Rank difference method-based wind speed-power curve fitting abnormal data filtering method

Abstract

The application discloses a wind speed power curve fitting abnormal data filtering method based on a rank difference method, and the method comprises the following steps: acquiring wind speed and power data; encoding ranks of the wind speed and power data respectively, and calculating rank differences of each data point; wherein each data point contains a group of wind speed and power value pairs, and the rank difference is the difference value of the power rank and the wind speed rank; centering the wind speed based on the rank difference; merging the centered wind speed into a data set and filtering; and fitting the data set into a curve based on the Bien method. Through the processing scheme of the disclosure, the data filtering effect is effectively improved.

Description

Technical Field

[0001] This invention relates to the field of wind turbine technology, and in particular to a method for filtering outomas in wind speed-power curve fitting based on the rank difference method. Background Technology

[0002] The wind speed-power characteristic curve is a crucial indicator for evaluating the performance of wind turbine generators and assessing their power generation capacity. Analyzing the wind speed-power characteristic curves of wind turbines provides important references for a range of issues, including turbine site selection, performance evaluation, assessment of power generation capacity, and estimation of power losses. Therefore, establishing a wind speed-power curve model that effectively characterizes the overall power output behavior of wind turbines is of great significance.

[0003] The input wind speed of a wind turbine is the main factor affecting its output active power. Therefore, ignoring the internal characteristics of the turbine and only considering the relationship between the input wind speed and the output active power, the following model is established:

[0004] P = f(V)

[0005] Where P is the active power output of the wind turbine, in kW; and V is the wind speed, in m / s.

[0006] The curve describing the relationship between wind speed and the active power output of a wind turbine generator is called the wind speed-power characteristic curve of the wind turbine.

[0007] Current research on wind speed-power curve modeling methods mainly focuses on the curve fitting stage. After basic filtering of the sample data, curve fitting can be performed. Power curve fitting methods can be broadly classified into parametric and non-parametric methods, discrete methods, and stochastic methods. Among these, the Bean method, a discrete method, is currently widely considered to be relatively effective.

[0008] Although research on methods for fitting wind speed-power curves is relatively mature, the validity of the data is the most fundamental determining factor for the quality of the fit. The presence of outliers can cause the power curve to deviate from the actual power characteristics of the wind turbine. Therefore, it is crucial to extract effective sample data and filter out outliers that do not represent the true performance of the wind turbine.

[0009] The reasons for the generation of abnormal data are as follows:

[0010] Power grid load shedding (referred to as power curtailment) and wind turbine load shedding due to their own reasons (referred to as self-derating) are both issues. Power grid load shedding is caused by grid dispatching, which limits the power output of wind turbines, preventing them from reaching full capacity. Wind turbine self-derating may be due to faulty operation, abnormal power output caused by extreme weather, etc. There are also some abnormal data caused by abnormal data acquisition and transmission.

[0011] The impact of outlier data on curve fitting is as follows: Figure 2 As shown, where, Figure 2-1 The curve fitting results are good for data that excludes power rationing and automatic derating. Figure 2-2 The inclusion of power rationing and self-derating data significantly affects the curve fitting effect.

[0012] While the above-mentioned outlier filtering method can be used to some extent for fitting wind speed-power curves, several shortcomings have been found in its structure during practical application, preventing it from achieving optimal performance. These shortcomings can be summarized as follows:

[0013] (1) The standard state filtering method for wind turbines is used, which selects data under normal power generation conditions. However, due to the inconsistency of turbine models and the inconsistent or inaccurate state judgment standards, the power curtailment conditions are not distinguished and therefore not filtered out. Some filtering methods incorporate blade angle for judgment, but the filtering effect is still not ideal. Moreover, this method cannot filter out self-derating data.

[0014] (2) Hard value filtering method, which is to filter the data by shifting the standard power curve up, down and left and right to obtain the upper and lower limits of the filter. However, since the shape and dispersion range of the actual curve are not fixed, this method still cannot achieve the ideal filtering effect.

[0015] Therefore, it is evident that the existing anomaly filtering methods described above still have inconveniences and shortcomings in their use, and urgently need further improvement. Creating a new anomaly filtering method has become a pressing goal for the industry. Summary of the Invention

[0016] In view of this, the present disclosure provides a method for filtering outomas in wind speed-power curve fitting based on the rank difference method, which at least partially solves the problems existing in the prior art.

[0017] In a first aspect, embodiments of this disclosure provide a method for filtering outomas in wind speed-power curve fitting based on the rank difference method, the method comprising the following steps:

[0018] Obtain wind speed and power data;

[0019] The wind speed and power data are ranked separately, and the rank difference of each data point is calculated; wherein each data point contains a pair of wind speed and power values, and the rank difference is the difference between the power rank and the wind speed rank;

[0020] The wind speed is centered based on the rank difference;

[0021] The centralized wind speeds are merged into a dataset and then filtered.

[0022] According to a specific implementation of this disclosure, centralizing the wind speed based on the rank difference includes the following steps:

[0023] The data points with a rank difference greater than 0 are divided into intervals according to a preset power interval value, and the average wind speed of the data points with a rank difference greater than 0 in each interval is calculated.

[0024] The wind speed of all data points within each interval is centered.

[0025] According to a specific implementation of this disclosure, the wind speed is centered by subtracting the average wind speed of the interval in which the data point is located from the wind speed of the data point.

[0026] According to one specific implementation of this disclosure, the preset power range value is 50kW.

[0027] According to a specific implementation of this disclosure, merging the centralized wind speeds into a dataset and filtering it includes the following steps:

[0028] The negative centralized wind speed is mapped to a positive centralized wind speed by taking the absolute value; the negative centralized wind speed, the centralized wind speed that is 0, and the centralized wind speed that is mapped to positive are merged into a dataset;

[0029] The confidence interval of the dataset is calculated based on the normal distribution of the dataset, and data with power less than a preset value and a centralized wind speed outside the confidence interval are filtered out.

[0030] According to one specific implementation of this disclosure, the method further includes performing basic filtering on the wind speed and power data.

[0031] According to a specific implementation of this disclosure, the step of ranking the wind speed and power data includes the following steps: sorting the wind speed of all data points in ascending order, wherein the wind speed rank of the data points is the ranking of the wind speed values ​​of the data points; and sorting the power of all data points in ascending order, wherein the power rank of the data points is the ranking of the power values ​​of the data points.

[0032] According to one specific implementation of the present disclosure, the method further includes fitting the dataset into a curve based on the Bean method and smoothing the curve using cubic spline interpolation.

[0033] Secondly, embodiments of this disclosure also provide an electronic device, the electronic device comprising:

[0034] At least one processor; and,

[0035] A memory communicatively connected to the at least one processor; wherein,

[0036] The memory stores instructions that can be executed by the at least one processor. When the instructions are executed by the at least one processor, they enable the at least one processor to perform the rank difference method for filtering abnormal data in wind speed-power curve fitting as described in the first aspect or any implementation thereof.

[0037] Thirdly, embodiments of this disclosure also provide a non-transitory computer-readable storage medium storing computer instructions that, when executed by at least one processor, cause the at least one processor to perform the rank difference-based wind speed-power curve fitting anomaly data filtering method in the first aspect or any implementation thereof.

[0038] Fourthly, embodiments of this disclosure also provide a computer program product, the computer program product including a computing program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, which, when executed by a computer, cause the computer to execute the rank difference method-based wind speed power curve fitting abnormal data filtering method in the first aspect or any implementation thereof.

[0039] The abnormal data filtering method for wind speed-power curve fitting based on the rank difference method in this embodiment solves the problem of unsatisfactory data filtering in the early stage of wind speed-power curve fitting, especially the problem of incomplete data filtering for power grid limited operation and wind turbine self-derating operation. This invention effectively improves the data filtering effect. Attached Figure Description

[0040] The above is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0041] Figure 1 A schematic flowchart of an anomaly data filtering method for wind speed-power curve fitting based on the rank difference method is provided in an embodiment of this disclosure.

[0042] Figure 2 This is a schematic diagram illustrating the impact of outlier data on the fitting of the wind speed-power curve.

[0043] Figure 3A schematic diagram of the effect of an anomaly filtering method for wind speed-power curve fitting based on the rank difference method provided in this embodiment of the present disclosure; wherein, (1) PositiveRank represents the positive rank difference point; (2) the solid line is the upper and lower limits of the centered wind speed obtained after wind speed centering; (3) Normal represents the extracted effective data points, which are used for subsequent curve fitting; (4) the fittedcurve is the wind speed-power curve fitted with the effective data points;

[0044] Figure 4 A schematic diagram comparing the filtering effect of the wind speed-power curve fitting anomaly data filtering method based on the rank difference method provided in this embodiment with existing filtering methods; wherein, (1) is a diagram showing the filtering effect of data and fitting curve using the state + blade angle method; (2) is a diagram showing the filtering effect of data and fitting curve using the hard value filtering method; (3) is a diagram showing the filtering effect of data and fitting curve using the wind speed-power curve fitting anomaly data filtering method based on the rank difference method of this invention; and

[0045] Figure 5 A schematic diagram of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0046] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0047] The following specific examples illustrate the implementation of this disclosure. Those skilled in the art can easily understand other advantages and effects of this disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. This disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this disclosure. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0048] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.

[0049] It should also be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this disclosure. The drawings only show the components related to this disclosure and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0050] Furthermore, specific details are provided in the following description to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.

[0051] This invention provides a method for filtering outomas in wind speed-power curve fitting based on the rank difference method. By extracting effective data used to fit the wind speed-power curve, specifically filtering data from the true ramp-up phase (before the unit reaches full power generation), a reliable data foundation is provided for the fitting phase in modeling. The characteristics of ramp-up phase data are that wind speed and power generally increase, but due to random factors, both power and wind speed have random errors and cannot be directly compared. However, the rankings of wind speed and power within their respective overall values ​​should be relatively similar; as wind speed ranking increases, so does power ranking. Therefore, the region with the highest concentration of true ramp-up data can be found based on the difference between power and wind speed rankings. The wind speed center for each power interval in this region is calculated, and then the region is expanded to a confidence interval, considering the data within the confidence interval as true ramp-up data.

[0052] Figure 1 This is a schematic diagram of the abnormal data filtering method for wind speed-power curve fitting based on the rank difference method provided in the embodiments of this disclosure.

[0053] like Figure 1 As shown, in step S110, wind speed and power data are acquired.

[0054] In this embodiment of the invention, the method further includes performing basic filtering on the wind speed and power data.

[0055] More specifically, the measured wind speed and power data are first filtered to select data where the wind speed and power are within the effective range.

[0056] More specifically, we now proceed to step S120.

[0057] In step S120, the wind speed and power data are ranked respectively, and the rank difference of each data point is calculated; wherein, each data point contains a pair of wind speed and power values, and the rank difference is the difference between the power rank and the wind speed rank.

[0058] In this embodiment of the invention, ranking the wind speed and power data respectively includes the following steps: sorting the wind speed of all data points in ascending order, with the wind speed rank of each data point being its ranking; and sorting the power of all data points in ascending order, with the power rank of each data point being its ranking. Because the sorting is in ascending order, the wind speed rank of the point with the lowest wind speed is 1, and the higher the wind speed, the higher the wind speed rank. The same applies to the power rank.

[0059] More specifically, wind speed and power are ranked separately, where the rank of a wind speed value is its ranking among all wind speed values; and the rank of a power value is its ranking among all power values.

[0060] Then, calculate the rank difference for each data point (a pair of wind speed and power values), which is the difference between the power rank and the wind speed rank. The rank difference calculation formula is:

[0061] R diff =R p -R w ...Equation 1

[0062] Among them, R diff For the rank difference, R w R is the rank of the wind speed. p Let be the rank of the power.

[0063] Next, proceed to step S130.

[0064] In step S130, the wind speed is centered based on the rank difference.

[0065] In this embodiment of the invention, centering the wind speed based on the rank difference includes the following steps:

[0066] The data points with a rank difference greater than 0 are divided into intervals according to a preset power interval value, and the average wind speed of the data points with a rank difference greater than 0 in each interval is calculated.

[0067] The wind speed of all data points within each interval is centered.

[0068] In this embodiment of the invention, the wind speed is centered by subtracting the average wind speed of the interval in which the data point is located from the wind speed of the data point.

[0069] More specifically, data points with a rank difference greater than 0 calculated in step S120 are selected to determine the center of the wind speed (hereinafter, these points are referred to as positive rank difference points, such as...). Figure 3 -(1) As shown. First, the data points are divided into intervals according to the preset power interval value. The average wind speed of the positive rank difference points in each interval is calculated to obtain the average wind speed of this interval. Then, the wind speed of all points in this interval is centered, that is, the wind speed is subtracted from the average wind speed.

[0070] In this embodiment of the invention, the data points are divided into intervals according to a preset power range value, wherein the preset power range value is preferably 50 kW. Based on a large amount of data verification, dividing the dataset with 50 kW as the window will not lose the distribution characteristics of the wind speed data, and will also ensure that the amount of data within the interval meets the statistical requirements and has statistical significance.

[0071] Next, proceed to step S140.

[0072] In step S140, the centralized wind speeds are merged into a dataset and then filtered.

[0073] In this embodiment of the invention, the step of merging the centralized wind speeds into a dataset and filtering it includes the following steps:

[0074] The negative centralized wind speed is mapped to a positive centralized wind speed by taking the absolute value; the negative centralized wind speed, the centralized wind speed that is 0, and the centralized wind speed that is mapped to positive are merged into a dataset;

[0075] Based on the normal distribution of the dataset, the confidence interval of the dataset is calculated, and data with power less than a preset value and a centralized wind speed outside the confidence interval are filtered out.

[0076] More specifically, negative centralized wind speeds are mapped to positive centralized wind speeds by taking their absolute values. Then, the negative centralized wind speeds, the zero centralized wind speeds, and the positive centralized wind speeds obtained from the mapping of negative centralized wind speeds are combined into a single dataset. The standard deviation σ is calculated, and the dataset follows a normal distribution N(0, σ). Its 95% confidence interval can be calculated as (-Zα / 2σ, +Zα / 2σ), where Z is the standard normal distribution, and Zα / 2 is the corresponding standard score. This standard score can be obtained by looking up a statistical table. The confidence interval is represented by [a, b]. Figure 3 The two solid lines in (2) represent the upper and lower limits of the confidence interval.

[0077] For a given set of sample data with mean μ and standard deviation σ, the 100(1-α)% confidence interval of the overall data mean is (μ-Zα / 2σ, μ+Zα / 2σ), where α is the significance level and the confidence interval is 95%, i.e., α = 0.05. Zα / 2 is the corresponding standard score, which can be obtained by consulting a statistical table.

[0078] For data points with power less than 80% of full capacity, filter out data where the centralized wind speed is outside the confidence interval [a, b]. This completes the data filtering process. Figure 3 In (3), Normal represents the extracted valid data points used for subsequent curve fitting.

[0079] The preferred method is to filter data points where the power is less than 80% of the full capacity. This is achieved by analyzing a large amount of historical data to determine the fluctuation of the power value around the full capacity. Selecting 80% covers a 95% confidence interval for the power after reaching full capacity. In this embodiment, in later practical applications, this value can be adjusted appropriately by analyzing the power distribution characteristics after reaching full capacity using historical data.

[0080] Next, proceed to step S150.

[0081] In step S150, the dataset is fitted into a curve based on the Bean method.

[0082] In this embodiment of the invention, the method further includes curve smoothing using cubic spline interpolation.

[0083] More specifically, curve fitting was performed using the widely used Bean method, which uses a wind speed interval of 0.5 m / s. Selecting a 0.5 m / s wind speed interval has been validated with extensive data. Dividing the dataset into 0.5 m / s windows ensures that the power data distribution characteristics are not lost and that the amount of data within each interval meets statistical requirements and has statistical significance. The average wind speed and power within each interval were calculated separately. Curve smoothing was performed using cubic spline interpolation. Figure 3 The curve in (4) is the wind speed-power curve fitted using valid data points.

[0084] The proposed anomaly data filtering method based on the rank difference method for wind speed-power curve fitting solves the problem of unsatisfactory data filtering in the early stages of wind speed-power curve fitting, particularly addressing the issue of incomplete data filtering for power grid-limited operation and wind turbine self-derating operation. This invention effectively improves the data filtering performance.

[0085] Figure 4A schematic diagram comparing the filtering effect of the wind speed-power curve fitting anomaly filtering method based on the rank difference method provided in this disclosure with existing filtering methods; wherein, Figure 4 -(1) is a graph showing the results of filtering data and fitting curves using the state + blade angle method. Figure 4 -(2) is a graph showing the results of filtering data and fitting curves using hard filtering. Figure 4 -(3) is a graph showing the effect of filtering data and fitting curves using the method of the present invention. It can be seen that the filtering effect of the present invention is significantly improved compared with the previous two.

[0086] See Figure 5 This disclosure also provides an electronic device 50, which includes:

[0087] At least one processor; and,

[0088] The memory is communicatively connected to the at least one processor; wherein,

[0089] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the rank difference method-based wind speed power curve fitting anomaly data filtering method in the foregoing method embodiments.

[0090] This disclosure also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the rank difference method-based wind speed-power curve fitting anomaly data filtering method in the foregoing method embodiments.

[0091] This disclosure also provides a computer program product, which includes a computing program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions that, when executed by a computer, cause the computer to perform the wind speed-power curve fitting anomaly data filtering method based on the rank difference method in the foregoing method embodiments.

[0092] The following is for reference. Figure 5 The diagram illustrates a structural schematic of an electronic device 50 suitable for implementing embodiments of the present disclosure. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0093] like Figure 5 As shown, the electronic device 50 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of the electronic device 50. The processing unit 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0094] Typically, the following devices can be connected to I / O interface 505: input devices 506 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 507 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 508 including, for example, magnetic tapes, hard disks, etc.; and communication devices 509. Communication device 509 allows electronic device 50 to communicate wirelessly or wiredly with other devices to exchange data. Although an electronic device 50 with various devices is shown in the figure, it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0095] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 509, or installed from a storage device 508, or installed from a ROM 502. When the computer program is executed by the processing device 501, it performs the functions defined in the methods of embodiments of this disclosure.

[0096] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0097] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0098] The aforementioned computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: acquire at least two Internet Protocol (IP) addresses; send a node evaluation request including the at least two IP addresses to a node evaluation device, wherein the node evaluation device selects an IP address from the at least two IP addresses and returns it; and receive the IP address returned by the node evaluation device; wherein the acquired IP address indicates an edge node in a content delivery network.

[0099] Alternatively, the aforementioned computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: receive a node evaluation request including at least two Internet Protocol (IP) addresses; select an IP address from the at least two IP addresses; and return the selected IP address; wherein the received IP address indicates an edge node in the content delivery network.

[0100] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0101] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0102] The units described in the embodiments of this disclosure can be implemented in software or in hardware. The name of a unit does not necessarily limit the unit itself; for example, the first acquisition unit can also be described as "a unit that acquires at least two Internet Protocol addresses".

[0103] It should be understood that the various parts of this disclosure can be implemented in hardware, software, firmware, or a combination thereof.

[0104] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. A method for filtering outlier data in wind speed-power curve fitting based on the rank difference method, characterized in that, The method includes the following steps: Obtain wind speed and power data; The wind speed and power data are ranked separately, and the rank difference of each data point is calculated; wherein each data point contains a pair of wind speed and power values, and the rank difference is the difference between the power rank and the wind speed rank; Centering the wind speed based on the rank difference includes the following steps: The data points with a rank difference greater than 0 are divided into intervals according to a preset power interval value, and the average wind speed of the data points with a rank difference greater than 0 in each interval is calculated. The wind speed is centered by subtracting the average wind speed of the interval in which the data point is located from the wind speed of the data point. The centralized wind speeds are merged into a dataset and then filtered, including the following steps: The negative centralized wind speed is mapped to a positive centralized wind speed by taking the absolute value; the negative centralized wind speed, the centralized wind speed that is 0, and the centralized wind speed that is mapped to positive are merged into a dataset; The confidence interval of the dataset is calculated based on the normal distribution of the dataset, and data with power less than a preset value and a centralized wind speed outside the confidence interval are filtered out.

2. The method for filtering outomas in wind speed-power curve fitting based on the rank difference method according to claim 1, characterized in that, The preset power range value is 50kW.

3. The method for filtering outomas in wind speed-power curve fitting based on the rank difference method according to claim 1, characterized in that, The method also includes basic filtering of the wind speed and power data.

4. The method for filtering outomas in wind speed-power curve fitting based on the rank difference method according to claim 1, characterized in that, The step of ranking the wind speed and power data separately includes the following steps: sorting the wind speed of all data points in ascending order, with the wind speed rank of each data point being the ranking of its wind speed value; and sorting the power of all data points in ascending order, with the power rank of each data point being the ranking of its power value.

5. The method for filtering outomas in wind speed-power curve fitting based on the rank difference method according to any one of claims 1 to 4, characterized in that, The method also includes fitting the dataset into a curve based on the Bean method and smoothing the curve using cubic spline interpolation.

6. An electronic device, characterized in that, The electronic device includes: At least one processor; and, 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, which, when executed by the at least one processor, cause the at least one processor to perform the rank difference method-based wind speed-power curve fitting anomaly data filtering method as described in any one of claims 1 to 5.

7. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions that, when executed by at least one processor, cause the at least one processor to perform the rank difference-based wind speed-power curve fitting anomaly data filtering method as described in any one of claims 1 to 5.

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