1101 results about "Smart grid network" patented technology

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 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 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 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 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 discloses an intelligent power grid operation and maintenance system based on federated learning and edge calculation, and the system comprises an intelligent electric meter enhancement module which is disposed at a power grid monitoring node and is used for collecting electric energy quality parameters and environment data in real time; the edge calculation layer is used for carrying out data preprocessing, power quality parameter anomaly detection and building a federated learning model; the secure communication framework is used for establishing a hybrid communication network and multi-level security protection, carrying out routing decision and carrying out encrypted transmission on interactive data among the modules; and the end analysis platform is used for integrating the multi-source heterogeneous data of the power grid, predicting the state of the power grid and generating a maintenance plan. According to the invention, comprehensive monitoring and predictive maintenance of the operation state of the power grid are realized.

The invention discloses a power transmission image compression quality evaluation method based on multichannel fusion, belongs to the technical field of smart power grids, and solves the problem of how to enhance adaptive evaluation performance of image quality in scenes with different compression ratios. A multichannel feature processing module introduces learnable offset to realize dynamic alignment and effective fusion of multichannel features; the multi-scale attention fusion module is combined with multi-scale feature extraction and an attention mechanism, learnable position codes are introduced, and the sensitivity to local structure changes is enhanced while the global perception ability is ensured; the self-adaptive quality evaluation module captures different scale features and local information by constructing residual connection and a multi-branch structure, performs weighted fusion on multi-channel fusion features in combination with a dynamic weight generation mechanism, and realizes accurate judgment of the quality of a complex scene image under different compression levels; while the image compression distortion perception capability is improved, the adaptive evaluation performance of the image quality in scenes with different compression ratios is effectively enhanced.

The invention relates to the technical field of electric energy meter metering, and discloses an electric energy meter metering anomaly analysis method and system, and the method comprises the steps: collecting data, such as voltage waveforms, to generate a metering feature vector set, and constructing an anomaly detection rule base; setting a scene parameter type set, and establishing an abnormal association judgment model; dynamically correcting the threshold value by combining the model, and generating an optimized error threshold value set and an abnormal triggering condition set; and updating a metering analysis strategy to generate an abnormal judgment scheme, and correcting data verification sequential logic. The system comprises a data acquisition module, an abnormal rule base construction module, a scene parameter configuration module, a correlation model training module, a dynamic threshold optimization module, a strategy updating module and a time sequence correction module. Through multi-dimensional data modeling, scene-based threshold configuration and dynamic time sequence calibration, the accuracy and adaptability of electric energy meter measurement anomaly detection are improved, and the method is suitable for measurement anomaly analysis of diversified power consumption scenes in a smart power grid.

The invention relates to the technical field of smart power grids, and discloses a source-grid load-storage cooperation method, which comprises the following steps: carrying out data fusion on real-time acquired source-side distributed power supply output fluctuation data, grid-side topology constraint parameters, load-side time-sharing load demand data and storage-side charge state data to obtain a multi-dimensional system state set; performing short-medium-long term multi-scale coupling prediction on the multi-dimensional system state set to obtain a source load dynamic balance prediction curve; constructing a multi-target collaborative optimization function according to the power fluctuation interval, and generating a target adjustment instruction set based on the multi-target collaborative optimization function; updating the target running state based on the adjustment instruction set; based on the deviation degree of the updated operation state and the prediction state, correcting a weight parameter of the multi-target collaborative optimization function, and based on the corrected multi-target collaborative optimization function, performing source-network-load-storage collaborative regulation and control on the target power platform; according to the invention, the efficiency of source-network load-storage collaboration can be improved.

The invention relates to the technical field of distributed energy storage and intelligent power grid control, in particular to an intelligent distributed energy storage cluster collaborative management system. Comprising a state acquisition module used for acquiring local power grid state parameters of an edge computing node in real time; the power grid state parameters comprise power grid frequency, power grid voltage and a heartbeat communication state with the cloud management platform; the risk calculation module is used for calculating quantified island risk indexes; the mode judgment module is used for determining a current operation mode decision; the mode switching module is used for switching the system from a vertical cooperative mode to a horizontal autonomous mode when the island risk index is greater than an island switching threshold value; and when the island risk index is smaller than or equal to the island switching threshold value, the system is maintained to operate in the vertical cooperation mode. According to the system, misjudgment and missed judgment of a traditional single threshold value mode are overcome, it is ensured that the mode switching module can be switched to the horizontal autonomous mode from the vertical cooperative mode in time under the real risk, and decision making is accurate and reliable.

The invention discloses an electricity utilization information acquisition intelligent configuration method based on a pattern recognition algorithm, and relates to the technical field of intelligent power grids, and the method comprises the steps: collecting a directional data stream, inputting a federal map neural network to construct a power distribution network physical connection relation, and generating a spatio-temporal topological feature vector; extracting a current effective value component of the directional data flow as an electrical load sequence, extracting an equipment state code to identify a voltage sag event, injecting voltage sag event associated disturbance into the electrical load sequence in combination with an event propagation path weight of a spatio-temporal topological feature vector, and generating an anti-fact sample set; and compressing the new configuration strategy through a knowledge distillation engine, and outputting an event response logic and a parameter adjustment instruction to form an executable configuration strategy. According to the method, through anti-fact sample generation and reinforcement learning optimization under spatial-temporal topological feature vector constraint, a physical rule deep embedding decision is realized.

The invention discloses a multi-time-scale charging load and energy storage cooperation probability prediction method, and relates to the technical field of power systems and smart grids, and the method comprises the steps: obtaining historical multi-source data of a target system, and constructing factor sub-graphs of different time scales; coupling the cause sub-graphs into a time cause feedback atlas through a structure mapping function, wherein the atlas comprises explicit time scale control nodes; executing cross-time-scale causal effect calculation based on the time factor feedback atlas to generate a causal effect matrix; using the causal effect matrix to construct a joint probability prediction model, and outputting a first prediction result; and when it is monitored that the deviation of the first prediction result exceeds a dynamic threshold value, triggering a time scale switching mechanism to generate a second prediction result. According to the method, multi-scale causal modeling is realized by constructing the time cause feedback atlas containing the time scale control nodes, dynamic scale switching and a residual compensation mechanism are combined, the prediction precision and the scheduling adaptability are remarkably improved, and multi-time-scale collaborative optimization management is realized.

The invention relates to the technical field of intelligent power grid management, in particular to a typhoon prediction and power grid maintenance system based on digital twinning and AI cooperation, which comprises a typhoon path evolution module, a power grid vulnerability mapping module, a risk link aggregation module, a maintenance strategy calibration module and a multi-stage maintenance tracking module. According to the method, through dynamic environment parameter extraction and path deviation trend analysis, continuous evolution judgment of a typhoon influence area can be realized, the problem of insufficient change capture of static prediction is solved, fine description and dynamic identification of a vulnerable part are realized, and through node connection relation induction and load intensity partitioning, a typhoon influence area can be identified. A risk link is identified and a potential fault chain is structurally presented; resource configuration and risk priority dynamic comparative analysis are maintained; a deployment logic can be calibrated; a task sequence is ensured to be consistent with a risk level; and the response efficiency and the scheduling accuracy are improved.

The invention discloses a high-low voltage linkage line loss comprehensive intelligent diagnosis system and method. The system comprises a distributed storage and high-performance calculation module, a data fusion and processing module, a multi-dimensional feature construction module, a high-low voltage linkage analysis module and a line loss intelligent diagnosis module. The method is used for carrying out line loss comprehensive intelligent diagnosis based on the system, and comprises the following steps: processing multi-source data in real time through the distributed storage and high-performance calculation module; bus-line-user archive data are fused, and a line loss index is calculated; extracting time, space and electrical three-dimensional characteristic indexes; performing high-low voltage linkage analysis based on the Pearson's correlation coefficient and the DTW distance; and fusing the system state model and the isolated forest anomaly detection model to output an anomaly diagnosis result. The method is suitable for accurate analysis and treatment of line loss in an intelligent power grid environment, and the efficiency and quality of line loss management can be improved.

The invention relates to the technical field of intelligent power grids, and provides an intelligent terminal supporting multi-parameter fusion perception, which comprises a data acquisition module for acquiring parameters of a transformer body and parameters of a far-end intelligent electric meter through multiple types of sensors; the data processing module is used for executing a dynamic window cleaning algorithm on the collected data, and the data fusion module is used for outputting power grid state characteristics by adopting a fixed weight and dynamic weight dual-mode fusion mechanism; a multi-mode wireless communication unit is arranged in the communication module, and the communication module uploads the fused data and the early warning information to a cloud master station platform; according to the method, the defects in the prior art are overcome, a sliding window self-adaptive adjustment mechanism is established, and the window length is automatically adjusted and calculated by analyzing data distribution characteristics in real time; when abnormal fluctuation is detected, the window is contracted to improve the sensitivity, and the window is expanded to enhance the immunity under a steady-state working condition.

The invention discloses a micro-grid power control method, system and device based on fuzzy modulation and a storage medium, and relates to the technical field of intelligent grid integration of a photovoltaic system and a micro-grid, and the method comprises the steps: firstly obtaining the real-time power data of a photovoltaic system, carrying out the control processing through fuzzy logic, and calculating a reference instruction; according to the reference instruction, hysteresis time is dynamically adjusted through modulation hysteresis control, and an inverter switching signal is generated; converting direct current into alternating current based on the switching signal and a power switching device, injecting the alternating current into the micro-grid, calculating active and reactive power reference values, ensuring that the system operates according to a unit power factor, and giving active power to the reference value; according to the method, the power control precision and adaptability of the micro-grid are improved, excessive switching of the inverter is avoided, loss is reduced, the service life is prolonged, power matching and stable transmission are achieved, reactive loss is reduced, bus voltage is stabilized, harmonic waves can be reduced, the electric energy quality is improved, and efficient and stable operation of the micro-grid is guaranteed.

The invention provides an intelligent power grid energy optimization management method and system, and relates to the technical field of data processing, and the method comprises the steps: 1, synchronously collecting photovoltaic output fluctuation data, user load demand data and power grid frequency state data, so as to obtain an original monitoring data set; step 2, performing spatial-temporal feature mapping on the original monitoring data set, extracting a multi-dimensional feature vector, determining a dominant feature shaft system based on covariance analysis, and calculating a dynamic coupling degree between the feature shaft systems to divide a regulation and control domain; time sequence key characteristic quantities are selected in a regulation and control domain, a load response state transition path is constructed, a user behavior correction coefficient is deduced according to the load response state transition path, dynamic load balance is achieved through a multi-period rolling strategy, and a load distribution scheme is generated. According to the invention, through real-time dynamic regulation and control of power supply output, an excitation electricity price signal is generated and user demand response is linked, so that intelligent power grid energy supply and demand balance optimization is realized.

The invention discloses an urban energy network intelligent allocation method and system, and the method comprises the steps: obtaining building energy consumption data and energy supply network topology information, building a supply and demand matching reference, recognizing a load peak value dense time period and a peak clipping and valley filling potential region, and exporting transferable load data to form a regional supply and demand distribution diagram; generating a peak shifting adjustment space in combination with the user response delay duration and the equipment start-stop period; performing gap matching analysis on the regional supply and demand distribution map and the peak shifting adjustment space, creating a hierarchical regulation instruction set, and differentially generating an instant response instruction and a delay response instruction; a regulation and control coordination parameter is formed through coordination configuration of the peak shifting revenue coefficient and the instant response instruction; finally, a regulation and control execution range is determined, cost benefit evaluation is implemented, an urban-level energy scheduling execution scheme is formed, accurate matching and efficient scheduling of urban energy supply and demand are achieved, and comprehensive and efficient scheduling decision support can be provided for application scenes such as an intelligent power grid, regional heat supply and comprehensive energy service.

The invention provides a smart power grid cloud edge collaborative heterogeneous data security access method, which comprises the following steps: collecting multi-source heterogeneous power equipment data in real time based on a deployed multi-mode sensor network; based on a lightweight federated learning edge inference engine deployed on an edge end network, performing adaptive semantic enhancement preprocessing on the multi-source heterogeneous power equipment data to generate an edge encryption feature tensor; performing three-dimensional credible security authentication and knowledge fusion on the edge encryption feature tensor based on an electric power space-time knowledge graph fusion engine deployed on the cloud side to generate secure and credible electric power data; and performing space-time causal reasoning and twin mirror image comparison on the secure and credible power data based on a federated block chain-driven digital twin decision engine deployed at a cloud end to generate a dynamic security policy map and store the dynamic security policy map. According to the scheme, the data security is enhanced, intelligent analysis and dynamic decision can be performed according to the real-time state of the power grid, and optimal scheduling and stable operation of the power grid are realized.

The invention relates to the field of power systems and intelligent power grids, in particular to an intelligent analysis system and method for the influence of multi-scene new energy power generation on a distribution network, and the system comprises a data collection module, a multi-scene modeling module, an influence analysis module, an intelligent evaluation module and an optimization suggestion generation module. And the data acquisition module, the multi-scene modeling module, the influence analysis module, the intelligent evaluation module and the optimization suggestion generation module are in communication connection with an external data bus. The hybrid digital twinborn model is constructed through the multi-scene modeling module, four typical scenes of new energy output fluctuation, extreme weather, AC / DC hybrid access and power grid topology change are dynamically simulated, and dynamic configuration of scene parameters (such as new energy permeability and load growth rate) is supported. According to the model, through fusion modeling (such as a power flow equation, an LSTM time sequence and a graph neural network GNN) driven by a physical mechanism and data, the running state of the distribution network under the multi-scene coupling effect can be reflected more truly.

The invention discloses a power grid state characterization method and system based on multi-modal fusion. The method comprises the following steps: characterizing power grid topological structure data through a graph embedding algorithm to generate topological feature vectors; encoding the time sequence operation data through a long short-term memory network to generate a time sequence feature vector; extracting an equipment state feature vector through a multi-layer perceptron; projecting the three types of feature vectors to a shared semantic space, and realizing cross-modal feature alignment by using cosine similarity loss; and adopting a gating multi-mode unit GMU to carry out adaptive weighted fusion, and generating a unified power grid state representation vector. The system comprises a multi-modal data acquisition and preprocessing module, a topological structure characterization module, a time sequence dynamic characterization module, an equipment state characterization module and a cross-modal fusion and decision module. According to the method, the problems of incomplete single-mode characterization, characteristic isomerism and insufficient dynamic characteristic capture are solved, the accuracy and robustness of power grid state sensing are improved, and the method is suitable for real-time monitoring and decision support of an intelligent power grid.

The invention discloses a high-temperature-resistant composite insulated cable for an intelligent power grid, and belongs to the technical field of cables. The objective of the invention is to solve the problems of single function, poor high-temperature resistance and easy overload failure of the existing cable. The cable sequentially comprises a compressed circular tinned copper conductor, an inner shielding layer, an XLPE inner insulating layer, an outer shielding layer, a self-repairing composite insulating layer, a metal shielding layer and an outer sheath layer from inside to outside, the self-repairing composite insulating layer is prepared by blending a self-repairing polymer matrix and a microencapsulation phase change material, and the melting point of the microencapsulation phase change material is accurately matched with the double dynamic bond response temperature of the self-repairing matrix. Through cooperation of self-repairing and thermal buffering functions, integration of multiple functions of high temperature resistance, self-repairing and thermal buffering is achieved, the long-term working temperature reaches 105 DEG C or above, no breakdown occurs during thermal shock at 180 DEG C, the overload repairing rate exceeds 80%, the high-temperature and overload working conditions of an intelligent power grid can be effectively adapted, the preparation steps are clear and reproducible, and remarkable technical advantages and application value are achieved.

The invention discloses an intelligent power grid fault prediction method, system and device, and relates to the technical field of power grid fault prediction, and the method comprises the steps: carrying out the real-time collection of the multi-source operation data of a target intelligent power grid, and obtaining a multi-dimensional power grid operation feature set; operation fault analysis is carried out, a fault feature vector group is generated, transfer learning is carried out, and a fault probability distribution matrix is generated; performing fault risk analysis according to the fault probability distribution matrix, and dividing a plurality of risk levels; and performing propagation path simulation of the target smart power grid on the plurality of fault early warning signals to generate a fault prediction report. According to the invention, the technical problems of low prediction precision and poor timeliness caused by fault prediction lagging and inaccurate fault propagation path simulation of the smart grid in the prior art are solved, and the technical effects of accurate prediction and risk early warning of the smart grid fault and improvement of the timeliness and accuracy of fault pre-judgment are realized.

The invention relates to an intelligent power grid cooperative scheduling method for automobile access, and relates to the field of electric automobile charging scheduling and intelligent power grid optimization. The method comprises the following steps: acquiring multi-source real-time data of an electric vehicle, a power grid and a charging station, constructing a collaborative scheduling graph structure, and performing multi-target optimization to generate an initial charging guide strategy and a charging station resource allocation scheme; then, reinforcement learning iteratively optimizes the guide strategy to dynamically adapt to environmental changes; further optimizing charging time and power parameters through a dynamic adaptive optimization algorithm, and realizing charging station congestion early warning and selection suggestions in combination with a probability prediction algorithm; a charging station power distribution strategy is optimized based on early warning information, fine optimization is carried out by adopting a genetic algorithm, and a scheduling scheme is evaluated and adjusted in real time through a feedback control algorithm. According to the invention, dynamic, refined and global optimization of electric vehicle charging scheduling is realized, the stability of a power grid, the operation efficiency of a charging station and the charging experience of a user are improved, and new energy consumption is promoted.

The invention discloses a power monitoring system mimicry defense architecture based on dynamic heterogeneous redundancy, which relates to the field of smart grid network security protection and comprises a physical layer, a twinborn layer and a simulation layer. All the layers communicate through encryption channels, a collaborative architecture of a physical layer, a twinborn layer, a simulation layer and an application layer is constructed, accurate mapping of a virtual environment and a physical power monitoring system is achieved by means of the digital twinborn technology, and a virtual carrier fitting the reality is provided for anomaly detection and attack simulation. The adaptability of the defense architecture to the power business logic is improved through analysis and simulation support on the power dedicated protocol; each layer ensures that the normal operation of the power monitoring system is not interfered through encryption communication and reasonable resource control; meanwhile, the digital twinborn body is used for replacing the traditional physical test environment, so that the construction, operation and maintenance cost of a defense system is reduced, the management efficiency is improved, and an adaptive service, efficient and reliable basic framework is laid for the safety protection of the power monitoring system.

The invention belongs to the technical field of smart power grids, and discloses a distributed photovoltaic cluster-based smart power grid optimal scheduling method and system, and the method comprises the steps: fusing meteorological and power grid data through a cross-modal sensing network, combining a meta-learning framework to quickly adapt to a new power station, and narrowing a fluctuation interval through a prediction-correction dual-channel mechanism; according to the method, implicit association between weather and a power grid is mined, prediction robustness of low-probability events is improved through extreme scene intensive training, output prediction precision is remarkably improved, and the problem that traditional prediction lags behind actual fluctuation is effectively relieved; a dynamic role multi-agent system is adopted, and a credit scoring mechanism of block chain evidence storage and an improved auction algorithm are combined, so that distributed efficient decision making is realized; the intelligent agent dynamically switches roles according to the load state, the credit condition is linked with the scheduling priority, the problem that the topology adaptability of centralized decision making is poor is solved, and a trusted collaborative environment is constructed through transaction records which cannot be tampered.

The invention provides a power transformer electromagnetic loss control method and device in an intelligent power grid environment, and relates to the field of intelligent power grids, and the method comprises the steps: carrying out the cooling demand prediction of a transformer according to a predicted power load sequence and a space temperature sequence, and outputting a predicted cooling load sequence; and with meeting of the predicted cooling load sequence as a constraint and minimization of energy consumption and oil temperature fluctuation as optimization targets, cooling parameters of the forced oil cooling device are optimized, an optimal cooling parameter sequence is output, and the forced oil cooling device is controlled to dissipate heat of the transformer. The method aims to solve the technical problems that a traditional control method lacks cooling strategy dynamic adjustment capacity, temperature rise abnormity is prone to occurring in a high-load or high-temperature environment, transformer iron core loss and winding loss are remarkably increased, and electromagnetic loss is high, accurate and efficient cooling control can be achieved, the electromagnetic loss is effectively reduced, and the service life of a transformer is prolonged. And the operation efficiency and stability of the transformer are improved.

The invention discloses a power data dynamic transmission access control and identity verification deployment strategy, and belongs to the field of power system data security. Aiming at the problems that traditional static access control is not adaptive to dynamic change of a power grid, centralized authentication is high in delay, cross-domain fine-grained authority management is missing and the like, a dynamic access control module, an identity authentication module and a cross-domain access module are constructed. Strategy self-adaption of dynamic access control is realized based on 12-dimensional environment parameters and improved particle swarm optimization; the security of identity authentication is improved by adopting a national cryptographic algorithm, a composite key and a bidirectional Schnorr protocol; attribute alignment of cross-domain access is achieved through dynamic attention and federal learning. Through testing, the maximum concurrency number reaches 12300 TPS, the average response time is 82 ms, the illegal access interception rate is 99.12%, and the real-time performance and safety requirements of an intelligent power grid are effectively met.

The invention discloses a key sharing and security detection scheme based on an intelligent electric meter, and aims to solve the problems that an untrusted electric meter in an intelligent power grid is difficult to identify effectively, the detection scale is uncontrollable and the communication security is insufficient. According to the scheme, the elliptic curve cryptography, the threshold secret sharing mechanism and the physical unclonable function are combined, and hardware-level binding of the unique identity of the equipment and the secret key is achieved in the registration stage. Through a threshold password mechanism, a plurality of intelligent electric meters cooperatively participate in key reconstruction and encryption communication, and the intelligent electric meters can still work normally when part of the electric meters fail or are broken through. The center node can actively identify a fault or a malicious ammeter and dynamically adjust a communication strategy according to the integrity and correctness of the feedback information. Meanwhile, the scheme supports dynamic identity updating and integrity verification, and effectively resists modeling attacks, Sybil attacks, replay attacks and DoS / DDoS attacks. According to the invention, secure identity authentication, reliable key distribution and efficient anomaly detection are realized, and the security of smart grid terminal communication is improved.