5179results about "Wind motor control" patented technology

A wind energy plant with a nacelle, a rotor, which features at least one rotor blade adjustable in its blade pitch angle, a central control device for controlling the wind energy plant and a control unit disposed in the rotor for controlling the blade pitch angle of the at least one rotor blade, wherein the central control device and the control unit in the rotor can exchange data with each other via a data link, which comprises at least one first sending and receiving device at the nacelle side and at least one second sending and receiving device at the rotor side, wherein a wireless network connection with a safety-oriented communication protocol is provided between the at least one first sending and receiving device and the at least one second sending and receiving device, and that one monitoring- and communication device at a time is associated to the first and / or the second sending and receiving device, which can monitor the function of the sending and receiving device and initiate a predetermined action in the case of an error.

The invention discloses an intelligent wind power plant fan monitoring system and method based on machine learning and digital twinning, and relates to the technical field of fan monitoring. Based on the dynamic probability digital twinning technology, the multi-dimensional operation state model of the fan is constructed in real time, and the state distribution and uncertainty quantification result is dynamically updated, so that the real-time operation state of the fan is accurately reflected, and the flexibility and accuracy of state modeling are improved; the machine learning module realizes accurate prediction of key operation parameters through a physical constraint neural network and a deep operation network, and evaluates potential fault risks, so that the equipment fault rate is effectively reduced; the monitoring module can visually display the running state of the fan and calibrate a fault area by using a real-time visualization technology, so that an abnormal problem can be conveniently and quickly positioned; the intelligent optimization control module is based on the deep reinforcement learning technology, dynamic adjustment of fan operation parameters is achieved, the power generation efficiency of the fan is improved, and meanwhile the operation stability and economical efficiency are optimized.

The invention relates to the technical field of wind turbine generator data analysis, and discloses an offshore wind turbine generator operation data analysis and prediction system. The system comprises a marine environment data integration module for collecting data to generate a multi-source time-space synchronization data set; the multi-modal feature fusion module is used for extracting cross-modal correlation features to generate a high-dimensional fusion feature tensor; the dynamic fault prediction module is used for constructing a two-way gating circulation network model to predict the degradation probability and the residual life of key components of the equipment; and the self-adaptive optimization control module is used for constructing a multi-target dynamic programming model to optimize a fan operation strategy. In addition, the system is further provided with a feedback correction module for correcting prediction model parameters, and a virtual sensor module based on a physical information neural network is used for monitoring tower stress and diagnosing sensor faults. According to the system, comprehensive monitoring, accurate fault prediction and optimal control of the offshore wind turbine generator are realized, the operation efficiency, reliability and safety of the wind turbine generator are effectively improved, and the operation and maintenance cost is reduced.

The invention relates to the technical field of wind turbine generator control, and discloses a wind turbine generator control optimization method based on dynamic changes of wind speed and wind direction. According to the method, real-time wind speed time sequence data and three-dimensional wind direction vector field data of a target wind field are received, a wind field dynamic analysis model is constructed by using a space-time convolutional neural network, and a wind field energy density distribution matrix and a turbulence intensity probability graph are generated. And constructing a multi-target adaptive optimization model on the basis, generating a unit control parameter instruction set, optimizing a cooperative adjustment coefficient according to a preset unit load-power generation efficiency balance equation, and outputting an optimal control action sequence to a wind turbine generator master control system through Bayesian optimization framework iterative updating. The method can accurately sense the wind field change, effectively balance the unit load and power generation efficiency, realize multi-unit cooperative control, improve the wind energy capture efficiency, and improve the operation stability and economic benefits of the wind turbine generator.

The invention discloses a pitch angle dynamic compensation method based on multi-radar space-time synchronization, and relates to the technical field of wind power generation. In order to solve the problem that the pitch angle control precision is insufficient due to the fact that three-dimensional wind field space-time characteristics cannot be captured in traditional single-point wind speed measurement, a wind wheel plane three-dimensional space-time speed field is synchronously constructed through a horizontal-vertical radar array, and blade segmented aerodynamic load distribution is calculated in real time in combination with a flow field-structure coupling model; establishing a vorticity transport equation under a rotating coordinate system based on a vorticity-shearing force transfer function, mapping dynamic load deviation through a neural network to generate a reference variable pitch instruction, and fusing the tower shadow interference feature vector to perform pre-compensation; a quadratic programming algorithm is adopted to resist saturation optimization, so that the variable pitch rate strictly follows a dynamic constraint curve of a hydraulic system. According to the method, the problem of aerodynamic load phase delay is effectively solved, periodic load fluctuation caused by the tower shadow effect is reduced, and technical guarantee is provided for stable operation of a large wind turbine generator under the complex wind condition.

The invention discloses a fan operation real-time dynamic adjustment control method and system based on environmental data. The method comprises the steps that multi-dimensional dynamic parameters of the environment where a fan is located are collected in real time based on a distributed sensor network; constructing a dynamic coupling prediction model, inputting the dynamic coupling prediction model into a bidirectional verification module based on multi-dimensional dynamic parameters, and obtaining an environment state prediction matrix in a future time window; based on the environment state prediction matrix, a combined adjustment instruction set of the fan pitch angle, the yaw angle and the generator torque is obtained, and the instruction set comprises main strategy parameters and an emergency tolerance interval; based on an incremental learning mechanism, model parameters are dynamically updated according to the deviation between actual environment parameters and predicted values, and the updating period is adaptively adjusted along with the change of turbulence intensity. The method has the advantages that accurate prediction and optimal control of the fan in a complex environment are achieved, the limitation that a traditional data driving model lacks physical interpretability is broken through, and the running stability and efficiency of the fan are 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 discloses a wind power generation system operation optimization method based on intelligent control, and the method comprises the following steps: S1, collecting the real-time data during the operation of a wind power generation system, including the wind speed, the wind direction, the rotating speed of a generator, the output power, and the environment temperature; s2, constructing a performance model of the wind power generation system according to the real-time data; s3, based on the performance model, adjusting the operation parameters of the generator in real time to optimize the power generation efficiency of the system; and S4, when detecting that the system is abnormal, automatically triggering a fault diagnosis module, generating a fault processing suggestion and executing an optimization strategy. Through real-time data acquisition, performance model construction, operation parameter dynamic adjustment and intelligent fault diagnosis, the problems that a traditional wind power generation system is low in efficiency and not timely in fault response are solved, and the system has the advantages of dynamically optimizing power generation efficiency, timely processing system abnormity and improving operation stability.

The invention relates to a wind power plant yaw control method based on a wind process. The method comprises the following steps: 1, establishing a wind power plant simulation model; 2, acquiring operation state data of the wind power plant as an initial state of the wind power plant simulation model; 3, acquiring a wind speed fluctuation sequence and a wind direction fluctuation sequence of the wind power plant as the state of the wind power plant yaw control model; 4, performing off-line learning to obtain optimal yaw angle set values under different wind regime sequences; enabling the wind power plant yaw control model to observe the environment and guide a wind turbine generator to act, and completing online learning; the wind power plant yaw control model is established, the optimal yaw angle of each wind turbine generator in the wind power plant is obtained through the wind speed sequence and the wind direction sequence, and the total generated power of the wind power plant is greatly improved; and the timeliness requirement of solving the wind power plant yaw control model in practical engineering is met.

The invention relates to the technical field of wind power generation control, in particular to a wind turbine generator wake flow optimization cooperative control system and method. According to the technical scheme, the wind turbine generator wake flow optimization cooperative control system comprises a feedforward LiDAR array which is deployed at the upstream 1-2 km of the prevailing wind direction of a wind power plant and used for collecting upstream three-dimensional wind field data in real time; the unit embedded sensor group comprises an ultrasonic anemograph and an inertial measurement unit IMU which are arranged in a cabin of each wind turbine unit, and the sampling frequency is not lower than 20Hz; the data fusion module is used for performing space-time alignment and noise filtering on the data of the LiDAR array and the unit embedded sensor through a convolutional neural network (CNN) and a Kalman filtering algorithm to generate a dynamic wind field digital twinborn model; and the LSTM wind field predictor predicts the wind speed and wind direction change trend in the future 30 seconds based on the dynamic wind field digital twinborn model. Through multi-source sensing fusion and dynamic game optimization, the operation efficiency and safety of the wind power plant under the dynamic wind condition are remarkably improved.

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 invention provides a wind power plant unit state monitoring and fault early warning system and method based on deep learning, and belongs to the field of wind power generation and artificial intelligence. According to the system, a cloud edge collaborative architecture is adopted, an edge computing terminal operates a data-driven space-time prediction model and a physical digital twinborn model in parallel, and abnormity is preliminarily screened by calculating a double-track residual error and comparing the double-track residual error with a dynamic early warning threshold value. And when an exception occurs, the cloud platform receives multi-modal data including a sensor, a model state and an operation and maintenance text, performs deep root cause analysis by using a diagnosis model fused with a wind power fault knowledge graph, and generates an interpretable diagnosis report. According to the method, deep fusion of data and a physical model is realized, and the accuracy of fault monitoring, the interpretability of diagnosis and the intelligent level of operation and maintenance decision are remarkably improved through a data-physical double-track driving mode.

The invention relates to the technical field of wind energy control systems, and discloses a wind power energy-saving control method and system based on big data analysis, and the system comprises a data collection and transmission layer, a data storage and management layer, a data analysis and prediction layer, an intelligent control layer, and an equipment full life cycle management layer. The data acquisition and transmission layer further comprises multi-source data acquisition system construction, edge computing data preprocessing and data transmission network construction. According to the intelligent control strategy, by means of a distributed cooperative control algorithm and an intelligent variable-pitch variable-speed control algorithm, the fan blade angle, the rotating speed and the power output are dynamically adjusted according to the real-time meteorological data, the power grid load requirement and the fan running state, wind power output is smoother through accurate regulation and control, and the wind power output efficiency is improved. The impact on the power grid caused by power fluctuation is effectively reduced, the wind power acceptance capability of the power grid is remarkably improved, and the stability and reliability of the operation of the power grid are guaranteed.

The invention relates to the technical field of fan blade deicing, and discloses an intelligent temperature control type fan blade electric heating deicing control system and method, and the system comprises a meteorological sensing module which is used for collecting the environment temperature, humidity, wind speed and rainfall type; the blade state monitoring module is used for acquiring blade surface temperature distribution and ice layer thickness data; the dynamic decision-making module is used for generating a partition heating instruction based on the data of the weather sensing module and the blade state monitoring module; the temperature control execution module is used for converting the instruction into a gradient signal; the heating array module is used for executing electric heating deicing operation in the blade surface subareas; and the energy coordination module is used for dynamically constraining the total power of the system. The meteorological sensing module and the blade state monitoring module are integrated through the system, real-time collection and fusion of multi-source data of blade surrounding environment parameters and the thickness of an ice layer are achieved through the design, comprehensiveness and accuracy of icing monitoring are improved, a reliable basis is provided for dynamic decision making, and therefore the response capacity and early warning precision of the system to icing risks are improved.

The invention relates to the field of wind turbine generator control, in particular to a wind turbine generator operation control optimization method and system based on deep learning, and the method comprises the steps: obtaining the vibration, bearing temperature and generator winding temperature of a wind turbine generator, and the wind speed, wind direction and air density of the wind turbine generator; inputting all acquired data into a control parameter acquisition model, and outputting an ideal control parameter set of the current wind turbine generator; calculating an accurate rated wind speed; adjusting the control frequency based on the wind speed and the wind direction; each control parameter is adjusted; the wind turbine generator operation control optimization system comprises a data monitoring module, a control parameter acquisition module, a control frequency adjustment module and a pitch angle adjustment module. The method has the advantages that limitation of a traditional single-machine control strategy is broken through, especially when the wind speed is lower than the rated wind speed, the ideal increment of the pitch angle is actively adjusted, and therefore the total generating capacity of the wind power plant is improved.

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.

A method for optimizing wind farm electric power generation using multivariable system identification, which reduces energy loss caused by the wind turbine wake effect by optimizing and adjusting the yaw angle and axial induction factor of each wind turbine, so as to maximize the total power generation of the wind farm. According to the method, accurate optimization gradient estimates are obtained by using the experimental data and the identification model, and then a gradient-ascend optimization with large initial step size and variable step size iteration is carried out, so that the problems of large model errors and of high computation cost of mechanism modeling are avoided. The method is easy to implement and has a high modeling accuracy and efficiency, and thus is suitable for solving the electric power generation optimization problem of large-scale real-life wind farms.

The invention discloses a wind turbine generator intelligent control system and method based on rotation laser radar and artificial intelligence fusion. The wind turbine generator intelligent control system comprises a dynamic scanning monitoring module which can carry out three-dimensional point cloud data collection on the azimuth angle range of 0-300 m and + / -90 degrees in front of a generator; the space-time feature processing module comprises a voxel grid denoising unit, a coordinate conversion unit and a space-time convolutional neural network ST-CNN; and a hybrid optimization control module. A variable-speed rotating multi-line laser radar is adopted, a dynamic scanning strategy is combined, and accurate monitoring of a large-range three-dimensional wind field in front of a unit is achieved. A space-time convolutional neural network ST-CNN is constructed, spatial features and time sequence information are creatively fused, and future wind conditions are accurately predicted. A hybrid control algorithm combining model predictive control MPC and deep reinforcement learning DRL is proposed, and control parameters are dynamically generated by taking power generation efficiency and unit load as optimization targets. From hardware deployment to software configuration, a complete and highly integrated intelligent control system is formed.

The invention discloses a wind and light storage coordination control method and system for a virtual synchronous machine, and the method comprises the steps: building a wind power plant-energy storage power station double-layer response mechanism at a first stage, and employing a multi-time scale coordination control strategy for a second-stage energy storage system; and in combination with the virtual synchronous control characteristics of the optical storage power station, establishing a multi-scene optimization model of a wind and light storage multi-source collaborative double-layer coupling mechanism, and realizing dynamic optimization of output of each unit among inertia support, frequency modulation capacity and energy storage health state. According to the method, wind power plant rotor kinetic energy release combined overspeed load shedding control, a fuzzy control means based on an energy storage charge state and a VSG control means based on a virtual inertia and damping coefficient self-adaption photovoltaic system are considered, then wind and light energy storage frequency modulation coordination is optimized, and the system inertia safety is effectively improved on various working conditions and different time scales.

Disclosed in the present invention are an anti-icing method and system for blades of a wind turbine generator set. The method comprises: establishing an icing prediction model; collecting in real time meteorological data of a wind turbine generator set, and collecting icing state data of blades of the wind turbine generator set; performing calculation on the basis of the meteorological data collected in real time and the icing prediction model, and on the basis of a calculation result, predetermining whether an icing condition is currently met; if the icing condition is currently met, heating the blades, and performing real-time calculation on the collected data, and when a non-icing condition is met, stopping heating; on the basis of the currently collected icing state data, determining whether an icing state is reached; and if the icing state is reached, heating the blades, and when the collected icing state data has met the non-icing condition, stopping heating. The method can achieve an effect of preventing icing in advance, data of blades can be monitored in real time, and repeated icing caused by meteorological changes can be prevented, thereby ensuring the normal operation of a wind turbine generator set.

The invention provides a self-adaptive variable-pitch control method and system based on turbulence intensity dynamic compensation, and relates to the field of wind power generation. Firstly, a real-time turbulence data set in the operation environment of a wind turbine generator is obtained, and the real-time turbulence data set comprises multi-dimensional turbulence characteristic units which are continuously collected and provided with time sequence marks; performing feature extraction on the real-time turbulence data set to obtain turbulence feature distribution containing spatial distribution features and dynamic change features, then generating a dynamic compensation strategy containing a related compensation parameter set according to the turbulence feature distribution, and adjusting control parameters of a preset variable pitch control model based on the dynamic compensation strategy to obtain the variable pitch control model. And finally, a pitch angle adjusting mechanism of the wind turbine generator is controlled through the corrected parameters, self-adaptive adjustment of the pitch angle is achieved, and therefore the operation stability and the output power stability of the wind turbine generator under the complex turbulence working condition can be improved, the damage risk of parts can be reduced, and the service life of the wind turbine generator can be prolonged.

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 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 wind power generation operation monitoring and control system and method, and belongs to the technical field of new energy power generation. The system comprises an analysis module, an optimization module, a control module and a feedback module. Wherein the analysis module is used for receiving operation data of wind power equipment, and realizing cross-domain data mapping, time sequence prediction and state evaluation based on a generative adversarial network and a Transform structure; the optimization module combines the prediction result and the equipment damage change, introduces uncertainty constraint and a multi-target weighting mechanism, and generates operation control parameters; the control module performs equipment operation control according to the parameters, and executes local emergency response under abnormal conditions; and the feedback module dynamically updates model parameters by using a control result, and optimizes a maintenance strategy through cloud reinforcement learning. The method can improve the operation stability, the operation and maintenance efficiency and the fault pre-judgment capability, and is suitable for the intelligent operation management of the wind power equipment.

The invention provides a single-point mooring floating type fan platform capable of actively yawing, and belongs to the technical field of offshore wind power. The problem that the aerodynamic efficiency of an impeller is reduced due to the fact that a fan platform needs to bear multiple coupling effects of ocean currents and waves and the fan platform deviates from the best windward direction due to the hydrodynamic force coupling effect when an included angle is formed between the ocean current direction and the wind direction under the action of the complex ocean environment is solved. The system comprises a wind turbine generator, a mast type tower, a platform foundation, a single-point mooring device and a flow guide device, the edge of the platform foundation is connected with the single-point mooring device and can rotate by 360 degrees around the single-point mooring device. The flow guiding device is installed at the edge position of the platform foundation and arranged opposite to the single-point mooring device, the horizontal section of the flow guiding device is in a symmetrical streamline shape, and the flow guiding device is provided with a driving device which drives the flow guiding device to rotate in the horizontal direction and adjusts the acting force of ocean current on the fan platform. The power generation device is mainly used for improving the power generation efficiency of the mast type tower offshore wind turbine platform.

A pitch change control method for a wind turbine, and a wind turbine. The pitch change control method comprises: on the basis of power data of a wind turbine, determining a corresponding blade roughness coefficient; on the basis of a preset pitch angle condition, determining a wind speed-pitch angle relation under the blade roughness coefficient; on the basis of the wind speed-pitch angle relation and wind speed data, determining a blade stall condition; and, in response to the wind turbine being in a blade stall state and according to the wind speed data and a preset pitch change control relation, controlling the wind turbine to change pitch. The control method can determine blade stall and perform a corresponding pitch change action while taking into account wind turbine power, so as to achieve a stall protection effect and improve the stability and reliability of the wind turbine.

The invention provides a wind-solar hydrogen production and ammonia synthesis system and a configuration method thereof, electronic equipment and a storage medium. The wind and light hydrogen production and ammonia synthesis system comprises a wind and light hydrogen production subsystem, an ammonia synthesis subsystem and a coordination controller which are relatively independent, the ammonia synthesis subsystem comprises a hydrogen storage tank, the hydrogen storage tank is connected with the wind and light hydrogen production subsystem, and the configuration method of the wind and light hydrogen production and ammonia synthesis system can be executed by the coordination controller. The configuration method of the wind-solar hydrogen production ammonia synthesis system comprises the following steps: firstly, determining capacity configuration parameters of a wind-solar hydrogen production subsystem by taking the minimum leveling hydrogen production cost as a target; and determining the capacity configuration parameters of the ammonia synthesis subsystem based on the capacity configuration parameters of the wind-solar hydrogen production subsystem by taking the maximization of the wind-solar absorption rate, the minimization of the total cost of the system and the minimization of the operation cost of the system as targets. The wind and light hydrogen production subsystem and the ammonia synthesis subsystem are subjected to two-stage decoupling type system configuration, and stable operation of the ammonia synthesis process is ensured while efficient utilization of green electricity is achieved.

Disclosed is an explainable method for monitoring a state of a generator of a wind turbine generator system on the basis of a spatio-temporal graph. The method includes: S1: acquiring data collected by a supervisory control and data acquisition (SCADA) system; S2: carrying out data understanding on the SCADA data, selecting features associated with the generator, and carrying out data preparation on the selected feature data, and obtaining valid data; S3: embedding the SCADA data, and forming a directed spatio-temporal graph data sequence; and S4: carrying out modeling of a normal behavior model of the generator on the constructed directed spatio-temporal graph data sequence, computing a full-graph-level residual and a node-level residual, computing a residual through an exponentially weighted moving average (EWMA) control chart method, carrying out full-graph-level state monitoring on the generator, forming a fault information transmission chain relation, and enhancing explainability and robustness of a monitoring result.

The invention discloses a variable-pitch coordinated optimization control method and system for a wind turbine generator, and relates to the technical field of wind turbine generator control, and the variable-pitch coordinated optimization control method comprises the following steps: obtaining operation characteristic data of the wind turbine generator; analyzing the obtained operation characteristic data of the wind turbine generator by using a factor analysis algorithm, and identifying key factors influencing the variable pitch performance of the wind turbine generator; predicting the variable pitch of the wind turbine generator at the future moment by using the variable pitch coordination control model to obtain a blade angle change trend; and dynamically adjusting the variable pitch angle of each blade based on the obtained blade angle change trend in combination with the real-time operation state of the wind turbine generator, and optimizing a variable pitch coordination control strategy. The variable-pitch coordinated control model is utilized to predict the variation trend of the blade angle, a scientific basis can be provided for variable-pitch angle adjustment at the future moment, the hysteresis of variable-pitch control is effectively reduced, and the control is more prospective and real-time adaptive.

The invention belongs to the technical field of comprehensive utilization of ocean renewable energy sources, and particularly relates to a pilot frequency coupling-based wind wave energy multi-degree-of-freedom broadband control method, which comprises the following steps of: performing frequency domain identification on wind wave environment characteristics, and constructing a wind wave pilot frequency energy coupling model; based on the motion response characteristics of the multi-degree-of-freedom floating platform, designing a pilot frequency energy regulation and conversion mechanism; a full-working-condition dynamic optimization control strategy is adopted, and cooperative operation and energy flow self-adaptive distribution of the wind energy conversion unit and the wave energy conversion unit are achieved. Aiming at the significant difference and relevance of wind energy and wave energy in frequency characteristics, time scale and spatial distribution, a pilot frequency wind wave coupling dynamic model is established, and broadband capture of low-frequency wind-induced and high-frequency wave-induced coupling energy is realized under complex sea conditions through multi-degree-of-freedom motion decoupling and state observation, so that the wind-induced and high-frequency wave-induced coupling energy is obtained. The energy utilization rate, the attitude stability and the structural safety of the system are effectively improved, and good robustness and engineering adaptability are achieved.