Semi-direct drive wind farm equivalent modeling method and device based on real-time data

Abstract

The present application proposes a semi-direct drive wind farm equivalent modeling method based on real-time data, which relates to the technical field of wind farm modeling in power systems. Among them, the method includes: collecting wind farm data; equivalenting the wind farm to a controlled current source, where the parameters of the controlled current source include amplitude and phase; and optimizing the parameters of the controlled current source. The present application adopting the above solution can efficiently and quickly obtain the equivalent model of the semi-direct drive wind farm, and the obtained results provide an important basis for the impact analysis of the wind farm connected to the power grid.

Description

Technical Field

[0001] The present application relates to the technical field of wind farm modeling in power systems, and particularly to a semi-direct drive wind farm equivalent modeling method and device based on real-time data. Background Art

[0002] With the successive completion of ten-million-kilowatt-level wind power bases, the large-scale centralized grid connection of wind turbines brings huge challenges to the safe and stable operation of power systems. Constructing an equivalent model that can accurately describe the overall characteristics of large-scale wind farms is the basis for studying the operation and control of high-proportion wind power systems, and the equivalence of detailed wind farm models is an important part of wind farm dynamic equivalence. The dynamic equivalent modeling of wind farms has become an important research means for analyzing the grid connection characteristics of large-scale wind farms. At present, the characteristics of single semi-direct drive wind turbines have been widely studied, but there is little introduction to the overall characteristics of wind farms. Due to the huge differences in the operation of wind turbines, multi-machine equivalent models can better reflect the dynamic characteristics of wind farms. Summary of the Invention

[0003] The present application aims to solve at least one of the technical problems in the related art to some extent.

[0004] To this end, the first object of the present application is to propose a semi-direct drive wind farm equivalent modeling method based on real-time data, which solves the technical problem that the existing methods have widely studied the characteristics of single semi-direct drive wind turbines but less studied the overall characteristics of wind farms, and can efficiently and accurately obtain the dynamic characteristics of wind power systems, providing an important basis for analyzing the impact of large-scale wind turbines connected to the grid.

[0005] The second object of the present application is to propose a semi-direct drive wind farm equivalent modeling device based on real-time data.

[0006] The third object of the present application is to propose a non-transitory computer-readable storage medium.

[0007] To achieve the above object, the first aspect embodiment of the present application proposes a semi-direct drive wind farm equivalent modeling method based on real-time data, including: collecting wind farm data; equivalenting the wind farm to a controlled current source, where the parameters of the controlled current source include amplitude and phase; and optimizing the parameters of the controlled current source.

[0008] Optionally, in an embodiment of the present application, the amplitude of the controlled current source is expressed as:

[0009]

[0010] where ρ represents air density, v represents wind speed, S represents the flow area of wind on the fan blades, C p represents the wind energy utilization coefficient of the fan, Um represents the amplitude of the grid connection point voltage of the wind turbine, p is a parameter, p = 0, … ∞, q represents the sampling sequence, t represents the starting moment, represents the phase.

[0011] Optionally, in an embodiment of the present application, the phase of the controlled current source is expressed as:

[0012]

[0013] where ρ represents the air density, v represents the wind speed, S represents the flow area of the wind on the wind turbine blades, C p represents the wind energy utilization coefficient of the wind turbine, U m represents the amplitude of the grid connection point voltage of the wind turbine, p is a parameter, p = 0,... ∞, q represents the sampling sequence, t represents the starting moment, represents the phase.

[0014] Optionally, in an embodiment of the present application, parameter optimization is performed on the controlled current source, specifically, the amplitude and phase of the controlled current source are optimized, expressed as:

[0015]

[0016] where I con_OPT represents the amplitude of the optimized controlled current source, I con represents the amplitude of the controlled current source, represents the phase of the optimized controlled current source, represents the phase of the controlled current source, I pp represents the amplitude of the current at the wind turbine collection line, represents the phase of the current at the wind turbine collection line,

[0017]

[0018]

[0019] where η m_con is the amplitude optimization coefficient, η φ_con is the phase optimization coefficient.

[0020] To achieve the above object, an embodiment of the second aspect of the present application proposes a semi-direct drive wind farm equivalent modeling device based on real-time data, including: a collection module, an equivalent module, and an optimization calculation module, where,

[0021] The collection module is used to collect wind farm data;

[0022] The equivalent module is used to equivalent the wind farm to a controlled current source, where the parameters of the controlled current source include amplitude and phase;

[0023] An optimization calculation module for optimizing the parameters of a controlled current source.

[0024] To achieve the above object, a third aspect embodiment of the present application proposes a non - transitory computer - readable storage medium, when the instructions in the storage medium are executed by a processor, it can execute a semi - direct - drive wind farm equivalent modeling method based on real - time data.

[0025] The semi - direct - drive wind farm equivalent modeling method based on real - time data, the semi - direct - drive wind farm equivalent modeling device based on real - time data, and the non - transitory computer - readable storage medium in the embodiments of the present application solve the technical problem that existing methods have studied the characteristics of a single semi - direct - drive wind turbine very extensively, but have studied the overall characteristics of the wind farm less. They can efficiently and accurately obtain the dynamic characteristics of the wind power system, providing an important basis for the impact analysis of large - scale wind turbines connected to the power grid.

[0026] The additional aspects and advantages of the present application will be partly given in the following description, partly will become obvious from the following description, or will be understood through the practice of the present application. Description of the Drawings

[0027] The above - mentioned and / or additional aspects and advantages of the present application will become obvious and easy to understand from the following description of the embodiments in conjunction with the drawings, where:

[0028] Figure 1 is a flowchart of a semi - direct - drive wind farm equivalent modeling method based on real - time data provided by Embodiment 1 of the present application;

[0029] Figure 2 is a structural schematic diagram of a semi - direct - drive wind farm equivalent modeling device based on real - time data provided by Embodiment 2 of the present application. Detailed Embodiments

[0030] The embodiments of the present application will be described in detail below. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals denote the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below with reference to the drawings are exemplary and are intended to explain the present application, and should not be construed as a limitation of the present application.

[0031] The semi - direct - drive wind farm equivalent modeling method and device based on real - time data in the embodiments of the present application will be described below with reference to the drawings.

[0032] Figure 1 is a flowchart of a semi - direct - drive wind farm equivalent modeling method based on real - time data provided by Embodiment 1 of the present application.

[0033] As Figure 1 shown, the semi - direct - drive wind farm equivalent modeling method based on real - time data includes the following steps:

[0034] Step 101, collect wind farm data;

[0035] Step 102, equivalent the wind farm to a controlled current source, where the parameters of the controlled current source include amplitude and phase;

[0036] Step 103, optimize the parameters of the controlled current source.

[0037] The equivalent modeling method of the semi-direct-drive wind farm based on real-time data in the embodiment of the present application collects wind farm data; equivalent the wind farm to a controlled current source, where the parameters of the controlled current source include amplitude and phase; optimize the parameters of the controlled current source. Thus, it can solve the technical problem that the existing methods have studied the characteristics of a single semi-direct-drive wind turbine very extensively, but have studied the overall characteristics of the wind farm less, and can efficiently and accurately obtain the dynamic characteristics of the wind power system, providing an important basis for the impact analysis of large-scale wind turbines connected to the power grid.

[0038] Collect the wind speed data of k wind turbines in the wind farm at the moment of t+q△T.

[0039] v = [v 1 (t+qΔT), v 2 (t+qΔT),......v k (t+qΔT)]

[0040] Where, v represents the wind speed, △T represents the sampling interval, q represents the sampling sequence. t represents the starting moment.

[0041] Collect the power factor angles of k wind turbines as:

[0042]

[0043] Furthermore, in the embodiment of the present application, the wind farm is equivalent to a controlled current source, and the amplitude of the controlled current source is expressed as:

[0044]

[0045] Where, ρ represents the air density, v represents the wind speed, S represents the flow area of the wind on the fan blades, C p represents the wind energy utilization coefficient of the fan, U m represents the amplitude of the fan grid connection point voltage, p is a parameter, p = 0,...∞, q represents the sampling sequence, t represents the starting moment, represents the phase.

[0046] Furthermore, in the embodiment of the present application, the phase of the controlled current source is expressed as:

[0047]

[0048] Among them, ρ represents the air density, v represents the wind speed, S represents the flow area of the wind on the fan blades, and C p represents the wind energy utilization coefficient of the fan, and U m represents the amplitude of the grid connection point voltage of the fan, p is a parameter, p = 0,...∞, q represents the sampling sequence, and t represents the starting moment, represents the phase.

[0049] Furthermore, in the embodiment of the present application, parameter optimization is performed on the controlled current source, specifically, the amplitude and phase of the controlled current source are optimized, which is expressed as:

[0050]

[0051] Among them, I con_OPT represents the amplitude of the optimized controlled current source, and I con represents the amplitude of the controlled current source, represents the phase of the optimized controlled current source, represents the phase of the controlled current source, and I pp represents the amplitude of the current at the fan collection line, represents the phase of the current at the fan collection line,

[0052]

[0053] Among them, η m_con is the amplitude optimization coefficient, and η φ_con is the phase optimization coefficient.

[0054] Figure 2 This is the structural schematic diagram of a semi-direct drive wind farm equivalent modeling device based on real-time data provided by the second embodiment of the present application.

[0055] As Figure 2 shown, the semi-direct drive wind farm equivalent modeling device based on real-time data includes: a collection module, an equivalent module, and an optimization calculation module. Among them,

[0056] The collection module 10 is used to collect wind farm data;

[0057] The equivalent module 20 is used to equivalent the wind farm to a controlled current source. Among them, the parameters of the controlled current source include amplitude and phase;

[0058] The optimization calculation module 30 is used to perform parameter optimization on the controlled current source.

[0059] The equivalent modeling device for a semi-direct drive wind farm based on real-time data according to the embodiments of the present application includes: a collection module, an equivalent module, and an optimization calculation module. Among them, the collection module is used to collect wind farm data; the equivalent module is used to equivalent the wind farm to a controlled current source, where the parameters of the controlled current source include amplitude and phase; the optimization calculation module is used to optimize the parameters of the controlled current source. Thus, it can solve the technical problem that the existing methods have a very extensive research on the characteristics of a single semi-direct drive wind turbine, but less research on the overall characteristics of the wind farm, and can efficiently and accurately obtain the dynamic characteristics of the wind power system, providing an important basis for the impact analysis of large-scale wind turbines connected to the power grid.

[0060] To implement the above embodiments, the present application also proposes a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the equivalent modeling method for a semi-direct drive wind farm based on real-time data in the above embodiments.

[0061] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0062] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" can explicitly or implicitly include at least one of such features. In the description of the present application, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0063] Any process or method description shown in the flowchart or described in other ways herein can be understood as representing a module, segment, or part of code including one or more executable instructions for implementing a customized logic function or process. The scope of the preferred embodiments of the present application includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in a reverse order according to the involved functions, rather than in the order shown or discussed, which should be understood by those skilled in the art of the embodiments of the present application.

[0064] The logic and / or steps represented in the flowchart or otherwise described herein can, for example, be considered a definitional sequence list of executable instructions for implementing logical functions, and can be embodied specifically in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device. As used in this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection portion having one or more wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable medium on which the program can be printed, as the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpretation, or otherwise processing as appropriate, and then storing it in a computer memory.

[0065] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), and the like.

[0066] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the method of implementing the above embodiments can be completed by a program instructing relevant hardware, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0067] In addition, each functional unit in various embodiments of the present application may be integrated into one processing module, or each unit may exist physically alone, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0068] The above-mentioned storage medium may be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A semi-direct drive wind farm equivalent modeling method based on real-time data, characterized in that, it includes the following steps: Collect wind farm data; Equivalent the wind farm to a controlled current source, and the parameters of the controlled current source include amplitude and phase, where: The amplitude of the controlled current source is expressed as: where ρ represents the air density, v represents the wind speed, S represents the flow area of the wind through the fan blades, C p represents the wind energy utilization coefficient of the fan, U m represents the amplitude of the grid connection point voltage of the fan, p is a parameter, p = 0, … ∞, q represents the sampling sequence, t represents the starting time, represents the phase; The phase of the controlled current source is expressed as: where ρ represents the air density, v represents the wind speed, S represents the flow area of the wind through the fan blades, C p represents the wind energy utilization coefficient of the fan, U m represents the amplitude of the grid connection point voltage of the fan, p is a parameter, p = 0,...∞, q represents the sampling sequence, t represents the starting time, represents the phase; Optimize the parameters of the controlled current source, specifically optimize the amplitude and phase of the controlled current source, expressed as: Among them, I con_OPT represents the magnitude of the optimized controlled current source, I con represents the magnitude of the controlled current source, represents the phase of the optimized controlled current source, represents the phase of the controlled current source, I pp represents the current magnitude at the fan collector line, represents the current phase at the fan collector line, Among them, η m_con is the amplitude optimization coefficient, and η φ_con is the phase optimization coefficient.

2. A semi-direct drive wind farm equivalent modeling device based on real-time data, characterized in that, it includes a collection module, an equivalent module, and an optimization calculation module, where, The collection module is used to collect wind farm data; The equivalent module is used to equivalent the wind farm to a controlled current source, and the parameters of the controlled current source include amplitude and phase, where: The amplitude of the controlled current source is expressed as: Among them, ρ represents the air density, v represents the wind speed, S represents the flow area of the wind through the fan blades, and C p represents the wind energy utilization coefficient of the fan, U m represents the amplitude of the grid connection point voltage of the fan, p is a parameter, p = 0,...∞, q represents the sampling sequence, and t represents the starting time, represents the phase; The phase of the controlled current source is expressed as: where ρ represents the air density, v represents the wind speed, S represents the flow area of the wind through the fan blades, C p represents the wind energy utilization coefficient of the fan, U m represents the amplitude of the grid connection point voltage of the fan, p is a parameter, p = 0,...∞, q represents the sampling sequence, t represents the starting time, represents the phase; The optimization calculation module is used to optimize the parameters of the controlled current source, specifically optimize the amplitude and phase of the controlled current source, expressed as: Among them, I con_OPT represents the amplitude of the optimized controlled current source, I con represents the amplitude of the controlled current source, represents the phase of the optimized controlled current source, represents the phase of the controlled current source, I pp represents the current amplitude at the fan collector line, represents the current phase at the fan collector line, Among them, η m_con is the amplitude optimization coefficient, and η φ_con is the phase optimization coefficient.

3. A non-transitory computer-readable storage medium, on which a computer program is stored, characterized in that, when the computer program is executed by a processor, it implements the method according to claim 1.

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