Method and device for determining regional wind power generation major weather process and medium

Abstract

The invention discloses a method and device for determining a regional wind power generation major weather process and a medium. The method comprises the following steps: collecting real-time wind speed data and installed capacity data of each wind power plant in a to-be-detected area; according to the real-time wind speed data and the installed capacity data, calculating a regional equivalent wind speed of the to-be-measured region; performing statistical analysis on the regional equivalent wind speed of the to-be-measured region in a preset historical period, and determining a low output threshold value and a high output threshold value of the to-be-measured region; and judging whether the condition that the forecast equivalent wind speed data sequence is lower than a low output threshold value or higher than a high output threshold value occurs in each time period of the to-be-measured area in the forecast time or not, and determining a major weather process early warning time period of the to-be-measured area in the forecast time according to the judgment result of each moment in the forecast time.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and more specifically, to a method, device and medium for determining a significant weather process for regional wind power generation. Background Art

[0002] As the global demand for renewable energy continues to increase, wind power generation, as a clean and sustainable form of energy, has been widely used and developed rapidly. However, the power generation of wind farms is directly affected by wind speed changes and has strong uncertainty and volatility. In a large-scale wind farm group, due to the distribution of geographical locations and the influence of weather systems, wind farms in different regions may be subject to different degrees of wind speed changes. This spatial and temporal inhomogeneity of wind speed poses a huge challenge to power dispatching.

[0003] At present, in the process of power dispatching, historical experience or simple numerical models are usually relied on to predict the output power of wind farms. However, these methods often ignore the interrelationships between multiple wind farms over a large area and cannot accurately capture the drastic power fluctuations in wind power generation systems caused by changes in weather systems. Especially in the face of some extreme weather events (such as cold fronts, typhoons, severe convective weather, etc.), the output power of wind farms may fluctuate significantly, or even rise or fall sharply in a short period of time. If these major weather processes cannot be identified and warned in advance, it may lead to untimely or improper grid dispatching, affecting the stability and security of the grid.

[0004] In addition, due to the complexity and randomness of the spatiotemporal distribution of wind resources, the traditional analysis method based on a single wind farm is difficult to effectively reflect the comprehensive impact of multiple wind farms in a large area. Therefore, there is an urgent need for a method that can comprehensively consider the wind power generation conditions of multiple wind farms in a large area to identify and warn of the changing characteristics of wind farm output power during major weather events, so that power dispatching agencies can take timely measures to ensure the safe operation of the power system. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a method, device and medium for determining a significant weather process for regional wind power generation.

[0006] According to one aspect of the present invention, a method for determining a significant weather process for regional wind power generation is provided, comprising:

[0007] Collect real-time wind speed data and installed capacity data of each wind farm in the area to be tested;

[0008] Calculate the regional equivalent wind speed of the area to be tested based on real-time wind speed data and installed capacity data;

[0009] Perform statistical analysis on the regional equivalent wind speed in the preset historical period of the area to be tested, and determine the low output threshold and the high output threshold of the area to be tested;

[0010] Determine whether the forecast equivalent wind speed data sequence in the tested area is lower than the low output threshold or higher than the high output threshold in each time period within the forecast time, and determine the warning time period for major weather processes in the tested area within the forecast time based on the judgment results at each moment within the forecast time.

[0011] Optionally, before calculating the regional equivalent wind speed of the area to be measured according to the real-time wind speed data and the installed capacity data, the method further includes:

[0012] The real-time wind speed data is preprocessed to remove outliers and noise in the real-time wind speed data to obtain effective real-time wind speed data.

[0013] Optionally, the regional equivalent wind speed of the area to be measured is calculated according to the real-time wind speed data and the installed capacity data, including:

[0014] Perform normalized weight calculation on installed capacity data to obtain the weight of each wind farm;

[0015] According to the weight of each wind farm and the real-time wind speed data, the regional equivalent wind speed of the area to be measured is calculated.

[0016] Optionally, the weight is calculated as:

[0017]

[0018] Among them, P i is the installed capacity data of the i-th wind farm in the region, is the total installed capacity data of all wind farms in the region, and n is the total number of wind farms.

[0019] Optionally, the regional equivalent wind speed is calculated as:

[0020]

[0021] Among them, w i is the weight of the i-th wind farm, V i (t) is the wind speed of the ith wind farm at time t, V eq (t) is the regional equivalent wind speed at time t.

[0022] Optionally, statistical analysis is performed on the regional equivalent wind speed in a preset historical period of the area to be measured to determine a low output threshold and a high output threshold of the area to be measured, including:

[0023] Conduct probability statistical analysis on the regional equivalent wind speed in the preset historical period of the area to be measured, and determine the percentile division data of the equivalent wind speed in the area to be measured;

[0024] The regional equivalent wind speed of the first preset percentile in the regional equivalent wind speed percentile division data is defined as a low output threshold;

[0025] The regional equivalent wind speed of the second preset percentile in the regional equivalent wind speed percentile division data is defined as the high output threshold.

[0026] Optionally, the first preset percentile is 15% and the second preset percentile is 85%.

[0027] Optionally, judging whether the forecast equivalent wind speed data sequence in the tested area is lower than the low output threshold or higher than the high output threshold in each time period within the forecast time, and determining the warning time period of the major weather process in the tested area within the forecast time according to the judgment results at each moment within the forecast time, including:

[0028] If there is a time period in the forecast wind speed data sequence in which the forecast wind speed data is less than the low output threshold, then the time period is determined to be a low output warning time period;

[0029] If there is a time period in which the forecast wind speed data is higher than the high output threshold in the forecast wind speed data sequence, then the time period is determined to be a high output warning time period;

[0030] If there is a time period in the forecast wind speed data sequence in which the forecast wind speed data is not less than the low output threshold and not higher than the high output threshold, then the time period is determined to be the standard output warning time period;

[0031] According to the low-output warning time period, high-output warning time period and standard-output warning time period corresponding to the forecast wind speed data sequence within the forecast time, the warning time period for major weather processes in the tested area within the forecast time is determined.

[0032] According to another aspect of the present invention, there is provided a device for determining a significant weather process for regional wind power generation, comprising:

[0033] A collection module is used to collect real-time wind speed data and installed capacity data of each wind farm in the area to be tested;

[0034] A calculation module is used to calculate the regional equivalent wind speed of the area to be tested based on the real-time wind speed data and installed capacity data;

[0035] A determination module is used to perform statistical analysis on the regional equivalent wind speed in a preset historical period of the area to be measured, and determine the low output threshold and the high output threshold of the area to be measured;

[0036] The analysis module is used to determine whether the forecast equivalent wind speed data sequence in the tested area is lower than the low output threshold or higher than the high output threshold in each time period within the forecast time, and determine the warning time period of major weather processes in the tested area within the forecast time based on the judgment results at each moment within the forecast time.

[0037] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and the computer program is used to execute the method described in any one of the above aspects of the present invention.

[0038] According to another aspect of the present invention, an electronic device is provided, comprising: a processor; a memory for storing instructions executable by the processor; the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method described in any one of the above aspects of the present invention.

[0039] Therefore, the present invention accurately identifies the start and end time of major wind power generation weather processes by weighted averaging and historical statistical analysis of wind speed data of multiple wind farms in the region, combined with the percentile division method. Through this innovative method, power dispatching agencies can give early warning of possible wind power fluctuations such as high output and low output, thereby making more accurate and timely dispatching decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] A more complete understanding of exemplary embodiments of the present invention may be obtained by referring to the following drawings:

[0041] Figure 1 It is a flow chart of a method for determining a significant weather process for regional wind power generation provided by an exemplary embodiment of the present invention;

[0042] Figure 2 is another flow chart of a method for determining a significant weather process for regional wind power generation provided by an exemplary embodiment of the present invention;

[0043] Figure 3 It is a schematic diagram of the structure of a device for determining a significant weather process for regional wind power generation provided by an exemplary embodiment of the present invention;

[0044] Figure 4 This is a structure of an electronic device provided by an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0045] Below, the exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described here.

[0046] It should be noted that the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

[0047] Those skilled in the art can understand that the terms "first" and "second" in the embodiments of the present invention are only used to distinguish different steps, devices or modules, etc., and neither represent any specific technical meaning nor indicate the necessary logical order between them.

[0048] It should also be understood that, in the embodiments of the present invention, “plurality” may refer to two or more than two, and “at least one” may refer to one, two or more than two.

[0049] It should also be understood that any component, data or structure mentioned in the embodiments of the present invention can generally be understood as one or more, unless explicitly limited or otherwise indicated in the context.

[0050] In addition, the term "and / or" in the present invention is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects before and after are in an "or" relationship.

[0051] It should also be understood that the description of the various embodiments of the present invention focuses on the differences between the various embodiments, and the same or similar aspects thereof can be referenced to each other, and for the sake of brevity, they will not be described one by one.

[0052] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0053] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0054] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0055] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0056] Embodiments of the present invention can be applied to electronic devices such as terminal devices, computer systems, servers, etc., which can operate with many other general or special computing system environments or configurations. Examples of well-known terminal devices, computing systems, environments and / or configurations suitable for use with electronic devices such as terminal devices, computer systems, servers, etc. include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, small computer systems, large computer systems, and distributed cloud computing technology environments including any of the above systems, etc.

[0057] Electronic devices such as terminal devices, computer systems, servers, etc. can be described in the general context of computer system executable instructions (such as program modules) executed by computer systems. Generally, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in a distributed cloud computing environment, where tasks are performed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules can be located on local or remote computing system storage media including storage devices.

[0058] Exemplary Methods

[0059] Figure 1 FIG. 1 is a flow chart of a method for determining a significant weather process for regional wind power generation provided by an exemplary embodiment of the present invention. This embodiment can be applied to electronic devices, such as Figure 1 As shown, the method 100 for determining a significant weather process for regional wind power generation includes the following steps:

[0060] Step 101, collecting real-time wind speed data and installed capacity data of each wind farm in the area to be tested;

[0061] Step 102, calculating the regional equivalent wind speed of the area to be measured according to the real-time wind speed data and the installed capacity data;

[0062] Step 103, performing statistical analysis on the regional equivalent wind speed in the preset historical period of the measured area to determine the low output threshold and the high output threshold of the measured area;

[0063] Step 104, determine whether the forecast equivalent wind speed data sequence in the test area is lower than the low output threshold or higher than the high output threshold in each time period within the forecast time, and determine the warning time period of major weather processes in the test area within the forecast time based on the judgment results at each moment within the forecast time.

[0064] Specifically, in response to the current problems of lack of indicators to characterize power generation fluctuations of multiple wind farms in a large region and unclear classification of major weather processes, the present invention proposes a process determination method and early warning system based on the weighted average and percentile division of wind speeds of all wind farms in the region. Through historical statistical analysis of regional equivalent wind speeds, combined with numerical weather forecast data, the start and end times of major wind power generation weather processes can be accurately divided and identified. This method can provide more accurate early warning information for power dispatching agencies, help improve the dispatching efficiency of the power grid and the economic benefits of wind farms, and reduce the risk of system imbalance and power shortage caused by wind speed fluctuations. Reference Figure 2 As shown, the specific process of the method provided by the present invention is as follows:

[0065] (1) Data collection and preprocessing

[0066] 1) Collect real-time wind speed data V of all wind farms in the region i (t) and installed capacity data P i .

[0067] 2) Wind speed data V of wind farm i (t) Perform preprocessing to remove outliers and noise to ensure the accuracy and reliability of the data.

[0068] (2) Calculation of regional equivalent wind speed

[0069] 1) For each wind farm’s installed capacity P i Perform normalized weight calculation and calculate the weight w of each wind farm i , the weight formula is as follows:

[0070]

[0071] Among them, P i is the installed capacity data of the i-th wind farm, is the total installed capacity of all wind farms in the region, and n is the total number of wind farms.

[0072] 2) Calculate the regional equivalent wind speed V eq (t), the formula is:

[0073]

[0074] Among them, V i (t) is the wind speed of the ith wind farm at time t, V eq (t) is the equivalent wind speed in the area at time t.

[0075] (3) Historical statistics and percentile classification of regional equivalent wind speed

[0076] 1) For the regional equivalent wind speed V in historical data eq(t) Conduct statistical analysis to obtain its distribution.

[0077] 2) Divide the regional equivalent wind speed into percentiles and determine the low output threshold V low and high output threshold V high , the specific formula is as follows:

[0078] Low output threshold V low Defined as the 15th percentile of the area's equivalent wind speed:

[0079] V low =Percentile(V eq (t), 15%)

[0080] High output threshold V high Defined as the 85th percentile of the area's equivalent wind speed:

[0081] V high =Percentile(V eq (t), 85%)

[0082] (4) Application of numerical weather forecast thresholds and warning of severe weather processes

[0083] 1) Set the threshold V determined above to low and V high Applied to real-time numerical weather forecast data to analyze future wind speed change trends.

[0084] 2) Set the warning conditions: If the wind speed V in the forecast data forecast (t) is lower than the low output threshold V low , then the corresponding time period [t start ,t end ] is marked as low output warning time period; if the wind speed V forecast (t) is higher than the high output threshold V high , then the corresponding time period [t start ,t end ] is marked as the high output warning time period.

[0085] 3) According to the warning time period, determine the start and end time of the major weather process for wind power generation, and notify the relevant power dispatching agencies to take countermeasures.

[0086] Therefore, the present invention accurately identifies the start and end time of major wind power generation weather processes by weighted averaging and historical statistical analysis of wind speed data of multiple wind farms in the region, combined with the percentile division method. Through this innovative method, power dispatching agencies can give early warning of possible wind power fluctuations such as high output and low output, thereby making more accurate and timely dispatching decisions.

[0087] This early warning mechanism fills the gap in the current field, namely the lack of identification and early warning indicators for typical processes of multiple wind farms in large areas. It can assist power dispatching agencies to reduce the negative impact on the power grid caused by severe fluctuations in wind speed and ensure the continuous and reliable operation of the power system. At the same time, the early warning system can optimize the utilization of wind power resources, improve the economic benefits of wind farms, and avoid power generation losses due to power fluctuations. In short, while enhancing the safe operation of wind farms, the present invention effectively improves the dispatching efficiency and economic benefits of the power system, providing important technical support for the large-scale grid connection of renewable energy.

[0088] Exemplary Devices

[0089] Figure 3 FIG. 1 is a schematic diagram of a device for determining a significant weather process for regional wind power generation provided by an exemplary embodiment of the present invention. Figure 3 As shown, the device 300 includes:

[0090] The collection module 310 is used to collect real-time wind speed data and installed capacity data of each wind farm in the test area;

[0091] A calculation module 320, used to calculate the regional equivalent wind speed of the area to be measured according to the real-time wind speed data and the installed capacity data;

[0092] A determination module 330 is used to perform statistical analysis on the regional equivalent wind speed in a preset historical period of the region to be measured, and determine a low output threshold and a high output threshold of the region to be measured;

[0093] The analysis module 340 is used to determine whether the forecast equivalent wind speed data sequence in the test area is lower than the low output threshold or higher than the high output threshold in each time period within the forecast time, and determine the warning time period of major weather processes in the test area within the forecast time based on the judgment results at each moment within the forecast time.

[0094] Optionally, before the calculation module 320, the apparatus 300 further includes:

[0095] The preprocessing module is used to preprocess the real-time wind speed data, remove abnormal values ​​and noise in the real-time wind speed data, and obtain effective real-time wind speed data.

[0096] Optionally, the calculation module 320 includes:

[0097] The acquisition submodule is used to perform normalized weight calculation on the installed capacity data to obtain the weight of each wind farm;

[0098] The calculation submodule is used to calculate the regional equivalent wind speed of the area to be measured according to the weight of each wind farm and the real-time wind speed data.

[0099] Optionally, the weight is calculated as:

[0100]

[0101] Among them, P i is the installed capacity data of the i-th wind farm in the region, is the total installed capacity data of all wind farms in the region, and n is the total number of wind farms.

[0102] Optionally, the regional equivalent wind speed is calculated as:

[0103]

[0104] Among them, w i is the weight of the i-th wind farm, V i (t) is the wind speed of the ith wind farm at time t, V eq (t) is the regional equivalent wind speed at time t.

[0105] Optionally, the determination module 330 includes:

[0106] The analysis submodule is used to perform probability statistical analysis on the regional equivalent wind speed in the preset historical period of the measured area, and determine the equivalent wind speed percentile division data of the measured area;

[0107] A first definition submodule, used to define a regional equivalent wind speed of a first preset percentile in the regional equivalent wind speed percentile division data as a low output threshold;

[0108] The second definition submodule is used to define the regional equivalent wind speed of a second preset percentile in the regional equivalent wind speed percentile division data as a high output threshold.

[0109] Optionally, the first preset percentile is 15% and the second preset percentile is 85%.

[0110] Optionally, the analysis module 340 includes:

[0111] A first determination submodule is configured to determine, if there is a time period in the forecast wind speed data sequence in which the forecast wind speed data is less than the low output threshold, that the time period is a low output warning time period;

[0112] The second determination submodule is used to determine that if there is a time period in which the forecast wind speed data is higher than the high output threshold in the forecast wind speed data sequence, the time period is a high output warning time period;

[0113] A third determination submodule is configured to determine that if there is a time period in the forecast wind speed data sequence in which the forecast wind speed data is not less than the low output threshold and not higher than the high output threshold, the time period is determined as a standard output warning time period;

[0114] The fourth determination submodule is used to determine the warning time period for major weather processes in the test area within the forecast time according to the low output warning time period, high output warning time period and standard output warning time period corresponding to the forecast wind speed data sequence within the forecast time.

[0115] Exemplary Electronic Devices

[0116] Figure 4 This is a structure of an electronic device provided by an exemplary embodiment of the present invention. Figure 4 As shown, the electronic device 40 includes one or more processors 41 and a memory 42 .

[0117] The processor 41 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0118] The memory 42 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 41 may run the program instructions to implement the methods of the software programs of the various embodiments of the present invention described above and / or other desired functions. In one example, the electronic device may also include: an input device 43 and an output device 44, which are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0119] In addition, the input device 43 may also include, for example, a keyboard, a mouse, etc.

[0120] The output device 44 can output various information to the outside, and can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto.

[0121] Of course, to simplify, Figure 4 Only some of the components related to the present invention in the electronic device are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device may further include any other appropriate components according to specific application conditions.

[0122] Exemplary computer program products and computer-readable storage media

[0123] In addition to the above-mentioned methods and devices, an embodiment of the present invention may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present invention described in the above-mentioned "Exemplary Method" section of this specification.

[0124] The computer program product may be written in any combination of one or more programming languages ​​to write program code for performing the operations of the embodiments of the present invention, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0125] In addition, an embodiment of the present invention may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present invention described in the above “Exemplary Method” section of this specification.

[0126] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, system or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable 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 above.

[0127] The basic principle of the present invention is described above in conjunction with specific embodiments. However, it should be pointed out that the advantages, strengths, effects, etc. mentioned in the present invention are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. must be possessed by each embodiment of the present invention. In addition, the specific details disclosed above are only for the purpose of illustration and facilitation of understanding, rather than limitation, and the above details do not limit the present invention to being implemented by adopting the above specific details.

[0128] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0129] The block diagrams of the devices, systems, equipment, and systems involved in the present invention are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, systems, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The word "such as" used here refers to the phrase "such as but not limited to", and can be used interchangeably with it.

[0130] The method and system of the present invention may be implemented in many ways. For example, the method and system of the present invention may be implemented by software, hardware, firmware or any combination of software, hardware, firmware. The above order of steps for the method is only for illustration, and the steps of the method of the present invention are not limited to the order specifically described above, unless otherwise specifically stated. In addition, in some embodiments, the present invention may also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present invention. Thus, the present invention also covers a recording medium storing a program for executing the method according to the present invention.

[0131] It should also be noted that in the system, device and method of the present invention, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. The above description of the disclosed aspects is provided to enable any technician in the field to make or use the present invention. Various modifications to these aspects are very obvious to those skilled in the art, and the general principles defined here can be applied to other aspects without departing from the scope of the present invention. Therefore, the present invention is not intended to be limited to the aspects shown here, but in accordance with the widest range consistent with the principles and novel features disclosed here.

[0132] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present invention to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A method for determining a significant weather process for regional wind power generation, characterized in that: include: Collect real-time wind speed data and installed capacity data of each wind farm in the area to be tested; Calculating the regional equivalent wind speed of the area to be measured according to the real-time wind speed data and the installed capacity data; Performing statistical analysis on the equivalent wind speed of the area to be measured within a preset historical period, and determining a low output threshold and a high output threshold of the area to be measured; Determine whether the forecast equivalent wind speed data sequence in the area to be tested is lower than the low output threshold or higher than the high output threshold in each time period within the forecast time, and determine the warning time period for major weather processes in the area to be tested within the forecast time based on the judgment results at each moment within the forecast time.

2. The method according to claim 1, characterized in that Before calculating the regional equivalent wind speed of the area to be measured according to the real-time wind speed data and the installed capacity data, the method further includes: The real-time wind speed data is preprocessed to remove abnormal values ​​and noise in the real-time wind speed data to obtain effective real-time wind speed data.

3. The method according to claim 1, characterized in that Calculating the regional equivalent wind speed of the area to be measured according to the real-time wind speed data and the installed capacity data includes: Performing normalized weight calculation on the installed capacity data to obtain the weight of each wind farm; The regional equivalent wind speed of the area to be measured is calculated according to the weight of each wind farm and the real-time wind speed data.

4. The method according to claim 3, characterized in that The calculation formula of the weight is: Among them, P i is the installed capacity data of the i-th wind farm in the region, is the total installed capacity data of all wind farms in the region, and n is the total number of wind farms.

5. The method according to claim 3, characterized in that: The calculation formula of the equivalent wind speed in the area is: Among them, w i is the weight of the i-th wind farm, V i (t) is the wind speed of the ith wind farm at time t, V eq (t) is the regional equivalent wind speed at time t.

6. The method according to claim 1, characterized in that Performing statistical analysis on the regional equivalent wind speed in the preset historical period of the region to be measured to determine the low output threshold and the high output threshold of the region to be measured, including: Performing probability statistical analysis on the equivalent wind speed of the area to be measured within a preset historical period to determine percentile division data of the equivalent wind speed of the area to be measured; defining the regional equivalent wind speed of the first preset percentile in the regional equivalent wind speed percentile division data as the low output threshold; The regional equivalent wind speed of a second preset percentile in the regional equivalent wind speed percentile division data is defined as the high output threshold.

7. The method according to claim 6, characterized in that The first preset percentile is 15%, and the second preset percentile is 85%.

8. The method according to claim 1, characterized in that Determining whether the forecast equivalent wind speed data sequence of the tested area is lower than the low output threshold or higher than the high output threshold in each time period within the forecast time, and determining the major weather process warning time period of the tested area within the forecast time according to the judgment results at each moment within the forecast time, including: If there is a time period in the forecast wind speed data sequence in which the forecast wind speed data is less than the low output threshold, determining the time period as a low output warning time period; If there is a time period in which the forecast wind speed data is higher than the high output threshold in the forecast wind speed data sequence, determining the time period as a high output warning time period; If there is a time period in which the forecast wind speed data is not less than the low output threshold and not higher than the high output threshold in the forecast wind speed data sequence, then determining the time period as the standard output warning time period; The major weather process warning time period of the test area within the forecast time is determined according to the low output warning time period, the high output warning time period and the standard output warning time period corresponding to the forecast wind speed data sequence within the forecast time.

9. A device for determining a significant weather process for regional wind power generation, characterized in that: include: A collection module is used to collect real-time wind speed data and installed capacity data of each wind farm in the area to be tested; A calculation module, used for calculating the regional equivalent wind speed of the area to be measured according to the real-time wind speed data and the installed capacity data; A determination module, configured to perform statistical analysis on the equivalent wind speed of the area to be measured within a preset historical period, and determine a low output threshold and a high output threshold of the area to be measured; The analysis module is used to determine whether the forecast equivalent wind speed data sequence in the test area is lower than the low output threshold or higher than the high output threshold in each time period within the forecast time, and determine the warning time period of major weather processes in the test area within the forecast time according to the judgment results at each moment within the forecast time.

10. The device according to claim 9, characterized in that Before calculating the module, it also includes: The preprocessing module is used to preprocess the real-time wind speed data, remove abnormal values ​​and noise in the real-time wind speed data, and obtain effective real-time wind speed data.

11. The device according to claim 9, characterized in that Computing module, including: An acquisition submodule is used to perform normalized weight calculation on the installed capacity data to obtain the weight of each wind farm; The calculation submodule is used to calculate the regional equivalent wind speed of the area to be measured according to the weight of each wind farm and the real-time wind speed data.

12. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and the computer program is used to execute the method according to any one of claims 1 to 8.

13. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing instructions executable by the processor; The processor is used to read the executable instructions from the memory and execute the instructions to implement the method described in any one of claims 1-8.

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