6570 results about "Wind power generator" patented technology

The invention provides a wind generating set fault monitoring method and system based on voiceprint recognition, and the method comprises the steps: collecting gear box voiceprint signal data in real time, recording signal fluctuation caused by gear surface wear, and obtaining an original signal data set containing a frequency spectrum high-frequency component enhancement feature; a time-frequency analysis method is adopted for the original signal data set, non-linear interference of vibration signals is recognized, multi-scale decomposition is carried out, initial wear frequency spectrum narrow-band characteristics and medium-term harmonic components are separated out, and a frequency spectrum component set is obtained; extracting characteristic parameters related to modulation depth abnormal fluctuation from the frequency spectrum component set, identifying a gear pair meshing frequency change rule, and determining a distribution mode of a tooth surface contact noise proportion; and if the feature matching result shows that the deviation between the spectrum high-frequency component diffusion distortion feature and the reference feature library exceeds a threshold value, adding the tooth surface fatigue crack noise feature into the feature library to obtain a target feature library.

The invention relates to the technical field of state monitoring of wind generating sets, and discloses a fault early warning and service life prediction method and system for a wind generating set, and the method comprises the steps: obtaining first state data, second state data and image data of a target wind generating set, and forming multi-dimensional data; fusing the multi-dimensional data by using a multi-modal fusion model to obtain multi-modal data fusion features of the target wind generating set; and performing fault early warning and / or life prediction on the target wind generating set based on the multi-modal data fusion features. By integrating the multi-modal data, the problem that fault features are difficult to comprehensively capture by a single data source is solved, fault early warning and service life prediction are performed by utilizing the multi-modal data fusion features, the false report and missing report rate of faults is reduced, accurate quantitative prediction of the remaining service life of the wind generating set is realized in combination with the data driving model, and the prediction efficiency is improved. By improving the accuracy of fault early warning and life prediction, the wind generating set is effectively operated and maintained in advance.

The invention discloses an intelligent fault detection system and method for a wind driven generator, and relates to the technical field of deep learning, and the method comprises the steps: collecting multi-dimensional data, and carrying out the preprocessing; extracting multi-dimensional features from the preprocessed multi-dimensional data, fusing the multi-dimensional features through a graph neural network and short-time Fourier transform, and generating a time-frequency domain signal; key features are extracted from the time-frequency domain signals, and fault features of the operation state of the wind driven generator are detected through a support vector machine model; based on the fault features, a fusion neural network model is constructed, fault prediction is generated, a diagnosis report is output, and a maintenance suggestion is provided; according to the method, the neural network model is fused, the convolutional neural network and the long-short-term memory network are combined, the spatial features and the time sequence data are extracted, and multi-dimensional characterization of the fault features of the wind driven generator is achieved.

The invention provides an equipment health management method and system for a wind generating set. The method belongs to the technical field of wind power generation, and comprises the following steps: acquiring multi-source data of a wind generating set, preprocessing the acquired multi-source data, and integrating the preprocessed multi-source data into a uniform data format by using a data fusion technology; based on domain knowledge and data characteristics, key features reflecting the health state of the equipment are extracted, and a feature subset with the highest prediction value for the health state of the equipment is screened out through a feature selection algorithm. Key components of the wind generating set are monitored in real time through a high-precision sensor network, and comprehensive collection of data is realized in combination with multi-source information such as a historical fault database and meteorological data.

The invention discloses a power equipment fault prediction method based on multi-modal data and related equipment, and belongs to the technical field of intelligent monitoring and fault prediction of power equipment, and the method comprises the steps: collecting and preprocessing the multi-modal operation data of the power equipment; extracting features of the preprocessed multi-modal operation data and performing global feature fusion to obtain multi-modal features; inputting the multi-modal features into a pre-trained fault prediction model to obtain a fault category and an occurrence probability of the power equipment; the fault prediction model is obtained by inputting the multi-modal features into a hybrid neural network for training. Multi-modal operation data of power equipment is collected, key features are extracted by using a deep learning model, multi-modal data fusion is performed, and a fault prediction model is constructed based on a hybrid neural network. Real-time fault early warning and maintenance suggestions are provided, the operation reliability of power equipment is improved, the unplanned shutdown risk is reduced, and the method is suitable for health management of transformer substations, high-voltage power transmission equipment and wind generating sets.

The invention relates to the field of mechanical detection, in particular to a fault diagnosis method for a wind driven generator based on a multi-modal large language model. According to the research, a GPT-3-based data set construction scheme is innovatively provided, and PDF-format mechanical fault related literatures are effectively converted into a structured high-quality question and answer pair data set. Then, fine adjustment is carried out on the base large language model by utilizing a data set, and the performance of the model in a professional task of mechanical fault diagnosis is successfully improved through fine adjustment parameter adjustment and exploration of a fine adjustment method. Besides, according to an actual use scene, a signal-to-text module is expanded, so that the signal-to-text module can receive multi-modal data of mechanical faults in real time, feature information contained in digital signals is changed into a text form and transmitted to a large language model module, and finally a diagnosis result is output by a large language model.

The invention relates to the technical field of fault diagnosis, in particular to a wind driven generator fault diagnosis method and system based on Mamba-ResNet. The method comprises the following steps: carrying out feature extraction and feature fusion by utilizing preprocessed data, namely constructing adaptive window short-time Fourier transform (AW-STFT) to carry out dynamic time-frequency resolution analysis, carrying out parallel feature extraction and constructing a multi-dimensional heterogeneous feature vector, and carrying out a cross-modal adaptive gating fusion mechanism based on a bidirectional cross gating unit; the method comprises the following steps: constructing a Mamba-ResNet hybrid deep network model architecture; performing model training on the constructed network model architecture; and performing fault diagnosis on the wind driven generator by using the trained model architecture. A tedious manual feature design process in a traditional method is avoided, and the automation level and adaptability of a diagnosis system are remarkably improved.

The invention belongs to the technical field of wind power generation, and discloses a wind driven generator multi-drive variable pitch control method based on big data, which comprises the following steps of: constructing a multi-source data acquisition network covering an environment, a unit, a power grid and a driver, and combining preprocessing means such as denoising and standardization and feature extraction methods such as principal component analysis; high-quality input is provided for a short-term prediction model; the model can accurately pre-judge wind conditions and power requirements in the future 10-30 minutes, and is matched with a global optimization algorithm to dynamically adjust a variable pitch strategy, so that the wind driven generator can still keep stable power output under complex working conditions of gust, power grid fluctuation and the like, and particularly has outstanding performance in areas with unstable wind resources. According to mechanical coupling characteristics of the multi-drive variable pitch system, mechanical parameters such as a gear transmission gap and shafting rigidity are fully included in global optimization, and power output, mechanical fatigue and load distribution are balanced through a multi-objective optimization algorithm.

The invention provides a gust-oriented wind generating set anti-overspeed control method and device and a medium, and belongs to the field of wind generating set control. The method comprises the following steps: acquiring real-time wind speed data and generator speed data; determining whether to enter a gust mode of the wind generating set according to the real-time wind speed data; under the condition that the wind generating set enters the gust mode, the minimum pitch angle of operation of the wind generating set is adjusted to be the minimum pitch angle of operation of the gust mode; calculating generator speed acceleration according to the generator speed data; determining whether to enter an acceleration anti-overspeed control strategy according to the generator rotating speed data and the generator rotating speed acceleration; and under the condition that it is determined that the acceleration anti-overspeed control strategy is entered, the preset variable-pitch rate is superposed on the normal variable-pitch rate. The pitch angle of the wind generating set in operation is increased when the wind generating set enters the gust mode, so that the attack angle margin of the blade can be kept in a safe range, and after the acceleration overspeed prevention control strategy is entered, the preset variable pitch rate is superposed on the normal variable pitch rate.

The invention discloses a wind driven generator oil leakage fault early warning method, which comprises the following steps: arranging vibration, temperature and oil sensors at a monitoring part, collecting multi-dimensional data for comprehensive analysis, analyzing a vibration signal by using a frequency spectrum to judge a mechanical fault, and combining a temperature trend and an oil parameter abnormal condition to realize early warning of an oil leakage fault of a wind driven generator. And carrying out classified early warning on the oil leakage fault by adopting a support vector machine algorithm. Furthermore, a Bayesian network algorithm is used to carry out accurate reasoning on the oil leakage position. According to the invention, the early warning result and the oil leakage position are uploaded to the monitoring center, and the processing suggestion is generated, so that intelligent and precise early warning of the oil leakage fault of the wind driven generator is realized. According to the multi-sensor fusion method, the accuracy and timeliness of oil leakage fault diagnosis are remarkably improved, the equipment loss and the maintenance cost are effectively reduced, and the method has important significance for guaranteeing safe and stable operation of the wind driven generator.

The invention relates to the technical field of offshore wind field operation and maintenance, and provides multi-modal and multi-source data fused deep learning fault diagnosis, which is characterized in that multi-modal data and multi-source data are acquired, cleaning work, unit conversion and format conversion work are carried out on the data, the data are integrated, and the fault diagnosis efficiency is improved. The time domain features, the frequency domain features, the time frequency features and the text features are taken as basic features to perform data feature extraction work, under the assistance of reducing feature dimension work, the calculation complexity is reduced, useful information is kept, an original model is generated through fusion, the original model is trained and optimized by using a deep learning model, and the calculation efficiency is improved. And fault diagnosis of offshore wind field operation and maintenance is completed for the fault diagnosis model. Multi-modal and multi-source heterogeneous data are combined, various information sources in a wind driven generator system are fully utilized, more accurate and comprehensive diagnosis services are provided, the accuracy and reliability of fault diagnosis are improved, the robustness of the system is enhanced, and therefore stable operation and efficient maintenance of the wind driven generator are guaranteed.

The invention discloses a matrix yaw system of a wind power plant. The matrix yaw system comprises a data acquisition module, a wind plant modeling module, a parameter extraction module and a yaw control module. The data acquisition module acquires a wind field original data set containing three-dimensional space coordinates and timestamps by using a multi-source sensor; the wind field modeling module calculates correlation between points by combining a covariance function through a Gaussian process regression algorithm, and constructs a three-dimensional dynamic wind field model; the parameter extraction module is used for extracting wind regime parameter vectors in the model by adopting a nearest neighbor interpolation algorithm on the basis of actual space coordinates of a fan; and the yaw control module utilizes a depth deterministic strategy gradient reinforcement learning model to generate a yaw angle adjustment instruction in combination with the wind regime parameter vector, the current yaw state of the fan and a preset maximum cumulative reward function. Through multi-module cooperation and intelligent algorithm optimization, accurate matching of wind field dynamic modeling and yaw control is achieved, and the energy efficiency and operation stability of the wind generating set are improved.

The floating type offshore wind turbine based on the tuned mass damper comprises a floating type foundation and a wind generating set, the wind generating set comprises a stand column, and the tuned mass damper is arranged in the stand column; the floating foundation comprises a buoy and a mounting platform, a base plate is slidably connected into the buoy, one end of the base plate is connected with the buoy through a damper spring, and the other end of the base plate is connected with the mounting platform through a supporting column. A submersible pump is arranged on the surface, close to the buoy, of the mounting platform, communicates with seawater through a first pipeline and communicates with the buoy through a second pipeline. And the buoys are distributed in a circumferential array along the mounting platform. The wind-resistant strength is improved, the cost is low, resetting is fast, the resetting effect is good, and offshore wind energy resources can be better utilized; the tuned mass damper is installed, vibration is reduced through compression and reset of the damping spring, and safety is improved; an air speed sensor is arranged, offshore air speed and air volume can be monitored, and the controller can conveniently control whether seawater is extracted or not.

The invention relates to the technical field of power system operation and control, and discloses a primary and secondary frequency modulation cooperative control system of a hybrid energy storage coupling wind generating set, which comprises a wind power generation unit, a hybrid energy storage unit and a central cooperative controller. A total power demand including inertia response and primary and secondary frequency modulation is synthesized, a primary and secondary frequency modulation cooperative distribution module generates a dynamic adjustment weight by using an S-type nonlinear function based on a frequency change rate and frequency deviation coupling relationship, and an energy relay compensation mechanism is introduced to fill a power gap in a switching process. And the SOC adaptive constraint and execution module applies direction selective boundary constraint to the state of charge, and controls hybrid energy storage to perform internal energy self-balancing in a frequency modulation dead zone. According to the invention, seamless connection of multi-time scale frequency modulation is realized, frequency secondary drop is effectively prevented, and the frequency support capability and the self-recovery capability of the system are improved.

The invention discloses a wind-solar complementary intelligent charging system and heat management optimization. The system comprises a wind power generation unit, a photovoltaic power generation unit, an energy storage unit, an intelligent control unit and a heat management unit, the wind power generation unit adopts an efficient wind generating set, is provided with an intelligent control system, and can automatically adjust a pitch angle and a yaw angle according to real-time wind speed and wind direction; the photovoltaic power generation unit selects a high-efficiency photovoltaic cell assembly and tracks the position of the sun in real time in combination with an intelligent light following system; and the energy storage unit adopts a high-performance energy storage battery pack and a battery management system. Through accurate prediction and analysis of local wind and light resources, dynamic configuration and cooperative power generation of the wind power generation unit and the photovoltaic power generation unit are realized. Compared with a traditional fixed configuration mode, local wind and light resources can be fully utilized, and the power generation efficiency is improved. Meanwhile, the multi-energy complementary cooperative power generation technology is adopted, organic combination of wind and light power generation and energy storage is achieved, and stable power supply of the system is ensured.

We provide a lubricating oil filtering and cooling device for a gearbox of a wind turbine generator. [Solution] The cooling assembly includes a circulation box (100) to which an oil discharge pipe communicating with the oil discharge port of the gearbox is attached, an oil pump is attached at its uppermost end, and an oil supply pipe (103) communicating with the oil supply port of the gearbox is connected at its outlet end, a refrigeration pipe (201) and a guide pipe (202) attached within the circulation box, an inner box (203) fixedly connected within the circulation box, and an equalizing member (204) provided within the circulation box, wherein both ends of the guide pipe are respectively connected to the oil pump and the inner box, and the inside of the circulation box is filled with cooling liquid, a filter fixedly connected within the inner box, a cleaning member provided above the filter, and a drive member movably connected to the circulation box.

The invention relates to the technical field of intelligent power distribution, in particular to an optical storage and charging integrated system based on a distributed soft bus, which comprises hardware equipment: a photovoltaic module, a wind driven generator, an energy storage module, a charging pile, acquisition equipment, a photovoltaic controller, a DCDC module and a mains supply / thermoelectric generator; communication and control: a distributed soft bus, a time sensitive network (TSN) and an intelligent control unit (EMS); according to the scheme, photovoltaic, wind power, energy storage, charging piles and other hardware equipment are connected through a distributed soft bus, real-time transmission of control instructions is achieved through a TSN, data are analyzed through intelligent algorithms such as a charging and discharging model and a multi-task learning model, an optimization strategy is generated, multi-energy cooperative control (including photovoltaic direct supply and energy recovery in the case of faults) is executed through an EMS, and the energy efficiency is improved. A user can set priorities through a terminal to participate in scheduling, and finally model parameters are corrected based on feedback data to form a light storage and charging integrated system of data acquisition, prediction optimization, intelligent control, user interaction and closed-loop evolution.

The invention relates to the technical field of wind power prediction, and particularly provides a generation power prediction method and system for a wind generating set, and the method comprises the steps: firstly obtaining a continuous operation data sequence of equipment state parameters and environment parameters containing timestamp marks, and then carrying out the time sequence feature analysis, generating an equipment state feature sequence and an environmental condition feature sequence, then performing correlation modeling on the two features through feature collaborative analysis operation to obtain a coupling feature sequence reflecting multi-factor collaborative influence, and inputting the coupling feature sequence into a pre-trained power prediction model to obtain a power prediction result; an initial power prediction sequence of a target time period is generated through time context learning and power value mapping operation, finally, an error calibration model is constructed based on historical data, the initial power prediction sequence is dynamically adjusted, and a calibration power prediction sequence is generated and output to a power dispatching system for power generation plan arrangement. And the accuracy of generating power prediction of the wind generating set is effectively improved.

The invention discloses a wind driven generator anomaly detection method and system based on SCADA periodic data. The method comprises the steps of obtaining operation data collected by an SCADA system of a wind driven generator in actual operation; according to the method, a normal data set is constructed besides the abnormal data of SCADA alarm, and the abnormal data collected by an SCADA system comprises a periodic type, a stable type and an irregular fluctuation type; performing preliminary labeling on three types of data in the abnormal data; performing data pattern recognition on the obtained data by using a neural network model to recognize periodic data; performing feature engineering processing on the data of which the output result is periodic, and extracting corresponding data features; enhancing the features in a mode of calculating abnormal scores by using an isolated forest; and performing anomaly classification on the feature vectors formed in the time window by using a machine learning model, and detecting abnormal data in the periodic data. According to the method, the accuracy of abnormal data detection of the wind driven generator can be improved.

The invention discloses a semi-submersible wave energy-wind energy integrated power generation platform and a damping regulation and control method of a damping regulation and control system of the semi-submersible wave energy-wind energy integrated power generation platform. The platform comprises a semi-submersible wind driven generator platform, a wind driven generator set, a wave energy power generation device, an energy storage system, a sea area detection system and the damping regulation and control system. The wave energy power generation devices are divided into two groups on the inner side and the outer side of the platform, each wave energy power generation device comprises a platform inner side wave energy power generation device and a platform outer side wave energy power generation device, and each wave energy power generation device comprises a swing arm floater, a universal hinge, a connecting arm and a damping adjusting module. The semi-submersible wind power generation device and the wave power generation device are combined, and offshore renewable resources are comprehensively utilized; the six-buoy design is adopted, buoyancy is dispersed, stability and safety are improved, and meanwhile space is provided for a wave power generation device; the floaters are symmetrically arranged in a hexagonal shape and carry a damping regulation and control system, and the whole structure is more easily stabilized by adjusting the damping of the floaters in all directions.

The invention provides a wind driven generator blade icing monitoring and deicing control system and method, and the system comprises a noise collection unit, a temperature collection unit, an actual calculation unit, a deicing scheme generation unit, and a deicing scheme selection unit. The operation parameters of the wind generating set are monitored and analyzed by combining with the operation parameters of the wind generating set, including wind speed, air temperature, humidity, blade rotating speed, power generation power and the like, and meanwhile, real-time monitoring, acquisition, processing and storage are carried out through a monitoring system and a sensor. Operation parameters and environment data of the wind generating set are monitored, collected, processed and stored, monitoring, alarming and analyzing of the icing condition of blades are achieved, the optimal deicing strategy and time arrangement are determined, the deicing effect is improved to the maximum extent, the energy consumption of the set is reduced, the power generation efficiency and economical efficiency of the set are improved, and the economic benefit is increased. The purposes of reliable, efficient and economical operation and maintenance of the wind generating set are achieved.

The invention relates to a wind power off-grid hydrogen production system and a control method thereof. The system comprises a wind driven generator, a black start power supply, an energy storage converter, a machine side converter, a grid side converter, a direct current bus capacitor, a first transformer, a second transformer, a third transformer, an AC-DC converter, a DC-DC converter, an electrolytic bath, a first switch, a second switch, a third switch and a fourth switch. The system comprises a black start mode, a normal operation mode and an emergency operation mode. In the black-start mode, the system is started from a closed state to an operating state through a black-start power supply; in the normal operation mode, the system power balance is maintained by adjusting the electrolysis power; in the emergency operation mode, the system power balance is maintained by adjusting the output power of the wind driven generator; according to the system, the flexible adjusting capacity of the load of the electrolytic cell can be fully explored, an energy storage device except a black-start power supply does not need to be additionally arranged, and wind power off-grid hydrogen production without energy storage can be achieved.

The invention discloses a dynamic monitoring method and system for settlement and inclination of a tower drum of a wind generating set, and relates to the technical field of safety monitoring of wind power generation infrastructure, the method comprises the following steps: synchronously collecting inclination data and positioning data of each monitoring point, and carrying out space-time alignment to obtain a fusion data set; decoupling the dynamic elastic response of the tower drum and the steady-state deformation of the foundation from the fused data through frequency domain analysis, and further calculating the overall inclination rate, the settlement amount and the non-uniform settlement rate of the tower drum; performing safety assessment based on the calculated parameters and a preset safety threshold, and determining a basic safety state and a risk mode; and finally, generating graded early warning information and decision information including risk positioning and trend prediction according to an evaluation result. Through the above mode, the method achieves the precise separation and monitoring of the dynamic deformation characteristics of the tower drum, improves the accuracy of safety evaluation and the timeliness of early warning, and provides an effective data support for the operation and maintenance decision of a wind generating set.

The invention relates to the technical field of parameter simulation, in particular to a parameterized simulation model design method based on a standard part structure. The method comprises the following steps: collecting bolt connection data of standard components in the wind generating set; blade surface vibration feature information is extracted according to the standard component bolt connection data; acquiring stress concentration distribution data at the joint based on the blade surface vibration characteristic information; calculating a local deformation accumulated value of the standard part according to the stress concentration distribution data at the joint; measuring the friction coefficient variation trend of the contact surface based on the local deformation accumulated value of the standard component; deducing pre-tightening force attenuation characteristics according to the change trend of the friction coefficient of the contact surface; and constructing a standard part fatigue damage prediction model based on the pre-tightening force attenuation characteristics. According to the method, the vibration characteristics of the blade surface are collected, the stress concentration distribution at the bolt connection position is indirectly extracted, and the bottleneck that the internal connection state is difficult to directly sense is broken through.

The invention provides a fan blade defect detection method based on a residual feature fusion network, and the method comprises the steps: 1, obtaining a multi-target data set and a multi-classification data set of a blade defect of a wind driven generator, and dividing the multi-target data set and the multi-classification data set into a training set, a verification set and a test set; step 2, building a wind driven generator blade defect detection model based on a residual feature fusion network RFFNet; 3, inputting the wind driven generator blade defect images in the training set and the verification set into a wind driven generator blade defect detection model for training to obtain an optimal parameter model; and step 4, inputting the test set into the optimal parameter model trained in the step 3, and outputting an accurate identification graph of the wind driven generator blade defect image. The network provided by the invention has remarkable advantages in the aspects of detecting the defects of the blades of the wind driven generator, improving false detection and missing detection and the like, effectively solves the problems existing in complex environments and multi-target defect detection, and has relatively high detection speed.

The invention discloses a wind driven generator blade detection method, system and device and a storage medium. First point cloud data of a target blade are acquired through a laser radar carried by an unmanned aerial vehicle; performing plane fitting and grid map conversion on the first point cloud data to obtain a fitting plane and a grid map; scanning and querying the grid map to obtain a plurality of blade occupation points, and clustering the blade occupation points to obtain blade positions; determining a hub position according to the blade position, and obtaining a blade angle according to the hub position and the blade position; and updating the orbital motion trajectory of the unmanned aerial vehicle according to the fitting plane and the hub position, and returning to the step of obtaining the first point cloud data of the target blade through the laser radar carried by the unmanned aerial vehicle until the currently obtained blade angle and the blade angle obtained in the last round meet a preset convergence condition. The method improves the accuracy of wind driven generator blade angle detection, also improves the quality of wind driven generator blade images, and can be applied to the technical field of machine vision.

The invention provides an active-disturbance-rejection full-wind-speed power smoothing method based on kinetic energy of a fan rotor, and belongs to the technical field of wind power control. The technical problem that the output power of a traditional wind turbine generator is unstable due to random fluctuation of the wind speed is solved. According to the technical scheme, the method comprises the following steps that S1, rotor kinetic energy is calculated based on a wind turbine kinetic model, and the effect of the rotor kinetic energy in power fluctuation stabilization is analyzed; s2, designing a first-order linear active disturbance rejection controller for rotating speed loop control; s3, designing a second-order linear active disturbance rejection controller for variable pitch control; and S4, dynamically generating an active power reference value by combining the kinetic energy of the rotor and an active disturbance rejection control technology, and controlling by coordinating a rotating speed ring and a variable pitch. The method has the beneficial effects that power fluctuation can be remarkably reduced under wind speed fluctuation and complex working conditions, the smoothness of power output is improved, meanwhile, the anti-interference capability and the dynamic stability of the system are enhanced, and efficient and stable operation of the wind driven generator system is ensured.

The invention discloses a wind generating set fault prediction method and system based on edge computing, and the method comprises the steps: collecting the vibration, temperature, rotating speed and other parameters of a wind generating set in real time through a data collection module deployed by edge computing equipment, carrying out the preliminary screening and packaging through a built-in data processing unit, and synchronizing to a constructed digital twinborn model; establishing an edge calculation fault analysis model based on model data, identifying potential fault features, comparing a physical unit operation state with a digital twin model simulation state parameter by parameter, performing preliminary diagnosis in combination with the fault features, evaluating a unit health state according to a fault mechanism model, and outputting a fault prediction result and part information. The system is correspondingly provided with six units including a data acquisition interaction unit, a data processing unit and a model construction updating unit, and all the units work cooperatively, so that accurate prediction and health management of faults of the wind generating set are realized.

The invention provides a variable-speed variable-pitch wind generating set torque control method and system and a computer readable medium, and the method comprises the steps: designing a rolling optimization torque and variable pitch instruction based on model predictive control (MPC) and sliding mode surface constraint on the basis of constructing a discretization model comprising a pneumatic mechanism, a transmission chain and a variable pitch mechanism; the robustness is enhanced in combination with the self-adaptive sliding mode surface design, the control gain is dynamically adjusted through fuzzy logic, and the power tracking precision and the mechanical loss suppression are further considered; meanwhile, the delay is reduced with the assistance of wind speed prediction feed-forward compensation, the method has high robustness and adaptive capacity, wind speed fluctuation and parameter time variation can be effectively handled, equipment is protected while the power generation efficiency is guaranteed, and an efficient and reliable solution is provided for wind power generation control.

The invention discloses a full life cycle test method, system and equipment for a variable pitch system of a wind generating set, and the method comprises the steps: constructing a digital twin model of the variable pitch system, and simulating the performance of the variable pitch system under an extreme working condition through the digital twin model; deploying a self-adaptive nano sensor network, and testing and optimizing the variable pitch system; carrying out remote installation and calibration guidance on the tested and optimized variable pitch system by utilizing an augmented reality technology; through an autonomous learning monitoring mechanism, all-weather monitoring is carried out on the pitch system which is operated after installation and calibration, and potential problems and damage in operation of the pitch system are predicted; based on a biological inspiration self-repairing technology, repairing potential damage of the variable pitch system; the method comprises the following steps: acquiring operation data of a variable pitch system, proposing an upgrading scheme for the variable pitch system by virtue of an expert diagnosis mechanism, and verifying the upgrading scheme through a digital twinborn model; and an intelligent decommissioning evaluation platform is established, the residual life of the variable pitch system is evaluated, and a recovery scheme is formulated.