78 results about "Power system topology" patented technology

The invention relates to the technical field of power operation and maintenance, in particular to a power system operation and maintenance method and system of an intelligent power distribution cabinet for weak current control. The method comprises the following steps: acquiring power operation data and sensing data of a power distribution cabinet, and analyzing a topological structure of a power system to obtain a dynamic topological structure of the power system; performing potential load anomaly analysis on the sensing data of the power distribution cabinet to obtain load anomaly node data of the power system; performing load anomaly influence structure division based on the load anomaly node data of the power system to obtain load anomaly influence nodes; performing abnormal node classification on the load abnormal influence nodes according to the sensing data of the power distribution cabinet to obtain three-phase power load offset nodes and three-phase power improper wiring nodes; and performing power distribution reconstruction on the three-phase power load offset node and the three-phase power improper wiring node to obtain abnormal node power distribution data. According to the invention, the weak current control efficiency and the power distribution energy efficiency can be improved.

The invention relates to a power system topological structure optimization method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: determining an optimization index of a power grid architecture according to the power grid architecture of a power system and distributed new energy power supply access data; according to the optimization index of the power grid architecture, establishing an optimization planning model of a power system topological structure; improving the ant colony algorithm by using a pheromone updating mode; and calculating and generating an optimization result of the topological structure of the power system according to the optimization planning model by using an improved ant colony algorithm. By adopting the method, the optimization result of the topological structure of the power system is calculated and generated based on the improved ant colony algorithm and the optimization planning model; the result meets the power grid operation constraint, the electric energy quality, the power supply reliability, the economical efficiency and the flexibility are remarkably improved, and meanwhile, the method adapts to distributed new energy power supply access proportion change and load dynamic change.

The invention discloses a demand defense control method and system based on digital twinning, and the method comprises the steps: building a digital twinning model through the multi-physics coupling and machine learning technology according to processed real-time operation data in combination with a power system topological structure and equipment parameters, and carrying out the dynamic calibration and optimization of the digital twinning model through the real-time operation data. According to the method, an electrical field and thermal field coupled digital twinborn model is constructed, time sequence features are extracted in combination with machine learning, and parameters are dynamically calibrated by using Kalman filtering, so that the problems of lack of multi-physical field interactive simulation, insufficient real-time data utilization and lack of a dynamic calibration mechanism of a traditional model are solved; the prediction accuracy of the model in a distributed power supply fluctuation and load sudden change scene is improved; according to the method, demand control, cost and user experience are taken as multi-objective optimization functions, an adjustment strategy is determined by adopting a particle swarm algorithm and the like, and closed-loop control from prediction, strategy generation to evaluation optimization is formed by calculating and controlling a multi-dimensional index evaluation effect such as response time and the like.

The invention discloses an electric power system node importance evaluation method based on a graph neural network, and the method comprises the following steps: 1), constructing an electric power system topological graph, abstracting an electric power system into a weighted graph G = (V, E), enabling a node V to represent electric power equipment, enabling an edge E to represent an electric power transmission path, and enabling the node and the edge to have electrical attributes; 2) aggregating electrical attributes and topological information of nodes through a graph convolutional network (GCN), and generating a node embedding vector; 3) optimizing node embedding representation by adopting self-supervised contrast learning, and calculating the similarity between the nodes; 4) updating node attributes and topological structures in real time according to dynamic power system monitoring data, and adjusting node importance scores; and 5) selecting the node with the highest importance score through a greedy algorithm, and optimizing the resource allocation of limited monitoring equipment.The method has the beneficial effects that powerful decision support is provided for scheduling, fault diagnosis and emergency response of the power system, and the monitoring efficiency and the overall safety of the power system are remarkably improved.

The invention discloses an LCC-HVDC operation range optimization method and system considering transient overvoltage constraint, and belongs to the field of power system automation. The method comprises the following steps: calculating the capacity of a matched capacitor based on the power system topology and the LCC-HVDC transmission power; carrying out load flow calculation in combination with a capacitor scheme, initializing equipment parameters, establishing a quasi-steady-state model of each equipment, and solving a quasi-steady state at the moment of maximum reactive power consumption during commutation failure; based on the model and the result, solving the quasi-steady state at the initial moment of the commutation failure recovery stage; and calculating the node voltage of the rectifier station, optimizing the LCC-HVDC power and equipment parameters along the gradient if the node voltage exceeds the limit, and iterating until the transient voltage safety is met, otherwise, determining that the current scheme is an operation scheme. The system comprises a corresponding function module and a data storage module. The method can be applied to an actual hybrid system, the transient overvoltage is accurately evaluated, the LCC-HVDC safety power range is determined, the power grid safety is guaranteed, and the new energy consumption efficiency is improved.

The invention relates to the technical field of electric carbon emission, in particular to an electric power system carbon emission flow metering error tracing method and system, and the method comprises the steps: building a carbon emission factor calculation model of each node based on the topological relation of an electric power system, and determining the upstream and downstream dependency relation between the nodes; according to an error source of the distributed metering system, quantifying relative uncertainty of a source side metering equipment error and an inter-node data transmission error; calculating the relative uncertainty of the carbon emission factors of each node from the source side to the user side step by step according to an error transmission rule, generating a global error propagation path, and calculating the total relative uncertainty of the carbon emission of the user side; and performing disturbance analysis on the power grid power data through a probability statistical simulation method, and identifying abnormal nodes. The invention aims to realize error traceability of carbon emission flow metering and avoid reduction of carbon emission metering precision caused by node errors in a power system.

The present invention provides a method, device, electronic device, and storage medium for evaluating transient stability of a power system. The method includes: obtaining power system flow information and power system topology information; determining a power system flow characteristic diagram based on the power system flow information and power system topology information; inputting node characteristics and topology characteristics of the power system flow characteristic diagram into a transient stability evaluation model to obtain the transient stability and transient stability index of the power system after each expected fault in the expected fault set output by the transient stability evaluation model; the transient stability evaluation model is used to perform coupled analysis of the transient stability and transient stability index of the power system by learning the characteristics of the power system flow characteristic diagram. The method, device, electronic device, and storage medium provided by the present invention enable rapid evaluation and prediction of the transient stability and transient stability index of the power system after each expected fault in the expected fault set.

The invention discloses an electric power system planning evaluation method and system based on multi-dimensional index decision support, and the method comprises the steps: constructing a scene set containing faults, fluctuation and load growth through obtaining a topological structure, line parameters and historical operation data of an electric power system; generating a topological risk index in the scene set by utilizing power flow calibration and topological risk identification, and dynamically adjusting a multi-dimensional index weight in combination with a phase factor in an impact and recovery stage; further fusing Monte Carlo distribution and Markov distribution through a control variable method to obtain a robust reliability index; mapping impact into stress, mapping function loss into strain, and constructing an elastic curve of the power system; and finally, extracting elastic indexes such as a yield point, ductility, recovery time and a function loss area, and outputting a comprehensive evaluation result of power system planning in combination with a robust reliability index so as to realize scientific comparison and optimization of different planning schemes.

The invention relates to an abnormal event tracing method and device of a power system, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring an operation state data set of a power system under the condition of receiving abnormal alarm information sent by a power monitoring system; performing anomaly detection on the operation state data set by adopting an isolated forest algorithm to obtain a candidate abnormal operation state data set; inputting the candidate abnormal operation state data set into a Gaussian mixture model to determine target abnormal operation state data in the candidate abnormal operation state data set; performing association mapping on the target abnormal operation state data and the topological structure of the power system, determining abnormal power equipment corresponding to the target abnormal operation state data, and obtaining time sequence data of the abnormal power equipment; and tracing the abnormal event based on the time sequence data of each abnormal power device, and generating an abnormal event tracing result. By adopting the method, the abnormity of the power grid system can be efficiently processed.

The invention relates to a resource networked modeling and scheduling optimization method based on a Neo4j electrical diagram database, and the method comprises the following steps: building a diagram database model based on a power system structure; flexible resources are extracted and managed based on a graph calculation method; and constructing a graph structure scheduling strategy system. Visual representation and multi-dimensional attribute nesting of the power system topology are achieved, real-time identification, dynamic updating and hierarchical classification are carried out on adjustable loads and controllable equipment, high expansibility, high real-time performance and high adaptability are achieved, the method can be widely applied to novel power systems represented by a power distribution network, a park micro-grid and a source-load-storage cooperative system, and the method is suitable for popularization and application. And accurate modeling and intelligent regulation and control of flexible resources are realized. Compared with an existing SQL (Structured Query Language) or relational database scheduling mode, the method has remarkable advantages in the aspects of modeling granularity, response speed and system integration.

The application discloses a novel power system dynamic simulation method and system based on an improved inverter model, relates to the technical field of novel power system stability analysis, and comprises the following steps: acquiring power system topology information, a control type of an inverter installed in the power system and an installation node, and constructing a node admittance matrix of an alternating current side system; according to the control type of the inverter, adding an adaptive bus model to the installation node of the inverter, and constructing an inverter state space model containing complete control links; performing power flow calculation on the alternating current side system by adopting a Newton-Raphson polar coordinate method, and taking the power flow calculation result as an initial value of electromechanical transient simulation calculation; respectively setting time domain simulation calculation methods of the alternating current side system electromechanical transient and the inverter side model, unifying simulation time and a step length, performing cyclic iteration solving of the alternating current side system and the inverter side model based on the initial value, and outputting a high-precision dynamic simulation calculation result of key parameters of the inverter until the simulation time ends.

The invention discloses a power grid synchronous broadband forced oscillation detection and positioning method and system and a medium, and the method comprises the steps: obtaining an oscillation data sequence of a power grid and a power system topological graph, and inputting the oscillation data sequence and the power system topological graph into a pre-trained multi-task graph sequence network, so as to obtain an oscillation source positioning detection result; comprising the following steps: performing information transmission on an oscillation data sequence through a convolutional layer, a pooling layer and an output feature obtained by a created embeddable sequence autoregression module and a topological graph of a power system through a constructed multi-task graph sequence network, and obtaining node features of each node through a gate control cycle unit GRU, and inputting a graph-level detection branch and a node-level detection branch to realize event type classification of the oscillation source and positioning of the oscillation source. The method aims at reducing interference of other information on broadband oscillation information, data are dynamically analyzed from the two dimensions of time and space, and broadband oscillation source positioning is rapidly and accurately achieved.

The application relates to the technical field of power system topology identification, in particular to a power distribution network framework topology identification method based on an improved graph neural network, which comprises the following steps: collecting real-time electrical quantity data of nodes and edges of a power distribution network, and modeling a power distribution network graph structure; constructing node, edge and hyperedge feature matrices by using real-time acquisition data, inputting the improved graph neural network topology identification model, dynamically weighting and adjusting edge weights through a graph attention network, splicing and fusing local topology features and global topology features, and generating a predicted adjacency matrix; when a dynamic change occurs in the power distribution network, locating a change area through adjacency matrix differences, and extracting a subgraph for incremental updating; constructing a topology structure of the power distribution network based on the final adjacency matrix, and outputting physical positions of nodes and connection relationships of edges. Compared with the prior art, the application significantly improves the real-time performance, generalization ability and applicability of the model, and solves the accuracy and efficiency problems of power distribution network topology identification in a dynamic environment.

The invention discloses a power system topology splitting method and device based on a transient stable operation domain, terminal equipment and a storage medium, and relates to the field of stability analysis, and the method comprises the steps: obtaining a static parameter and a node admittance matrix at a moment before a fault after the fault, and obtaining a current state parameter value in real time, constructing and solving a first sum of squares planning problem to obtain a pre-fault stability domain equation, calculating a first stability judgment coefficient based on the current state parameter value, and if the coefficient exceeds a threshold value, obtaining a plurality of single-line splitting schemes; according to the single-line splitting scheme and the split node admittance matrix, a second quadratic sum planning problem is constructed and solved, a pre-split stability domain equation is obtained, and a second stability judgment coefficient is calculated based on the current state parameter value; and determining a target splitting scheme according to the second stability judgment coefficients of all the schemes, and performing active splitting. By implementing the invention, the topology splitting efficiency is improved, and the problem of long time consumption due to dependence on simulation model calculation in the prior art is solved.

The embodiment of the invention provides a power load prediction method and device based on a structure perception aggregation function, and the method comprises the steps: obtaining the node features of each node and the node features of a neighbor node in a power system topology model, aggregating the node features of each node and the node features of neighbor nodes in the power system topology model based on a structure sensing aggregation function to obtain the node features of each node after aggregation; acquiring historical time sequence features of each node in the power system topology model, and performing fusion processing on the historical time sequence features of each node and the aggregated node features to obtain fusion feature data of each node; and inputting the fusion feature data of each node into a trained deep neural network prediction model to obtain a power load prediction result of each node.

The invention discloses a power grid topology updating method and device, electronic equipment and a storage medium, and relates to the technical field of power system topology analysis. The power grid topology updating method comprises the following steps: acquiring a power grid topological graph of a plant station, determining equipment identifiers of each node and a switch in each connecting line in the power grid topological graph, and determining a connection relationship between each node and the switch in each connecting line according to the power grid topological graph; determining a closed state of each switch, and determining an adjacent matrix of the nodes according to the connection relationship, the equipment identifier and the closed state; when the closed state is updated, determining an association node associated with the updated closed state, and updating the adjacency matrix according to a connection relationship corresponding to the association node to obtain a target adjacency matrix; and updating the power grid topology according to the target adjacent matrix. According to the method, only the local associated nodes are adjusted during switch displacement, the calculation complexity of topology updating is remarkably reduced, and the processing efficiency of power grid topology updating is improved.

The invention discloses a method and a system for measuring and calculating a user side green electricity use ratio based on power flow tracking. The measuring and calculating method comprises the following steps: S1, constructing a complete power system topology model based on a power grid SCADA system and GIS data, and abstracting a power entity set into nodes and edges; s2, calculating power transmission paths and proportions from the power generation nodes to the user nodes by adopting a power flow tracking algorithm; s3, identifying the electric quantity generated by the green power supply, and recording the flow path and proportion change of the green power supply in the power grid; s4, calculating the proportion of the electric quantity received by the user from each green power supply, and summing to obtain the use proportion of the user side green power; and S5, storing the key calculation process and result to support traceability verification. According to the method, accurate, dynamic and verifiable measurement and calculation are realized, full-link tracking of electric power from a power generation side to a user side is realized, reliable data support is provided for green power transaction and carbon emission accounting, and fairness and transparency of green power consumption determination are remarkably improved.

The embodiment of the application provides a kind of power load prediction method and device based on structure perception aggregation function, its method includes: obtaining the node feature of each node in the power system topology model and the node feature of its neighbor node, and based on structure perception aggregation function, the node feature of each node in the power system topology model and the node feature of its neighbor node are aggregated, to obtain the node feature after aggregation of each node;Obtain the historical time series feature of each node in the power system topology model, and by fusing the historical time series feature of each node with the aggregated node feature, obtain the fusion feature data of each node;By inputting the fusion feature data of each node into the trained deep neural network prediction model, the power load prediction result of each node is obtained.

The present invention relates to a method, system, equipment and medium for evaluating and optimizing the accuracy of electric power data. The method comprises the following steps: collecting electric power system topology data and electric power data of each node of the electric power system; constructing an inter-node correlation topology matrix based on the electric power system topology data and the electric power data of each node of the electric power system; calculating the composite weight of each node based on the inter-node correlation topology matrix; constructing an electric power data accuracy evaluation model, evaluating the accuracy of the electric power data of each node based on the composite weight of each node, and obtaining an electric power data accuracy evaluation index for each node; iteratively optimizing the electric power data of nodes whose electric power data accuracy evaluation index is less than a preset threshold, stopping the iteration until an iterative convergence condition is reached, and obtaining optimized electric power data of the current node.

The application provides an intelligent edge processing method with logical relationship power switch quantity, and the specific steps include: S1, two input ports are formed by collecting signals and high level signals, and the signals are stored in gateway memory through an AND gate circuit as current data of switches; the data stored in the gateway is expressed as follows: A x ={S1, S2, S3, S4, S5...S n} (1); S2, the data of all current switches are re-collected at a preset switch collection frequency; the data set is: A' x ={S'1, S'2, S'3, S '4 , S'5...S' n} (3); S3, if S n +S' n =0, it is indicated that the switch is not in a variable position, the result is input into the gateway memory as a feedback signal, and the switch state at the latest time is updated; if S n +S' n =1, it is indicated that the switch has been in a variable position, the signal is sent to a background in real time, and a power system topology is updated according to the signal.

A transient stability evaluation method based on topology and closed neural network, which utilizes differential equations to establish the nonlinear relationship between the transient power angle stability state and the power dynamic variables, and introduces an adjacency matrix to represent the topology of the power system, solves the long-standing problems of explainable and targeted modeling of complex continuous-time nonlinear dynamics of the power grid. Specifically, the present application combines differential equations, closed continuous-time neural networks and neural circuit strategies, wherein the closed continuous-time neural network greatly simplifies the modeling of continuous-time dynamics and avoids the limitations of numerical solutions of differential equations; and the neural circuit strategy ingeniously combines the adjacency matrix of the power system to simulate the adjacency relationship between buses, so that the model structure is highly sparse, which is conducive to improving the calculation efficiency. At the same time, when the topology of the power system changes, the method can quickly update the model to meet the accuracy requirements under the new topology.

The invention discloses a distribution line fault positioning method based on an adaptive relation matrix, which overcomes the problems of low positioning accuracy and low positioning speed of a distribution line fault positioning method in the prior art, and comprises the following steps: constructing an N-dimensional network topology matrix according to the number of distribution line switches; monitoring whether the power system has a fault or not in real time, if so, acquiring fault information, and judging a fault occurrence position by using a fault positioning method; if no fault occurs, detecting the change of the topological structure of the power system, if the change of the topological structure of the power system exists, performing adaptive adjustment on the N-dimensional network topological matrix, and then monitoring whether the power system has the fault in real time; otherwise, directly monitoring whether the power system has a fault or not in real time. The method can adapt to the change of the topological structure of the distribution line, and can quickly and accurately position all fault points when the line breaks down, thereby providing an accurate basis for further fault isolation and power supply recovery, and improving the power supply reliability of the system.

The invention discloses a key tie line accurate identification method under cyber-physical collaborative network attack, which comprises the following steps: inputting a power system topological structure, a generator output parameter and node load data, and initializing a binary decision variable for network division; constructing a single-layer mixed integer linear programming model; establishing a topological division constraint; establishing a regional net power calculation constraint, a flow and division association constraint and a physics power flow balance constraint; establishing a power generation surplus-load deficit matching and total load proportion practical constraint; and solving the model containing all constraints to obtain an optimal node division scheme for minimizing the number of connection lines, extracting to obtain a final key connection line set, and performing load shedding risk assessment under collaborative attack. According to the method, the problem that a traditional model is complex in calculation and not real in hypothesis is solved, and data driving and accurate identification of the functional key tie line in the power system are achieved.

The invention belongs to the technical field of power system fault prediction, and particularly relates to an anticipated fault analysis chart parallel computing method and system.The method includes the steps that when parallel computing is conducted on a power system anticipated fault analysis chart, a power system topological structure chart is established firstly; establishing a power flow model of the power system under the expected fault according to the power topological graph; converting the power flow model into a matrix form to form a matrix form equation set; and finally, carrying out parallel calculation on the matrix form equation set. According to the scheme, rapid analysis under the anticipated fault of the power system can be realized, and the practicability of static analysis of the power grid is improved.

The invention discloses a topology analysis method and device of a power system, electronic equipment and a storage medium, and is used for solving the technical problem that a current power system cannot carry out power grid cross-station analysis. The method comprises the following steps: firstly, acquiring equipment mode information of the whole station of the power system; constructing a complete topology model of the power system according to the equipment mode information; then performing invalid connection edge-node simplification processing on the complete topology model to obtain an optimized topology model; and finally, performing connectivity analysis on the optimized topology model through breadth-first search to obtain a connected component result of the equipment mode information. Therefore, by constructing unified and complete power system topology information, full-topology modeling of data management platform equipment data of the power system is realized, and cross-station connectivity analysis of the whole station of the power system is further realized.

The present application relates to the technical field of electric carbon emission, in particular to a power system carbon emission flow metering error traceability method and system, the method comprises: based on the topological relation of power system, constructing the carbon emission factor calculation model of each node, and determining the upstream and downstream dependence relation among nodes; according to the error source of distributed metering system, the relative uncertainty of source side metering equipment error and data transmission error among nodes is quantified; according to the error transmission rule, the relative uncertainty of carbon emission factor of each node from source side to user side is calculated step by step, the global error propagation path is generated, and the overall relative uncertainty of user side carbon emission is calculated; through the disturbance analysis of power grid power data by probability statistical simulation method, the abnormal node is identified. The purpose is to realize the error traceability of carbon emission metering, and avoid the decrease of carbon emission metering accuracy caused by node error in power system.

The present invention provides a method and system for detecting and defending against cross-domain threats in a power system. The method comprises the following steps: performing anomaly identification on the operational monitoring data of physical domain nodes within a target power grid area to obtain anomaly identification results; performing denial of service attack identification on the network traffic data of information domain nodes to obtain attack identification results; performing anomaly event correlation analysis on the two to obtain a cross-domain attack anomaly identification result; and obtaining a corresponding power system topology map and communication network topology map. An attack tracing graph is generated based on the key nodes and key risk propagation paths in the cross-domain attack chain generated based on a graph theory algorithm. Furthermore, based on the vulnerability information of the key nodes in the graph, active defense rules are iteratively generated and distributed to the nodes based on a game theory algorithm and a reinforcement learning algorithm. The present invention uses cross-domain anomaly event correlation analysis to accurately perceive cross-domain attack risks in real time and execute adaptive security defense, thereby improving the comprehensiveness and reliability of power system security protection.

The embodiment of the application provides a power load prediction method and device based on graph convolutional neural network and time series fusion, which comprises the following steps: acquiring the topological relationship between devices in a power system, and constructing a power system topological model according to the topological relationship between devices in the power system; acquiring the node features of each node in the power system topological model, and updating the node features of each node in the power system topological model based on a graph convolutional neural network to obtain the updated node features of each node; acquiring the historical time series features of each node in the power system topological model, and obtaining the fusion feature data of each node by fusing the historical time series features of each node with the updated node features; and inputting the fusion feature data of each node into a trained deep neural network prediction model to obtain the power load prediction result of each node.

The invention discloses an electric power system short-circuit current suppression method and system based on network construction type equipment, and belongs to the technical field of electric power. The method comprises the following steps: establishing a power system topology and solving a steady-state operating point; establishing a converter control model based on the steady-state point, and constructing a current-limiting control logic by adopting three criteria of current, phase angle and voltage; deducing a short-circuit current analytical expression through fault analysis, and establishing a relationship between the output current of the network construction type equipment and the total short-circuit current; and dynamically adjusting the output current phase by adopting a self-adaptive saturation current angle strategy based on the relationship. The technical problems that a traditional current limiting method is difficult in parameter setting and the current limiting mode cannot be automatically quitted after a fault occurs are solved, the short-circuit current of the power system can be restrained to the maximum extent, reliable on-off of a circuit breaker is ensured, and the safety and stability of the system are improved.

The invention relates to the technical field of topological structures of power systems, and discloses a high-capacity direct-current hydrogen production power supply with a multi-winding integrated transformer, which comprises a clamping circuit, an inverter circuit, a multi-winding isolation conversion rectifying circuit and a non-isolation regulating circuit which are connected in sequence, the input end of the multi-winding isolation conversion rectification circuit is connected with the output end of the inverter circuit, and the output ends of the multi-winding isolation conversion rectification circuit are mutually independent; the input ends of the non-isolated regulating circuits are connected with the output ends of the multi-winding isolated conversion rectifying circuits in a one-to-one correspondence manner, the output ends of the non-isolated regulating circuits adopt a single-path port output manner or a multi-path port output manner, and the output ends of the non-isolated regulating circuits are connected with the electrolytic bath. The requirements of different electrolytic cell capacities and voltage grades are met, the capacity and power density of the direct-current hydrogen production power supply are greatly improved, and the application requirements of renewable energy source volatility, intermittency and engineering compactness are met.