5527 results about "Offshore wind power" patented technology

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 an offshore energy platform coordinated scheduling method based on multi-energy complementation and hierarchical optimization, which combines multi-energy complementation characteristic modeling, multi-target opportunity constraint optimization and rolling optimization, and realizes offshore multi-energy coordinated scheduling by constructing a hierarchical decoupling optimization and control system. Based on prediction and historical data of multiple types of energy such as offshore wind power, photovoltaic energy and tidal energy, complementarity and flexibility of the energy are quantified, high-quality data support is provided for scheduling optimization, a day-ahead layered optimization model containing renewable energy priority consumption and flexible standby configuration is constructed, and a medium-and-long-term output strategy is formulated. Output of various energy sources is dynamically adjusted through a rolling optimization mechanism, and flexible response to renewable energy fluctuation is achieved. And finally, second-level frequency and voltage support is realized by using a virtual synchronous machine and droop control, and the self-adaptive capability of the system is enhanced. According to the invention, the cooperative regulation capability and operation stability of the offshore platform multi-energy system can be effectively improved, and the dependence on a traditional standby power supply is reduced.

The invention provides a service life prediction method based on structural fatigue analysis of floating type offshore wind power equipment, and relates to the technical field of wind power generation. Equipment structure data of the floating type offshore wind power equipment is acquired, a sensor network is connected to acquire environmental load information, and a multi-body coupling simulation model is established based on the equipment structure data; environmental load information is input into the multi-body coupling simulation model for stress amplitude and load change, fatigue damage accumulation data is calculated based on the multi-body fatigue load spectrum, and the residual life of the floating type offshore wind power equipment is predicted through the fatigue damage accumulation data. The technical problem that in the prior art, unique stress characteristics and dynamic response of floating type offshore wind power equipment are ignored, and consequently the service life of the wind power equipment is predicted inaccurately is solved. The technical effects that the fatigue accumulation process of the floating type offshore wind power equipment is accurately calculated, and the operation safety and the service life prediction precision of the floating type wind power equipment are improved are achieved.

The invention relates to the technical field of power system stability control, in particular to a broadband oscillation suppression method and system for an offshore wind power flexible direct current grid-connected system and a medium, and the method comprises the steps: sequentially injecting positive and negative sequence perturbation sine harmonic signals at a grid-connected point of a wind turbine generator, constructing a multi-input multi-output impedance matrix, reducing the order of the multi-input and multi-output impedance matrix into single-input and single-output equivalent positive sequence impedance; the stability margin of the offshore wind power flexible direct current grid-connected system is obtained based on equivalent positive sequence impedance quantification; when the stability margin is lower than a preset margin threshold value, reconstructing the fan grid-side converter into a matrix control structure by cascading a weighting coefficient matrix between the inner and outer ring control loops of the fan grid-side converter; and iteratively optimizing the matrix type control structure based on impedance sensitivity, and synchronously connecting a low-pass filter in series in a voltage and current feedback channel to carry out impedance remodeling so as to finish broadband oscillation risk suppression. According to the invention, broadband oscillation suppression of the offshore wind power flexible direct current grid-connected system is realized through impedance modeling and a matrix type control strategy.

A mooring system for a floating offshore wind power platform, a monitoring system, and a monitoring method. The mooring system comprises a static mooring system and a dynamic mooring system. The static mooring system comprises three mooring cable bundles. Upright columns (1) are consistent with the centers of the mooring cable bundles. The dynamic mooring system comprises a power unit, propeller units (4), and a control unit. A group of propeller units (4) is installed below each upright column (1) of the platform, and each group of propeller units (4) comprises one to four propeller units (4). The monitoring system monitors the platform in real time, obtains the real-time motion level and mooring tension of the platform, and on the basis of offset and inclination requirements and mooring tension requirements of the platform in a real-time motion state, calculates the power required by the propeller units (4) for execution. A conventional mooring method is combined with a power positioning system to reduce the load on the mooring cables and reduce the maximum offset and the inclination angle of the platform foundation, benefiting both the efficiency and service life of dynamic cables and wind turbines.

The invention relates to the technical field of data processing, and discloses an operation mode switching control method and device for an offshore wind turbine generator. The method comprises the following steps: carrying out model classification on historical operation data of a unit to obtain operation mode classification data and a characteristic parameter library; performing reinforcement learning analysis on the current state evaluation data and the short-term prediction data of the unit to obtain a target operation mode; performing fuzzy control processing on the target operation mode to obtain a mode switching strategy and a parameter adjustment sequence; performing multi-objective optimization and cooperative control analysis on the mode switching strategy and the parameter adjustment sequence to obtain accurate control signals of each actuator in the target offshore wind turbine generator; and generating an operation mode switching control strategy according to the accurate control signal of each actuator, and performing mode switching control on the target offshore wind turbine generator through the mode switching control strategy. According to the invention, the efficiency and accuracy of the operation mode switching control of the offshore wind turbine generator are improved.

According to the economic arrangement method for the current collection line of the offshore wind plant based on dynamic partition optimization, the topological optimization problem of a current collection system is converted into the minimum spanning tree problem, a dynamic edge weight minimum spanning tree algorithm is introduced, and on the basis of an existing minimum spanning tree, edge addition and deletion operation is dynamically processed so as to keep updating of the minimum spanning tree. According to the method, the algorithm is applied, and submarine cable type selection and submarine cable cross avoidance functions are added, so that a radial topological structure is formed. And dynamic partition design: for the condition of multiple fans or multiple booster stations, performing dynamic partition by utilizing fan grouping based on a scanning method and fan grouping based on adjacent position variation, and improving the optimization speed. Compared with a traditional algorithm, the economic arrangement method for the current collection lines of the offshore wind power plant based on dynamic partition optimization can converge to a better current collection system design scheme more quickly, and the full life cycle cost of the current collection system is remarkably reduced.

An offshore wind power plant composite ring current collection system topology planning method based on an N-1 safety criterion comprises the following steps that S1, wind power plant basic data including wind turbine and transformer substation node coordinates, wind turbine rated power, cable electrical parameters and the like are obtained; s2, generating a candidate cable set and a cable cross avoidance constraint pair set based on the basic data; s3, a scene set containing a normal scene and all single cable fault scenes is constructed, an optimization model with the minimum total cost of the whole life cycle as the target is established, and cable construction / operation variables and direct current power flow power balance, node degrees and cable cross avoidance constraints are integrated; s4, solving the model to obtain an initial topology scheme; and S5, carrying out full fault set N-1 security verification on the initial scheme, and bringing a violation scene into a fault set to resolve until the scheme meets an N-1 criterion. According to the method, through non-predefined topological optimization and planning, reliability and economical efficiency are taken into consideration, calculation complexity is reduced, and the method can be expanded to a cross-substation scene.

The invention relates to the technical field of offshore wind power operation risk analysis, and discloses an offshore wind power operation risk analysis system based on deep learning, and the system comprises a data collection and preprocessing module which is used for collecting wind power equipment operation state data and environment data of multiple types of sensors, and carrying out the cleaning and preprocessing of the data; the multi-modal data fusion and feature extraction module is used for extracting and fusing multi-modal features from different data sources; and the real-time risk prediction and intelligent decision module is used for analyzing the operation state of the wind power equipment in real time based on a deep learning model, and performing prediction and alarm when a fault trend is detected. By introducing a self-attention mechanism and a Transform architecture, deep fusion analysis is performed on multi-modal data from wind power equipment, and information of different data sources can be weighted and integrated in a unified feature space, so that the system can adaptively focus on important features, and the analysis precision and fault detection capability of the multi-modal data are remarkably improved.

The invention relates to an offshore wind turbine generator data acquisition method and system, and the method comprises the steps: dividing a target offshore wind power plant into a plurality of inspection regions, and obtaining the structure parameters and topographic data of a wind turbine generator of each inspection region; according to the wind turbine generator structure parameters and topographic data, respectively constructing an unmanned aerial vehicle flight path of each inspection area, and presetting a surrounding radius and a vehicle position angle; based on the flight path, the surrounding radius and the camera position angle, controlling the unmanned aerial vehicle to carry out surrounding flight on the wind turbine generator in each inspection area, and collecting a wind turbine generator image and point cloud data in the surrounding flight process; and constructing a wind turbine generator three-dimensional model based on the wind turbine generator image and the point cloud data, constructing a three-dimensional map in combination with the topographic data, and fusing a flight path into the three-dimensional map to complete data acquisition of the target offshore wind plant. According to the invention, autonomous intelligent inspection of the offshore wind turbine generator can be realized.

The invention relates to the technical field of offshore wind power, and discloses a bearing performance degradation evaluation method for an offshore wind power foundation structure, which effectively solves the problem of neglecting corrosion heterogeneity in the prior art by acquiring initial three-dimensional geometric point cloud data and historical service data and constructing a first finite element model. The model better fits the actual structure state; the local damage evolution and cumulative effect are simulated by means of a second finite element model, meanwhile, material degradation evaluation is combined, the interaction of three key factors including corrosion thinning, local damage and material mechanical property degradation is comprehensively considered, and the defect that the multi-factor coupling influence is not fully solved in the prior art is overcome; the extreme environment load condition is introduced to construct the third finite element model, the performance degradation law of the structure in the extreme environment can be analyzed, and then the reliability and applicability of evaluation are improved by establishing a mathematical relation model and predicting the full life cycle bearing performance.

The invention discloses an offshore wind power blade defect detection method, and belongs to the technical field of wind power blade detection. The detection method comprises the following steps: S01, offshore wind power blade defect detection: identifying the position of a blade defect, outputting a positioning mark frame of the defect on an original image, and providing the coordinate of the center position of the mark frame and the corresponding length and width; s02, leaf image splicing: carrying out step-by-step shooting; splicing each detected image by means of an image splicing technology to form a complete fan blade image; and S03, determining the actual physical position of the blade defect in the single image: in the spliced total image, determining the actual physical coordinate of the blade defect in the original single image corresponding to the fan blade. According to the detection method, multiple detection means such as computer vision, feature extraction, image splicing and edge detection technologies are fused, blade surface defect information can be comprehensively obtained, and the detection precision and accuracy are greatly improved.

The invention discloses a wind power equipment coating corrosion state monitoring method and device, equipment, a storage medium and a program product, and relates to the technical field of wind power generation, and the wind power equipment coating corrosion state monitoring method comprises the steps: obtaining equipment parameters of offshore wind power equipment and environment parameters collected by a sensor on the offshore wind power equipment; based on the equipment parameters and the environment parameters, establishing a digital twinborn model corresponding to the offshore wind power equipment; according to the digital twinborn model, the change condition of the coating state of the offshore wind power equipment is simulated, predicted coating state parameters are output, and the predicted coating state parameters at least comprise the coating corrosion state and the coating thickness of the offshore wind power equipment at the current time point. The technical problems that an existing wind power equipment coating corrosion state monitoring scheme is greatly limited by the environment, the workload is high, and the detection cost is high are solved.

The invention relates to the technical field of geotechnical engineering, and discloses a method, device and equipment for predicting the bearing capacity of an offshore wind power single pile and a medium, and the method comprises the steps that actual measurement data of different stratums where the single pile is located are obtained based on a pore pressure static sounding test; determining side friction resistance and unit pile end resistance of different stratums where the single pile is located based on measured data; the BP neural network model is trained based on preset single pile attribute data, the side friction resistance and the single pile end resistance, and a single pile bearing capacity prediction model is obtained; optimizing the single-pile bearing capacity prediction model based on a genetic algorithm to obtain a data processing type single-pile bearing capacity prediction model with gradually increased fitness; and solving the data processing type single-pile bearing capacity prediction model with gradually increased fitness to obtain an optimal solution of the single-pile bearing capacity. According to the method, the purpose that the accurate single-pile bearing capacity can be obtained only by conducting the pore pressure static sounding test subsequently is achieved, and the prediction accuracy is improved.

An offshore wind power plant current collection system optimization method considering cable type selection comprises the following steps: S1, acquiring wind power plant basic data including fan and transformer substation coordinates, fan rated power, multi-specification cable electrical and cost parameters and the like; s2, on the basis of the basic data, generating a candidate cable set by using clustering partitions and a distance threshold value, completing candidate set reduction, and synchronously generating a cable crossing avoidance constraint pair set; s3, an optimization model with the minimum total cost of the whole life cycle is established, the double-side ring path does not need scene variables, and the composite ring path needs to integrate scene driving variables; s4, carrying out path accelerated solution, carrying out hot start on a double-sided ring, carrying out cross constraint simplification, and carrying out additional fault set dimension reduction on a composite ring to obtain an initial scheme; and S5, performing full fault set N-1 verification on the initial scheme, bringing a violation scene into a fault set for resolution until the scheme meets an N-1 criterion, and outputting a final result. According to the method, topology and cable type selection integrated optimization is realized, and the solving quality and efficiency are considered.

The invention provides a method for evaluating the stability of an offshore wind power pile foundation under the coupling action of submarine landslide and wind wave impact load, and develops a finite element model of pile-soil interaction under the action of complex lateral load. Based on an established seabed landslide impact load quantification formula, the lateral load of the single pile under the coupling action of the seabed landslide, wind, wave and flow in the complex environment is given. And finally, carrying out single pile stability analysis under the action of complex lateral load, and discussing the stability of the single pile foundation by considering the sediment strength of different sea areas at the same time. Through the technical scheme of the invention, the method can judge the stability evaluation of the single pile foundation under the action of the wind wave current load and the potential landslide load in the research area, provides reference for site selection of offshore wind power, establishment of the offshore wind power and the like, and reduces the economic loss caused by the instability of the offshore wind power.

The invention discloses a multi-modal offshore wind power ultra-short-term prediction method in the field of offshore wind power plant cluster power prediction, and aims to solve the technical problems of spatial-temporal feature splitting and insufficient dynamic dependency relationship modeling. The method comprises the steps of performing anomaly detection and restoration on fan data, and generating a corrected wind power cluster data set; extracting a mean value, a standard deviation and a latest value of core operation data of each fan through a dynamic time window, and constructing a multi-dimensional node feature; a static geographic similarity matrix is generated based on geographic coordinates, a basic wake effect matrix is generated in combination with real-time wind direction data, correction is carried out through the maximum mutual information quantization time-delay effect, and then a dynamic adjacency matrix is obtained through self-adaptive fusion; and integrating the multi-dimensional node features and the dynamic adjacency matrix into a space-time diagram sequence data architecture, inputting the space-time diagram sequence data architecture into a multi-scale wake flow perception diagram space-time prediction model, and outputting a multi-fan power prediction value. According to the invention, high-precision multi-fan power prediction can be realized.

The invention relates to a high-performance solid-waste-based offshore wind power grouting material and a preparation method thereof, and a solid-waste-based cementing material is a hydraulic cementing material prepared by processing and grinding one or more solid wastes such as water-quenched blast furnace slag, steel slag, desulfurized gypsum, fly ash, red mud, tailings, carbide slag and phosphogypsum as raw materials according to a certain proportion. The solid waste-based cementing material is adopted to replace 525 Portland cement, the prepared grouting material product is excellent in performance, the carbon emission of the grouting material product is remarkably reduced, the cost is also reduced, resource utilization of industrial solid waste is facilitated, meanwhile, the performance of the grouting material is obviously improved, and the grouting material is suitable for popularization and application. The advantages of ultrahigh strength, elasticity modulus, high flowability, low shrinkage and high durability are realized.

This invention introduces a kinetic energy recovery wind-wave integrated system for offshore wind power generation. The system consists of a semi-submersible platform equipped with a fan and an internal wave energy device. The device includes a shell housing a Power Take-Off (PTO) system, featuring a permanent magnet synchronous linear motor and an active controller. The motor's stator is fixed inside the shell, while its mover is connected to a counterweight block outside the stator, linked to the shell's top via a spring. Limiters are installed at both ends of the shell to restrict the counterweight block's movement. This system utilizes the wave energy device to absorb kinetic energy, which otherwise affects wind turbine stability, and converts it into usable electrical energy via the PTO system. This enhances the stability and safety of offshore wind turbine power generation.

The invention provides a marine ecological environment influence tracking and monitoring method based on an offshore wind power project, and relates to the technical field of marine ecological environment, and the method comprises the steps: carrying out the ecological risk index calculation based on an obtained multi-modal feature vector, and obtaining ecological risk data, and configuring a space-time sequence based on the preprocessed comprehensive data source and the ecological risk data, and predicting a pollution diffusion path and migration blocking based on the preprocessed comprehensive data source. By introducing a dynamic evaluation model of an ecological risk index and combining time sequence modeling and spatial distribution modeling, dynamic analysis and future trend prediction of the ecological risk are realized, and by generating a risk thermodynamic diagram in real time, identifying a high-risk area and simulating a pollutant diffusion path and a biological migration blocking condition, the risk of the ecological risk can be predicted. According to the invention, the monitoring strategy can be dynamically adjusted according to the real-time monitoring data, a scientific basis is provided for construction optimization and ecological restoration, and the accuracy and timeliness of monitoring and evaluation are improved.

The invention discloses an offshore wind power low-frequency networking system based on hybrid topology and a control method, in N onshore receiving end nodes of the offshore wind power low-frequency networking system, one node adopts an M3C current converter, N-1 nodes adopt DRU and MMC current converters, an offshore low-frequency power grid voltage is established through the M3C current converter, and the M3C current converter is connected with the M3C current converter; the power flow control of other power control nodes is realized through the DRU and the MMC, and by adopting the technical scheme of the invention, the engineering construction cost can be obviously reduced under the condition of ensuring the reliable operation of the system, and the method has a good application prospect.

The embodiment of the invention discloses an offshore wind turbine generator operation and maintenance method and device based on operation and maintenance time window calculation, and the method comprises the steps: responding to fault warning information of any one wind turbine generator, and predicting the remaining life of the other wind turbine generators, so as to judge the life states of the other wind turbine generators; collecting historical operation and maintenance data and calculating an operation and maintenance time window; and constructing an operation and maintenance optimization model, and generating an operation and maintenance scheduling mode of the wind turbine generator based on the operation and maintenance optimization model. According to the embodiment, potential problems can be found in time, prevention measures can be taken, the operation and maintenance time window is calculated with the minimum operation and maintenance economic cost in the life cycle as the target, the operation and maintenance optimization model considering operation and maintenance emergencies is solved through an intelligent optimization algorithm, the corresponding wind turbine generator maintenance mode, personnel and equipment distribution and ship dispatching and operation and maintenance routes are obtained, and the maintenance efficiency is improved. Finally, single-time operation and maintenance work scheduling arrangement is completed, maintenance tasks and resources can be arranged more reasonably based on the scheduling mode of the operation and maintenance time window, and unnecessary waiting time and resource waste are reduced.

The invention provides a floating attitude adjustment and optimization system for a floating type offshore wind power equipment group, and relates to the technical field of offshore wind power equipment, and the system comprises a sensing monitoring module which collects an equipment operation attitude parameter set and a marine environment data set in real time; the information is transmitted to the central control unit through the data transmission module, and is analyzed and processed by the equipment attitude prediction module to generate equipment attitude prediction information; the adjustment strategy generation module obtains a target attitude adjustment strategy based on the equipment attitude prediction information and the construction information of the buoyancy attitude adjustment device; the adjustment parameter generation module obtains attitude dynamic adjustment parameters; and the attitude adjustment execution module performs multi-point attitude adjustment and dynamic feedback optimization on the equipment group. The technical problem that an existing floating type wind power equipment set lacks a flexible floating posture adjusting means and is difficult to cope with floating posture changes caused by a complex marine environment is solved, the operation stability of the equipment set is improved, and therefore the power generation efficiency is improved, and the service life of the equipment is prolonged.

The invention relates to an offshore wind speed prediction method based on a TVFEMD-FE-TCN-Transform model, and the method comprises the steps: obtaining historical offshore wind speed data, carrying out the preprocessing of the data, decomposing the original wind speed data through TVFEMD to obtain a plurality of IMF components, improving the data stability, and carrying out the prediction of the offshore wind speed. According to the method, four types of signals including a high-frequency signal, an intermediate-frequency signal, a low-frequency signal and a trend signal are generated through reconstruction according to IMF component complexity through fuzzy entropy FE, calculation complexity is reduced, a time domain convolutional network TCN is adopted to extract reconstructed signal features, fusion is performed, the reconstructed signal features are input into Transform for wind speed prediction, meanwhile, Transform model parameters are optimized through MWOA, and the wind speed prediction accuracy is improved. Predicting the offshore wind speed by using the optimal parameter combination Transform model to obtain a final offshore wind speed prediction value; according to the method provided by the invention, the problems of insufficient signal decomposition, weak feature extraction capability and low prediction precision of a single prediction model are effectively solved, and powerful support is provided for operation and maintenance management and power grid dispatching of the offshore wind power plant under the condition that the offshore wind speed has intermittent and fluctuation characteristics.

The invention provides an offshore wind turbine generator fault diagnosis method and system based on multi-source sensor data fusion. The method comprises the steps that a vibration signal from at least one component of a wind turbine generator is acquired through a vibration sensor; performing time-frequency conversion on the vibration signal by applying synchronous compression wavelet transform to obtain time-frequency representation of the vibration signal; when the reconstruction error exceeds a preset threshold value, it is judged that an abnormal event exists in the vibration signal; obtaining the position of a part corresponding to the abnormal event; starting an image sensor and an acoustic sensor according to the position of the component, and acquiring an image signal and a sound signal of the component according to the image sensor and the acoustic sensor; according to the DS evidence theory, the vibration signal, the image signal and the sound signal, obtaining the confidence of the fault type; the fault type of the component is judged according to the maximum confidence allocation principle, high-resolution time-frequency analysis can be achieved through synchronous compression wavelet transform (SST), and the fault feature identification degree is improved in combination with the self-encoding neural network and the D-S evidence theory.

The invention provides a corrosion risk assessment method and system for an offshore wind power steel structure, and the method comprises the steps: monitoring the corrosion state of the offshore wind power steel structure, and obtaining corrosion state data; based on the corrosion state data, corrosion rate parameters are calculated according to a corrosion rate model; calculating and analyzing the electric steel structure mechanical model to obtain a structure safety coefficient; checking the remaining service life of the steel structure according to the corrosion rate parameter and the structural safety coefficient to obtain a remaining service life parameter of the steel structure; and a corrosion risk grading standard is formulated, and the corrosion risk grade of the offshore wind power steel structure is obtained through evaluation. By comprehensively considering the corrosion state, the corrosion rate, the structure safety and the remaining service life, the corrosion risk can be recognized as soon as possible, technical support is provided for operation and maintenance work of the offshore wind power steel structure, then it is ensured that the structure can still keep sufficient safety under the influence of corrosion, the operation and maintenance cost and efficiency are optimized, and the service life of the structure is prolonged.

The invention relates to a method and a system for determining the elevation of an offshore wind turbine foundation scouring analysis datum plane, and the method comprises the steps: carrying out three-dimensional modeling through seabed three-dimensional point cloud data, analyzing the thickness of shallow sludge through high-frequency and low-frequency sounding data, and obtaining stratum distribution through combining with shallow profile data; calculating a theoretical scouring depth based on the water flow data, and reconstructing a three-dimensional grid data model; calculating and screening a suitable area through a terrain gradient factor, and constructing a seabed base plane calculation data set; and calculating the average elevation of each fan basic area as the datum level elevation of scouring analysis. According to the method, refined reconstruction of the regional landform of the offshore wind turbine foundation is realized, the accuracy and scientificity of determining the elevation of the reference surface are improved, reliable data support is provided for stability evaluation and design optimization of the offshore wind turbine foundation, the structural safety risk caused by scouring is remarkably reduced, and the economic benefit is improved. And the overall safety and economic benefits of the offshore wind power plant are improved.

The invention discloses an offshore wind power plant micro-siting optimization method considering regular layout. The method comprises the following steps: firstly, obtaining wind data, wind power plant parameters, fan parameters and wake flow parameters; performing multi-angle rotation on the wind power plant based on an equivalent rotation coordinate method, constructing a gridding mixed integer quadratic programming model, and performing rotation rule layout optimization (RRLO-1) to obtain an initial fan layout; on the basis of the initial layout, a hybrid algorithm (GA-ISSA) of a genetic algorithm and a sparrow search algorithm is adopted, the position of a draught fan and the rotation angle of the wind power plant are finely adjusted, and fine rule layout optimization (RRLO-2) is completed; and finally, the wake flow influence is calculated through a Cosine wake flow model and a quadratic sum superposition model, a fan power curve is processed in a piecewise linearization mode, and the final layout is output by taking the maximization of the total generating capacity as the target. According to the method, a fan position configuration scheme meeting the engineering rule layout requirement is provided for early-stage planning of the offshore wind plant, the wake effect is reduced, the power generation efficiency is improved, and development of the offshore wind power industry and low-carbon target implementation are facilitated.

A method for configuring reactive power capacity of a power grid to which offshore wind power is connected on a large scale includes the following steps. Static voltage stability index of substations in a regional power grid to which the offshore wind power is connected on the large scale and at multiple points and relative dynamic voltage drop area index for an N-2 fault are calculated. Thus, weak nodes in voltage stability are located, thereby implementing a prediction of a risk to the voltage stability of the regional power grid. These nodes are used as candidate reactive power compensation configuration nodes. An objective function is established as the minimum total cost of reactive power compensation devices at the candidate reactive power compensation configuration nodes. A multi-type reactive power optimization configuration model including static voltage stability constraints, voltage stability constraints under faulty state, and other constraints is solved.

The invention relates to an offshore wind power prediction method, and aims to improve the accuracy and reliability of prediction. The method comprises the following steps: (1) a data preprocessing stage: detecting an abnormal value in data by using a DBSCAN clustering algorithm, reconstructing the abnormal value by using a KNN interpolation method, detecting time sequence abnormity through an LSTM automatic encoder, and performing regression reconstruction by using LSTM to ensure the retention of time sequence features; (2) a feature engineering stage: screening out key features through correlation analysis, generating a label column through K-means clustering and wind direction classification, and extracting features such as wind direction change rate, time periodicity and wind speed interaction; and (3) a model construction stage: constructing a composite model in combination with multi-scale convolution (Inception), a bidirectional long short-term memory neural network (BiLSTM) and a multi-head self-attention mechanism, extracting local features through a convolution layer, capturing a time dependency relationship through a bidirectional LSTM layer, enhancing the attention of key features by using the multi-head self-attention mechanism, and finally realizing high-precision wind power prediction.