Comprehensive monitoring method and system for wind power generation equipment based on multiple sensors and power distribution cabinet

Abstract

The invention relates to the field of wind power generation control, in particular to a wind power generation equipment comprehensive monitoring method and system based on multiple sensors and a power distribution cabinet. Comprising the following steps: collecting wind power parameters of power generation equipment in a target area through a monitoring unit, generating a wind power diagram, and verifying by combining a position vector between the power generation equipment and a target wind power vector in the wind power diagram; generating N groups of input data based on the wind power parameters, the equipment parameters and the N operation modes, and respectively calculating wake flow distribution, generating capacity and equipment loss value corresponding to each group of data through a wake flow prediction model and a power generation prediction model; and superposing the wake flow information into the wind power data of other equipment, finally calculating the total power generation amount and the total loss amount of all wake flow branches, and selecting the optimal operation mode of each power generation equipment through multi-objective optimization. Cooperative control of power generation equipment is realized, and power generation efficiency and equipment loss are effectively balanced.

Description

Technical Field

[0001] The present invention relates to the field of wind power generation control, and in particular to a comprehensive monitoring method, system and distribution cabinet for wind power generation equipment based on multiple sensors. Background Art

[0002] Existing wind power equipment monitoring technologies mostly rely on a single sensor or local data collection, which makes it difficult to fully reflect the dynamic interaction of multiple wind power generation equipment in a complex wind farm. For example, traditional methods usually ignore the impact of the wake effect on downstream equipment, resulting in a significant deviation between power generation prediction and actual operating conditions. In addition, most monitoring systems lack the ability to dynamically balance the equipment operation mode and loss, often pursuing short-term power generation efficiency at the expense of equipment life, or operating in a conservative mode, resulting in insufficient utilization of power generation potential. Similar prior art includes a Chinese patent with publication number CN118934443A, which proposes a method and system for optimizing the power generation efficiency of a wind turbine based on wind direction and wind speed monitoring, wherein the method includes: analyzing the data of wind direction and wind speed monitoring; based on the analysis results, retrieving the corresponding working parameter set from the pre-configured working mode library of the wind turbine; and configuring the wind turbine based on the working parameter set. The invention's method for optimizing the power generation efficiency of a wind turbine based on wind direction and wind speed monitoring monitors changes in wind direction and wind speed in real time, adjusts the operating status of the wind turbine, optimizes the speed of the unit to meet the power output requirements by monitoring and predicting changes in wind conditions, and thereby achieves maximum wind energy conversion. In addition, similar prior art includes a European patent with publication number EP2111509B1, which discloses a wind farm monitoring and control system, the system comprising: at least one wind farm; at least one intelligent management server that can be connected to the at least one wind farm through a data communication network; at least one wind farm configuration. A tool related to the intelligent management server is used to establish a connection to the at least one wind farm. The present invention proposes a system for monitoring and controlling a wind farm. Monitoring and control are performed so that multiple wind farms can be uniformly monitored and controlled. Therefore, different wind farms can be monitored and controlled simultaneously with uniform output, and data from wind farms can also be compared. The technical solutions of the above two patents both solve the problem of monitoring wind power generation, but neither of them considers analyzing the relationship between wind turbine wake, power generation and equipment loss, thereby achieving a balance between increasing power generation and reducing equipment loss. Summary of the invention

[0003] The present invention provides a comprehensive monitoring method, system and distribution cabinet for wind power generation equipment based on multiple sensors, which significantly improves the monitoring accuracy and operation efficiency of wind power generation equipment through multi-sensor data fusion and collaborative control mechanism. The method includes: Step S1: obtaining wind information at each power generation device in the target area through a monitoring unit, obtaining a wind map based on the wind information, and verifying the wind information through position vectors between adjacent power generation devices and a target wind vector in the wind map; Step S2: acquiring N groups of input information based on the wind information, equipment information and N operation modes of the power generation equipment, and acquiring N groups of wake information and corresponding power generation and equipment loss according to each group of the input information, the wake prediction model and the power generation prediction model; Step S3: adding each set of wake information to the corresponding wind power information of other power generation equipment, repeating step S2, obtaining the prediction information of the corresponding power generation equipment on each wake branch, and calculating the total power generation and total equipment loss on each wake branch; Step S4: selecting the optimal operation mode corresponding to each power generation device based on the total power generation and the total device loss.

[0004] As a preferred technical solution of the present invention, the equipment information is the structural parameters of the generator set, including at least the windmill height, rotor diameter and blade angle.

[0005] As a preferred technical solution of the present invention, verifying the wind information by using the position vector between adjacent power generation equipment and the target wind vector in the wind map includes: According to the position information of the first power generation device and the second power generation device, the position vector pointing from the first power generation device to the second power generation device is obtained, and according to the wind information monitored by the first power generation device and the second power generation device, the first wind map and the second wind map are respectively obtained, and the first wind map and the second wind map are compared. According to the comparison result, the target wind vector at the position with the maximum wind difference is obtained, and the target wind vector is compared with the position vector, and the wind information corresponding to the second power generation device is verified according to the comparison result. Through this step, the wind information of each power generation device is verified, wherein the first power generation device is located upstream of the second power generation device.

[0006] As a preferred technical solution of the present invention, the wake information, power generation and equipment loss corresponding to each group of the input data are obtained respectively, including: The power generation equipment in the target area is sorted according to the location information, and the wind information and equipment information of the corresponding power generation equipment after inspection are used as the first information according to the sorting, and the first information is respectively combined with the different operating modes corresponding to the power generation equipment to obtain multiple groups of input information, and each group of the input information is respectively input into the wake prediction model and the power generation prediction model to obtain the wake information, the power generation and the equipment loss corresponding to each group of the input information, wherein the operating mode includes a high-speed mode, a normal mode and a low-speed mode.

[0007] As a preferred technical solution of the present invention, the total power generation and total equipment loss on each of the tail flow branches are calculated, including: Add the N wake information of the power generation equipment to the wind power information corresponding to the corresponding i-th power generation equipment to obtain N new wind power information, and combine each of the new wind power information, equipment information and each of the N working modes corresponding to the i-th power generation equipment to obtain The group inputs information, and repeats step S3 to obtain the corresponding The prediction information includes wake information, power generation and equipment loss, and repeating this step to obtain the prediction information of each wake branch corresponding to each power generation device; Based on the prediction information of each power generation equipment on each wake branch, the total power generation and total equipment loss of the M power generation equipment on each wake information branch are calculated.

[0008] As a preferred technical solution of the present invention, the optimal operation mode corresponding to each power generation device is selected based on the total power generation and the total equipment loss on each wake branch, including: Based on the total power generation and the total equipment loss of each of the wake branches, a first value of the total power generation of each wake branch and a second value corresponding to the total equipment loss are calculated, and several wake branches to be selected whose first value is greater than the second value and whose ratio between the second value and the total power generation is the smallest are selected. The sum of the square differences between the equipment loss of each power generation equipment on each of the wake branches to be selected and the average equipment loss are calculated, and the operating mode of each power generation equipment on the wake branch to be selected that corresponds to the smallest sum of the square differences is taken as the optimal operating mode.

[0009] As a preferred technical solution of the present invention, the step S4 further includes a step S5: When each of the power generation equipment operates in the optimal operation mode, the real-time power generation and real-time equipment loss of the power generation equipment are also collected in real time, the real-time power generation is compared with the predicted power generation to obtain a first difference, the real-time equipment loss is also compared with the predicted equipment loss to obtain a second difference, the first difference and the second difference are weighted to obtain a weighted result, and whether the power generation equipment is abnormal is determined based on the weighted result.

[0010] The present invention also provides a wind power generation equipment comprehensive monitoring system based on multiple sensors, which is used to implement the above method. The system comprises: A monitoring unit, used to obtain wind information at each power generation device in the target area, and obtain a wind map based on the wind information; A verification unit, used to verify the wind information through the position vectors between adjacent power generation devices and the target wind vector in the wind map; A prediction unit is used to obtain N groups of input information based on the wind information, equipment information and N operating modes of the power generation equipment, and obtain N groups of wake information and corresponding power generation and equipment loss according to each group of the input information, the wake prediction model and the power generation prediction model; each group of the wake information is added to the corresponding wind information of other power generation equipment, and step S3 is repeated to obtain the prediction information of the corresponding power generation equipment on each wake branch; The calculation unit is used to calculate the total power generation and the total equipment loss on each of the tail flow branches, and select the operation mode corresponding to each power generation device based on the total power generation and the total equipment loss.

[0011] The present invention also provides a power distribution cabinet for comprehensive monitoring of wind power generation equipment based on multiple sensors, the power distribution cabinet comprising: a memory and at least one processor, the memory storing instructions; The at least one processor calls the instructions in the memory to enable the power distribution cabinet for comprehensive monitoring of wind power generation equipment based on multiple sensors to execute the above method.

[0012] The present invention also provides a computer-readable storage medium, on which instructions are stored, and the above method is implemented when the instructions are executed by a processor.

[0013] The beneficial effects of the present invention are as follows: The present invention significantly improves the monitoring accuracy and operating efficiency of wind power generation equipment through multi-sensor data fusion and collaborative control mechanism. Traditional monitoring systems rely on single sensors or local data collection, which makes it difficult to fully reflect the dynamic interaction of multiple wind power generation equipment in complex wind farms. In particular, the impact of wake effects on downstream equipment is often ignored, resulting in power generation prediction deviations and imbalanced equipment life management. The present invention collects wind parameters in real time and generates wind maps, combines the position vectors of adjacent equipment with the target wind vector for data verification, effectively reduces sensor errors and environmental interference, and ensures the accuracy of input data. On this basis, combined with equipment structural parameters and multiple operating modes, multiple sets of input data are generated. The power generation and equipment loss under different operating modes are evaluated through the wake prediction model and the power generation prediction model, and the wake information is recursively superimposed on the wind data of the downstream equipment to form a global chain impact calculation. This method breaks through the limitations of single-machine optimization, comprehensively considers the mutual shielding effect of equipment in the wind farm, and selects the optimal balance point between power generation efficiency and equipment loss through multi-objective optimization. For example, in strong winds, the load of downstream equipment is reduced to reduce losses while maintaining overall power generation efficiency. In addition, the system collects operating data in real time and compares it with the predicted value. It judges equipment abnormalities through weighted differences, supports timely maintenance and fault prevention, and improves system reliability. Through dynamic collaborative control, it not only solves the problem of overestimation of power generation caused by the wake effect in traditional methods, but also realizes the uniform distribution of equipment loss, which is convenient for synchronous maintenance planning and extending equipment life. This technical solution flexibly adapts to different wind farm conditions and equipment configurations, takes into account short-term power generation potential and long-term equipment health management, and provides effective support for the intelligent and efficient operation of wind farms. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0015] Figure 1 It is a flow chart of the comprehensive monitoring method of wind power generation equipment based on multiple sensors of the present invention; Figure 2 A flow chart of a method for verifying wind power information of a power generation device; Figure 3 Schematic diagram of the wake branch of the present invention; Figure 4 It is a structural diagram of the wind power generation equipment comprehensive monitoring system based on multiple sensors of the present invention. DETAILED DESCRIPTION

[0016] Embodiments of the present invention provide a comprehensive monitoring method, system and distribution cabinet for wind power generation equipment based on multiple sensors. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0017] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 As shown, an embodiment of a comprehensive monitoring method for wind power generation equipment based on multiple sensors in an embodiment of the present invention includes: Step S1: obtaining wind information at each power generation device in the target area through a monitoring unit, obtaining a wind map based on the wind information, and verifying the wind information through position vectors between adjacent power generation devices and a target wind vector in the wind map; Specifically, the wind parameters at each device are monitored in real time through multiple sensors in the monitoring unit. The sensors include wind sensors. The discrete sensor data are converted into a continuous wind field distribution map using a spatial interpolation algorithm, such as Kriging interpolation. The wind map includes a wind map for each wind device. The wind strength and direction in different areas are intuitively displayed. The direction vector is calculated based on the geographic coordinates of the adjacent devices, such as the vector from the upstream device A to the downstream device B. The actual measured value of the wind direction between the devices is extracted from the wind map. If the deviation between the position vector and the wind vector direction exceeds a threshold, data correction is triggered. Through the above technical solution, sensor errors or environmental interference can be reduced, ensuring the accuracy of the input data of the wake prediction model, and avoiding misjudgment of the power generation efficiency of downstream devices due to erroneous wind information.

[0018] Step S2: acquiring N groups of input information based on the wind information, equipment information and N operation modes of the power generation equipment, and acquiring N groups of wake information and corresponding power generation and equipment loss according to each group of the input information, the wake prediction model and the power generation prediction model; Specifically, in combination with real-time wind parameters, equipment structural parameters (such as blade angle, tower height) and preset operating modes (high-speed mode, normal mode, low-speed mode), N groups of input combinations are generated, for example, the value of N is 3, and based on the wind information of each power generation equipment, that is, the wind field information at the location or the wind field information after the wake information of the upstream power generation equipment is superimposed, the above equipment information, that is, the equipment structural parameters and N operating modes, are respectively input into the above wake prediction model and the power generation prediction model to calculate the power generation and loss of each device under different operating modes, wherein the above wake prediction model and the power generation prediction model both use historical wind information, operating mode, and equipment information as variables, and use the historical wake information and historical power generation and historical equipment loss corresponding to the above variables as the results of the above wake prediction model and the power generation prediction model, respectively. Through the above technical solution, the shielding effect of the upstream equipment on the downstream can be accurately evaluated, and the problem of overestimation of power generation caused by ignoring the wake in the traditional method is solved, and the prediction results of multiple operating modes are provided for each device, such as the high-speed mode has high power generation but large loss, laying a foundation for subsequent acquisition of the optimal operating mode.

[0019] Step S3: adding each set of wake information to the corresponding wind power information of other power generation equipment, repeating step S2, obtaining the prediction information of the corresponding power generation equipment on each wake branch, and calculating the total power generation and total equipment loss on each wake branch; Specifically, the wake distribution of each device in step S2, such as the wind speed attenuation area, is recursively superimposed on the wind data of the downstream device to form a chain influence calculation. For example, device A affects device B, and device B further affects device C. The total power generation and total loss of each wake branch, such as A→B→C, are accumulated to form a global evaluation result, breaking through the limitations of single-machine optimization, comprehensively considering the mutual influence of equipment in the wind farm, avoiding local optimal solutions, and supporting multi-device collaborative control under complex wind farm conditions. For example, in strong winds, the operating load of downstream equipment is reduced to reduce losses.

[0020] Step S4: selecting the optimal operation mode corresponding to each power generation device based on the total power generation and the total device loss.

[0021] Specifically, by maximizing total power generation and minimizing total losses, the optimal balance point between power generation efficiency and equipment life is screened out, and a combination of operating modes with high total power generation and uniform equipment loss distribution is selected. While ensuring equipment health, the overall power generation efficiency is improved, and the wear of key components is reduced by dynamically adjusting the operating mode, such as enabling low-loss mode in strong winds.

[0022] Furthermore, the equipment information is the structural parameters of the generator set, including at least the windmill height, rotor diameter and blade angle.

[0023] Further, the wind information is verified by the position vectors between adjacent power generation equipment and the target wind vector in the wind map, such as Figure 2 As shown, including: According to the position information of the first power generation device and the second power generation device, the position vector pointing from the first power generation device to the second power generation device is obtained, and according to the wind information monitored by the first power generation device and the second power generation device, the first wind map and the second wind map are respectively obtained, and the first wind map and the second wind map are compared. According to the comparison result, the target wind vector at the position with the maximum wind difference is obtained, and the target wind vector is compared with the position vector, and the wind information corresponding to the second power generation device is verified according to the comparison result. Through this step, the wind information of each power generation device is verified, wherein the first power generation device is located upstream of the second power generation device.

[0024] Specifically, when the target area encounters severe convective weather, the wind sensor may be disturbed by strong winds or the sensor may drift, resulting in inaccurate wind information monitoring of the second power generation device, which will affect the subsequent turbulence prediction and loss prediction. Therefore, through the position information of the first power generation device and the second power generation device, that is, the position coordinates, the position coordinates are the coordinates of the first power generation device and the second power generation device in the target area, and the difference between the ordinate and the abscissa in the position coordinates of the second power generation device and the first power generation device is used as the position vector pointed from the first power generation device to the second power generation device. In addition, according to the wind information of the first power generation device and the second power generation device, the first wind map and the second wind map corresponding to the first power generation device are respectively obtained, wherein the first wind map and the second wind map are wind intensity distribution maps in different directions. Since the wake of the first power generation device will affect the wind field of the second power generation device and reduce the wind force in the wake direction of the second power generation device, the first wind map and the second wind map are compared. By comparison, the target wind direction with a larger wind difference between the second wind diagram and the first wind diagram, i.e., the comparison result, is obtained, which is also the wake direction of the first power generation equipment blowing toward the second power generation equipment. Since the wake is a fan-shaped area behind the first power generation equipment, the wake direction is consistent with the direction of the position vector. Therefore, the target wind vector can be obtained through the target wind direction, and the target wind vector is compared with the position vector. When the direction of the target wind vector is consistent with the direction of the position vector, the wind information of the second power generation equipment is accurate. On the contrary, when there is a deviation between the directions, it means that the wind information of the second power generation equipment is inaccurate, and the deviation of the wind information of the second power generation equipment is corrected by the deviation angle between the two. For example, if the angle between the target wind vector and the position vector is +5 degrees, the wind direction in the wind information of the second power generation equipment is compensated by 5 degrees. Through the technical solution, more accurate wind information of each power generation equipment can be obtained, laying a foundation for further predicting the power generation and loss of each power generation equipment according to the wind information.

[0025] Further, wake information, power generation and equipment loss corresponding to each group of the input data are obtained respectively, including: The power generation equipment in the target area is sorted according to the location information, and the wind information and equipment information of the corresponding power generation equipment after inspection are used as the first information according to the sorting, and the first information is respectively combined with the different operating modes corresponding to the power generation equipment to obtain multiple groups of input information, and each group of the input information is respectively input into the wake prediction model and the power generation prediction model to obtain the wake information, the power generation and the equipment loss corresponding to each group of the input information, wherein the operating mode includes a high-speed mode, a normal mode and a low-speed mode.

[0026] Specifically, the power generation equipment in the target area is sorted according to the position information, that is, according to the direction of the wake, the upstream power generation equipment is sorted first, and the downstream power generation equipment is sorted last, wherein the front power generation equipment is not affected by the wake. This step is carried out for the front power generation equipment. The wind information and equipment information of the power generation equipment after verification are used as the first information, wherein the equipment information is the structural parameters of the generator set, including at least: windmill height, rotor diameter, blade angle, and the first information is respectively combined with different operating modes of the power generation equipment, and the operating modes include high-speed mode, normal mode and protection mode. A plurality of groups of input data are obtained, and each group of the input data is respectively input into the wake prediction model and the power generation prediction model, and the corresponding wake information, power generation and equipment loss under the input data conditions are respectively obtained. Through the technical solution, the wake information, power generation and equipment loss of the power generation equipment under different operating modes can be obtained, laying a foundation for further calculating the total power generation and total equipment loss in the target area.

[0027] Further, the total power generation and total equipment loss on each of the wake branches are calculated, including: Add the N wake information of the power generation equipment to the wind power information corresponding to the corresponding i-th power generation equipment to obtain N new wind power information, and combine each of the new wind power information, equipment information and each of the N working modes corresponding to the i-th power generation equipment to obtain The group inputs information, and repeats step S3 to obtain the corresponding The prediction information includes wake information, power generation and equipment loss, and repeating this step to obtain the prediction information of each wake branch corresponding to each power generation device; Based on the prediction information of each power generation equipment on each wake branch, the total power generation and total equipment loss of the M power generation equipment on each wake information branch are calculated.

[0028] Specifically, each set of wake information of the above-mentioned power generation equipment is added to the wind power information corresponding to the corresponding other power generation equipment, the above-mentioned i-th power generation equipment is the downstream power generation equipment of the above-mentioned power generation equipment, and the wake information corresponding to N different input information in the above-mentioned power generation equipment is added to the wind power information corresponding to the above-mentioned other power generation equipment, and N new wind power information is obtained, and each of the above-mentioned N new wind power information is combined with the equipment information of the above-mentioned i-th power generation equipment into N second information, and each of the above-mentioned second information is combined with each of the N working modes of the above-mentioned other power generation equipment to obtain The method of step S3 is repeated to obtain the wake information, power generation information and equipment loss corresponding to each input information of the above-mentioned i-th power generation equipment, and the method of this step is repeated to obtain multiple sets of wake information, power generation information and equipment loss corresponding to each power generation equipment downstream of the above-mentioned i-th power generation equipment; take two power generation equipment as an example, such as Figure 3 As shown, the pth group of prediction information corresponding to the pth input information of the first power generation device, namely the lth wake information, the pth power generation information and the pth equipment loss, the pth wake information is added to the wind power information of the second power generation device to form new wind power information, and combined with the kth input information composed of the equipment information of the second power generation device and the jth power generation mode, k=(p-1)*3+j, and the kth prediction information corresponding to the second power generation device is obtained based on the kth input information, that is, Figure 2 In the formula, the kth wake information, kth power generation information and kth equipment loss corresponding to the second device are included, and each branch of the pth prediction information of the first power generation device pointing to the kth prediction information corresponding to the second power generation device is used as a wake branch, and the total power generation and total equipment loss of all the M power generation devices in each of the above wake branches are calculated, wherein the value of M is a positive integer greater than or equal to 2 and less than or equal to 4. When performing wind power generation, especially in severe convection weather, it is not only necessary to consider the power generation of the power generation equipment, but also the equipment loss of the power generation equipment. If the increase in power generation is at the cost of higher equipment loss, and the cost of equipment loss is greater than the value of power generation, then even if the power generation is increased, it is meaningless. Therefore, by calculating the total power generation and total equipment loss on each wake branch, a foundation is laid for further selecting the optimal operating mode for each power generation equipment.

[0029] Further, based on the total power generation and the total equipment loss on each wake branch, an optimal operation mode corresponding to each power generation equipment is selected, including: Based on the total power generation and the total equipment loss of each of the wake branches, a first value of the total power generation of each wake branch and a second value corresponding to the total equipment loss are calculated, and several wake branches to be selected whose first value is greater than the second value and whose ratio between the second value and the total power generation is the smallest are selected. The sum of the square differences between the equipment loss of each power generation equipment on each of the wake branches to be selected and the average equipment loss are calculated, and the operating mode of each power generation equipment on the wake branch to be selected that corresponds to the smallest sum of the square differences is taken as the optimal operating mode.

[0030] Specifically, by calculating the total power generation and total equipment loss of each of the above-mentioned wake branches, the power generation and equipment loss on each wake branch can be understood as a whole, thereby avoiding local optimal solutions. Usually, the ideal state in the process of wind power generation is that the more power generation, the smaller the equipment loss, the better. Therefore, by calculating the value of the total power generation on each wake branch and taking it as the first value, that is, the product of the total power generation and the electricity price, the total equipment loss value is also calculated and taken as the second value, that is, the sum of the products of the equipment loss corresponding to each equipment on the above-mentioned wake branch and the total equipment price, and the above-mentioned first value is greater than the above-mentioned second value, and the above-mentioned first value The several tail flow branches with the smallest ratio to the total power generation are used as the above-mentioned tail flow branches to be selected, that is, the several tail flow branches with the smallest power generation cost. The average equipment loss on each of the above-mentioned tail flow branches to be selected is also calculated, and the sum of the square differences between each power generation equipment on the above-mentioned tail flow branches to be selected and the average equipment loss is calculated. The above-mentioned tail flow branch to be selected with the smallest sum of the average differences is used as the target tail flow branch, that is, the tail flow branch to be selected with a relatively uniform distribution of equipment loss, so that the life of the power generation equipment in the above-mentioned target area is less different. Through the above-mentioned technical solution, it is convenient to arrange preventive maintenance, overhaul or replacement simultaneously, and reduce frequent maintenance activities.

[0031] Furthermore, the step S4 further includes a step S5: When each of the power generation equipment operates in the optimal operation mode, the real-time power generation and real-time equipment loss of the power generation equipment are also collected in real time, the real-time power generation is compared with the predicted power generation to obtain a first difference, the real-time equipment loss is also compared with the predicted equipment loss to obtain a second difference, the first difference and the second difference are weighted to obtain a weighted result, and whether the power generation equipment is abnormal is determined based on the weighted result.

[0032] Specifically, when the equipment is operating in the optimal operating mode, its power generation and equipment loss are collected in real time, the deviation between the real-time power generation and the predicted power generation, and the deviation between the real-time equipment loss and the predicted equipment loss are collected, and the above two deviations are weighted, for example: the weights of power generation and equipment loss are both 0.5, and whether the corresponding power generation equipment is abnormal is judged based on the weighted result, wherein when the above weighted result is greater than or equal to the set threshold, the above power generation equipment is abnormal, and when the above weighted result is less than the above set threshold, the above power generation equipment is normal. Through the above technical solution, the abnormal state of the above power generation equipment can be obtained in real time, and when the power generation equipment is abnormal, measures can be taken immediately to avoid the situation from worsening.

[0033] The present invention also provides a wind power generation equipment comprehensive monitoring system based on multiple sensors, which is used to implement the above method, such as Figure 4 As shown, the system comprises: A monitoring unit is used to obtain wind information at each power generation device in the target area and obtain a wind map based on the wind information. A verification unit, used to verify the wind information through the position vectors between adjacent power generation devices and the target wind vector in the wind map; A prediction unit is used to obtain N groups of input information based on the wind information, equipment information and N operating modes of the power generation equipment, and obtain N groups of wake information and corresponding power generation and equipment loss according to each group of the input information, the wake prediction model and the power generation prediction model; each group of the wake information is added to the corresponding wind information of other power generation equipment, and step S3 is repeated to obtain the prediction information of the corresponding power generation equipment on each wake branch; The calculation unit is used to calculate the total power generation and the total equipment loss on each of the tail flow branches, and select the operation mode corresponding to each power generation device based on the total power generation and the total equipment loss.

[0034] The present invention also provides a power distribution cabinet for comprehensive monitoring of wind power generation equipment based on multiple sensors, the power distribution cabinet comprising: a memory and at least one processor, the memory storing instructions; The at least one processor calls the instructions in the memory to enable the power distribution cabinet for comprehensive monitoring of wind power generation equipment based on multiple sensors to execute the above method.

[0035] The present invention also provides a computer-readable storage medium, on which instructions are stored, and the above method is implemented when the instructions are executed by a processor.

[0036] In summary, the present invention significantly improves the monitoring accuracy and operating efficiency of wind power generation equipment through multi-sensor data fusion and collaborative control mechanism. Traditional monitoring systems rely on a single sensor or local data collection, which makes it difficult to fully reflect the dynamic interaction of multiple wind power generation equipment in a complex wind farm. In particular, the impact of the wake effect on downstream equipment is often ignored, resulting in power generation prediction deviations and imbalanced equipment life management. The present invention collects wind parameters in real time and generates a wind map, combines the position vectors of adjacent equipment with the target wind vector for data verification, effectively reduces sensor errors and environmental interference, and ensures the accuracy of input data. On this basis, combined with equipment structural parameters and multiple operating modes, multiple sets of input data are generated. The power generation and equipment loss under different operating modes are evaluated through the wake prediction model and the power generation prediction model, and the wake information is recursively superimposed on the wind data of the downstream equipment to form a global chain impact calculation. This method breaks through the limitations of single-machine optimization, comprehensively considers the mutual shielding effect of equipment in the wind farm, and selects the optimal balance point between power generation efficiency and equipment loss through multi-objective optimization. For example, in strong winds, the load of downstream equipment is reduced to reduce losses while maintaining overall power generation efficiency. In addition, the system collects operating data in real time and compares it with the predicted value. It judges equipment abnormalities through weighted differences, supports timely maintenance and fault prevention, and improves system reliability. Through dynamic collaborative control, it not only solves the problem of overestimation of power generation caused by the wake effect in traditional methods, but also realizes the uniform distribution of equipment loss, which is convenient for synchronous maintenance planning and extending equipment life. This technical solution flexibly adapts to different wind farm conditions and equipment configurations, takes into account short-term power generation potential and long-term equipment health management, and provides effective support for the intelligent and efficient operation of wind farms.

[0037] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0038] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the whole or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0039] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A comprehensive monitoring method for wind power generation equipment based on multiple sensors, characterized in that: include: Step S1: obtaining wind information at each power generation device in the target area through a monitoring unit, obtaining a wind map based on the wind information, and verifying the wind information through position vectors between adjacent power generation devices and a target wind vector in the wind map; Step S2: acquiring N groups of input information based on the wind power information, equipment information and N operation modes of the power generation equipment, and acquiring N groups of wake information and corresponding power generation and equipment loss according to each group of the input information, the wake prediction model and the power generation prediction model; Step S3: adding each set of wake information to the corresponding wind power information of other power generation equipment, repeating step S2, obtaining the prediction information of the corresponding power generation equipment on each wake branch, and calculating the total power generation and total equipment loss on each wake branch; Step S4: selecting the optimal operation mode corresponding to each power generation device based on the total power generation and the total device loss.

2. The method according to claim 1, characterized in that The equipment information is the structural parameters of the generator set, including at least the windmill height, rotor diameter and blade angle.

3. The method according to claim 1, characterized in that Verifying the wind information by using position vectors between adjacent power generation devices and a target wind vector in the wind map includes: According to the position information of the first power generation device and the second power generation device, a position vector pointing from the first power generation device to the second power generation device is obtained, and according to the wind information monitored by the first power generation device and the second power generation device, a first wind map and a second wind map are respectively obtained, the first wind map and the second wind map are compared, and a target wind vector at the position with the maximum wind difference is obtained according to the comparison result, the target wind vector and the position vector are compared, and the wind information corresponding to the second power generation device is verified according to the comparison result, and through this step, the wind information of each power generation device is verified, wherein the first power generation device is located upstream of the second power generation device.

4. The method according to claim 1, characterized in that: Obtain multiple sets of wake information and corresponding power generation and equipment losses, including: The power generation equipment in the target area is sorted according to the location information, and the wind information and equipment information of the corresponding power generation equipment are used as the first information according to the sorting, and the first information is respectively combined with different operating modes to obtain different multiple groups of input information, and each group of the input information is respectively input into the wake prediction model and the power generation prediction model to obtain the wake information, the power generation and the equipment loss corresponding to each group of the input information.

5. The method according to claim 1, characterized in that Calculate the total power generation and total equipment loss of each power generation equipment in the target area under different operating conditions, including: Add the N wake information of the power generation equipment to the wind power information corresponding to the corresponding i-th power generation equipment to obtain N new wind power information, and combine each of the new wind power information, equipment information and each of the N working modes corresponding to the i-th power generation equipment to obtain The group inputs information, and repeats step S3 to obtain the corresponding The prediction information includes wake information, power generation and equipment loss, and repeating this step to obtain the prediction information of each wake branch corresponding to each power generation device; Based on the prediction information of each power generation equipment on each wake branch, the total power generation and total equipment loss of the M power generation equipment on each wake information branch are calculated.

6. The method according to claim 1, characterized in that Selecting the optimal operation mode corresponding to each power generation device based on the total power generation and the total equipment loss on each wake branch includes: Based on the total power generation and the total equipment loss of each of the wake branches, a first value of the total power generation of each wake branch and a second value corresponding to the total equipment loss are calculated, and several wake branches to be selected whose first value is greater than the second value and whose ratio between the second value and the total power generation is the smallest are selected. The sum of the square differences between the equipment loss of each power generation equipment on each of the wake branches to be selected and the average equipment loss are calculated, and the operating mode of each power generation equipment on the wake branch to be selected that corresponds to the smallest sum of the square differences is taken as the optimal operating mode.

7. The method according to claim 1, characterized in that The step S4 further includes a step S5: When each of the power generation equipment operates in the optimal operation mode, the real-time power generation and real-time equipment loss of the power generation equipment are also collected in real time, the real-time power generation is compared with the predicted power generation to obtain a first difference, the real-time equipment loss is also compared with the predicted equipment loss to obtain a second difference, the first difference and the second difference are weighted to obtain a weighted result, and whether the power generation equipment is abnormal is determined based on the weighted result.

8. A wind power generation equipment integrated monitoring system based on multiple sensors, used to implement the method according to any one of claims 1 to 7, characterized in that: The system comprises: A monitoring unit is used to obtain wind information at each power generation device in the target area and obtain a wind map based on the wind information. A verification unit, used to verify the wind information through the position vectors between adjacent power generation devices and the target wind vector in the wind map; A prediction unit is used to obtain N groups of input information based on the wind information, equipment information and N operating modes of the power generation equipment, and obtain N groups of wake information and corresponding power generation and equipment loss according to each group of the input information, the wake prediction model and the power generation prediction model; each group of the wake information is added to the corresponding wind information of other power generation equipment, and step S3 is repeated to obtain the prediction information of the corresponding power generation equipment on each wake branch; The calculation unit is used to calculate the total power generation and the total equipment loss on each of the tail flow branches, and select the operation mode corresponding to each power generation device based on the total power generation and the total equipment loss.

9. A distribution cabinet for comprehensive monitoring of wind power generation equipment based on multiple sensors, characterized in that: The device comprises: a memory and at least one processor, wherein instructions are stored in the memory; The at least one processor calls the instructions in the memory to enable the power distribution cabinet for comprehensive monitoring of wind power generation equipment based on multiple sensors to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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