Method and device for adjusting power oscillation in multi-synchronous-machine grid-connected wind power plant

Abstract

The invention provides a method and a device for adjusting power oscillation in a multi-synchronous-machine grid-connected wind power plant. The method comprises the following steps: determining risk parameters for calculating risk indexes; the risk index is used for representing the oscillation risk of the power in the multi-synchronous-machine grid-connected wind power plant, and normalized data is obtained after processing; calculating an influence factor of the risk index at the next moment based on the normalized data; the risk index at the next moment represents the risk index of power oscillation in the wind power plant which is connected to the grid through the multiple synchronous machines at the next moment; calculating a predicted value of the risk index at the next moment based on the influence factor; adjusting the output power in the wind power plant according to the predicted value; according to the power oscillation risk index of grid connection of the multiple synchronous units, when oscillation occurs in the wind power plant, prediction can be carried out in advance, the output power of the wind power plant can reach a stable state by controlling the machine-side converter, and the stability of the power in the wind power plant which is subjected to grid connection of the multiple synchronous machines is ensured.

Description

Technical Field

[0001] The present disclosure relates to the technical field of power systems, and in particular to a method and device for regulating power oscillation in a wind farm with multiple synchronous machines connected to a grid. Background Art

[0002] As the scale of wind power application continues to increase, the stability of the system frequency in wind farms will gradually decrease in places where large-scale wind power is concentrated, which may cause power oscillations in wind farms. At the same time, due to the volatility and uncertainty of wind energy, the output power of the generator sets in the wind farm may also fluctuate, resulting in unstable system frequency, which in turn causes power oscillations within the power system, affecting the safe and stable operation of the entire power system.

[0003] In the related technologies, the prediction and analysis methods of wind power oscillation are mainly based on the analysis method of transient large disturbance free oscillation response signal and the method based on random response signal under environmental excitation. Because these methods require detailed system models, wind power converter control structure and parameters, they are only applicable to simple wind farms. If they are applied to multiple synchronous machines running in parallel or large-scale wind power gathering stations, modeling difficulties and large amount of calculations will occur, which will easily lead to the calculation of "dimensionality disaster" problems, making it difficult to accurately predict and analyze the power oscillation risks in wind farms. Summary of the invention

[0004] The present disclosure provides a method and device for adjusting power oscillation in a wind farm with multiple synchronous machines connected to the grid, so as to at least solve the above technical problems existing in the prior art.

[0005] According to a first aspect of the present disclosure, a method for adjusting power oscillation in a wind farm with multiple synchronous machines connected to the grid is provided, the method comprising:

[0006] Determining a risk parameter for calculating a risk index; the risk index is used to characterize the risk of power oscillation in a wind farm with multiple synchronous machines connected to the grid;

[0007] Acquire risk data of the risk parameter and obtain normalized data after processing; the risk data is a time series of the risk parameter;

[0008] Calculating the impact factor of the risk index at the next moment based on the normalized data; the risk index at the next moment represents the risk index of power oscillation in the wind farm connected to the grid via multiple synchronous machines at the next moment;

[0009] Calculate the predicted value of the risk index at the next moment based on the influencing factors;

[0010] The output power in the wind power plant is adjusted according to the predicted value.

[0011] In one possible implementation, the risk parameters include:

[0012] The wind speed at the location of the wind farm, the output power of the wind turbines, the total active output of the wind farm, the total reactive output of the wind farm, and the frequency at the junction of multiple synchronous machines in the wind farm.

[0013] In one possible implementation manner, obtaining normalized data after the processing includes:

[0014] The risk data is normalized to obtain normalized data.

[0015] In one possible implementation, the risk data is normalized in the following manner:

[0016]

[0017] in, t m The normalized value of the wind speed at the location of the wind farm at the moment; t m The normalized value of the wind turbine output power at the moment; t m The time is the normalized value of the total active power output of the wind farm; t m The time is the normalized value of the total reactive power output of the wind farm; t m The moment is the normalized value of the frequency at the convergence point of multiple synchronous machines in the wind farm; V WG,max , V WG,min t 1 ,t 2 ,...,t m ,...,t n The maximum and minimum wind speeds at the wind farm at the time; P JZ,max , P JZ,min t 1 ,t 2 ,...,t m ,...,t n The maximum and minimum output power of the wind turbine group at the moment; P FDC,max , P FDC,min t 1 ,t 2 ,...,t m ,...,t n The maximum and minimum values ​​of the total active output of the wind farm at the time; Q FDC,max , Q FDC,min t 1 ,t 2 ,...,t m,...,t n The maximum and minimum values ​​of the total reactive power output of the wind farm at the moment; f HJ,max , f HJ,min t 1 ,t 2 ,...,t m ,...,t n The maximum and minimum frequencies at the junction of multiple synchronous machines in the wind farm at the moment.

[0018] In one possible implementation, the impact factor of the risk index at the next moment is calculated in the following manner:

[0019]

[0020] in, is the influence factor of the wind speed at the wind farm on the power oscillation risk index of the wind farm connected to the grid via multiple synchronous machines at the next moment; is the influence factor of the output power of the wind turbine on the power oscillation risk index of the wind farm connected to the grid via multiple synchronous machines at the next moment; is the influence factor of the total active power output of the wind farm on the power oscillation risk index of the wind farm connected to the grid via multiple synchronous machines at the next moment; is the influencing factor of the power oscillation risk index of the wind farm connected to the grid via multiple synchronous machines at the next moment based on the total reactive power output of the wind farm; It is the influence factor of the frequency at the gathering point of multiple synchronous machines in the wind farm on the power oscillation risk index in the wind farm connected to the grid via multiple synchronous machines at the next moment.

[0021] In one possible implementation,

[0022]

[0023] In one possible implementation manner, adjusting the output power in the wind power plant according to the predicted value includes:

[0024] comparing the predicted value with a preset threshold;

[0025] If the predicted value is less than the preset threshold, the output power of the machine-side converter in the wind farm is maintained; otherwise, the output power of the machine-side converter in the wind farm at the next moment is reduced.

[0026] In one possible implementation, the risk index is calculated in the following manner:

[0027]

[0028] Among them, t 1 ,t 2 ,...,t m ,...,t nis a fixed time interval of n moments, where n is a natural number, n∈1,2,..., m is the mth moment, and m is a natural number, m∈{1,2,...,n}; t m The wind speed at the wind farm at the moment; V WG,N It is the rated wind speed at the location where the wind farm is operating stably; t m The output power of the wind turbine at the moment; P JZ,N The rated output power of the wind turbines when the wind farm is operating stably; t m The total active power output of the wind farm at the moment; P FDC,N It is the total active output rating of the wind farm when the wind farm is operating stably; t m The total reactive power output of the wind farm at any given moment; Q FDC,N It is the total reactive power output rating of the wind farm when the wind farm is operating stably; t m The frequency at the point where multiple synchronous machines in the wind farm converge at the moment; f HJ,N V is the rated frequency of the wind farm's multi-synchronous machines when the wind farm is running stably; WG,max , V WG,min t 1 ,t 2 ,...,t m ,...,t n The maximum and minimum wind speeds at the wind farm at the time; P JZ,max , P JZ,min t 1 ,t 2 ,...,t m ,...,t n The maximum and minimum output power of the wind turbine group at the moment; P FDC,max , P FDC,min t 1 ,t 2 ,...,t m ,...,t n The maximum and minimum values ​​of the total active output of the wind farm at the time; Q FDC,max , Q FDC,min t 1 ,t 2 ,...,t m ,...,t n The maximum and minimum values ​​of the total reactive power output of the wind farm at the moment; f HJ,max , f HJ,min t 1 ,t 2 ,...,tm ,...,t n The maximum and minimum frequencies at the junction of multiple synchronous machines in the wind farm at the moment.

[0029] According to a second aspect of the present disclosure, a power oscillation adjustment device in a wind farm with multiple synchronous machines connected to the grid is provided, the device comprising:

[0030] A determination module, used to determine a risk parameter for calculating a risk index; the risk index is used to characterize the power oscillation risk in a wind farm with multiple synchronous machines connected to the grid;

[0031] An acquisition module, used to acquire risk data of the risk parameter and obtain normalized data after processing; the risk data is a time series of the risk parameter;

[0032] A first calculation module is used to calculate the impact factor of the risk index at the next moment based on the normalized data; the risk index at the next moment represents the risk index of power oscillation in the wind farm connected to the grid via multiple synchronous machines at the next moment;

[0033] A second calculation module, used to calculate a predicted value of the risk index at the next moment based on the influencing factor;

[0034] The adjustment module is used to adjust the output power in the wind power plant according to the predicted value.

[0035] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[0036] at least one processor; and

[0037] a memory communicatively connected to the at least one processor; wherein,

[0038] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the present disclosure.

[0039] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute the method described in the present disclosure.

[0040] The disclosed method and device for adjusting power oscillation in a wind farm connected to the grid by multiple synchronous machines can predict the power oscillation risk in a wind farm connected to the grid by multiple synchronous machines at the next moment through historical data, and adjust the operation of the machine-side converter control system in the wind farm at the next moment according to the prediction result. This is helpful for the safe operation of wind turbines and power equipment, improves the operational stability of the power system, and promotes the coordinated development of wind farms and power grids.

[0041] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The above and other objects, features and advantages of the exemplary embodiments of the present disclosure will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present disclosure are shown in an exemplary and non-limiting manner, in which:

[0043] In the drawings, the same or corresponding reference numerals represent the same or corresponding parts.

[0044] Figure 1 A schematic diagram of the implementation process of the method for adjusting power oscillation in a wind farm with multiple synchronous machines connected to the grid according to an embodiment of the present disclosure is shown;

[0045] Figure 2 A schematic diagram of the structure of a power oscillation adjustment device in a wind farm with multiple synchronous machines connected to the grid according to an embodiment of the present disclosure is shown;

[0046] Figure 3 A schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0047] In order to make the purpose, features, and advantages of the present disclosure more obvious and easy to understand, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present disclosure.

[0048] A method and device for adjusting power oscillations in a wind farm with multiple synchronous machines connected to the grid will be described below in conjunction with the accompanying drawings.

[0049] like Figure 1 As shown, the present application provides a method for adjusting power oscillation in a wind farm with multiple synchronous machines connected to the grid, the method comprising:

[0050] S101, determining a risk parameter for calculating a risk index; the risk index is used to characterize the power oscillation risk in a wind farm with multiple synchronous machines connected to the grid;

[0051] It is understandable that the present application first defines the power oscillation risk index in a wind farm connected to the grid by multiple synchronous machines. The risk index is calculated in the following way:

[0052]

[0053] Among them, t 1 ,t 2 ,...,t m ,...,t n is a moment at a fixed time interval of n, for example, a time interval of t = 1s, where n is a natural number, which can be n = 225, n∈1,2,..., m is the mth moment, and m is a natural number, m∈{1,2,...,n}; t m The wind speed at the wind farm at the moment; V WG,N It is the rated wind speed at the location where the wind farm is operating stably; t m The output power of the wind turbine at the moment; P JZ,N The rated output power of the wind turbines when the wind farm is operating stably; t m The total active power output of the wind farm at the moment; P FDC,N It is the total active output rating of the wind farm when the wind farm is operating stably; t m The total reactive power output of the wind farm at any given moment; Q FDC,N It is the total reactive power output rating of the wind farm when the wind farm is operating stably; t m The frequency at the point where multiple synchronous machines in the wind farm converge at the moment; f HJ,N V is the rated frequency of the wind farm's multi-synchronous machines when the wind farm is running stably; WG,max , V WG,min t 1 ,t 2 ,...,t m ,...,t n The maximum and minimum wind speeds at the wind farm at the time; P JZ,max , P JZ,min t 1 ,t 2 ,...,t m ,...,t n The maximum and minimum output power of the wind turbine group at the moment; P FDC,max , P FDC,min t 1 ,t 2 ,...,t m ,...,t n The maximum and minimum values ​​of the total active output of the wind farm at the time; Q FDC,max , Q FDC,min t 1 ,t 2 ,...,t m,...,t n The maximum and minimum values ​​of the total reactive power output of the wind farm at the moment; f HJ,max , f HJ,min t 1 ,t 2 ,...,t m ,...,t n The maximum and minimum frequencies at the junction of multiple synchronous machines in the wind farm at the moment.

[0054] According to formula (1), the risk parameters of the risk index include the wind speed at the location of the wind farm, the output power of the wind turbines, the total active output of the wind farm, the total reactive output of the wind farm, and the frequency at the gathering point of multiple synchronous machines in the wind farm.

[0055] S102, acquiring risk data of the risk parameter, and obtaining normalized data after processing; the risk data is a time series of the risk parameter;

[0056] Specifically, according to the risk parameters of the power oscillation risk index in the wind farm connected to the grid by multiple synchronous machines, the historical data of the risk parameters are measured, and the time series of the parameters related to the power oscillation risk index in the wind farm connected to the grid by multiple synchronous machines is established.

[0057]

[0058] Then, the risk data of the risk parameter of the power oscillation risk index in the wind farm connected to the grid with multiple synchronous machines is normalized to obtain

[0059]

[0060] in, t m The normalized value of the wind speed at the location of the wind farm at the moment; t m The normalized value of the wind turbine output power at the moment; t m The time is the normalized value of the total active power output of the wind farm; t m The time is the normalized value of the total reactive power output of the wind farm; t m The moment is the normalized value of the frequency at the convergence point of multiple synchronous machines in the wind farm; V WG,max , V WG,min t 1 ,t 2 ,...,t m ,...,t n The maximum and minimum wind speeds at the wind farm at the time; P JZ,max , PJZ,min t 1 ,t 2 ,...,t m ,...,t n The maximum and minimum output power of the wind turbine group at the moment; P FDC,max , P FDC,min t 1 ,t 2 ,...,t m ,...,t n The maximum and minimum values ​​of the total active output of the wind farm at the time; Q FDC,max , Q FDC,min t 1 ,t 2 ,...,t m ,...,t n The maximum and minimum values ​​of the total reactive power output of the wind farm at the moment; f HJ,max , f HJ,min t 1 ,t 2 ,...,t m ,...,t n The maximum and minimum frequencies at the junction of multiple synchronous machines in the wind farm at the moment.

[0061] S103, calculating an impact factor of a risk index at a next moment based on the normalized data; the risk index at a next moment represents a risk index of power oscillation in a wind farm connected to the grid via multiple synchronous machines at a next moment;

[0062] Specifically, the influence factor of the power oscillation risk index in the wind farm connected to the grid by multiple synchronous machines at the next moment is calculated by using the obtained normalized data.

[0063]

[0064] in, is the influence factor of the wind speed at the wind farm on the power oscillation risk index of the wind farm connected to the grid via multiple synchronous machines at the next moment; is the influence factor of the output power of the wind turbine on the power oscillation risk index of the wind farm connected to the grid via multiple synchronous machines at the next moment; is the influence factor of the total active power output of the wind farm on the power oscillation risk index of the wind farm connected to the grid via multiple synchronous machines at the next moment; is the influencing factor of the power oscillation risk index of the wind farm connected to the grid via multiple synchronous machines at the next moment based on the total reactive power output of the wind farm; It is the influence factor of the frequency at the gathering point of multiple synchronous machines in the wind farm on the power oscillation risk index in the wind farm connected to the grid via multiple synchronous machines at the next moment.

[0065] S104, calculating a predicted value of the risk index at the next moment based on the influencing factor;

[0066] According to the calculated impact factor, the predicted value of the power oscillation risk index in the wind farm connected to the grid by multiple synchronous machines at the next moment can be calculated.

[0067]

[0068] S105: Adjust the output power in the wind power plant according to the predicted value.

[0069] In some embodiments, adjusting the output power in the wind power plant according to the predicted value includes:

[0070] comparing the predicted value with a preset threshold;

[0071] If the predicted value is less than the preset threshold, the output power of the machine-side converter in the wind farm is maintained; otherwise, the output power of the machine-side converter in the wind farm at the next moment is reduced.

[0072] Specifically, the present application adjusts the output power of the machine-side converter in the wind farm according to the predicted value of the power oscillation risk index in the wind farm connected to the grid by multiple synchronous machines at the next moment. The specific adjustment method is as follows: if the predicted value δ ZDGL-next If the result is less than the preset threshold value of 0.582, it is considered that the power oscillation risk in the wind farm connected to the grid by multiple synchronous machines at the next moment is small, and it is only necessary to maintain the normal output power of the machine-side converter in the wind farm at the next moment; if the predicted value δ ZDGL-next If the result is greater than or equal to the preset threshold value of 0.582, it is considered that the risk of power oscillation in the wind farm connected to the grid via multiple synchronous machines at the next moment is relatively high, which may endanger the safety of wind turbines and station equipment. Therefore, it is necessary to reduce the output power of the machine-side converter in the wind farm at the next moment to suppress the power oscillation in the wind farm.

[0073] The power oscillation adjustment method for a wind farm with multiple synchronous machines connected to the grid provided by the present application can predict the power oscillation risk of a wind farm with multiple synchronous machines connected to the grid at the next moment based on the risk parameter of the calculated risk index and then through the historical data of the risk parameter, and adjust the operation of the machine-side converter control system in the wind farm at the next moment according to the prediction result. The present application is conducive to the safe operation of wind turbines and power equipment, improves the operational stability of the power system, and promotes the coordinated development of wind farms and power grids.

[0074] like Figure 2 As shown, an embodiment of the present application provides a power oscillation adjustment device in a wind farm with multiple synchronous machines connected to the grid, the device comprising:

[0075] A determination module 201 is used to determine a risk parameter for calculating a risk index; the risk index is used to characterize the power oscillation risk in a wind farm with multiple synchronous machines connected to the grid;

[0076] An acquisition module 202 is used to acquire risk data of the risk parameter and obtain normalized data after processing; the risk data is a time series of the risk parameter;

[0077] A first calculation module 203 is used to calculate the impact factor of the risk index at the next moment based on the normalized data; the risk index at the next moment represents the risk index of power oscillation in the wind farm connected to the grid via multiple synchronous machines at the next moment;

[0078] A second calculation module 204, configured to calculate a predicted value of the risk index at the next moment based on the influencing factor;

[0079] The adjustment module 205 is used to adjust the output power in the wind power plant according to the predicted value.

[0080] The power oscillation adjustment device for a wind farm with multiple synchronous machines connected to the grid provided in the present application comprises a determination module 201 which determines a risk parameter for calculating a risk index; the risk index is used to characterize the power oscillation risk in a wind farm with multiple synchronous machines connected to the grid; an acquisition module 202 acquires risk data of the risk parameter and obtains normalized data after processing; the risk data is a time series of the risk parameter; a first calculation module 203 calculates an influencing factor of the risk index at the next moment based on the normalized data; the risk index at the next moment characterizes the risk index of power oscillation in a wind farm with multiple synchronous machines connected to the grid at the next moment; a second calculation module 204 calculates a predicted value of the risk index at the next moment based on the influencing factor; and an adjustment module 205 adjusts the output power in the wind farm according to the predicted value.

[0081] An embodiment of the present application provides an electronic device, including:

[0082] at least one processor; and

[0083] a memory communicatively connected to the at least one processor; wherein,

[0084] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the present disclosure.

[0085] Figure 3A schematic block diagram of an example electronic device 300 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0086] like Figure 3 As shown, the device 300 includes a computing unit 301, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 302 or a computer program loaded from a storage unit 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the device 300 can also be stored. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0087] A number of components in the device 300 are connected to the I / O interface 305, including: an input unit 306, such as a keyboard, a mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a disk, an optical disk, etc.; and a communication unit 309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 309 allows the device 300 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0088] The computing unit 301 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 301 performs the various methods and processes described above, such as a power oscillation adjustment method in a wind farm with multiple synchronous machines connected to the grid. For example, in some embodiments, the power oscillation adjustment method in a wind farm with multiple synchronous machines connected to the grid may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 300 via the ROM 302 and / or the communication unit 309. When the computer program is loaded into the RAM 303 and executed by the computing unit 301, one or more steps of the power oscillation adjustment method in a wind farm with multiple synchronous machines connected to the grid described above may be performed. Alternatively, in other embodiments, the calculation unit 301 may be configured to execute the power oscillation adjustment method in a wind farm with multiple synchronous machines connected to the grid in any other appropriate manner (for example, by means of firmware).

[0089] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0090] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0091] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0092] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0093] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0094] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0095] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0096] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present disclosure, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0097] The above is only a specific embodiment of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present disclosure, which should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A method for adjusting power oscillation in a wind farm with multiple synchronous machines connected to the grid, characterized in that: The method comprises: Determining a risk parameter for calculating a risk index; the risk index is used to characterize the risk of power oscillation in a wind farm with multiple synchronous machines connected to the grid; Acquire risk data of the risk parameter and obtain normalized data after processing; the risk data is a time series of the risk parameter; Calculating the impact factor of the risk index at the next moment based on the normalized data; the risk index at the next moment represents the risk index of power oscillation in the wind farm connected to the grid via multiple synchronous machines at the next moment; Calculate the predicted value of the risk index at the next moment based on the influencing factors; The output power in the wind power plant is adjusted according to the predicted value.

2. The method according to claim 1, characterized in that The risk parameters include: The wind speed at the location of the wind farm, the output power of the wind turbines, the total active output of the wind farm, the total reactive output of the wind farm, and the frequency at the junction of multiple synchronous machines in the wind farm.

3. The method according to claim 1, characterized in that: The normalized data obtained after the processing includes: The risk data is normalized to obtain normalized data.

4. The method according to claim 3, characterized in that The risk data is normalized in the following manner: in t m The normalized value of the wind speed at the location of the wind farm at the moment; t m The normalized value of the wind turbine output power at the moment; t m The time is the normalized value of the total active power output of the wind farm; t m The time is the normalized value of the total reactive power output of the wind farm; t m The moment is the normalized value of the frequency at the convergence point of multiple synchronous machines in the wind farm; V WG,max , V WG,min are t1, t2, ..., t m ,...,t n The maximum and minimum wind speeds at the wind farm at the time; P JZ,max , P JZ,min are t1, t2, ..., t m ,...,t n The maximum and minimum output power of the wind turbine group at the moment; P FDC,max , P FDC,min are t1, t2, ..., t m ,...,t n The maximum and minimum values ​​of the total active output of the wind farm at the time; Q FDC,max , Q FDC,min are t1, t2, ..., t m ,...,t n The maximum and minimum values ​​of the total reactive power output of the wind farm at the moment; f HJ,max , f HJ,min are t1, t2, ..., t m ,...,t n The maximum and minimum frequencies at the junction of multiple synchronous machines in the wind farm at the moment.

5. The method according to claim 1, characterized in that The impact factor of the risk index at the next moment is calculated in the following way: in, is the influence factor of the wind speed at the wind farm on the power oscillation risk index of the wind farm connected to the grid via multiple synchronous machines at the next moment; is the influence factor of the output power of the wind turbine on the power oscillation risk index of the wind farm connected to the grid via multiple synchronous machines at the next moment; is the influence factor of the total active power output of the wind farm on the power oscillation risk index of the wind farm connected to the grid via multiple synchronous machines at the next moment; is the influencing factor of the power oscillation risk index of the wind farm connected to the grid via multiple synchronous machines at the next moment based on the total reactive power output of the wind farm; It is the influence factor of the frequency at the gathering point of multiple synchronous machines in the wind farm on the power oscillation risk index in the wind farm connected to the grid via multiple synchronous machines at the next moment.

6. The method according to claim 1, characterized in that The predicted value of the risk index at the next moment is calculated in the following way:

7. The method according to claim 1, characterized in that The step of adjusting the output power in the wind power plant according to the predicted value comprises: comparing the predicted value with a preset threshold; If the predicted value is less than the preset threshold, the output power of the machine-side converter in the wind farm is maintained; otherwise, the output power of the machine-side converter in the wind farm at the next moment is reduced.

8. The method according to claim 1, characterized in that The risk index is calculated in the following way: Among them, t1, t2, ..., t m ,...,t n is a fixed time interval of n moments, where n is a natural number, n∈1,2,..., m is the mth moment, and m is a natural number, m∈{1,2,...,n}; t m The wind speed at the wind farm at the moment; V WG,N It is the rated wind speed at the location where the wind farm is operating stably; t m The output power of the wind turbine at the moment; P JZ,N The rated output power of the wind turbines when the wind farm is operating stably; t m The total active power output of the wind farm at the moment; P FDC,N It is the total active output rating of the wind farm when the wind farm is operating stably; t m The total reactive power output of the wind farm at any given moment; Q FDC,N It is the total reactive power output rating of the wind farm when the wind farm is operating stably; t m The frequency at the point where multiple synchronous machines in the wind farm converge at the moment; f HJ,N V is the rated frequency of the wind farm's multi-synchronous machines when the wind farm is running stably; WG,max , V WG,min are t1, t2, ..., t m ,...,t n The maximum and minimum wind speeds at the wind farm at the time; P JZ,max , P JZ,min are t1, t2, ..., t m ,...,t n The maximum and minimum output power of the wind turbine group at the moment; P FDC,max , P FDC,min are t1, t2, ..., t m ,...,t n The maximum and minimum values ​​of the total active output of the wind farm at the time; Q FDC,max , Q FDC,min are t1, t2, ..., t m ,...,t n The maximum and minimum values ​​of the total reactive power output of the wind farm at the moment; f HJ,max , f HJ,min are t1, t2, ..., t m ,...,t n The maximum and minimum frequencies at the junction of multiple synchronous machines in the wind farm at the moment.

9. A power oscillation adjustment device in a wind farm with multiple synchronous machines connected to the grid, characterized in that: The device comprises: A determination module, used to determine a risk parameter for calculating a risk index; the risk index is used to characterize the power oscillation risk in a wind farm with multiple synchronous machines connected to the grid; An acquisition module, used to acquire risk data of the risk parameter and obtain normalized data after processing; the risk data is a time series of the risk parameter; A first calculation module is used to calculate the impact factor of the risk index at the next moment based on the normalized data; the risk index at the next moment represents the risk index of power oscillation in the wind farm connected to the grid via multiple synchronous machines at the next moment; A second calculation module, used to calculate a predicted value of the risk index at the next moment based on the influencing factor; The adjustment module is used to adjust the output power in the wind power plant according to the predicted value.

10. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 8.

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