Distributed arrangement method for wind power plant sensors

Abstract

The invention provides a wind power plant sensor distributed arrangement method which comprises the steps that part of wind turbine generators in a wind power plant serve as characteristic wind turbine generators, the other wind turbine generators in the wind power plant serve as non-characteristic wind turbine generators, and the multiple characteristic wind turbine generators and the multiple non-characteristic wind turbine generators are distributed at intervals; arranging a plurality of sensors in the characteristic wind turbine generator, and obtaining environment data and operation data of the characteristic wind turbine generator by using the plurality of sensors; and according to the environment data and the operation data of the plurality of characteristic wind turbine generators around the non-characteristic wind turbine generator, utilizing a weighted average method to calculate the environment data and the operation data of the non-characteristic wind turbine generator. According to the distributed arrangement method for the wind power plant sensors, a large number of sensors and sensing data arranged in non-characteristic wind turbine generators are omitted, so that the arrangement difficulty and the use cost of the wind power plant are greatly reduced while the operation efficiency of the wind power plant is improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of wind power generation, and in particular to a distributed arrangement method of wind farm sensors. Background Art

[0002] Wind turbines are devices that convert wind energy into electrical energy. During operation, they need to constantly collect environmental and operational data in order to timely correct the operating status of the unit and operate at the best power generation efficiency. However, a wind turbine is often equipped with hundreds of sensors to collect data on thousands of parameters. The number of sensors and data in a wind farm is in the tens of thousands, but the data collected by sensors in wind turbines at different locations is often the same. Some of the collected data is only used for trend analysis and is not analyzed in real time. This not only causes a large amount of data redundancy and reduces the operating efficiency of the wind farm, but also makes the layout of the wind farm more difficult and the cost of use higher. Summary of the invention

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

[0004] To this end, an object of the present disclosure is to provide a method for distributed deployment of wind farm sensors.

[0005] To achieve the above-mentioned purpose, the present disclosure provides a method for distributed arrangement of wind farm sensors, comprising: taking some wind turbines in the wind farm as characteristic wind turbines, and taking the remaining wind turbines in the wind farm as non-characteristic wind turbines, wherein a plurality of the characteristic wind turbines and a plurality of the non-characteristic wind turbines are distributed at intervals; arranging a plurality of the sensors in the characteristic wind turbines, and using the plurality of the sensors to obtain environmental data and operating data of the characteristic wind turbines; and calculating the environmental data and operating data of the non-characteristic wind turbines by using a weighted average method based on the environmental data and operating data of a plurality of the characteristic wind turbines around the non-characteristic wind turbines.

[0006] Optionally, the method also includes: obtaining the distance between adjacent characteristic wind turbines located at a certain position of the non-characteristic wind turbine group; when the distance is greater than a preset distance, selecting the environmental data and operating data of one of the characteristic wind turbines closest to the non-characteristic wind turbine group in the position for inferring the environmental data and operating data of the non-characteristic wind turbine group; when the distance is not greater than the preset distance, selecting the environmental data and operating data of two characteristic wind turbines closest to the non-characteristic wind turbine group in the position for inferring the environmental data and operating data of the non-characteristic wind turbine group.

[0007] Optionally, the method further includes: setting the calculation weights of a plurality of the characteristic wind turbine groups located at a certain position of the non-characteristic wind turbine group to decrease in sequence in a direction away from the non-characteristic wind turbine group.

[0008] Optionally, the method further includes: setting the calculation weights of a plurality of the characteristic wind turbine groups located around the non-characteristic wind turbine group and closest to the non-characteristic wind turbine group to be the same.

[0009] Optionally, the method also includes: obtaining the wind direction at the non-characteristic wind turbine group based on environmental data of multiple characteristic wind turbine groups around the non-characteristic wind turbine group; setting the calculation weight of the characteristic wind turbine group located upwind of the non-characteristic wind turbine group to be greater than the calculation weight of the characteristic wind turbine group located downwind of the non-characteristic wind turbine group.

[0010] Optionally, the method further includes: taking the wind turbine set at the corner of the wind farm as the characteristic wind turbine set; taking the characteristic wind turbine set at the corner as the starting set, and sequentially selecting wind turbine sets at intervals as the characteristic wind turbine sets.

[0011] Optionally, the method further comprises: arranging a plurality of the characteristic wind turbines to be distributed equidistantly or unequally.

[0012] Optionally, the method further includes: setting a non-characteristic wind turbine set between adjacent characteristic wind turbine sets; or setting a plurality of non-characteristic wind turbine sets between adjacent characteristic wind turbine sets.

[0013] Optionally, the environmental data includes one or more of: wind speed, wind direction, wave velocity, flow direction, temperature, and density.

[0014] Optionally, the operating data includes: one or more of rotational speed, torque, vibration acceleration, displacement, temperature, voltage, and current.

[0015] The technical solution provided by the present disclosure may have the following beneficial effects:

[0016] The wind turbines in the wind farm are divided into characteristic wind turbines and non-characteristic wind turbines distributed at intervals, and sensors are arranged in the characteristic wind turbines. The environmental data and operating data of the characteristic wind turbines are obtained by using the sensors, and the environmental data and operating data of the non-characteristic wind turbines are inferred by using the environmental data and operating data of the characteristic wind turbines. In this way, not only can the environmental data and operating data of all wind turbines be obtained to ensure the normal operation of the wind farm, but also a large number of sensors and sensor data arranged in the non-characteristic wind turbines are saved, thereby greatly reducing the layout difficulty and use cost of the wind farm while improving the operating efficiency of the wind farm.

[0017] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will be obvious from the following description or learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above and / or additional aspects and advantages of the present disclosure will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0019] Figure 1 is a flow chart of a method for distributing wind farm sensors according to an embodiment of the present disclosure;

[0020] Figure 2 is a schematic diagram of the arrangement of a wind farm in a distributed arrangement method of wind farm sensors proposed in an embodiment of the present disclosure;

[0021] Figure 3 is a schematic diagram of the arrangement of a wind farm in a distributed arrangement method of wind farm sensors proposed in an embodiment of the present disclosure;

[0022] As shown in the figure: 1. Characteristic wind turbine, 2. Non-characteristic wind turbine. DETAILED DESCRIPTION

[0023] Embodiments of the present disclosure are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present disclosure, and are not to be construed as limitations of the present disclosure. On the contrary, the embodiments of the present disclosure include all changes, modifications, and equivalents that fall within the spirit and connotation of the appended claims.

[0024] like Figure 1 , Figure 2 and Figure 3 As shown, the embodiment of the present disclosure proposes a distributed arrangement method of wind farm sensors, including:

[0025] S1: taking some wind turbines in the wind farm as characteristic wind turbines 1, and taking the remaining wind turbines in the wind farm as non-characteristic wind turbines 2, wherein a plurality of characteristic wind turbines 1 and a plurality of non-characteristic wind turbines 2 are distributed at intervals;

[0026] S2: arranging a plurality of sensors in the characteristic wind turbine generator set 1, and using the plurality of sensors to obtain environmental data and operating data of the characteristic wind turbine generator set 1;

[0027] S3: Calculate the environmental data and operating data of the non-characteristic wind turbine group 2 by using a weighted average method based on the environmental data and operating data of multiple characteristic wind turbine groups 1 around the non-characteristic wind turbine group 2.

[0028] It can be understood that the wind turbines in the wind farm are divided into characteristic wind turbines 1 and non-characteristic wind turbines 2 that are distributed at intervals, and sensors are arranged in the characteristic wind turbine 1, and the environmental data and operating data of the characteristic wind turbine 1 are obtained by using the sensors, and the environmental data and operating data of the non-characteristic wind turbine 2 are inferred by using the environmental data and operating data of the characteristic wind turbine 1. As a result, not only can the environmental data and operating data of all wind turbines be obtained to ensure the normal operation of the wind farm, but also a large number of sensors and sensor data arranged in the non-characteristic wind turbine 2 are omitted, thereby greatly reducing the difficulty of layout and use cost of the wind farm while improving the operating efficiency of the wind farm.

[0029] It should be noted that a wind turbine is a device that converts wind energy into electrical energy. By utilizing multiple sensors in the characteristic wind turbine 1, the environmental data and operating data of the characteristic wind turbine 1 can be obtained. By utilizing the environmental data and operating data of the characteristic wind turbine 1, the environmental data and operating data of the non-characteristic wind turbine 2 can be inferred. Thus, by utilizing the obtained environmental data and operating data, the operating status of the unit can be corrected in a timely manner so that it can operate at the optimal power generation efficiency.

[0030] This embodiment arranges sensors at intervals among a plurality of wind turbines, thereby effectively reducing the number and cost of sensors arranged in the wind farm. At the same time, the method of extrapolating data from surrounding wind turbines does not affect data acquisition of wind turbines where no sensors are arranged.

[0031] The characteristic wind turbine set 1 is used for arranging sensors, and the non-characteristic wind turbine set 2 is not arranged with sensors. A plurality of characteristic wind turbine sets 1 and a plurality of non-characteristic wind turbine sets 2 are distributed at intervals and are all wind turbine sets in the wind farm.

[0032] Among them, the multiple sensors arranged in the characteristic wind turbine set 1 are used to obtain environmental data and operation data of the characteristic wind turbine set 1, and the multiple sensors may include: wind speed sensor, wind direction sensor, rotation speed sensor, torque sensor and other types of sensors.

[0033] The weighted average method assigns a weight value to each data point, which determines the importance of the data point in calculating the average value. The calculation formula of the weighted average is:

[0034] Weighted average = (value 1 × weight 1 + value 2 × weight 2 + … + value n × weight n) ÷ (weight 1 + weight 2 + … + weight n).

[0035] In this formula, each value is multiplied by its corresponding weight, then those products are added together, and finally the sum of those products is divided by the sum of all weights to get the weighted average.

[0036] In this embodiment, each parameter of the non-characteristic wind turbine generator set 2 is calculated by using the weighted average method based on the corresponding parameters of the characteristic wind turbine generator sets 1 around it.

[0037] In some embodiments, the method further comprises:

[0038] Obtaining the spacing between adjacent characteristic wind turbines 1 located at a certain position of a non-characteristic wind turbine 2;

[0039] When the spacing is greater than the preset spacing, the environmental data and operating data of a characteristic wind turbine set 1 closest to the non-characteristic wind turbine set 2 in terms of orientation are selected to calculate the environmental data and operating data of the non-characteristic wind turbine set 2;

[0040] When the spacing is not greater than the preset spacing, the environmental data and operating data of the two characteristic wind turbines 1 closest to the non-characteristic wind turbine 2 in azimuth are selected to infer the environmental data and operating data of the non-characteristic wind turbine 2.

[0041] It can be understood that when the spacing is greater than the preset spacing, it indicates that the spacing between the non-characteristic wind turbine 2 and the adjacent characteristic wind turbine 1 at that location is large, and the non-characteristic wind turbine 2 is far away from the characteristic wind turbine 1 at that location, and the data correlation is weak. Therefore, the environmental data and operating data of a characteristic wind turbine 1 that is closest to the non-characteristic wind turbine 2 in the location are selected to infer the environmental data and operating data of the non-characteristic wind turbine 2, thereby ensuring the accurate calculation of the data of the non-characteristic wind turbine 2, and then ensuring the high-efficiency operation of the wind farm.

[0042] When the spacing is not greater than the preset spacing, it indicates that the spacing between the adjacent characteristic wind turbines 1 of the non-characteristic wind turbine 2 at this position is small, the non-characteristic wind turbine 2 is close to the characteristic wind turbine 1 at this position, and the data correlation is strong. Therefore, the environmental data and operating data of the two characteristic wind turbines 1 closest to the non-characteristic wind turbine 2 in the position are selected to infer the environmental data and operating data of the non-characteristic wind turbine 2, thereby ensuring the accurate calculation of the data of the non-characteristic wind turbine 2, and then ensuring the high-efficiency operation of the wind farm.

[0043] It should be noted that the preset spacing can be set according to actual needs and there is no limitation on this.

[0044] In some embodiments, the method further comprises:

[0045] The calculation weights of a plurality of characteristic wind turbines 1 located at a certain position of a non-characteristic wind turbine 2 are set to decrease in sequence in a direction away from the non-characteristic wind turbine 2 .

[0046] It can be understood that the characteristic wind turbine 1 is close to the non-characteristic wind turbine 2 in a certain direction of the non-characteristic wind turbine 2, and the distance between it and the non-characteristic wind turbine 2 is smaller, the environment they are in is closer, and the data correlation is stronger. Therefore, in the calculation process, the calculation weight of the characteristic wind turbine 1 is set larger, so that the environmental data and operation data of the non-characteristic wind turbine 2 can be obtained more accurately to ensure the high-efficiency operation of the wind farm.

[0047] The characteristic wind turbine 1 is far away from the non-characteristic wind turbine 2 in a certain direction. The distance between it and the non-characteristic wind turbine 2 is large, the environment they are in is slightly different, and the data correlation is weak. Therefore, in the calculation process, the calculation weight of the characteristic wind turbine 1 is set smaller, so that the environmental data and operation data of the non-characteristic wind turbine 2 can be obtained more accurately to ensure the high-efficiency operation of the wind farm.

[0048] Therefore, the calculation weights of multiple characteristic wind turbines 1 located at a certain position of the non-characteristic wind turbine 2 are set to decrease successively in the direction away from the non-characteristic wind turbine 2, which can achieve accurate calculation of the environmental data and operation data of the non-characteristic wind turbine 2, thereby ensuring the high-efficiency operation of the wind farm.

[0049] In some embodiments, the method further includes: setting the calculation weights of a plurality of characteristic wind turbines 1 located around the non-characteristic wind turbine 2 and closest to the non-characteristic wind turbine 2 to be the same.

[0050] It can be understood that the multiple characteristic wind turbines 1 located around the non-characteristic wind turbine 2 and closest to the non-characteristic wind turbine 2 are all closely spaced from the non-characteristic wind turbine 2, are in relatively close environments, and have strong data correlation. Therefore, in the calculation process, the calculation weights of the multiple characteristic wind turbines 1 are set to be the same, which can achieve accurate calculation of the environmental data and operating data of the non-characteristic wind turbine 2, thereby ensuring the high-efficiency operation of the wind farm.

[0051] For example, when the wind turbines in the wind farm are distributed in a matrix shape, the multiple characteristic wind turbines 1 around the non-characteristic wind turbine 2 and closest to the non-characteristic wind turbine 2 may be the four characteristic wind turbines 1 in front, behind, left and right of the non-characteristic wind turbine 2.

[0052] In some embodiments, the method further comprises:

[0053] Obtaining the wind direction at the non-characteristic wind turbine group 2 according to the environmental data of multiple characteristic wind turbine groups 1 around the non-characteristic wind turbine group 2;

[0054] The calculation weight of the characteristic wind turbine group 1 located in the upwind direction of the non-characteristic wind turbine group 2 is set to be greater than the calculation weight of the characteristic wind turbine group 1 located in the downwind direction of the non-characteristic wind turbine group 2.

[0055] It can be understood that since the multiple characteristic wind turbines 1 located around the non-characteristic wind turbine group 2 are closely spaced from the non-characteristic wind turbine group 2, their environments are relatively close, and the data correlation is relatively strong, the wind direction at the non-characteristic wind turbine group 2 can be obtained based on the environmental data of the multiple characteristic wind turbines 1 around the non-characteristic wind turbine group 2, and the environment in which the characteristic wind turbine group 1 located upwind of the non-characteristic wind turbine group 2 is closer to the non-characteristic wind turbine group 2. Therefore, the calculation weight of the characteristic wind turbine group 1 located upwind of the non-characteristic wind turbine group 2 is larger, while the calculation weight of the characteristic wind turbine group 1 located downwind of the non-characteristic wind turbine group 2 is smaller. As a result, the environmental data and operating data of the non-characteristic wind turbine group 2 can be accurately calculated, thereby ensuring the high-efficiency operation of the wind farm.

[0056] For example, if the wind direction is from the front to the rear of the non-characteristic wind turbine group 2 , the calculation weight of the characteristic wind turbine group 1 in front of the non-characteristic wind turbine group 2 is greater than the calculation weight of the characteristic wind turbine group 1 behind the non-characteristic wind turbine group 2 .

[0057] like Figure 2 and Figure 3 As shown, in some embodiments, the method further includes:

[0058] The wind turbine at the corner of the wind farm is taken as the characteristic wind turbine 1;

[0059] The characteristic wind turbine group 1 at the corner is taken as the starting unit, and wind turbine groups are selected at intervals in sequence as the characteristic wind turbine group 1.

[0060] It can be understood that the wind turbine at the corner is used as the characteristic wind turbine 1 and is used to estimate the environmental data and operating data of other non-characteristic wind turbines 2, thereby effectively reducing the calculation error of the data and improving the accuracy of data estimation of the non-characteristic wind turbine 2.

[0061] like Figure 2 and Figure 3 As shown, in some embodiments, the method further includes:

[0062] Multiple characteristic wind turbine generator sets 1 are arranged to be distributed equidistantly or non-equidistantly.

[0063] It can be understood that no matter whether multiple characteristic wind turbines 1 are equidistantly distributed or unequally distributed, the environmental data and operating data of the characteristic wind turbine 1 can be used to accurately infer the environmental data and operating data of the non-characteristic wind turbine 2, thereby improving the operating efficiency of the wind farm while reducing the use cost of the wind farm.

[0064] It should be noted that when multiple characteristic wind turbines 1 are equidistantly distributed, one characteristic wind turbine 1 can be arranged for every other non-characteristic wind turbine 2, or one characteristic wind turbine 1 can be arranged for every two non-characteristic wind turbines 2, or one characteristic wind turbine 1 can be arranged for every three non-characteristic wind turbines 2, and there is no limitation to this.

[0065] When multiple characteristic wind turbines 1 are not distributed equidistantly, one characteristic wind turbine 1 may be arranged every other non-characteristic wind turbine 2. At the same time, at other locations in the same wind farm, one characteristic wind turbine 1 may be arranged every two non-characteristic wind turbines 2, or one characteristic wind turbine 1 may be arranged every three non-characteristic wind turbines 2. There is no restriction on this.

[0066] like Figure 2 and Figure 3 As shown, in some embodiments, the method further includes:

[0067] It is set that there is one non-characteristic wind turbine set 2 between adjacent characteristic wind turbine sets 1; or, it is set that there are multiple non-characteristic wind turbine sets 2 between adjacent characteristic wind turbine sets 1.

[0068] It can be understood that, by providing a non-characteristic wind turbine 2 between adjacent characteristic wind turbines 1, an equidistant distribution of multiple characteristic wind turbines 1 is achieved, while ensuring that there is a smaller spacing between adjacent characteristic wind turbines 1; by providing multiple non-characteristic wind turbines 2 between adjacent characteristic wind turbines 1, an equidistant distribution of multiple characteristic wind turbines 1 is achieved, while ensuring that there is a larger spacing between adjacent characteristic wind turbines 1.

[0069] Among them, no matter there is one non-characteristic wind turbine 2 between adjacent characteristic wind turbines 1 or there are multiple non-characteristic wind turbines 2 between adjacent characteristic wind turbines 1, the environmental data and operation data of the non-characteristic wind turbine 2 can be accurately calculated using the weighted average method.

[0070] In some embodiments, the environmental data includes one or more of wind speed, wind direction, wave speed, current direction, air temperature, and density.

[0071] For example, the wind turbines in the wind farm are divided into characteristic wind turbines 1 and non-characteristic wind turbines 2 that are distributed at intervals, and sensors are arranged in the characteristic wind turbines 1, and the wind speed data of the characteristic wind turbines 1 is obtained by using the sensors, and the wind speed data of the non-characteristic wind turbines 2 is inferred by using the wind speed data of the characteristic wind turbines 1.

[0072] For example, the wind turbines in the wind farm are divided into characteristic wind turbines 1 and non-characteristic wind turbines 2 that are distributed at intervals, and sensors are arranged in the characteristic wind turbines 1, and the wind direction data of the characteristic wind turbines 1 is obtained by using the sensors, and the wind direction data of the non-characteristic wind turbines 2 is inferred by using the wind direction data of the characteristic wind turbines 1.

[0073] In some embodiments, the operating data includes one or more of rotation speed, torque, vibration acceleration, displacement, temperature, voltage, and current.

[0074] For example, the wind turbines in the wind farm are divided into characteristic wind turbines 1 and non-characteristic wind turbines 2 that are distributed at intervals, and a sensor is arranged in the characteristic wind turbine 1, and the speed data of the characteristic wind turbine 1 is obtained by using the sensor, and the speed data of the non-characteristic wind turbine 2 is inferred by using the speed data of the characteristic wind turbine 1.

[0075] For example, the wind turbines in the wind farm are divided into characteristic wind turbines 1 and non-characteristic wind turbines 2 that are distributed at intervals, and sensors are arranged in the characteristic wind turbines 1, and the torque data of the characteristic wind turbines 1 is obtained by using the sensors, and the torque data of the non-characteristic wind turbines 2 is inferred by using the torque data of the characteristic wind turbines 1.

[0076] It should be noted that for environmental data such as wind speed, wind direction, wave speed, flow direction, temperature, density, etc., since the environments in which each wind turbine is located are similar, the weighted average method can be used to accurately calculate them. For operating data such as speed, torque, vibration acceleration, displacement, temperature, voltage, current, etc., since the various wind turbines arranged in the wind farm are basically of the same model and production batch, and are in similar environments, the weighted average method can be used to accurately calculate them.

[0077] Among them, for some parameters, there are large differences between wind turbines and the estimation accuracy is low, for example: power generation and other parameters that are affected by multiple factors.

[0078] It should be noted that, in the description of the present disclosure, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present disclosure, unless otherwise specified, the meaning of "plurality" is two or more.

[0079] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present disclosure includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present disclosure belong.

[0080] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0081] Although the embodiments of the present disclosure have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present disclosure. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present disclosure.

Claims

1. A distributed arrangement method of wind farm sensors, characterized in that: include: Taking some of the wind turbines in the wind farm as characteristic wind turbines, and taking the rest of the wind turbines in the wind farm as non-characteristic wind turbines, wherein a plurality of the characteristic wind turbines and a plurality of the non-characteristic wind turbines are distributed at intervals; Arranging a plurality of the sensors in the characteristic wind turbine group, and using the plurality of the sensors to obtain environmental data and operating data of the characteristic wind turbine group; The environmental data and operating data of the non-characteristic wind turbine group are calculated by using a weighted average method according to the environmental data and operating data of a plurality of the characteristic wind turbine groups around the non-characteristic wind turbine group.

2. The distributed arrangement method of wind farm sensors according to claim 1, characterized in that: The method further comprises: Obtaining the distance between adjacent characteristic wind turbine groups located at a certain position of the non-characteristic wind turbine group; When the interval is greater than a preset interval, selecting the environmental data and operating data of one of the characteristic wind turbines closest to the non-characteristic wind turbine in the direction for inferring the environmental data and operating data of the non-characteristic wind turbine; When the interval is not greater than a preset interval, the environmental data and operating data of the two characteristic wind turbines closest to the non-characteristic wind turbine in the direction are selected for calculating the environmental data and operating data of the non-characteristic wind turbine.

3. The distributed arrangement method of wind farm sensors according to claim 1, characterized in that: The method further comprises: The calculation weights of the plurality of characteristic wind turbine groups located at a certain position of the non-characteristic wind turbine group are set to decrease in sequence in a direction away from the non-characteristic wind turbine group.

4. The distributed arrangement method of wind farm sensors according to claim 1, characterized in that: The method further comprises: The calculation weights of a plurality of the characteristic wind turbine groups located around the non-characteristic wind turbine group and closest to the non-characteristic wind turbine group are set to be the same.

5. The distributed arrangement method of wind farm sensors according to claim 1, characterized in that: The method further comprises: Acquire the wind direction at the non-characteristic wind turbine group according to the environmental data of a plurality of the characteristic wind turbine groups around the non-characteristic wind turbine group; The calculation weight of the characteristic wind turbine group located upwind of the non-characteristic wind turbine group is set to be greater than the calculation weight of the characteristic wind turbine group located downwind of the non-characteristic wind turbine group.

6. The distributed arrangement method of wind farm sensors according to claim 1, characterized in that: The method further comprises: Taking the wind turbine group at the corner of the wind farm as the characteristic wind turbine group; The characteristic wind turbine group at the corner is taken as the starting group, and wind turbine groups are selected in sequence as the characteristic wind turbine groups.

7. The distributed arrangement method of wind farm sensors according to claim 1, characterized in that: The method further comprises: A plurality of the characteristic wind turbine groups are arranged to be distributed equidistantly or unequally.

8. The distributed arrangement method of wind farm sensors according to claim 1, characterized in that: The method further comprises: There is one non-characteristic wind turbine set between adjacent characteristic wind turbine sets; or, A plurality of the non-characteristic wind turbine groups are arranged between adjacent characteristic wind turbine groups.

9. The distributed arrangement method of wind farm sensors according to claim 1, characterized in that: The environmental data includes: one or more of wind speed, wind direction, wave velocity, flow direction, temperature, and density.

10. The distributed arrangement method of wind farm sensors according to claim 1, characterized in that: The operating data includes: one or more of rotation speed, torque, vibration acceleration, displacement, temperature, voltage, and current.

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