28069 results about "Power grid" patented technology

Disclosed in the present invention are a fuzzy-logic-control-based coordination method and system for a power grid requirement response and an energy storage system, the method comprising: S1, collecting real-time power grid data and prediction data, and constructing a corresponding real-time power grid data set and a corresponding prediction data set; S2, using a fuzzy algorithm to convert the real-time power grid data set, the prediction data set and multi-dimensional renewable energy information into a fuzzy set; S3, customizing a power grid requirement response measure and an operation strategy of an energy storage system; S4, executing the strategy customized in step S3; S5, monitoring in real time the execution effect of the strategy and collecting operation data such as a power grid load matching degree, energy storage device response speed and efficiency, and a requirement response participation degree; and S6, periodically updating a decision model of a fuzzy logic controller. In the present invention, the fuzzy logic controller is used to process and analyze power grid data in real time, such that the uncertainty and ambiguity during power grid operation can be effectively handled, especially for the production capacity fluctuation of renewable energy and the rapid changes of power loads.

The invention discloses an energy storage system operation and maintenance strategy optimization method based on digital twinning, and belongs to the technical field of electric energy storage and intelligent power grids. Establishing a digital twinning synchronous model of the energy storage system and calibrating the digital twinning synchronous model; generating prediction data at the current moment based on the digital twinborn model, performing residual analysis on the prediction data and real-time data, and generating a quantitative diagnosis index; according to the quantitative diagnosis index and the fault mode, adjusting parameters of the digital twinning synchronization model, and ensuring that the model and the actual state of the energy storage system are kept synchronous; inputting real-time data into the adjusted digital twinborn model, calculating a future operation index of the energy storage system, and generating simulation operation data; and generating a non-periodic operation and maintenance strategy according to the simulation operation data, the fault mode and the operation and maintenance rule base. According to the method, the adaptive calibration digital twinborn model is adopted, real-time diagnosis and prospective optimization can be fused, and the operation and maintenance efficiency and reliability of the energy storage system are remarkably improved.

The invention relates to the technical field of power system dispatching, in particular to an emerging load power supply potential improvement method based on dynamic prediction, which comprises the following steps of: acquiring emerging load power consumption, meteorological environment and power grid schedulable resource data in a target area through an Internet of Things sensing terminal, and performing two-channel modeling to obtain a new load power supply potential improvement model; a deep space-time network is used to predict a load curve, a model is established to quantify resource regulation potential, a scheduling priority list and a capacity allocation strategy are generated by means of a matching rule base according to load fluctuation and resource evaluation results, an actual scheduling effect is fed back to the prediction model, parameters are corrected through error back propagation, a closed-loop optimization link is formed, and the scheduling efficiency is improved. The method improves load prediction accuracy and resource scheduling adaptability, is suitable for emerging load power supply optimization in a novel power system, and guarantees stable and efficient operation of a power grid.

The invention discloses a box-type substation state monitoring and early warning method based on artificial intelligence, relates to the technical field of intelligent power grids, and aims to solve the problems of missing report, false report and response lag caused by the fact that an existing static threshold ignores multi-physical coupling and a depth model highly depends on scarce fault samples. According to the scheme, sliding window kernel density estimation is carried out on a multi-channel time sequence signal, a dynamic coupling matrix is constructed through recursion Copula decomposition, a three-level threshold surface is generated through time-varying quantile regression, abnormal samples and graph attention network extraction state representation are generated in combination with a conditional variation auto-encoder, lightweight recursion pruning is carried out, and the dynamic coupling matrix is obtained. An abnormal score is generated through a multilayer Bayesian network and particle filtering, a multi-step risk trend is discriminated through a Gaussian kernel derivative slope, and finally unscented Kalman filtering is used for smoothing and online threshold correction; according to the method, the detection sensitivity and the early warning recall rate of the box-type substation to the transient coupling fault are remarkably improved, the response speed is improved, and the false alarm frequency is effectively reduced.

The invention relates to the technical field of energy storage systems, in particular to a source network load storage coordination control method for multi-configuration network type energy storage coordination operation. According to the technical scheme, the source-network-load-storage coordinated control method for multi-construction-network-type energy storage coordinated operation comprises the following steps: generating a new energy output and load change trend through a dynamic prediction model based on historical power data, real-time load requirements and meteorological information; optimization is carried out to obtain a power generation plan containing a clean energy consumption target and economical efficiency constraints; decomposing the power generation plan into a photovoltaic power adjusting instruction, an energy storage charging and discharging instruction and a load control instruction according to the power grid safety priority, wherein the emergency control instruction is executed prior to the economic dispatching instruction; and controlling parallel operation of a plurality of pieces of networking type energy storage equipment, and realizing multi-machine power distribution and circulating current suppression through a virtual synchronous control technology. And through layered optimization scheduling and multi-machine cooperative control, the new energy consumption capability is remarkably improved, and the phenomenon of abandoning light and wind is reduced.

The invention relates to the technical field of intelligent power grids, in particular to a power distribution network battery digital dynamic management system based on digital twinning. Comprising a data acquisition unit; the digital twinborn modeling unit is used for constructing a battery-power grid-environment multi-dimensional dynamic twinborn body and realizing virtual-real bidirectional mapping and adaptive updating by combining a multi-physics field coupling model and a long-short-term memory network time sequence prediction algorithm; a dynamic optimization unit; and executing the feedback unit. Through a distributed heterogeneous sensing network of a data acquisition unit, multi-dimensional operation data of a battery pack and a key node of a power distribution network are acquired, and a high-fidelity data set containing four-dimensional labels of a battery state, a power grid parameter, time and a position is generated in combination with a spatial-temporal feature extraction technology; the deep fusion of the full life cycle state of the battery and the global operation data of the power distribution network is realized, and the comprehensive data support covering the global is provided for the optimization decision.

The invention discloses a power grid load prediction and scheduling optimization system based on artificial intelligence, particularly relates to the technical field of power system automation, and solves the technical problems of low power grid load prediction precision, poor scheduling strategy robustness and insufficient source grid load storage coordination in the prior art. Multi-source heterogeneous data space-time alignment is realized by constructing a data acquisition layer based on edge calculation, a load prediction result is generated by adopting an AI prediction module fused by a graph convolutional network and an attention mechanism, and a source-network-load-storage collaborative scheduling scheme is generated through a multi-target risk hedging optimization algorithm. And closed-loop optimization is realized by using digital twinborn pre-check and incremental learning. And finally, the load prediction accuracy, the scheduling decision reliability and the system adaptive capability in the new energy access environment are improved.

The invention relates to the technical field of image data processing, and discloses a power grid drawing intelligent review method and system based on a knowledge graph, and the method comprises the steps: driving a multi-mode large language model based on a domain specific prompt project, extracting a power grid domain knowledge triple from structured text data, and then carrying out the quality evaluation and conflict detection, performing automatic resolution on the conflict knowledge to obtain a candidate knowledge set; checking and confirming the candidate knowledge set through a man-machine cooperation verification mechanism, and constructing a power grid design specification knowledge graph; and identifying to-be-reviewed elements in the to-be-reviewed power grid drawing through the multi-modal large language model, querying the power grid design specification knowledge graph according to the generated structured query statement, performing compliance judgment on the to-be-reviewed power grid drawing based on the obtained query data packet, and generating a review report. According to the method and the device, the efficiency and the reliability of automatic drawing review can be improved, and meanwhile, the interpretability and the traceability of review results are ensured.

The present disclosure relates to the technical field of intelligent monitoring and management of power systems, and specifically relates to an intelligent collaborative power consumption regulation and control method, apparatus and system for source-grid-load-storage, an electronic device and a storage medium. Said system comprises an energy regulation and control center and energy regulation and control units provided in microgrids; the energy regulation and control units use a temporal attention mechanism-based LRCN dual-layer network combined model to predict power consumption amounts, so as to generate power consumption surpluses and shortages within a future preset time; and on the basis of the power consumption surpluses and shortages and latest current electricity prices of the microgrids, the energy regulation and control center uses a fusion multi-objective algorithm based on a Pareto front curve and a fuzzy algorithm to generate a microgrid collaborative power consumption regulation and control solution, and sends the regulation and control solution to the energy regulation and control units for execution, so as to ensure the balance of energy supply and demand of the microgrids. Therefore, the present disclosure achieves efficient, intelligent and refined energy management for microgrid clusters, reducing energy consumption and costs, and providing solid support for sustainable development of microgrids.

The invention relates to a power distribution system optimization method considering a space-time game under vehicle-station-network interaction, and the method employs a double-layer optimization framework integrating the space-time game and dynamic electricity price, an upper decision maker is a power distribution system operator, and the power distribution system operation cost is minimized and the renewable energy consumption rate is maximized. Generating a dynamic electricity price signal in a time-sharing and partition manner; the lower-layer main body comprises an electric vehicle user and a charging station operator, and the electric vehicle user optimizes a charging time period and a charging station and forms charging demand distribution through a non-cooperative game based on a dynamic electricity price signal and a charging service charge by taking the maximum self-charging decision utility as a target; and a charging station operator optimizes and adjusts a charging service fee decision by taking charging demand distribution as input and taking revenue maximization as a target. Compared with the prior art, the method can collaboratively optimize the economic operation of the power grid and the consumption of renewable energy sources, promotes the efficient flow and balanced distribution of resources among regions, and provides decision support for the dispatching of the power distribution network.

The invention belongs to the technical field of power system safety, and discloses a high-voltage transmission line safety assessment method based on an intelligent algorithm. A database is constructed through multi-source data acquisition and fusion processing, a deep learning model is utilized to analyze relevance between meteorological conditions and an icing formation mechanism, an evolution law of the meteorological conditions and the icing formation mechanism is mined, multi-scene simulation and sensitivity analysis are performed in combination with physical characteristics and landform information of a power transmission line, and a line icing risk partition assessment system is established. According to the method, a critical value of line mechanical strength and icing thickness is calculated based on a mechanical model, a grading early warning threshold standard is formulated, a time sequence prediction algorithm and a risk propagation model are constructed to realize intelligent early warning, and a prevention and control strategy is formed through optimal configuration of anti-icing resources and self-adaptive generation of a deicing scheme. According to the invention, the capability of resisting icing disasters of the high-voltage transmission line is effectively improved, and the safe and stable operation level of a power grid is enhanced.

The invention discloses a self-generating and self-using distributed photovoltaic power station power anti-countercurrent control method and system, and relates to the technical field of photovoltaic anti-countercurrent control, and the method comprises the steps: obtaining the real-time load power of a grid-connected point of a power grid and the output power of a photovoltaic inverter, and calculating the net load power deviation; when the net load power deviation is smaller than a preset countercurrent risk threshold value, it is judged that a countercurrent risk exists, and an anti-countercurrent adjusting instruction is generated; in response to the anti-countercurrent regulation instruction, dynamically correcting the maximum output power limit value of the photovoltaic inverter, and synchronously activating the charge and discharge compensation mode of the energy storage system; an anti-countercurrent control coefficient is calculated, and the power reduction proportion of the photovoltaic inverter and the compensation power of the energy storage system are synchronously adjusted based on the anti-countercurrent control coefficient, so that the power flow of the grid-connected point of the power grid is kept to be zero or forward flow; according to the method, dynamic and accurate cooperation of photovoltaic reduction and energy storage compensation power in time and magnitude is realized, and the efficiency of a self-generating and self-using distributed photovoltaic power station is improved.

The invention discloses a cloud edge collaborative multi-source data fusion safety management and control system for intelligent power distribution equipment, relates to the technical field of intelligent power grids and power distribution automation, and is used for managing and controlling 10-35 kV power distribution equipment. The system comprises an edge data acquisition module, a preprocessing module, a cloud storage management module, a cloud edge collaborative scheduling module, a multi-source data fusion module, an intelligent risk assessment module, a safety control execution module, a safety protection module and a man-machine interaction module. The modules are interacted through a 5G / industrial Ethernet; the acquisition module obtains multiple parameters, the preprocessing module cleans standardized data, the cloud side performs hierarchical storage, the scheduling module allocates tasks, the fusion module integrates data, the evaluation module performs grading risk, the protection module guarantees safety, and the interaction module performs visual alarm. The method improves the power distribution data quality and risk assessment precision, optimizes the cloud edge cooperation efficiency, enhances the safety protection capability, achieves the preventive operation and maintenance of equipment, reduces the fault and power failure time, reduces the operation and maintenance cost, and provides support for the safe and efficient operation of a power distribution network.

The invention relates to the field of image processing, and discloses an intelligent drawing checking method and system based on a multi-modal large language model, and the method comprises the steps: obtaining a to-be-checked target engineering design drawing and a to-be-checked task description; generating a global overview drawing based on the target engineering design drawing; performing global semantic analysis according to the global overview map and the review task description through a multi-modal large language model, and generating a global semantic analysis result and to-be-reviewed local area proposal information; cutting a local image from the design drawing; performing element identification analysis on the local image through a multi-modal large language model to obtain local structured information; and performing information fusion processing on the local structured information and the global semantic analysis result, generating complete drawing information, performing compliance verification and defect positioning on the complete drawing information and the structured specification knowledge base, and generating a review report. According to the method, intelligent review of the power grid engineering design drawing can be realized, the review efficiency and accuracy are improved, and meanwhile, the resource consumption is reduced.

The invention discloses a V2G-based dynamic discharge power optimization control method, and belongs to the field of smart power grid and electric vehicle cooperative control, and the method comprises the following steps: collecting the battery state information of an electric vehicle and the load demand data of a power grid in real time; determining a demand response priority through a multi-objective optimization algorithm based on the battery state information of the electric vehicle and the load demand data of the power grid; calculating a power distribution strategy scheme based on the response priority; when the power distribution strategy scheme does not meet the battery health constraint, a safe discharge power range is adjusted based on a battery state evaluation result; generating a correction parameter based on the adjusted discharge power range and the environment variable; and monitoring the operation state of bidirectional energy flow in real time, and when the operation state of bidirectional energy flow does not achieve the response effect, adaptively adjusting the power distribution strategy based on the correction parameter and updating the execution parameter.

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.

A system and method for integrated optimization of autonomous electric vehicle fleets across transportation and electricity networks which employs artificial intelligence to dynamically allocate autonomous electric vehicles between mobility services and electricity grid services based on real-time conditions. The platform acquires data including energy mix forecasts, earth observation measurements, vehicle owner schedules, and emission-based route penalties to generate coordinated allocation decisions. Vehicle owners specify availability through a scheduling interface. The system optimizes vehicle utilization through a hierarchical optimization approach implementing mobility demand-side flexibility and electricity demand-side flexibility simultaneously. Multi-objective genetic algorithm optimization balances revenue generation, energy costs, emissions reduction, and battery health. The integrated approach maximizes value creation across both transportation and energy domains, reducing urban emissions while enhancing grid stability through coordinated management of distributed energy resources in autonomous electric vehicle fleets.

The invention discloses a power transmission line inspection unmanned aerial vehicle obstacle intelligent detection and obstacle avoidance method. The method comprises the steps that multi-source sensing data is acquired, and alignment is completed through calibration and timestamp matching; heterogeneous data preprocessing and feature enhancement; constructing a high-precision environment fusing a geometric structure and a semantic tag, mapping a two-dimensional target detection result output by the recognition network to a three-dimensional coordinate system through spatial transformation, and fusing the two-dimensional target detection result with a point cloud structure to construct a semantic occupation grid map; performing preliminary route planning according to a preset power grid topological structure and task coverage requirements, and generating a barrier-free flight path covering the whole inspection area; reinforcing learning of a dynamic obstacle avoidance strategy; track dynamic reconstruction and energy consumption optimization scheduling are carried out; the technical problems that an existing technical system has defects in the aspects of obstacle recognition accuracy, complex environment adaptability, data fusion capacity and obstacle avoidance strategy intelligence, and the requirements for high-reliability, low-energy-consumption and high-efficiency unmanned aerial vehicle power transmission line inspection are difficult to meet are solved.

The invention discloses an optical storage and charging integrated micro-grid energy management system, relates to the technical field of micro-grid energy management, and is used for solving the problems of low energy scheduling efficiency and insufficient stability in an optical storage and charging system. The system comprises a power generation monitoring module, an energy storage management module, a charging load control module and an energy coordination module. The photovoltaic power generation state and environmental parameters are monitored in real time through a multi-type sensor network, and the running state of the photovoltaic module is judged; based on battery monitoring data and photovoltaic output characteristics, a differential energy storage management strategy is formulated; the charging power is dynamically distributed in combination with the energy state to realize charging and discharging intelligent regulation and control; by converging multi-source information, source storage and load interaction is coordinated to cope with different power states; accurate monitoring, intelligent scheduling and efficient cooperation of the photovoltaic, storage and charging integrated micro-grid are realized, and the energy utilization efficiency and the operation stability are remarkably improved.

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

The invention discloses a flexible load multi-target collaborative scheduling system and method, and relates to the technical field of collaborative optimization of power systems. The method is used for solving the problem of lack of accurate prediction and multi-target coordination of agricultural electricity and water utilization regulation and control. Firstly, based on meteorological data, soil moisture content and crop growth characteristics, an irrigation demand prediction model is constructed, irrigation water demand is predicted, and a water pump load power baseline is generated; then, a dynamic baseline constraint condition is generated in combination with historical behavior data and water pump start-stop logic; establishing a power grid side objective function, a user side objective function and a water affair side objective function, and introducing an underground water and carbon emission punishment mechanism; thirdly, dividing the power distribution network into sub-regions, adopting an alternating direction multiplier method to solve a region regulation and control strategy in parallel, coordinating water resource distribution conflicts through virtual interactive variables, and outputting a global scheduling instruction; and finally, collecting real-time response data and correcting the prediction model on line to realize closed-loop adaptive optimization.

The invention relates to the technical field of electric power system intelligent monitoring, and discloses an intelligent power supply system state monitoring and fault early warning method and system. According to the system, power grid operation parameters are collected in real time through a heterogeneous sensor array, multi-dimensional features are extracted through wavelet transform, a fault diagnosis model is constructed based on deep learning, precise early warning is achieved in combination with a dynamic threshold optimization algorithm, an optimal disposal scheme is generated based on an expert knowledge base, and remote data transmission is achieved through dual-channel communication. Real-time monitoring, fault early warning and intelligent decision support of the state of the power supply network are realized, and the operation reliability and the operation and maintenance efficiency of the power grid are remarkably improved.

The invention discloses a line loss treatment method based on low-voltage power grid multi-source data fusion, and belongs to the technical field of information, and the method comprises the steps: reconstructing a power supply path based on a power grid topological structure and a changed power access point; extracting reverse power flow characteristics of the frequency points for correcting a node impedance matrix of the current power supply path to obtain a power supply access point impedance parameter set; updating a power transmission path through the reverse power flow characteristic to obtain a power distribution characteristic; and a power grid topological structure, a current power supply path, a power supply access point impedance parameter set and power distribution characteristics are fused to construct a line loss distribution prediction model and identify a line loss abnormal region, and the line loss abnormal region is treated. Through implementation of the method and the device, the problem that the line loss treatment effect is poor due to the fact that the influence of factors such as transformer area topological structure change, power access point change and load fluctuation on the line loss is not considered in the prior art, and the predicted line loss distribution has a large error compared with the actual situation can be solved.

The invention discloses an intelligent fault diagnosis method fusing state monitoring and a multi-modal large model, and the method specifically comprises the steps: synchronously collecting time sequence data and a space image through a heterogeneous sensor group and monitoring equipment disposed in power grid equipment, and forming original data; based on the original data, a physical constraint feature vector is generated in combination with an equipment thermodynamic equation and a material deformation rule; performing health index prediction through the lightweight LSTM network based on the physical constraint feature vector; when detecting that the health indexes continuously decrease, clustering an HI time sequence curve by adopting a Gaussian mixture model, judging a degradation stage according to a clustering center distance, and obtaining a stage recognition result; and based on finite element simulation parameters, introducing a reinforcement learning model, optimizing the simulation parameters by taking maintenance cost minimization as a target, and outputting a predictive maintenance work order. According to the invention, intelligent fault diagnosis and accurate maintenance of the power grid equipment are realized, the fault processing efficiency and accuracy are improved, and the power failure loss is reduced.

The invention discloses a fault ride-through hierarchical control method for a network-constructed converter, and the method comprises the steps: introducing a virtual resistor formed by fault current negative feedback into the voltage and current inner loop control of the network-constructed converter based on VSG control through a current inner loop dynamic amplitude limiting control layer after the converter enters a fault ride-through state, the size of the virtual resistor is adjusted in real time along with the amplitude of the fault current so as to carry out dynamic current limiting control on the virtual resistor; and the power outer loop dynamic compensation control layer dynamically adjusts an active power instruction value and a reactive power instruction value of the network construction type converter based on VSG control during the fault ride-through period according to the voltage drop degree of the power grid after the converter enters the fault ride-through state. And respectively performing active-frequency control and reactive-voltage control based on the active power instruction value and the reactive power instruction value. In addition, the method also comprises a state coupling and parameter self-adaption module. According to the method, the GFM converter can have better dynamic performance in the fault recovery stage.

The invention relates to the technical field of smart power grids, in particular to a hybrid game-based virtual power plant optimization operation method under multiple markets, and the method comprises the steps: building an electricity-certificate-carbon multi-market coupling transaction framework, deeply analyzing a master-slave game relationship between a virtual power plant operation platform and a VPP alliance, and building a price guidance-based double-layer interaction mechanism; according to the method, research is carried out aiming at the condition that a virtual power plant belongs to different interest subjects, a resource complementation-based alliance cooperation model is proposed, and a VPP alliance cooperation optimization framework considering demand side load elasticity is established by introducing a refined model of a flexible load; and establishing a virtual power plant operation platform and VPP alliance mixed game optimization model. Solving a master-slave game stage by adopting a double-layer iterative algorithm; and solving a cooperative game stage in combination with an alternating direction multiplier method. The virtual power plant is guided to participate in multi-market cooperative regulation and control through a mixed game mechanism, safe and stable operation of the power system is ensured, and the operation economy and environmental protection property are improved.

Disclosed are a second-level optimal control method and apparatus for joint frequency regulation of heterogeneous load resources, and an electronic device, a storage medium, a computer program product and a computer program. The method comprises: establishing a standardized operation model for heterogeneous load resources; on the basis of parameters of the standardized operation model for the resources and historical frequency regulation signal data of a frequency regulation market, predicting frequency regulation signal distribution and price distribution over a future time period; solving an optimal operation model involving the joint participation of multiple heterogeneous resources in energy and frequency regulation markets, in order to determine a baseline energy output and a frequency regulation capacity; constructing an optimal decomposition problem of load resources and performing variable substitution, and calculating the marginal cost and adjustable range of power adjustment for each resource; and in response to receiving a frequency regulation signal sent by a power grid, starting dispatching from the resource having the lowest marginal regulation cost, and updating the power of each load-side resource until power adjustment requirements are met.

The invention discloses a power transmission line insulator defect detection method and system based on machine vision, and relates to the technical field of machine vision, and the method comprises the steps: combining a pre-trained DenseCLIP model with a YOLOv11 network, extracting an insulator image feature through a visual encoder of the DenseCLIP model, converting the insulator image feature into a visual embedded vector, and carrying out the detection of the defect of the insulator through the YOLOv11 network; constructing a text prompt according to a defect category, converting the text prompt into a language embedded vector, calculating the similarity between the vision embedded vector and the language embedded vector, generating a pixel-text matching score graph to guide YOLOv11 network learning, and constructing an insulator defect detection model; and constructing a mixed training set by using an artificial sample and a real sample, carrying out training by adopting a transfer learning staged training strategy, importing a to-be-detected insulator image into an insulator defect detection model, obtaining an enhanced feature graph and a pixel-text score graph after visual and language embedding vector fusion, and generating a final prediction result. According to the invention, the detection precision and real-time performance of insulator defects are improved, and the intelligent operation and maintenance efficiency of a power grid is remarkably improved.

The invention discloses an intelligent self-monitoring temperature management system for a box-type substation, and relates to the technical field of intelligent power grid equipment monitoring. The problems that an existing system is large in temperature measurement deviation, low in reliability, delayed in early warning, inaccurate in hot spot positioning and extensive in heat dissipation control are solved. According to the scheme, multi-source signals are acquired in parallel through a data acquisition module, and a temperature time sequence is extracted; a boundary calibration module is adopted to fuse data to generate a three-dimensional boundary condition; the multi-physical field solving module obtains an internal temperature / stress field; the physical information prediction module is fused with a heat transfer physical constraint training graph neural network to predict a hotspot migration trend; the hierarchical scheduling module is used for solving a fan and oil pump collaborative optimization instruction in real time based on model predictive control; according to the invention, the accuracy of internal temperature monitoring of the box transformer substation, the reliability of hot spot prediction and the accuracy of heat dissipation control are remarkably improved, the insulation life of equipment is effectively prolonged, and the operation safety and reliability of the system are improved.

The invention relates to the technical field of electric vehicle charging management, and discloses an electric vehicle charging optimization management system. The system comprises a dynamic charging demand prediction module which constructs a model based on historical data, collects voltage fluctuation, current phase deviation and charging power pulse characteristics of a charging pile in real time, and outputs a theoretical charging load value; the multi-dimensional demand difference analysis module is used for performing three-dimensional comparison of time accumulation deviation, frequency response difference and pulse characteristic similarity on theoretical and actually measured load values to generate a charging pile group level difference characteristic tensor; the hot spot region association positioning module inputs the difference feature tensor into a spatial topology mapping network, generates a fault probability thermal distribution diagram in combination with power grid node capacity and charging station position information, and positions a charging bottleneck region; and the self-adaptive regulation and control strategy generation module is used for starting millisecond-level monitoring on a high fault probability region according to the thermal distribution diagram and applying a power disturbance test on adjacent nodes. The system can improve the accuracy and effectiveness of charging management.