CN113836762B - Wind turbine and wind power plant digital mirror image simulation display system - Google Patents

Abstract

The invention relates to a wind turbine and wind power plant digital mirror image simulation display system, which comprises: the blade state monitoring and predicting module is used for monitoring sound and light of the fan impeller and monitoring vibration deformation of the blades; the device comprises a tower drum vibration monitoring and stress analyzing and predicting module, a stress analyzing and predicting module and a fatigue analyzing and predicting module, wherein the tower drum vibration monitoring and stress analyzing and predicting module is used for predicting the accumulated fatigue level of a tower drum; the unit dynamic wake flow simulation display module is used for displaying the forced operation condition of the wake flow interference fan of the wind power plant through high-precision simulation and 3D virtual reality; and the self-adaptive optimization control module is used for adjusting the control mode of the yaw system and the control mode of the unit in real time based on the monitored unit operation data. The invention can simulate and display wake flow interference, important component load and predicted component life under the running state of the wind turbine and the wind power plant, and combines an optimal wake flow cluster control strategy and a fan noise reduction control strategy to realize the optimal target of the whole power generation of the wind power plant, improve the whole power generation, reduce the load of the unit, prolong the service life of the unit and improve the quality and the efficiency.

Description

Wind turbine and wind power plant digital mirror image simulation display system

Technical Field

The invention relates to the field of wind power generation, in particular to a wind turbine and a wind power plant digital mirror image simulation display system.

Background

The digital twin is taken as a new technical means, so that problems can be found and solved before real production, virtual prototype verification is realized, and the development of the technology is facilitated. The digital twin is a simulation process integrating multidisciplinary, multi-physical quantity, multi-scale and multi-probability by fully utilizing data such as a physical model, sensor updating, operation history and the like, and mapping is completed in a virtual space, so that the full life cycle process of corresponding entity equipment is reflected.

Digital twinning is a technical system aimed at creating an aggregate or digital model (also called a digital twinner) for a physical system that expresses all its knowledge. By monitoring the system state in real time and dynamically updating the digital model, the diagnosis, evaluation and prediction capabilities of the digital twin body can be improved; meanwhile, the operation and the maintenance of the actual system are optimized on line, the structural design redundancy is reduced, frequent periodic overhaul and maintenance are avoided, and the safety of the system is ensured.

The digital twin can analyze design, manufacture and operation data of the fan equipment in a gathering manner, and inject the data into a brand new equipment design model, so that the design is continuously optimized in an iterative manner. With the digital twin, the abnormal function can be identified in the early stage, so that the defects of the equipment can be eliminated and the quality can be improved when the production is not carried out.

There are 216,000 wind turbine generators operating globally, with approximately 3,8000 wind turbine generator failure events per year. Studies have shown that preventative maintenance costs are 25% lower than passive maintenance, while predictive maintenance costs are 47% lower.

For the wind power industry, the value of digital twins is to use data to understand the health of the wind turbine or the entire wind farm. Therefore, how to use the digital twin prediction to provide the residual service life of equipment components, the unplanned maintenance is converted into planned activities, the maintenance plan is made in an auxiliary mode, the downtime of the fan is reduced to the maximum extent, the efficiency is improved, and the cost is reduced. How to improve operation and maintenance and reliability, optimize control and improve the annual energy production of the whole field. How to customize various wind turbine configurations in the early planning and design stage so as to ensure that the optimal wind turbine is built at the proper position of the wind power plant. How to collect and analyze data from actual operation after the wind farm is put into operation and provide operation and maintenance recommendations to make it more efficient. And how to simulate the real scene of the whole wind power plant with the highest precision, the whole life cycle cost from the design to the retirement of the wind turbine is reduced, and a more perfect means is provided for verifying the design quality of the wind turbine.

Disclosure of Invention

The invention aims to provide a digital mirror image simulation display system for a wind turbine and a wind power plant, which combines a mathematical model for describing the physical principle of operation of the wind turbine and the wind power plant with sensor data collected and processed from actual assets in the actual operation process, and constructs a digital platform for preventive and predictive maintenance by using a sensor, big data analysis and advanced visualization and analysis tools so as to know the behavior and the condition of a wind turbine in real time and predict faults and plan maintenance, thereby reducing the maintenance cost and the downtime.

The invention provides a wind turbine and wind power plant digital mirror image simulation display system, which comprises:

the blade state monitoring and predicting module is used for correlating acoustic signals with the running state of an impeller tower drum based on sound-light monitoring of a fan impeller and monitoring of vibration deformation of the blade, monitoring the running state of the impeller and a rotating part of the impeller in real time, carrying out online monitoring and data analysis through a numerical calculation model and a fault diagnosis and early warning model, and sending out fault early warning information;

the device comprises a tower drum vibration monitoring and stress analysis and prediction module, a stress analysis and prediction module and a control module, wherein the tower drum vibration monitoring and stress analysis and prediction module is used for monitoring the vibration amplitude and frequency of a tower drum by installing displacement or speed sensors at corresponding positions of the tower drum, calculating and analyzing results by combining stress modes, predicting the accumulated fatigue level of the tower drum and sending out fault early warning information;

the unit dynamic wake flow simulation display module is used for displaying the forced operation condition of the wake flow interference fan of the wind power plant through high-precision simulation and 3D virtual reality;

the self-adaptive optimization control module is used for reducing overall negative effects caused by wake flow in the wind power plant by optimizing the distribution of a full wake flow field, improving the full power generation capacity and reducing the overall fatigue load level, and adjusting the control mode of the yaw system and the control mode of the unit in real time based on the monitored unit operation data so as to reduce the equipment vibration condition of the unit.

Furthermore, the blade state monitoring and predicting module is used for monitoring sound and light of the fan impeller and monitoring vibration deformation of the blades;

the acousto-optic monitoring of the fan impeller comprises:

monitoring the appearance and the outer layer structure of the blade through acoustic and video sensors arranged at corresponding positions of the blade and a tower barrel, extracting characteristic values of collected sound signals in the working process of the blade, analyzing the time domain, the energy spectrum and the power spectrum, comparing the monitored sound signals of the blade with sound signals of intact blades, judging whether the blade is damaged or not by analyzing the sound signals, and monitoring and early warning the blade;

blade vibrations deformation monitoring includes image monitoring and blade load monitoring, image monitoring includes: acquiring an image at the junction of an impeller and a cabin through camera monitoring equipment, performing binarization and edge detection pretreatment on the acquired image, positioning points on a blade through a visual ranging principle, comparing relative positions of edge points of the blade with relative positions of corresponding points of the blade in an initial static state, analyzing deformation of the blade, and calculating clearance between a tower barrel and the blade;

the blade load monitoring comprises: the blade load and vibration conditions are continuously monitored through strain gauges arranged at key positions of blade stress concentration so as to detect the damage condition of the blade.

Further, the blade state monitoring and predicting module is also used for blade stress simulation analysis prediction, blade surface wear simulation and aerodynamic noise simulation;

the blade stress simulation analysis and prediction comprises the following steps:

establishing a blade three-dimensional model based on blade design parameters and material property attributes;

analyzing the stress of the blade by adopting a finite element method, and analyzing the stress and the strain of the blade under different simulation parameters under various wind speed environmental conditions, including fatigue damage accumulation;

extracting stress simulation result data, establishing a corresponding query relation between the stress simulation result and the real-time monitoring data, and displaying the data at intervals of days or hours in the operation process;

establishing an association relation by using a simulation result in a life cycle and the predicted wind speed and stress, using a monitored stress change curve as correction, establishing an association prediction model, and predicting the strain condition in a certain time period in the future by using an actual monitored wind speed variable parameter as an input condition so as to guide operation and maintenance;

the blade surface wear simulation comprises:

establishing a blade three-dimensional model based on blade design parameters and material property attributes;

based on the attribute information of the blade material, wind resource prediction data and real wind measurement data provided by WRF software are used as input conditions, and a wear rate and a wear degree of the blade in the operation period of the blade are calculated by using a wear and erosion model;

establishing a correlation prediction model based on the simulation result and the environmental parameters, and predicting the wear rate and the wear degree in the next half year and one year by taking the actual monitoring variable parameters as input conditions and the result predicted in the accumulated time as correction along with the time in the operation process so as to guide the operation and maintenance;

the aerodynamic noise simulation includes:

establishing a three-dimensional model of the unit, including a tower, a cabin and blades;

simulating the noise of the rotating state of the whole blade under various wind conditions and the noise level of the monitoring position of the blade in a static state by using a noise model;

simulating noise of different blades in damage states, wherein the noise comprises single blades, multiple blades cracked and different worn blades, and the noise of wind power plants in arctic weather regions in icing states of different blades is simulated;

comparing and verifying the simulation data with the actual measurement data, and updating and correcting the simulation result;

and establishing a blade noise and fault information base according to the corresponding relation between the shape, the wind speed and the like and the noise level.

Further, the tower drum vibration monitoring and stress analyzing and predicting module is specifically used for tower drum vibration monitoring, blade root load monitoring and tower drum stress analyzing and predicting;

the tower vibration monitoring comprises:

simultaneously installing tilt sensors at the top end and the middle part of the tower barrel, installing 2-axis acceleration sensors at the top end to detect the direction and the magnitude of acceleration so as to correct tilt values, and compensating measured values to obtain relatively real values;

installing a low-frequency acceleration sensor at the top of the tower cylinder, continuously monitoring the shaking of the tower cylinder, and acquiring the dynamic deformation displacement of the tower cylinder by adopting an integral algorithm;

the inclination angle sensor and the acceleration sensor of the tower barrel are installed in pairs, sampling is carried out synchronously, and the static, quasi-static and dynamic deformation of the tower barrel are obtained through common analysis;

the blade root load monitoring comprises:

arranging 6 strain sensors at the blade root of each group of blades, and respectively monitoring the bending moment of the blade root in the shimmy direction, the bending moment in the waving direction and the torque of the blade root of the fan; the two pairs of strain sensors are respectively used for monitoring the strain of the blade root in the axial direction, are arranged at intervals of 90 degrees in the circumferential direction and respectively test the strain in the shimmy direction and the waving direction; the rest two sensors are arranged at the circumferential tangent of the same section position at 180-degree intervals, and are used for monitoring the torsional strain under the action of load; a signal acquisition instrument is arranged in the hub, is used for acquiring and preprocessing signals, is communicated with the engine room in a wireless mode, and transmits the signals to the tower footing industrial control cabinet; monitoring the strain of a blade root under the load effect, and analyzing the stress characteristic and the stress-strain state of the blade root to obtain the load state of the blade of the wind generating set under each operating condition;

before data sampling, calibrating a blade root bending moment signal by using static load calibration to determine the relation between a strain signal and a load signal, and verifying the accuracy of a numerical calculation model and a theoretical calculation model of a blade root stress-strain state;

the tower stress analysis and prediction comprises the following steps:

1) Establishing a tower drum stress analysis model based on tower drum design parameters and material attributes;

2) Based on given wind speed and turbulence input boundary conditions, characteristics of a tower drum such as limit load and accumulated fatigue load are calculated and analyzed, and a relation function of wind speed turbulence and stress load is established;

3) And (3) calculating and analyzing the stress load level by using the full-time wind speed and the turbulence of the fan under the actual operation condition as input conditions and using the process in the step 2), evaluating the proportion of the accumulated fatigue load level in the full life cycle, and predicting the residual life.

Further, the wake flow simulation display process of the unit dynamic wake flow simulation display module includes:

(1) Establishing a whole wind power plant model:

establishing a digital model based on the actual physical topography of the wind power plant, establishing a simulation calculation domain by taking the central position of the wind power plant as a central point and at least 20 times of the radius of the wind power plant as a radius, considering meteorological conditions, and simulating wake flow interference under the typical working condition of the wind power plant by taking wind speed, wind direction and turbulence as input conditions; wherein, the wind speed condition is input and determined by combining the meteorological prediction with the data verification of the actual wind measuring tower; typical working conditions include a main wind direction, a secondary wind direction and a maximum wind speed; the wind direction interval takes maximum 15 degrees as an interval, the wind speed takes 0.25-0.5m/s as an interval, and the wind direction and wind speed interval is further reduced for sensitive working conditions;

(2) Calculating wake flow simulation and creating a database:

performing wake simulation calculation according to the model established in the step (1) and the determined calculation condition to form a database; the wind turbine model adopts a virtual blade model, simulates data post-processing, and extracts and calculates the full-field fan wake flow speed, turbulence intensity, power and power loss corresponding to the working condition; the database data at least comprises two basic data, namely an image, video format data and a parameter variable point data set;

(3) Based on the established wind power plant model, acquiring wind field aerodynamic characteristic data containing wind turbine blades under various statistical wind conditions according to main flow wind speed and wind direction data analyzed by big data, analyzing wake influence and mutual interference between the wake and the wake, acquiring wake influence data and mutual interference data between the wake and the wake, and evaluating and optimizing a fan operation adjustment strategy under each wind condition;

(4) And (3) displaying the simulation data calculated in the step (2), displaying the data of the anemometer tower and the power prediction production tower in a real-time state parameter mode, and displaying the SCADA monitoring data of the fan, wherein the SCADA monitoring data comprises real-time temperature, pressure and other meteorological data.

Further, the displaying mode in the step (4) comprises view-angle-division displaying, multiple view angles in the whole and local modes, the trail interference condition of all wind turbine trails of the whole wind power plant is displayed in the whole situation, and data are updated in a cycle of 10 minutes; displaying the calculated dynamic wake flow of the fan in a smoke or air particle simulation visualization mode, supporting full-field 3D wake flow field display, and having a VR interaction function; and the environmental reference object around the wind power plant is established by an actual reference object.

Further, the adaptive optimization control module performs wake flow control through a pre-constructed wake flow detection model and then through an online identification mode, and includes:

when wind field fan group control is implemented for the first time, the condition of wake track influence of the fan is identified, a wake flow interference control model is constructed, power and load data are analyzed, and optimal yaw, pitch and speed control parameters are searched;

establishing a wind field model, inputting actual wind measurement data and fan yaw angle data of a wind field into the established wind field model, judging the occurrence condition and degree of wake flow influence, and marking the data with the wake flow influence;

constructing a wake flow influence identification model in a data set by utilizing real-time power generation power data, cabin vibration data, vibration data in a CMS (content management system) system and load data and marked fan running state data through data cleaning and mode clustering;

for the unit which is detected to have serious wake flow interference and needs to be adjusted, the total power and the load of the unit are optimized by adopting a control strategy based on a multi-loop ESC, so that the controlled unit can achieve the purposes of maximizing the generating capacity and reducing the load of a controlled fan subgroup in a self-optimizing mode.

By means of the scheme, by means of the wind turbine and wind power plant digital mirror image simulation display system, wake flow interference, important component loads and predicted component service lives under the operation states of the wind turbine and the wind power plant can be simulated and displayed, an optimal wake flow cluster control strategy and a fan noise reduction control strategy are combined, the optimal target of the whole power generation amount of the wind power plant is achieved, the whole power generation amount is improved, the unit load is reduced, the unit service life is prolonged, and the quality and the efficiency are improved.

The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical solutions of the present invention more clearly understood and to implement them in accordance with the contents of the description, the following detailed description is given with reference to the preferred embodiments of the present invention and the accompanying drawings.

Drawings

FIG. 1 is a schematic diagram of the corresponding construction of a physical model and a digital model according to the present invention;

FIG. 2 is a schematic view of the acoustic and video sensor mounting location of the present invention;

FIG. 3 is a schematic view of the sensor mounting location of the present invention;

FIG. 4 is a schematic view of the static load calibration of the bending moment signal at the root of the blade according to the present invention;

FIG. 5 is a first schematic diagram of a group control strategy of wind farm fans of the present invention;

FIG. 6 is a schematic diagram of a wind farm fan group control strategy of the invention II;

fig. 7 is a layout diagram of four strain sensors of fig. 3 in accordance with the present invention.

Detailed Description

The following detailed description of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.

The embodiment provides a wind turbine and wind power plant digital mirror image simulation display system (platform), which comprises a plurality of parts, such as a blade state monitoring and predicting module, a tower vibration monitoring and stress analysis predicting module, a unit dynamic wake flow simulation display module, a self-adaptive optimization control module and the like.

The blade state monitoring and predicting module and the tower vibration monitoring and stress analysis predicting module are integrated with monitoring of sound and light of a fan impeller, fan load, tower vibration, foundation and noise of a wind power plant into a whole, and integrated online monitoring and data analysis of the impeller, the tower and the foundation of a unit can be realized through a corresponding numerical calculation model and a fault diagnosis and early warning model, so that fault hidden dangers are effectively discovered, reasons of the blade noise, the tower vibration and the foundation hidden dangers are researched and judged, the fatigue life and the safety of the unit are evaluated, a load basis is provided for optimization of a fan control strategy, and an optimized control effect is verified.

The unit dynamic wake flow simulation display module displays and displays the forced operation conditions (the operation parameters in time and the basic state parameters of the unit, including wind speed, power, load, environmental parameter variables and the like) of the wake flow interference fan of the wind power plant through a high-precision simulation technology and a 3D virtual reality technology.

The self-adaptive optimization control module enables overall negative influence caused by wake flow in the wind power plant to be reduced by optimizing distribution of a full-field wake flow field, so that full-field generating capacity is improved, overall fatigue load level is reduced, a control mode of a yaw system and a control mode of a unit are adjusted in real time based on monitored unit operation data, equipment vibration condition of the unit is reduced, and safe operation of the unit is guaranteed. The system platform realizes state monitoring and residual service life (RUL) estimation, carries out health assessment on the in-service wind generating set for the wind power plant, provides technical support for fault analysis and safety early warning, realizes intelligent operation and maintenance, and improves economic benefits.

A wind field model is established through FAST.farm/FAST software, wind resource data and real wind measurement data provided by WRF (WRF software interfaces with FAST.farm software through vtk file format) software are used for driving the fan model to obtain operation data, a digital twin model of the wind field is established through visual software, and a user can visually observe the wake of the wind field, the power generation condition of the fan, the wind speed at the height of a hub, the margin of the fan, the generated noise and the vibration condition transmitted to the bottom of a tower barrel in the digital twin, so that the operation and safety conditions of the fan can be visually known. In addition, the system also displays the actual measurement data of the wind turbine instantaneous power, the loads of key components such as blades and bases and the like in real time in the form of visual data. Using FAST, FARM or SOFWA to establish a dynamic wake flow model of a machine group, participating in the analysis of the power and load of the machine group through a self-adaptive optimization control system, and supporting the optimization of a cluster control strategy. The corresponding construction principle of the physical model and the digital model is shown in figure 1.

The digital mirror image simulation display system for the wind turbine and the wind power plant can simulate and display wake flow interference, important part loads and predicted part service lives under the running state of the wind turbine and the wind power plant, and combines an optimal wake flow cluster control strategy and a fan noise reduction control strategy to achieve the optimal target of the whole power generation capacity of the wind power plant, improve the whole power generation capacity, reduce the unit load, prolong the unit service life and improve the quality and the efficiency.

The modules of the platform are described in detail below.

1. Blade condition monitoring and prediction module

1.1 blade monitoring

The module comprises a fan impeller acousto-optic monitoring part and a blade vibration deformation monitoring part. Based on a big data analysis technology and a high-performance calculation analysis technology, acoustic signals and the running state of an impeller tower are correlated, the running state of an impeller and a rotating part of the impeller are monitored in real time, possible fault problems are prevented and found in advance, the fault rate is reduced to the maximum extent, technical support is provided for operation and maintenance detection of a fan impeller and a part of the fan impeller of a wind farm, a technical transformation scheme is provided by analyzing running data, detecting by using instruments if necessary and analyzing by means of experts, a problem part of equipment is transformed, the performance and the reliability of the equipment are improved, and the economic benefit of the wind farm is finally improved.

1) Sound and light monitor of fan impeller (blade pneumatic noise monitor)

And in the acousto-optic monitoring of the impeller, acoustic and video sensors are arranged at corresponding positions of the blade and the tower barrel, so that the appearance and the outer structure of the blade are monitored. In the position a in fig. 2 (the tower barrel corresponds to the blade tip position, the tower barrel height H, the impeller radius R, and the mounting position H = H-R), 4 sound sensors are mounted in a pasting manner, the pneumatic noise of the blades in the rotating state is collected, the sound sensors are arranged at intervals of 90 degrees in the circumferential direction, the monitoring in any wind direction is realized, and signals are transmitted to the tower footing industrial control cabinet through communication cables or wireless transmission. The collected sound signals in the working process of the blades are subjected to characteristic value extraction, analysis is mainly carried out from angles such as time domains, energy spectrums, power spectrums and the like, the monitored sound signals of the blades are compared with sound signals of intact blades, whether the blades are damaged or not can be distinguished by analyzing the sound signals, and the purposes of monitoring and early warning of the blades are achieved.

2) Blade deformation monitoring

The method comprises two parts, namely a first part image monitoring part. The camera shooting monitoring equipment is fixed at the position B where an impeller and an engine room are intersected in the figure 1, the camera shooting monitoring equipment is transmitted to a tower footing industrial control cabinet through a fan network, collected images are subjected to image preprocessing such as binaryzation and edge detection, the positioning of upper points of blades is realized through a visual ranging principle, the relative positions of the edge points of the blades are compared with the relative positions of corresponding points of the blades in an initial static state, then, the deformation of the blades can be analyzed, and the clearance between a tower barrel and the blades is calculated.

The second part is blade load monitoring. The blade is the key part of wind power generator, and in the in-process of electricity generation, the fan blade has born powerful stress as direct wind-force receiving mechanism, in the work of day after day, along with the time lapse, often causes structural damage, influences the service life. The strain gauge is installed at the key position of stress concentration of the blade, the strain gauge is mainly of a front embedded type, the strain gauge is embedded in the production process of the blade, and the damage condition of the blade can be detected in the early stage by continuously monitoring the load and the vibration condition of the blade.

3) Noise control

For the unit with the noise reduction requirement, a noise control strategy (power reduction operation) is operated by a specific sector so as to achieve the optimal balance between wind field benefit and environmental friendliness. And predicting the noise of a specific point of the wind field area by using an accurate noise model, and adjusting the unit operation mode according to a prediction result to reduce the noise level.

1.2 blade stress analysis prediction module

1) Stress simulation analysis prediction

The main function is to realize the simulation analysis of the blade stress and simulate the stress state of the blade under various wind conditions.

The method mainly comprises the following steps:

(1) And establishing a three-dimensional model of the blade by using three-dimensional modeling software based on the blade design parameters and the material property attributes.

(2) The finite element method is adopted to analyze the stress of the blade, and the stress, strain and the like of the blade under different simulation parameters under various wind speed environment conditions (including maximum wind speed) are analyzed, including fatigue damage accumulation.

(3) And establishing a stress database. And extracting stress simulation result data, establishing a corresponding query relation between the stress simulation result and the real-time monitoring data, and displaying the system at intervals of days or hours in the operation process.

(4) And establishing a correlation prediction model. Establishing an incidence relation by using a simulation result in a life cycle and the predicted wind speed and stress, taking a monitored stress change curve as correction, establishing a prediction model, taking an actual monitored wind speed variable parameter as an input condition, predicting the strain condition within a certain period of time (1-3 months, within one year) in the future, and guiding operation and maintenance.

2) Blade surface wear simulation

The main function is to simulate the abrasion of the blades of the wind turbine generator along with the time in the actual operation process based on environmental information (temperature, atmospheric particles, wind speed and wind sand), and pertinently guide the blades to protect key parts based on a calculation result. For a newly installed wind turbine, the node deployment and installation position of the stress sensor can be guided according to the simulated stress calculation result, and a beneficial reference is provided for the operation and maintenance of the wind turbine.

The method mainly comprises the following implementation processes:

(1) And establishing a three-dimensional model of the blade by using three-dimensional modeling software based on the blade design parameters and the material property attributes.

(2) Based on the attribute information of the blade material, wind resource prediction data and real wind measurement data provided by WRF software are used as input conditions, and the wear rate and the wear degree of the blade in the operation period of the blade are calculated by using wear and erosion models.

(3) And establishing a correlation prediction model based on the simulation result and the environmental parameters, and predicting the wear rate and the wear degree in the next half year and one year by taking the actual monitoring variable parameters as input conditions and taking the predicted result in the accumulated time as correction along with the time lapse in the operation process so as to guide the operation and maintenance.

3) Pneumatic noise simulation

And the pneumatic noise simulation module is used for simulating the noise level of the coupling operation of the fan blade and the tower drum under various working conditions, and establishing a database after the verification and the correlation of the noise level and the actually measured noise data.

The detailed process comprises the following steps:

(1) And establishing a three-dimensional model of the unit by using three-dimensional modeling software, wherein the three-dimensional model comprises a tower, a cabin and blades, the shape of the model is as consistent as possible with the appearance shape of actual equipment, and the influences of the terrain around the unit equipment and the roughness of the ground are considered.

(2) Noise of the entire blade rotation state in various wind conditions and noise level of the monitoring position in the blade stationary state are simulated using the noise model.

(3) The method is used for simulating the noise of different blades in the damaged state, and comprises the steps of single blade cracking, multi-blade cracking and different worn blades, and simulating the noise of different blades in the icing state in the wind power plant in the extremely cold weather region.

(4) And comparing and verifying the simulation data with the actual measurement data, and updating and correcting the simulation result. Because various running states cannot be actually tested in the actual process, the test data of typical working conditions in a laboratory and a wind farm field is compared with the simulation data to verify the correlation, the error possibly existing in the simulation is evaluated, and the corresponding simulation result is corrected and updated based on the test data.

(5) And establishing a blade noise and fault information base according to the corresponding relation between the shape, the wind speed and other conditions and the noise level. The noise monitoring system of the acousto-optic monitoring system is called, the monitoring level value is compared with the database query, and early warning improvement is provided.

2. Tower drum vibration monitoring and stress analysis prediction module

The module mainly monitors the vibration state of the tower drum, a displacement or speed sensor is installed at a corresponding position, the vibration amplitude and frequency of the tower drum are monitored, the accumulated fatigue level of the tower drum is predicted by combining a stress modal calculation analysis result, corresponding prediction and prompt are given at each operation stage, and fault information is prevented in advance.

2.1 Tower vibration monitoring

The tower barrel of the wind generating set is a bearing part in the wind generating set, and mainly plays a supporting role in the wind generating set and absorbs the vibration of the wind generating set. The tower barrel bears complex and variable loads such as thrust, bending moment, torque load and the like, so that the tower barrel can generate vibration such as swinging, twisting and the like with a certain amplitude in the operation process of the wind generating set; in addition, the tower may be subject to material deformation, component failure, and foundation settlement, which may cause the tower to tilt. The normal operation of the wind generating set can be influenced by the overlarge inclined deformation of the tower barrel, and safety accidents can be seriously caused. The tower barrel is under the alternating load of large vibration for a long time, and the bolt fastening force is easy to reduce or loosen.

The real-time vibration and the whole deformation condition of a tower section of thick bamboo of monitoring, the detection that can be better to the deformation of each connecting cylinder of a tower section of thick bamboo moreover obtains the nonlinear deformation curve of a tower section of thick bamboo center at horizontal direction offset and a tower section of thick bamboo, and the static state, the quasi-static state and the dynamic deformation of a comprehensive, accurate judgement tower section of thick bamboo to can carry out real-time record ripples to tower section of thick bamboo vibration. Therefore, measures can be taken in time for maintenance, and the alarm threshold value and the proper working condition interval of unit operation can be scientifically established.

(1) Because the displacement of the tower during the inclination of the tower is nonlinear, inclination angle sensors are simultaneously installed at the top end and the middle part of the tower, and a 2-axis acceleration sensor is installed at the top end to detect the direction and the magnitude of the acceleration so as to correct the inclination angle value. The specific installation location is at C of fig. 3, located at the interface of the tower and the nacelle. And data acquisition, processing and transmission of a single fan are realized through the industrial control cabinet of each fan tower foundation.

Because the inclination sensor works in the dynamic shaking of the tower, the liquid level in the inclination sensor is influenced by the shaking acceleration, and the measured value needs to be compensated to obtain a relatively real value. The compensation model is as follows:

wherein α is a measured value, a _{Acceleration of a vehicle} For measured acceleration, g is acceleration of gravity, α _{Practice of} For supplementingThe compensated inclination angle value. Since the acceleration is directional, the acceleration value measured here is a vector composition in the corresponding direction of the acceleration in the X-Y direction of the 2-axis acceleration sensor.

(2) And a low-frequency acceleration sensor is arranged at the top of the tower drum, the shaking of the tower drum is continuously monitored, and the dynamic deformation displacement of the tower drum can be obtained by adopting an integral algorithm. The inclination angle sensor and the acceleration sensor of the tower barrel are installed in pairs, sampling is carried out synchronously, and static, quasi-static and dynamic deformation of the tower barrel are obtained through common analysis. Wherein static deflection is related to the base settlement of the tower, quasi-static deflection is related to the relatively stable wind load of the tower, and dynamic deflection is related to wind turbine turbulence and other transient impacts.

(3) The bottom load of a tower section of thick bamboo is monitored, combines the structural style characteristics and the load test requirement of a tower section of thick bamboo, arranges 4 strain sensor in tower section of thick bamboo bottom, and the test direction is tower section of thick bamboo axial direction, as shown in figure 7, installs inside the steel tower section of thick bamboo 4 strain sensor (the dot is shown in figure 7), arranges at interval 90 on the D point department along the circumferencial direction as figure 3, and 4 sensors all are located same horizontal cross-section, waves and the bending moment in the pitch direction to the steel tower section of thick bamboo and tests.

2.2 blade root load monitoring

The blade load sensor is arranged at E of fig. 3. And 6 strain sensors (respectively positioned at positions 1, 2, 3, 4, 5 and 6) are arranged at the blade root of each group of blades, and the bending moment in the shimmy direction, the bending moment in the flapping direction and the torque of the blade root of the fan are respectively monitored. Two pairs of strain sensors (positions 1-4 in figure 3) respectively monitor the strain of the axial direction of the blade root of the blade, are arranged at intervals of 90 degrees in the circumferential direction and respectively test the strain in the shimmy direction and the waving direction; the remaining two sensors (fig. 3, positions 5-6) are arranged at 180 degrees intervals in the circumferential tangential direction of the same cross section position, and are used for monitoring the torsional strain under the action of load. The hub is internally provided with a signal acquisition instrument for acquiring and preprocessing signals, wirelessly communicates with the engine room, and transmits the signals to the tower footing industrial control cabinet. The blade load state of the wind generating set under each operating condition is further obtained by monitoring the strain of the blade root under the load effect and combining the stress characteristic and the stress-strain state analysis of the blade root.

Before data sampling, the static load calibration is used for calibrating the blade root bending moment signal so as to determine the relation between the strain signal and the load signal, and the relation is used for verifying the accuracy of a numerical calculation model and a theoretical calculation model of the blade root stress-strain state, as shown in fig. 4.

The blade load monitoring system mainly measures the wavelength output by the optical fiber sensor, and the corresponding relation of the strain and the wavelength is

Wherein c is a sensor coefficient.

2.3 Tower tube stress analysis and prediction module

(1) Building a tower model

And establishing a tower tube stress analysis model based on the tower tube design parameters and the material attributes.

(2) Tower drum stress analysis

And (3) giving wind speed and turbulence input boundary conditions, calculating and analyzing the characteristics of the tower drum such as limit load, accumulated fatigue load and the like, and establishing a relation function of wind speed turbulence and stress load.

(3) Tower life prediction

And (3) calculating and analyzing the stress load level by using the full-time wind speed and the turbulence of the fan under the actual operation condition as input conditions and using the process in the step (2), evaluating the proportion of the accumulated fatigue load level in the full life cycle, and predicting the residual life.

3. Unit dynamic wake flow simulation display module

The module shows the wake flow influence condition of the wind power plant under the actual operation working condition, and the wake flow influence and the generated energy loss degree of each fan and the whole wind power plant can be visually known from the global and local visual angles. Meanwhile, a vivid virtual wind farm is created by using a virtual reality technology (VR) on the basis of a high-definition digital map and a high-fidelity surrounding environment. The provided analysis method can be used for evaluating the influence of the complex terrain and the trail of the wind turbine on the power output of the wind power plant, and the accuracy of the wind power plant power prediction under the complex turbulence working condition is improved. The module also provides theoretical guidance for the adaptive optimization control module.

The wake flow simulation display process is as follows:

(1) And establishing a whole wind power plant model. A digital model is established based on actual wind power plant physical topography, a simulation calculation domain is established by taking the wind power plant central position as a central point and at least 20 times of wind power plant radius as a radius, meteorological conditions are considered, wind speed, wind direction and turbulence are taken as input conditions, and wake flow interference under typical working conditions of the wind power plant is simulated. The wind speed and other conditions are input and determined after the combination of the meteorological prediction and the actual anemometer tower data verification. Typical working conditions cover working conditions of main wind direction, secondary wind direction, great wind speed and the like which affect the benefits of the wind power plant and the safety of the fan. In order to cover possible working conditions under actual operating conditions as much as possible, the interval of wind directions is 15 degrees at the maximum, the interval of wind speeds is 0.25-0.5m/s, and the interval of the wind directions and the wind speeds can be further reduced for sensitive working conditions.

(2) Calculating wake flow simulation and creating a database. The method comprises the steps of wake flow calculation, data post-processing and database establishment. And performing wake flow simulation calculation according to the model established in the first step and the determined calculation condition to form a database. The wind turbine model adopts a virtual blade model, so that the calculation accuracy is ensured, the calculation amount is reduced, and the calculation speed is accelerated. And (4) performing analog data post-processing, and extracting variable data such as the wake flow speed, turbulence intensity, power and power loss of the full-field fan corresponding to the calculated working condition. And establishing a database, wherein the database data at least comprises two basic data, namely image data, video format data and parameter variable point data sets.

(3) Based on the established wind power plant model, according to main stream wind speed and wind direction data analyzed by big data, wind power plant aerodynamic characteristic data containing wind turbine blades under various statistical wind conditions are obtained, wake influence and mutual interference between the wake and the wake are analyzed, wake influence data and mutual interference data between the wake and the wake are obtained, and fan operation adjustment strategies under various wind conditions are evaluated and optimized.

(4) And (5) displaying the system. Comprises two parts. One is the simulated data calculated in the second step. And secondly, wind measuring tower and power prediction production tower data, and fan SCADA monitoring data including real-time meteorological data such as temperature and pressure. The second part is a real-time state parameter presentation. Under the global view angle, the display (curve or digital jumping form) of key parameters such as real-time power, wind speed and the like is embedded under the 3-dimensional visualization angle.

The display mode is as follows: and displaying multiple visual angles such as a sub-visual angle display, a global visual angle display and a local visual angle display, globally displaying the trail interference condition of all wind turbine trails of the whole wind power plant, and updating data by taking 10 minutes as a period. The dynamic wake flow of the fan is displayed and calculated in a visual form of simulating smoke or air particles and the like, full-field 3D wake flow field display is supported, and the VR interaction function is achieved. The intelligent wind power plant environment reference object is established by the actual reference object. In the modeling process of the new energy simulation system, in order to enable the established three-dimensional model to have a high-precision simulated three-dimensional effect, on-site real data need to be acquired, relevant documents, videos and the like need to be sorted, and meanwhile, a large amount of various types of data such as three-view pictures, physical photos, section pictures and the like are referenced for modeling, and an unmanned aerial vehicle is used for aerial photography to quickly acquire high-precision three-dimensional terrain data and high-definition wind power plant digital images including villages, trees, hilly roads, landscapes, buildings and the like, so that a high-definition digital map (the landscape such as villages, trees, hilly roads, overhead lines, roads, communication facilities and the like, the size proportion, the mutual distance and the height between the building requirements and the real scenes) is realized on the whole site.

4. Self-adaptive optimization control module

Due to wake effects, maximizing the energy capture of a single wind turbine does not result in maximizing the energy capture of the entire wind farm. Therefore, the power maximization of the wind field is realized through the extreme value search control principle of the wind field. By optimizing the distribution of the full-field wake flow field, the overall negative influence caused by the wake flow in the wind power field is reduced, so that the full-field power generation is improved, and the overall fatigue load level is reduced. And adjusting the control mode of the yaw system and the control mode of the unit in real time based on the monitored unit operation data, reducing the equipment vibration condition of the unit and ensuring the safe operation of the unit.

For the condition that a plurality of fans are arranged in a wind field, the influence of the wake of the front exhaust fan on the rear exhaust fan is the main reason for reducing the whole generating capacity of the wind field and increasing the fluctuating load of the fans. The module is used for realizing the control targets of maximizing the power generation power of the sub-groups and reducing the load of the sub-groups by utilizing a wind field fan wake flow group control scheme for the wind farm or the fan sub-groups in the wind farm.

The wake flow generated by the wind turbine is inconvenient to observe, and no special sensor is used for carrying out real-time online detection on the wake flow, so that the wake flow is controlled in an online identification mode by constructing a wake flow detection model in advance. The specific scheme is as follows.

Because the generation of the wake of the fan is closely related to the states of different types, wind directions, wind speeds, fan yawing and the like, when the wind field fan group controller is implemented for the first time, the data are needed to be used for identifying the wake influence of the fan, constructing a wake interference control model, analyzing power and load data, and searching for optimal yawing, variable pitch and variable speed control parameters. In order to accurately identify the time, the range and the degree of the occurrence of the wake flow of the fan, advanced wind field modeling software FAST. The method has the main advantage that the influence of FAST.farm on the wind field fan wake flow is simulated and identified by adopting FAST.farm simulation, and the defect that the conventional wake flow model cannot model the obvious influence factors of wake flow meandering, wake deflection and the like on load and power generation amount is overcome.

The wake flow influence among the wind turbines is mainly reflected in the aspects of fluctuation of the generated power of the wind turbines, increase of fluctuating loads and the like, a marked running state of the wind turbines is utilized to construct a wake flow influence identification model in a data set through a big data technology by utilizing the data set formed by data such as real-time generated power data and cabin vibration data in a main control system, vibration data in a CMS system, load data in a basic monitoring system of a wind turbine impeller tower drum and the like, and the steps of data cleaning, mode clustering and the like are carried out.

For the unit which detects serious wake interference, the total power and load of the unit are optimized by evaluating the unit which needs to be adjusted and adopting a control strategy based on a multi-loop ESC (NLESC) in a self-adaptive mode, so that the controlled unit achieves the aims of maximizing the generated energy of a controlled fan subgroup and reducing the load in a self-optimization mode. ESC is a self-optimizing control scheme that can search for unknown/time-varying optimal input parameter settings in real time. It can be seen as dynamically implementing a gradient search. The fan control is perturbed with a sinusoidal signal, then a gradient proportional signal is extracted by filtering, and the gradient is driven to zero by an integrator. The model is a simple-structure 'model-free' control scheme. The basic principle of NLESC is similar to that of a single ESC controller, and the NLESC controller is an extension of the NLESC controller in the aspect of wind field fan group control, and the optimization goal of the NLESC controller is the maximization of the total power of fans in a whole loop. The schematic diagrams of the wind farm and wind farm airspace strategy are shown in fig. 5 and 6.

In fig. 6, the wind speed is directed from the wind turbine 1 to the wind turbine n. Wind turbine i +1 through wind turbine n are located behind wind turbine i. The control objective is to maximize the total power from wind turbine i to wind turbine n by controlling wind turbine i. The measurement for wind turbine i is the sum of the powers from wind turbine i to n. The control input is generator torque. Pi is the power of wind turbine i, ki is the generator torque gain of wind turbine i, ω i is the generator speed of wind turbine i, τ _C Torque is controlled for the wind turbine.

Fan limit search control:

1) Estimating input dynamic parameters based on an open loop control test;

2) Selecting a dithering frequency within an input dynamic bandwidth;

3) Designing a high pass filter to pass frequencies higher than the dither frequency;

4) Low pass filters are designed to block higher frequencies;

5) Determining a phase angle that compensates for phase angle deviations caused by input dynamics and a high pass filter to improve performance tracking;

6) Jitter amplitude: ensuring the jitter frequency to jitter and output;

7) The compensator is designed based on robust stability criteria.

The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, it should be noted that, for those skilled in the art, many modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.

Claims (7)

1. The utility model provides a wind energy conversion system and wind-powered electricity generation field digital mirror image simulation display system which characterized in that includes:

the blade state monitoring and predicting module is used for correlating acoustic signals with the running state of an impeller tower based on the sound-light monitoring of the fan impeller and the vibration deformation monitoring of the blades, monitoring the running state of the impeller and the running state of a rotating part of the impeller in real time, carrying out online monitoring and data analysis through a numerical calculation model and a fault diagnosis and early warning model, and sending out fault early warning information;

the device comprises a tower drum vibration monitoring and stress analysis and prediction module, a stress analysis and prediction module and a fault early warning module, wherein the tower drum vibration monitoring and stress analysis and prediction module is used for monitoring the vibration amplitude and frequency of a tower drum by installing displacement or speed sensors at corresponding positions of the tower drum, calculating an analysis result by combining a stress mode, predicting the accumulated fatigue level of the tower drum and sending fault early warning information;

the unit dynamic wake flow simulation display module is used for displaying the forced operation condition of the wake flow interference fan of the wind power plant through high-precision simulation and 3D virtual reality;

the self-adaptive optimization control module is used for reducing the overall negative influence caused by the wake flow in the wind power plant by optimizing the distribution of the wake flow field of the whole plant, improving the power generation capacity of the whole plant and reducing the overall fatigue load level, and adjusting the control mode of the yaw system and the control mode of the unit in real time based on the monitored unit operation data so as to reduce the equipment vibration condition of the unit.

2. The wind turbine and wind farm digital mirror image analog display system of claim 1, wherein the blade condition monitoring and prediction module is used for wind turbine impeller acousto-optic monitoring and blade vibration deformation monitoring;

the acousto-optic monitoring of the fan impeller comprises:

monitoring the appearance and the outer layer structure of the blade through acoustic and video sensors arranged at corresponding positions of the blade and a tower barrel, extracting characteristic values of collected sound signals in the working process of the blade, analyzing the time domain, the energy spectrum and the power spectrum, comparing the monitored sound signals of the blade with sound signals of intact blades, judging whether the blade is damaged or not by analyzing the sound signals, and monitoring and early warning the blade;

the monitoring of blade vibrations deformation includes image monitoring and blade load monitoring, image monitoring includes: acquiring an image at the junction of an impeller and a cabin through camera monitoring equipment, performing binarization and edge detection pretreatment on the acquired image, positioning points on a blade through a visual ranging principle, comparing relative positions of edge points of the blade with relative positions of corresponding points of the blade in an initial static state, analyzing deformation of the blade, and calculating clearance between a tower barrel and the blade;

the blade load monitoring comprises: the blade load and vibration conditions are continuously monitored through strain gauges arranged at key positions of the stress concentration of the blade so as to detect the damage condition of the blade.

3. The wind turbine and wind farm digital mirror image analog display system of claim 2, wherein the blade condition monitoring and prediction module is further configured for blade stress analog analysis prediction, blade surface wear simulation, and aerodynamic noise simulation;

the blade stress simulation analysis prediction comprises the following steps:

establishing a blade three-dimensional model based on blade design parameters and material property attributes;

analyzing the stress of the blade by adopting a finite element method, and analyzing the stress and the strain of the blade under different simulation parameters under various wind speed environmental conditions, including fatigue damage accumulation;

extracting stress simulation result data, establishing a corresponding query relation between the stress simulation result and the real-time monitoring data, and displaying the data at intervals of days or hours in the operation process;

establishing an association relation by using a simulation result in a life cycle and predicted wind speed and stress, taking a monitored stress change curve as correction, establishing an association prediction model, and predicting a strain condition in a certain time period in the future by using an actual monitored wind speed variable parameter as an input condition so as to guide operation and maintenance;

the blade surface wear simulation comprises:

establishing a blade three-dimensional model based on blade design parameters and material property attributes;

based on the attribute information of the blade material, wind resource prediction data and real wind measurement data provided by WRF software are used as input conditions, and a wear rate and a wear degree of the blade in the operation period of the blade are calculated by using a wear and erosion model;

establishing a correlation prediction model based on a simulation result and an environmental parameter, and predicting the wear rate and the wear degree in the next half year and one year by taking an actual monitoring variable parameter as an input condition and taking a predicted result in accumulated time as correction along with the time lapse in the operation process so as to guide the operation and maintenance;

the aerodynamic noise simulation includes:

establishing a three-dimensional model of the unit, including a tower, a cabin and blades;

simulating the noise of the rotating state of the whole blade under various wind conditions and the noise level of the monitoring position of the blade in a static state by using a noise model;

simulating noise of different blades in damage states, wherein the noise comprises single blades, multiple blades cracked and different worn blades, and the noise of wind power plants in arctic weather regions in icing states of different blades is simulated;

comparing and verifying the simulation data with the actual measurement data, and updating and correcting the simulation result;

and establishing a blade noise and fault information base according to the corresponding relation between the shape, the wind speed and other conditions and the noise level.

4. The wind turbine and wind farm digital mirror image simulation display system of claim 1, wherein the tower vibration monitoring and stress analysis and prediction module is specifically used for tower vibration monitoring, blade root load monitoring, and tower stress analysis and prediction;

the tower vibration monitoring comprises:

simultaneously installing tilt sensors at the top end and the middle part of the tower barrel, installing 2-axis acceleration sensors at the top end to detect the direction and the magnitude of acceleration so as to correct tilt values, and compensating measured values to obtain relatively real values;

the method comprises the following steps of mounting a low-frequency acceleration sensor at the top of a tower drum, continuously monitoring the shaking of the tower drum, and acquiring the dynamic deformation displacement of the tower drum by adopting an integral algorithm;

the inclination angle sensor and the acceleration sensor of the tower barrel are installed in pairs, sampling is carried out synchronously, and the static, quasi-static and dynamic deformation of the tower barrel are obtained through common analysis;

the blade root load monitoring comprises:

arranging 6 strain sensors at the blade root of each group of blades, and respectively monitoring the bending moment of the blade root in the shimmy direction, the bending moment in the waving direction and the torque of the blade root of the fan; the two pairs of strain sensors are respectively used for monitoring the strain of the blade root in the axial direction, are arranged at intervals of 90 degrees in the circumferential direction and respectively test the strain in the shimmy direction and the waving direction; the rest two sensors are arranged at the circumferential tangent of the same section position at 180-degree intervals, and are used for monitoring the torsional strain under the action of load; a signal acquisition instrument is arranged in the hub, is used for acquiring and preprocessing signals, is communicated with the engine room in a wireless mode, and transmits the signals to the tower footing industrial control cabinet; monitoring the strain of a blade root under the load effect, and analyzing the stress characteristic and the stress-strain state of the blade root to obtain the load state of the blade of the wind generating set under each operating condition;

before data sampling, calibrating a blade root bending moment signal by using static load calibration to determine the relation between a strain signal and a load signal, and verifying the accuracy of a numerical calculation model and a theoretical calculation model of a blade root stress-strain state;

the tower stress analysis and prediction comprises the following steps:

1) Establishing a tower tube stress analysis model based on tower tube design parameters and material attributes;

2) Based on given wind speed and turbulence input boundary conditions, characteristics such as limit load, accumulated fatigue load and the like of a tower are calculated and analyzed, and a relation function between wind speed turbulence and stress load is established;

3) And (3) calculating and analyzing the stress load level by using the full-time wind speed and the turbulence of the fan under the actual operation condition as input conditions and using the process in the step 2), evaluating the proportion of the accumulated fatigue load level in the full life cycle, and predicting the residual life.

5. The wind turbine and wind farm digital mirror image simulation display system according to claim 1, wherein the wake simulation display process of the unit dynamic wake simulation display module comprises:

(1) Establishing a whole wind power plant model:

establishing a digital model based on the actual physical topography of the wind power plant, establishing a simulation calculation domain by taking the central position of the wind power plant as a central point and at least 20 times of the radius of the wind power plant as a radius, and simulating wake flow interference under the typical working condition of the wind power plant by taking the wind speed, the wind direction and the turbulence as input conditions and taking meteorological conditions into consideration; wherein, the wind speed condition is input and determined by combining the meteorological prediction with the data verification of the actual wind measuring tower; typical working conditions cover a main wind direction, a secondary wind direction and a maximum wind speed; the wind direction interval takes maximum 15 degrees as an interval, the wind speed takes 0.25-0.5m/s as an interval, and the wind direction and wind speed interval is further reduced for sensitive working conditions;

(2) Calculating wake flow simulation and creating a database:

performing wake flow simulation calculation according to the model established in the step (1) and the determined calculation condition to form a database; the wind turbine model adopts a virtual blade model, simulates data post-processing, and extracts and calculates the wake flow speed, turbulence intensity, power and power loss of the full-field fan corresponding to the working condition; the database data at least comprises two basic data, namely an image data, a video format data and a parameter variable point data set;

(3) Based on the established wind power plant model, acquiring wind field aerodynamic characteristic data containing wind turbine blades under various statistical wind conditions according to main flow wind speed and wind direction data analyzed by big data, analyzing wake influence and mutual interference between the wake and the wake, acquiring wake influence data and mutual interference data between the wake and the wake, and evaluating and optimizing a fan operation adjustment strategy under each wind condition;

(4) And (3) displaying the simulation data calculated in the step (2), displaying the data of the anemometer tower and the power prediction production tower in a real-time state parameter mode, and displaying the fan SCADA monitoring data including real-time meteorological data such as temperature and pressure.

6. The digital mirror image analog display system for the wind turbines and the wind power plants according to claim 5, wherein the display mode in the step (4) comprises split view display, global and local multiple view angles, the trail interference condition of all wind turbine trails of the whole wind power plant is displayed globally, and data are updated in a cycle of 10 minutes; displaying the calculated dynamic wake flow of the fan in a smoke or air particle simulation visualization mode, supporting full-field 3D wake flow field display, and having a VR interaction function; and establishing the reference object of the surrounding environment of the wind power plant by using the actual reference object.

7. The wind turbine and wind farm digital mirror image simulation display system according to claim 1, wherein the adaptive optimization control module performs wake control through a pre-constructed wake detection model and then through an online identification manner, comprising:

when wind field fan group control is implemented for the first time, the condition of wake track influence of the fan is identified, a wake flow interference control model is constructed, power and load data are analyzed, and optimal yaw, pitch and speed control parameters are searched;

establishing a wind field model, inputting actual wind measurement data and fan yaw angle data of a wind field into the established wind field model, judging the occurrence condition and degree of wake flow influence, and marking the data with the wake flow influence;

constructing a wake flow influence identification model in a data set by utilizing real-time power generation power data, cabin vibration data, vibration data in a CMS (content management system) system and load data and marked fan running state data through data cleaning and mode clustering;

for the unit which is detected to have serious wake flow interference and needs to be adjusted, the total power and the load of the unit are optimized by adopting a control strategy based on a multi-loop ESC, so that the controlled unit can achieve the purposes of maximizing the generating capacity and reducing the load of a controlled fan subgroup in a self-optimizing mode.

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