820 results about "Wind measurement" patented technology

The invention discloses a wind power plant generating capacity improving method based on active wake flow yaw control optimization. The method comprises the following steps: 1) acquiring basic information data of a wind power plant; 2) establishing a CFD simulation model of the wind power plant according to the basic information data; 3) optimizing the arrangement scheme of the laser wind finding radar, and obtaining the operation data of the wind turbine generator in real time; 4) constructing a single-machine analysis wake flow model and a multi-machine wake flow superposition model, respectively comparing with the flow field simulation result data and the wind measurement result of the laser wind measurement radar, and correcting a wake flow speed distribution expression to obtain wind speed and wind direction prediction data at the hub center height of each machine site in the wind power plant; and 5) constructing a whole field optimization control model, taking maximum output as an optimization target to obtain an optimal yaw control strategy set of each wind turbine generator, and enabling the related wind turbine generators to execute yaw actions. The invention belongs to the technical field of wind power generation control, and solves the problem that the wake flow loss among wind power units in a wind power plant is difficult to reduce by the existing control strategy.

The invention provides a wind measurement laser radar optical automatic calibration method and system based on reinforcement learning, and relates to the technical field of calibration, and the method comprises the steps: obtaining the initial state information and environment parameters of a wind measurement laser radar optical system, constructing a three-dimensional simulation model of the wind measurement laser radar optical system, performing a multi-field coupling simulation test on the three-dimensional simulation model to obtain wind measurement data and wind measurement error data under the current environmental condition, and constructing a mapping model of wind measurement errors and environmental parameters and wind measurement laser radar optical system state parameters; and inputting the wind measurement error data and the mapping model into a preset reinforcement learning model to obtain an optimal calibration parameter set in a current state, generating a calibration instruction of the wind measurement laser radar optical system according to the optimal calibration parameter set, and performing aberration correction by combining online wavefront detection and adaptive optical compensation to obtain a calibration result of the wind measurement laser radar optical system. And automatic calibration of the wind measurement laser radar optical system is completed.

The invention provides a wind turbine generator array wake flow management method and system for Doppler laser wind measurement, and relates to the technical field of laser, and the method comprises the steps: collecting the real-time wind speed data, wind direction data and turbulence intensity data of each generator in the front and back direction through deploying a Doppler laser radar in a wind turbine generator array; establishing a wake flow influence model containing a wake flow influence coefficient, an attenuation coefficient and a diffusion coefficient based on the data, and calculating to obtain a wake flow influence range, loss power and a recovery distance; according to the method, the wind turbine generator array is cooperatively controlled, and the power generation efficiency of the wind turbine generator array is optimized by adjusting the yaw angle or the pitch angle of the upstream unit and the rotating speed of each unit. The wind speed data acquisition precision of the wind field is improved, the wake flow influence evaluation accuracy is enhanced, and the overall power generation efficiency of the wind field is remarkably improved.

The invention relates to the technical field of wind power generation, and discloses a wind power plant energy management method based on laser radar wind measurement, comprising the following steps: S1, deploying laser radar equipment, and collecting real-time data of wind speed, wind direction and turbulence intensity in a wind power plant; s2, constructing a three-dimensional dynamic model of a wind field, and modeling wind speed, wind direction and turbulence distribution in combination with laser radar data and historical meteorological data; s3, performing short-term prediction on the future wind speed by using the deep learning model, and outputting minute-level to hour-level wind energy prediction data; s4, optimizing a dynamic scheduling strategy of the wind turbine generator based on the prediction data; and S5, docking an energy storage system, optimizing charging and discharging behaviors of energy storage equipment, and realizing smooth power output. According to the invention, by fusing the laser radar wind measurement data, the deep learning prediction model and the intelligent optimization algorithm, high-precision wind condition monitoring, dynamic scheduling optimization and power output stability improvement of the wind power plant are realized.

The invention relates to the technical field of new energy power generation prediction, in particular to a new energy power prediction method fusing typhoon meteorological information and a micrometeorological prediction result, which comprises the following steps: collecting multi-source observations such as a satellite scatterometer, a radar wind profile and laser wind measurement, and unifying coordinates; constructing a mesoscale wind field by adopting four-dimensional variational assimilation, inferring a micrometeorological field by utilizing a spectrum embedding diffusion network, and generating a multi-scale meteorological field by frequency domain phase consistency fusion; probabilistic wind speed is sampled in the countercurrent model meeting the condition of mass and momentum conservation, an energy conservation graph neural network is input, the wake effect is coupled, and a unit power quantile value is obtained; the power probability is sent to a risk weighting model, the weight is adjusted in real time according to the peak load, the reserve capacity and the climbing rate risk, a dispatching power curve and uncertainty are output, and grid-connected power errors are used for periodically updating the weight and the parameters of the last layer of the reversible model. According to the invention, the safety acceptance margin of the power grid to new energy is obviously improved.

The invention relates to the technical field of wind power generation, and discloses a cross-scale wind power plant wind resource evaluation method based on a numerical mode, comprising the following steps: step 1, acquiring multi-source observation data of a target area; step 2, screening a WRF optimal configuration scheme adaptive to the regional terrain; step 3, through a WRF power downscaling method, generating annual scale wind resource data as initial wind resource data; 4, performing partition correction on the initial wind resource data to obtain wind measurement data; step 5, constructing a WRF-WFP model coupling the WRF mode and the wind power plant parameterized model; step 6, optimizing WRF lower boundary conditions; 7, simulating the atmospheric flow of the target area, and quantifying the wake flow evolution characteristics of the wind power plant; 8, constructing a wake flow analysis model; and step 9, formulating a collaborative productivity plan of the wind power plant group. The power generation capacity of the wind power plant to be developed can be accurately predicted, and a reliable basis is provided for scientific decision-making of resource distribution development and planning of the million-kilowatt wind power plant.

The invention relates to the technical field of wind power plants, and provides a wind energy resource high-precision simulation method and system, and the method comprises the steps: obtaining geographic data, wind measurement data and mesoscale meteorological data of a target region; the method comprises the following steps: constructing a terrain model based on geographic data, dividing the terrain model into a plurality of sectors, and generating a wind profile as a boundary condition of each sector by using mesoscale meteorological data; the method comprises the following steps: selecting multiple groups of turbulence models based on mesoscale data and wind measurement data of a selected wind measurement tower, performing directional calculation in combination with boundary conditions, constructing a relationship between the selected wind measurement tower and a target wind measurement tower based on a directional calculation result, obtaining a simulation value of the target wind measurement tower through interpolation, and evaluating an error between the simulation value of the target wind measurement tower and the wind measurement data. Selecting an optimal turbulence model based on the error; and performing comprehensive calculation based on the wind measurement data and a directional calculation result corresponding to the optimal turbulence model to obtain a whole-field wind energy resource. And the wind resource simulation accuracy is improved.

The invention relates to the technical field of wind power plant wake flow testing, and discloses a 3D scanning type laser radar planning method suitable for wind power plant wake flow testing, and the method comprises the steps: determining a wind power plant wake flow wind measurement region according to a wind measurement demand; determining a mainstream wind direction and a wind speed interval of the wind power plant wake flow wind measurement area based on the wind power plant wind resource data, and obtaining unit coordinates and unit parameters of wind turbine units in the wind power plant wake flow wind measurement area; according to the mainstream wind direction, the wind speed interval, the unit coordinates and the unit parameters, performing simulation calculation on the wake flow field of the wind turbine generator in the wake flow wind measurement area of the wind power plant to generate a wake flow area cloud picture; according to the method, the arrangement position of the 3D scanning type laser radar is determined based on the wake flow area cloud picture and the actual landform of the site, the situation that the arrangement position of the 3D scanning type laser radar is repeatedly adjusted for multiple times due to arrangement errors possibly occurring during arrangement according to experience can be avoided, and the wake flow testing efficiency is improved.

The invention discloses a laser radar system for simultaneously measuring an atmospheric cloud height and a wind field. The laser radar system comprises an outer cover module and an inner cavity, the outer cover module comprises an automatic detection station shell, an all-in-one meteorological sensor connected with the automatic detection station shell, a cloud measurement window embedded in the automatic detection station shell and a wind measurement window embedded in the automatic detection station shell; a reference module, a control module, a cloud measurement optical receiving module, a cloud measurement laser transmitting module and a wind measurement optical transceiving module are arranged in the inner cavity; wherein the control module, the cloud measurement optical receiving module, the cloud measurement laser transmitting module and the wind measurement optical transceiving module are all integrated on the reference module; through the above structure, the laser radar system can obtain the wind profile data, the cloud height measurement data, the atmospheric extinction coefficient profile and the visibility of the current environment at the same time.

The invention provides an air tightness self-adaptive control method and system for a wind measurement laser radar shell, and relates to the technical field of air tightness control, and the method comprises the steps: reconstructing the three-dimensional shape of a sealing surface of the wind measurement laser radar shell through a machine vision method, and building a mapping model of a multi-scale shape characteristic parameter and a gas leakage rate; obtaining the position coordinates of a sensitive area, and carrying out the dynamic pressure regulation of the sealing surface of the wind measurement laser radar housing according to the position coordinates of the sensitive area; the method comprises the following steps: acquiring air tightness control parameters under different working conditions, performing self-learning optimization on the air tightness control parameters to obtain an optimal control parameter matrix, storing the control parameter matrix to a controller, and predicting the air tightness performance of a wind measurement laser radar shell by adopting a pre-trained air tightness prediction model to obtain the air tightness performance of the wind measurement laser radar shell. And when the predicted gas leakage rate exceeds a preset early warning threshold value, outputting a gas tightness alarm signal, and starting a gas tightness self-adaptive control flow according to the stored optimal control parameter matrix.

The invention relates to the technical field of mine intelligent ventilation, and discloses a mine intelligent ventilation system which comprises a mine ventilation environment parameter monitoring system, an air door and air window automation and remote control system, a local ventilator intelligent control system, a main ventilator intelligent monitoring system and an analysis decision platform. In the mine ventilation environment parameter monitoring system, according to the principle of mine ventilation resistance measurement, a ventilation route of a mine is selected, multi-parameter sensors are arranged at nodes, air pressure, temperature, humidity, wind speed and wind quantity at the starting end and the tail end of each roadway of the mine are monitored in real time, and node air density is calculated. According to the intelligent ventilation system for the mine, real-time wind measurement improves the wind measurement precision and frequency of the mine and reduces the workload of manual wind quantity detection, meanwhile, the number of wind measurement staff can be properly reduced, manual inspection tour and unattended operation of wind measurement work are achieved, and therefore expenditure is reduced.

The invention relates to a spaceborne laser radar wind field data correction method based on a ground-based laser wind finding radar, and belongs to the field of radio, and the method comprises the steps: calculating horizontal wind speed components U1 and V1, and horizontal wind speed components U2 and V2 of different height layers according to collected data; determining an optimal nonlinear regression correction model; generating U and V wind fields with high spatial resolution; constructing a power spectrum correction model, and generating power spectrum signals of the corrected high-spatial-resolution grids U and V; generating a satellite-borne laser wind finding radar antenna direction weighting function, and obtaining a weight coefficient corresponding to each grid point in the high-spatial-resolution grid; and constructing a satellite-borne laser wind-finding radar wind field data correction model, and finally obtaining corrected satellite-borne laser wind-finding radar wind field data. According to the invention, accurate simulation of the power spectrums of the U and V components of the satellite-borne laser wind-finding radar with low spatial resolution is realized, and accurate and efficient correction of the U and V components of the satellite-borne laser wind-finding radar is realized.

The invention provides a wind power plant ultra-short-term power prediction optimization method based on a laser radar, and the method employs the laser radar to scan a wind power plant region, and obtains the wind speed and direction data based on a coherent Doppler wind measurement principle. The wind speed extrapolation prediction is carried out by adopting an algorithm combining EMD and ARCH, the EMD is firstly used for decomposing a wind speed time sequence, then an ARCH model is established for each intrinsic mode function, and finally the ultra-short-term wind speed is obtained by combining prediction results. Then, an algorithm combining SVR and GA is used for power preliminary prediction, the SVR establishes a nonlinear relation between input parameters and power output, and the GA optimizes SVR parameters; then, the preliminary prediction result is processed and corrected based on an FNN algorithm, and the laser radar prediction wind speed and the preliminary power prediction value are input into a fuzzy neural network, so that the prediction accuracy is improved. And performing precision evaluation on the correction result according to specification requirements, and if the result does not reach the standard, adjusting parameters or improving the method. And when the precision reaches the standard, outputting a wind power forecast time sequence power value in the future 3-5 hours.

The invention discloses a radar detection blind area wind field fine reconstruction method based on anemograph collaborative networking, and the method comprises the specific steps: firstly, separating wind speed measurement deviation caused by vibration displacement in real time, eliminating vibration interference, carrying out the automatic diagnosis and restoration of data based on machine learning, and optimizing the integrity of wind speed data and the measurement continuity; secondly, an ultrasonic anemograph array optimization arrangement method oriented to global wind field sensing is established, time-space consistency calibration of multi-sensor data is achieved, and the precision and reliability of wind speed measurement are improved; and finally, carrying out data-physical fusion bridge flow field high-resolution intelligent reconstruction, and improving the data integrity of a complex flow field area. The reconstruction method effectively cooperates with the wind measurement laser radar and the anemograph, the advantages of high-frequency single-point measurement and wide-area space scanning of the wind measurement laser radar and the anemograph can be exerted, high-precision reconstruction of the global flow field of the bridge is expected to be achieved, and reliable support is provided for guaranteeing wind resistance safety of a large-span bridge.

The airport available runway monitoring and early warning system and method based on multi-source data fusion are high in real-time performance, good in forecasting effect and large in monitoring range. The system comprises a laser wind finding radar (1), a CFD numerical simulation module (2), a mesoscale weather forecast module (3), a machine learning model (4) and a system server (5). The method comprises the following steps that: a system server performs data assimilation by combining real-time wind shear detection data measured by a laser wind measurement radar and forecast data of a current time point output by a mesoscale weather forecast module, processes the assimilated data by applying a CFD numerical simulation module, and calculates the real-time wind shear detection data by combining terrain and building parameters near an airport; according to the method, real-time sub-meter gridding three-dimensional wind field data is generated, and a sub-meter three-dimensional wind field structure is reconstructed, so that future wind shear of an airport is forecast and early warned, and then available runway prediction is carried out. The method is applied to the technical field of wind field prediction and judgment.

The invention provides a three-dimensional wind field inversion method of a coherent wind lidar, and relates to the technical field of lidar wind measurement, and the method comprises the steps: building a mathematical model of a coherent wind lidar receiving echo signal, and determining an optimal target detection distance; under the optimal target detection distance, emitting a modulated laser detection signal to a target wind field, and processing echo signals which are received by a laser radar and are mixed at different frequencies to obtain an echo signal light field distribution condition; calculating the radial velocity of the wind in the wind field of the target based on the light field distribution condition of the echo signal; the elevation angle of the laser radar is fixed, the four orientations are scanned, radial wind speeds corresponding to the four orientations are obtained, the atmospheric three-dimensional wind field is inverted, and real-time atmospheric wind field three-dimensional detection with high temporal-spatial resolution and high wind measurement precision can be realized.

The invention discloses a multi-source data fusion wind power plant micro-siting dynamic evaluation method, relates to the technical field of wind power plant siting, and solves the problem that a mainstream method still depends on single anemometer tower data or static GIS analysis during wind power plant siting in the prior art. The method comprises the following steps: firstly, acquiring wind measurement data of multiple sources, unifying the wind measurement data of different sampling frequencies into a set time window, and realizing spatial scale alignment through an interpolation method; then obtaining a wind energy score according to the annual average wind speed and the main wind direction frequency, obtaining a suitability score according to the distance to the residential area or the main road, obtaining an environmental impact factor according to the noise and the ecological protection area, and constructing a target function based on the wind energy score, the suitability score and the environmental impact factor; and finally, combining a genetic algorithm and a real-time optimization model of a wake flow interference model, and dynamically adjusting the evaluation tendency according to the evaluation score of the fan layout inversion site, so that the evaluation result is more comprehensive, scientific and reasonable.

The invention provides a method, device and equipment for determining a buoyancy generation item of atmospheric turbulence kinetic energy and a storage medium, and the method comprises the steps: obtaining the wind speed at a plurality of moments through a wind measurement radar within a specified time length, and obtaining the buoyancy generation item of the atmospheric turbulence kinetic energy according to the wind speed at the plurality of moments; the dissipation rate and pulsation components corresponding to the wind speed in different coordinate axis directions are determined, a turbulence trend item, a shear generation item and a turbulence transmission item are determined according to the pulsation components corresponding to the different coordinate axis directions, and the wind speed is acquired by adopting a wind measurement radar, so that the spatial resolution and the data continuity of data are improved, and the detection range is expanded. And more accurate dissipation rate, turbulence trend term, shear generation term and turbulence transmission term are determined, so that the buoyancy generation term of the atmospheric turbulence kinetic energy can be more accurately deduced according to the dissipation rate, turbulence trend term, shear generation term and turbulence transmission term, and the accuracy of the buoyancy generation term is ensured.

The invention discloses a wind power plant unit load reduction optimization method and system based on cabin type laser radar real-time wind measurement, and relates to the technical field of wind power generation. Constructing a load estimation model; performing real-time detection on a blade root bending moment predicted value sequence and a tower bottom vibration acceleration predicted value sequence, constructing a multi-objective optimization function including blade root bending moment, tower bottom vibration acceleration and power generation power, and setting a dynamic weight coefficient of the multi-objective optimization function according to three-dimensional wind field prediction sequence data; obtaining an optimal control variable through a multi-objective genetic algorithm based on the multi-objective optimization function; and according to the optimal control variable and vertical wind shearing in the three-dimensional area, differential adjustment is conducted on the pitch angle of each blade, and a personalized variable pitch angle sequence of each blade is obtained. According to the method, by establishing the optimization objective function fusing the load and the power, multi-variable collaborative optimization is achieved, and the safety and the economical efficiency of wind power plant unit operation are remarkably improved.

The invention discloses a multi-sensor fusion meteorological data comprehensive analysis platform, and relates to the technical field of meteorological monitoring. The platform comprises a three-level sensing layer, a dynamic collaborative architecture and a meteorological data analysis layer. The third-level sensing layer works cooperatively through a ground total-factor meteorological station, an unmanned aerial vehicle cluster and a FY-4 satellite and is integrated with a bimodal ultrasonic wind measurement sensor, a miniature meteorological sounding cabin and an HRIT / LRIT data receiving module, so that multi-dimensional data acquisition from the ground to a 5km high altitude is realized; the dynamic collaborative architecture is based on an edge computing node and a cloud fusion module, adopts a lightweight LSTM (Long Short Term Memory) model to detect abnormity in real time, and dynamically optimizes the weight of multi-source data through Kalman filtering; and the meteorological data analysis layer is combined with the element coincidence index, the layering stability index and the risk assessment model to generate a graded early warning signal. According to the scheme, the problems of vertical data fault, low multi-source data fusion efficiency, early warning lag and the like in the traditional technology are solved, and the severe convective weather prediction precision is remarkably improved.

The invention relates to a wind shear wind speed extrapolation calculation method based on a neutral equivalent wind speed. The method comprises the following steps: (1) determining input wind speed measurement data; (2) classifying the atmospheric stability, and converting the wind speed at each moment into an equivalent wind speed in a neutral state; (3) performing extrapolation wind speed calculation according to the neutral equivalent wind speed; and (4) outputting an extrapolated wind speed result, and calculating wind shear. According to the method, the wind speed extrapolation error is effectively reduced, the wind speed extrapolation calculation is more accurate, and particularly, the method has outstanding performance in complex terrains or marine environments.

The invention discloses a radar wind measurement method and system based on terrain matching, relates to the technical field of radar control, and solves the problem of insufficient radar wind measurement precision under complex terrain conditions in the prior art. The method comprises the following steps: firstly, collecting time-space synchronous multi-source data and preprocessing the multi-source data, wherein the multi-source data at least comprises data of a laser radar, an unmanned aerial vehicle and a sensor network; then constructing a three-dimensional vortex adaptive function and combining smooth partitioning, performing grid division and setting an overlapping buffer region with a certain width between different regions; and finally, calculating a terrain complexity index, and when the terrain complexity index obtained through real-time calculation exceeds a threshold value, adjusting parameters of the radar. The radar wind measurement data error is effectively compensated, the accuracy of the wind measurement data and the generalization ability of the model are improved, the pertinence and efficiency of radar scanning are improved, more accurate wind field information is obtained, and compared with a traditional fixed scanning mode, the wind measurement precision is obviously improved.

The invention relates to the technical field of three-dimensional real-time wind speed measurement, and discloses a large-span roof three-dimensional ultrasonic anemometer and a measurement method thereof, and the method comprises the steps: building a three-dimensional coordinate system through laser ranging, and analyzing and compensating installation errors; an initial calibration coefficient is generated through a self-inspection and optimization algorithm, and the initial calibration coefficient is compared and analyzed with a standard instrument for initial calibration; ultrasonic pulse emission is controlled, full-path scanning and redundancy measurement of all axes are completed, signal quality is improved through multi-modal signal processing detection, and abnormal values are eliminated through weighted median values; the sound velocity is corrected based on real-time temperature data, the influence of humidity and air pressure on the sound velocity is adjusted through a nonlinear regression model, and a Reynolds stress correction term is introduced to compensate for sound wave path bending in an eddy current field; a three-dimensional turbulence intensity distribution diagram is constructed by fusing ultrasonic and LiDAR data, meteorological parameters are calculated, an extreme weather event is early warned by using a machine learning model, a visual three-dimensional wind speed field is provided through a cloud platform, and a potential risk area is identified.

The invention provides a measurement method of a shipborne three-dimensional scanning wind measurement laser radar, and the method comprises the steps: obtaining the attitude information of a ship through a combined inertial navigation system, and the attitude information comprises a heading angle, a pitching angle and a rolling angle; according to the attitude information and a coordinate transformation model between a preset radar coordinate system and a geographic coordinate system, determining an azimuth angle and a pitch angle of a laser beam of the shipborne three-dimensional scanning wind measurement laser radar in the geographic coordinate system; according to the azimuth angle and the pitch angle, controlling the shipborne three-dimensional scanning wind measurement laser radar to emit a laser beam, and receiving a reflection echo signal; and acquiring three-dimensional velocity component information of the wind field according to the reflection echo signal to obtain a three-dimensional wind field.

The invention discloses a laser radar-assisted wind power generation wind shear modeling method, and relates to the technical field of wind shear modeling, and the method comprises the steps: collecting the wind speed measurement data of a laser radar at a plurality of heights in a target fan region, and constructing a measurement volume integral operator matrix; constructing a unit induction field correction model based on large eddy simulation in combination with a gradient boosting tree; coupling the measurement volume integral operator with the unit induction field correction model, and obtaining a high-precision wind speed vertical section and a corresponding wind shear parameter through inversion; on the basis of the high-precision wind speed vertical section and the corresponding wind shear parameters, physical constraints are fused, a wind shear model is generated, and uncertainty evaluation is carried out; according to the wind shear modeling method, the problems that the measurement error of the laser radar is difficult to correct, the wind shear modeling lacks the physical constraint and the result credibility cannot be quantified are solved by constructing the volume measurement integral operator and the unit induction field correction model for coupling inversion and fusing the physical constraint and the Bayesian uncertainty evaluation method.

The invention discloses a Doppler differential interferometer imaging position drift monitoring method, which solves the problem of inaccurate wind speed measurement result caused by drift of an interferometer image surface due to temperature change and the like. According to the method, periodic grooves are etched in the grating diffraction surface of the interferometer, namely, groove patterns serve as scales, the drift distance of the interferometer to be monitored is calculated, Doppler differential interferometer image surface drift caused by temperature changes can be effectively monitored, then the drift monitoring result is used for correcting the imaging position drift of the interferometer, and the drift distance of the interferometer to be monitored is calculated. Therefore, the accuracy of Doppler phase measurement is improved, the wind measurement accuracy of the interferometer is improved, and sub-pixel monitoring of the imaging position drift of the interferometer is realized.

The invention discloses an acousto-optic radar wind measurement data correction system based on adaptive noise suppression, and relates to the technical field of data correction. Environmental parameters and acousto-optic radar system parameters are combined through a dynamic adjustment module to dynamically adjust a signal-to-noise ratio threshold; the echo signal quality analysis module inputs the frequency spectrum characteristic data and the dynamically adjusted signal-to-noise ratio threshold value into an echo quality model and judges whether the current echo signal is valid or not, if yes, interpolation is carried out by adopting a spatial smooth interpolation method, if yes, the dominant frequency of the echo signal is extracted, Doppler frequency shift is calculated, and according to the detection angle and the laying direction, the Doppler frequency shift is calculated; and calculating a radial wind speed, inverting a three-dimensional wind speed vector and carrying out physical consistency correction. According to the correction system, the dynamic adjustment module is introduced, real-time environment parameters and system operation parameters are combined, and a signal-to-noise ratio threshold value required by judgment is dynamically adjusted, so that a judgment basis can be intelligently self-adapted along with a measurement environment, and the accuracy and robustness of echo signal quality judgment are remarkably improved.

The invention discloses a yaw wind error compensation method and system based on wind measurement data, and relates to the technical field of wind power generation equipment control, and the method comprises the steps: setting a wind measurement point position, and collecting an original wind field data set; calculating an incoming flow and a cabin wind direction angle according to the original wind field data set; performing deviation calculation to obtain a yaw wind error angle; triggering a dual-channel compensation module, accumulating errors, constructing an error queue, and performing batch compensation to obtain a compensation result; yawing control parameters are called, the cabin attitude is analyzed according to the compensation result, the yawing control parameters are updated, and self-adaptive compensation of yawing to the wind error is achieved. The technical problems that in the operation process of the wind driven generator, due to wake flow, terrain, turbulent flow and other environmental factors and equipment loss, a traditional yaw system cannot accurately detect the wind direction and compensate the wind alignment error, the power generation efficiency is low, and the potential safety hazard of equipment is large are solved, and the purposes of accurately improving the wind alignment precision of a wind wheel and improving the power generation efficiency are achieved. The power generation efficiency is obviously improved; and the equipment operation safety is enhanced.

The invention discloses a wind power prediction method and system for a region lacking wind measurement data by adopting a BP neural network, and relates to the field of wind power prediction of a wind power plant. The method includes: collecting wind speed, wind direction and other historical feature data and actual wind power of a wind power plant; downloading ERA5 reanalysis data provided by the ECMWF in the wind power plant, interpolating the ERA5 data to each wind turbine generator in the wind power plant by using an inverse distance weight method, and constructing sample data; singular point elimination is carried out on each feature element in the sample data; building a BP neural network model, carrying out normalization processing on the processed sample data, constructing a data set, and grouping and dividing the data set into a training set and a test set; training and outputting the neural network model, and calculating a root-mean-square error output by the model; and outputting the model with the minimum error and testing to obtain the predicted wind power. The method is simple in data acquisition and high in prediction precision, and can provide a reference for predicting the wind power in a region lacking actually measured data.

The invention provides a methane emission real-time monitoring system and method.The methane emission real-time monitoring system comprises a multi-point sampler, a methane sensor and a control terminal, the multi-point sampler is provided with a plurality of gas inlet channels, a gas inlet of each gas inlet channel corresponds to each monitoring point, gas outlets of the gas inlet channels are all connected to the methane sensor, and the methane sensor is connected with the control terminal; the control terminal can control the multi-point sampler to carry out gas collection on each monitoring point according to a preset circulation sequence; a temperature and humidity sensor and a wind speed measuring instrument are arranged near the multipoint sampler, and the control terminal is used for correcting methane concentration data by fusing methane concentration data acquired by the methane sensor, temperature data and humidity data acquired by the temperature and humidity sensor and wind speed data acquired by the wind speed measuring instrument through a Kalman filtering algorithm. The problems of random mistaken killing and large environmental interference under the scenes of non-uniform animal house gas distribution, large temperature and humidity difference, complex ventilation environment and the like can be avoided, so that methane concentration data can be obtained more accurately.