764 results about "Wind direction" patented technology

The invention discloses a wind power generation abnormal data analysis method and system, and relates to the technical field of data analysis, and the method comprises the following steps: constructing a wind direction change rate enhanced perception model, analyzing the potential omen of wind direction abrupt change based on an ultra-short time scale wind direction change trend curve and a wind speed fluctuation coupling index collected at multiple measurement points, and determining the wind direction abrupt change. Generating a risk early warning label of wind wheel pointing deviation; and based on the risk early warning label, executing a high-frequency yaw disturbance prediction mechanism, and predicting an inflow angle continuous offset window caused by yaw response lag by using a nonlinear time sequence evolution trend and a short-term wind direction reversal probability curve. The wind direction sudden change early warning is realized through multi-measuring-point wind direction enhanced perception and wind speed coupling analysis, the response precision is improved and the energy consumption is reduced in combination with predictive yaw compensation and torque balance, the yaw parameters are dynamically optimized by using adaptive closed loop and reinforcement learning, the inflow angle is kept stable for a long time, and the power generation efficiency and the structural safety are improved.

The invention discloses an SAR sea surface flow velocity inversion method based on physical guidance and data driving fusion, and relates to the field of flow velocity inversion, and the method comprises the steps: carrying out the non-geophysical Doppler frequency shift correction of the Doppler frequency shift observed by a spaceborne synthetic aperture radar, and obtaining the geophysical Doppler frequency shift; predicting based on a wave-induced Doppler frequency shift model according to a radar incident angle, reanalyzed wind direction data, a sea surface 10-meter height wind speed inverted by an SAR (Synthetic Aperture Radar), a simulated wind wave parameter, a simulated surge parameter and a simulated maximum wave parameter to obtain a wave-induced Doppler frequency shift; the wave-induced Doppler frequency shift model is obtained based on physical guidance and data-driven modeling; doppler frequency shift generated by sea surface flow is determined according to the geophysical Doppler frequency shift and the wave-induced Doppler frequency shift; according to the method, the Doppler frequency shift generated based on the sea surface flow is converted to obtain the sea surface radial flow velocity, the wave-induced Doppler frequency shift is accurately removed, and reliable inversion of the sea surface flow velocity is realized.

The invention provides a method for monitoring and controlling cable-stayed buckling construction of a concrete-filled steel tube arch bridge based on a digital twin system, and the method comprises the steps: collecting the monitoring data, such as wind speed and wind direction, arch rib stress displacement, tower bottom stress, tower deviation and cable force, through a sensor; establishing a concrete filled steel tube arch bridge construction digital twinning system based on a three-dimensional model, real-time monitoring, data analysis and a visual platform; time sequence data is decomposed into different frequency components through variational mode decomposition (VMD), a bridge construction stage prediction model is established through a long short-term memory network (LSTM), and real-time accurate prediction of the construction progress and state is achieved. And secondly, based on the prediction model and on-site real-time monitoring conditions, an intelligent load adjustment construction control method based on a digital twin system is provided, and high-precision control over automatic load adjustment of cable hoisting and real-time rectification of tower displacement is achieved. The problems that in a traditional monitoring and control method, early warning is not timely, the assembling precision is insufficient, and the arch axis shape control difficulty is large are solved.

The invention discloses an automatic cruise system for a cleaning robot of a power insulator of a transformer substation, and relates to the technical field of intelligent operation and maintenance of the transformer substation, the automatic cruise system comprises a data extraction module, a processing module, a decision module and an execution module, and solves the technical problems of excessive shutdown when a wind field approaches danger and discontinuous operation when the wind field is safe. The decision-making module sets safety indexes according to the weights of the wind speed and the wind direction, divides the wind speed into four risk intervals, divides the wind direction into three grades, calculates the total safety score of the wind field through weighted summation, corresponds to three operation grades of safety, yellow early warning and red early warning, generates differential instructions according to different grades, and sends the differential instructions to the wind field. And real-time path updating during early warning level switching is supported, and safe operation under the dynamic change of the wind field is realized.

The invention relates to a radar synthetic wind speed resolving method, device and equipment and a storage medium, and is applied to the field of radar speed measurement, the radar synthetic wind speed resolving method comprises the steps that radar sight wind speed and the rotation angle of a radar are acquired, and the rotation angle is the angle of a radar roll angle; performing radar beam azimuth calculation according to the rotation angle to obtain a radar beam vector; and performing synthetic wind speed calculation according to the radar sight wind speed and the radar beam vector, and generating scalar wind speed and wind direction. The method has the technical effect that the calculation accuracy of the synthetic wind speed is improved.

The invention relates to the field of wind and sand prevention and control, in particular to a desert photovoltaic large base peripheral prevention and control system and method. The prevention and control system comprises a main air guide direction prevention and control system and a secondary air guide direction prevention and control system; the main wind direction prevention and control system is sequentially provided with three high vertical sand blocking fences, a lateral anti-lateral quicksand fence, a first sand stabilization grid area with a grid structure, two first plant sand blocking forest belts, a first plant sand stabilization area and a windproof sand blocking dust suppression fence in the direction from the upwind direction to the photovoltaic base. And the secondary wind guide direction prevention and control system comprises a second sand stabilization grid area, a second plant sand blocking forest belt and a second plant sand stabilization area. The sand prevention system can effectively solve the problems that an existing sand prevention technology is low in efficiency, unsustainable and the like, erosion and damage of sand grains to photovoltaic panels and infrastructures are reduced, the power generation efficiency is improved, the maintenance cost is reduced, long-term stable and efficient operation of a desert photovoltaic large base is guaranteed, and powerful support is provided for energy development and ecological protection balance in the desert area.

The invention discloses a short-term wind power prediction method based on dynamic graph and multi-scale space-time fusion. A dynamic space-time convolution attention network is constructed. Aiming at the dynamic topology and sequence complex characteristics of the wind power plant, a space-time topology module based on metric learning is designed, the physical distance and real-time characteristics are adaptively fused, and dynamic space correlation caused by the wind direction and wake flow effect is captured. The core ST-CAN module integrates graph convolution, time convolution and a self-attention mechanism, and through a mixed strategy of local convolution and global attention, modeling spatial dependence, local high-frequency fluctuation and a long-range period rule in a collaborative manner. According to the method, the prediction precision and generalization ability are remarkably improved, and power prediction challenges under complex wind conditions can be effectively coped with. According to the method, MAE, RMSE and MAPE indexes under different prediction step lengths are superior to those of a traditional method and an existing deep learning model, the prediction precision is high, the robustness is high, and the method is suitable for a complex wind field environment.

The invention relates to the technical field of meteorological observation, in particular to a parameter measurement method and device based on a dual-band mode, equipment and a storage medium. Generating a dual-band excitation signal according to the aligned sine wave signal; analyzing the echo signal according to a preset gain coefficient, a time difference method and a preset attitude compensation formula to obtain a reconstructed quasi-unipolar pulse, an actual wind speed, a horizontal wind direction angle, a pitch angle, a three-dimensional wind speed vector, a roll angle and a real wind direction; acquiring real-time air pressure, and correcting a preset standard state sound velocity according to a preset sound velocity correction formula and the real-time air pressure to obtain a corrected sound velocity; according to the scheme, a dual-band signal attached to a medium is generated, after efficient transmission analysis, high-resolution pulses and multi-dimensional wind field parameters are obtained by means of gain, a time difference method, attitude compensation and the like, the sound velocity is corrected in combination with air pressure, comprehensive, accurate and rapid updating of data are considered, and the measurement reliability is improved.

The invention relates to the technical field of data detection, in particular to an unmanned aerial vehicle atmospheric data anomaly detection and correction system based on deep learning, and provides the following scheme: an air mass reference coordinate system is constructed based on the attitude of an unmanned aerial vehicle and a relative wind direction; re-projecting the original measurement data to obtain an atmosphere data sequence with a stable reference direction; then, generating a frequency mask dynamically changing along with time, wherein the frequency mask is used for constraining positioning of disturbance energy of the rotor wing in the time-frequency analysis process; performing multi-scale decomposition on the time-frequency coefficient matrix based on a multi-channel frequency mask, and respectively extracting an aerodynamic disturbance estimation signal, a background atmosphere signal and a turbulence structure signal; and inputting the three types of signals into a preset deep learning model to realize identification and corresponding correction of atmospheric data anomaly. According to the invention, rotor disturbance and a real atmospheric structure can be distinguished, and the quality and reliability of atmospheric observation data are improved.

The utility model provides a wind direction and wind speed measuring device and a steel structure tower device. The wind direction and wind speed measuring device comprises a sampling main body, the sampling main body comprises a plurality of air inlets and at least two air channels, the plurality of air inlets are correspondingly communicated with the at least two air channels, openings of the plurality of air inlets face at least two different directions, and the at least two air channels are mutually independent and extend in different directions; the first temperature detection assemblies are arranged in the air ducts and used for collecting temperature information of at least two different positions in the extending direction of the air ducts. The second temperature detection assembly is arranged inside or outside the air duct and used for collecting environment temperature information. The humidity detection assembly is arranged inside or outside the air duct and used for collecting environment humidity information. The controller is used for calculating wind power data according to the temperature information and the humidity information, and the wind power data comprises wind direction and wind speed. The 360-degree omni-directional wind speed and wind direction measuring device is beneficial to measuring 360-degree omni-directional wind speed and wind direction, wide in measuring range, high in reliability, easy to maintain and high in measuring precision.

The invention discloses a cabin type laser radar wind field detection method and system based on multi-beam expansion. The cabin type laser radar wind field detection method and system are low in cost, high in sampling density, small in fitting error and high in measurement precision. The method comprises the following steps: transmitting a plurality of groups of uniformly distributed radial detection wave beams through a cabin type wind measurement laser radar, and carrying out omnibearing scanning on a target wind field; collecting radial wind speed observation data of each group of beams, performing nonlinear fitting processing on the observation data based on a cosine fitting model, and determining a mapping relation between fitting parameters and azimuth angles; the real wind speed and wind direction of the target wind field are calculated through the fitting model; the system comprises a transmitting module (1), a receiving module (2), a fitting calculation module (3) and an output module (4). The method is applied to the technical field of wind measurement laser radars.

The invention discloses an air conditioner control method, device and equipment in a sleep application scenario and a medium, and the air conditioner control method in the sleep application scenario comprises the steps that point cloud data of a user sleep area is continuously obtained through a millimeter wave radar, and the covering states of covering objects of all parts of a body are accurately recognized; the defects that a traditional infrared or temperature and humidity sensor cannot penetrate through the fabric, and local exposure is difficult to judge are overcome. Based on the recognition result, the system generates wind direction, wind speed and other wind feeling adjusting instructions in real time, air supply of the air conditioner is prevented from directly blowing exposed parts, and the thermal comfort is improved. The self-adaptive optimization of the strategy is realized by continuously monitoring the body movement frequency and the turn-over times of the user before and after adjustment, evaluating the adjustment effect and carrying out positive or negative weight adjustment on the control rule. The problems that in the prior art, due to the fact that the coverage state cannot be accurately perceived, air supply is uncomfortable, cold catching is likely to happen, and adjustment lags are solved, and the air conditioner use experience in the sleep scene is remarkably improved.

The invention relates to the field of wind driven generators, and particularly discloses a floating type vertical axis wind turbine multi-partition variable pitch control strategy which comprises the steps that S1, basic parameters and operating condition parameters of a unit are obtained; s2, establishing an aerodynamic model based on a double-disk multi-flow management theory and a superposition wind speed method; s3, measuring parameters through a sensor, and calculating a theoretical attack angle of the blade; s4, establishing a multi-partition pitch angle change function based on the theoretical attack angle; s5, constructing an NSGA-II multi-target optimization model, and taking maximization of an average power coefficient and minimization of a blade thrust standard deviation as targets; s6, substituting a function to obtain an optimal variable pitch control factor and an optimal pitch angle; and S7, the optimal pitch angle is converted into a variable pitch actuator instruction. The problems that an existing strategy is limited in partition, the wind direction influence is ignored, and optimal parameters for working condition adaptation are lacked are solved, the wind energy capture efficiency is improved, the load fluctuation is reduced, and the variable pitch control accuracy is improved.

An offshore wind-resistant photovoltaic platform supporting structure belongs to the technical field of offshore photovoltaic equipment installation, and comprises an installation seat and a photovoltaic panel, the interior of the installation seat is slidably connected with a rotating ring, the inner wall of the rotating ring is provided with an inclination angle adaptation mechanism, and a downward movement pressure reduction mechanism is arranged above the installation seat. The dip angle adaptation mechanism comprises a movable block arranged above the rotating ring, when the photovoltaic panel is subjected to weak wind, the movable block moves in a small range to buffer instantaneous sea wind pressure, when the photovoltaic panel is subjected to strong wind, the movable block drives the long edge of the photovoltaic panel to rotate to directly face the wind direction, and the wind load is reduced to prevent the structure from being damaged due to instantaneous impact of the strong wind. A rotary wind-resistant mechanism is arranged above the movable block, the rotary wind-resistant mechanism comprises a sliding disc arranged at the top of the movable block, and the sliding disc is extruded to move when the photovoltaic panel is subjected to the wind pressure of sea wind, so that the photovoltaic panel changes the pitch angle around the horizontal axis of the photovoltaic panel to parallel the wind direction, and the stability is enhanced while the wind area and the wind pressure of the photovoltaic panel are reduced.

The invention relates to the technical field of wind power generation control, and discloses a yaw calibration method and system based on a fan sensor, and the method comprises the steps: carrying out the frequency domain and multi-resolution decomposition of a real-time wind direction signal, and obtaining a short-term disturbance level; if yes, recursive state estimation is executed to obtain a smooth wind direction trend; calculating a long-term drift value and carrying out probability state simulation and power loss model mapping to obtain nonlinear influence estimation; if high turbulence is judged, a dynamic compensation threshold value is calculated to generate an adaptive yaw instruction; the actuator is driven, and the alignment precision grade is determined according to the corrected feedback position; and iteratively updating the reference turbulence intensity index to match and optimize the compensation control parameter. According to the method, accurate identification of short-term disturbance and long-term change under complex turbulent flow can be realized, yaw maloperation is effectively reduced, and the alignment precision and the power generation efficiency are improved.

The invention relates to a wind power plant unit operation optimization method and system based on artificial intelligence, and the method comprises the following steps: S1, data collection and processing: collecting the output power data, vibration data, temperature data, rotating speed data, yaw angle data and blade root strain data of each unit, meanwhile, acquiring upwind speed, wind direction, wind shear and turbulence intensity by using a laser radar at the top of a cabin, and performing time synchronization and preliminary noise filtering on the data; according to the invention, unit output power data, vibration data, temperature data, rotating speed data, yaw angle data and blade root strain data are continuously acquired through a data acquisition module, upwind speed, wind direction, wind shear and turbulence intensity are acquired by a laser radar at the top of a cabin, and time synchronization and preliminary noise filtering processing are combined to obtain a wind speed and a wind direction; discrete noisy points generated by wind speed pulsation, equipment jitter and environment interference can be effectively eliminated, so that the acquired data is more stable and reliable.

The invention discloses a wind turbine group wake flow control method based on radar wind measurement, and the method comprises the following steps: S1, radar wind measurement: installing laser wind measurement radars on cabins of an upstream wind turbine generator and a downstream wind turbine generator, and enabling the laser wind measurement radars to collect wind direction data in real time; and S2, multi-machine radar data fusion and whole wind flow field reconstruction are carried out, radar wind measurement data of upstream and downstream wind turbine generators are synchronously collected, a three-dimensional wind flow field of a wind direction area of the wind power plant is reconstructed, and the three-dimensional wind flow field outputs whole-field unified wind field indexes as wake flow model input data. The method has the advantage of being good in control effect, and solves the problems that when wake flow of an existing wind turbine group is controlled, self-adaptive adjustment is inconvenient to carry out on upstream and downstream wind turbine units according to radar wind measurement data, so that upstream and downstream fans are lack of cooperation, the wake flow control efficiency is affected, and the power generation benefits of the upstream and downstream fan groups are reduced.

According to the bionic butterfly and the flight control system thereof disclosed by the invention, intelligent flight of the bionic butterfly is realized in a breakthrough manner by fusing a high-precision radio navigation module and a multi-stage environment perception control system; based on polar coordinate positioning of VOR / DME signals and a course deviation dynamic correction algorithm, the navigation precision is improved to a navigation level standard; an original five-mode switching architecture (including a sudden strong wind emergency mechanism) realizes millisecond-level mode arbitration and wing-tail cooperative control through three-dimensional perception of real-time wind speed, wind direction and duration. The deep combination of bionic and engineering mechanics not only reproduces the wave type energy-saving flight path of the butterfly in the nature, but also greatly improves the wind resistance and reduces the energy consumption through the innovative designs of free falling obstacle avoidance, asymmetric flapping and the like.

The invention belongs to the technical field of multi-rotor unmanned aerial vehicle control, and particularly relates to a multi-rotor unmanned aerial vehicle self-adaptive feedforward control method based on a mesoscopic wind field model. Comprising the following steps: firstly, based on a relaxation time model of a lattice Boltzmann method, establishing a mapping relation between a Knudsen number and turbulence intensity and thermal noise intensity of a macroscopic wind field, and realizing parameterized representation of a mesoscopic wind field; then, constructing a mesoscopic wind field model by synthesizing a real-time wind field containing average wind, turbulent flow, gust and thermal noise components; then, calculating the wind resistance according to the predicted wind speed, designing a self-adaptive gain mechanism fusing a real-time tracking error, an error wind direction included angle and a historical error trend, and generating a dynamic feedforward control quantity; and finally, combining the feedforward control quantity with a feedback control quantity based on gravity compensation to form a comprehensive wind resistance control law. Starting from the mesoscale, the disturbance suppression capability and trajectory tracking precision of the unmanned aerial vehicle in complex environments such as strong wind and turbulent flow are enhanced.

The embodiment provides a wind turbine yaw-to-wind error calculation method and system based on goodness of fit, a terminal and a medium, and belongs to the technical field of wind power generation. The embodiment first preprocesses the original data to exclude the interference of variable pitch, shutdown, power limitation and the like on power generation; then performs interval data set division based on wind speed intervals and yaw angles; then uses a hypothesis testing method to check the power distribution of each wind speed interval sub-set, selects a corresponding fitting method through the actual distribution of each sub-set, and determines the yaw wind direction of the maximum power in the interval; and finally comprehensively considers the calculated yaw error angles in each wind speed interval. If the yaw error angles obtained in each wind speed interval have high consistency, and the goodness of the fitting result meets the preset requirement, it is considered that there is a significant yaw error angle in the wind farm, and the yaw error angle is calculated. The present application improves the accuracy of yaw error calculation.

The invention discloses a meteorological risk analysis early warning system based on meteorological data, and relates to the technical field of meteorological perception early warning, the meteorological data and structure operation data of an area where a power transmission tower is located are collected, wind direction abrupt change feature recognition is carried out to obtain a wind direction abrupt change rate V theta, a sand and dust invasion change index Esant is constructed, and the wind direction abrupt change rate V theta is obtained; the method comprises the following steps: performing sand-dust invasion state evaluation on a sand-dust invasion judgment threshold Ethresh, performing sand-dust impact response analysis when the sand-dust invasion judgment threshold Ethresh is evaluated as a sand-dust impact period, constructing an instantaneous shear wind pressure stress sigma sh, further obtaining a foundation erosion response coefficient Rfd in combination with a sand-dust invasion change index Esant, and performing coupling modeling on the instantaneous shear wind pressure stress sigma sh and the foundation erosion response coefficient Rfd to obtain a foundation erosion response coefficient Rfd. And the structural fatigue rate PLfat is obtained to evaluate the structural safety state of the power transmission tower, so that the response sensitivity of the early warning system to high-frequency sand and dust disturbance is improved, and safe and stable operation of power transmission facilities under extreme weather conditions is ensured.

The invention discloses a mooring unmanned aerial vehicle take-up and pay-off anti-interference control method based on image recognition, and relates to the technical field of unmanned aerial vehicle cable anti-interference control systems. Comprising the following steps: acquiring a wind speed, a wind direction, a cable direction, an actually measured tension, a winch speed and a cable image with aligned timestamps, and projecting the wind speed along the cable direction to obtain a wind disturbance component along the cable; inputting the actually measured tension, the winch speed and the wind disturbance component into a tension prediction model to output predicted tension at the next moment; comparing the predicted tension with a safety interval to adjust the take-up and pay-off speed of the winch; and in combination with the image, identifying the cable form, performing visual correction on the winding and unwinding speed of the winch through loosening eliminating logic and vibration suppression logic, and issuing the corrected winding and unwinding speed of the winch to execute and synchronize to the unmanned aerial vehicle so as to adaptively cooperate with the winding and unwinding action of the winch. Through tension prediction and multi-link evaluation adjustment, the problem that the unmanned aerial vehicle shakes and drifts in the air due to the fact that the tension is abnormal and the take-up and pay-off speed is not matched is effectively solved.

The invention relates to the technical field of sandstorm disaster monitoring, and relates to a sandstorm flow real-time monitoring method and system under regional environment characteristics. According to the method, the soil moisture content, the soil compactness and the vegetation coverage are collected, the initial dusting wind speed threshold value is determined in combination with the real-time wind direction and the historical log, the dusting wind speed threshold value is dynamically corrected through the earth surface wind resistance deviation coefficient, and the dusting wind speed threshold value is compared with the real-time wind speed collected in the to-be-detected area to judge whether the wind sand flowing risk exists or not. When the wind sand flowing risk exists, the collected surface sand layer height change data and the real-time wind speed are subjected to coupling analysis to determine the real-time wind sand index, so that prevention and control personnel visually master the wind sand damage degree, and meanwhile, the current wind sand severity level and the wind sand flowing change trend are determined according to the real-time wind sand index; the prevention and control work is changed from passive disaster response to active prevention in advance, time is won for reinforcing protection facilities in advance and storing prevention and control materials, and the effect of effectively reducing sand storm disaster loss is achieved.

The invention relates to the technical field of high-speed train pneumatic safety performance optimization, in particular to a high-speed train crosswind pneumatic safety control blowing device which comprises a blowing assembly and an adjusting assembly. The air blowing assembly comprises a plurality of air blowing pipes, an air pump and an air conveying pipe, the air blowing pipes are arranged on the tops of the two sides of the train in a sectional mode or close to the top of the train, and a plurality of air blowing holes are formed in the sides, away from the train, of the air blowing pipes at equal intervals in the axial direction. The air pump is installed on the train and connected with the air blowing pipe through the air conveying pipe. The adjusting assembly comprises a motor, a wind speed and direction sensor and a controller. The motor is in transmission connection with the air blowing pipe and used for adjusting the angle of the air blowing hole. The wind speed and direction sensor is installed on the roof of the train and used for monitoring the wind speed outside the train in real time. The controller is arranged in a safety guarantee system of the train, and the controller is connected with the motor, the wind speed and direction sensor and an inertial measurement unit of the train.

The invention provides an intelligent wind speed and direction prediction method and system for an unmanned aerial vehicle. The method comprises the steps of obtaining offline flight data of an unmanned aerial vehicle, performing preprocessing, obtaining offline flight optimization data, performing data primary selection and fine selection processing, obtaining an optimal flight feature set corresponding to a target wind speed component, performing model training, obtaining an offline wind speed prediction model, and transmitting the offline wind speed prediction model to a preset intelligent flight control system of the unmanned aerial vehicle. Obtaining a real-time wind speed predicted value and a real-time wind direction based on the real-time flight data of the unmanned aerial vehicle; according to the method, a three-dimensional target wind speed component differentiation modeling strategy is adopted, Pearson primary selection and RFECV fine selection are fused, and an optimal flight feature set is automatically configured for each target wind speed component, so that unmanned aerial vehicle intelligent prediction of real-time wind speed and real-time wind direction perception is realized, the system is used for realizing the method, and the system is suitable for large-scale popularization and application. The three traditional problems of large load of a physical sensor, strong model dependence and deep learning computing power bottleneck are overcome, and the precision and real-time performance of wind speed and wind direction prediction are ensured.

The invention discloses an offshore high-power wind power plant rapid prediction system and method based on wind resource refined evaluation and field network integration. The system comprises a wind resource fine evaluation module used for comprehensively considering wind speed, wind direction, turbulence intensity, atmospheric stability and marine environment parameters to obtain spatial and temporal distribution of wind energy resources; the high-power wind turbine comprehensive modeling module is used for establishing a wind power plant unit power output model in combination with the power curve and a Jensen wake flow model; the neural network prediction module is used for realizing rapid prediction of future power; and the field network integrated rapid solution and grid connection module combines the predicted power with the power grid constraint condition, and outputs the adjusted power curve and power grid operation characteristics by adopting a rapid solution and optimization method. According to the method, the impact of wind power fluctuation on the power grid can be effectively reduced, the wind power grid-connected friend performance and the power grid stability are improved, rapid solving and dispatching optimization of the generating capacity of the high-power wind power plant in the deep and far sea are achieved, and the method has high engineering application value.

The invention relates to the technical field of yaw strategy optimization, in particular to a wind power plant yaw strategy rolling optimization method based on model predictive control. The method comprises the following steps: acquiring real-time and historical wind power plant operation state data of each fan in a wind power plant at a current control moment, and preprocessing the wind power plant operation state data to form a state vector; performing short-term prediction on the wind direction and the wind speed in a future prediction time domain by using a support vector regression model based on the state vector, and generating a wind field evolution prediction sequence for control decision; and coupling the wind field evolution prediction sequence with the yaw angle variable, and constructing a wind power plant power prediction model in a prediction time domain. According to the method, the refined power prediction model integrating wind field short-term prediction, the fan space topological relation and yaw angle variable coupling is constructed, and the influence of yaw operation on the wake flow propagation path, the speed loss and the downstream fan inflow condition can be accurately described.

The invention provides a dynamic yaw error compensation method and system based on a cabin type wind lidar. The method comprises the following steps: firstly, acquiring three-dimensional wind field data at a preset distance in front of a wind turbine, simultaneously acquiring operation state parameters of the wind turbine, then extracting unstable transverse component and longitudinal velocity gradient distribution in an incoming wind flow direction, and then calculating a real-time yaw error amount caused by dynamic change of a wind field; and converting the real-time yaw error amount into a feed-forward compensation control signal by combining the dynamic response characteristic of the yaw system of the wind turbine, finally inputting the feed-forward compensation control signal into a main controller of the wind turbine, and adjusting an angle set value of the yaw system so as to complete yaw error compensation of the laser radar. According to the technical scheme, the alignment precision, the wind energy capturing efficiency and the operation stability of the wind generating set under the complex changing wind condition are improved, and high-precision and high-reliability compensation of the dynamic yaw error is achieved.

The utility model discloses a multifunctional deep sea environment simulation system, and relates to the technical field of deep sea engineering experiment devices. Comprising a wave flow experiment water tank which is a containing cavity capable of bearing water, and the top of the water tank is of an open structure; a deepwater experiment area, a transition experiment area and a shallow water experiment area are sequentially arranged in the water tank according to the wind direction; the fan array is used for providing a wind field with a preset condition required by an experiment above the water body; the flow guide array is used for guiding and controlling air generated by the fan array; the current generation array is used for generating current and completing circulation of the water body in the wave current experiment water tank through a backflow channel; the wave making array is used for generating waves with different periods and wave heights for the water body; and the wave absorbing plate is used for dissipating energy of the generated waves. According to the system, multi-factor combined action experiment simulation can be synchronously carried out, and the design precision is remarkably improved; the wind wave coupling effect on the structure is achieved, and the real sea wind wave environment is simulated.

The invention discloses a fire source positioning method and terminal based on an Internet of Things smoke detector, and relates to the technical field of fire source positioning, and the method comprises the steps: 1, collecting the detection data in a monitoring region in real time, and when the detection data meets a preset triggering condition, executing the step 2: starting a time-limited data collection window, step 2, verifying the rationality of the detection data by adopting an RANSAC algorithm, and filtering abnormal detection data; step 3, carrying out multi-modal wind direction estimation, and carrying out wind direction fusion calculation; 4, establishing a three-dimensional Gaussian plume model, simulating spatial distribution of smoke under the action of wind direction and turbulence diffusion, and obtaining a plurality of candidate fire source positioning points; multi-source data mutual verification and compensation are carried out through a multi-modal data fusion mechanism, system errors of a single data source are eliminated, data reliability is improved by matching with an enhanced RANSAC anomaly detection algorithm and an environment adaptive Gaussian plume model, and accurate positioning of a fire source when an indoor fire occurs is realized by matching with time control and confidence evaluation.