A wind farm sensor distributed arrangement method

Abstract

The present disclosure provides a wind farm sensor distributed arrangement method, comprising: regarding part of wind turbines in a wind farm as feature wind turbines, and regarding the rest of wind turbines in the wind farm as non-feature wind turbines, wherein the feature wind turbines and the non-feature wind turbines are distributed at intervals; arranging multiple sensors in the feature wind turbines, and obtaining environmental data and operation data of the feature wind turbines by using the multiple sensors; and calculating the environmental data and operation data of the non-feature wind turbines by using a weighted average method according to the environmental data and operation data of the feature wind turbines around the non-feature wind turbines. In the wind farm sensor distributed arrangement method, a large number of sensors arranged in the non-feature wind turbines and sensor data are saved, so that the operation efficiency of the wind farm is improved, and the arrangement difficulty and use cost of the wind farm are greatly reduced.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the field of wind power generation technology, in particular to a wind farm sensor distributed arrangement method. BACKGROUND

[0002] A wind turbine is a device for converting wind energy into electrical energy, which needs to collect environmental data and operating data at all times during operation in order to timely correct the operating state of the unit and operate at the optimal power generation efficiency. However, a wind turbine often installs hundreds of sensors to collect data of thousands of parameters, and the number of sensors and data in a wind farm is in the tens of thousands, but the data collected by the sensors in different positions of the wind turbine are often the same, and some collected data are only used for trend analysis and are 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 arrangement of the wind farm difficult and the use cost high. SUMMARY

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

[0004] To this end, the purpose of the present disclosure is to provide a wind farm sensor distributed arrangement method.

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

[0006] Optionally, the method further comprises: obtaining the distance of an adjacent characteristic wind turbine located at a certain direction of the non-characteristic wind turbine; when the distance is greater than a preset distance, selecting the environmental data and operating data of the characteristic wind turbine closest to the non-characteristic wind turbine at the direction for calculating the environmental data and operating data of the non-characteristic wind turbine; and when the distance is not greater than the preset distance, selecting the environmental data and operating data of the two characteristic wind turbines closest to the non-characteristic wind turbine at the direction for calculating the environmental data and operating data of the non-characteristic wind turbine.

[0007] Optionally, the method further comprises: setting the calculation weight of the plurality of feature wind turbines located at a certain direction of the non-feature wind turbine to decrease in turn along the direction away from the non-feature wind turbine.

[0008] Optionally, the method further comprises: setting the calculation weight of the plurality of feature wind turbines located around the non-feature wind turbine and closest to the non-feature wind turbine to be the same.

[0009] Optionally, the method further comprises: obtaining the wind direction at the non-feature wind turbine according to the environmental data of the plurality of feature wind turbines around the non-feature wind turbine; and setting the calculation weight of the feature wind turbine located at the upwind direction of the non-feature wind turbine to be greater than the calculation weight of the feature wind turbine located at the downwind direction of the non-feature wind turbine.

[0010] Optionally, the method further comprises: taking the wind turbines at the corners in the wind farm as the feature wind turbines; and taking the feature wind turbines at the corners as the starting turbines, and selecting the wind turbines as the feature wind turbines in turn and at intervals.

[0011] Optionally, the method further comprises: setting the plurality of feature wind turbines to be distributed at equal intervals or at unequal intervals.

[0012] Optionally, the method further comprises: setting one non-feature wind turbine between the adjacent feature wind turbines; or setting a plurality of non-feature wind turbines between the adjacent feature wind turbines.

[0013] Optionally, the environmental data comprises one or more of wind speed, wind direction, sea wave flow speed, flow direction, air temperature, and density.

[0014] Optionally, the operation data comprises one or more of rotating speed, torque, vibration acceleration, displacement, temperature, voltage, and current.

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

[0016] The wind turbines in the wind farm are divided into the feature wind turbines and the non-feature wind turbines distributed at intervals, the sensors are arranged in the feature wind turbines, the environmental data and the operation data of the feature wind turbines are obtained by using the sensors, and the environmental data and the operation data of the non-feature wind turbines are calculated by using the environmental data and the operation data of the feature wind turbines. Thus, the environmental data and the operation data of all the wind turbines can be ensured to be obtained, the normal operation of the wind farm is ensured, a large number of sensors arranged in the non-feature wind turbines and the sensing data are saved, the operation efficiency of the wind farm is improved, and the arrangement difficulty and the use cost of the wind farm are greatly reduced.

[0017] Additional aspects and advantages of the present disclosure will be made apparent from the following description, which proceeds with reference to the accompanying drawings, wherein: BRIEF DESCRIPTION OF DRAWINGS

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

[0019] Figure 1 is a flowchart of a wind farm sensor distributed arrangement method according to an embodiment of the present disclosure;

[0020] Figure 2 is a schematic diagram of a wind farm according to an embodiment of the present disclosure;

[0021] Figure 3 is a schematic diagram of a wind farm according to 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 with reference to the accompanying drawings, wherein the same or similar notations are used to represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present disclosure, and cannot be understood as a limitation of the present disclosure. On the contrary, the embodiments of the present disclosure include all changes, modifications and equivalents falling within the spirit and scope of the appended claims.

[0024] As shown in Figure 1 , Figure 2 and Figure 3 , the present disclosure proposes a wind farm sensor distributed arrangement method, comprising:

[0025] S1: Part of the wind turbines in the wind farm are taken as characteristic wind turbines 1, and the rest of the wind turbines in the wind farm are taken as non-characteristic wind turbines 2, wherein the plurality of characteristic wind turbines 1 and the plurality of non-characteristic wind turbines 2 are distributed at intervals;

[0026] S2: A plurality of sensors are arranged in the characteristic wind turbines 1, and the environmental data and operating data of the characteristic wind turbines 1 are obtained by using the plurality of sensors;

[0027] S3: The environmental data and operating data of the non-characteristic wind turbines 2 are calculated by using the weighted average method according to the environmental data and operating data of the plurality of characteristic wind turbines 1 around the non-characteristic wind turbines 2.

[0028] It can be understood that the wind turbines in the wind farm are divided into the interval-distributed characteristic wind turbines 1 and non-characteristic wind turbines 2, and the sensors are arranged in the characteristic wind turbines 1, and the environmental data and the operation data of the characteristic wind turbines 1 are obtained by using the sensors, and the environmental data and the operation data of the non-characteristic wind turbines 2 are calculated by using the environmental data and the operation data of the characteristic wind turbines 1, so that the environmental data and the operation data of all the wind turbines can be ensured to be obtained, the normal operation of the wind farm is ensured, and a large number of sensors arranged in the non-characteristic wind turbines 2 and the sensing data are saved, so that the operation efficiency of the wind farm is improved, and the arrangement difficulty and the use cost of the wind farm are greatly reduced.

[0029] It should be noted that the wind turbines are devices for converting wind energy into electric energy, the environmental data and the operation data of the characteristic wind turbines 1 can be obtained by using the multiple sensors in the characteristic wind turbines 1, and the environmental data and the operation data of the non-characteristic wind turbines 2 can be calculated by using the environmental data and the operation data of the characteristic wind turbines 1, so that the obtained environmental data and the operation data can be used to timely correct the operation state of the wind turbines to operate at the optimal power generation efficiency.

[0030] The embodiment effectively reduces the number and the cost of the sensors arranged in the wind farm by arranging the sensors in the multiple wind turbines, and the data acquisition of the wind turbines without the sensors is not affected by the method of calculating the data of the surrounding wind turbines.

[0031] The characteristic wind turbines 1 are used to arrange the sensors, the non-characteristic wind turbines 2 are not used to arrange the sensors, and the multiple characteristic wind turbines 1 and the multiple non-characteristic wind turbines 2 are interval-distributed and are all the wind turbines in the wind farm.

[0032] The multiple sensors arranged in the characteristic wind turbines 1 are used to obtain the environmental data and the operation data of the characteristic wind turbines 1, and the multiple sensors can include various sensors such as a wind speed sensor, a wind direction sensor, a rotating speed sensor, a torque sensor, and the like.

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

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

[0035] In this formula, each value is multiplied by the corresponding weight, and then the products are added, and finally the sum of the products is divided by the sum of all the weights, so that the weighted average number is obtained.

[0036] In this embodiment, each parameter of the non-characteristic wind turbine 2 is calculated by using the weighted average method through the corresponding parameter of the characteristic wind turbine 1 in the four directions.

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

[0038] obtaining the distance of the adjacent characteristic wind turbine 1 located in a certain direction of the non-characteristic wind turbine 2;

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

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

[0041] It can be understood that when the distance is greater than the preset distance, it indicates that the distance of the adjacent characteristic wind turbine 1 located in the direction of the non-characteristic wind turbine 2 is large, the non-characteristic wind turbine 2 is far away from the characteristic wind turbine 1 in the direction, and the data correlation is weak. Therefore, the environmental data and the operating data of the characteristic wind turbine 1 closest to the non-characteristic wind turbine 2 in the direction are selected to calculate the environmental data and the operating data of the non-characteristic wind turbine 2, so as to ensure the accurate calculation of the data of the non-characteristic wind turbine 2, and further ensure the efficient operation of the wind farm.

[0042] When the distance is not greater than the preset distance, it indicates that the distance of the adjacent characteristic wind turbine 1 located in the direction of the non-characteristic wind turbine 2 is small, the non-characteristic wind turbine 2 is close to the characteristic wind turbine 1 in the direction, and the data correlation is strong. Therefore, the environmental data and the operating data of the two characteristic wind turbines 1 closest to the non-characteristic wind turbine 2 in the direction are selected to calculate the environmental data and the operating data of the non-characteristic wind turbine 2, so as to ensure the accurate calculation of the data of the non-characteristic wind turbine 2, and further ensure the efficient operation of the wind farm.

[0043] It should be noted that the preset distance can be set according to actual needs, and no limitation is made thereto.

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

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

[0046] It can be understood that the characteristic wind turbine 1 close to the non-characteristic wind turbine 2 in a certain direction of the non-characteristic wind turbine 2 has a smaller distance from the non-characteristic wind turbine 2, a more similar environment, and stronger data correlation, so that the calculation weight of the characteristic wind turbine 1 is set to be larger in the calculation process, so as to more accurately obtain the environmental data and operation data of the non-characteristic wind turbine 2, and ensure the high efficiency of the wind farm.

[0047] The characteristic wind turbine 1 far away from the non-characteristic wind turbine 2 in a certain direction of the non-characteristic wind turbine 2 has a larger distance from the non-characteristic wind turbine 2, a slightly different environment, and weaker data correlation, so that the calculation weight of the characteristic wind turbine 1 is set to be smaller in the calculation process, so as to more accurately obtain the environmental data and operation data of the non-characteristic wind turbine 2, and ensure the high efficiency of the wind farm.

[0048] Therefore, the calculation weights of the multiple characteristic wind turbines 1 located in a certain direction of the non-characteristic wind turbine 2 are sequentially reduced in the direction away from the non-characteristic wind turbine 2, so as to accurately calculate the environmental data and operation data of the non-characteristic wind turbine 2, and further ensure the high efficiency of the wind farm.

[0049] In some embodiments, the method further comprises: setting the calculation weights of the multiple 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 all have a smaller distance from the non-characteristic wind turbine 2, a more similar environment, and stronger data correlation, so that the calculation weights of the multiple characteristic wind turbines 1 are set to be the same in the calculation process, so as to accurately calculate the environmental data and operation data of the non-characteristic wind turbine 2, and further ensure the high efficiency of the wind farm.

[0051] For example, when the wind turbines of the wind farm are distributed in a matrix shape, the multiple characteristic wind turbines 1 located around the non-characteristic wind turbine 2 and closest to the non-characteristic wind turbine 2 can be the four characteristic wind turbines 1 in front of, behind, left of, 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 2 according to the environmental data of the multiple characteristic wind turbines 1 around the non-characteristic wind turbine 2;

[0054] Setting the calculation weight of the characteristic wind turbine 1 located in the upwind direction of the non-characteristic wind turbine 2 to be larger than the calculation weight of the characteristic wind turbine 1 located in the downwind direction of the non-characteristic wind turbine 2.

[0055] It can be understood that, since the multiple feature wind turbines 1 located around the non-feature wind turbine 2 are all small in distance from the non-feature wind turbine 2 and close in environment, the data correlation is strong, thus the wind direction at the non-feature wind turbine 2 can be obtained according to the environmental data of the multiple feature wind turbines 1 around the non-feature wind turbine 2, and the feature wind turbine 1 located upwind of the non-feature wind turbine 2 is closer in environment to the non-feature wind turbine 2, thus the calculation weight of the feature wind turbine 1 located upwind of the non-feature wind turbine 2 is set to be larger, and the calculation weight of the feature wind turbine 1 located downwind of the non-feature wind turbine 2 is set to be smaller, thereby the environmental data and the operation data of the non-feature wind turbine 2 can be accurately calculated, and the efficient operation of the wind farm is ensured.

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

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

[0058] The wind turbine at the corner in the wind farm is set as the feature wind turbine 1.

[0059] The feature wind turbine 1 at the corner is set as the starting turbine, and the wind turbines are selected as the feature wind turbines 1 in turn and at intervals.

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

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

[0062] The multiple feature wind turbines 1 are set to be equidistantly distributed or non-equidistantly distributed.

[0063] It can be understood that, whether the multiple feature wind turbines 1 are equidistantly distributed or non-equidistantly distributed, the environmental data and the operation data of the non-feature wind turbines 2 can be accurately calculated by using the environmental data and the operation data of the feature wind turbines 1, thereby improving the operation efficiency of the wind farm while reducing the use cost of the wind farm.

[0064] It should be noted that when multiple characteristic wind turbine units 1 are distributed at equal intervals, one characteristic wind turbine unit 1 can be arranged every other non-characteristic wind turbine unit 2, one characteristic wind turbine unit 1 can be arranged every two non-characteristic wind turbine units 2, or one characteristic wind turbine unit 1 can be arranged every three non-characteristic wind turbine units 2. There are no restrictions on this.

[0065] When multiple characteristic wind turbine units 1 are not equidistantly distributed, a characteristic wind turbine unit 1 can be arranged every other non-characteristic wind turbine unit 2. At the same time, in other locations within the same wind farm, a characteristic wind turbine unit 1 can be arranged every two non-characteristic wind turbine units 2, or every three non-characteristic wind turbine units 2, without any restrictions.

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

[0067] The adjacent characteristic wind turbine 1 is provided with one non-characteristic wind turbine 2; or, the adjacent characteristic wind turbine 1 is provided with multiple non-characteristic wind turbine 2.

[0068] It is understandable that setting one non-characteristic wind turbine 2 between adjacent characteristic wind turbine 1 achieves an equidistant distribution of multiple characteristic wind turbine 1s while ensuring a small gap between adjacent characteristic wind turbine 1s; setting multiple non-characteristic wind turbine 2s between adjacent characteristic wind turbine 1s achieves an equidistant distribution of multiple characteristic wind turbine 1s while ensuring a large gap between adjacent characteristic wind turbine 1s.

[0069] In this case, whether there is one non-characteristic wind turbine 2 between adjacent characteristic wind turbine 1 or multiple non-characteristic wind turbine 2 between adjacent characteristic wind turbine 1, the environmental data and operation data of non-characteristic wind turbine 2 can be accurately calculated using the weighted average method.

[0070] In some embodiments, environmental data includes one or more of the following: wind speed, wind direction, ocean current speed, current direction, 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, which are distributed at intervals. 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. The wind speed data of the non-characteristic wind turbines 2 is calculated 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 interval-distributed characteristic wind turbines 1 and non-characteristic wind turbines 2, and sensors are arranged in the characteristic wind turbines 1, and wind direction data of the characteristic wind turbines 1 are obtained by using the sensors, and wind direction data of the non-characteristic wind turbines 2 are calculated by using the wind direction data of the characteristic wind turbines 1.

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

[0074] For example, the wind turbines in the wind farm are divided into interval-distributed characteristic wind turbines 1 and non-characteristic wind turbines 2, and sensors are arranged in the characteristic wind turbines 1, and rotation speed data of the characteristic wind turbines 1 are obtained by using the sensors, and rotation speed data of the non-characteristic wind turbines 2 are calculated by using the rotation speed data of the characteristic wind turbines 1.

[0075] For example, the wind turbines in the wind farm are divided into interval-distributed characteristic wind turbines 1 and non-characteristic wind turbines 2, and sensors are arranged in the characteristic wind turbines 1, and torque data of the characteristic wind turbines 1 are obtained by using the sensors, and torque data of the non-characteristic wind turbines 2 are calculated 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, sea wave flow speed, flow direction, air temperature, density, etc., the environment of each wind turbine is close, so the weighted average method can be used to accurately calculate, and for operation data such as rotation speed, torque, vibration acceleration, displacement, temperature, voltage, current, etc., each wind turbine arranged in the wind farm basically belongs to the same model, production batch, and the environment is close, so the weighted average method can be used to accurately calculate.

[0077] For some parameters, the difference between wind turbines is large, and the calculation accuracy is low, for example: parameters such as power generation affected by multiple factors.

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

[0079] Any procedural or methodological descriptions in flow charts or otherwise described herein can be understood to represent modules, segments, or portions of code that include executable instructions for implementing the logic functions or procedures described, and the scope of preferred embodiments of the present disclosure includes additional implementations in which the functions can be performed in an order different from that shown or discussed, including substantially simultaneously or in reverse order, as appropriate, according to the functionality involved, as will be understood by those skilled in the art to which embodiments of the present disclosure pertain.

[0080] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present disclosure. In the present specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Also, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in an appropriate manner.

[0081] Although the embodiments of the present disclosure have been shown and described above, it is understood that the above-described embodiments are exemplary and are not to be construed as limiting the present disclosure, and those skilled in the art can make changes, modifications, replacements, and variations to the above-described embodiments within the scope of the present disclosure.

Claims

1. A method of distributed arrangement of wind farm sensors, characterized in that, The method comprises: selecting part of wind turbines in the wind farm as characteristic wind turbines and the rest of wind turbines in the wind farm as non-characteristic wind turbines, wherein the characteristic wind turbines and the non-characteristic wind turbines are distributed at intervals; arranging a plurality of sensors in the characteristic wind turbines and obtaining environmental data and operating data of the characteristic wind turbines by using the plurality of sensors; obtaining environmental data and operating data of the non-characteristic wind turbines by using a weighted average method according to the environmental data and operating data of the characteristic wind turbines around the non-characteristic wind turbines; The method further comprises: obtaining the distance between the adjacent characteristic wind turbines located at a certain direction of the non-characteristic wind turbine; when the distance is greater than a preset distance, selecting the environmental data and operating data of the characteristic wind turbine closest to the non-characteristic wind turbine at the direction for calculating the environmental data and operating data of the non-characteristic wind turbine; when the distance is not greater than the preset distance, selecting the environmental data and operating data of the two characteristic wind turbines closest to the non-characteristic wind turbine at the direction for calculating the environmental data and operating data of the non-characteristic wind turbine; The method further comprises: setting the calculation weights of the plurality of characteristic wind turbines located at a certain direction of the non-characteristic wind turbine to decrease in turn along the direction away from the non-characteristic wind turbine; The method further comprises: obtaining the wind direction at the non-characteristic wind turbine according to the environmental data of the plurality of characteristic wind turbines around the non-characteristic wind turbine; setting the calculation weight of the characteristic wind turbine located at the upwind direction of the non-characteristic wind turbine to be greater than the calculation weight of the characteristic wind turbine located at the downwind direction of the non-characteristic wind turbine; The method further comprises: setting the plurality of characteristic wind turbines to be distributed at equal intervals or non-equal intervals.

2. The wind farm sensor distributed arrangement method according to claim 1, characterized in that, The method further comprises: setting the calculation weights of the plurality of characteristic wind turbines located around the non-characteristic wind turbine and closest to the non-characteristic wind turbine to be the same.

3. The wind farm sensor distributed arrangement method according to claim 1, wherein, The method further comprises: selecting the wind turbines at the corners of the wind farm as the characteristic wind turbines; selecting the characteristic wind turbines at the corners as the starting wind turbines and selecting the wind turbines at intervals as the characteristic wind turbines in turn.

4. The wind farm sensor distributed arrangement method of claim 1, wherein, The method further comprises: setting one non-characteristic wind turbine between the adjacent characteristic wind turbines; or, setting a plurality of non-characteristic wind turbines between the adjacent characteristic wind turbines.

5. The wind farm sensor distributed arrangement method of claim 1, wherein, The environmental data comprises one or more of wind speed, wind direction, sea wave flow speed, flow direction, air temperature, and density.

6. The wind farm sensor distributed arrangement method of claim 1, wherein, The operating data comprises one or more of rotating speed, torque, vibration acceleration, displacement, temperature, voltage, and current.

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