Offshore wind plant wireless data return system and method based on 5G technology

Abstract

The invention provides an offshore wind plant wireless data return system and method based on the 5G technology, and relates to the technical field of wireless data communication. The system comprises a wireless return data acquisition module, a transmission quality evaluation and judgment module, a fan operation evaluation and judgment module and a fan optimization evaluation feedback module. According to the invention, the wireless backhaul data is acquired and transmitted to the preset wind power plant monitoring center, and then the transmission quality of the wireless backhaul data is evaluated to judge whether the wind turbine generator operation coincidence degree evaluation is executed or not; and then obtaining a wind turbine generator operation evaluation index after wind turbine generator operation coincidence degree evaluation is executed, judging whether to execute wind turbine generator operation state optimization or not, and finally, comprehensively judging whether to execute aging detection reminding or not, thereby realizing improvement of wireless data return stability of the offshore wind plant. The problem that in the prior art, the stability of wireless data return of an offshore wind power plant is low is solved.

Description

Technical Field

[0001] The present invention relates to the field of wireless data communication technology, and in particular to a 5G-based offshore wind farm wireless data backhaul system and method. Background Art

[0002] Offshore wind farms are usually located in sea areas far from the shore, with complex environments and widely distributed wind power generation equipment. As the scale of wind farms expands, wind turbines will generate a large amount of operating data (such as wind speed, power generation, equipment status, etc.), which need to be transmitted back to the control center on land in real time for monitoring and management. Therefore, higher requirements are placed on data transmission. As the fifth generation of mobile communication technology, 5G has outstanding advantages in high bandwidth, low latency, large-scale connection and high reliability. Through the high-bandwidth, low-latency and wide-coverage 5G network, real-time transmission and remote management of offshore wind farm data can be realized, providing technical support for the intelligent and automated operation of wind farms.

[0003] Existing wireless data backhaul systems for offshore wind farms mostly use wireless communication technology to transmit wind farm data back to the operation and management center on land. Various sensors installed on wind turbines collect data in real time, such as wind speed, rotation speed, temperature, and power generation. The sensor data is aggregated and processed by the local control system, and the processed data is transmitted to the management center on land via wireless communication. Through microwave communication, 4G / 5G cellular networks, and satellite communications, combined with sensor networks and edge computing technologies, data backhaul and remote management of offshore wind farms are realized.

[0004] Remote monitoring and maintenance of offshore wind farms have emerged in the context of the rapid development of the offshore wind power industry. However, offshore wind farms also face greater operation and maintenance challenges because they are usually located in remote sea areas, with complex offshore environments and bad weather, high operation and maintenance costs and difficulties. The harsh marine environment, wind, waves, salt spray and other factors will have a negative impact on the life and performance of equipment, especially communication equipment, sensors, etc., which are exposed to the marine environment for a long time and are susceptible to corrosion and damage. At the same time, offshore communication equipment relies on power supply, and unstable power supply or insufficient battery power may cause communication equipment to frequently drop out or weak signals, thereby affecting the stability of data transmission.

[0005] In the existing technology, in the process of remote monitoring and maintenance of offshore wind farms, timely response and optimization adjustment are crucial to ensure the operational stability of offshore wind farms. For example, in a low temperature environment, wind turbine blades may freeze or be affected by pollutants such as dust and salt, which may reduce the aerodynamic performance of the blades and increase the risk of vibration, thereby affecting the wireless data backhaul of the offshore wind farm. There is a problem of low stability of wireless data backhaul of offshore wind farms. Summary of the invention

[0006] The present invention solves the problem of low stability of wireless data backhaul from offshore wind farms in the prior art by providing a wireless data backhaul system and method for offshore wind farms based on 5G technology, thereby improving the stability of wireless data backhaul from offshore wind farms.

[0007] The present invention provides an offshore wind farm wireless data backhaul system based on 5G technology, including a wireless backhaul data acquisition module, a transmission quality assessment and judgment module, a wind turbine operation assessment and judgment module and a wind turbine optimization assessment and feedback module; wherein the wireless backhaul data acquisition module is used to acquire the wireless backhaul data of the offshore wind farm within a preset time interval, and transmit the wireless backhaul data to a preset wind farm monitoring center at the same time; the transmission quality assessment and judgment module is used to evaluate the transmission quality of the wireless backhaul data to obtain a backhaul data quality assessment index, and judge whether to perform the wind turbine operation compliance assessment based on the backhaul data quality assessment index and a preset backhaul quality assessment threshold range; the backhaul data quality assessment index is used to quantify the wireless backhaul data quality. The wind turbine operation evaluation and judgment module is used to obtain the wind turbine operation evaluation index after the wind turbine operation compliance evaluation is performed, and whether to perform the wind turbine operation status optimization is judged based on the wind turbine operation evaluation index and the preset unit operation compliance threshold range, and the wind turbine operation evaluation index is used to comprehensively quantify the compliance of the wind turbine operation; the wind turbine optimization evaluation feedback module is used to obtain the wind turbine optimization effect evaluation index after the wind turbine operation status optimization is performed, and comprehensively judge whether to perform the aging detection reminder according to the feedback data quality evaluation index and the wind turbine operation evaluation index, and the wind turbine optimization effect evaluation index is used to quantitatively evaluate the effect of the wind turbine operation status optimization.

[0008] Optionally, the specific steps of evaluating the transmission quality of wireless backhaul data to obtain a backhaul data quality evaluation index are as follows: obtaining backhaul data quality related values ​​and quality evaluation related values ​​at preset time intervals, the backhaul data quality related values ​​including data hash check sending values, data hash check receiving values, the total number of wireless backhaul lost data packets, the total number of wireless backhaul sent data packets, data packet sending time and data packet receiving time, the quality evaluation related values ​​including ambient atmospheric pressure measurement values, ambient signal propagation distance measurement values ​​and ambient signal interference intensity measurement values; comparing the data hash check sending value with the data hash check receiving value to obtain a data hash check compliance value; obtaining an environmental impact correction coefficient based on the quality evaluation related value combined with the standard atmospheric pressure and the influencing factors obtained from a preset database, the influencing factors including the ambient atmospheric pressure influencing factor, the ambient distance influencing factor and the ambient signal interference influencing factor; combining the data hash check compliance value, the backhaul data quality related value, the environmental impact correction coefficient and the transmission quality evaluation weight obtained from the preset database to obtain the backhaul data quality evaluation index, the transmission quality evaluation weight including the data effective quality evaluation weight, the data complete quality evaluation weight and the data timeliness quality evaluation weight.

[0009] Optionally, the method for obtaining the return data quality evaluation index is as follows:

[0010]

[0011]

[0012] δ h =1-[τ1*(P-P0)+τ2*Gd+τ3*Gq];

[0013] Where Df represents the backhaul data quality assessment index, α represents the data validity quality assessment weight, β represents the data integrity quality assessment weight, γ represents the data delay quality assessment weight, d represents the number of wireless backhaul data packets, d = 1, 2, ..., D, D represents the total number of wireless backhaul data packets, H d Indicates the data hash checksum of the dth wireless backhaul data packet, h d Indicates the data hash check value sent by the dth wireless backhaul data packet, h ′ d represents the data hash check received value of the dth wireless backhaul data packet, Ld represents the total number of wireless backhaul lost data packets, Zd represents the total number of wireless backhaul sent data packets, t d represents the data packet sending time of the dth wireless backhaul data packet, t ′ d represents the packet receiving time of the dth wireless backhaul packet, Δt represents the reference packet delay deviation, δg represents the environmental impact correction coefficient, τ1 represents the environmental air pressure impact factor, P represents the environmental atmospheric pressure measurement value, P0 represents the standard atmospheric pressure, τ2 represents the environmental distance impact factor, Gd represents the environmental signal propagation distance measurement value, τ3 represents the environmental signal interference impact factor, and Gq represents the environmental signal interference intensity measurement value.

[0014] Optionally, the specific steps of obtaining the wind turbine operation evaluation index after performing the wind turbine operation compliance assessment are as follows: obtaining operation measurement related values ​​of preset measurement points within a preset time interval, the operation measurement related values ​​including actual power generation of the wind turbine, actual voltage of the wind turbine, actual frequency of the wind turbine, wind wheel radius and ambient wind speed; obtaining a power quality assessment value by processing the actual voltage of the wind turbine, the actual frequency of the wind turbine and the fluctuation compliance factor and reference related values ​​obtained from a preset database, the fluctuation compliance factor including a voltage fluctuation compliance factor and a frequency fluctuation compliance factor, the reference related values ​​including a standard reference voltage of the wind turbine and a standard reference frequency of the wind turbine; processing the power quality assessment value in combination with the air density and the reference data obtained from the preset database to obtain the wind turbine operation evaluation index, the reference data including the standard reference power generation of the wind turbine, the reference power generation change value, the reference wind energy utilization, the operation power compliance weight, the wind energy utilization compliance weight and the power quality compliance weight.

[0015] Optionally, the specific steps for judging whether to perform wind turbine operation status optimization based on the wind turbine operation evaluation index and the preset unit operation compliance threshold range are as follows: compare the wind turbine operation evaluation index with the preset unit operation compliance threshold range: if the wind turbine operation evaluation index is within the preset unit operation compliance threshold range, the wind turbine operation status optimization is not performed, and the wind turbine operation evaluation index is uploaded to cloud storage; if the wind turbine operation evaluation index exceeds the preset fluctuation compliance threshold range, the wind turbine operation status optimization is performed, and the wind turbine operation evaluation index is fed back to the preset personnel.

[0016] Optionally, the specific steps for performing wind turbine operating state optimization are as follows: A1, obtaining initial operating related data of the wind turbine, increasing the initial pitch angle of the wind turbine in sequence until the maximum pitch angle is reached, judging whether the wind turbine operating evaluation index is within the preset unit operating compliance threshold range, if so, stopping the wind turbine operating state optimization, otherwise executing A2, the initial operating related data include the initial pitch angle, the initial wind turbine speed and the initial output voltage; A2, successively reducing the initial wind turbine speed of the wind turbine until the minimum wind turbine speed is reached, judging whether the wind turbine operating evaluation index is within the preset unit operating compliance threshold range, if so, stopping the wind turbine operating state optimization, otherwise executing A3; A3, successively reducing the initial output voltage of the wind turbine until the minimum output voltage is reached, and simultaneously obtaining the wind turbine optimization effect evaluation index after performing wind turbine operating state optimization.

[0017] Optionally, the specific steps for obtaining the wind turbine optimization effect evaluation index after performing wind turbine operating state optimization are as follows: obtaining initial relevant measurement values ​​of preset measurement points to be optimized within a preset time interval to be optimized, the initial relevant measurement values ​​including initial actual average generated power, initial output power, initial average output power, initial average energy consumption and initial total power generation; obtaining optimization relevant measurement values ​​of preset optimization measurement points within the preset optimization time interval, the optimization relevant measurement values ​​including optimized actual average generated power, optimized output power, optimized average output power, optimized average energy consumption and optimized total power generation; obtaining a power fluctuation compliance evaluation value by the difference between the results of processing the initial output power and the initial average output power and the results of processing the optimized output power and the optimized average output power; processing the initial relevant measurement values, the optimization relevant measurement values, the power fluctuation compliance evaluation values ​​and the optimization relevant reference data obtained from a preset database to obtain the wind turbine optimization effect evaluation index, the optimization relevant reference data including a reference power generation efficiency change, a reference power fluctuation change, a reference energy consumption change, a power generation efficiency optimization evaluation weight, a power fluctuation optimization evaluation weight and an energy consumption change evaluation weight.

[0018] The present invention provides a wireless data backhaul method for an offshore wind farm based on 5G technology, comprising the following steps: S1, obtaining wireless backhaul data of an offshore wind farm within a preset time interval, and transmitting the wireless backhaul data to a preset wind farm monitoring center; S2, evaluating the transmission quality of the wireless backhaul data to obtain a backhaul data quality evaluation index, judging whether to perform a wind turbine operation compliance assessment based on the backhaul data quality evaluation index and a preset backhaul quality evaluation threshold range, and the backhaul data quality evaluation index is used to quantify the transmission quality of the wireless backhaul data; S3, obtaining a wind turbine operation evaluation index after performing a wind turbine operation compliance assessment, judging whether to perform a wind turbine operation state optimization based on the wind turbine operation evaluation index and a preset unit operation compliance threshold range, and the wind turbine operation evaluation index is used to comprehensively quantify the wind turbine operation compliance; S4, obtaining a wind turbine optimization effect evaluation index after performing a wind turbine operation state optimization, and comprehensively judging whether to perform an aging detection reminder based on the backhaul data quality evaluation index and the wind turbine operation evaluation index, and the wind turbine optimization effect evaluation index is used to quantitatively evaluate the effect of wind turbine operation state optimization.

[0019] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0020] 1. By evaluating the transmission quality of wireless backhaul data to determine whether to perform the wind turbine operation compliance assessment, then obtaining the wind turbine operation assessment index after performing the wind turbine operation compliance assessment and determining whether to perform wind turbine operation status optimization, and finally making a comprehensive judgment on whether to perform aging detection reminders, the evaluation of wireless backhaul data transmission quality and the evaluation and optimization of wind turbine operation status are achieved, thereby achieving the improvement of the stability of wireless data backhaul in offshore wind farms, and effectively solving the problem of low stability of wireless data backhaul in offshore wind farms in the prior art.

[0021] 2. By obtaining the return data quality related values ​​and quality assessment related values ​​at preset time intervals, and obtaining the data hash check compliance value and the environmental impact correction coefficient at the same time, and then combining the data hash check compliance value, the return data quality related value, the environmental impact correction coefficient and the transmission quality assessment weight obtained from the preset database to obtain the return data quality assessment index, the numerical evaluation of the wireless return data transmission quality is realized, and then a more accurate evaluation of the wireless return data transmission quality is realized.

[0022] 3. By obtaining the operation measurement related values ​​of the preset measurement points within the preset time interval, and then processing the actual voltage and frequency of the wind turbine, the fluctuation compliance factor obtained from the preset database, and the reference related values, the power quality assessment value is obtained. Finally, the power quality assessment value is combined with the air density and the reference data obtained from the preset database to obtain the wind turbine operation evaluation index, thereby realizing the numerical evaluation of the wind turbine operation compliance, and then realizing a more accurate evaluation of the wind turbine operation compliance. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 A schematic diagram of the structure of the wireless data backhaul system for offshore wind farms based on 5G technology provided by the present invention;

[0025] Figure 2 Schematic diagram of the change of the backhaul data quality evaluation index provided by the present invention, wherein (a) is a schematic diagram of the change of the backhaul data quality evaluation index with the total number of wireless backhaul lost data packets, and (b) is a schematic diagram of the change of the backhaul data quality evaluation index with |data packet sending time - data packet receiving time|;

[0026] Figure 3 A flow chart of the offshore wind farm wireless data backhaul method based on 5G technology provided by the present invention. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] Unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. Similarly, words such as "one", "one" or "the" do not indicate a quantitative limitation, but indicate the existence of at least one. Words such as "include" or "comprise" mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Words such as "connect" or "connected" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect.

[0029] It should be noted that the terms "up", "down", "left", "right", "front", "back", etc. used in the present invention are only used to indicate relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0030] In view of the problem of low stability of wireless data backhaul in offshore wind farms in the prior art, the present invention provides a wireless data backhaul system and method for offshore wind farms based on 5G technology, thereby improving the stability of wireless data backhaul in offshore wind farms.

[0031] like Figure 1As shown, the present invention provides a structural schematic diagram of an offshore wind farm wireless data backhaul system based on 5G technology. The offshore wind farm wireless data backhaul system based on 5G technology provided by the present invention includes a wireless backhaul data acquisition module, a transmission quality assessment and judgment module, a wind turbine operation assessment and judgment module, and a wind turbine optimization assessment feedback module; wherein the wireless backhaul data acquisition module is used to obtain the wireless backhaul data of the offshore wind farm within a preset time interval, and transmit the wireless backhaul data to a preset wind farm monitoring center; the transmission quality assessment and judgment module is used to evaluate the transmission quality of the wireless backhaul data to obtain a backhaul data quality assessment index, and judge whether the wind turbine group operation is in compliance with the program based on the backhaul data quality assessment index and the preset backhaul quality assessment threshold range. The wind turbine operation evaluation and judgment module is used to obtain the wind turbine operation evaluation index after the wind turbine operation compliance evaluation is performed, and whether to perform wind turbine operation status optimization is determined based on the wind turbine operation evaluation index and the preset unit operation compliance threshold range. The wind turbine operation evaluation index is used to comprehensively quantify the compliance of wind turbine operation; the wind turbine optimization evaluation feedback module is used to obtain the wind turbine optimization effect evaluation index after the wind turbine operation status optimization is performed, and whether to perform aging detection reminder is comprehensively determined based on the return data quality evaluation index and the wind turbine operation evaluation index. The wind turbine optimization effect evaluation index is used to quantitatively evaluate the effect of wind turbine operation status optimization.

[0032] In this embodiment, a preset wind farm monitoring center is used to receive and store wireless backhaul data and monitor the operating status of wind turbines in the wind farm; the wireless backhaul data includes wind turbine operating data and environmental data; the wind turbine operating data includes wind turbine power generation, wind turbine speed, wind turbine output voltage and wind turbine output current; the environmental data includes ambient wind speed, ambient temperature and ambient air pressure.

[0033] Specifically, the power generation of the wind turbine is obtained through the electric energy metering device deployed at the output end of the generator; the speed of the wind turbine is obtained through the speed sensor deployed on the output shaft of the wind turbine rotor; the output voltage and output current of the wind turbine are obtained through the voltage sensor and current sensor deployed at the output end of the generator; and the environmental data is obtained through the wind speed sensor, temperature sensor and air pressure sensor deployed on the top of the nacelle of the wind turbine.

[0034] Among them, the acquired wireless backhaul data is packaged in binary data format and compressed through lossless compression algorithm to obtain wireless backhaul data packet, and then the wireless backhaul data packet is encrypted through hash function. Then, the wireless backhaul data is transmitted to the preset wind farm monitoring center through 5G technology. 5G technology has the advantages of providing high bandwidth, low latency and wide coverage, which enhances the stability of wireless backhaul data transmission. By obtaining wireless backhaul data, comprehensive monitoring of offshore wind farms is achieved, thereby improving the reliability of wireless backhaul data of offshore wind farms.

[0035] The specific steps of evaluating the quality of wireless backhaul data transmission to obtain the backhaul data quality evaluation index are as follows: obtaining the backhaul data quality related values ​​and the quality evaluation related values ​​at the preset time intervals, the backhaul data quality related values ​​include the data hash check sending value, the data hash check receiving value, the total number of wireless backhaul lost data packets, the total number of wireless backhaul sent data packets, the data packet sending time and the data packet receiving time, and the quality evaluation related values ​​include the ambient atmospheric pressure measurement value, the ambient signal propagation distance measurement value and the ambient signal interference strength measurement value; comparing the data hash check sending value with the data hash check receiving value to obtain the data hash check compliance value (i.e., H in the backhaul data quality evaluation index). d ); the environmental impact correction coefficient (i.e., the δ in the returned data quality assessment index) is obtained based on the quality assessment related values ​​combined with the standard atmospheric pressure and the impact factors obtained from the preset database. h ), the influencing factors include the environmental air pressure influencing factor, the environmental distance influencing factor and the environmental signal interference influencing factor; the data hash check compliance value, the return data quality related value, the environmental impact correction coefficient and the transmission quality assessment weight obtained from the preset database are used to obtain the return data quality assessment index, and the transmission quality assessment weight includes the data effective quality assessment weight, the data complete quality assessment weight and the data timeliness quality assessment weight.

[0036] The method for obtaining the return data quality evaluation index is as follows:

[0037]

[0038]

[0039] δ h =1-[τ1*(P-P0)+τ2*Gd+τ3*Gq];

[0040] Where Df represents the backhaul data quality assessment index, α represents the data validity quality assessment weight, β represents the data integrity quality assessment weight, γ represents the data delay quality assessment weight, d represents the number of wireless backhaul data packets, d = 1, 2, ..., D, D represents the total number of wireless backhaul data packets, Hd Indicates the data hash checksum of the dth wireless backhaul data packet, h d Indicates the data hash check value sent by the dth wireless backhaul data packet, h ′ d represents the data hash check received value of the dth wireless backhaul data packet, Ld represents the total number of wireless backhaul lost data packets, Zd represents the total number of wireless backhaul sent data packets, t d represents the data packet sending time of the dth wireless backhaul data packet, t ′ d represents the packet receiving time of the dth wireless backhaul packet, Δt represents the reference packet delay deviation, δ h represents the environmental impact correction coefficient, τ1 represents the environmental air pressure impact factor, P represents the environmental atmospheric pressure measurement value, P0 represents the standard atmospheric pressure, τ2 represents the environmental distance impact factor, Gd represents the environmental signal propagation distance measurement value, τ3 represents the environmental signal interference impact factor, and Gq represents the environmental signal interference intensity measurement value.

[0041] In this embodiment, the total number of wireless backhaul lost data packets and the total number of wireless backhaul sent data packets are obtained through the data packet statistics information in the network interface; the data packet sending time and the data packet receiving time are obtained through the time function; the ambient atmospheric pressure measurement value is obtained through the air pressure sensor deployed on the top of the wind turbine cabin; the ambient signal propagation distance measurement value is obtained through the GPS (Global Positioning System Module) module; the ambient signal interference intensity measurement value is obtained through a wireless communication module such as the LoRa (Long Range Module) module, and the reference data packet delay deviation is obtained from a preset database, wherein the reference data packet delay deviation is represented by the result of summing and averaging the collected historical data packet transmission delays.

[0042] Among them, the ambient atmospheric pressure measurement value, standard atmospheric pressure, ambient signal propagation distance measurement value, and ambient signal interference intensity measurement value are all dimensionally processed.

[0043] It should be understood that the ambient pressure impact factor is used to describe the degree of influence of the ambient pressure on the environmental impact correction coefficient. Specifically, the ambient pressure impact factor is the impact factor corresponding to the ambient pressure preset in the preset database, and represents the numerical value of the degree of influence of the ambient pressure on the environmental impact correction coefficient. When used, the preset ambient pressure impact factor can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the ambient pressure and the impact factor corresponding to the ambient pressure preset in the preset database form a mapping set, and the real-time ambient pressure is input into the mapping set to obtain the corresponding impact factor. The mapping relationship can be one-to-one or many-to-one. In this example, the value range is [0, 1].

[0044] The environmental distance impact factor is used to describe the degree of influence of the environmental signal propagation distance on the environmental impact correction coefficient. Specifically, the environmental distance impact factor is the impact factor corresponding to the environmental distance preset in the preset database, and represents the numerical value of the influence of the environmental signal propagation distance on the environmental impact correction coefficient. When used, the preset environmental distance impact factor can be directly obtained from the preset database, and the corresponding relationship can be a preset mapping relationship. For example, the signal propagation distance and the impact factor corresponding to the preset environmental distance in the preset database form a mapping set, and the real-time environmental signal propagation distance is input into the mapping set to obtain the corresponding impact factor. The mapping relationship can be one-to-one or many-to-one. In this example, the value range is [0, 1].

[0045] The environmental signal interference impact factor is used to describe the degree of influence of the environmental signal propagation interference intensity on the environmental impact correction coefficient. Specifically, the environmental signal interference response factor is the impact factor corresponding to the environmental signal interference preset in the preset database, which represents the numerical value of the influence of the environmental signal propagation interference intensity on the environmental impact correction coefficient. When used, the preset environmental signal interference impact factor can be directly obtained from the preset database, and the corresponding relationship can be a preset mapping relationship. For example, the environmental signal propagation interference intensity and the impact factor corresponding to the environmental signal interference preset in the preset database form a mapping set, and the real-time signal propagation interference intensity is input into the mapping set to obtain the corresponding impact factor. The mapping relationship can be one-to-one or many-to-one. In this example, the value range is [0, 1].

[0046] Among them, the data effective quality assessment weight is used to describe the influence of the effectiveness of wireless backhaul data transmission on the backhaul data quality assessment index. Specifically, the data effective quality assessment weight is the weight corresponding to the data effective quality assessment preset in the preset database, which represents the numerical value of the influence of the wireless backhaul data transmission effectiveness on the backhaul data quality assessment index. When used, the weight corresponding to the preset data effective quality assessment can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the wireless backhaul data transmission effectiveness and the weight corresponding to the data effective quality assessment preset in the preset database form a mapping set, and the real-time wireless backhaul data transmission effectiveness is input into the mapping set to obtain the corresponding weight. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship. In this example, the value range is [0, 1].

[0047] The data integrity quality assessment weight is used to describe the degree of influence of the wireless backhaul data transmission integrity on the backhaul data quality assessment index. Specifically, the data integrity quality assessment weight is the weight corresponding to the data integrity quality assessment preset in the preset database, which represents the numerical value of the influence of the wireless backhaul data transmission integrity on the backhaul data quality assessment index. When used, the weight corresponding to the preset data integrity quality assessment can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the wireless backhaul data transmission integrity and the weight corresponding to the data integrity quality assessment preset in the preset database form a mapping set, and the real-time wireless backhaul data transmission integrity is input into the mapping set to obtain the corresponding weight. The mapping relationship can be one-to-one or many-to-one. In this example, the value range is [0, 1].

[0048] In this example, the sum of the data validity quality assessment weight, the data integrity quality assessment weight, and the data delay quality assessment weight is 1. The data delay quality assessment weight is used to describe the impact of the wireless backhaul data transmission delay on the backhaul data quality assessment index.

[0049] like Figure 2As shown, it is a change diagram of the backhaul data quality evaluation index provided in an embodiment of the present application, wherein the following settings are made: the data effective quality evaluation weight is 0.4, the data integrity quality evaluation weight is 0.3, the data delay quality evaluation weight is 0.3, the total number of wireless backhaul data packets is 50, the total number of wireless backhaul sent data packets is 50, the reference data packet delay deviation is 0.1 seconds, the ambient air pressure impact factor is 0.05, the ambient distance impact factor is 0.02, the ambient interference impact factor is 0.03, the ambient atmospheric pressure measurement value is 1013hPa, the standard atmospheric pressure is 1013.25hPa, the ambient signal propagation distance measurement value is 10 kilometers, the ambient signal interference intensity measurement value is 5 decibels, the data hash check compliance value of the wireless backhaul data packet is 1, and the data packet sending time of the wireless backhaul data packet is evenly distributed in the range of 0 to 1 second; it should be noted that The absolute value of the difference between the packet sending time and the packet receiving time generally ranges from 0 seconds to 0.5 seconds; (a) is a schematic diagram of the change of the backhaul data quality evaluation index with the total number of wireless backhaul lost packets. When the absolute value of the difference between the packet sending time and the packet receiving time is a fixed value (0.25 seconds), as the total number of wireless backhaul lost packets increases, the backhaul data quality evaluation index decreases accordingly; (b) is a schematic diagram of the change of the backhaul data quality evaluation index with |packet sending time-packet receiving time|. When the total number of wireless backhaul lost packets is 0, if the packet sending time is equal to the packet receiving time, the backhaul data quality evaluation index reaches the maximum value. As the absolute value of the difference between the packet sending time and the packet receiving time is taken, that is, the time delay of data reception gradually increases, the backhaul data quality evaluation index decreases accordingly.

[0050] The backhaul data quality evaluation index is used to quantify the transmission quality of wireless backhaul data. Specifically, the data hash checksum compliance value, the total number of wireless backhaul lost packets, and the packet reception time all directly affect the backhaul data quality evaluation index. The backhaul data quality evaluation index includes multiple parameters, and each parameter is related to each other and does not exist independently. For example, a lost packet will cause the data hash checksum compliance value (i.e., H in the backhaul data quality evaluation index) to be negative. d ) is invalid (data packet is not received and cannot pass hash check), thus reducing the overall data validity; in addition, as the data packet reception time increases, the received data packet may fail to pass the check, thus affecting the data hash check compliance value (i.e. H in the return data quality evaluation index). d); at the same time, when the measured value of the ambient atmospheric pressure gradually deviates from the standard atmospheric pressure, the propagation characteristics of the communication signal change accordingly. For example, the lower the air pressure, the more likely it is that the signal attenuation will increase; and as the measured value of the ambient signal interference intensity increases, that is, the worse the communication quality, the bit error rate increases, the probability of data packet loss increases, and the total number of wireless backhaul lost data packets increases accordingly. Therefore, a comprehensive analysis is performed to obtain the backhaul data quality evaluation index; the numerical evaluation of the wireless backhaul data transmission quality is realized, and the wireless backhaul data transmission quality is judged through the numerical evaluation, thereby improving the reliability of the wireless backhaul data transmission quality judgment, and further improving the accuracy of the judgment of the abnormal situation of the wireless backhaul data transmission.

[0051] Among them, the specific steps of judging whether to execute the wind turbine operation compliance assessment based on the backhaul data quality assessment index and the preset backhaul quality assessment threshold range are as follows: compare the backhaul data quality assessment index with the preset backhaul quality assessment threshold range: if the backhaul data quality assessment index is within the preset backhaul quality assessment threshold range, the wind turbine operation compliance assessment is executed, and the wireless backhaul data and the backhaul data quality assessment index are backed up in real time and uploaded to the cloud storage; if the backhaul data quality assessment index exceeds the preset backhaul quality assessment threshold range, the wind turbine operation compliance assessment is not executed, and the preset personnel are reminded to re-acquire the wireless backhaul data.

[0052] In this embodiment, the preset backhaul quality assessment threshold range is obtained from a database. Specifically, the preset backhaul quality assessment threshold range is set by professionals according to standards in the field. For example, the preset backhaul quality assessment threshold range is generally set to 0.8 to 1. By combining the preset backhaul quality assessment threshold range for judgment, a more accurate evaluation of the wireless backhaul data transmission quality is achieved.

[0053] The specific steps of obtaining the wind turbine operation evaluation index after the wind turbine operation compliance evaluation is performed are as follows: obtaining the operation measurement related values ​​of the preset measurement points within the preset time interval, the operation measurement related values ​​including the actual power generation of the wind turbine, the actual voltage of the wind turbine, the actual frequency of the wind turbine, the radius of the wind wheel and the ambient wind speed; obtaining the power quality evaluation value (i.e., the DP in the wind turbine operation evaluation index) by processing the actual voltage of the wind turbine, the actual frequency of the wind turbine, the fluctuation compliance factor obtained from the preset database and the reference related values; b), the fluctuation compliance factor includes the voltage fluctuation compliance factor and the frequency fluctuation compliance factor, and the reference related values ​​include the standard reference voltage of the wind turbine and the standard reference frequency of the wind turbine; the power quality assessment value is combined with the air density and the reference data obtained from the preset database to obtain the wind turbine operation assessment index, and the reference data includes the standard reference power generation of the wind turbine, the reference power generation change value, the reference wind energy utilization, the operating power compliance weight, the wind energy utilization compliance weight and the power quality compliance weight.

[0054] The method for obtaining the wind turbine operation evaluation index is as follows:

[0055]

[0056]

[0057] Where Wy represents the wind turbine operation evaluation index, Indicates that the operating power meets the weight, Indicates that wind energy utilization meets the weight, Indicates the power quality compliance weight, b indicates the number of the preset measurement point in the preset time interval, b=1,2,...,B, B indicates the total number of preset measurement points in the preset time interval, P b represents the actual power generation of the wind turbine at the bth preset measurement point, P ’ represents the standard reference power generation of the wind turbine, ΔP represents the reference power generation change value, ρ represents the air density, R represents the radius of the wind wheel, V represents the ambient wind speed, ΔC represents the reference wind energy utilization coefficient, DP b represents the power quality assessment value of the bth preset measurement point, ε1 represents the voltage fluctuation compliance factor, V b,s Indicates the actual voltage of the fan at the bth preset measurement point, V ′ represents the standard reference voltage of the fan, ε2 represents the frequency fluctuation compliance factor, F b,s Indicates the actual frequency of the fan at the bth preset measurement point, F ′ Indicates the standard reference frequency of the fan, C y Indicates the preset return quality assessment threshold range.

[0058] In this embodiment, the actual power generation, actual voltage and actual frequency of the wind turbine are obtained through the SCADA (Supervisory Control and Data Acquisition) system; the radius of the wind wheel is obtained through a laser ranging sensor; the ambient wind speed is obtained through a wind speed sensor deployed on the top of the nacelle of the wind turbine, and reference related values, standard reference power generation of the wind turbine, reference power generation change value and reference wind energy utilization are obtained from a preset database; wherein, the standard reference voltage of the wind turbine is represented by summing and averaging the collected historical output voltages of the wind turbines, and the standard reference frequency of the wind turbine is represented by summing and averaging the collected historical output frequencies of the wind turbines; the air density is 1.225 kilograms per cubic meter.

[0059] Specifically, the reference wind energy utilization is represented by summing and averaging the collected historical wind energy utilization, the reference power generation change value is represented by summing and averaging the collected historical wind turbine power generation change values, and the standard reference power generation of the wind turbine is represented by summing and averaging the collected historical wind turbine power generation.

[0060] It should be understood that the voltage fluctuation compliance factor is used to describe the degree of influence of the voltage fluctuation compliance on the power quality assessment value. Specifically, the voltage fluctuation compliance factor is the compliance factor corresponding to the voltage fluctuation preset in the preset database, which represents the numerical value of the influence of the voltage fluctuation compliance on the power quality assessment value. When used, the preset voltage fluctuation compliance factor can be directly obtained from the preset database, and the corresponding relationship can be a preset mapping relationship. For example, the voltage fluctuation compliance and the compliance factor corresponding to the voltage fluctuation preset in the preset database form a mapping set, and the real-time voltage fluctuation is input into the mapping set to obtain the corresponding compliance factor. The mapping relationship can be one-to-one or many-to-one. In this example, the value range is [0, 1]; in this example, the sum of the voltage fluctuation compliance factor and the frequency fluctuation compliance factor is 1, and the frequency fluctuation compliance factor is used to describe the influence of the frequency fluctuation compliance on the power quality assessment value.

[0061] Among them, the operating power compliance weight is used to describe the influence of the compliance of the wind turbine operating power on the wind turbine operation evaluation index. Specifically, the operating power compliance weight is the weight corresponding to the operating power compliance preset in the preset database, which represents the numerical value of the influence of the wind turbine operating power compliance on the wind turbine operation evaluation index. When used, the preset operating power compliance corresponding weight can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the compliance of the wind turbine operating power and the corresponding weight of the operating power compliance preset in the preset database form a mapping set, and the real-time wind turbine operating power compliance is input into the mapping set to obtain the corresponding weight. The mapping relationship can be one-to-one or many-to-one. In this example, the value range is [0, 1].

[0062] The wind energy utilization compliance weight is used to describe the influence of the wind energy utilization of the wind turbine group on the wind turbine group operation evaluation index. Specifically, the wind energy utilization compliance weight is the corresponding weight of the wind energy utilization compliance preset in the preset database, which represents the numerical value of the influence of the wind energy utilization of the wind turbine group on the wind turbine group operation evaluation index. When used, the preset wind energy utilization compliance corresponding weight can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the wind energy utilization of the wind turbine group and the corresponding weight of the wind energy utilization compliance preset in the preset database form a mapping set, and the real-time wind energy utilization of the wind turbine group is input into the mapping set to obtain the corresponding weight. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship. In this example, the value range is [0, 1].

[0063] In this example, the sum of the operating power compliance weight, wind energy utilization compliance weight and power quality compliance weight is 1. The power quality compliance weight is used to describe the impact of the power quality compliance of the wind turbine on the wind turbine operation evaluation index.

[0064] The wind turbine operation evaluation index is used to comprehensively quantify the compliance of the wind turbine operation; among them, the wind turbine operation evaluation index is affected by the calculation of the compliance of the wind turbine operation power, wind energy utilization efficiency and power quality compliance; specifically, the wind turbine operation evaluation index is obtained by comprehensive analysis through the correlation and mutual influence between multiple parameters. For example, as the ambient wind speed increases, the actual output power of the wind turbine also changes, which in turn causes the actual voltage and actual frequency of the wind turbine to fluctuate, thereby reducing the power quality evaluation value; in addition, the actual power generation of the wind turbine is directly affected by the ambient wind speed, and the change of the actual power generation of the wind turbine will also cause the actual voltage and actual frequency of the wind turbine to fluctuate, thereby affecting the power quality, that is, as the power quality evaluation value decreases, it indicates that the voltage stability and frequency stability of the wind turbine are reduced, thereby affecting the overall power quality, and the compliance of the wind turbine operation is reduced. Therefore, through a quantitative method, a numerical evaluation of the compliance of the wind turbine operation is realized, and the compliance of the wind turbine operation is judged through a numerical evaluation, thereby realizing the improvement of the reliability of the judgment of the compliance of the wind turbine operation under abnormal wireless backhaul data transmission.

[0065] Among them, the specific steps of judging whether to execute the wind turbine operation status optimization based on the wind turbine operation evaluation index and the preset unit operation compliance threshold range are as follows: compare the wind turbine operation evaluation index with the preset unit operation compliance threshold range: if the wind turbine operation evaluation index is within the preset unit operation compliance threshold range, the wind turbine operation status optimization is not executed, and the wind turbine operation evaluation index is uploaded to the cloud storage; if the wind turbine operation evaluation index exceeds the preset fluctuation compliance threshold range, the wind turbine operation status optimization is executed, and the wind turbine operation evaluation index is fed back to the preset personnel.

[0066] In this embodiment, the preset unit operation compliance threshold range is obtained from a database. Specifically, the preset unit operation compliance threshold range is set by professionals according to standards in the field. For example, the preset unit operation compliance threshold range is generally set to 0.7 to 1. By combining the preset unit operation compliance threshold range for judgment, a more accurate assessment of the degree of compliance of the wind turbine operation is achieved.

[0067] Among them, the specific steps for executing the operation state optimization of the wind turbine are as follows: A1, obtain the initial operation related data of the wind turbine, increase the initial pitch angle of the wind turbine in sequence until the maximum pitch angle is reached, and judge whether the wind turbine operation evaluation index is within the preset unit operation compliance threshold range. If so, stop the wind turbine operation state optimization, otherwise execute A2, the initial operation related data includes the initial pitch angle, the initial wind turbine speed and the initial output voltage; A2, reduce the initial wind turbine speed of the wind turbine in sequence until the minimum wind turbine speed is reached, and judge whether the wind turbine operation evaluation index is within the preset unit operation compliance threshold range. If so, stop the wind turbine operation state optimization, otherwise execute A3; A3, reduce the initial output voltage of the wind turbine in sequence until the minimum output voltage is reached, and at the same time obtain the wind turbine optimization effect evaluation index after the wind turbine operation state optimization is executed.

[0068] In this embodiment, the initial pitch angle is used to describe the angle between the airfoil chord line at the top of the wind turbine blade and the rotation plane, and is obtained by a pitch angle sensor deployed at the center of the wind turbine blade; the maximum pitch angle is used to describe the maximum adjustable angle of the wind turbine blade; the initial wind turbine speed is used to describe the speed of the generator shaft, and is obtained by a speed sensor deployed at the output end of the generator; the initial output voltage is used to describe the output voltage of the generator, and the initial output voltage is obtained by a voltage sensor deployed at the output end of the generator; the minimum wind turbine speed is used to describe the lowest speed of the wind turbine; the minimum output voltage is used to describe the lowest output voltage of the wind turbine; the maximum pitch angle, the minimum wind turbine speed and the minimum output voltage are obtained from a preset database; and the wind turbine operating state optimization is performed, thereby realizing the adjustment of the wind turbine operating state under abnormal wireless backhaul data transmission conditions, thereby realizing the improvement of the wind turbine operating stability during wireless backhaul data transmission.

[0069] Among them, the specific steps of obtaining the wind turbine optimization effect evaluation index after the optimization of the wind turbine operating state is as follows: obtain the initial relevant measurement values ​​of the preset measurement points to be optimized within the preset time interval to be optimized, the initial relevant measurement values ​​include the initial actual average power generation power, the initial output power, the initial average output power, the initial average energy consumption and the initial total power generation; obtain the optimization related measurement values ​​of the preset optimization measurement points within the preset time interval of optimization, the optimization related measurement values ​​include the optimized actual average power generation power, the optimized output power, the optimized average output power, the optimized average energy consumption and the optimized total power generation; obtain the power fluctuation compliance evaluation value (i.e., U in the wind turbine optimization effect evaluation index) by the difference between the results of processing the initial output power and the initial average output power and the results of processing the optimized output power and the optimized average output power; process the initial relevant measurement values, the optimization related measurement values, the power fluctuation compliance evaluation values ​​and the optimization related reference data obtained from the preset database to obtain the wind turbine optimization effect evaluation index, the optimization related reference data include the reference power generation efficiency change, the reference power fluctuation change, the reference energy consumption change, the power generation efficiency optimization evaluation weight, the power fluctuation optimization evaluation weight and the energy consumption change evaluation weight.

[0070] The method for obtaining the fan optimization effect evaluation index is as follows:

[0071]

[0072]

[0073] Where Wx represents the wind turbine optimization effect evaluation index, ω1 represents the power generation efficiency optimization evaluation weight, ω2 represents the power fluctuation optimization evaluation weight, ω3 represents the energy consumption change evaluation weight, and W after,avg Represents the optimized actual average power generation, W a ′ fter Indicates the reference optimized power generation, W before,avg Represents the initial actual average power generation, W b ′ efore represents the reference initial power generation, ΔW represents the reference power generation efficiency change, U represents the power fluctuation compliance evaluation value, ΔB represents the reference power fluctuation change, m represents the number of the preset measurement point to be optimized within the preset time interval to be optimized, m=1,2,...,M, M represents the total number of preset measurement points to be optimized within the preset time interval to be optimized, n represents the number of the preset optimization measurement point within the preset optimization time interval, n=1,2,...,N, N represents the total number of preset optimization measurement points within the preset optimization time interval, represents the initial output power of the mth preset measurement point to be optimized, P before,avg represents the initial average output power, represents the optimized output power of the nth preset optimized measurement point, P after,avg represents the optimized average output power, E before,avg represents the initial average energy consumption, E after,avg represents the optimized average energy consumption, D before,avg represents the initial total power generation, D after,avg represents the optimized total power generation, ΔE represents the reference energy efficiency change, J y Indicates that the preset unit operation meets the threshold range.

[0074] In this embodiment, the initial actual average generated power is used to describe the average generated power within the preset time interval to be optimized, the initial output power is used to describe the output power of the preset measurement point to be optimized within the preset time interval to be optimized, the initial average output power is used to describe the average output power within the preset time interval to be optimized, the initial average energy consumption is used to describe the average energy consumption within the preset time interval to be optimized, and the initial total power generation is used to describe the total power generation within the preset time interval to be optimized; the optimized actual average generated power is used to describe the average generated power within the optimized preset time interval, the optimized output power is used to describe the output power of the preset optimized measurement point within the optimized preset time interval, the optimized average output power is used to describe the average output power within the optimized preset time interval, the optimized average energy consumption is used to describe the average energy consumption within the optimized preset time interval, and the optimized total power generation is used to describe the total power generation within the optimized preset time interval; the initial relevant measurement values ​​and the optimization relevant measurement values ​​are obtained through the SCADA (Supervisory Control and Data Acquisition) system.

[0075] It should be understood that the reference power generation efficiency change, the reference power fluctuation change and the reference energy consumption change are obtained from a preset database; specifically, the reference power generation efficiency change is represented by summing and averaging the collected historical wind turbine power generation efficiencies, the reference power fluctuation change is represented by summing and averaging the collected historical wind turbine power fluctuation changes, and the reference energy consumption change is represented by summing and averaging the collected historical wind turbine energy consumption changes.

[0076] Among them, the power generation efficiency optimization evaluation weight is used to describe the influence of the power generation efficiency optimization of the wind turbine on the wind turbine optimization effect evaluation index. Specifically, the power generation efficiency optimization evaluation weight is the weight corresponding to the power generation efficiency optimization evaluation preset in the preset database, which represents the numerical value of the influence of the power generation efficiency optimization of the wind turbine on the wind turbine optimization effect evaluation index. When used, the weight corresponding to the preset power generation efficiency optimization evaluation can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the power generation efficiency optimization of the wind turbine and the weight corresponding to the power generation efficiency optimization evaluation preset in the preset database form a mapping set, and the real-time wind turbine power generation efficiency optimization is input into the mapping set to obtain the corresponding weight. The mapping relationship can be one-to-one or many-to-one. In this example, the value range is [0, 1].

[0077] The power fluctuation optimization evaluation weight is used to describe the influence of the power fluctuation of the wind turbine on the wind turbine optimization effect evaluation index. Specifically, the power fluctuation optimization evaluation weight is the weight corresponding to the power fluctuation optimization evaluation preset in the preset database, which represents the numerical value of the influence of the power fluctuation optimization of the wind turbine on the wind turbine optimization effect evaluation index. When used, the weight corresponding to the preset power fluctuation optimization evaluation can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the power fluctuation optimization of the wind turbine and the weight corresponding to the power fluctuation optimization evaluation preset in the preset database form a mapping set, and the real-time wind turbine power fluctuation optimization situation is input into the mapping set to obtain the corresponding weight. The mapping relationship can be one-to-one or many-to-one. In this example, the value range is [0, 1].

[0078] In this example, the sum of the power generation efficiency optimization evaluation weight, the power fluctuation optimization evaluation weight and the energy consumption change evaluation weight is 1. The energy consumption change evaluation weight is used to describe the influence of the energy consumption optimization of the wind turbine on the wind turbine optimization effect evaluation index.

[0079] Specific assumptions: The optimization evaluation weight of power generation efficiency is 0.4, the optimization evaluation weight of power fluctuation is 0.3, the evaluation weight of energy consumption change is 0.3, the reference optimization power generation is 550 kW, the initial actual average power generation is 480 kW, the reference initial power generation is 500 kW, the reference power fluctuation change is 10, the reference power generation efficiency change is 20, the total number of preset measurement points to be optimized within the preset time interval to be optimized is 10, the total number of preset optimization measurement points within the preset time interval to be optimized is 10, and the initial output powers of the preset measurement points to be optimized are 480 kW, 490 kW, 475 kW, 485 kW, 495 kW, 490 kW, respectively. watts, 480 kW, 470 kW, 500 kW, 495 kW, the initial average output power is 486 kW, the optimized output powers of the preset optimized measurement points are 500 kW, 510 kW, 505 kW, 515 kW, 500 kW, 505 kW, 510 kW, 495 kW, 520 kW, 515 kW, respectively, the optimized average output power is 507.5 kW, the initial average energy consumption is 1000 kWh, the initial total power generation is 1200 kWh, and the reference energy consumption change is 100; the above method can be used to calculate the wind turbine optimization effect evaluation index, and the statistical table of the change of the wind turbine optimization effect evaluation index is shown in Table 1:

[0080] Table 1 Statistics of changes in fan optimization effect evaluation index

[0081]

[0082] It can be seen from Table 1 that when the optimized actual average power generation, the optimized average energy consumption and the power fluctuation compliance evaluation value are fixed values, as the optimized total power generation increases, the wind turbine optimization effect evaluation index increases accordingly, as shown in the second and third groups of data in the table; as the power generation efficiency increases after optimization, the effect of wind turbine operation state optimization is better; at the same time, the optimized power fluctuation and the optimized unit power generation energy consumption directly affect the wind turbine optimization effect evaluation index; specifically, for example, when the power fluctuation decreases, that is, the power fluctuation compliance evaluation value increases, the wind turbine optimization effect evaluation index increases accordingly.

[0083] In addition, the wind turbine optimization effect evaluation index includes parameters from multiple aspects, and each parameter is related to each other. It does not exist independently and is not obtained by simple combination and addition. For example, with the increase of wind speed in offshore wind farms, the optimized actual average power generation of wind turbines increases, and the optimized total power generation increases. In order to ensure the change of wind speed, the adjustment frequency needs to be increased, which leads to an increase in the optimized average energy consumption. At the same time, with the reduction of power fluctuations, the stability of the optimized actual average power generation increases, and the power generation efficiency increases, that is, the power generation increases under the same energy consumption; therefore, through quantitative methods, the correlation and mutual influence between various factors are considered, and the wind turbine optimization effect evaluation index is obtained through comprehensive analysis; the numerical evaluation of the optimization effect of the wind turbine operating state is realized, and the optimization effect of the wind turbine operating state is judged through numerical evaluation, thereby achieving the improvement of the accuracy of the evaluation of the optimization effect of the wind turbine operating state.

[0084] Among them, the specific steps of comprehensively judging whether to execute the aging detection reminder based on the feedback data quality evaluation index and the wind turbine operation evaluation index are as follows: judge whether the obtained feedback data quality evaluation index is within the preset feedback quality evaluation threshold range; if the feedback data quality evaluation index is within the preset feedback quality evaluation threshold range, the aging detection reminder is not executed, otherwise, judge whether the obtained wind turbine operation evaluation index is within the preset unit operation compliance threshold range; if the wind turbine operation evaluation index is within the preset unit operation compliance threshold range, the aging detection reminder is not executed, otherwise, judge whether the obtained wind turbine optimization effect evaluation index is within the preset wind turbine optimization effect threshold range; if the fan optimization effect evaluation index is within the preset fan optimization effect threshold range, the aging detection reminder is not executed, otherwise the aging detection reminder is executed.

[0085] In this embodiment, the preset wind turbine optimization effect threshold range is obtained from a database. Specifically, the preset wind turbine optimization effect threshold range is set by professionals according to standards in the field. For example, the actual average power generation power range (generally 500kW to 3MW), the power fluctuation compliance evaluation value range (generally 0 to 50), the average energy consumption range (generally 500kWh to 5000kWh) and the total power generation range (generally 500kWh to 10000kWh) are set to optimize the preset wind turbine optimization effect threshold range, thereby achieving accurate evaluation of the optimization effect of the wind turbine operating status.

[0086] like Figure 3As shown, it is a flow chart of the offshore wind farm wireless data backhaul method based on 5G technology provided by the present invention. The offshore wind farm wireless data backhaul method based on 5G technology provided by the present invention comprises the following steps: S1, obtaining the wireless backhaul data of the offshore wind farm within a preset time interval, and transmitting the wireless backhaul data to a preset wind farm monitoring center; S2, evaluating the transmission quality of the wireless backhaul data to obtain a backhaul data quality evaluation index, judging whether to perform the wind turbine operation compliance evaluation based on the backhaul data quality evaluation index and the preset backhaul quality evaluation threshold range, and the backhaul data quality evaluation index is used to quantify the wireless backhaul quality. The data transmission quality; S3, obtain the wind turbine operation evaluation index after executing the wind turbine operation compliance assessment, and judge whether to execute the wind turbine operation status optimization based on the wind turbine operation evaluation index and the preset unit operation compliance threshold range. The wind turbine operation evaluation index is used to comprehensively quantify the compliance of the wind turbine operation; S4, obtain the wind turbine optimization effect evaluation index after executing the wind turbine operation status optimization, and make a comprehensive judgment on whether to execute the aging detection reminder based on the feedback data quality assessment index and the wind turbine operation evaluation index. The wind turbine optimization effect evaluation index is used to quantitatively evaluate the effect of the wind turbine operation status optimization.

[0087] In this embodiment, aging detection reminders are provided by text messages or emails. Specifically, the aging detection reminders include reminding preset personnel to perform aging detection of key parts such as gear boxes and suggesting increased equipment lubrication. By comprehensively judging whether to perform aging detection, processing and feedback of abnormal wireless backhaul data transmission situations are achieved, thereby achieving improved stability of wireless data backhaul in offshore wind farms.

[0088] There are a few points to note:

[0089] (1) The drawings of the embodiments of the present invention only relate to the structures related to the embodiments of the present invention, and other structures may refer to the general design.

[0090] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present invention, the thickness of the layers or regions is exaggerated or reduced, that is, these drawings are not drawn according to the actual scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or there may be intermediate elements.

[0091] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0092] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A wireless data backhaul system for offshore wind farms based on 5G technology, characterized in that: It includes wireless backhaul data acquisition module, transmission quality assessment and judgment module, wind turbine operation assessment and judgment module and wind turbine optimization assessment feedback module; The wireless backhaul data acquisition module is used to obtain the wireless backhaul data of the offshore wind farm within a preset time interval, and transmit the wireless backhaul data to a preset wind farm monitoring center; The transmission quality evaluation and judgment module is used to evaluate the transmission quality of the wireless backhaul data to obtain a backhaul data quality evaluation index, and determine whether to perform the wind turbine operation compliance evaluation based on the backhaul data quality evaluation index and a preset backhaul quality evaluation threshold range. The backhaul data quality evaluation index is used to quantify the transmission quality of the wireless backhaul data; The wind turbine operation evaluation and judgment module is used to obtain the wind turbine operation evaluation index after the wind turbine operation compliance evaluation is performed, and judge whether to perform the wind turbine operation state optimization based on the wind turbine operation evaluation index and the preset unit operation compliance threshold range, and the wind turbine operation evaluation index is used to comprehensively quantify the compliance of the wind turbine operation; The wind turbine optimization evaluation feedback module is used to obtain the wind turbine optimization effect evaluation index after the wind turbine operating status optimization is executed, and to make a comprehensive judgment on whether to execute the aging detection reminder based on the feedback data quality evaluation index and the wind turbine operating evaluation index. The wind turbine optimization effect evaluation index is used to quantitatively evaluate the effect of the wind turbine operating status optimization.

2. The offshore wind farm wireless data backhaul system based on 5G technology according to claim 1 is characterized in that: The specific steps of evaluating the transmission quality of wireless backhaul data to obtain the backhaul data quality evaluation index are as follows: Obtaining return data quality related values ​​and quality assessment related values ​​at preset time intervals, wherein the return data quality related values ​​include data hash check sending value, data hash check receiving value, total number of wireless return data packets lost, total number of wireless return data packets sent, data packet sending time, and data packet receiving time, and the quality assessment related values ​​include ambient atmospheric pressure measurement value, ambient signal propagation distance measurement value, and ambient signal interference intensity measurement value; Compare the data hash check sent value with the data hash check received value to obtain the data hash check compliance value; Obtaining an environmental impact correction factor according to the quality assessment related value combined with the standard atmospheric pressure and the impact factors obtained from the preset database, wherein the impact factors include an environmental pressure impact factor, an environmental distance impact factor, and an environmental signal interference impact factor; The data hash check compliance value, the return data quality related value, the environmental impact correction coefficient and the transmission quality assessment weight obtained from the preset database are combined to obtain the return data quality assessment index, and the transmission quality assessment weight includes the data validity quality assessment weight, the data integrity quality assessment weight and the data timeliness quality assessment weight.

3. The offshore wind farm wireless data backhaul system based on 5G technology according to claim 2 is characterized in that: The method for obtaining the return data quality evaluation index is as follows: d h =1-[τ1*(P-P0)+τ2*Gd+τ3*Gq]; Where Df represents the backhaul data quality assessment index, α represents the data validity quality assessment weight, β represents the data integrity quality assessment weight, γ represents the data delay quality assessment weight, d represents the number of wireless backhaul data packets, d = 1, 2, ..., D, D represents the total number of wireless backhaul data packets, H d Indicates the data hash checksum of the dth wireless backhaul data packet, h d Indicates the data hash check value sent by the dth wireless backhaul data packet, h ′ d represents the data hash check received value of the dth wireless backhaul data packet, Ld represents the total number of wireless backhaul lost data packets, Zd represents the total number of wireless backhaul sent data packets, t d represents the data packet sending time of the dth wireless backhaul data packet, t ′ d represents the packet receiving time of the dth wireless backhaul packet, Δt represents the reference packet delay deviation, δ h represents the environmental impact correction coefficient, τ1 represents the environmental air pressure impact factor, P represents the environmental atmospheric pressure measurement value, P0 represents the standard atmospheric pressure, τ2 represents the environmental distance impact factor, Gd represents the environmental signal propagation distance measurement value, τ3 represents the environmental signal interference impact factor, and Gq represents the environmental signal interference intensity measurement value.

4. The offshore wind farm wireless data backhaul system based on 5G technology according to claim 1 is characterized in that: The specific steps of judging whether to perform the wind turbine operation compliance assessment based on the feedback data quality assessment index and the preset feedback quality assessment threshold range are as follows: Compare the return data quality assessment index with the preset return quality assessment threshold range: If the feedback data quality assessment index is within the preset feedback quality assessment threshold range, the wind turbine operation compliance assessment is performed, and the wireless feedback data and the feedback data quality assessment index are backed up in real time and uploaded to the cloud storage; If the feedback data quality assessment index exceeds the preset feedback quality assessment threshold range, the wind turbine operation compliance assessment will not be performed, and the preset personnel will be reminded to re-acquire the wireless feedback data.

5. The offshore wind farm wireless data backhaul system based on 5G technology according to claim 1 is characterized in that: The specific steps of obtaining the wind turbine operation evaluation index after the wind turbine operation compliance evaluation is performed are as follows: Obtaining operation measurement related values ​​of preset measurement points within a preset time interval, wherein the operation measurement related values ​​include actual power generation of the wind turbine, actual voltage of the wind turbine, actual frequency of the wind turbine, wind wheel radius, and ambient wind speed; The power quality assessment value is obtained by processing the actual voltage and actual frequency of the fan, the fluctuation compliance factor obtained from the preset database, and the reference related value, wherein the fluctuation compliance factor includes the voltage fluctuation compliance factor and the frequency fluctuation compliance factor, and the reference related value includes the standard reference voltage and the standard reference frequency of the fan; The power quality assessment value is combined with the air density and the reference data obtained from the preset database to obtain the wind turbine operation assessment index, wherein the reference data includes the standard reference power generation of the wind turbine, the reference power generation change value, the reference wind energy utilization, the operating power compliance weight, the wind energy utilization compliance weight and the power quality compliance weight.

6. The offshore wind farm wireless data backhaul system based on 5G technology according to claim 5 is characterized in that: The specific steps of determining whether to perform wind turbine operation state optimization based on the wind turbine operation evaluation index and the preset unit operation compliance threshold range are as follows: Compare the wind turbine operation evaluation index with the preset unit operation compliance threshold range: If the wind turbine operation evaluation index is within the preset unit operation compliance threshold range, the wind turbine operation status optimization will not be performed, and the wind turbine operation evaluation index will be uploaded to the cloud storage; If the wind turbine operation evaluation index exceeds the preset fluctuation compliance threshold range, the wind turbine operation status optimization is executed, and the wind turbine operation evaluation index is fed back to the preset personnel.

7. The offshore wind farm wireless data backhaul system based on 5G technology according to claim 6 is characterized in that: The specific steps of performing wind turbine operating state optimization are as follows: A1, obtaining the initial operation related data of the wind turbine set, increasing the initial pitch angle of the wind turbine set in sequence until the maximum pitch angle is reached, judging whether the wind turbine set operation evaluation index is within the preset unit operation compliance threshold range, if so, stopping the wind turbine set operation state optimization, otherwise executing A2, the initial operation related data including the initial pitch angle, the initial wind turbine speed and the initial output voltage; A2, successively reduce the initial wind turbine speed of the wind turbine until it reaches the minimum wind turbine speed, and determine whether the wind turbine operation evaluation index is within the preset unit operation compliance threshold range. If so, stop the wind turbine operation status optimization, otherwise execute A3; A3, sequentially reducing the initial output voltage of the wind turbine generator set until the minimum output voltage is reached, and at the same time obtaining the wind turbine optimization effect evaluation index after the wind turbine generator set operation state optimization is performed.

8. The offshore wind farm wireless data backhaul system based on 5G technology according to claim 1 is characterized in that: The specific steps of obtaining the wind turbine optimization effect evaluation index after the wind turbine operating state optimization is performed are as follows: Obtaining initial relevant measurement values ​​of a preset measurement point to be optimized within a preset time interval to be optimized, wherein the initial relevant measurement values ​​include initial actual average generated power, initial output power, initial average output power, initial average energy consumption, and initial total power generation; Obtaining optimization-related measurement values ​​of preset optimization measurement points within a preset optimization time interval, wherein the optimization-related measurement values ​​include optimizing actual average power generation, optimizing output power, optimizing average output power, optimizing average energy consumption, and optimizing total power generation; The power fluctuation compliance evaluation value is obtained by the difference between the results of processing the initial output power and the initial average output power and the results of processing the optimized output power and the optimized average output power; The initial relevant measurement values, optimization relevant measurement values, power fluctuation compliance evaluation values ​​and optimization relevant reference data obtained from a preset database are processed to obtain a wind turbine optimization effect evaluation index, wherein the optimization relevant reference data include a reference power generation efficiency change, a reference power fluctuation change, a reference energy consumption change, a power generation efficiency optimization evaluation weight, a power fluctuation optimization evaluation weight and an energy consumption change evaluation weight.

9. The offshore wind farm wireless data backhaul system based on 5G technology according to claim 1 is characterized in that: The specific steps of comprehensively judging whether to perform aging detection reminder based on the feedback data quality evaluation index and the wind turbine operation evaluation index are as follows: Determine whether the obtained return data quality assessment index is within a preset return quality assessment threshold range; If the feedback data quality assessment index is within the preset feedback quality assessment threshold range, the aging detection reminder is not executed. Otherwise, it is determined whether the obtained wind turbine operation assessment index is within the preset unit operation compliance threshold range; If the wind turbine operation evaluation index is within the preset unit operation threshold range, the aging detection reminder is not executed. Otherwise, it is determined whether the obtained wind turbine optimization effect evaluation index is within the preset wind turbine optimization effect threshold range. If the fan optimization effect evaluation index is within the preset fan optimization effect threshold range, the aging detection reminder is not executed, otherwise the aging detection reminder is executed.

10. A wireless data backhaul method for an offshore wind farm based on 5G technology, characterized in that: The following steps are involved: S1, obtaining wireless backhaul data of the offshore wind farm within a preset time interval, and transmitting the wireless backhaul data to a preset wind farm monitoring center; S2, evaluating the transmission quality of the wireless backhaul data to obtain a backhaul data quality evaluation index, and determining whether to perform a wind turbine operation compliance evaluation based on the backhaul data quality evaluation index and a preset backhaul quality evaluation threshold range, wherein the backhaul data quality evaluation index is used to quantify the transmission quality of the wireless backhaul data; S3, obtaining a wind turbine operation evaluation index after executing the wind turbine operation compliance evaluation, and judging whether to execute wind turbine operation state optimization based on the wind turbine operation evaluation index and a preset wind turbine operation compliance threshold range, wherein the wind turbine operation evaluation index is used to comprehensively quantify the wind turbine operation compliance; S4, obtaining a wind turbine optimization effect evaluation index after executing wind turbine operating status optimization, and making a comprehensive judgment on whether to execute aging detection reminder based on the feedback data quality evaluation index and the wind turbine operating evaluation index. The wind turbine optimization effect evaluation index is used to quantitatively evaluate the effect of wind turbine operating status optimization.

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