 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 capacitance of the target equivalent machine; C f is the filter capacitance 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 power generation 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] The embodiment obtains the equivalent parameters of the target equivalent machine through a series of calculation manners provided in the first formula based on the device parameters of each power generation unit itself through the capacity weighting method, thereby ensuring the effectiveness of the equivalent parameters on the basis of simplifying the calculation process of the equivalent parameters, and further improving the efficiency and effectiveness of the grouping.

[0098] As an optional embodiment, the method further comprises:

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

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

[0101] wherein the target optimization algorithm comprises a group optimization algorithm or a gradient optimization algorithm;

[0102] optimizing the correction parameter according to the target optimization algorithm, and updating the correction parameter according to the optimization result.

[0103] After the equivalent parameters are determined, a feasible optimization algorithm can be determined according to the topological structure of the system, such as a group intelligence algorithm based on group behavior or a gradient optimization algorithm based on mathematical mechanism, and then the to-be-corrected parameter in the equivalent parameters can be optimized, so as to adjust the corresponding parameters of the equivalent machine, thereby further ensuring the effectiveness of the equivalent parameters, and further improving the effectiveness of the grouping result.

[0104] The application also provides a photovoltaic power generation unit equivalent modeling device, which comprises:

[0105] an acquisition module configured to acquire a topological structure of a target photovoltaic power station and device parameters and steady-state active power of each power generation unit;

[0106] wherein the topological structure comprises the number, layout and collection line connection of the power generation units in the target photovoltaic power station;

[0107] a processing module configured to determine a grouping number and a grouping demarcation point according to the topological structure and the steady-state active power of each power generation unit;

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

[0109] the processing module is further configured to determine equivalent parameters and equivalent collection 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 application can determine the number of groups and the group division point of the reference index for grouping of the current target photovoltaic power station by obtaining the topological structure of the target photovoltaic power station and the device parameters and the steady-state active power parameters of each power generation unit, and thus the equivalent grouping of the power generation units in the target photovoltaic power station with a fixed number of groups can be realized. The corresponding grouping operation is performed according to the number of groups and the group division point to generate target equivalent machines, and then the equivalent parameters and the equivalent collection line parameters of each target equivalent machine are calculated and obtained, and the target equivalent model is calculated to complete the grouping of the target photovoltaic power station, thereby reducing the problems of unstable number of groups and low physical interpretability of the grouping index caused by the clustering method in some embodiments. Meanwhile, the method is simple to calculate, reduces the data processing amount, and improves the efficiency of the equivalent modeling of the photovoltaic power station.

[0111] As an optional embodiment, the specific manner of determining the number of groups and the group division point of the reference index according to the topological structure and the steady-state active power of each power generation unit comprises:

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

[0113] determining the number of groups and the group division point of the steady-state active power according to the operating state of each power generation unit.

[0114] The operating state of each power generation unit can be further determined by the topological structure and the steady-state active power of each power generation unit in the photovoltaic power station, so that the grouping index is more interpretable in the physical sense. The most suitable number of groups and the group division point of the steady-state active power based on the number of groups can be determined according to the operating state, so that the power generation units with similar operating states are allocated to the same group, and the efficiency and effectiveness of the grouping are improved.

[0115] As an optional embodiment, the operating state comprises high-power output, medium-power output and low-power output, the number of groups comprises three-machine grouping, and the group division point comprises 0.7pu and 0.35pu, pu being a power unit used to indicate the rated output power of the corresponding power generation unit.

[0116] The specific manner of performing the grouping operation on each power generation unit according to the number of groups and the group division point to generate a plurality of target equivalent machines by the processing module comprises:

[0117] determining the power generation units with the steady-state active power in the interval (0.7pu, 1pu] as high-power output units, and grouping each high-power output unit to generate a first equivalent machine;

[0118] determine the power generation units with steady-state active power in the interval (0.35pu, 0.7pu] as medium-power output units, and group each of the medium-power output units to generate a second equivalent machine;

[0119] determine the power generation units with steady-state active power in the interval [0pu, 0.35pu] as small-power output units, and group each of the small-power output units to generate a third equivalent machine.

[0120] The present embodiment can further determine the operating state of each power generation unit in the photovoltaic power station based on the topology structure and the steady-state active power of each power generation unit, and the operating state can be further subdivided into high-power output, medium-power output and small-power output, which can be used to indicate the operating environment and the operating condition of each power generation unit, so that the grouping index has more interpretability in physical meaning. Based on the number of groups, the grouping demarcation points of the steady-state active power are determined as 0.7pu and 0.35pu, so that the power generation units with similar operating states are allocated to the same group, and the corresponding equivalent machine is generated, thereby improving the efficiency and effectiveness of the grouping.

[0121] As an optional embodiment, the processing module is further configured to:

[0122] determine real-time weather data;

[0123] optimize the determined number of groups and the grouping demarcation points of the steady-state active power based on the real-time weather data, and use the optimized data as the number of groups and the grouping demarcation points.

[0124] The present embodiment optimizes the determined number of groups and the grouping demarcation points of the steady-state active power by determining real-time weather data and related auxiliary data such as temperature, humidity, illumination, time, region, date, etc., and dynamically adjusts the number of groups and the grouping demarcation points based on the actual operating environment, thereby improving the effectiveness of the grouping.

[0125] As an optional embodiment, the specific way in which the processing module determines the equivalent collection line parameters includes:

[0126] calculating the network loss based on the topology structure;

[0127] determining the equivalent collection line parameters based on the network loss.

[0128] The present embodiment first calculates the total network loss based on the original topology structure before grouping, and then determines the equivalent collection line parameters based on the calculated network loss, thereby ensuring the effectiveness of the grouping and improving the calculation efficiency of the system parameters after the 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 target equivalent machine according to the topological structure and the device parameters of each power generation unit includes:

[0131] According to a first formula, the equivalent parameters of each target equivalent machine are determined by a capacity weighting method;

[0132] The first formula includes:

[0133]

[0134] Wherein, G eq is the light intensity of the target equivalent machine, m is the number of power generation units in the current group, G i is the light intensity of each power generation unit in the group; 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 capacitance of the target equivalent machine; C f is the filter capacitance 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 power generation 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] The present embodiment obtains the equivalent parameters of the target equivalent machine by a series of calculation methods provided in the first formula based on the device parameters of each power generation unit itself by a capacity weighting method, thereby ensuring the effectiveness of the equivalent parameters on the basis of simplifying the calculation process of the equivalent parameters, and further improving the efficiency and effectiveness of the grouping.

[0136] As an optional implementation, the processing module is further configured to:

[0137] determine at least one correction parameter in the equivalent parameters;

[0138] determine a target optimization algorithm according to the topology structure;

[0139] The target optimization algorithm includes a swarm optimization algorithm or a gradient optimization algorithm.

[0140] The correction parameter is optimized according to the target optimization algorithm, and the correction parameter is updated according to an optimization result.

[0141] After the equivalent parameter is determined, a feasible optimization algorithm can be determined according to the topology structure of the system, for example, a swarm intelligence algorithm based on swarm behavior or a gradient optimization algorithm based on mathematical mechanism, and then the to-be-corrected parameter in the equivalent parameter can be optimized and processed, so that the corresponding parameter of the equivalent machine is adjusted, thereby further ensuring the effectiveness of the equivalent parameter and improving the effectiveness of the grouping result.

[0142] It should be noted that the division of each module of the above device is only a logical functional division, and all or part of it can be integrated into a physical entity, or physically separated. And these modules can all be implemented in the form of software called by the processing element; all can be implemented in the form of hardware; some modules can be implemented in the form of software called by the processing element, and some modules can be implemented in the form of hardware. For example, the processing module can be a separate processing element, or it can be integrated into a chip of the above device, in addition, it can also be stored in the form of program code in the memory of the above device, and the function of the above determination module is called and executed by a processing element of the above device. The implementation of other modules is similar. In addition, all or part of these modules can be integrated together, or they can be independently implemented. The processing element here can be an integrated circuit with signal processing capability. In the implementation process, each step of the above method or each module can be completed by the integrated logic circuit of the hardware in the processor element or the instruction in the form of software.

[0143] As shown in Figure 2 , as shown in Figure 2 An internal structure schematic diagram of a computer device provided by the embodiment of the present application, the computer device 300 can be provided as a server. Referring to 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 program. The application program stored in the memory 301 can include one or more than one module 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 can 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 can 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 can understand that, Figure 2 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. A specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0146] The embodiment of the present application provides a storage medium, the storage medium stores computer readable instructions, and the computer readable instructions are executed by one or more processors, so that the one or more processors execute the method provided by any one of the embodiments.

[0147] Finally, it should be noted that in this document, relational terms such as first and second and the like can only be used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the terms "comprises", "comprising" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or apparatus that includes a list of elements does not only include those elements, but also includes other elements not explicitly listed or other elements inherent to such process, method, article or apparatus. Without more limitations, an element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article or apparatus that includes the element.

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

[0149] The foregoing description of the disclosed embodiments enables a person skilled in the art to make or use the application. Modifications of these embodiments will occur to persons of skill in the art, and that the generic principles defined herein can be applied to other embodiments without departing from the spirit or scope of the application. Therefore, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for equivalent modeling of photovoltaic power generation units, characterized in that, The method includes: Obtain the topology of the target photovoltaic power station and the device parameters and steady-state active power of each power generation unit; The topology includes the number, layout, and power collection line connection of the power generation units in the target photovoltaic power station; Based on the topology and the steady-state active power of each power generation unit, determine the number of clusters and the cluster boundary points; Based on the number of clusters and the cluster boundary points, perform clustering operations on each of the power generation units to generate multiple target isostats. Based on the topology 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. The step of determining the number of clusters and the cluster boundary point based on the topology and the steady-state active power of each power generation unit includes: Based on the topology and the steady-state active power of each power generation unit, the operating state of each power generation unit is determined; Based on the operating status of each power generation unit, determine the number of groups and the grouping boundary point of the steady-state active power; The operating states include high power output, medium power output, and low power output; the number of groups includes three-unit groups; and the grouping boundaries include 0.7 pu and 0.35 pu, where pu is a per-unit power value used to indicate the rated output power of the corresponding power generation unit. In addition, the equivalent parameters include: equivalent light intensity, inverter equivalent capacity, filter inductance, filter resistance, filter capacitor, transformer equivalent capacity, and equivalent impedance; The step of determining the equivalent parameters of each target equivalent machine based on the topology and the device parameters of each power generation unit includes: According to the first formula, the equivalent parameters of each target isomechanism are determined by the capacity weighting method; The first formula includes: ； Among them, G eq Let G be the light intensity of the target equivalent machine, m be the number of the power generation units in the current machine group, and G be the light intensity of the target equivalent machine. i S represents the light intensity of each power generation unit in the cluster; eq S represents the inverter capacity of the target equivalent machine. PV R represents the inverter capacity of a single power generation unit. f_eq R is the filter resistor of the target isomechanical unit. f L is the filter resistor for a single power generation unit. f_eq L is the filter inductance of the target equivalent machine. f C is the filter inductance for a single power generation unit. f_eq C is the filter capacitor of the target isostat; f S is the filter capacitor for a single power generation unit. T_eq S represents the transformer capacity of the target equivalent machine. T Z represents the transformer capacity of a single power generation unit. T_eq Z represents the transformer impedance of the target isostat. T The transformer impedance of a single power generation unit.

2. The method according to claim 1, characterized in that, The step of performing a grouping operation on each of the power generation units based on the number of groups and the grouping boundary point to generate multiple target isostats includes: The power generation units whose steady-state active power is in the range of (0.7pu, 1pu) are identified as high-power output units, and the high-power output units are grouped to generate the first equivalent machine. The power generation units whose steady-state active power is in the range of (0.35pu, 0.7pu) are identified as medium-power output units, and the medium-power output units are grouped to generate a second equivalent machine. The power generation units whose steady-state active power is in the range of [0pu, 0.35pu] are identified as low-power output units, and the low-power output units are grouped to generate a third equivalent machine.

3. The method according to claim 1, characterized in that, The method further includes: Determine real-time meteorological data; Based on the real-time meteorological data, the determined number of clusters and the cluster boundary point of the steady-state active power are optimized, and the optimized data are used as the number of clusters and the cluster boundary point.

4. The method according to claim 1, characterized in that, The methods for determining the equivalent collector line parameters include: Based on the aforementioned topology, the network loss is calculated. The equivalent collector line parameters are determined based on the network loss.

5. The method according to claim 1, characterized in that, The method further includes: At least one correction parameter is determined from the equivalent parameters; Based on the aforementioned topology, determine the target optimization algorithm; 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.

6. A photovoltaic power generation unit equivalent modeling device, characterized in that, The device includes: The acquisition module is used to acquire the topology of the target photovoltaic power station and the device parameters and steady-state active power of each power generation unit; The topology includes the number, layout, and power collection line connection of the power generation units in the target photovoltaic power station; The processing module is used to determine the number of clusters and the cluster boundary points based on the topology and the steady-state active power of each of the power generation units. The processing module is further configured to perform a grouping operation on each of the power generation units according to the number of groups and the grouping boundary point, and generate multiple target isostats. The processing module is further configured to determine the equivalent parameters and equivalent collector line parameters of each target equivalent machine based on the topology and the device parameters of each of the power generation units, and to establish a target equivalent model; The processing module determines the number of clusters and the specific method for determining the cluster boundary points based on the topology and the steady-state active power of each power generation unit, including: Based on the topology and the steady-state active power of each power generation unit, the operating state of each power generation unit is determined; Based on the operating status of each power generation unit, determine the number of groups and the grouping boundary point of the steady-state active power; Furthermore, the operating states include high power output, medium power output, and low power output; the number of groups includes three-unit groups; and the grouping boundaries include 0.7 pu and 0.35 pu, where pu is a per-unit power value used to indicate the rated output power of the corresponding power generation unit. In addition, the equivalent parameters include: equivalent light intensity, inverter equivalent capacity, filter inductance, filter resistance, filter capacitor, transformer equivalent capacity, and equivalent impedance; The processing module determines the specific method for the equivalent parameters of each target equivalent machine based on the topology and the device parameters of each power generation unit, including: According to the first formula, the equivalent parameters of each target isomechanism are determined by the capacity weighting method; The first formula includes: ； Among them, G eq Let G be the light intensity of the target equivalent machine, m be the number of the power generation units in the current machine group, and G be the light intensity of the target equivalent machine. i S represents the light intensity of each power generation unit in the cluster; eq S represents the inverter capacity of the target equivalent machine. PV R represents the inverter capacity of a single power generation unit. f_eq R is the filter resistor of the target isomechanical unit. f L is the filter resistor for a single power generation unit. f_eq L is the filter inductance of the target equivalent machine. f C is the filter inductance for a single power generation unit. f_eq C is the filter capacitor of the target isostat; f S is the filter capacitor for a single power generation unit. T_eq S represents the transformer capacity of the target equivalent machine. T Z represents the transformer capacity of a single power generation unit. T_eq Z represents the transformer impedance of the target isostat. T The transformer impedance of a single power generation unit.

7. A computer device, characterized in that, The method includes one or more processors and a memory storing computer-readable instructions that, when executed by the one or more processors, perform the steps of the method as described in any one of claims 1-5.

8. A storage medium, characterized in that, The storage medium stores computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the method as described in any one of claims 1-5.

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