6495results about "Ac network circuit arrangements" patented technology

The invention discloses an anomaly detection and intelligent diagnosis method, system and device based on digital power grid multi-source data and a medium, and belongs to the technical field of anomaly detection, and the method comprises the steps: obtaining multi-source operation data from a power grid operation process, carrying out the preprocessing, and generating a standardized data set; time sequence features are extracted based on historical data, a power grid state reference model is constructed, and normal operation states in different load scenes are represented; on the basis of deviation calculation of the standardized data set and the power grid state reference model, abnormal candidate signals are detected, and high-confidence-coefficient abnormal signals are screened and generated; determining an abnormal source based on the high-confidence abnormal signal in combination with a power grid topological structure, and performing analysis to obtain fault type information; and generating a control instruction according to the fault type information and issuing the control instruction to a power grid control system. According to the method, a complete technical scheme of multi-dimensional data fusion, dynamic deviation detection, high-confidence anomaly screening, anomaly source accurate positioning and fault type rapid diagnosis is realized.

The invention relates to the technical field of collaborative optimization, in particular to a virtual power plant power generation-utilization-price collaborative optimization system based on an AI large model, and the system comprises a load confidence matching module, a resource stability mapping module, a source-load capacity coupling module, an electricity price interval adjustment module and a comprehensive regulation and control linkage module. According to the method, the confidence interval prediction of the load demand is realized based on the hybrid neural network modeling of the load behavior data and the equipment temperature control characteristic sequence, and the scheduling matching confidence is measured according to the boundary overlapping condition of the prediction interval and the power generation response characteristic; a stability screening mechanism for adjusting resources is constructed in combination with the output fluctuation ratio and the equipment inertia characteristic, the controllability of load adjustment and the real-time performance of source side response are improved, the price adjustment rhythm is corrected through an electricity price response delay factor, dynamic closed-loop linkage between load adjustment and price guidance is achieved, and the load adjustment efficiency is improved. The execution priority is dynamically updated under the condition that multiple response conditions are matched, and the certainty of resource scheduling and the sensitivity of response are improved.

The invention relates to the technical field of power grid monitoring, in particular to a power operation risk identification method, system and device based on multi-modal data fusion and a storage medium. In order to solve the problems of multi-modal data splitting, topological constraint missing and the like in traditional power disturbance analysis, an improved BERT model is constructed, and electrical signal time-frequency features and text semantic information are mapped to a unified vector space through a multi-modal embedding mechanism; a time sequence attention mechanism is adopted to establish a time dependency relationship between signals and texts, and a graph attention network is combined to realize risk propagation modeling under power grid topology constraints; and collaborative optimization of disturbance classification, risk prediction and trend analysis is carried out through a multi-task learning framework. The technical problems that heterogeneous data fusion is difficult and risk identification precision is insufficient are effectively solved, accurate identification and intelligent early warning of electric power operation risks are achieved, and the safe operation level of a power grid is improved.

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 relates to the technical field of electric power monitoring analysis, and discloses an intelligent analysis system for electric power monitoring data based on a mutual inductor. The system comprises a mutual inductor data acquisition and structuring module which acquires a current waveform, a voltage waveform and a harmonic component in real time, and generates a standardized monitoring data unit through preprocessing, field mapping and association identification establishment; the theoretical monitoring value calculation module constructs a dynamic calculation model according to historical power data, and an input standard data unit outputs a theoretical value; the rule conformity verification module is used for matching the equipment type with the operation rule and generating single compliance judgment; a multi-dimensional difference analysis module compares a theoretical value with a measured value from time domain deviation, frequency domain deviation and waveform distortion, and generates an equipment-level difference coefficient matrix; the association network construction module is used for constructing a multi-monitoring-point association map according to the equipment identifier and the position information; and the anomaly positioning and strategy generation module combines the map, compliance judgment and a difference matrix, calculates a risk score, generates an anomaly probability distribution map, positions anomaly and matches a monitoring strategy.

The invention provides a power transmission network equipment fault diagnosis and life prediction method and system, and relates to the technical field of fault diagnosis, and the method comprises the steps: obtaining the time sequence electrical characteristic data of a plurality of monitoring nodes, carrying out the window segmentation and statistical characteristic extraction, building a dynamic association graph structure based on a space-time association constraint model, and obtaining the time sequence electrical characteristic data; and calculating the abnormal contribution degree of each node, marking candidate abnormal nodes, determining a fault propagation path through reverse tracing and path analysis, and finally outputting a fault positioning result. According to the invention, abnormal nodes can be accurately identified, a fault propagation path can be accurately tracked, and the accuracy and timeliness of power transmission network fault diagnosis are improved.

The invention discloses a photovoltaic power generation power prediction method and system based on a large language model. The method comprises the following steps: converting historical power data and numerical weather forecast data into time sequence embedded representation; through cross-modal semantic alignment, semantic embedding representation is generated; constructing a natural language prompt containing task context information, encoding the natural language prompt into prompt embedding, combining prompt embedding with semantic embedding representation to form a fusion input sequence, inputting the fusion input sequence into a pre-trained large language model, and outputting implicit features; synchronously generating an initial power prediction result and a weather prediction result obtained by correcting the numerical weather prediction data through a parallel collaborative prediction mechanism; and taking the meteorological prediction result as a correction signal, performing joint optimization on the preliminary power prediction result, and outputting a power generation power prediction value. According to the method, the problem of deep fusion of heterogeneous data is effectively solved, and the prediction accuracy is improved.

The invention belongs to the technical field of power system load prediction, and relates to a long-range multivariable load prediction method and system based on time-frequency domain collaboration, and the system carries out the normalization and stabilization of a multivariate load time sequence through a data preprocessing module; the feature embedding module performs linear embedding on the block sequence to construct high-dimensional feature representation; the state space coding module extracts long-range dependency features and generates depth time sequence representation; the decoding prediction module maps the coding features into a preliminary prediction sequence; the time sequence alignment module identifies a leading-lagging relation among multiple variables and aligns a time sequence; the frequency domain optimization module realizes frequency domain component fusion based on adaptive filtering; and the model training optimization module is used for performing training and optimization through a signal attenuation loss function. The method can effectively improve the precision and robustness of long-range multivariable load prediction, and especially has obvious advantages in the aspects of processing complex dependency relationships and dynamic time delay.

The invention relates to the technical field of electric power control, in particular to a multi-virtual power plant collaborative scheduling method and system based on mixed games and carbon transactions. The method comprises the following steps: acquiring parameters of each virtual power plant, reading a carbon quota allocation scheme and a carbon transaction market price released by a power grid, and establishing a virtual power plant operation cost expression containing a carbon transaction cost; constructing a non-cooperative game model, and obtaining a preliminary optimal output strategy of each virtual power plant by taking minimization of the operation cost of the virtual power plant as a target; the operation state of the virtual power plant is detected, if the risk that renewable energy consumption is insufficient or the total carbon emission exceeds the standard exists, a cooperative optimization mechanism is triggered, a plurality of virtual power plants are selected to form a joint optimization group, and the joint optimization group is regarded as an independent participant to obtain an optimal output strategy again; and issuing the optimized scheduling instruction to each virtual power plant for execution according to the optimal output strategy, and participating in carbon market transaction according to the actual carbon emission and the quota difference in the settlement period.

The invention discloses a power supply equipment fault prediction method and device based on deep learning, and relates to the technical field of power system equipment fault prediction and deep learning application. The method comprises the following steps: acquiring a power grid topological structure, an equipment operation state, a historical fault record, a real-time equipment load and environmental condition data; forming a space-time correlation basic diagram according to the power grid topology and the equipment operation state, and calculating the correlation strength by using a diagram neural network; calculating fault time delay and determining a transmission path set by using a long short-term memory network in combination with association strength and historical fault records; fusing multiple data to calculate a cross-regional fault propagation probability, and generating a predicted fault path list; and the fault prediction output of the long-short-term memory network input is updated, and the real-time operation data verification optimization of the power grid is combined, so that accurate cross-regional cascade fault prediction is realized, and safe and stable operation of the power grid is ensured.

The invention relates to a grid-connected scheduling management method and device constructed in combination with a knowledge graph, equipment and a medium. According to the method, a comprehensive data set is constructed by integrating multi-source data such as new energy output, power grid topology, load, weather and historical fault records, and then a dynamic knowledge graph is formed by using entity recognition and relation extraction technologies; a probability causal graph model is constructed by extracting a causal path and adding probability parameters, and uncertainty propagation intensity is quantified in combination with a sequence diagram neural network; on the basis of a propagation model, risk index conditional probability is calculated by adopting probability causal reasoning, and a fault propagation sequence is simulated through a cascade failure theory to realize multi-level risk assessment; based on a multi-objective optimization model and deep reinforcement learning, an adaptive scheduling strategy is generated, a complete technical closed loop from data fusion and causal reasoning to intelligent decision is realized, and the technical effects of describing a new energy uncertainty propagation path, prospectively evaluating a power grid risk situation and dynamically generating an optimal grid-connected scheduling scheme are achieved.

The invention discloses a power distribution network fault identification driving accurate isolation method based on an edge computing architecture, and relates to the technical field of power system automation, and the method comprises the following steps: S001, collecting voltage and current signals at each edge computing node of a power distribution network, analyzing the signal fluctuation amplitude and propagation time delay of different nodes based on the same fault event, and obtaining a fault event; and calculating a fault identification judgment difference degree between the nodes. According to the method, the identification credibility is quantified and abnormal node judgment is corrected through fault identification judgment difference analysis and topological constraint modeling; self-adaptive identification logic optimization is realized in combination with historical and real-time data; control priority and pre-simulation analysis are introduced, so that the feasibility and safety of the isolation action are improved; and finally, a closed-loop mechanism integrating recognition, simulation and feedback is constructed, and the fault response accuracy, coordination and operation stability of the power distribution network under a distributed architecture are remarkably improved.

The invention discloses a distribution network fault disaster damage analysis and intelligent disposal decision-making system based on big data and artificial intelligence, and relates to the technical field of power system distribution network fault processing. According to the system, multi-source heterogeneous data is integrated through the data intelligent acquisition module, main and distribution network topology connection is realized, and accurate fault identification and positioning, influence range evaluation and economic loss quantification are realized by using the disaster damage analysis module in combination with algorithms such as random forest, CNN and graph convolutional neural network. And constructing a closed-loop management system and generating an optimal disposal strategy and preventive maintenance suggestions based on the intelligent decision-making module. According to the method, the problems of lagging disaster damage assessment, insufficient positioning precision, dependence on artificial experience on decision making and the like in traditional distribution network fault processing are solved, rapid and accurate fault positioning, disaster damage dynamic assessment and intelligent decision making support are realized, the fault response efficiency and the power supply reliability are remarkably improved, and distribution network operation and maintenance are promoted to be transformed to an active defense and intelligent decision making mode.

The invention relates to the technical field of charging parking space guiding control, and particularly discloses a parking space charging reservation and intelligent guiding method, device, equipment and medium, and the method comprises the steps: obtaining vehicle battery state data and a user reservation time window through an Internet of Things terminal; synchronously acquiring charging pile state data, real-time traffic flow data and power grid load information in the parking lot; fusing the obtained multi-source heterogeneous data based on a federated learning framework, constructing a dynamic charging demand prediction model, and predicting charging pile occupancy rate distribution in a preset time period by using a space-time diagram convolutional network; through the combination of the dynamic charging demand prediction model and the space-time diagram convolutional network, the occupancy rate distribution of the charging piles is accurately predicted, the scheduling strategy of the charging piles is adjusted in real time, the problem of unreasonable resource allocation is avoided, and the waiting time of a user is shortened; the application of the quantum genetic algorithm optimizes the matching process of the charging pile through global optimal solution search, thereby ensuring the balance of the power grid load and the rationality of the charging strategy.

The invention provides a spatio-temporal data fusion-based power distribution network load prediction method and system, and relates to the technical field of power distribution network load prediction, and the method comprises the steps: collecting related data of a power distribution network, carrying out the wavelet transform decomposition of historical load data, obtaining a load feature matrix, constructing a hierarchical graph convolution network based on topological structure data, and extracting topological correlation features; and generating a spatial-temporal feature tensor through tensor decomposition fusion, training a depth probability prediction model adopting a variational auto-encoder structure, and adjusting prediction probability distribution in combination with environmental data. According to the method, the prediction precision is improved, a complex space-time dependency relationship can be captured, and reliable uncertainty quantization is provided.

The invention provides a meteorological-distributed power supply-load long-term joint prediction method, and relates to the technical field of power system load prediction, and the method comprises the steps: introducing a multi-channel attention fusion mechanism, and constructing a feature fusion layer; performing multi-source feature coding and feature weighted fusion by using a meteorological encoder, a power encoder, a load encoder and a feature fusion layer to obtain a sample training data set; based on the time weighted loss function, adopting the sample training data set to supervise and train the long-short term memory network until convergence; and executing generation power prediction and power load prediction by using the long-term trend prediction plug-in. According to the method and the device, the technical problem of insufficient coupling modeling between prediction objects in the prior art can be solved, the technical target of collaborative modeling between the weather, the distributed power supply and the load is realized, and the technical effect of improving the power prediction precision is achieved.

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 relates to the technical field of power systems, and provides a power distribution network multi-dimensional regulation and control decision method, system and device and a medium, and the method comprises the steps: inputting the preprocessed multi-source operation data into a preset state perception model, and obtaining a multi-dimensional state vector representing the operation state of a power distribution network; a multi-dimensional state vector is used as a state space, regulation and control operation is used as an action space, a composite reward function is established according to a power distribution network operation target, and modeling is carried out to obtain a Markov decision process framework; interacting with a power distribution network simulation environment by adopting a deep reinforcement learning algorithm, obtaining a current state from a state space, selecting and executing regulation and control operation in an action space according to a strategy network, updating strategy network parameters based on feedback of a composite reward function until an optimal regulation and control strategy network is obtained, and obtaining a deep reinforcement learning strategy model; and performing strategy rolling updating based on the real-time monitoring data to obtain a target regulation and control strategy. According to the invention, comprehensive optimal regulation and control of a complex operation scene can be realized.

The invention provides a virtual power plant dynamic response scheduling method and system based on a cognitive spectrum network, which can improve the reliability, self-adaptive capability calculation efficiency and risk management and control capability of a virtual power plant communication system, and relates to the technical field of virtual power plants, the method comprises the following steps: establishing a cognitive spectrum self-organizing network; collecting current operation state data of each power resource under the virtual power plant and historical operation state data under different working conditions; obtaining a resource response characteristic of each power resource; outputting a preliminary optimization result; converting the preliminary optimization result into a credible optimization result; generating a system risk partition topology report; a market risk hedging strategy capable of being automatically executed is generated; and generating a final scheduling instruction containing the power set value, the execution time, the hedging operation and the risk constraint condition of each resource. According to the method, a feasible technical path is provided for large-scale efficient utilization of distributed resources under the background of the energy internet.

The invention belongs to the technical field of multi-energy micro-grid cooperative regulation and control. The invention discloses a multi-energy micro-grid coordinated regulation and control system based on cross-layer knowledge injection and federated distillation. The system is characterized by comprising a cross-layer knowledge injection network; a federal knowledge distillation module; and the uncertainty map regulation and control module is used for constructing an MEMG uncertainty association map, learning the influence weight of each uncertainty factor on a regulation and control decision through a map attention mechanism, and realizing a dynamic risk avoidance strategy. The invention discloses a multi-energy micro-grid cooperative regulation and control method based on cross-layer knowledge injection and federated distillation. The method is characterized by comprising the following steps: step 1, MEMG topology knowledge coding; 2, cross-layer knowledge injection training is carried out; step 3, federal knowledge distillation optimization; and step 4, performing uncertainty map regulation and control. According to the system and the method, the precision, the robustness and the data privacy protection capability of MEMG regulation and control are improved, and the system and the method are suitable for efficient collaborative optimization of the park-level multi-energy microgrid.

The invention relates to the technical field of power distribution network fault processing, in particular to a power distribution network fault automatic reconstruction control method of a power system. The method comprises the following steps: collecting operation state data of the power distribution network; dividing the power distribution network operation state data into power distribution network operation fault data and power distribution network operation state candidate data to be detected; performing disturbance interference potential state simulation analysis on the to-be-detected candidate data of the operation state of the power distribution network to generate disturbance interference potential state simulation data of the power distribution network; performing disturbance fault analysis through the power distribution network disturbance potential state simulation data to generate power distribution network disturbance fault data; based on the power distribution network operation fault data and the power distribution network disturbance fault data, intelligent reconstruction control decision design is carried out, and power distribution network intelligent reconstruction control decision data is generated; and power distribution network fault automatic reconstruction control operation is executed through the power distribution network intelligent reconstruction control decision data. According to the invention, efficient automatic reconstruction control is realized when the power distribution network fails.

The invention discloses a power distribution network layered optimization method oriented to source-network-load interaction, and relates to the technical field of power system automation. The method comprises the following steps: constructing a dynamic adaptive collaborative architecture, and dynamically generating a three-layer logic structure of global coordination, regional autonomy and local execution according to the real-time state of the power distribution network; operating a three-layer mixed game mechanism, and determining an optimal strategy of each main body through master-slave, cooperation and bilateral interactive games; executing an evaluation-planning-operation three-level closed-loop optimization process, and taking a key performance index fed back by an operation layer as a basis for scheme correction of a planning layer; and a mixed intelligent strategy combining multi-agent deep reinforcement learning and an improved particle swarm optimization algorithm is adopted for solving. According to the method, the problems of hierarchical solidification, benefit coordination imbalance and planning operation splitting in the prior art are effectively solved, and the adaptive capacity, the economical efficiency and the flexibility of the power distribution network in the high-permeability source-network-load interaction scene are improved.

The invention belongs to the technical field of load prediction in a low-voltage distribution area, and particularly relates to a time sequence prediction method for attention mixed multi-scale decomposition, which comprises the following steps: S1, preprocessing original time sequence data to obtain a standardized sequence X; s2, inputting X into MJDA, and outputting uniform high-dimensional representation U after feature enhancement; s3, inputting U into TCDA, and carrying out cross-dimension dependence modeling and deep nonlinear transformation to obtain a final enhanced feature U1; s4, inputting U1 output by the TCDA into a hybrid expert predictor group; the predictor group is composed of K parallel expert predictors, and a corresponding expert prediction result is obtained; meanwhile, U output by the MJDA is processed through a noise perception gating network, and weight distribution U used for expert predictor fusion is generated; and according to the U, carrying out weighted summation on the output of the K expert predictors to obtain a prediction result. According to the method, high-precision and high-stability load prediction can be realized in a low-voltage distribution area environment with limited resources.

The invention relates to the technical field of hydropower station optimization scheduling, in particular to a hydropower station multi-target scheduling decision-making method and system, and the method comprises the steps: obtaining the multi-source heterogeneous data of a target cascade hydropower station, and constructing and dynamically updating a scheduling knowledge graph fusing the cascade hydraulic coupling and collaborative operation association relationship; identifying a reference scheduling time period and a non-reference scheduling time period and establishing a differential output constraint; performing feature compression on the scheduling knowledge graph, extracting a key feature sub-graph influencing a scheduling decision, and predicting a state evolution path of related scheduling elements of the target cascade hydropower station in a future scheduling time domain; constructing and solving a multi-target dynamic decision model, and generating a candidate scheduling scheme set; and performing cross-scale conflict detection based on the candidate scheduling scheme set, performing hierarchical re-optimization on the candidate scheduling scheme set according to a detection result, and outputting and executing a scheduling decision result. The objective of the invention is to adapt to the dynamic demand of the power market for cascade hydropower station scheduling and realize rapid and accurate collaborative scheduling decision.

The invention discloses a short-term power load prediction method, system and device based on multi-intelligent-model fusion and a medium, and belongs to the technical field of short-term power load prediction, and the method comprises the steps: obtaining regional historical load data and meteorological data; performing data cleaning on the obtained load data and meteorological data; measuring linear and nonlinear correlation between the power load and the meteorological factors, and screening meteorological data with high load correlation; decomposing the load data into a time sequence by using an empirical mode decomposition method based on combination of multi-scale permutation entropy to obtain a multi-scale sub-data sequence; respectively predicting the multi-scale sub-data sequences to obtain prediction results; carrying out weighted fusion on the prediction result through a long short-term memory network model to obtain a load prediction result, and optimizing model parameters to obtain a trained multi-model prediction model; and predicting the test set data by using the trained model to obtain a final load prediction result. According to the invention, the precision and adaptability of load prediction are effectively improved.

The invention discloses a distributed photovoltaic cluster power prediction method and device based on multi-modal fusion. The method comprises the following steps: acquiring historical photovoltaic data and historical photovoltaic images of a photovoltaic region to be predicted; analyzing a time sequence relationship in the historical photovoltaic data, extracting photovoltaic time sequence characteristics, and giving a first prediction result in combination with the data time sequence prediction model; extracting spatial features in the historical photovoltaic image, reconstructing the historical photovoltaic image, and giving a second prediction result in combination with the image prediction model; and in combination with a preset fusion weight, fusing the first prediction result and the second prediction result to obtain a target prediction power, and completing power prediction of the distributed photovoltaic cluster, thereby effectively capturing the influence of sudden weather events on photovoltaic power generation, improving the accuracy of conventional cloud picture data when coping with complex and changeable cloud layer motion, and improving the prediction efficiency of the distributed photovoltaic cluster. Therefore, the accuracy of power prediction is improved.

The invention discloses an electric power utilization anomaly detection and early warning method based on the Internet of Things, which relates to the technical field of electric power utilization and comprises the following steps of: performing fitting comparison on identified inductive current fluctuation and a behavior residual error template generated based on a real load behavior; analyzing a response time window, a fluctuation amplitude track and a behavior continuity characteristic, and judging whether the inductive current fluctuation generated by a non-target loop belongs to a real load change behavior or not under the condition that a plurality of loops are grounded together according to the response time window, the fluctuation amplitude track and the behavior continuity characteristic; and combining the behavior residual error fitting score, the phase consistency value and the frequency response stability value to generate a signal credibility judgment sequence, and carrying out credibility grade division on the inductive current fluctuation. According to the method, the problem of false fluctuation misjudgment caused by inductive signal interference under the condition that multiple loops share the grounding condition is solved, accurate identification and dynamic regulation and control on the authenticity of inductive current fluctuation are achieved, and therefore the accuracy and reliability of power utilization anomaly detection and early warning are improved.

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

The invention discloses a virtual power plant bidding optimization method fusing LLM knowledge reasoning and MAPPO, and the method comprises the steps: constructing the operation state input of a virtual power plant participating in a spot market, and guiding LLM to carry out the semantic extraction of historical market clearing data, energy storage state, electricity price trend and bidding rules through a Prompt mechanism; and forming a task stage semantic state and a structured reward function, and embedding the task stage semantic state and the structured reward function into an Actor-Critic network of the MAPPO. A semantic state generated by the LLM is introduced into the Actor network to serve as auxiliary input, and the semantic state and the environment state jointly generate a quotation action; structured semantic rewards are introduced into the Critic network, and the perception ability of value estimation on market rules is improved. A market clearing result is fed back to the intelligent agent to form a revenue signal, the revenue signal and the reward generated by the LLM are superposed to participate in dominant function calculation, and finally strategy convergence is completed under a PPO cutting optimization mechanism.

The invention relates to an unmanned aerial vehicle electric power inspection monitoring method and system, and relates to the technical field of electric power inspection, and the method comprises the steps: obtaining multi-source data of a monitoring area, determining potential defect risk information based on the multi-source data, a power grid digital twin model and a fault propagation knowledge graph, and determining a potential defect priority based on the defect risk information, an inspection task is generated in combination with available unmanned aerial vehicle resources and an optimization algorithm, the inspection task is sent to an unmanned aerial vehicle cluster to collect data, so that the unmanned aerial vehicle cluster performs data collection on each to-be-verified risk area according to the unmanned aerial vehicle inspection task, and defects are identified based on the collected monitoring data and a defect identification AI model. And finally generating a maintenance decision suggestion based on the identification result and the risk information. The technical effects of accurately determining the potential defect risk of the power system, reasonably distributing the unmanned aerial vehicle resources for routing inspection, accurately identifying the defects and generating effective maintenance decision suggestions are achieved, and the efficiency and accuracy of power routing inspection monitoring are improved.