129 results about "Wind park" patented technology

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 belongs to the technical field of wind power plant management, and relates to a method and system for real-time positioning and trajectory analysis of wind power plant workers. The method comprises the following steps of: preprocessing multi-source position data by utilizing an edge computing node; fusing the preprocessed multi-source position data to generate a high-precision positioning result; performing trajectory analysis by using a deep learning algorithm to obtain a trajectory analysis result; performing intelligent task allocation by combining wind power plant equipment state data and environment data; on the basis of the physiological data and the environmental data of the staff, health condition monitoring and safety risk assessment of the staff are carried out; generating an equipment maintenance strategy by using a predictive maintenance algorithm in combination with the trajectory analysis result and the wind power plant equipment state data; and a worker real-time trajectory analysis result, an intelligent task allocation result, a monitoring and evaluation result and a standby maintenance strategy are visually presented. The positioning precision is improved, track analysis is accurate and reasonable, real-time safety monitoring of workers can be achieved, and the intelligent degree is high.

Provided is a method of operating a hybrid power plant, including energy generating units including a wind park and at least one unit of another type of renewable energy source and including an energy storage system, the method including: creating a power production schedule based on an actual forecast of power production, an actual forecast of energy price and actual hybrid power plant status, the power production schedule including at least scheduled power for points in time in the future; and controlling, during the points in time in the future the energy generating units and the energy storage system based on the power production schedule, the hybrid power plant status at the points in time in the future, in particular energy storage system status, such as to dispatch power according to the power production schedule, but complying with any grid operator reference at the points in time in the future.

The invention discloses a regional power grid wind power generation power prediction optimization method which comprises the following specific steps: firstly, collecting data such as wind speed and wind direction through meteorological sensors arranged in a wind power plant and around the wind power plant, and carrying out cleaning and de-noising preprocessing; secondly, decomposing the meteorological data into intrinsic mode function components with different frequencies by adopting variational mode decomposition, constructing a convolutional neural network to perform feature extraction on each component, and adaptively adjusting the size of a convolution kernel according to the periodic characteristics of the data; then, a bidirectional long-short-term memory network based on an attention mechanism is used for fusing features, time sequence information is captured, power prediction is achieved through a full-connection neural network, and the weight of the FCNN is initialized through a random forest regression model to improve the initial performance of the model; according to the method, the prediction precision is improved through multi-level characteristic decomposition and a self-adaptive mechanism, the real-time response capability is good, and the requirements of the regional power grid for the precision and timeliness of wind power generation power prediction are met.

The invention discloses an energy internet operation state evaluation method and system, and relates to the technical field of energy internet, and the method comprises the following specific steps: building a micrometeorological model: combing the geographic position information of various energy facilities distributed in the energy internet, according to the method, a regional differentiation comprehensive evaluation mechanism considering ecological benefits and energy utilization efficiency is established by fusing dynamically quantified ecological environment related indexes and energy operation state indexes obtained based on micrometeorological differences, so that real-time influences of the energy internet on the ecological environment in different regions are comprehensively and accurately mastered; according to the method and the system, comprehensive evaluation results such as the interference degree of a wind power plant on a bird migration path, the ecological interference degree of a solar power station on land vegetation and the influence range and intensity of electromagnetic radiation on surrounding ecology are evaluated, and energy internet optimization operation strategies and ecological protection measures which are more practical and more targeted are formulated based on the comprehensive evaluation results.

The invention discloses a wind power plant fan fault prediction method based on a deep learning algorithm, and the method comprises the following steps: obtaining and inputting fan operation state data, and obtaining a matrix capable of representing the characteristics of the operation state data; reducing the data size scale of a high-frequency signal sequence generated by the fan sensor by using a box line downsampling method; aiming at the fault sequence data of the positive sample, combining prior knowledge and observation data by using a Bayesian multivariate autoregression model; for negative sample data, normal behavior sequence data is mapped to approximate posterior probability distribution of potential variables output by an encoder by using a variational auto-encoder model in combination with an attention mechanism, a decoder samples from the distribution through a re-parameterization technique, and reconstructed data similar to input data distribution is generated; a self-adaptive threshold adjustment strategy is used, and a classification decision can be dynamically optimized by automatically adjusting the threshold of positive and negative reconstruction errors. According to the method, the fan fault prediction precision can be effectively improved.

The invention relates to the field of wind power generation control, and particularly discloses a cooperative control analysis system and method of a distributed wind power generation system. The artificial intelligence technology based on the deep learning field is used for calculating wind intensity values of a plurality of preset time points in a preset time period of the wind power plant, wind frequency values of the plurality of preset time points in the preset time period and wind frequency values of the plurality of preset time points in the preset time period. And performing feature extraction and coding on the wind speed values at the plurality of preset time points in the preset time period and the wind direction values at the plurality of preset time points in the preset time period to obtain a classification result of the type of the wind driven generator which is suitable for running. Therefore, wind energy can be effectively utilized by intelligently selecting the type of the wind driven generator which runs properly, and the stability and reliability of power generation of the wind power plant are improved.

The invention discloses an offshore wind power plant control method and system, and relates to the technical field of wind power generation control, and the method comprises the steps: carrying out the collection through a sensor array disposed at a wind generating set, and forming a three-dimensional environment parameter vector; inputting the real-time environment parameters into a pre-trained classification model, and matching the real-time environment parameters to a plurality of preset environment operation states; constructing a Markov state transition probability matrix based on the historical operation data, and recording environmental parameter change rate characteristics during state transition; determining the adaptation degree of the preset control parameters of the wind generating set in each environment operation state to other environment operation states; fusing to generate a mixed prediction result; and triggering a control strategy according to the comprehensive judgment of the prediction result and the adaptation degree evaluation. The method has the advantages that dynamic Markov state transition prediction and cross-state adaptation degree evaluation are combined, so that the adaptive capacity of the offshore wind power plant to complex sea conditions is remarkably improved.

The invention belongs to the technical field of wind power generation, and particularly relates to a fault diagnosis and early warning method and system for a wind power plant, and the method comprises the steps: collecting the state data of a plurality of physical fields through a plurality of types of sensor arrays disposed at key parts of a fan; performing preprocessing and feature extraction on the data to obtain high-dimensional feature data; driving the field-level digital twinborn model to perform real-time simulation based on the high-dimensional feature data, and determining an initial fault recognition result and severity by comparing a simulation predicted value with actual sensing data and combining with an unsupervised anomaly detection algorithm; a diagnosis result is calibrated through multi-modal high-dimensional feature data fusion; predicting the remaining useful life of the key component based on the fault severity and the evolution trend thereof, and generating a collaborative operation and maintenance decision by using a multi-objective optimization algorithm; and an operation and maintenance decision is automatically executed through a closed-loop feedback execution mechanism. According to the invention, the accuracy of fault diagnosis and early warning of the wind power plant is improved.

The invention relates to an equivalent reduced-order modeling method of an offshore wind power flexible direct output system. The method comprises the following steps: constructing an equivalent reduced-order model of a wind power plant; constructing an equivalent order reduction model of the modular multilevel converter; constructing a broadband equivalent reduced-order model of the receiving-end power grid; constructing an equivalent order reduction model of the offshore wind power flexible direct output system based on the equivalent order reduction model of the wind power plant, the equivalent order reduction model of the modular multilevel converter and the broadband equivalent order reduction model of the receiving end power grid; according to the method, the wind power plant, the MMC and the receiving-end power grid are subjected to order reduction and integration, the number of overall nodes and elements is reduced in a multiplied mode, the voltage-current transient consistency of grid-connected points is still kept, the CPU calculation amount is remarkably reduced, and a larger-scale fan group can be loaded on an HIL platform at a time.

A system for transporting fluid generated by a wind turbine includes at least one wind turbine for generating electrical power, wherein the wind turbine includes a fluid producing unit configured for generating a fluid by using the generated electrical power, a fluid pipeline system coupled to the wind turbine for transporting the generated fluid, and a pressure control system coupled to the fluid pipeline system for controlling the fluid flow of the fluid in the pipeline system. The pipeline system includes a transporting pipeline and a connection pipeline, wherein the connection pipeline is coupled to the wind turbine and the connection pipeline such that the fluid is transportable from the wind turbine to the transporting pipeline via the connection pipeline.

The invention discloses an intelligent wind power prediction method and system based on physical knowledge guidance, and the method comprises the steps: firstly collecting data of a wind power plant, carrying out the preprocessing, and dividing the preprocessed data into a training set, a test set and a verification set; secondly, constructing a wind power prediction model, fusing physical priori knowledge, and inputting data of the training set into the wind power prediction model for training; and finally, performing multi-step prediction by using the trained wind power prediction model through data in the test set and the verification set to obtain predicted power. The system comprises a data acquisition unit, a data preprocessing unit, a model construction unit, a model training unit and a model prediction unit. The method improves the prediction precision and generalization ability in a complex environment, can achieve the more accurate prediction of the wind power of a wind power plant under different meteorological conditions, and improves the prediction robustness.

The invention discloses a wear-resistant wind energy cable, relates to the technical field of cables, and solves the problem that a wind energy cable in the prior art is difficult to meet the reliability requirement of long-term operation in a wind power generation scene due to the poor protection effect of a sheath layer. Comprising a cable core structure, and the cable core structure comprises a plurality of conductors distributed in a longitudinal array, insulation shielding layers wrapping the conductors and wrapping tape layers wrapping the insulation shielding layers; the cable also comprises an outer sheath which wraps the cable core structure, and the outer sheath comprises an outer protection layer and a lining layer which are arranged outside the belting layer. The functional layer comprises an armor layer and a pressure-resistant buffer layer, and the armor layer is arranged between the inner side of the lining layer and the outer side of the belting layer; according to the design scheme of the cable outer sheath, by optimizing the structural design, the structural bearing capacity of the cable is remarkably improved, and meanwhile the protection effect of the cable is enhanced. The design can meet the reliability requirement of long-term operation of the cable in a wind power generation scene.

The invention discloses a wind turbine generator blade icing degree prediction method and device, equipment and a medium, relates to the technical field of wind power generation, and aims to obtain unit environment monitoring information and weather forecast information of a wind power plant under different time granularities, and perform standardization processing and serialization processing to obtain a wind turbine generator blade icing degree prediction result. Performing feature extraction on the processed unit environment monitoring information and weather forecast information by using a neural network model to obtain environment monitoring time sequence features and weather forecast time sequence features; calculating the environment monitoring time sequence characteristics and the weather forecast time sequence characteristics based on an attention mechanism to obtain an environment monitoring weight coefficient and a weather forecast weight coefficient; and inputting the environment monitoring weight coefficient, the environment monitoring time sequence characteristics, the weather forecast weight coefficient and the weather forecast time sequence characteristics into a neural network model, outputting a blade icing degree probability, and predicting the wind turbine blade icing degree according to the blade icing degree probability, thereby improving the overall operation efficiency of a wind power plant, and ensuring the safety of the unit, personnel and equipment assets.

The invention belongs to the field of data transmission of wind power plants, and particularly discloses a secure data transmission method and system for a wind power plant, and the method comprises the steps: firstly obtaining wind speed, wind direction and other wind power plant parameters, grouping the parameters in sequence, mining a data sequence dependency relationship by means of an optimized long-short-term memory network, and extracting a feature vector; associating and integrating the equipment space and the operation relationship into a feature matrix; constructing a multi-dimensional data encryption model based on multiple dimensions of parameters, and encrypting the feature matrix by adopting a dynamic key generation mechanism; and establishing a secure channel to transmit encrypted data through a self-adaptive routing selection strategy based on a link state, and restoring the data by a receiving end by using a corresponding key and a decryption algorithm. The system is composed of a parameter acquisition unit, a data grouping unit and other eight units, all the units work cooperatively, the safety guarantee of the whole process of wind power plant data from acquisition and encryption processing to safe transmission and decryption reduction is achieved, and the data transmission safety and reliability are effectively improved.

The invention belongs to the field of wind power generation, and discloses a wind speed ultra-short-term prediction method based on transfer learning, which comprises the following steps of: 1, performing correlation analysis on high-dimensional SCADA (Supervisory Control and Data Acquisition) data through a data preprocessing and feature selection module, and selecting features related to wind speed; step 2, capturing transferable time scale features through a feature extractor and a discriminator; step 3, extracting a key dynamic state of time feature transfer through a self-attention mechanism and a maximum mean value difference; and step 4, performing ultra-short-term prediction on the wind speed through the prediction network. By adopting transfer learning, learning of the target field can be helped by utilizing knowledge of the source field, so that the generalization ability of the model in the new field is improved. The method aims at overcoming the limitation of a traditional method in the aspects of data imbalance, domain transfer and the like, more accurate and stable wind speed prediction is achieved, and reliable technical support is provided for safe operation of a wind power plant and stable supply of a power system.

The variable VAR module utilizes an electronic inverter operating at a “boost voltage” that is only a small fraction of the full line voltage. Fo a distribution line voltage of 38 kV, a “boost voltage” of about 10% of the line voltage (i.e., 3.8 kV) is typically sufficient to boost the voltage and inject the VARs necessary to cause the power generated by load-side generation, such as a solar or wind farm, to flow into the power grid. Reducing the operating voltage of the high-power electronics by 90% produces a tremendous reduction in the cost of the power flow controller—in comparison to those operating at the full line voltage. The module also provides “four quadrant” phase angle control injecting variable VARs into the power line causing the load-side generation to flow in the desired direction while maintaining the desired voltage levels in response to monitored power line conditions.

A method is provided of controlling an amount of active damping to be applied by an active damping system during operation of a wind turbine, the method including (a) receiving a power reference signal indicative of the power to be produced by the wind turbine, (b) determining a relative power reference change indicative of a change in the power reference signal, (c) determining the amount of active damping to be applied based on the relative power reference change, and (d) outputting the determined amount of active damping to the active damping system. Furthermore, a corresponding device, a wind turbine including such a device, a wind park including such wind turbines, and a computer program product are provided.

The invention discloses a wind-solar power generation power combined prediction method and platform, and relates to the technical field of power management, and the method comprises the steps: collecting multi-dimensional power generation data of a wind power plant and a photovoltaic power plant in real time based on a data sensing assembly, and extracting multi-dimensional prediction features after preprocessing; historical wind and light power generation data are collected, a combined prediction model is generated through training, the multi-dimensional prediction features are used as input for combined prediction, and predicted wind and light power generation power is obtained; and meanwhile, the deviation between the actual wind-solar power generation power and the predicted wind-solar power generation power is monitored in real time, and the combined prediction model is dynamically optimized. The method solves the technical problem that the existing method cannot effectively combine the synergistic effect of wind power and photovoltaic power generation and lacks a self-adaptive updating mechanism in the face of environment change, so that the prediction accuracy is insufficient, and achieves the purpose that the prediction accuracy is low through combined modeling of the wind-solar power generation power and introduction of a dynamic optimization mechanism. And the prediction precision and the adaptive capability are obviously improved.

The invention provides a permanent magnet generator fault monitoring method and device, electronic equipment and a storage medium, and relates to the technical field of motor fault detection, and the method comprises the steps: obtaining the historical operation data of a target permanent magnet generator; according to the historical operation data, establishing a motor model of the target permanent magnet generator in each working condition state; determining time domain features and frequency domain features of the target permanent magnet generator in each working condition state based on the motor model; and performing fault monitoring on the target permanent magnet generator based on the time domain feature and the frequency domain feature. Therefore, compared with the prior art that data acquisition is carried out through installation of a sensor, high-frequency voltage is injected through an inverter, then corresponding high-frequency inductance is calculated, and then identification analysis of specific harmonic waves is completed, the scheme in the invention can realize continuous monitoring and diagnosis, fault occurrence can be effectively prevented, and the fault diagnosis accuracy is improved. The dependence on sensors and monitoring conditions is small, and the operation efficiency and economic benefits of the whole wind power plant can be remarkably improved.

The invention discloses a wind power plant monitoring method and system based on a real-time database, and relates to the technical field of wind power plants, and the wind power plant monitoring method comprises the following steps: storing obtained real-time operation characteristic data in the real-time database; identifying operation fault data of the current wind power plant; based on the identified operation fault data of the current wind power plant, key factors influencing the operation fault of the wind power plant are obtained; and dynamically adjusting operation parameters of wind power plant equipment and optimizing real-time monitoring. According to the method, the real-time operation characteristic data is analyzed based on the fault diagnosis algorithm, the current operation fault data of the wind power plant can be quickly identified, the fault can be found more quickly and accurately, the abnormal condition of equipment can be mastered in time, and the equipment damage risk caused by delayed detection is reduced.

A method for optimizing performance of a wind farm having at least one wind turbine includes maneuvering a first unmanned aerial vehicle (UAV) having at least one sensor to a first location near the at least one wind turbine of the wind farm; collecting, via the at least one sensor of the first UAV, data corresponding to one or more wind conditions at the at least one wind turbine; receiving the data corresponding to the one or more wind conditions at the at least one wind turbine via a controller; generating a control action for the at least one wind turbine using the data corresponding to the one or more wind condition at the at least one wind turbine; and implementing, via the controller, the control action.

A concrete tower for a wind turbine, comprising: a plurality of tower sections stackable one another to form the tower such that a diameter of the tower sections varies from a first diameter to a second diameter the tower, having an outer diameter between 5.0 and 7.0 m at a predetermined height, falling between first and second ends of the tower, that is proximate to a height of a tip of at least one blade arranged on the concrete tower or a wind turbine with the concrete tower and oriented according to six o'clock. Also, a wind turbine, a wind farm, a method of manufacturing a concrete tower and a method of manufacturing a wind turbine.

A kind of wind farm dynamic equivalent model construction method based on terrain distribution influence belongs to power system optimization operation technical field, the present wind power generation field dynamic equivalent model is constructed to solve the problem that actual power and the difference in operating state caused by complex terrain distribution are not considered, error is larger in the case of complex wind environment of wind farm.It includes: selecting wind turbine of same model parameter in wind power generation field to form wind turbine generator unit, obtaining power variation and variation rate of each wind turbine in wind turbine generator unit;Obtain the relative sensitivity of wind condition change influence of wind turbine, and the relative sensitivity of wind condition change influence is used as representation index to obtain the relationship between transient potential derivative and relative sensitivity;Obtain the equivalent parameter of equivalent unit in wind condition change dynamic process, and construct wind farm dynamic equivalent model according to equivalent parameter.The present application is used for the construction of wind turbine group equivalent model of same parameter.

The application discloses a pitch angle control method considering fast active power regulation of a wind driven generator, which can reduce pitch angle fluctuation of all PMSGs during fast active power control, minimize fatigue damage of the wind driven generator, prolong the service life of the wind driven generator and reduce maintenance cost. P ; S2, a wake effect model of the wind farm containing mathematical models of the plurality of PMSGs is constructed to determine a plurality of pitch angles beta from the power coefficient c P ; and S3, a fast active power control model of the wind farm is constructed to satisfy minimum pitch angle beta fluctuation of the PMSGs.

The invention belongs to the field of offshore wind power generation system control strategies, and discloses a wind power plant converter station parameter optimization method based on a samson swarm algorithm, and the method comprises the steps: S1, constructing a structure of an offshore wind power plant converter station; s2, analyzing control systems of the sending-end converter station and the receiving-end converter station, and determining parameters needing to be set in the control systems; and S3, optimizing parameters which need to be set by adopting a salmons algorithm. The offshore wind power plant transmits electric energy to a power utilization side through a high-voltage direct-current power transmission line through a sending-end converter station and a receiving-end converter station; the sending end converter station and the receiving end converter station adopt MMC converters, and a control system comprises a large number of parameters needing to be set, and the parameters can be obtained through a samcat swarm optimization algorithm.

The present application relates to the technical field of wind power plant construction, and particularly relates to a wind power plant road construction earthwork intelligent allocation method, system and equipment. The allocation method comprises the following steps: collecting wind power plant road earthwork engineering data to be built and preprocessing to obtain a standardized engineering data set; an earthwork intelligent allocation model based on A* algorithm and linear programming fusion is constructed, the model takes the transport volume priority as the core criterion, cancels the allocation priority mechanism, and realizes the optimal allocation by calculating the minimum transport volume of the global excavation pile number and the filling pile number; presetting the allocation limit condition, configuring the limit condition matrix, and based on the standardized engineering data set, the earthwork intelligent allocation model is used to generate the earthwork allocation scheme of the wind power plant road. The present application adapts to the topological characteristics of the wind power plant road, realizes the efficient and reasonable allocation of the wind power plant road earthwork through the global transport volume priority, and effectively controls the construction cost.

Methods and apparatuses for controlling reactive power of a wind turbine, and a wind farm are provided. An exemplary method includes: obtaining operation data of single wind turbines in a wind turbine group at the current time point; determining the total maximum capacitive reactive capacity and total minimum inductive reactive capacity, satisfying a safety constraint condition at the next time point, of the wind turbine group; calculating a deviation value of a wind turbine group reactive instruction at the current time point; and updating the wind turbine group reactive instruction on the basis of the acquired, determined, and calculated data so as to perform reactive power control.

The invention discloses a high-altitude multi-layer road connected building fire-extinguishing anti-seismic greening facility combination system which is applied to building of a wind power plant and anti-seismic and fireproof multi-layer road connected wind driven generators of high-rise residents. A road is supported by double stand columns, short beams, long beams, pull rods and pull ropes are connected to the double stand columns to form a high-altitude multi-layer supporting frame and the road, and multi-layer wind driven generators are connected to the high-altitude multi-layer supporting frame and the road to form a wind power plant. A wind driven generator can be connected to a high-altitude multi-layer fireproof and shockproof fire extinguishing road connected to a high-rise building, and a fan is connected to the intersection of the road or an open area with a certain distance away from the building. The top layer road of the building can be extended upwards in multiple layers, multiple layers of fans are connected to the top layer road, and power transmission lines of the fans extend downwards along the stand columns and are integrated into a network.

The utility model relates to the technical field of wind power plant construction, and discloses an underground embedded part for wind power plant construction, which comprises a plurality of bases arranged at equal intervals. According to the underground embedded part for construction of the wind power plant, a locking mechanism is installed in a conical positioning column, the conical positioning column and soil can be locked, in the locking mechanism, a locking block can be inserted into the soil through a through groove by pulling a locking rod upwards, and a gap in the conical positioning column is used for locking the conical positioning column. Cement is poured into the conical positioning columns by constructors, so that the stability of the bases is further improved, and the six bases are welded and fixed in a triangular mode by utilizing the fact that a triangle is one of the most stable shapes in geometry and has excellent bearing capacity and deformation resistance, so that the stability of the bases is improved. And two stable triangular structures which support each other can be formed, so that the overall stability of the fan foundation is remarkably improved.