8773 results about "Wind power" patented technology

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

The invention provides a wind power booster station equipment fault prediction and diagnosis method and system, and relates to the technical field of power equipment fault diagnosis, and the method comprises the steps: constructing an equipment topological relation through a knowledge graph, employing a double-flow heterogeneous graph neural network to extract space-time cooperation features, generating a candidate path based on multi-hop reasoning, extracting a key evidence chain, and calculating a credibility score. And combining multi-scale fault feature reconstruction and Tsallis entropy calculation to obtain a diagnosis result. According to the invention, the fault root cause can be accurately identified, the diagnosis accuracy is improved, the false alarm rate is reduced, and decision support is provided for wind power plant equipment maintenance.

The invention provides a combined wind power prediction method suitable for a distributed wind power plant, and the method comprises the steps: collecting the real-time meteorological data and historical power data of a wind power plant cluster, carrying out the cross-wind-plant data collaborative cleaning, and generating a time-space aligned standardized data set. Constructing an adaptive spatio-temporal feature extractor, outputting a spatio-temporal feature matrix, and inputting the spatio-temporal feature matrix into the spatio-temporal adaptive neural network, the graph attention prediction model and the physical constraint decision tree model to generate three prediction sequences. And the sequences are fused through a space-time collaborative attention mechanism to generate a dynamic weighted combination prediction result. And performing physical constraint correction on the result by using a space-time residual error correction network to generate a final prediction sequence. And updating the neural network topological structure based on the prediction error distribution, and outputting a prediction result with uncertainty evaluation to a power grid dispatching system. According to the method, the precision and reliability of wind power prediction of the distributed wind power plant can be improved, and the stability and economy of power grid dispatching are improved.

The invention discloses an online prediction method for a transient frequency track of a power grid under coexistence of wind power low voltage ride through and off-grid, and belongs to the technical field of operation and control of a power system. A multi-source heterogeneous data fusion monitoring system is constructed, power grid and wind turbine generator data are collected, faults are recognized through an improved algorithm, and feature vectors are output; building an energy flow model based on a fault result, calculating a trajectory divergence index by using technologies such as phase-space reconstruction, estimating power vacancy, and obtaining a power unbalance sequence; designing a prediction algorithm by using the sequence, predicting a frequency trajectory in combination with an improved K-nearest neighbor algorithm and a trajectory feature library, and introducing a confidence coefficient to evaluate a correction error; and finally, establishing a three-level control response system, and implementing multi-time scale cooperative control according to a prediction result. The method can accurately predict the frequency trajectory, effectively deal with the wind power fault, improve the stability of the power grid and the wind power consumption capability, and provide powerful guarantee for the safe and stable operation of the power grid.

The invention provides a wind power prediction method and system based on time sequence decomposition and multi-model fusion, and the method comprises the steps: collecting the historical power generation power and meteorological data of a target wind power plant; decomposing the historical power generation power and the meteorological data to obtain a trend component, a seasonal component and a residual component; fusing with meteorological data to construct a trend feature matrix, a periodic feature matrix and a residual feature matrix; different modeling schemes are adopted to construct corresponding single models; dividing into a training set, a verification set and a test set according to a time sequence; performing training optimization on the single model by using the training set, the verification set and the test set, and constructing a wind power short-term power prediction model; and inputting the real-time meteorological data and the generated power to the wind power short-term power prediction model, and outputting the generated power prediction value of the target wind power plant, thereby effectively improving the comprehensiveness, accuracy and stability of model prediction.

A method and system for robust optimization of microgrid scheduling, relating to the technical field of microgrid scheduling. The method comprises: constructing a multi-interval uncertainty set by means of an uncertainty prediction parameter (S1); on the basis of the established multi-interval uncertainty set, constructing a robust scheduling model of a microgrid (S2); and using a column constraint generation algorithm to iteratively solve the constructed robust scheduling model, to obtain a net load curve and an operation plan for energy output (S3). In the present invention, on the basis of traditional single-interval robust optimization, a multi-interval uncertainty set is constructed on the basis of wind power prediction data, a multi-interval two-stage robust optimization model is established on the basis of the foregoing, and the conservative nature of single-interval robustness is reduced. A nested column and a constraint generation algorithm are used for solving. In the first stage, using minimum net load fluctuation as a goal, planning is carried out on the basis of the prediction data, and in the second stage, considering the uncertainty of wind power, wind power output in a worst-case scenario is searched for, and the policy of the first stage is adjusted, so that the stability of the microgrid is ensured.

The invention discloses a pole climbing robot for a wind power tower pole, which is characterized in that the robot mainly comprises a robot body, 4-8 pole climbing mechanisms, a snake-shaped swing arm and a movable studio, wherein the snake-shaped swing arm and the movable studio are mounted on one side of the robot body; the robot body is an annular body formed by combining two semicircular cambered bodies or three equal cambered bodies; the diameter of an internal ring of the circular body is matched with the diameter of the wind power tower pole; the pole climbing mechanisms mainly comprise upper foot wheels, lower foot wheels, connecting arms, driving gears, thrust screw rods, nut gears and supporting seats; the upper foot wheels are connected with the lower foot wheels by the connecting arms through wheel shafts; the connecting arms are connected with the thrust screw rods by connecting pin shafts on the connecting arms, and form movable hinges; the 4-8 pole climbing mechanisms are distributed in the circular robot body uniformly; the snake-shaped swing arm consists of three swing arm parts, namely a large arm, a middle arm and a small arm; and the movable studio comprises two paralleled linear guide rails, a walk driving device and an operation room.

The invention relates to the technical field of wind power equipment monitoring, and discloses a wind power gear box intelligent fault early warning method and system based on machine learning. The method comprises the steps that multi-source monitoring data such as vibration signals, temperature data and oil analysis data of the wind power gear box are acquired, and multi-scale operation characteristics are extracted through time-frequency conjoint analysis; key fault sensitive features are determined through an adaptive feature selection algorithm, and a dynamic fault feature weight matrix is constructed in combination with a historical fault case library; multi-modal data fusion is adopted to generate an enhanced fault feature set, and modal decomposition is carried out on the enhanced fault feature set to obtain a trend component and a fluctuation component; a fault evolution feature space is constructed by using a deep neural network based on two components, then a fault development mode is identified by using a time sequence mode matching algorithm, and finally a graded early warning signal is generated according to a matching degree with a preset mode, so that fault features can be comprehensively captured, and safe operation of a wind power gear box is ensured.

The invention relates to a wind power equipment blade fault detection technology, discloses an unsupervised wind power equipment blade fault detection method based on a phase perception parallel attention mechanism, and solves the problems that an existing wind power equipment blade fault detection method is high in dependence on labeled data, insufficient in generalization ability under strong noise and variable working conditions and high in fault detection efficiency. And a weak transient fault signal and a dynamic change characteristic are difficult to capture robustly. According to the scheme of the invention, the method comprises the steps: collecting a blade operation audio signal, and extracting a dual-channel time-frequency feature containing an amplitude spectrum and a phase spectrum through improved short-time Fourier transform; a deep adversarial auto-encoder is constructed by using an encoder containing a phase perception parallel attention module, a decoder and an auxiliary encoder, and normal working condition feature distribution is learned by reconstructing an error loss, potential representation consistency loss, adversarial loss and phase consistency loss optimization model during off-line training; in the reasoning stage, the fault is judged based on the feature distance score and the reconstruction error score.

The invention discloses a fan blade state monitoring method based on multi-sensor fusion, relates to the technical field of wind power, and is suitable for wind energy prime mover equipment manufacturing and blade state monitoring technologies of onshore and offshore wind generating sets. The method comprises the following steps: acquiring operation data, a vibration signal, an acoustic signal and a pulse signal of a fan; the current working condition state of the fan is recognized, common-mode fault verification, local damage positioning and transient stress damage analysis are carried out on the vibration signals and the acoustic signals, and a fault analysis result and a first damage analysis result are obtained; performing phase-locked amplification analysis on the vibration signal and the acoustic signal through active excitation to obtain a second damage analysis result; and finally, a comprehensive state monitoring report of the fan blade is generated, so that the problems of difficulty in identification of weak damage and high false alarm rate of blades of land and offshore wind generating sets in wind energy prime mover equipment manufacturing under a non-stable working condition are solved, and the equipment operation and maintenance intelligent level in the wind energy prime mover equipment manufacturing industry is effectively improved.

The invention discloses a fan blade diagnosis method and system based on voiceprint perception, and relates to the technical field of intelligent monitoring of wind power equipment, and the method comprises the steps: carrying out frequency domain compensation processing, stripping environment wind noise components, and generating a pure voiceprint signal based on a wind flow characteristic parameter set; based on the pure voiceprint signal, identifying a sensitive acoustic frequency band of the fan blade, exciting a sound wave phase synergistic effect in the sensitive acoustic frequency band, and generating an enhanced voiceprint signal; quantifying the internal damage depth of the fan blade material according to the enhanced voiceprint signal, and calculating a sound energy flow disorder degree index; according to the sound energy flow disorder degree index, the blade health state of the fan is judged through a multi-dimensional acoustic characteristic state space mapping mechanism, and a three-dimensional health assessment report is generated; through a physical level wind noise stripping technology, a transfer function matrix is generated based on vortex interference modeling and sound wave-airflow phase offset correction, frequency domain energy reweighted decoupling is realized, and environmental wind noise is accurately stripped in a strong turbulence environment.

The invention discloses an optimal design method for a floating wind power-wave energy multi-energy complementary power generation platform, which comprises the following steps of: constructing an integrated and parameterized system model which is a fully-coupled and parameterized numerical model comprising all key components of the floating wind power-wave energy multi-energy complementary power generation platform; all key design parameters influencing the system performance are set as parameterized variables; establishing a multidisciplinary coupling dynamic simulation model; defining a multi-objective optimization problem including decision variables, objective functions and constraint conditions; the decision variable selects a group of core variables from the parameterized variables as optimization input; and combining the multi-objective optimization problem with a multidisciplinary coupling dynamic simulation model, executing a multi-objective optimization cycle, and generating and deciding a Pareto optimal solution set to obtain typical design schemes with different characteristics. According to the method, the global optimization design of the floating wind power-wave energy multi-energy complementary power generation platform can be realized.

The invention belongs to the technical field of wind power equipment fault diagnosis, and relates to a wind turbine generator maintenance method based on multi-modal data fusion and a knowledge graph. Comprising the steps of obtaining text description data, component image data and equipment operation time sequence data of a wind turbine generator; predicting the residual life of the component based on the equipment operation time sequence data, and generating a residual life prediction value; performing feature extraction on the data to obtain corresponding features; performing fusion processing on the features to obtain joint feature representation; analyzing a fault evolution time sequence mode of the wind turbine generator based on the joint feature representation; performing matching retrieval in a historical case library according to a fault evolution time sequence mode; and inputting a matching retrieval result into the dynamic knowledge graph for reasoning, generating a fault traceability result, and generating a maintenance decision scheme including a maintenance priority list in combination with the residual life prediction value. According to the method, accurate fault diagnosis, automatic source tracing of root causes and dynamic optimization of maintenance strategies are realized, the operation and maintenance efficiency is remarkably improved, and the cost is reduced.

The invention discloses a low-altitude wind field prediction method and system based on a space-time diagram convolutional network, and relates to the technical field of weather forecast and wind energy utilization, and the method comprises the steps: collecting wind field observation data and physical field data of all nodes of a target region, a dynamic space-time diagram is constructed based on a flow function-vorticity theory through a dynamic diagram construction module; extracting spatial information through a graph attention network to obtain a spatial feature tensor; the spatial feature tensor and the physical field data are processed by a PhysFusion-TransTCN encoder to obtain the deep spatial and temporal features of the wind field; performing hierarchical feature aggregation on the wind field deep spatial-temporal features through an output module to obtain a wind field prediction result of the target area; a wind field physical mechanism is deeply fused, multi-scale spatial-temporal feature fusion is realized, and prediction result precision and physical rationality are ensured.

A wind-photovoltaic ratio system optimization configuration method for a new energy hydrogen production park, the method comprising the steps of: S1) calculating an output time series of local wind power and photovoltaic power generation, and analyzing wind power and photovoltaic output characteristics and a wind-photovoltaic correlation; S2) establishing equivalent device models of a hydrogen energy storage unit and an electrochemical energy storage unit to form an electric bus power balance and hydrogen bus power balance relationship, and taking the minimization of a full life cycle cost as an objective function and taking into consideration constraint conditions such as the power balance, device operation characteristics and a land restriction to establish a system planning optimization configuration model for a new energy hydrogen production park, which model takes a wind-photovoltaic ratio into consideration; S3) integrating the objective function and the constraint conditions to obtain a system planning and optimization configuration scheme for the new energy hydrogen production park and the total system configuration cost; and S4) taking into consideration an electricity price factor and an energy storage operation characteristic constraint, performing annual hydrogen production timing operation simulation, and analyzing a park electricity purchase demand and a new energy consumption capacity under the present planning optimization configuration scheme.

The invention discloses a multi-modal data fusion and intelligent analysis platform and method for an unmanned aerial vehicle inspection system for a wind power plant. The platform comprises a multi-modal data acquisition module, a feature extraction and standardization module, a multi-modal information fusion module, a joint learning and optimization module, a domain knowledge injection module and an intelligent decision and application module. The system processes multi-source heterogeneous data through an integrated learning and deep learning fusion strategy, projects features to a shared semantic space by using joint training and comparative learning to enhance the anomaly discrimination ability, and performs verification and semantic enhancement on a supervised retrieval result in combination with a knowledge base in the wind power field. And finally, outputting a high-reliability diagnosis report and a maintenance suggestion. According to the invention, accurate identification and positioning of the fan fault are realized, and the inspection efficiency and the system decision reliability are significantly improved.

The invention discloses a new energy equipment troubleshooting method based on a knowledge graph and a large model. The method is used for solving the problems of fault positioning and troubleshooting of photovoltaic and wind power new energy equipment and the like. The method mainly comprises the following steps: performing data cleaning and preprocessing on a new energy equipment troubleshooting professional field document, and constructing a structured knowledge graph of equipment types, fault types, fault information and solution nodes in a graph database through Cypher; the big language model analyzes the user fault description, extracts and maps node attributes in the knowledge graph, and generates a graph query statement; the question-tracing type multi-round interaction is realized, the missing information is dynamically complemented, and the basic fault is accurately positioned in a step-by-step reasoning mode; according to the method, multi-modal resources such as texts, schematic diagrams and videos stored in the knowledge graph are combined, a step-by-step reasoning path and a solution with illustrative features are generated through a preset template, the whole reasoning process is visualized in a branch thinking graph form, and the accuracy, interpretability and user experience of troubleshooting are remarkably improved.

The invention discloses a new energy equipment intelligent operation and maintenance system and method based on digital twinning, and relates to the technical field of new energy equipment operation and maintenance management. The system comprises a data acquisition module, a digital twin construction module, a model adaptive module, an intelligent analysis module, a decision optimization module and a knowledge closed-loop module. The data acquisition module realizes multi-source heterogeneous data fusion and standardization; the digital twin construction module generates geometric, physical and behavior models and is linked with real-time data; the model adaptive module dynamically calibrates the key parameters; the intelligent analysis module completes anomaly detection, fault diagnosis and life prediction; the decision optimization module formulates a maintenance scheduling and operation strategy and realizes a control closed loop; and the knowledge closed-loop module constructs a structured fault graph through text analysis to continuously optimize the model. The method can be widely applied to wind power, photovoltaic, energy storage and other scenes, and efficient and intelligent operation and maintenance of new energy equipment are achieved.

The invention discloses a water electrolysis hydrogen production intelligent control system and method based on artificial intelligence. The system and method are suitable for a large-scale water electrolysis hydrogen production scene under the power supply condition of fluctuating renewable energy sources such as wind power and photovoltaic. The system comprises a data acquisition layer, an edge calculation layer, an intelligent control layer and an execution layer. Dynamic modeling of the running state of the hydrogen production system is achieved through multi-parameter real-time monitoring and feature extraction. The intelligent control layer fuses an LSTM prediction module and a reinforcement learning controller, the LSTM prediction module is used for predicting future renewable energy input and hydrogen demand trends, and the reinforcement learning controller calculates an optimal current density set value based on a prediction result so as to maximize hydrogen production efficiency per unit energy consumption. And meanwhile, a digital twinning technology and a safety protection mechanism are combined, thermoelectric dual regulation and control of the electrolysis process are achieved, and the control response speed and the system stability are improved. According to the invention, energy consumption can be effectively reduced, the service life of the stack is prolonged, and the operation efficiency and reliability of the hydrogen production system under complex load are improved.

The invention relates to the technical field of quality detection, in particular to a gas atomization powder sphericity degree multi-dimensional online analysis system and a method thereof.The gas atomization powder sphericity degree multi-dimensional online analysis system comprises a dynamic dual-mode imaging module, a transverse wind field auxiliary detection unit and a multi-dimensional feature fusion analysis module; the transverse wind field auxiliary detection unit is used for applying controllable transverse wind power through an airflow nozzle orthogonal to the powder falling direction and measuring the deflection track of gas atomization powder ball particles in combination with a laser displacement sensor. The online calibration module is used for spraying standardized spherical particles to the powder flow according to a preset period; the traditional density detection depends on destructive sampling and off-line measurement, the efficiency is low, and the whole production line particles cannot be covered; through an orthogonal wind field trajectory inversion technology, a density value and an internal defect mark are synchronously output through non-contact dynamic measurement, 100% lossless online full inspection of a production line is realized, and sampling limitation and aging bottleneck of a traditional means are broken through.

The utility model is suitable for the technical field of household appliances, and provides a moisture absorption and exhaust system, a drying module and clothes processing equipment, the moisture absorption and exhaust system comprises a moisture absorption and exhaust shell and a rotatable moisture absorption and exhaust part arranged in the moisture absorption and exhaust shell, and the moisture absorption and exhaust part comprises a moisture absorption part and a desorption part; a first opening and a second opening which are communicated through a moisture absorption part are formed in the end faces, along the two axial sides of the moisture absorption and exhaust part, of the moisture absorption and exhaust shell, and an air inlet duct part and an air outlet duct part which are located on the two axial sides of the desorption part and communicated through the desorption part are further arranged on the end faces, along the two axial sides of the moisture absorption and exhaust part, of the moisture absorption and exhaust shell. The outer air guide face gradually inclines towards the desorption part in the direction facing the central axis of the moisture absorption and exhaust part and is used for guiding air in the main drying air channel to reach the moisture absorption part of the moisture absorption and exhaust assembly and / or guiding air flowing out of the moisture absorption part, it is avoided that air vortexes are generated in the main drying air channel, wind energy loss is caused, the air passing speed of the moisture absorption and exhaust system is guaranteed, and the service life of the system is prolonged. The water desorption efficiency is ensured, the energy consumption is reduced, and the drying efficiency is improved.

The invention relates to the technical field of wind power tower monitoring, and discloses a wind power tower health monitoring method and system. The method comprises the following steps: acquiring structural vibration signals and environmental load data of a wind power tower drum, extracting dynamic response characteristics of the tower drum, and establishing a basic monitoring data set of the running state of the tower drum in combination with the environmental load data; performing multi-dimensional decomposition processing on the basic monitoring data set, separating an inherent frequency component, a damping characteristic parameter and an external excitation coupling component of the tower drum, and constructing a first evaluation index of tower drum structure health according to the relevance between the inherent frequency component and the damping characteristic parameter; historical damage records and material degradation data of the tower drum are obtained, the historical damage records and the external excitation coupling component are subjected to time sequence alignment analysis, and a second evaluation index of tower drum damage evolution is generated; and fusing the first evaluation index and the second evaluation index, establishing a comprehensive scoring model of the health state of the tower drum, dividing the health level of the tower drum, and outputting a real-time monitoring report.

The invention relates to the technical field of wind power, in particular to a dynamic response test method for a wind power variable pitch system. Comprising the steps of building a high-fidelity digital twinborn model, performing virtual testing and case optimization, performing physical self-adaptive testing execution, performing real-time analysis and decision-making driven by machine learning, dynamically adjusting testing stress, performing data feedback and model calibration and generating a comprehensive health report. Compared with the prior art which mainly depends on a fixed physical test process and artificial experience judgment and has the defects of low test efficiency, large resource consumption and difficulty in comprehensively covering extreme working conditions, the method innovatively introduces a high-fidelity digital twinning technology and a virtual test optimization process; according to the method, massive tests are executed in the virtual environment in a risk-free manner, and the enhanced test cases are intelligently generated, so that test forward movement and accurate optimization are realized, the pertinence and efficiency of physical tests are remarkably improved, and the test cost and period are greatly reduced.

The invention relates to a power distribution network voltage partition control method and system based on a hierarchical K-means clustering algorithm, and belongs to the technical field of power distribution system voltage partition optimization. According to the technical scheme, power distribution network node parameters are collected in a self-adaptive partition mode, a node sensitivity coefficient and an eigenvector of an electrical distance are constructed, and then a K-means algorithm is used for fine region division; selecting a dominant node: solving a Jacobi matrix through load flow calculation, extracting a voltage sensitivity coefficient, and selecting a node with the maximum sensitivity as a dominant node in each partition; multi-objective optimization: constructing an optimization model with minimum network loss, minimum voltage deviation and highest voltage stability as objectives; and partition cooperative control: accessing wind power and photovoltaic power to the dominant node, adjusting reactive power output in real time according to an optimization result, and realizing partition autonomy and global cooperation. According to the invention, voltage fluctuation and out-of-limit are inhibited, system network loss is reduced, control efficiency and economy are improved, and the method is suitable for complex topology and high permeability scenes.

The invention discloses a wind power multi-scale decomposition prediction method. At present, single-point prediction is not comprehensive and accurate enough, and cannot adapt to quantitative accurate requirements of a wind power plant and a power grid dispatching mechanism in risk management. The method comprises the following steps of: forming an original wind power sequence from actually acquired wind power data, sequentially performing feature selection and data decomposition processing to form multi-scale modal data, and constructing a depth prediction model according to the multi-scale modal data; a probability prediction interval determination process is completed in the residual error distribution mode depth prediction model through adaptive bandwidth kernel density estimation; after actually obtained wind power data form an original wind power sequence, an initial model is established, feature selection processing is performed on the initial model, that is, weighted marginal contribution is calculated for each feature of the initial model according to all involved feature subsets by using an SHAP algorithm based on a Shapley value in a game theory, and the weighted marginal contribution of each feature of the initial model is calculated; and completing a feature data acquisition process of accurately quantifying interdependence and interaction effect between features.

The invention provides an optimization scheduling method and system for power distribution of an electrolytic cell group under a wind power fluctuation condition, and relates to the technical field of wind power grid-connected power generation control, and the method comprises the steps: collecting wind power plant power data and electrolytic cell operation parameters, carrying out the adaptive segmentation processing of the power data, constructing a fluctuation feature space, predicting the fluctuation risk, and carrying out the optimization scheduling of the power distribution of the electrolytic cell group. And an electrolytic cell regulation capability evaluation system is established and dynamically grouped, and a power regulation matrix is constructed to calculate an optimal power distribution proportion, so that coordinated regulation of wind power fluctuation by the electrolytic cell group is realized, and the system operation stability and the energy utilization efficiency are improved.

The invention discloses an existing wind power blade electrothermal film deicing control method based on big data, and relates to the technical field of wind turbine generator operation control and ice prevention and removal. Frost ice / glaze ice probability output by an ice type discrimination model does not only give an alarm any more, but is directly converted into an increase / decrease coefficient constrained by boundary and monotonicity; energy can be fully put in a glaze ice scene, and overheating and over-consumption can be avoided in a frost ice scene; according to the method, the change trend of wind speed, temperature and relative humidity is introduced as modulation factors, the working condition of forming / turning glaze ice is responded in advance, and insufficient deicing caused by static threshold hysteresis is reduced; according to power deviation correction, a unit wind speed-power curve serves as a reference, a dead zone, segmented gain and accumulative saturation structure is adopted, on the premise of not depending on an external icing sensor, extrinsic performance of pneumatic degradation is closed into controlled quantity, sensitivity and stability are considered, and start-stop oscillation and duration drift are reduced.

The invention discloses a distributed new energy and energy storage primary frequency modulation multi-agent cooperative control method and system, and belongs to the technical field of power system frequency modulation. The method comprises the following steps: constructing a multi-agent power distribution network simulation environment based on power grid topology and integrated wind power / photovoltaic / energy storage models and constraints; distributing an independent intelligent agent for each unit, and defining an observation space containing information such as frequency deviation and an output adjustment action space; an MADDPG framework containing a shared Critic network and an independent Actor network is built, and cooperative training is achieved through experience playback; designing a comprehensive reward function fusing the frequency, the voltage, the energy storage SOC and the electricity abandoning rate; and outputting a real-time output instruction by using the trained model to realize primary frequency modulation cooperative control. The method can significantly improve the response speed and precision of the distributed energy participating in the primary frequency modulation, guarantees the system safety, reduces the power abandonment rate of the new energy, and meets the power grid safety requirements of the high-permeability new energy.

The invention discloses a big language model wind power prediction method fusing decomposition and prompt, relates to the technical field of wind power prediction, and solves the technical problem that the wind power prediction method is difficult to consider multi-modal modeling, multi-scale perception and migration generalization ability at the same time. The method comprises the following steps: performing CEEMDAN decomposition on input wind power and related meteorological variables, and extracting an intrinsic mode function component and a residual term; performing standardization processing to obtain an embedded sequence, dividing the embedded sequence into patches with fixed lengths, and encoding each patch into an embedded vector; inputting the embedded vector into a reprogramming module and converting the embedded vector into reprogramming embedding; a natural language prompt is constructed for each intrinsic mode function component and the residual sequence, and after the natural language prompt is reprogrammed, embedded and spliced with the corresponding component, a unified input sequence is formed; performing lightweight fine tuning through a LoRA method to obtain a large language model; and inputting the unified input sequence into the large language model to obtain a wind power prediction value. According to the invention, an efficient and generalizable large language model wind power prediction method is constructed.

The invention relates to the technical field of power dispatching management and control, and discloses a power distribution network power dispatching method based on a virtual power plant AI large model and demand response, and the method comprises the steps: collecting the operation data of a power distribution network, and constructing a feature vector; an AI large model is adopted to calculate and predict load output, and joint uncertainty information is output; calculating a system power unbalance amount, and generating a scheduling strategy; issuing a scheduling instruction corresponding to the scheduling strategy and executing the scheduling instruction; comprehensive performance evaluation indexes are calculated, and whether a performance reduction reason diagnosis mechanism is started or not is judged; according to the method, the AI large model is adopted, the load power, the photovoltaic output and the wind power output are predicted at the same time through a multi-task learning strategy, and the correlation among multiple variables is fully utilized; by constructing a multi-objective optimization model, comprehensively considering economy, safety and reliability and adopting an improved particle swarm optimization algorithm for solving, coordinated optimization configuration of demand response resources is realized, power grid fluctuation is effectively reduced, and power supply reliability is improved.