14727 results about "Electric generator" patented technology

The present invention relates to the technical field of power system equipment and control. The method of the present invention comprises: installing sensors to acquire data for online real-time monitoring, and constructing a comprehensive condition online monitoring model for comprehensive data analysis; comprehensively evaluating the health condition of a generator on the basis of a comprehensive analysis result, and identifying fault causes; and constructing a generator comprehensive data intelligent monitoring and dynamic early warning model to perform insulation degradation trend analysis and prediction on the generator. In the present invention, by comprehensively monitoring the condition of a turbine generator, various key indicators of the generator are captured in real time, and trends and patterns underlying the data are revealed, providing technical support for accurately evaluating the health condition of the generator; and continuous monitoring for the insulation condition of the generator allows for proactive identification of potential risks, thereby providing decision-making support for preventive maintenance, helping operators take measures promptly, preventing faults, improving the reliability and safety of the generator.

The invention discloses a virtual synchronous generator grid-connected current harmonic suppression method and system, and relates to the technical field of power systems, and the method comprises the steps: S1, carrying out the digital sampling of intermittent high-frequency disturbance through a wind power plant grid-connected current signal in a power grid through an analog-to-digital converter, and obtaining an original digital current sequence; s2, according to the original digital current sequence, analyzing a wind load frequency component by adopting a fast Fourier transform algorithm, and determining amplitude and phase characteristics of a high-frequency harmonic component; s3, if the amplitude of the high-frequency harmonic component exceeds a preset threshold value, extracting an intermittent disturbance harmonic sequence from the frequency component to obtain a to-be-suppressed harmonic subset; according to the virtual synchronous generator grid-connected current harmonic suppression method and system, the stability of a remote wind power plant power grid is remarkably improved, continuous monitoring and real-time suppression of intermittent disturbance are achieved, and high efficiency and reliability of power grid operation are guaranteed.

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 provides a thermoelectric unit intelligent early warning method and system based on an artificial intelligence model. The method comprises the steps that firstly, multi-source operation monitoring information of different operation dimensions of core components such as a turbine, a boiler and a generator of a thermoelectric unit is obtained; performing time sequence correlation analysis on each modal monitoring data, constructing a dynamic correlation matrix, generating a modal conflict correction coefficient, and obtaining a time sequence dependence feature set; inputting the model into a hierarchical evolutionary early warning model, and obtaining an evolutionary fusion early warning feature vector through modal conflict correction and dynamic feature evolution modeling; generating a working condition self-adaptive dynamic early warning threshold value based on the current operation working condition; through comparison and abnormal traceability of the traceability verification module, an abnormal root component and a propagation path are determined, and a targeted traceability early warning instruction is output, so that the early warning accuracy and adaptability of the thermoelectric unit are improved, and safe and stable operation of the unit is guaranteed.

The invention relates to the technical field of extended-range electric vehicle control, and discloses an engine starting control method and system for an extended-range electric vehicle, and the method comprises the following steps: obtaining the operation information of the extended-range electric vehicle, the operation information comprises the actual temperature of an engine, the actual rotating speed of a range extender, the state of charge (SOC) of a power battery and the maximum dischargeable power; when the starting request mark position of the engine is set, the generator is controlled to enter a torque control mode, and the engine enters a starting mode; and based on the actual temperature of the engine and the actual rotating speed of the range extender, the pre-control torque of the generator is activated through the enabling flag bit. By accurately controlling the rotating speed and the torque of the generator, smooth starting of the engine is achieved, the requirement for starting the engine in various vehicle using scenes is met, meanwhile, the actual temperature of the engine, the SOC of a power battery and the like are considered, the target rotating speed of the started engine and the starting torque of the generator are flexibly controlled, and the starting efficiency is improved. The starting performance of the engine is improved.

The invention discloses a low sample hydro-generator fault diagnosis method based on transfer learning. The method comprises the following steps: collecting a current signal, a vibration signal, a temperature signal and a voiceprint signal; executing preprocessing; performing label labeling, and dividing the data into a source domain data set and a target domain data set; operating parameters of the hydro-generator are collected, processed and combined into working condition feature vectors; training a source domain base model on the source domain data set to generate a pre-training parameter set; establishing a physical constraint module, and binding the physical constraint module with the parameter updating process of the source domain base model; initializing a transfer learning model, and calling a physical constraint module to apply constraint to generate a physically constrained transfer learning model; performing increment fine adjustment; and outputting a diagnosis result vector through the transfer learning model after increment fine tuning. According to the method, transfer learning and physical constraints are combined, hydro-generator fault diagnosis is optimized through increment fine tuning, and the method has the advantages of high precision, small sample adaptability and physical consistency verification.

A vehicle having a primary mover, a charging system, multiple electric loads, and a controller. The charging system includes a charge storing device and an alternator. The alternator is configured to convert mechanical energy generated by the prime mover into electrical energy to charge the charge storing device. The multiple electrical loads are electrically coupled to the charging system via a power distribution module. The controller is communicably coupled to the charging system and is configured to receive an indication that an electrical output of the charging system is unable to provide sufficient electrical energy to each electrical load in the electrical loads. The controller is also configured to provide a control signal to the power distribution module in response to the indication. The control signal is configured to cause the power distribution module to decouple at least one of the electrical loads from the charging system.

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 relates to the technical field of micro-grid dispatching, and particularly provides an intelligent dispatching method and system for a photovoltaic-storage-diesel micro-grid, and the method comprises the steps: obtaining the operation data of an energy storage battery, the operation data of a diesel generator, and the global operation data of the micro-grid; evaluating the power response capability of the energy storage battery according to the operation data of the energy storage battery, and evaluating the quick response capability of the diesel generator according to the operation data of the diesel generator; analyzing the stability risk level of the micro-grid according to the global operation data, the power response capability and the quick response capability of the micro-grid; adjusting scheduling optimization target emphasis according to the stability risk level; emphatically acquiring an energy storage battery charging and discharging instruction, a diesel generator starting and stopping instruction and an output instruction according to the adjusted scheduling optimization target, and executing all the acquired instructions; according to the method, the problem that the system stability is sacrificed due to blind pursuit of cost minimization in a traditional strategy can be avoided.

The invention discloses a system and a method for analyzing stability and oscillation transmission of a network-forming converter system, and belongs to the technical field of virtual synchronous generator control, and the system comprises an active power control loop which takes an active power deviation value as input, and outputs a power angle of a VSG system through an active integration part and a feedback part; the reactive power control loop takes the reactive power deviation ratio as input, and outputs the voltage amplitude of the VSG system through an active integration part and a feedback part; according to the analysis method, whether each state variable is stable or not is judged firstly, and then the intensity of oscillation transmission between the state variables is judged. According to the method, the stability of the system can be evaluated more accurately, and the accuracy of system stability analysis under dynamic disturbance is ensured; an oscillation transfer effect function is introduced, and the mutual influence between different state variables is quantitatively analyzed; and the dynamic fluctuation range of each state variable in the system can be accurately evaluated.

The invention relates to the technical field of new energy power generation monitoring, in particular to a new energy wind power system fault detection method, system and device based on multi-parameter fusion and a medium. The method comprises the following steps: acquiring various operating parameters such as wind speed, wind direction, generator speed, temperature, voltage and current of a wind power system in real time, and performing feature extraction on the operating parameters to obtain key features related to a fault; analyzing the key features based on a deep learning algorithm, and judging the fault type of the wind power system; and outputting a fault diagnosis result in a visual mode, and sending the fault diagnosis result to a control center for remote monitoring and fault early warning. The technical problems that fault detection of a traditional wind power system is poor in real-time performance, not high in accuracy, low in intelligent degree and the like are solved, and reliable guarantee is provided for safe and stable operation of the wind power system.

An autonomous flying device achieving a large payload and a long continuous flight time and also accurately adjust position and orientation while flying. The device includes: a main rotor and the like that provide main thrust; a sub rotor and the like that controls the orientation; an engine that generates energy for rotating the main rotor and the like and the sub rotor and the like; and an arithmetic control device that controls rotation of the sub rotor and the like. Also, the main rotor and the like are rotated by being drivingly connected to the engine, whereas the sub rotor and the like are rotated by motors driven by electric power generated from generator and the like operated by the engine. Further, when orientation control to tilt the fuselage is performed, the arithmetic control device increases the output distribution ratio of the sub rotor to above the output distribution ratio of the sub rotor when hovering is performed.

The invention belongs to the technical field of permanent magnet synchronous generator demagnetization fault detection, and particularly discloses a permanent magnet motor fault early warning system based on big data, which comprises a high-frequency injection and grid-connected control module, a fault early warning module and a fault early warning module, actively injecting a high-frequency disturbance signal of which the frequency is higher than the fundamental frequency into a control loop of the permanent magnet synchronous generator; the multi-source synchronous acquisition module is used for synchronously acquiring multi-dimensional operation parameters of the permanent magnet synchronous generator and performing time domain alignment and frequency domain separation on the multi-dimensional operation parameters to obtain synchronous acquisition signals; the composite feature extraction module is used for extracting a multi-dimensional demagnetization criterion of the permanent magnet synchronous generator based on the synchronous acquisition signal; the self-adaptive threshold regression prediction module is used for performing fusion evaluation on the multi-dimensional demagnetization criteria by using the demagnetization health index model to generate a demagnetization judgment result of the demagnetization degree of the permanent magnet synchronous generator; the method has the advantages that the demagnetization recognition accuracy is improved, and early warning is achieved.

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 pressure storage and heat storage coupled regenerative heat pump power storage system and a regulation and control method thereof. The system is composed of an electric-power generation integrated unit, an energy storage compression and energy release expansion integrated unit, an energy storage expansion and energy release compression integrated unit, a high-temperature heat exchanger, a backheating heat exchanger, a cold energy absorption heat exchanger, a heat storage and cold storage storage tank, a high-pressure gas storage tank and the like. And an energy storage / energy release loop, a heat storage / heat release loop, a cold storage / cold release loop and a pressure heat coupling loop are formed by connecting pipelines. In the energy storage mode, the compressor unit is driven by electric energy to conduct multi-stage compression, and compression heat and expansion cold are stored in the high-temperature storage tank and the low-temperature storage tank correspondingly. In the energy release mode, the heat storage medium and the cold storage medium are sequentially released to supply heat or cold to the circulating working medium, and the expansion unit is driven to drive the generator to generate electricity. According to the method, bidirectional operation of energy storage and energy release is achieved through valve switching and circulation control, and power output and load change are adjusted within a second level through a pressure-heat coupling mechanism.

The invention discloses an electric power control method based on a diesel generator, commercial power and photovoltaic grid connection. The method comprises the following steps: acquiring operation data of commercial power, a diesel generator and photovoltaic-energy storage through an energy management system, constructing a multi-target dynamic optimization model, and distributing power by taking the lowest comprehensive operation cost, the highest power supply reliability and the minimum carbon emission as targets; the power analysis and regulation unit adopts a double-loop mechanism of outer-loop Kalman filtering prediction compensation and inner-loop model prediction control, synchronizes the voltage, frequency and phase of the side to be connected with the grid and the power grid side, and smoothly switches the power change rate of each energy source in the process through a self-adaptive sliding mode control strategy; and the grid-connected switching unit intelligently pre-judges the grid-connected switching opportunity by using a long short-term memory (LSTM) network and fuzzy logic fusion model. According to the invention, the energy utilization efficiency and the power supply stability are improved, multi-energy intelligent dynamic regulation and control are realized, the comprehensive operation cost of the system is effectively reduced, and reliable power supply under complex working conditions is ensured.

The embodiment of the invention provides a digital simulation modeling method and device of a tandem type hybrid electric propulsion system, computer equipment and a medium, and relates to the technical field of simulation modeling, and the method comprises the following steps: constructing a turboshaft prime mover model, constructing a generator model, connecting the turboshaft prime mover model and the generator model in series to generate a generator set, the power generation groups are connected in parallel to construct a turbine power generation system; constructing a propulsion system; constructing a battery energy storage system; constructing a power transmission and distribution system, and respectively connecting the power transmission and distribution system with a turbine power generation system, a propulsion system and a battery energy storage system; and constructing a comprehensive control and energy management system, and outputting a digital simulation modeling result through a turbine power generation system, a power transmission and distribution system and a battery energy storage system. According to the scheme, a simulation modeling method based on multi-physics field coupling double-power-source dynamic response and energy flow regulation and control provides reliable modeling technical support for engineering application of a tandem type hybrid electric propulsion system.

An intelligent energy management system, includes: a drive device including an engine configured to output power to a wheel of the vehicle, a drive motor configured to output power to the wheel, and an electric generator connected to the engine and driven by the engine to generate electricity; a power battery configured to supply electricity to the drive motor and charged with an alternating current outputted from the electric generator or the drive motor; and a control device configured to acquire multi-domain data fusion information, predict, according to the multi-domain data fusion information, a route-specific vehicle energy consumption corresponding to a preset travel route, plan, according to a road section-specific vehicle energy consumption corresponding to each road section, a target SOC corresponding to each road section, and control, according to the target SOC and an actual vehicle demand corresponding to each road section, the drive device.

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 diesel generator turn-to-turn short circuit fault protection method and system based on multi-parameter characteristics, and relates to the technical field of intelligent fault diagnosis of power equipment, and the method comprises the steps: carrying out the feature extraction of operation state monitoring data, carrying out the integration according to a time sequence, and generating a real-time feature sequence set; performing spatio-temporal feature coding and fusion on the real-time feature sequence set to generate a multi-dimensional spatio-temporal feature map; mapping the comprehensive matching score set into fault confidence according to a linear relation, obtaining a fault confidence sequence, judging a fault level in combination with a characteristic amplitude overrun condition, and generating a fault diagnosis result set; and executing hierarchical response based on the fault diagnosis result set, generating a hierarchical protection action execution record, and generating a short-circuit fault protection report in combination with the fault diagnosis result set. According to the method, quantitative similarity analysis of real-time features and standard modes is realized, the fault probability is accurately evaluated through a multi-parameter weighted fusion mechanism, and the reliability and interpretability of a diagnosis result are improved.

The invention relates to the technical field of wind power generation, and particularly discloses a duct double-turbine supercharged windmill which comprises a duct and a blade type rotating structure arranged in the duct. The duct sequentially comprises a first contraction section, a first straight section, a second contraction section and a second straight section in the air flow direction, and the diameter of the first straight section is larger than that of the second straight section. The blade type rotating structure comprises a rotating shaft, a first-stage turbine and a second-stage turbine, the first-stage turbine and the second-stage turbine are arranged on the rotating shaft, the first-stage turbine and the second-stage turbine are correspondingly arranged on the first straight section and the second straight section respectively, and the rotating shaft is connected with the generator through a transmission structure. Structural improvement is carried out through the blade type rotating structure and the duct, the turbofan located at the front end drives the turbofan located at the rear end to rotate, and therefore the wind energy utilization rate is increased.

The invention relates to the technical field of wind power operation and maintenance management, and particularly provides an abnormal state detection method and system for a wind driven generator system. Joint feature extraction is carried out on the multi-source monitoring data flow through a feature coding network, a state evolution feature sequence and a cross-source association feature set are generated, a pre-trained anomaly diagnosis model is called to carry out fusion analysis on the features, and an anomaly diagnosis result containing anomaly confidence distribution information is generated; and after the abnormal type, the time dimension development track and the space dimension influence component identifier are analyzed, an equipment early warning instruction containing time and space positioning information is generated and sent to the target management terminal, so that the abnormal state of the wind driven generator system can be comprehensively and accurately detected, detailed abnormal information is provided in time, and the safety of the wind driven generator system is improved. And the equipment fault risk and the operation and maintenance cost are effectively reduced.

The invention belongs to the technical field of multi-body dynamics analysis, and discloses a wind driven generator transmission chain rigid-flexible coupling multi-body dynamics analysis method based on dynamic mode decomposition, and the method comprises the steps: firstly, enabling multi-degree-of-freedom time series data to be non-linearly embedded into a high-dimensional feature space through an encoder neural network; extracting a dominant mode by utilizing intrinsic orthogonal decomposition (POD), and constructing a low-dimensional feature space; parameterized dynamic mode decomposition and radial basis function regression are adopted, a mapping relation between system parameters and Koopman operators is established, and accurate prediction of dynamic characteristics under variable working conditions is achieved; and finally, reconstructing a physical response through a decoder, and optimizing model parameters in combination with an error driving mechanism. The problems that a traditional method is low in calculation efficiency, poor in nonlinear adaptability and difficult in multi-parameter coupling prediction are effectively solved, the efficiency and precision of transmission chain dynamic analysis are remarkably improved, and reliable technical support is provided for state monitoring and service life prediction of the wind turbine generator.

An electric generator according to the present technology includes a coil element. The coil element includes a magnetic core and two or more windings. The magnetic core causes a magnetic flux change in an axial direction. The two or more windings that are each helically wound around the magnetic core in such a manner that each of the two or more windings is adjacent to the other winding in the axial direction of the magnetic core and are independent windings each capable of being electrically separated from the other winding.

A landing pad receives and stores packages delivered from an aerial vehicle or awaiting pickup from an aerial vehicle. The landing pad can be placed outside of a window and can contain a transmitter for sending out an identification signal via radio frequency to aid aerial vehicles in finding the landing pad. The landing pad contains a landing platform with a trapdoor that leads to a storage compartment. The trapdoor can be configured to only open when it receives a signal from an authorized aerial vehicle. The storage compartment can be accessed via a storage compartment door which can contain a locking mechanism. The storage compartment can be climate controlled. The landing pad can also have a transmitter that emits sounds to discourage animals from nesting on or near the landing pad. The landing pad can also include a solar power generator as a source of electrical energy.

The invention relates to the technical field of wind driven generator monitoring, in particular to a fan fault early warning and diagnosis method fusing multi-modal time sequence data, which comprises the following steps: respectively acquiring vibration time sequence data, current time sequence data and temperature time sequence data of a fan, and respectively calculating a first feature sequence, a second feature sequence and a third feature sequence; generating a fault early warning signal based on a three-state polarity synchronous counting mechanism; calculating a dispersion statistical value, and executing chronic fault diagnosis; when the fan is in a low-load steady-state working condition, active lubrication state detection and feedback are executed; when the first feature sequence is calculated, synchronously executing adjoint oil product pollution diagnosis; and the environment temperature is judged through the temperature time sequence data, and low-temperature working condition adaptability compensation is executed under the low-temperature working condition. The main purpose of the invention is to solve the problems in the prior art that early fault signs cannot be effectively captured due to information loss during fusion processing of multi-modal data, and real faults and external interference are difficult to distinguish.

The invention relates to the technical field of wind power generation, artificial intelligence and fluid mechanics, and discloses a prediction method and device for a multi-working-condition flow field of a wind driven generator based on a neural network, and the prediction method comprises the steps: obtaining a sample data set which comprises multiple groups of multi-working-condition data; and inputting the sample data set into a physical information field adversarial neural network model for training to obtain the total loss of forward propagation of the sample data in the field adversarial neural network model. According to the total loss, determining whether training of the wake flow field prediction model is completed; and under the condition that a new round of training is carried out on the wake flow field prediction model, back propagation is carried out on the total loss, and neural network parameters are optimized. In this way, a dual-branch loss collaborative optimization mechanism is formed. A physical information neural network and a domain adversarial neural network are combined, and complementary advantages of the two are fully exerted. When the data is limited or the distribution difference is large, high-precision and physically consistent wake flow field prediction can be realized.

The invention discloses a fatigue test method for a cabin structural member of an offshore wind power generator. A test piece is subjected to surface treatment, a multi-sensor array is arranged, the test piece is placed in a combined system of a multi-axis dynamic loading unit and a collaborative environment simulation cabin, an intelligent control and analysis platform synchronously applies a multi-axis alternating load, salt spray corrosion and a circulating temperature and humidity environment, and real marine service conditions are simulated; the state of a test piece is evaluated in real time based on criteria such as a strain concentration coefficient and acoustic emission energy, a strengthening test is started for potential damage risks, a load strategy is adjusted to track crack propagation when microcracks are monitored, and after the test is ended, a fatigue life prediction model is established or calibrated by combining failure test piece analysis data. And outputting a life evaluation result and a structure improvement suggestion. Through the multi-factor collaborative simulation and self-adaptive control technology, the authenticity and efficiency of the test are improved, and technical support is provided for design optimization and reliability guarantee of offshore wind power cabin structural parts.

The invention discloses a wind driven generator temperature monitoring method based on a graph space-time attention network, and the method comprises the following steps: carrying out the preprocessing of collected SCADA data, and obtaining a data set; each sensor is used as a node, the top-k neighbor relation of each node is calculated, edge weights among the nodes are calculated through a Gaussian kernel function, a weighted adjacency matrix is constructed based on the neighbor relation and the edge weights of all the nodes, and therefore a multi-sensor time sequence diagram of the wind driven generator is obtained, time sequence characteristics are given to the nodes by time sequence data collected by different timestamps, and a multi-sensor time sequence diagram of the wind driven generator is obtained. Forming a space-time diagram structure, and outputting time sequence characteristics of each sensor; a graph attention network is introduced into wind driven generator temperature state monitoring, and global and connectivity characteristics of a multi-sensor network are modeled. According to the method, the generator temperature prediction precision is remarkably improved by fusing a graph space-time two-dimensional attention mechanism and key variable screening, redundant variables are eliminated by adopting the maximum information coefficient MIC, and noise interference is reduced.

The invention relates to the technical field of wind driven generator driving control, and discloses a wind driven generator rotor turning-over driving control method based on torque and rotating speed cooperation, which comprises the following steps: before operation, controlling a motor to execute forward and reverse quasi-static micro-motion, and collecting an average electromagnetic torque; pure gravity and friction components are separated by using torque arithmetic sum and difference, gravity model gain is corrected, and friction compensation is configured; a feed-forward torque is calculated in combination with a real-time angle, and friction compensation and speed loop feedback are superposed to generate a current instruction; according to the method, gravity and friction are decoupled through physical symmetry, model parameter errors are eliminated, hidden dangers of integral lag and out-of-control rotating speed when the variable polarity load passes through a dead point are avoided, and stability and safety of the turnover process of the hundred-ton rotor are ensured.