169 results about "Grid security" patented technology

The invention relates to a reconfigurable battery energy storage system grid-connected and off-grid seamless switching control method and device. The method comprises the following steps of: sensing a real-time power grid state, pre-judging a system operation mode, and acquiring a millisecond-level panoramic information flow of the power grid state through a high-speed sampling voltage sensor, a high-speed sampling frequency sensor, a high-speed sampling phase sensor and a high-speed sampling communication interface. And pre-judging the stability and disturbance trend of the power grid through a deep learning algorithm, executing a power distribution and voltage support algorithm in grid-connected and off-grid modes, and calculating an optimal switching opportunity point and a system reconstruction target topology scheme. Meanwhile, the harmonic distortion rate and the voltage sag depth are calculated in real time through discrete Fourier transform, so that a mode pre-judgment mechanism is automatically triggered. After switching, key parameters such as voltage, frequency and power are automatically corrected through real-time sampling and observation, and zero-sensing switching is achieved. The method can effectively improve the response speed and stability of the battery energy storage system when the power grid fails, and guarantees the safe operation of the power grid.

The invention discloses a substation dispatching data network security communication method based on quantum tunnel encryption, relates to the technical field of large-scale power grid security assurance, and aims to solve the problems of endpoint security loss, boundary solidification, weak quantum threat resistance and inflexible deployment of a dispatching data network. According to the method, the establishment of a secure session and the encapsulation, encryption and restoration of data are realized by integrating quantum key distribution and a post quantum cryptographic operation module through quantum secure communication terminals deployed in a dispatching master station and a transformer substation. According to the method, application layer encryption and signature are carried out on telecontrol protocol data, a double-layer encryption tunnel is constructed, and transmission of various physical network media is supported. By means of the scheme, end-to-end protection is achieved, quantum attacks are effectively resisted, double encryption is provided, and networking flexibility and smooth evolution are improved.

The invention relates to the technical field of large-scale power grid safety guarantee, in particular to an optimal decentralized coordination control method for a micro-grid in a new energy power system, which comprises the following steps of: performing virtual disconnection verification on key power transmission lines one by one based on online acquired power grid topology and real-time line power flow data, and establishing a whole-grid cascade risk quantitative index. According to the method, power grid topology and line power flow data are acquired in real time, virtual disconnection verification of key power transmission lines is implemented one by one, simulation and analysis of a line fault scene are realized, whole-network cascade risk quantitative indexes are established on the basis, and an initial fault line with the maximum threat to system safety is identified by comparing the indexes with a safety threshold value in real time, so that the safety of the system is improved. A fragile line risk contribution list is generated, and high-risk lines are positioned and prevented in advance; and further directly calculating the power adjustment amount required by the micro-grid group according to the risk contribution list, and forming a forward-looking tide dredging instruction set.

The invention discloses an electric power artificial intelligence model security protection method and system based on a zero-trust architecture, and belongs to the technical field of power grid security protection. The method comprises the following steps: constructing an encryption service request according to identity recognition information and environment information of an access user; verifying the request by adopting a multi-factor identity verification method, and generating an authentication credible score; performing weighted calculation by combining the user historical behavior score, the equipment safety score and the environmental factor score to obtain a dynamic comprehensive credibility score; and generating a permission configuration strategy based on the score to realize fine-grained access control. The system comprises a user request access module, an information collection module, a credibility evaluation module and an access control module, and through continuous verification and dynamic permission adjustment, safe access of the electric power artificial intelligence model is ensured. The defect that a traditional security technology depends on static authentication and boundary protection is overcome, data leakage, model tampering and illegal access are effectively prevented, and the overall security and stability of a power system are improved.

The invention discloses a power grid industrial control system security protection scheme based on a block chain, and belongs to the field of power grid automation security. Aiming at the problems of high centralized authentication risk, weak physical layer protection and the like of a traditional industrial control system, a trusted execution environment and intelligent contract response system is constructed through fusion of a block chain distributed account book and a hardware security module (HSM). According to the scheme, equipment physical identity binding and vibration monitoring are achieved through an HSM, the consensus efficiency is optimized through an improved PBFT algorithm, instruction legality, parameter compliance and execution logic rationality are dynamically verified in combination with an intelligent contract, a security baseline is generated in real time, and the instruction stream deviation degree is monitored. The method can dynamically balance the security and real-time performance of the instruction, reduce manual intervention, improve the abnormal response efficiency to a second level, is suitable for industrial control systems such as SCADA (Supervisory Control And Data Acquisition) and the like, remarkably improves the protection capability in scenes such as DDoS attack resistance and physical tampering detection, and provides an extensible intelligent protection normal form for power grid security.

The invention discloses a micro-grid safety monitoring method and system based on attack detection data, and relates to the field of power grid safety monitoring, and the method comprises the steps: outputting a state vector of each node; obtaining a predicted attacked result of each node; outputting an estimated value set and a measured value set of the operation data of each prediction node; and obtaining an attack detection result of each prediction node and executing corresponding operation. According to the method, the data channel safety degree and the multi-dimensional evaluation parameters are fused, a state estimation and attack probability recognition mechanism combining extended Kalman filtering and a time sequence model is introduced, a hierarchical response mechanism is set according to attack levels, the elastic defense and active recovery capability of the system under different attack intensities is improved, and stable operation of the micro-grid is guaranteed.

The invention relates to the technical field of power system security, and particularly discloses a ground wire management operation system based on ground wire cabinet monitoring, which comprises a physical sensing layer, an edge computing layer, a block chain consensus layer and an application service layer. The physical sensing layer collects the identity, the in-place state and the electrical parameters of the grounding wire; the edge calculation layer carries out local data processing and event judgment; the block chain consensus layer realizes data credible evidence storage and cross-domain sharing through an intelligent contract and a distributed account book; and the application service layer provides asset panoramic display, cooperative verification and risk early warning services. Through the above architecture, unified management and safety collaboration of data of the whole life cycle of the grounding wire are realized, and the safety operation and maintenance level of a power grid is improved.

The invention discloses a main-distribution-micro-cooperation fault processing method and system, and belongs to the technical field of power system dispatching and fault handling, and the method comprises the steps: carrying out the fault perception and risk analysis of main-distribution-micro-cooperation after a fault is triggered, and generating an event fault event; through grid identification and power-loss monitoring, analyzing a power-loss condition and determining a power-loss influence level; generating an alarm event based on the event fault event and the grid identification and power loss monitoring result; and according to the alarm event and the power loss condition analysis result, generating a main-distribution-micro cooperative processing scheme, and supporting visual layered display. According to the method, the defects of fragmentation, low cooperation efficiency and scattered disposal schemes of main and distribution micro fault disposal in the prior art are overcome, the fault positioning accuracy, provincial and regional cooperation efficiency and the scientificity of the disposal schemes are remarkably improved, and the influence of faults on safe operation of a power grid and power supply of users is effectively reduced; the method is suitable for a main and distribution micro-grid fault handling scene containing distributed resources.

The invention discloses a power system network abnormal data clustering vulnerability detection technology, and belongs to the field of power system network security. Aiming at the problems of insufficient high-dimensional data processing capability, poor dynamic adaptability, high false alarm rate and the like of a traditional anomaly detection method, a detection technology based on an improved FCM algorithm is provided. According to the technology, through a dynamic feature weight matrix design module, a time adaptive membership updating module, a physical constraint regularization module, an incremental density peak initialization module and the like, an intelligent clustering framework fusing spatial-temporal features and physical constraints is constructed. And extracting multi-dimensional features by using GCN and Transform, dynamically adjusting the weight in combination with an attention mechanism, and introducing a time decay factor and a fuzzy logic constraint to realize accurate clustering. Experiments show that when the technology is applied to a provincial power grid, the anomaly detection accuracy is improved to 94.3%, the decision generation time is shortened to 14.8 ms, the constraint violation rate is reduced to 2.1%, and the real-time performance and reliability of power grid safety protection are remarkably improved.

The invention relates to the technical field of power grid safety evaluation, in particular to a power system short-circuit parameter prediction method, system and device based on time-space relevance and a storage medium. A multi-dimensional state vector including a node voltage vector, a branch current vector, a short-circuit impedance matrix, spatial position information and a time feature vector is constructed, and a space-time correlation feature vector including a time-dimensional feature, a spatial-dimensional feature, a space-time coupling feature and a state feature is extracted. Wavelet transform, Fourier analysis and graph theory analysis are adopted to identify a parameter change rule and a propagation effect. A multi-level prediction model of a statistical prediction layer, a deep learning layer and a fusion layer is constructed, and comprehensive description of dynamic characteristics of short-circuit parameters is realized through spatio-temporal feature fusion. A staged training strategy is adopted, and comprises the steps of establishing a basic model through offline training, updating parameters in real time through online learning, and quantifying uncertainty through a probability prediction framework. And a prediction correction mechanism and a reliability evaluation system are established, and a complete prediction-evaluation-correction closed loop is formed.

The invention relates to a power grid security defense system based on artificial intelligence and a block chain, and the system comprises an AI defense layer, a block chain trust layer, a cooperative control layer, an attack traceability and attribution layer, a physical-information fusion verification layer, a scene adaptive configuration layer and a risk prediction module. Data credibility is guaranteed by reputation weighted consensus, dynamic defense is completed by means of intelligent contracts and cross-layer cooperation, and beforehand early warning and computing power adaptation are realized through a risk prediction module. The system effectively solves the problems that in the prior art, an AI model is prone to being attacked, block chain response lags behind, virtual and real data are not synchronous, multi-scene adaptability is poor, pre-risk early warning is lacked, and computing power dispatching lags behind, can be widely applied to scenes such as a power transmission network, a power distribution network, a micro-grid and energy storage grid connection, and has high practicability. The anti-attack ability of the power grid to false data injection, equipment backdoor attack, collaborative DDoS and other threats is significantly improved, and the operation stability and safety of the power grid are guaranteed.

The invention provides an intelligent scheduling method and system based on source network load storage efficient collaboration, and relates to the technical field of source network load storage systems.The method comprises the steps that operation data of all nodes in a power grid are collected in real time through Internet of Things equipment, integrity verification and preliminary evaluation are conducted on the operation data, and an initial operation data set is formed; performing data cleaning and integration on the initial operation data set to form a unified operation data set; performing feature extraction and state analysis based on the operation data set to obtain a multi-dimensional feature vector; according to the multi-dimensional feature vector and an artificial intelligence technology, constructing a source network load storage collaborative energy management model; and obtaining a power grid operation state and load demand prediction result based on the model. According to the method, a full-process closed loop from data acquisition, processing, strategy generation to execution feedback is constructed, so that source-grid-load-storage efficient collaborative scheduling under data driving is realized, and finally, the goals of safe, stable and economic optimized operation of the power grid are achieved.

The invention discloses a day-ahead optimal scheduling decision method and device considering the climbing speed difference of a thermal power generating unit. The method comprises the following steps: expanding an optimal scheduling decision period of the thermal power generating unit; modeling peak regulation intervals of different climbing rates in the operation process of the thermal power generating unit by adopting a 0-1 variable; establishing a thermal power generating unit optimization scheduling objective function considering different state operation cost and deep peak regulation compensation cost, and thermal power generating unit operation constraint conditions reserved by considering different working condition start-stop, segmented climbing rate, start-stop process output and standby climbing capability; combining hydropower operation, system balance, new energy operation and power grid security constraints to construct a high-proportion new energy power grid unit combination optimization scheduling decision model; and accessing a planning day power grid topology model and operation boundary data, and generating a day-ahead unit commitment plan and an output plan based on the unit commitment optimization scheduling decision model. According to the invention, the power grid security risk caused by insufficient climbing ability can be reduced, and new energy consumption is promoted.

A kind of suitable for network type and follow network type energy storage isolated network collaborative control method and system, real-time monitoring network type energy storage remaining available capacity;Determine whether the active remaining available capacity of network type energy storage is less than active threshold, further determine whether the active remaining available capacity of follow network type energy storage is greater than power threshold, if greater, frequency deviation control is adopted to network type energy storage, and frequency additional control strategy is adopted to follow network type energy storage;Determine whether the reactive remaining available capacity of network type energy storage is less than reactive threshold, further determine whether the reactive remaining available capacity of follow network type energy storage is greater than reactive threshold, if greater, voltage amplitude deviation control is adopted to network type energy storage, and voltage additional control strategy is adopted to follow network type energy storage.The present application realizes the collaborative control of network type energy storage and follow network type energy storage in microgrid in isolated network mode, reduces artificial intervention operation, and ensures that power grid is safe, stable operation.

The invention relates to the technical field of power system dispatching, and discloses a power grid multi-target coordinated dispatching optimization method, device and equipment and a medium, and the method comprises the steps: constructing a dispatching target function which at least comprises a power abandoning penalty term and a safety out-of-limit risk penalty term; establishing a security constraint set of power grid operation; scheduling scheme solving is carried out based on the security constraint set, and when it is detected that power abandoning is caused by relaxation constraints, self-adaptive relaxation is carried out on the corresponding relaxation constraints according to a preset relaxation priority sequence; in the relaxed and updated feasible region, carrying out optimization solution by adopting a hierarchical reinforcement learning framework, and outputting an optimal scheduling instruction; wherein the upper layer of the hierarchical reinforcement learning framework is used for generating a scheduling feasible region meeting safety requirements, and the lower layer of the hierarchical reinforcement learning framework is used for performing rolling optimization of unit output based on the scheduling feasible region. According to the invention, on the premise of guaranteeing the safety of the power grid, the clean energy power abandonment rate is reduced and the optimization solution speed is improved.

The invention relates to the technical field of power systems, in particular to a grid-connected detection method for a virtual power plant operation platform based on a power grid demand analog signal, which comprises the following steps of: fusing historical data, real-time data and external factors to generate a high-precision analog signal, and constructing a mathematical model of the power grid demand analog signal; quantizing grid-connected detection indexes of the virtual power plant operation platform; based on the construction result of the mathematical model and the quantification result of the virtual power plant operation platform grid-connected detection indexes, grid-connected simulation testing and fault detection are carried out on the virtual power plant operation platform, a comprehensive score is obtained, a collaborative optimization model of the target virtual power plant operation platform grid-connected strategy and control parameters is constructed, and the target virtual power plant operation platform grid-connected detection indexes are obtained. And continuously optimizing and improving the grid-connected detection model and optimizing a target virtual power plant control strategy. The problems that in the prior art, the grid-connected efficiency is not high, and the safe and stable operation risk of a power grid is large are effectively solved, the grid-connected performance of the virtual power plant is comprehensively improved, and safe and stable operation of the power grid is guaranteed.

The invention discloses a power grid analysis method, device, equipment and medium based on a time sequence text heterogeneous graph, and the method comprises the steps: constructing a time sequence text attribute heterogeneous graph based on power grid data, and storing the time sequence text attribute heterogeneous graph in an attribute graph database, the time sequence text attribute heterogeneous graph comprises multiple types of nodes and edges, the nodes and / or edges carry text attributes, and the text attributes are stored in the attribute graph database; the topology and the attribute of the time sequence text attribute heterogeneous graph are dynamically changed along with time; based on the attribute graph database, exporting an event sequence from the attribute graph database according to a time sequence, or aggregating operation state data of the power grid equipment according to a time window to generate a window snapshot and exporting the window snapshot; and performing operation state analysis, fault detection or intelligent scheduling decision on the power grid according to the exported event sequence or window snapshot. According to the technical scheme provided by the embodiment of the invention, the comprehensiveness of power grid operation state analysis, the accuracy of fault detection and the scientificity of intelligent scheduling decision are remarkably improved, and a powerful guarantee is provided for safe, efficient and economical operation of the power grid.

The invention belongs to the technical field of large-scale power grid safety guarantee, and particularly relates to a secondary virtual loop intelligent checking method and system based on a substation configuration file. According to the method, an electrical information fusion graph fusing primary wiring and secondary functions is constructed by analyzing a substation configuration file; secondly, performing reverse tracing to form an independent virtual loop causal chain aiming at an execution outlet node; calculating a full-link time sequence deviation of each virtual loop causal chain; and finally, performing weighted summation on respective time sequence deviations of a plurality of causal chains which are gathered at the same outlet and have mutually exclusive starting end function logics to obtain comprehensive risk weighted time delay, and judging whether virtual loop configuration conflicts exist or not according to a comparison result of an index and a preset threshold value. According to the method, accumulated time delay risks can be found, and hidden deep logic configuration conflicts among different functions can be identified and quantified, so that the substation configuration file checking depth and precision are remarkably improved, and the safety of large-scale power grid operation is ensured.

The invention discloses a power grid transient stability evaluation system and method based on an enhanced physical information neural network. The technical problem that an existing power grid transient stability evaluation system cannot meet the requirements of power grid online scheduling and real-time early warning is solved. The system comprises a power grid data acquisition module, a preprocessing module, a core evaluation module and an output and decision support module. The power grid data acquisition module is used for acquiring running state data of a power grid; the preprocessing module is used for preprocessing the operation state data to obtain a power grid operation state feature vector; the core evaluation module is used for performing transient stability reasoning according to the power grid operation state feature vector by adopting a target enhanced physical information neural network model based on a domain decomposition strategy, and outputting a power grid transient stability evaluation result; and the output and decision support module is used for outputting a power grid safety decision according to the power grid transient stability evaluation result and visualizing the power grid transient stability evaluation result and the power grid safety decision.

The invention belongs to the technical field of power supply, and particularly relates to a waterproof heat dissipation electrical cabinet with power grid safety guarantee for power supply, which comprises a power supply control cabinet, a cooling box, a ventilation mechanism, an air guide mechanism and an explosion-proof trigger mechanism, and is characterized in that the power supply control cabinet comprises a cabinet body, a sealing cabinet door, a pipe interface, a clamping frame and a connecting hole; a sealing cabinet door is arranged on the front side of the cabinet body, pipe connectors are arranged at the top and the bottom of the right side wall of the cabinet body, a clamping frame is arranged on the right side wall of an inner cavity of the cabinet body, and a connecting hole is formed in the right side wall of the cabinet body; when a fire breaks out in the electrical cabinet due to a circuit fault, the increased temperature triggers the anti-explosion triggering mechanism, the anti-explosion triggering mechanism makes contact with the ventilation mechanism, a low-temperature medium in the ventilation mechanism is sprayed into the electrical cabinet for fire extinguishing, the anti-explosion effect is improved, and the safety of the electrical cabinet is guaranteed; therefore, safety and stability of power supply are guaranteed, and safe power supply of the power system is realized.

The invention relates to a classification association rule mining-based identification method and device for transmission power constraints of working lines in a power grid safety unit combination, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: constructing a line blocking state recognition sample set according to historical operation data of a pre-constructed security constraint unit commitment model in a plurality of historical scheduling cycles; the line blocking state identification sample set comprises a first sample set under a ground state line transmission power constraint and a second sample set under a fault state line transmission power constraint; performing merging processing on items in the item set, and constructing rule items about a line blocking state; constructing a classification association rule set according to frequent rule items corresponding to the same line blocking state sample labels in the rule items; and according to the classification association rule set, detecting whether the line transmission power constraint in the new scheduling period plays a role or not. By adopting the method, the recognition precision of the transmission power constraint of the working line can be improved.

The application discloses a kind of AI coordination edge computing's distributed power grid monitoring system, it is related to the technical field of distributed power grid monitoring based on AI model;Including distributed data acquisition module, acquisition multiple operating data, support multi-protocol access;Edge node preprocessing module, local processing original data, classified storage;AI collaborative analysis module, edge and cloud model cooperation, iteration updates model;Abnormal intelligent identification module, constructs rule base and identifies multiple abnormalities, generates alarm;Hierarchical data transmission module, hierarchical transmission data, with link redundancy;Visual management module, show power grid state, support query analysis;Security protection module, encrypted data, hierarchical authorization, detect attack, double backup.The application eliminates power grid monitoring blind area, improves data processing real-time and transmission efficiency;AI collaborative analysis improves the accuracy of abnormal identification, hierarchical disposal improves fault efficiency;Adapt to multiple scenarios, reduce operation and maintenance cost, help power grid safe operation.

A method and system for synchronizing WAMS time-series data across security zones based on parallel processing and traffic splitting is proposed. In Security Zone I, the WAMS time-series data synchronization master service dynamically groups all PMU measurement points connected to the WAMS master station. Based on the grouping, multiple time-series data synchronization instances are automatically started in parallel processing to generate WAMS time-series data files containing grouping information. These files are then transmitted synchronously across security zones using different communication channels. In Security Zone III, the file parsing master service automatically determines and starts multiple file parsing instances based on the grouping information in the WAMS time-series data file names, achieving split parsing of data files and parallel submission and saving of WAMS time-series data. This invention, based on the automatic dynamic loading of multiple time-series data synchronization instances in Security Zone I and the parallel processing of multiple file parsing instances in Security Zone III, improves the cross-security zone WAMS data synchronization capability, ensures the sampling rate and integrity of WAMS time-series data in Security Zone III, and provides strong data support for ensuring the safe and stable operation of the power grid.

The invention discloses a power grid key section screening method and system based on reinforcement learning, a processor and a medium, and belongs to the technical field of power grid safe operation. According to the method, a power grid dynamic state space is constructed by collecting multi-source data and utilizing a graph convolutional neural network, a partition risk is evaluated in combination with a deep Q network to obtain candidate key sections, meanwhile, a reward function and weight parameters thereof are adaptively optimized, a simulation fault scene is searched through a Monte Carlo tree to calculate a risk index, and the risk index is calculated. And finally, screening out a key section and carrying out online iterative optimization on model parameters. According to the method, the problems that a traditional method is difficult to process multi-source dynamic data, lacks complex fault scene simulation and cannot dynamically balance a multi-dimensional optimization target are solved, the dynamic risk characteristics of the power grid can be accurately described, the accuracy and the real-time performance of key section screening are improved, and effective support is provided for safe and stable operation of the power grid.

The embodiment of the invention provides a power grid evaluation index system construction method and device, electronic equipment, a computer readable storage medium and a program product, and relates to the technical field of power grid monitoring. According to the method, a multi-dimensional index system covering safety coverage, risk weighting and safety toughness is constructed by obtaining a long-time-sequence safety statistical table and operation parameters, and the stable operation capacity of a power grid under the long-time scale can be comprehensively reflected; a safety coverage index is used for evaluating basic safety, a risk weighting index is combined with a fault weight and an operation parameter to quantify risk strength, a safety toughness index is used for describing continuity and aggregation of an unsafe state, and the three indexes cooperate to form a power grid evaluation index system. Under the condition that manual intervention is not needed, systematic, differentiated and dynamic evaluation of the safety level of the power grid is automatically achieved, and a technical basis with higher guiding value is provided for operation optimization, planning decision making and emergency response.

The invention discloses a power grid operation instruction verification method, a power grid operation instruction verification device and a readable storage medium, and belongs to the technical field of power grid control. According to the method, the execution priority of each operation instruction in the operation instruction queue is determined, and the operation instructions with the front priorities are executed in sequence according to the execution priorities, so that the power grid operation instructions can be subjected to dynamic safety verification and priority ranking, and timely execution of high-priority instructions is facilitated; the problem that the current power grid static protection cannot accurately identify the power protection action of the equipment so that the security protection of the power grid has loopholes is solved.

This application provides a two-layer prevention and control method and system for grid cascading faults considering the integration of new energy sources. Based on the probability density function and output power function of wind farms and photovoltaic farms, the Monte Carlo method is used to calculate stochastic power flow. Assuming an initial fault occurs on a line and it is disconnected by backup protection, for the remaining lines other than the line with the initial fault, an expression for whether a cascading fault occurs in the grid is established based on the calculated power flow value and its corresponding setting value. The node injection power combination is obtained based on the calculated power flow value, and a grid safety margin is established based on the node injection power combination. An inner-layer optimization model is constructed with minimizing the grid safety margin as the first objective function, and an outer-layer optimization model is constructed with maximizing the grid safety margin and minimizing grid loss cost as the second objective function. The two-layer prevention and control model for cascading faults is solved to obtain a cascading fault prevention and control strategy, thereby enabling early identification and precise and effective prevention and control of cascading faults.

The application discloses a power grid real-time partition power distribution scheduling method and device, medium and equipment, and belongs to the power distribution scheduling field. The application obtains multi-source data of a power grid, inputs a partition optimization model for similarity matching. If a similar scene is matched, a historical optimization data is used to initialize a population, otherwise, an initial population is randomly generated according to power grid safety constraints. Then, a multi-objective cooperative evolution algorithm is used to mine Pareto optimal information, an initial population is optimized to generate an optimal partition parameter set, and a multi-objective reinforcement learning algorithm is used to optimize hyperparameters. Based on the optimized hyperparameters and real-time operation parameters, the power grid structure characteristics and topological connectivity are taken as constraints, the partition strategy is dynamically adjusted to meet the physical structure limit of the power grid, and finally, constraint verification is performed and real-time partition power distribution scheduling instructions are output. The application effectively solves the problem that the prior art cannot accurately and efficiently perform real-time partition power distribution scheduling of the power grid.

The embodiment of the invention provides a quantum-enhanced power grid multi-agent cooperative authentication and session key generation method and system, and belongs to the technical field of power grid security. The method comprises the following steps: selecting a collaborative authentication main body and initializing the collaborative authentication main body; initiating an authentication request; generating a multi-subject collaborative challenge; generating a joint authentication response; judging a local authentication result; aggregating and judging collaborative authentication results; collaboratively confirming the session key; and feeding back an authentication result and executing a security policy. According to the method, the safety, reliability and credibility of the power grid system in a complex operation environment are improved.

The application provides a power load anomaly detection method and device, and belongs to the technical field of power grid safety. The method comprises the following steps: standardizing power load data and processing the power load data in blocks. Then, a multi-head self-attention network is used to calculate an attention matrix representation between blocks and within blocks, and the attention matrix representation is respectively up-sampled. Next, a divergence loss function of the two is calculated, and an anomaly score of each point is calculated according to the divergence loss function. Finally, whether the power load data is abnormal is determined through a preset hyperparameter threshold. Through the method, the distance between normal and abnormal user features is maximized, the distance between features of the same type of users is minimized, an effective power data representation is learned, different user power consumption data features are actively compared, and abnormal power consumption behaviors are effectively identified.