A 5G technology-based wireless data backhaul system and method for offshore wind farms

Abstract

The application provides a kind of offshore wind farm wireless data backhaul system and method based on 5G technology, it is related to wireless data communication technical field.The system includes: wireless backhaul data acquisition module, transmission quality evaluation judgment module, wind turbine operation evaluation judgment module and wind turbine optimization evaluation feedback module.The application obtains wireless backhaul data and transmits to the preset wind farm monitoring center, then the transmission quality of wireless backhaul data is evaluated to judge whether to execute wind turbine generator set operation compliance evaluation, then the wind turbine generator set operation evaluation index after executing wind turbine generator set operation compliance evaluation is obtained and whether to execute wind turbine generator set operation state optimization is judged, finally whether to execute aging detection prompt is comprehensively judged, the stability of offshore wind farm wireless data backhaul is improved, and the problem of low stability of offshore wind farm wireless data backhaul in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of wireless data communication technology, and in particular to a wireless data backhaul system and method for offshore wind farms based on 5G technology. Background Technology

[0002] Offshore wind farms are typically located in complex environments far from shore, with a wide distribution of wind power generation equipment. As wind farms expand, wind turbines generate a large amount of operational data (such as wind speed, power generation, and equipment status). This data needs to be transmitted back to the onshore control center in real time for monitoring and management, thus placing higher demands on data transmission. 5G, as the fifth-generation mobile communication technology, has significant advantages in high bandwidth, low latency, massive connectivity, and high reliability. Through the high bandwidth, low latency, and wide coverage of 5G networks, real-time data transmission and remote management of offshore wind farms can be achieved, providing technical support for the intelligent and automated operation of wind farms.

[0003] Existing offshore wind farm wireless data backhaul systems mostly employ wireless communication technology to transmit wind farm data back to the onshore operation and management center. Data such as wind speed, rotational speed, temperature, and power generation are collected in real time by various sensors installed on the wind turbines. The sensor data is aggregated and processed by the local control system, and the processed data is transmitted to the onshore management center via wireless communication. By combining microwave communication, 4G / 5G cellular networks, and satellite communication with sensor networks and edge computing technology, data backhaul and remote management of offshore wind farms have been realized.

[0004] Remote monitoring and maintenance of offshore wind farms has emerged in response to the rapid development of the offshore wind power industry. However, offshore wind farms also face greater operation and maintenance challenges because they are typically located in remote sea areas with complex marine environments and harsh weather, resulting in high operation and maintenance costs and difficulties. The harsh marine environment, with its wind, waves, and salt spray, negatively impacts the lifespan and performance of equipment, especially communication equipment and sensors, which are susceptible to corrosion and damage due to long-term exposure. Furthermore, offshore communication equipment relies on power supplies, and unstable power supply or insufficient battery power can lead to frequent disconnections or weak signals, thus affecting the stability of data transmission.

[0005] In existing technologies, timely response and optimization are crucial for ensuring the operational stability of offshore wind farms during remote monitoring and maintenance. For example, in low-temperature environments, wind turbine blades may freeze or be affected by pollutants such as dust and salt, which can reduce the aerodynamic performance of the blades, increase the risk of vibration, and thus affect the wireless data transmission of offshore wind farms, resulting in low stability of wireless data transmission. Summary of the Invention

[0006] This invention provides a wireless data backhaul system and method for offshore wind farms based on 5G technology, which solves the problem of low stability of wireless data backhaul in existing technologies and improves the stability of wireless data backhaul in offshore wind farms.

[0007] This invention provides a 5G-based wireless data backhaul system for offshore wind farms, comprising a wireless backhaul data acquisition module, a transmission quality assessment module, a wind turbine operation assessment module, and a wind turbine optimization assessment feedback module. The wireless backhaul data acquisition module acquires wireless backhaul data from the offshore wind farm within a preset time interval and transmits the data to a preset wind farm monitoring center. The transmission quality assessment module evaluates the transmission quality of the wireless backhaul data to obtain a backhaul data quality assessment index. Based on the backhaul data quality assessment index and a preset backhaul quality assessment threshold range, it determines whether to perform a wind turbine operation compliance assessment. The backhaul data quality assessment index is used to quantify the wireless backhaul data quality. The transmission quality of the 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 assessment is performed, and to determine whether to perform 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 degree 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 to comprehensively determine whether to perform aging detection reminder based on the backhaul data quality evaluation index and the wind turbine operation index. The wind turbine optimization effect evaluation index is used to quantitatively evaluate the effect of wind turbine operation status optimization.

[0008] Optionally, the specific steps for evaluating the transmission quality of wireless backhaul data to obtain the backhaul data quality assessment index are as follows: Obtain backhaul data quality correlation values ​​and quality assessment correlation values ​​at preset time intervals. The backhaul data quality correlation values ​​include data hash check transmission value, data hash check reception value, total number of lost wireless backhaul data packets, total number of transmitted wireless backhaul data packets, data packet transmission time, and data packet reception time. The quality assessment correlation 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 transmission value and the data hash check reception value to obtain a data hash check compliance value. Based on the quality assessment correlation value, standard atmospheric pressure, and influence factors obtained from a preset database, obtain an environmental impact correction coefficient. The influence factors include ambient atmospheric pressure influence factor, ambient distance influence factor, and ambient signal interference influence factor. Combine the data hash check compliance value, backhaul data quality correlation value, environmental impact correction coefficient, and transmission quality assessment weights obtained from a preset database to obtain the backhaul data quality assessment index. The transmission quality assessment weights include data validity quality assessment weights, data integrity quality assessment weights, and data timeliness quality assessment weights.

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

[0010]

[0011]

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

[0013] In the formula, 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 latency quality assessment weight, d represents the wireless backhaul data packet number, d = 1, 2, ..., D, D represents the total number of wireless backhaul data packets, and H d h represents the data hash checksum of the d-th wireless backhaul data packet. d h represents the data hash checksum of the d-th wireless backhaul data packet. ′ d Ld represents the received data hash checksum of the d-th wireless backhaul data packet, Zd represents the total number of lost wireless backhaul data packets, and t represents the total number of transmitted wireless backhaul data packets. d t represents the data packet transmission time of the d-th wireless backhaul data packet. ′ d The data packet reception time of the d-th wireless backhaul data packet is represented by Δt, which represents the reference data packet delay deviation, and δ.g The environmental impact correction coefficient is represented by τ1, the environmental air pressure influence factor is represented by P, the measured value of environmental atmospheric pressure is represented by P0, the standard atmospheric pressure is represented by τ2, the environmental distance influence factor is represented by Gd, the measured value of environmental signal propagation distance is represented by τ3, the environmental signal interference influence factor is represented by Gq, and the measured value of environmental signal interference intensity is represented by Gq.

[0014] Optionally, the specific steps for obtaining the wind turbine operation evaluation index after performing the wind turbine operation compliance assessment are as follows: Obtain the operation measurement correlation values ​​of preset measurement points within a preset time interval. The operation measurement correlation values ​​include the actual power generation of the wind turbine, the actual voltage of the wind turbine, the actual frequency of the wind turbine, the rotor radius, and the ambient wind speed. Process the actual voltage of the wind turbine, the actual frequency of the wind turbine, and the fluctuation compliance factors and reference correlation values ​​obtained from a preset database to obtain a power quality evaluation value. The fluctuation compliance factors include voltage fluctuation compliance factors and frequency fluctuation compliance factors. The reference correlation values ​​include the standard reference voltage and the standard reference frequency of the wind turbine. Combine the power quality evaluation value with air density and reference data obtained from a preset database to obtain the wind turbine operation evaluation index. 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.

[0015] Optionally, the specific steps for determining whether to perform wind turbine operation status optimization based on the wind turbine operation evaluation index and a preset threshold range for turbine operation are as follows: compare the wind turbine operation evaluation index with the preset threshold range for turbine operation; if the wind turbine operation evaluation index is within the preset threshold range for turbine operation, then wind turbine operation status optimization is not performed, and the wind turbine operation evaluation index is uploaded to the cloud for storage; if the wind turbine operation evaluation index exceeds the preset fluctuation threshold range, then 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 optimizing the wind turbine's operating status are as follows: A1, obtain the initial operating data of the wind turbine, sequentially increase the initial pitch angle of the wind turbine until it reaches the maximum pitch angle, and determine whether the wind turbine's operating evaluation index is within the preset threshold range for turbine operation. If yes, stop the wind turbine's operating status optimization; otherwise, proceed to A2. The initial operating data includes the initial pitch angle, the initial turbine speed, and the initial output voltage. A2, sequentially decrease the initial turbine speed of the wind turbine until it reaches the minimum turbine speed. Determine whether the wind turbine's operating evaluation index is within the preset threshold range for turbine operation. If yes, stop the wind turbine's operating status optimization; otherwise, proceed to A3. A3, sequentially decrease the initial output voltage of the wind turbine until it reaches the minimum output voltage, and simultaneously obtain the wind turbine optimization effect evaluation index after optimizing the wind turbine's operating status.

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

[0018] This invention provides a wireless data backhaul method for offshore wind farms based on 5G technology, comprising the following steps: S1, acquiring wireless backhaul data of the offshore wind farm within a preset time interval, and simultaneously 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 assessment 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, acquiring a wind turbine operation evaluation index after performing the wind turbine operation compliance assessment, and determining whether to perform wind turbine operation status optimization based on the wind turbine operation evaluation index and a preset turbine operation compliance threshold range, wherein the wind turbine operation evaluation index is used to comprehensively quantify the compliance of wind turbine operation; S4, acquiring a wind turbine optimization effect evaluation index after performing wind turbine operation status optimization, and comprehensively determining whether to perform an aging detection reminder based on the backhaul data quality evaluation index and the wind turbine operation evaluation index, wherein the wind turbine optimization effect evaluation index is used to quantify the effect of wind turbine operation status optimization.

[0019] One or more technical solutions provided in the embodiments of this 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 a wind turbine operation compliance assessment, then obtaining the wind turbine operation assessment index after the 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 system achieves the assessment of wireless backhaul data transmission quality and the assessment and optimization of wind turbine operation status. This improves the stability of wireless data backhaul in offshore wind farms and effectively solves the problem of low stability of wireless data backhaul in existing technologies.

[0021] 2. By acquiring the backhaul data quality-related values ​​and quality assessment-related values ​​at preset time intervals, and simultaneously acquiring the data hash verification value and environmental impact correction coefficient, the backhaul data quality assessment index is obtained by combining the data hash verification value, backhaul data quality-related values, environmental impact correction coefficient, and transmission quality assessment weights obtained from the preset database. This enables a numerical assessment of the wireless backhaul data transmission quality, thereby achieving a more accurate assessment of the wireless backhaul data transmission quality.

[0022] 3. By acquiring the relevant values ​​of the operation measurement points within a preset time interval, the actual voltage of the wind turbine, the actual frequency of the wind turbine, and the fluctuation compliance factor and reference relevant values ​​obtained from the preset database are processed to obtain the power quality assessment value. Finally, the power quality assessment value is combined with the air density and reference data obtained from the preset database to obtain the wind turbine operation assessment index, thereby realizing the numerical assessment of the wind turbine operation compliance degree, and thus realizing a more accurate assessment of the wind turbine operation compliance degree. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

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

[0025] Figure 2 The diagram illustrates the change of the backhaul data quality assessment index provided by the present invention, wherein (a) is a diagram illustrating the change of the backhaul data quality assessment index with the total number of lost data packets in wireless backhaul, and (b) is a diagram illustrating the change of the backhaul data quality assessment index with |data packet sending time - data packet receiving time|.

[0026] Figure 3 A flowchart of the wireless data backhaul method for offshore wind farms based on 5G technology provided by the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0028] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms “first,” “second,” and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms “an,” “a,” or “the,” and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. The terms “comprising,” “including,” or “including,” and similar terms mean that the element or object preceding the word encompasses the element or object listed following the word and its equivalents, without excluding other elements or objects. The terms “connected,” “linked,” or “connected,” and similar terms are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect.

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

[0030] This invention addresses the problem of low stability in wireless data backhaul for offshore wind farms in existing technologies by providing a 5G-based wireless data backhaul system and method for offshore wind farms, thereby improving the stability of wireless data backhaul for offshore wind farms.

[0031] like Figure 1The diagram shows the structure of a 5G-based wireless data backhaul system for offshore wind farms provided by this invention. The system includes a wireless backhaul data acquisition module, a transmission quality assessment module, a wind turbine operation assessment module, and a wind turbine optimization assessment feedback module. The wireless backhaul data acquisition module acquires wireless backhaul data from the offshore wind farm within a preset time interval and transmits the data to a preset wind farm monitoring center. The transmission quality assessment module evaluates the transmission quality of the wireless backhaul data to obtain a backhaul data quality assessment index. Based on the backhaul data quality assessment index and a preset backhaul quality assessment threshold range, it determines whether to implement the wind turbine operation compliance program. The system comprises three modules: a wind turbine operation evaluation module and a wind turbine optimization evaluation module. The wind turbine operation evaluation index is used to quantify the transmission quality of wireless backhaul data. The wind turbine operation evaluation judgment module obtains the wind turbine operation evaluation index after the wind turbine operation compliance evaluation is performed. Based on the wind turbine operation evaluation index and the preset unit operation compliance threshold range, it determines whether to perform wind turbine operation status optimization. The wind turbine operation evaluation index is used to comprehensively quantify the compliance degree of wind turbine operation. The wind turbine optimization evaluation feedback module obtains the wind turbine optimization effect evaluation index after the wind turbine operation status optimization is performed. Based on the backhaul data quality evaluation index and the wind turbine operation index, it comprehensively determines whether to perform aging detection reminder. The wind turbine optimization effect evaluation index is used to quantify the effect of wind turbine operation status optimization.

[0032] In this embodiment, a pre-set 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 an energy metering device deployed at the generator output end; the wind turbine speed is obtained through a 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 voltage and current sensors deployed at the generator output end; and environmental data is obtained through wind speed, temperature, and air pressure sensors deployed on the top of the wind turbine nacelle.

[0034] The acquired wireless backhaul data is packaged in binary format and then compressed using a lossless compression algorithm to obtain wireless backhaul data packets. These packets are then encrypted using a hash function. Finally, the wireless backhaul data is transmitted to a pre-set wind farm monitoring center via 5G technology. 5G technology offers advantages such as high bandwidth, low latency, and wide coverage, enhancing the stability of wireless backhaul data transmission. By acquiring this wireless backhaul data, comprehensive monitoring of offshore wind farms is achieved, thereby improving the reliability of wireless backhaul data from offshore wind farms.

[0035] The specific steps for evaluating the quality of wireless backhaul data transmission to obtain the backhaul data quality assessment index are as follows: First, obtain backhaul data quality-related values ​​and quality assessment-related values ​​at preset time intervals. Backhaul data quality-related values ​​include the transmitted data hash checksum value, the received data hash checksum value, the total number of lost wireless backhaul data packets, the total number of transmitted wireless backhaul data packets, data packet transmission time, and data packet reception time. Quality assessment-related values ​​include ambient atmospheric pressure measurements, ambient signal propagation distance measurements, and ambient signal interference intensity measurements. Second, compare the transmitted data hash checksum value with the received data hash checksum value to obtain the data hash checksum compliance value (i.e., the H value in the backhaul data quality assessment index). d Based on the relevant values ​​of the quality assessment, combined with standard atmospheric pressure and impact factors obtained from the preset database, the environmental impact correction coefficient (i.e., δ in the quality assessment index of the returned data) is obtained. h The influencing factors include environmental air pressure, environmental distance, and environmental signal interference. The data hash verification value, the data quality correlation value, the environmental impact correction coefficient, and the transmission quality assessment weight obtained from the preset database are used to obtain the data quality assessment index. 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.

[0036] The method for obtaining the quality assessment index of the returned data is as follows:

[0037]

[0038]

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

[0040] In the formula, 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 latency quality assessment weight, d represents the wireless backhaul data packet number, d = 1, 2, ..., D, D represents the total number of wireless backhaul data packets, and Hd h represents the data hash checksum of the d-th wireless backhaul data packet. d h represents the data hash checksum of the d-th wireless backhaul data packet. ′ d Ld represents the received data hash checksum of the d-th wireless backhaul data packet, Zd represents the total number of lost wireless backhaul data packets, and t represents the total number of transmitted wireless backhaul data packets. d t represents the data packet transmission time of the d-th wireless backhaul data packet. ′ d The data packet reception time of the d-th wireless backhaul data packet is represented by Δt, which represents the reference data packet delay deviation, and δ. h The environmental impact correction coefficient is represented by τ1, the environmental air pressure influence factor is represented by P, the measured value of environmental atmospheric pressure is represented by P0, the standard atmospheric pressure is represented by τ2, the environmental distance influence factor is represented by Gd, the measured value of environmental signal propagation distance is represented by τ3, the environmental signal interference influence factor is represented by Gq, and the measured value of environmental signal interference intensity is represented by Gq.

[0041] In this embodiment, the total number of lost wireless backhaul data packets and the total number of transmitted wireless backhaul data packets are obtained through data packet statistics information in the network interface; the data packet transmission time and data packet reception time are obtained through a time function; the ambient atmospheric pressure measurement value is obtained through a barometric pressure sensor deployed on the top of the wind turbine nacelle; the ambient signal propagation distance measurement value is obtained through a GPS (Global Positioning System Module); the ambient signal interference intensity measurement value is obtained through a wireless communication module such as a LoRa (Long Range 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 summation and averaging of the collected historical data packet transmission delays.

[0042] Among them, the measured values ​​of ambient atmospheric pressure, standard atmospheric pressure, environmental signal propagation distance, and environmental signal interference intensity have all been dimensionless.

[0043] It is important to understand that the ambient air pressure influence factor is used to describe the degree of influence of ambient air pressure on the environmental impact correction coefficient. Specifically, the ambient air pressure influence factor is the influence factor corresponding to the ambient air pressure preset in the preset database. It represents the numerical value of the degree of influence of ambient air pressure on the environmental impact correction coefficient. When using it, the preset ambient air pressure influence factor can be directly obtained from the preset database. The correspondence can be a pre-set mapping relationship. For example, the ambient air pressure and the influence factor corresponding to the preset ambient air pressure in the preset database form a mapping set. The real-time ambient air pressure is input into the mapping set to obtain the corresponding influence 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 influence 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 influence factor is the influence factor corresponding to the environmental distance preset in the preset database. It represents the numerical value of the degree of influence of the environmental signal propagation distance on the environmental impact correction coefficient. When using it, the preset environmental distance influence factor can be directly obtained from the preset database. The correspondence can be a pre-set mapping relationship. For example, the signal propagation distance and the influence factor corresponding to the preset environmental distance in the preset database form a mapping set. The real-time environmental signal propagation distance is input into the mapping set to obtain the corresponding influence 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 intensity of environmental signal propagation interference on the environmental impact correction coefficient. Specifically, the environmental signal interference impact factor is the impact factor corresponding to the environmental signal interference preset in the preset database. It represents the numerical value of the degree of influence of the intensity of environmental signal propagation interference on the environmental impact correction coefficient. When using it, the preset environmental signal interference impact factor can be directly obtained from the preset database. The correspondence can be a pre-set mapping relationship. For example, the intensity of environmental signal propagation interference and the preset environmental signal interference impact factor in the preset database form a mapping set. 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 validity quality assessment weight is used to describe the degree of influence of the validity of wireless backhaul data transmission on the backhaul data quality assessment index. Specifically, the data validity quality assessment weight is the weight corresponding to the preset data validity quality assessment in the preset database, which represents the numerical value of the degree of influence of the validity of wireless backhaul data transmission on the backhaul data quality assessment index. When using it, the weight corresponding to the preset data validity quality assessment can be directly obtained from the preset database. The correspondence can be a pre-set mapping relationship. For example, the validity of wireless backhaul data transmission and the weight corresponding to the preset data validity quality assessment in the preset database form a mapping set. The validity of real-time wireless backhaul data transmission 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].

[0047] The data integrity quality assessment weight is used to describe the degree of influence of 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 preset data integrity quality assessment in the preset database, representing the numerical value of the degree of influence of wireless backhaul data transmission integrity on the backhaul data quality assessment index. When using it, the weight corresponding to the preset data integrity quality assessment can be directly obtained from the preset database. The correspondence can be a pre-set mapping relationship. For example, the wireless backhaul data transmission integrity and the weight corresponding to the preset data integrity quality assessment in the preset database form a mapping set. Input the real-time wireless backhaul data transmission integrity 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 latency quality assessment weight is 1. The data latency quality assessment weight is used to describe the degree of influence of wireless backhaul data transmission latency on the backhaul data quality assessment index.

[0049] like Figure 2The diagram shows the variation of the data return quality assessment index provided in this embodiment of the application. The settings are as follows: data validity quality assessment weight is 0.4, data integrity quality assessment weight is 0.3, data latency quality assessment weight is 0.3, the total number of wireless return data packets is 50, the total number of wireless return transmitted data packets is 50, the reference data packet delay deviation is 0.1 seconds, the environmental air pressure influence factor is 0.05, the environmental distance influence factor is 0.02, the environmental interference influence factor is 0.03, the measured environmental atmospheric pressure is 1013 hPa, the standard atmospheric pressure is 1013.25 hPa, the measured environmental signal propagation distance is 10 kilometers, the measured environmental signal interference intensity is 5 dB, the data hash checksum of all wireless return data packets is 1, and the data packet transmission time of the wireless return data packets is uniformly distributed within the range of 0 to 1 second. It should be noted that... The absolute value of the difference between the data packet transmission time and the data packet reception time is generally between 0 seconds and 0.5 seconds. Among them, (a) is a schematic diagram of the change of the backhaul data quality assessment index with the total number of lost data packets in wireless backhaul. When the absolute value of the difference between the data packet transmission time and the data packet reception time is a fixed value (0.25 seconds), the backhaul data quality assessment index decreases as the total number of lost data packets in wireless backhaul increases. Among them, (b) is a schematic diagram of the change of the backhaul data quality assessment index with |data packet transmission time - data packet reception time|. When the total number of lost data packets in wireless backhaul is 0, if the data packet transmission time and the data packet reception time are equal, the backhaul data quality assessment index reaches its maximum value. As the absolute value of the difference between the data packet transmission time and the data packet reception time, i.e., the data reception time delay, gradually increases, the backhaul data quality assessment index decreases.

[0050] The backhaul data quality assessment index is used to quantify the transmission quality of wireless backhaul data. Specifically, the data hash checksum, the total number of lost data packets during wireless backhaul, and the data packet reception time all directly affect the backhaul data quality assessment index. The backhaul data quality assessment index includes multiple parameters, and these parameters are interconnected and not independent. For example, lost data packets will affect the data hash checksum (i.e., the H value in the backhaul data quality assessment index). d Invalid (data packets not received and unable to pass hash verification) reduces overall data validity; furthermore, as data packet reception time increases, received data packets may fail verification, thus affecting the data hash verification compliance value (i.e., the H in the return data quality assessment index). dMeanwhile, as the measured ambient atmospheric pressure gradually deviates from the standard atmospheric pressure, the propagation characteristics of the communication signal change accordingly. For example, lower atmospheric pressure may lead to increased signal attenuation. Furthermore, as the measured intensity of environmental signal interference increases, i.e., the communication quality deteriorates, the bit error rate increases, the probability of data packet loss increases, and the total number of lost data packets in wireless backhaul increases. Therefore, a comprehensive analysis yields a backhaul data quality assessment index. This enables a numerical assessment of the wireless backhaul data transmission quality. By judging the wireless backhaul data transmission quality through numerical evaluation, the reliability of wireless backhaul data transmission quality judgment is improved, thereby enhancing the accuracy of judging abnormal situations in wireless backhaul data transmission.

[0051] The specific steps for determining whether to perform a 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, then the wind turbine operation compliance assessment is performed, and the wireless backhaul data and the backhaul data quality assessment index are backed up in real time and uploaded to cloud storage; If the backhaul data quality assessment index exceeds the preset backhaul quality assessment threshold range, then the wind turbine operation compliance assessment is not performed, and the preset personnel are reminded to reacquire the wireless backhaul data.

[0052] In this embodiment, the preset backhaul quality assessment threshold range is obtained from the database. Specifically, the preset backhaul quality assessment threshold range is set by professionals according to industry standards. 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 assessment of the wireless backhaul data transmission quality is achieved.

[0053] The specific steps for obtaining the wind turbine operation assessment index after the wind turbine operation compliance assessment are as follows: Obtain the relevant operation measurement values ​​for preset measurement points within a preset time interval. These values ​​include the actual power generation of the wind turbine, the actual voltage of the wind turbine, the actual frequency of the wind turbine, the rotor radius, and the ambient wind speed. Process the actual voltage of the wind turbine, the actual frequency of the wind turbine, and the fluctuation compliance factor and reference relevant values ​​obtained from a preset database to obtain the power quality assessment value (i.e., the DP in the wind turbine operation assessment index). bThe fluctuation compliance factors include voltage fluctuation compliance factors and frequency fluctuation compliance factors. The reference relevant values ​​include the wind turbine standard reference voltage and the wind turbine standard reference frequency. The power quality assessment value is combined with air density and reference data obtained from a preset database to obtain the wind turbine operation assessment index. The reference data includes the wind turbine standard reference power generation, reference power generation change value, reference wind energy utilization, operating power compliance weight, wind energy utilization compliance weight, and power quality compliance weight.

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

[0055]

[0056]

[0057] In the formula, Wy represents the wind turbine operation evaluation index. This indicates that the operating power conforms to the weight. This indicates that wind energy utilization conforms to the weighting. This indicates that the power quality conforms to the weight, b represents the number of the preset measurement point within the preset time interval, b = 1, 2, ..., B, B represents the total number of preset measurement points within the preset time interval, and P b P represents the actual power generation of the wind turbine at the b-th preset measurement point. ’ DP represents the standard reference power generation of the wind turbine, ΔP represents the variation in reference power generation, ρ represents air density, R represents the rotor radius, V represents the ambient wind speed, and ΔC represents the reference wind energy utilization coefficient. b V represents the power quality assessment value at the b-th preset measurement point, ε1 represents the voltage fluctuation compliance factor, and V b,s This represents the actual voltage of the fan at the b-th preset measurement point, in V. ′ The standard reference voltage for the wind turbine is represented by ε2, which represents the frequency fluctuation compliance factor, and F. b,s F represents the actual frequency of the fan at the b-th preset measurement point. ′ Indicates the standard reference frequency of the wind turbine, C y This indicates the preset range of return quality assessment thresholds.

[0058] In this embodiment, the actual power generation, actual voltage, and actual frequency of the wind turbine are obtained through a SCADA (Supervisory Control and Data Acquisition) system; the rotor radius is obtained through a laser rangefinder; the ambient wind speed is obtained through a wind speed sensor deployed on the top of the wind turbine nacelle; and reference correlation values, standard reference power generation, reference power generation variation, and reference wind energy utilization are obtained from a preset database. The standard reference voltage is represented by summing and averaging the collected historical wind turbine output voltages, and the standard reference frequency is represented by summing and averaging the collected historical wind turbine output frequencies. The air density is 1.225 kg / m³.

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

[0060] It is important to understand that the voltage fluctuation compliance factor is used to describe the degree of influence of voltage fluctuation compliance on the power quality assessment value. Specifically, the voltage fluctuation compliance factor is a preset compliance factor corresponding to voltage fluctuation in the preset database, representing the numerical value of the degree of influence of voltage fluctuation compliance on the power quality assessment value. When using it, the preset voltage fluctuation compliance factor can be directly obtained from the preset database. The correspondence can be a pre-set mapping relationship. For example, the voltage fluctuation compliance and the preset voltage fluctuation compliance factor in the preset database form a mapping set. 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. The frequency fluctuation compliance factor is used to describe the degree of influence of frequency fluctuation compliance on the power quality assessment value.

[0061] Among them, the operating power compliance weight is used to describe the degree of 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 degree of influence of the operating power compliance of the wind turbine on the wind turbine operation evaluation index. When using it, the weight corresponding to the preset operating power compliance can be directly obtained from the preset database. The correspondence can be a pre-set mapping relationship. For example, the operating power compliance of the wind turbine and the weight corresponding to the preset operating power compliance in the preset database form a mapping set. The real-time operating power compliance of the wind turbine 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 degree of influence of the wind energy utilization status of wind turbine units on the wind turbine unit operation evaluation index. Specifically, the wind energy utilization compliance weight is the weight corresponding to the wind energy utilization compliance preset in the preset database. It represents the numerical value of the degree of influence of the wind energy utilization status of wind turbine units on the wind turbine unit operation evaluation index. When using it, the weight corresponding to the preset wind energy utilization compliance can be directly obtained from the preset database. The correspondence can be a pre-set mapping relationship. For example, the wind energy utilization status of wind turbine units and the weight corresponding to the preset wind energy utilization compliance in the preset database form a mapping set. The real-time wind energy utilization status of wind turbine units 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].

[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 degree of influence of the power quality compliance of the wind turbine on the wind turbine operation evaluation index.

[0064] The wind turbine operation assessment index is used to comprehensively quantify the compliance level of wind turbine operation. The index is influenced by calculations of wind turbine operating power compliance, wind energy utilization efficiency, and power quality compliance. Specifically, the index is derived through a comprehensive analysis of the correlations and mutual influences among multiple parameters. For example, as ambient wind speed increases, the actual output power of the wind turbine changes, leading to fluctuations in the actual voltage and frequency, thus reducing the power quality assessment value. Furthermore, the actual power generation is directly affected by ambient wind speed, and changes in actual power generation also cause fluctuations in the actual voltage and frequency, further impacting power quality. A decrease in the power quality assessment value indicates reduced voltage and frequency stability, thus affecting overall power quality and decreasing the compliance level of wind turbine operation. Therefore, this quantitative approach enables a numerical assessment of wind turbine operation compliance. This numerical assessment improves the reliability of wind turbine operation compliance assessment in the event of abnormal wireless backhaul data transmission.

[0065] The specific steps for determining whether to perform wind turbine operation status optimization based on the wind turbine operation evaluation index and the preset threshold range are as follows: Compare the wind turbine operation evaluation index with the preset threshold range: If the wind turbine operation evaluation index is within the preset threshold range, 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 threshold range, wind turbine operation status optimization is performed, 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 the database. Specifically, the preset unit operation compliance threshold range is set by professionals according to the 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 wind turbine operation compliance degree is achieved.

[0067] The specific steps for optimizing the wind turbine's operating status are as follows: A1. Obtain the initial operating data of the wind turbine, and sequentially increase the initial pitch angle of the wind turbine until it reaches the maximum pitch angle. Determine whether the wind turbine's operating evaluation index is within the preset threshold range. If yes, stop the wind turbine's operating status optimization; otherwise, proceed to A2. The initial operating data includes the initial pitch angle, the initial turbine speed, and the initial output voltage. A2. Sequentially decrease the initial turbine speed of the wind turbine until it reaches the minimum turbine speed. Determine whether the wind turbine's operating evaluation index is within the preset threshold range. If yes, stop the wind turbine's operating status optimization; otherwise, proceed to A3. A3. Sequentially decrease the initial output voltage of the wind turbine until it reaches the minimum output voltage, and simultaneously obtain the wind turbine optimization effect evaluation index after the wind turbine's operating status optimization.

[0068] In this embodiment, the initial pitch angle describes the angle between the airfoil chord at the tip of the wind turbine blade and the plane of rotation, and is obtained by a pitch angle sensor deployed at the center of the wind turbine blade; the maximum pitch angle describes the maximum adjustable angle of the wind turbine blade; the initial turbine speed describes the rotational speed of the generator shaft, and is obtained by a speed sensor deployed at the generator output; the initial output voltage describes the output voltage of the generator, and is obtained by a voltage sensor deployed at the generator output; the minimum turbine speed describes the lowest operating speed of the wind turbine; and the minimum output voltage describes the lowest output voltage of the wind turbine. By obtaining the maximum pitch angle, minimum turbine speed, and minimum output voltage from a preset database, and by performing wind turbine operation state optimization, the wind turbine operation state is adjusted under abnormal wireless backhaul data transmission conditions, thereby improving the operational stability of the wind turbine during wireless backhaul data transmission.

[0069] The specific steps for obtaining the wind turbine optimization effect evaluation index after optimizing the wind turbine operating status are as follows: First, obtain the initial relevant measurement values ​​of the preset measurement points to be optimized within the preset time interval. These initial relevant measurement values ​​include the initial actual average power generation, initial output power, initial average output power, initial average energy consumption, and initial total power generation. Second, obtain the optimization relevant measurement values ​​of the preset optimization measurement points within the preset optimization time interval. These optimization relevant measurement values ​​include the optimized actual average power generation, optimized output power, optimized average output power, optimized average energy consumption, and optimized total power generation. Third, obtain the power fluctuation compliance evaluation value (i.e., U in the wind turbine optimization effect evaluation index) by processing the initial relevant measurement values, optimization relevant measurement values, power fluctuation compliance evaluation value, and optimization relevant reference data obtained from the preset database. The optimization relevant reference data includes the reference power generation efficiency change, reference power fluctuation change, reference energy consumption change, power generation efficiency optimization evaluation weight, power fluctuation optimization evaluation weight, and energy consumption change evaluation weight.

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

[0071]

[0072]

[0073] In the formula, 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 W represents the optimized actual average power generation. a ′ fter Indicates the reference optimized power generation capacity, W before,avg W represents the initial actual average power generation. b ′ efore Here, ΔW represents the reference initial power generation, ΔW represents the change in reference power generation efficiency, U represents the power fluctuation conforming to the assessment value, ΔB represents the change in reference power fluctuation, m represents the number of the preset measurement points to be optimized within the preset time interval, m = 1, 2, ..., M, where M represents the total number of preset measurement points to be optimized within the preset time interval, and n represents the number of the preset optimization measurement points within the preset optimization time interval, n = 1, 2, ..., N, where N represents the total number of preset optimization measurement points within the preset optimization time interval. P represents the initial output power of the m-th preset measurement point to be optimized. before,avg This represents the initial average output power. P represents the optimized output power at the nth preset optimization measurement point. after,avg E represents the optimized average output power. before,avg E represents the initial average energy consumption. after,avg D represents the optimized average energy consumption. before,avg D represents the initial total power generation. after,avg This represents the optimized total power generation, where ΔE represents the change in reference energy efficiency, and J y This indicates that the preset unit operation meets the threshold range.

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

[0075] It is important to understand that the reference power generation efficiency change, reference power fluctuation change, and reference energy consumption change are obtained from a pre-set 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 degree of influence of the wind turbine power generation efficiency optimization on the wind turbine optimization effect evaluation index. Specifically, the power generation efficiency optimization evaluation weight is the weight corresponding to the preset power generation efficiency optimization evaluation in the preset database, which represents the numerical value of the degree of influence of the wind turbine power generation efficiency optimization on the wind turbine optimization effect evaluation index. When using it, the weight corresponding to the preset power generation efficiency optimization evaluation can be directly obtained from the preset database. The correspondence can be a pre-set mapping relationship. For example, the wind turbine power generation efficiency optimization and the weight corresponding to the preset power generation efficiency optimization evaluation in the preset database form a mapping set. 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 degree of influence of wind turbine power fluctuation on the wind turbine optimization effect evaluation index. Specifically, the power fluctuation optimization evaluation weight is the weight corresponding to the preset power fluctuation optimization evaluation in the preset database. It represents the numerical value of the degree of influence of the wind turbine power fluctuation optimization on the wind turbine optimization effect evaluation index. When using it, the weight corresponding to the preset power fluctuation optimization evaluation can be directly obtained from the preset database. The correspondence can be a pre-set mapping relationship. For example, the wind turbine power fluctuation optimization and the weight corresponding to the preset power fluctuation optimization evaluation in the preset database form a mapping set. The real-time wind turbine power fluctuation 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].

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

[0079] Specific assumptions: The evaluation weight for power generation efficiency optimization is 0.4, the evaluation weight for power fluctuation optimization is 0.3, the evaluation weight for energy consumption change is 0.3, the reference optimized 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 is 10, the total number of preset optimization measurement points within the preset optimization time interval is 10, and the initial output power of the preset measurement points to be optimized is 480 kW, 490 kW, 475 kW, 485 kW, 495 kW, and 490 kW respectively. The initial average output power was 486 kW, and the optimized output power at the preset optimization measurement points were 500 kW, 510 kW, 505 kW, 515 kW, 500 kW, 505 kW, 510 kW, 495 kW, 520 kW, and 515 kW, respectively, with an optimized average output power of 507.5 kW. The initial average energy consumption was 1000 kWh, the initial total power generation was 1200 kWh, and the reference energy consumption change was 100. The wind turbine optimization effect evaluation index can be calculated using the above method. The statistical table of changes in the wind turbine optimization effect evaluation index is shown in Table 1.

[0080] Table 1. Statistical Table of Changes in the Evaluation Index of Wind Turbine Optimization Effect

[0081]

[0082] As shown in Table 1, when the optimized actual average power generation, optimized average energy consumption, and power fluctuation compliance evaluation value are fixed values, the wind turbine optimization effect evaluation index increases with the increase of the total optimized power generation, as shown in the second and third sets of data in the table. The better the optimization effect of the wind turbine operation status, the more the optimized power generation efficiency increases. 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, when the power fluctuation compliance evaluation value increases, the wind turbine optimization effect evaluation index increases accordingly.

[0083] Furthermore, the wind turbine optimization effect evaluation index incorporates multiple parameters, and these parameters are interconnected, not independent entities, and are not obtained through simple addition. For example, as offshore wind speeds increase, the optimized actual average power generation of the wind turbine increases, along with the total optimized power generation. To accommodate wind speed changes, the regulation frequency needs to be increased, leading to a corresponding increase in optimized average energy consumption. Simultaneously, as power fluctuations decrease, the stability of the optimized actual average power generation increases, resulting in higher power generation efficiency—that is, increased power generation with the same energy consumption. Therefore, by quantifying the correlations and mutual influences among various factors, a comprehensive analysis yields the wind turbine optimization effect evaluation index. This enables a numerical evaluation of the optimization effect of wind turbine operation status, allowing for the judgment of the optimization effect and thus improving the accuracy of the wind turbine operation status optimization effect evaluation.

[0084] The specific steps for determining whether to execute an aging detection reminder based on the backhaul data quality assessment index and the wind turbine operation assessment index are as follows: First, determine if the acquired backhaul data quality assessment index is within the preset backhaul quality assessment threshold range. If the backhaul data quality assessment index is within the preset backhaul quality assessment threshold range, then the aging detection reminder is not executed; otherwise, determine if the acquired wind turbine operation assessment index is within the preset turbine operation compliance threshold range. If the wind turbine operation assessment index is within the preset turbine operation compliance threshold range, then the aging detection reminder is not executed; otherwise, determine if the acquired wind turbine optimization effect assessment index is within the preset wind turbine optimization effect threshold range. If the wind turbine optimization effect assessment index is within the preset wind turbine optimization effect threshold range, then 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 the database. Specifically, the preset wind turbine optimization effect threshold range is set by professionals according to industry standards. For example, the preset wind turbine optimization effect threshold range is determined by setting the actual average power generation 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), thereby achieving an accurate evaluation of the wind turbine unit operation status optimization effect.

[0086] like Figure 3The diagram shows a flowchart of the wireless data backhaul method for offshore wind farms based on 5G technology provided by the present invention. The method includes the following steps: S1, acquiring wireless backhaul data from the offshore wind farm within a preset time interval, and simultaneously 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 assessment 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 wireless backhaul... S3, Obtain the wind turbine operation evaluation index after the wind turbine operation compliance assessment. Based on the wind turbine operation evaluation index and the preset unit operation compliance threshold range, determine whether to perform wind turbine operation status optimization. The wind turbine operation evaluation index is used to comprehensively quantify the compliance of wind turbine operation. S4, Obtain the wind turbine optimization effect evaluation index after the wind turbine operation status optimization. Based on the returned data quality evaluation index and the wind turbine operation evaluation index, comprehensively determine whether to perform aging detection reminder. The wind turbine optimization effect evaluation index is used to quantitatively evaluate the effect of wind turbine operation status optimization.

[0087] In this embodiment, aging detection reminders are sent via SMS or email. Specifically, the aging detection reminders include reminding designated personnel to conduct aging detection on key components such as gearboxes and suggesting increased equipment lubrication. By comprehensively judging whether to perform aging detection, the system can handle and provide feedback in case of abnormal wireless data transmission, thereby improving the stability of wireless data transmission in offshore wind farms.

[0088] The following points need to be explained:

[0089] (1) The accompanying drawings of the embodiments of the present invention only involve the structures involved in the embodiments of the present invention. Other structures can refer to the general design.

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

[0091] (3) Where there is no conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other to obtain new embodiments.

[0092] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A wireless data backhaul system for offshore wind farms based on 5G technology, characterized in that, It 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 and feedback module; The wireless backhaul data acquisition module is used to acquire wireless backhaul data of offshore wind farms 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 assess the transmission quality of wireless backhaul data to obtain a backhaul data quality assessment index. Based on the backhaul data quality assessment index and a preset backhaul quality assessment threshold range, it determines whether to perform a wind turbine operation compliance assessment. The backhaul data quality assessment index is used to quantify the transmission quality of 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 assessment is performed, and to determine whether to perform 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 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. Based on the feedback data quality evaluation index and the wind turbine operation evaluation index, a comprehensive judgment is made on whether to perform aging detection reminder. The wind turbine optimization effect evaluation index is used to quantitatively evaluate the effect of wind turbine operation status optimization.

2. The 5G-based wireless data backhaul system for offshore wind farms according to claim 1, characterized in that, The specific steps for evaluating the transmission quality of wireless backhaul data to obtain the backhaul data quality assessment index are as follows: The system acquires backhaul data quality-related values ​​and quality assessment-related values ​​at preset time intervals. The backhaul data quality-related values ​​include data hash check sending value, data hash check receiving value, total number of lost wireless backhaul data packets, total number of wireless backhaul data packets sent, data packet sending time, and data packet receiving time. The quality assessment-related values ​​include ambient atmospheric pressure measurement value, ambient signal propagation distance measurement value, and ambient signal interference intensity measurement value. The data hash check sent value is compared with the data hash check received value to obtain the data hash check match value; The environmental impact correction coefficient is obtained by combining the relevant values ​​of quality assessment with standard atmospheric pressure and the impact factors obtained from a preset database. The impact factors include environmental air pressure impact factor, environmental distance impact factor and environmental signal interference impact factor. The data hash verification 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. 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 5G-based wireless data backhaul system for offshore wind farms according to claim 2, characterized in that, The method for obtaining the quality assessment index of the returned data is as follows: d h =1-[τ1*(P-P0)+τ2*Gd+τ3*Gq]; In the formula, 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 latency quality assessment weight, d represents the wireless backhaul data packet number, d = 1, 2, ..., D, D represents the total number of wireless backhaul data packets, and H d h' represents the data hash checksum of the d-th wireless backhaul data packet, h' represents the data hash checksum sent value of the d-th wireless backhaul data packet. d Ld represents the received data hash checksum of the d-th wireless backhaul data packet, Zd represents the total number of lost wireless backhaul data packets, and t represents the total number of transmitted wireless backhaul data packets. d Let t′ represent the data packet transmission time of the d-th wireless backhaul data packet. d The data packet reception time of the d-th wireless backhaul data packet is represented by Δt, which represents the reference data packet delay deviation, and δ. h The environmental impact correction coefficient is represented by τ1, the environmental air pressure influence factor is represented by P, the measured value of environmental atmospheric pressure is represented by P0, the standard atmospheric pressure is represented by τ2, the environmental distance influence factor is represented by Gd, the measured value of environmental signal propagation distance is represented by τ3, the environmental signal interference influence factor is represented by Gq, and the measured value of environmental signal interference intensity is represented by Gq.

4. The 5G-based wireless data backhaul system for offshore wind farms according to claim 1, characterized in that, The specific steps for determining whether to perform a wind turbine operation compliance assessment based on the backhaul data quality assessment index and a preset backhaul quality assessment threshold range are as follows: Compare the returned data quality assessment index with the preset returned data 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 will not be performed, and the preset personnel will be reminded to reacquire the wireless backhaul data. If the backhaul data quality assessment index exceeds the preset backhaul quality assessment threshold, the wind turbine operation compliance assessment will be performed, and the wireless backhaul data and backhaul data quality assessment index will be backed up in real time and uploaded to cloud storage.

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

6. The 5G-based wireless data backhaul system for offshore wind farms according to claim 5, characterized in that, The specific steps for determining whether to perform wind turbine operation status optimization based on the wind turbine operation evaluation index and the preset threshold range for turbine operation are as follows: Compare the wind turbine operation evaluation index with the preset threshold range for turbine operation: If the wind turbine operation evaluation index is within the preset 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 for storage. If the wind turbine operation assessment index exceeds the preset fluctuation threshold range, the wind turbine operation status optimization will be performed, and the wind turbine operation assessment index will be fed back to the preset personnel.

7. The 5G-based wireless data backhaul system for offshore wind farms according to claim 6, characterized in that, The specific steps for optimizing the operating status of wind turbine units are as follows: A1. Obtain the initial operation-related data of the wind turbine, and sequentially increase the initial pitch angle of the wind turbine until the maximum pitch angle is reached. Determine whether the wind turbine operation evaluation index is within the preset unit operation compliance threshold range. If yes, stop the wind turbine operation status 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, sequentially reduce the initial turbine speed of the wind turbine unit until the minimum turbine speed is reached, determine whether the wind turbine unit operation evaluation index is within the preset unit operation compliance threshold range, if so, stop the wind turbine unit operation status optimization, otherwise execute A3; A3, sequentially reduce the initial output voltage of the wind turbine until it reaches the minimum output voltage, and at the same time obtain the wind turbine optimization effect evaluation index after performing wind turbine operation status optimization.

8. The 5G-based wireless data backhaul system for offshore wind farms according to claim 1, characterized in that, The specific steps for obtaining the wind turbine optimization effect evaluation index after optimizing the wind turbine operating status are 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, initial output power, initial average output power, initial average energy consumption, and initial total power generation. Obtain the optimization-related measurement values ​​of preset optimization measurement points within the preset time interval. The optimization-related measurement values ​​include optimized actual average power generation, optimized output power, optimized average output power, optimized average energy consumption, and optimized total power generation. The power fluctuation meets the 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. The wind turbine optimization effect evaluation index is obtained by processing the initial relevant measurement values, optimized relevant measurement values, power fluctuation compliance evaluation values, and optimized relevant reference data obtained from the preset database. The optimized relevant reference data includes reference power generation efficiency change, reference power fluctuation change, reference energy consumption change, power generation efficiency optimization evaluation weight, power fluctuation optimization evaluation weight, and energy consumption change evaluation weight.

9. The 5G-based wireless data backhaul system for offshore wind farms according to claim 1, characterized in that, The specific steps for determining whether to issue an aging detection reminder based on the comprehensive judgment of the backhaul data quality assessment index and the wind turbine operation assessment index are as follows: Determine whether the quality assessment index of the acquired returned data is within the preset returned data quality assessment threshold range; If the returned data quality assessment index is within the preset returned data quality assessment threshold range, the aging detection reminder will not be executed; otherwise, it will be determined whether the obtained wind turbine operation assessment index is within the preset unit operation threshold range. If the wind turbine operation evaluation index is within the preset threshold range, the aging detection reminder will not be executed; otherwise, it will be determined whether the obtained wind turbine optimization effect evaluation index is within the preset wind turbine optimization effect threshold range. If the wind turbine optimization effect evaluation index is within the preset wind turbine optimization effect threshold range, the aging test reminder will not be executed; otherwise, the aging test reminder will be executed.

10. A wireless data backhaul method for offshore wind farms based on 5G technology, characterized in that, Includes the following steps: S1, acquire wireless backhaul data of offshore wind farm within a preset time interval, and transmit the wireless backhaul data to the preset wind farm monitoring center. S2, evaluate the transmission quality of wireless backhaul data to obtain a backhaul data quality evaluation index, and determine whether to perform wind turbine operation compliance evaluation based on the backhaul data quality evaluation index and the preset backhaul quality evaluation threshold range. The backhaul data quality evaluation index is used to quantify the transmission quality of wireless backhaul data. S3, obtain the wind turbine operation evaluation index after the wind turbine operation compliance assessment, and determine whether to perform 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 wind turbine operation. S4. Obtain the wind turbine optimization effect evaluation index after the wind turbine operation status optimization is performed. Based on the feedback data quality evaluation index and the wind turbine operation evaluation index, make a comprehensive judgment on whether to perform aging detection reminder. The wind turbine optimization effect evaluation index is used to quantitatively evaluate the effect of wind turbine operation status optimization.

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