1838 results about "Smart grid network" patented technology

The invention discloses an electric energy metering box fault prediction method and system based on big data analysis, relates to the technical field of smart power grids, and solves the problems of progressive aging missing detection and instantaneous interference misjudgment caused by dependence on single parameter threshold alarm and fault positioning misalignment caused by multi-source data isolated analysis in the prior art. According to the scheme, electrical, environment and equipment state parameters are collected in real time through a multi-dimensional sensing network; the error drift of the mutual inductor is dynamically predicted based on an LSTM-Kalman filtering model, and core breakdown early warning is realized in combination with wavelet transform; outputting a corrected resistance value and a fault mark by using a BP neural network; predicting the life of the piezoresistor by adopting a gradient boosting decision tree and fusing lightning overvoltage characteristics; the transient interference is suppressed through the combination of a Transform self-attention mechanism and dynamic time warping; according to the method, the aging detection precision and the complex environment adaptability are remarkably improved, the misjudgment rate is reduced, and the multi-fault associated positioning and active defense capability is realized.

The invention discloses an intelligent power grid optimal scheduling method and system based on multivariate energy storage cooperative scheduling, and relates to the technical field of power grid optimal scheduling, and the method comprises the following steps: building a prediction model based on first data, generating prediction data, coupling energy storage characteristic parameters of different types of energy storage equipment with the prediction data, and obtaining a prediction model; establishing a multi-energy collaborative scheduling model; dynamically screening the energy storage scheduling strategy set based on a preset real-time performance evaluation index to generate an optimal strategy subset; according to the optimal strategy subset, performing differentiated charging and discharging control instructions on the energy storage equipment cluster; and collecting second data in the charge and discharge control process, calculating a deviation value between the second data and the prediction data, converting the deviation value into a feature vector, inputting the feature vector into a preset incremental learning algorithm, and optimizing parameters of the multi-energy collaborative scheduling model. Layered screening is implemented in combination with real-time performance evaluation indexes, and it is ensured that the optimal scheduling scheme can be rapidly selected in different time periods and under the uncertain disturbance condition.

The invention provides a power grid fault detection and positioning method and system for an intelligent power grid system. The method comprises the following steps: collecting a fault traveling wave signal when a power distribution line has a fault; preprocessing the fault traveling wave signal to obtain a preprocessed target fault traveling wave signal; carrying out traveling wave front edge detection on the target fault traveling wave signal, and determining the signal arrival time of the fault traveling wave signal; fault signal features are extracted from the target fault traveling wave signals through a frequency domain analysis method and a time domain analysis method; estimating the fault line characteristics of the fault line with the fault in combination with the fault signal characteristics and the signal arrival time; outputting the line type and the fault type of the fault line through a feature recognition model; selecting an optimal fault positioning algorithm by using a decision tree algorithm based on the line type and the fault type; and determining the line position of the fault line according to the target fault traveling wave signal by adopting an optimal fault positioning algorithm. The method has the effect of intelligently selecting the optimal positioning algorithm according to the line type.

The invention discloses an intelligent operation and maintenance system and method for a high-voltage switch cabinet based on digital twinning, relates to the technical field of intelligent power grids, and solves the problems of nonlinear effect modeling distortion, cross-spatio-temporal scale coupling deviation accumulation, time sequence real-time contradiction and insufficient extreme working condition adaptation in the prior art. Hysteresis parameters of the ferromagnetic material are dynamically calibrated through a quantum annealing optimization algorithm, and electromagnetic-thermal field strong coupling synchronous calculation is realized by combining multi-scale mesh generation and an implicit thermal field iterative algorithm; constructing an incremental transfer learning framework to fuse aging features and real-time data, and correcting boundary conditions of the model by adopting four-dimensional variational assimilation; establishing a hybrid verification platform to dynamically feed back extreme working condition parameters, and generating a credible operation and maintenance instruction in combination with a block chain; according to the method, the contact temperature rise prediction precision, the residual life evaluation reliability and the circuit breaker transient response real-time performance are remarkably improved, and active immune type intelligent operation and maintenance of the high-voltage switch cabinet under the extreme working condition are achieved.

The invention relates to the technical field of smart power grids, and discloses a smart power grid power dispatching optimization method, which comprises the following steps of: firstly, acquiring power grid operation data, and processing data missing and noise problems by utilizing federal learning; and constructing a load prediction model through a dynamic time warping algorithm and a specific network. A multi-energy coupling scheduling model and a demand response game model are constructed, and a multi-time scale rolling optimization framework is established. And carrying out sensitivity analysis on scheduling parameters, designing a hierarchical collaborative optimization mechanism, and constructing a robust optimization model to cope with the power flow uncertainty. And integrating a scheduling instruction verification module, and deploying an online incremental learning mechanism. The method can effectively process data, accurately predict load, optimize multi-energy scheduling, guide demand response, deal with uncertainty, verify scheduling instructions and update the model in real time, improves the safety, reliability and economy of smart grid power scheduling, and realizes optimal configuration of power resources.

The invention relates to the technical field of intelligent power grid regulation, in particular to a static concentration and dynamic distribution coordination control method for reactive power / voltage of a power distribution network. The method comprises the following steps: acquiring basic data of the power distribution network; performing multi-scale voltage stability domain analysis according to the basic data of the power distribution network to generate multi-scale voltage stability domain data; performing static centralized control strategy processing on the multi-scale voltage stability domain data to generate a static centralized control strategy; performing dynamic event trigger control processing according to the multi-scale voltage stability domain data to generate a dynamic distribution control strategy; and performing multi-region real-time cooperative control execution by using a static centralized control strategy and a dynamic distribution control strategy, and generating corrected cooperative control scheme data. By performing multi-scale voltage stability management and intelligent coordination control on the power distribution network, rapid dynamic response of reactive power and voltage is realized, and the voltage out-of-limit problem caused by active power fluctuation output is effectively suppressed.

The invention provides an intelligent power grid relay protection reliability evaluation and lifting control method and system, and relates to the technical field of intelligent power grid relay protection, and the method comprises the steps: collecting the operation data of a relay protection device, carrying out the preprocessing and multi-domain feature extraction, and inputting a deep hybrid neural network to obtain a comprehensive feature vector; a multi-stage fault diagnosis model is utilized to identify a fault mode, a collaborative anomaly and a reliability level at a device level, an interval level and a station level respectively, and a fault propagation path and influence are predicted based on a causal reasoning network. And finally, constructing a hierarchical cooperative control strategy, and through multi-objective optimization, distributed game optimization and hierarchical rolling optimization, realizing coordinated optimization of a protection fixed value and improving the reliability of a relay protection device, thereby reducing the fault propagation probability and ensuring safe and stable operation of a power grid.

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 source-grid-load-storage integrated optimal scheduling method and system for a micro-grid, and relates to the technical field of smart grids and micro-grids, and the method comprises the steps: firstly collecting multi-source data in a specified micro-grid, and carrying out the optimization storage of the multi-source data in a time sequence database; constructing a time sequence prediction model, predicting generating capacity and load demand according to historical data, generating a preliminary scheduling strategy according to the generating capacity and load demand, realizing comprehensive cost minimization, analyzing the strategy, evaluating deviation and abnormity, and finally correcting the strategy according to an evaluation result; comprising an intelligent data aggregation module, a time sequence data prediction module, a preliminary scheduling generation module, a preliminary scheduling strategy analysis module and a real-time optimization feedback module, influences of various data sources are considered, the model can be adjusted in real time, variable power demands and power generation conditions in a micro-grid environment can be better coped with, and the real-time optimization feedback is realized. The accuracy of a prediction result is greatly improved, so that a powerful basis is provided for scheduling decision making, and risks possibly caused by prediction deviation are avoided.

The invention relates to the technical field of data fusion, and discloses a thunder-vision intelligent fusion system and method adapted to a power transmission scene, and the system comprises a radar module which detects the specific information of a power transmission line in real time; the visual module is used for identifying obstacles near the power transmission line, external damage and potential safety hazards; the dynamic adjustment module automatically adjusts the working mode of the sensor; the data processing module is used for comprehensively processing radar and visual data through a data fusion algorithm; the multi-dimensional perception fusion module is used for realizing omnibearing perception; the communication module transmits the detected data to a remote monitoring center in real time; the intelligent analysis and early warning module is used for automatically identifying faults and abnormal conditions and generating an early warning signal; through efficient data fusion and intelligent analysis, the accuracy and response speed of power transmission line monitoring are remarkably improved, the safety and stability of the system are enhanced, powerful support is provided for operation and maintenance personnel, and development of an intelligent power grid is promoted.

The invention discloses a smart power grid line loss intelligent detection method based on Internet of Things big data, and relates to the technical field of smart power grid electric energy metering, and the method comprises the following steps: S1, extracting a voltage signal and a current signal of each electric energy metering node in a set time window, carrying out the frequency domain transformation to obtain a frequency spectrum, calculating the energy concentration index of a high-frequency band, and calculating the energy concentration index of the high-frequency band; and identifying a high-frequency harmonic interference time section according to the change of the energy concentration degree. According to the method, a closed-loop mechanism of high-frequency harmonic interference identification, error traceability, cause discrimination and dynamic correction is constructed, fragment-level and node-level error compensation is realized, and the accuracy and the stability of line loss anomaly identification of the smart grid are improved by combining electric quantity consistency verification and trend model updating; and the reliability and the intelligent level of the system in a complex interference environment are obviously enhanced.

The invention discloses a power distribution fusion terminal real-time video reasoning method based on an SDNN, relates to the technical field of intelligent power grid monitoring, and aims to solve the problems of high delay, high bandwidth consumption, low power consumption and the like caused by insufficient utilization of computing power resources of an existing power distribution terminal and dependence on a cloud on video processing in the prior art. And the problem of poor adaptability of the model in a complex power grid scene is solved. Firstly, a power grid special sample library is constructed, and data quality is optimized through time domain key frame extraction and a stratified sampling mechanism; carrying out enhanced training on the lightweight model by adopting transfer learning and multi-modal confrontation; designing an intermediate representation conversion and hierarchical caching mechanism to realize efficient model compiling and heterogeneous hardware deployment; edge-side real-time reasoning is realized based on dynamic frame sampling and statistical threshold detection; finally, a self-adaptive feedback closed loop is driven through reinforcement learning, and incremental updating of the model and dynamic allocation of resources are achieved. The real-time performance of power distribution equipment anomaly detection is remarkably improved, and meanwhile the model memory occupancy rate is reduced.

The invention relates to the technical field of intelligent power grids, in particular to an intelligent power distribution room sensing system and method based on data fusion. The multi-source data acquisition module is used for synchronously acquiring electrical parameters, mechanical vibration signals, temperature distribution data and environment monitoring data of power equipment in a power distribution room; the edge computing gateway is connected to the multi-source data acquisition module and is configured to perform time alignment, abnormal value elimination and feature extraction on the original sensing data; the data fusion analysis server is connected to the edge computing gateway through a network and comprises a space-time alignment unit used for unifying monitoring data of different sampling frequencies to the same time reference; the self-adaptive weight fusion unit is used for dynamically adjusting fusion weight according to the reliability of the data of each sensor; the state evaluation unit is used for generating an equipment health degree score and a fault early warning signal based on a fusion result; according to the scheme, monitoring blind areas and misjudgment risks caused by data islands can be fundamentally solved.

The invention provides a hydropower station generator set excitation system fault prediction method and device, and relates to the technical field of intelligent power grids, and the method comprises the steps: carrying out the continuous sampling of a generalized state observation vector through a sliding time window, so as to form an operation parameter observation tensor; fusing the operation parameter observation tensor and the boundary constraint type operation parameters of the generator, and constructing a fault observation tensor fused by multi-source operation parameters; introducing a structural causal atlas in a modeling layer, establishing a causal path network embedded based on causal reasoning and topological time sequence for a fault observation tensor, and dividing system interaction faults encountered by excitation system prediction into a plurality of typical modes; and performing graph attention modeling on the evolution trend and the instability boundary of each key parameter in the fault causal chains of different typical modes contained in the causal path network, and dynamically identifying the path characteristics of the excitation system entering the fault critical state. According to the method, the interaction type fault of the excitation system of the hydropower station generator set can be predicted.

The invention provides a micro-grid cooperative scheduling method and device, and relates to the technical field of smart grids, and the method comprises the steps: obtaining historical operation data and real-time operation data of a micro-grid system, and data of an external information system; generating load demand and energy equipment output prediction information based on the historical operation data and the data of the external information system; constructing a layered multi-time-scale decision architecture, and performing decision optimization on each layer of agents by adopting a reinforcement learning algorithm; constructing a plurality of heterogeneous agents, and carrying out cooperative scheduling on the plurality of heterogeneous agents by adopting a centralized training and distributed execution multi-agent reinforcement learning algorithm; inputting the prediction information and the real-time operation data into a decision framework, and outputting a real-time control instruction; and setting a security constraint condition, and realizing optimization of the security constraint in combination with a Lyapunov function, a Lagrange multiplier method, a security layer mechanism and a reinforcement learning algorithm. According to the method provided by the invention, the safe, efficient and reliable operation of the micro-grid in the grid-connected / off-grid mode can be realized.

The invention relates to the technical field of information security, and discloses a data security management system and method in an intelligent power grid, and the system comprises a dynamic encryption and key management module which is used for carrying out the encryption and key management of the data of the intelligent power grid, determining an encryption algorithm according to the sensitivity of the data, and generating a corresponding key, the dynamic encryption and key management module transmits encrypted data and key information to the adaptive access control module so as to ensure the security of data access; and the self-adaptive access control module is used for managing access permissions of the user and the equipment and performing dynamic authorization according to the attribute label and the context information. Through dynamic encryption and adaptive key management, an encryption algorithm and a key length are adjusted in real time according to data sensitivity and a network environment, a quantum key distribution technology is combined, high security and quantum attack resistance of a key are ensured, and then security and real-time performance of data transmission are ensured.

The invention discloses an electric equipment energy-saving control method and system based on the Internet of Things, and relates to the technical field of smart power grid and Internet of Things application, and the method comprises the steps: S1, obtaining the operation data and historical use records of user electric equipment, extracting the habit diversity and personalized preference analysis characteristics of a user, obtaining an equipment use preference model, and carrying out the operation of the user electric equipment; s2, extracting an operation rule of each device from the preference model, judging a priority adjustment parameter of the device, and obtaining a dynamic priority sequence, and S3, calculating an available power threshold value under current power resource limitation through real-time power grid load monitoring data, and determining a distribution power value required by important device guarantee; according to the electric equipment energy-saving control method and system based on the Internet of Things, the preference model and the priority sequence can be updated in time, and optimal distribution of electric power resources is achieved. The method can effectively balance the power demand of the user and the load of the power grid, improves the utilization efficiency of power resources, and provides a new solution for the management of the intelligent power grid.

The invention belongs to the technical field of power load prediction, and particularly relates to a Mama-based endogenous and endogenous variable fusion power load prediction method, which comprises the following steps of: collecting and preprocessing a data set; embedding an endogenous variable, dividing a power load endogenous sequence into N non-overlapping Patch blocks, adding position codes to each block, generating a block token Pen of block fine granularity through linear projection and position codes, and introducing a learnable global token Gen to represent the macroscopic state of the sequence; global embedding of exogenous variables; performing cross-granularity fusion on internally and externally generated tokens; carrying out Mama time sequence dependence modeling; and performing multi-step prediction output, mapping the time sequence characteristics into future multi-step load prediction values through a full connection layer, restoring the original data scale after reverse normalization, and outputting a final prediction result. According to the method, through a block embedding strategy and a dynamic parameter selection mechanism, the adaptability of the model to a complex power scene is remarkably improved, and reliable technical support is provided for efficient scheduling and marketization operation of an intelligent power grid.

The invention relates to the technical field of smart power grids, in particular to a fine tuning method and system based on a power failure multi-modal model and a medium, and the method comprises the steps: generating a knowledge retention mask according to a pre-training weight matrix of a multi-modal large language model; generating a sparse low-rank matrix according to the space-time sparse characteristic of the structured multi-modal data set; generating a conflict mitigation regularization item according to the knowledge retention mask and the sparse low-rank matrix, constructing a power task joint loss function according to the conflict mitigation regularization item, and performing fine tuning training on the multi-modal large language model to obtain a fine tuning multi-modal large language model; and inputting the real-time operation data of the power grid into the fine-tuning multi-mode large language model for reasoning to obtain a power grid fault diagnosis result, and performing isolation control on a power grid fault section according to the power grid fault diagnosis result. According to the method, efficient fine tuning is performed on the multi-modal large language model by using the power grid data space-time sparsity modeling and the conflict mitigation regularization strategy, and rapid and accurate diagnosis of the power grid fault is realized.

The invention provides an intelligent power grid state monitoring method and system based on digital twinning, and the method comprises the steps: obtaining multi-source data in a power grid operation environment, and forming a scene feature vector and a corresponding operation scene label; selecting a basic twinborn model template according to the operation scene label, mapping the scene feature vector into a node parameter in the basic twinborn model template, and obtaining a corresponding parameter set and a digital twinborn model instance; according to the digital twinborn model instance, constructing a causal graph structure of the operation state between the devices so as to reflect a causal dependence path between current power grid state variables; and performing trend risk judgment according to the abnormal causal path set, and outputting structured early warning response information. According to the method, the problem of insufficient generalization ability caused by the static state of the traditional twin structure is effectively solved.

The invention provides an intelligent power grid load dynamic monitoring method and system, and the method comprises the steps: collecting real-time power consumption data, and generating a real-time power consumption data sequence; constructing a time sequence model based on historical synchronous data, generating an expected power load data sequence, and comparing the real-time power consumption data sequence with the expected power load data sequence to obtain a load trend prediction result; analyzing by applying a self-adaptive dynamic clustering algorithm based on a graph neural network, generating a configuration scheme of a clustering center and a clustering number, determining an optimal power distribution scheme in combination with a bilevel programming model and a robust optimization algorithm, and evaluating the risk of the optimal power distribution scheme to generate a power distribution strategy; based on a power distribution strategy, a demand side management strategy based on the game theory is formulated to generate an intelligent load response strategy so as to realize dynamic load monitoring; according to the invention, the management level of the smart power grid is improved, and a solid foundation is provided for future energy management and optimal scheduling.

The present invention discloses a method and system for smart grid fault self-healing based on generative artificial intelligence, which relates to the field of smart grid fault self-healing. The method includes: obtaining and preprocessing multi-source heterogeneous data; generating a pre-trained language model and performing domain adaptability fine-tuning, adding a task-specific output layer; performing feature fusion using a multimodal data fusion method, and using annotated historical fault data sets for supervised training to obtain fault diagnosis results. Fault self-healing is modeled as a Markov decision process, and a deep reinforcement learning algorithm is used to interactively learn the optimal decision-making strategy with a power grid simulation environment, and an optimal fault self-healing plan is generated and converted into specific operation instructions. The present invention integrates multi-source heterogeneous data, performs accurate positioning and description generation, improves the reliability and self-healing ability of the smart grid, and ensures rapid recovery of normal operation when a power grid fails.

The invention belongs to the technical field of power grids, and discloses a user behavior data mining and prediction analysis system in a smart power grid environment, which comprises a data acquisition module, a multi-dimensional feature extraction module, a behavior pattern recognition module, a demand prediction and trend analysis module, a personalized service recommendation module and a data storage and security module. According to the method, the accuracy of user behavior pattern recognition is improved, complex features can be extracted from the power consumption behaviors of the users through combination of multi-dimensional feature extraction and a deep learning algorithm, and therefore the behavior patterns of the users are recognized more accurately. Compared with simple clustering of traditional single-dimensional features, the system can recognize multi-level behavior features such as power utilization peak and valley periods and seasonal preference, and the comprehensiveness of user behavior portraits is improved. Through a demand prediction technology combining time sequence analysis and an integrated learning model, accurate short-term and long-term power consumption prediction can be carried out, and a demand change trend is judged.

The invention discloses a standardized centralized DTU multi-level fault diagnosis and self-healing method and system, and relates to the technical field of intelligent power grid fault diagnosis, and the method comprises the steps: building a dynamic topological graph according to a unified coding rule, extracting spatial-temporal characteristics through a graph convolution network, and obtaining a spatial-temporal characteristic power grid topological graph; building a power grid digital twin environment based on a multi-level standardized fault diagnosis result, generating a plurality of sets of self-healing strategy candidate sets by adopting a reinforcement learning algorithm, screening an optimal self-healing strategy, and converting the optimal self-healing strategy into a control instruction conforming to a substation automation standard protocol; the control instruction is sent to the DTU and analyzed, and the intelligent power switch cabinet is controlled to execute self-healing operation. By automatically correcting the abnormal diagnosis result, false alarm and missing alarm are reduced, and the accuracy of fault diagnosis is improved. The intelligent level of power grid management is obviously improved, the maintenance cost is reduced, and the operation efficiency and the service quality of a power system are optimized.

The invention relates to the technical field of intelligent power grids, in particular to an intelligent design method and system for electric power engineering cloud resources based on distributed computing. Comprising the following steps: constructing an electric power engineering multi-dimensional resource modeling system, dividing calculation nodes into three types of heterogeneous resource units including a real-time processing core, a data analysis core and a disaster recovery backup core, and establishing a dynamic attribute matrix containing time delay sensitivity, an energy consumption coefficient and a risk assessment value; the real-time processing core is configured with a hardware acceleration instruction set and supports microsecond response; a self-adaptive dynamic clustering algorithm is adopted, elastic resource clusters facing task requirements are generated according to the spatial topological relation of resource units and the load change trend, a quantum genetic optimization mechanism is introduced in the clustering process to dynamically adjust the inter-cluster coupling degree, and a cross-cluster communication link based on credibility evaluation is established; according to the design, the problems of low resource utilization efficiency and service quality degradation caused by a static allocation mode can be fundamentally solved.

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.

The invention provides an artificial intelligence-driven energy storage system health state monitoring and early warning mechanism. The mechanism comprises five levels of data acquisition, processing, artificial intelligence analysis, decision support and user interaction. System operation data is collected in real time through a sensor network, and after cleaning and standardization, machine learning and deep learning algorithms are used for analysis. Based on the analysis results, the system provides optimization suggestions, fault diagnosis and predictive maintenance plans, and displays system status and sends alarms through a user interface. According to the invention, intelligent power grid optimization, battery health state monitoring and fault diagnosis early warning are realized, and through multi-source data fusion, real-time artificial intelligence analysis and adaptive learning, the efficiency and reliability of the energy storage system are improved, and the service life of the energy storage system is prolonged. The system can predict the energy demand, evaluate the health state of the battery and identify an abnormal mode, thereby actively making a maintenance plan, reducing accidental shutdown, and providing a comprehensive solution for intelligent management of the energy storage system.

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 smart power grids and artificial intelligence, in particular to a substation autonomous intelligent inspection method and system based on multi-modal data, and the method comprises the steps: obtaining inspection data of multiple modals, and generating fusion features; identifying system alarm information; performing intention recognition and task classification to generate an executable task sequence; generating a multi-device cooperative scheduling scheme; executing the multi-device cooperative scheduling scheme; iterative optimization is carried out; according to the intelligent inspection method provided by the invention, more accurate and more robust multi-mode perception and diagnosis are realized, and deep understanding of complex instructions and safe and efficient cooperation of multiple devices are also realized; and meanwhile, through dynamic re-planning and a verification type feedback learning mechanism, high real-time performance and robustness are ensured, and meanwhile, the system is endowed with the capability of iterative optimization.

The invention provides a power grid evolution behavior modeling method and system based on dynamic digital twinning, and relates to the technical field of intelligent power grid digital twinning. According to the method, interaction characteristics of power station output, user load response and an energy storage scheduling strategy are captured in real time through a digital twin interface, behavior coupling factors containing complementarity and conflict weight are formed, and a power grid response relation and a multi-scale demand elastic curved surface are constructed by using an implicit tensor fusion technology and nonlinear projection. And through the steps of virtual behavior anchor point disturbance and the like, offset is separated to identify an unbalance region, an evolution trajectory is generated, an optimal collaborative path is extracted, and the weight of a behavior coupling factor is dynamically adjusted, so that rapid convergence of a global collaborative stability domain is realized. The method covers a behavior coupling factor generation module and the like, supports various operations, effectively solves many problems in power grid resource interaction, and improves dynamic adaptability and cooperation efficiency of power grid evolution modeling.