524 results about "Topology identification" patented technology

The present disclosure relates to the technical field of power systems. Disclosed are a distributed fault self-healing method and system for an active distribution network, and a device and a medium. In the present disclosure, using a two-terminal waveform difference as a faulty section locating principle is applicable to an active distribution network, which contains various types of DGs and different DG permeabilities. The present disclosure has a relatively low requirement for data computation and storage regarding an STU, and topology identification and update can be automatically completed during operation by simply configuring basic static network topology information for the STU, thereby adapting to a change in the operation mode of the network. By means of the present disclosure, a rational fault recovery rule is set, and important load nodes can be recovered as much as possible while meeting a network power constraint. In a recovery solution, the presence of distributed generation is taken into consideration, and different processing methods are designed according to whether the distributed generation can operate in an island manner for a long period of time.

The invention relates to the technical field of power system topology identification, in particular to a power distribution network frame topology identification method based on an improved graph neural network, and the method comprises the steps: collecting the real-time electric quantity data of nodes and edges of a power distribution network, and carrying out the modeling of a power distribution network graph structure; the method comprises the following steps: constructing a node, edge and hyperedge feature matrix by using real-time collected data, inputting an improved graph neural network topology identification model, dynamically weighting and adjusting an edge weight through a graph attention network, splicing and fusing local topological features and global topological features, and generating a prediction adjacency matrix; when the power distribution network is dynamically changed, a change area is positioned through adjacency matrix difference, and a sub-graph is extracted for incremental updating; and constructing a topological structure of the power distribution network based on the final adjacent matrix, and outputting a connection relationship between the physical positions of the nodes and the edges. Compared with the prior art, the real-time performance, generalization ability and applicability of the model are remarkably improved, and the problems of accuracy and efficiency of power distribution network topology identification in the dynamic environment are solved.

The invention discloses a low-voltage power distribution area topology identification method and system based on clustering analysis and a graph theory, and relates to the technical field of low-voltage power distribution area topology classification. The system can accurately identify the topological structure of each partition through the combination of a clustering analysis model and a graph theory algorithm, and automatically calculate a topological characteristic coefficient Tp; therefore, the complex topological structure of the power distribution area can be classified and analyzed more carefully and comprehensively. According to a traditional method, accurate classification is often difficult when a complex branch structure is faced, and a topology recognition module ensures the accuracy of a classification result through a calculation mode of a graph theory. Through the feedback management module, when the power performance risk coefficient Lpjx of the partition exceeds a safety threshold R, a fault early warning signal can be sent out in time, abnormal nodes are automatically identified, faults are isolated, meanwhile, load transfer is executed, the power demand is rapidly transferred to the normally operating partition, and the power failure range and time are reduced.

The invention discloses a power distribution network topology identification and state estimation method based on a message passing neural network and online deep learning, and the method comprises the steps: training a neural network through historical measurement data, building a power distribution network topology identification model, combining the obtained topology information with corresponding power grid measurement data, and constructing a graph data set; taking the obtained graph data set as a training set to train a message passing neural network to obtain a power distribution network state estimation model; and finally, inputting the real-time measurement data into a state estimation model of the topology identification model to obtain a real-time state estimation result under the section. According to the method, the power distribution network topology can be accurately identified under limited measurement data, accurate state estimation is provided for a power distribution system, and technical support is provided for subsequent safety assessment and optimized operation.

The invention provides a power grid topology identification method and system, a terminal and a storage medium, and the method comprises the steps: obtaining the real-time data of a power grid, the real-time data of the power grid comprises the real-time operation data and the real-time topology data of the power grid, and the real-time operation data comprises the real-time operation information of each node and the line fault information; the real-time topological data comprises a connection relationship of each node; the power grid real-time data is input to a preset topology recognition model, so that the topology recognition model carries out topology recognition on the power grid according to the power grid real-time data, a topology recognition result is output, and the topology recognition result comprises a topology structure diagram, node state information, fault positioning information and future topology state information. And the accuracy and comprehensiveness of power grid topology identification are effectively improved.

The invention belongs to the technical field of supply chain management, and discloses an intelligent purchasing system and method of a supply chain. Comprising the steps of collecting demand prediction data and supplier performance data; carrying out deep mining on the demand prediction data, and predicting future purchase demands; generating a capability portrait based on the performance data of the suppliers, and performing quantitative evaluation on performance scores of different suppliers according to a pre-constructed evaluation system; historical order data is acquired, a supplier relation network topology is constructed in combination with the capability portraits, and a synergistic effect between suppliers is identified; fusing the performance score, the future purchase demand and the synergistic effect, and recommending an optimal supplier combination; acquiring purchase management data, and intelligently formulating a purchase strategy in combination with the supplier performance data of the optimal supplier combination; according to the invention, the purchase efficiency can be improved, and the flexibility and toughness of the supply chain are enhanced, so that the overall response speed, cost control capability and operation quality of the supply chain are effectively improved.

The invention relates to the technical field of electric power communication, and discloses a district physical topology identification method based on dual-mode communication signal strength, comprising the following steps: S1, building a district network; s2, topology identification is started, self initialization is completed, and necessary environment preparation before topology identification is carried out; s3, adding STA operation participation data one by one until all STA data are added; s4, identification calculation is started, branch calculation is carried out through HPLC signals of neighbor STAs by adopting a mode of selecting a rated threshold value, meter box calculation is carried out through HRF signals of the neighbor STAs, and relevancy ranking is determined; and S5, obtaining a topology result, and ending identification. According to the method, identification of the physical topology of the transformer area can be completed without additional equipment and manual intervention, and the method is safe, convenient and high in practicability. In the recognition process, the method is not affected by load voltage and current changes and the length of a power line, and compared with an existing impedance calculation and time delay calculation scheme, the recognition accuracy is high.

Disclosed in the present invention are a low-voltage transformer area topology identification method and apparatus, a storage medium, and a chip device. The low-voltage transformer area topology identification method comprises: obtaining electricity amount time series data of devices to be identified in a low-voltage transformer area to obtain original electricity amount data; performing dimension reduction processing on the original electricity amount data to obtain low-dimensional electricity amount data of branch boxes and low-dimensional electricity amount data of meter boxes; determining a transformer area master meter on the basis of the low-dimensional electricity amount data of the branch boxes, updating branch boxes waiting for determination of a connection relationship to obtain a first connection relationship of the branch boxes, and obtaining final-stage branch boxes; on the basis of low-dimensional electricity amount data of the final-stage branch boxes and the low-dimensional electricity amount data of the meter boxes, determining a second connection relationship of the meter boxes; and on the basis of the first and second connection relationships, obtaining a topological structure of the low-voltage transformer area. According to the low-voltage transformer area topology identification method in embodiments of the present invention, the interference caused by redundant information in high-dimensional data can be reduced, and the topological structure of the low-voltage transformer area can be accurately obtained.

The invention discloses a power operation mode intelligent arrangement and risk prevention and control method under mass new energy access, and relates to the technical field of power system operation control, and the method comprises the steps: constructing a main distribution network architecture data model, carrying out the fusion processing of dynamic data of different voltage levels and professional attributes, and generating a system operation state model; based on the system operation state model, topological structure analysis is executed, and a network connection relation model is established; and calculating a new energy access optimization scheme according to the network connection relation model, and generating an operation mode arrangement result and a risk prevention and control strategy. According to the method, the problems of data islands and inaccurate topology identification in the prior art are effectively solved. By constructing the multi-objective optimization model, the new energy access capacity can be scientifically evaluated, the optimal operation scheme can be generated, and the operation efficiency, safety and reliability of a power system under the condition of massive new energy access are remarkably improved.

The invention discloses an equipment management system and a server, and relates to the technical field of servers, and the equipment management system is characterized in that a topology identifier storage assembly for storing slot position signal source identifiers is moved to a server mainboard end, so that the occupation of mainboard connector pins by information transmission is reduced; and meanwhile, the management control component establishes and stores a corresponding relation between the address of the arbitration component and the screen printing area of the case. The adapter card does not need to be pre-bound and configured, can automatically identify the screen printing area of the case through the relationship by inserting any communication interface of the mainboard, and generates slot screen printing information in combination with the pre-stored signal source identifier. The problems that a large number of pins are occupied due to the fact that the adapter card hardware address signal identification configuration needs to be transmitted through a connector, and the adapter card needs to be bound with the fixed configuration and lacks flexibility in the prior art are solved. The technical effects of reducing pin occupation, realizing flexible plugging of the adapter card and automatic identification of screen printing, reducing hardware development cost and meeting the requirement of the server for reserving more bus interfaces for the connector are achieved.

The invention discloses a power distribution area line topology identification method and system based on an embedded system. The method comprises the following steps: acquiring and preprocessing electrical operation data and equipment state data in a power distribution area line; constructing a graph neural network model based on the preprocessed electrical operation data, and constructing a static graph structure in combination with the preprocessed equipment state data to train the graph neural network model; monitoring the real-time load change degree of the distribution line, and judging whether to trigger a dynamic update mechanism based on the load change degree, including dynamically updating the parameters of the static graph structure according to the load change degree to obtain an optimized model; and obtaining a topology recognition result according to the optimized graph neural network model, and regularly evaluating and optimizing the topology recognition result. The dynamic updating mechanism is triggered by monitoring the load change in real time, and the model can be adjusted in real time according to the actual operation condition of the power distribution area, so that the topology identification can accurately reflect the real-time state of the power distribution area.

The invention discloses a metering processing method for an electric energy meter topology application and an electric energy meter of a transformer area topology. The metering processing method comprises the following steps: S1, constructing a measurement data set based on an equipment identifier, a power factor and a sampling timestamp of the electric energy meter; s2, generating an initial graph model containing user nodes and electric energy transmission edges based on the data set; s3, inputting a dynamic coupling backflow enhancement graph diffusion network to execute topology identification; s4, constructing a fusion path diagram and initializing an edge weight; s5, calculating an edge weight regulation factor, and updating the edge weight; s6, asynchronous propagation is carried out to output a transformer area power supply topological graph; s7, constructing a metering path based on the topological graph and identifying an abnormal path segment; and S8, correcting the abnormal path, and generating an optimization topology and evaluation report. According to the method, the precise identification of the power supply topology of the transformer area and the self-adaptive correction of the abnormal metering path are realized.

The invention relates to the technical field of electric power topology identification, and discloses an electric power topology rapid identification method based on depth map matching, and the method comprises the following steps: S1, carrying out the multi-source heterogeneous data fusion and adaptive noise reduction, and employing an adaptive threshold noise reduction algorithm; s2, modeling a dynamic space-time diagram; s3, attention-driven graph matching is carried out, and a two-channel graph neural network is used; and S4, updating the incremental topology library. For the condition that the real-time data of the low-voltage power distribution network generates significant noise due to equipment aging and acquisition errors, a self-adaptive threshold noise reduction algorithm is adopted, high-frequency noise components are accurately filtered out according to a threshold value, missing data are intelligently repaired in combination with a space-time Kriging interpolation method, the purity and integrity of the data are ensured, and a data basis is provided for subsequent topology recognition.

The invention discloses a power distribution area topology identification method and system based on an intelligent fusion terminal and correlation coefficients, and the method comprises the steps: collecting the multi-source data of a power distribution area through an edge fusion terminal, and carrying out the data preprocessing and feature extraction at a terminal side; carrying out parallel calculation on a voltage fluctuation Pearson's correlation coefficient and a load change Kendall rank correlation coefficient, and constructing a correlation coefficient matrix in different time periods; calculating an adaptive weight based on the load fluctuation entropy, and fusing a dual-mode correlation coefficient; carrying out topology generation by using a graph neural network, and outputting an edge existence probability and a node hierarchy; performing physical constraint optimization on the initial topology by adopting a genetic algorithm; the system comprises an edge fusion terminal cluster, a cloud analysis platform, a topology verification module and a terminal management platform. According to the method, topology recognition precision and dynamic adaptability are remarkably improved, bimodal correlation coefficients and graph neural network space modeling are creatively fused, and a complex topological structure is precisely restored.

The invention relates to the technical field of power system power distribution network fault detection, in particular to an arc fault diagnosis and analysis method based on an artificial intelligence algorithm, and the method comprises the four steps: synchronous data collection and topological excitation, deployment of terminals at transformer area nodes, injection of characteristic current, and synchronous collection of response waveforms; signal preprocessing: separating a key frequency band through double-digital band-pass filtering, and calculating energy ratio and other enhanced fault features; performing multi-source feature fusion and intelligent diagnosis, constructing a vector containing statistical features, time domain distortion features and topology identification, and inputting a gradient boosting decision tree and deep neural network hybrid model to obtain fault confidence and type; and based on fault positioning and verification of topology, scheduling multi-node cooperative monitoring, and determining a fault point in combination with a topological relation. The method improves the data reliability and diagnosis precision, achieves the precise positioning of a fault, and guarantees the safe operation of a power distribution network.

The invention discloses a power distribution network topology identification method and device, and the method comprises the steps: obtaining the original measurement information of a power distribution network; based on the original measurement information, constructing a mixed integer nonlinear programming topology measurement identification model based on a branch current method; utilizing the target data physical fusion driving linearization model to perform linearization processing on a nonlinear constraint and measurement equation to be used in the topology measurement identification model of the mixed integer nonlinear programming based on the branch current method to obtain a processing result; and constructing a topology measurement identification model of mixed integer linearization programming based on a branch current method by using a processing result so as to output a topology identification result of the power distribution network. Therefore, the problems that in the related technology, the efficiency of maintaining the topology file based on a manual and data statistics method is low, a topology error identification model based on an optimization class method is poor in adaptability and low in accuracy, the accuracy of topology identification is reduced, and the operation stability of a power distribution network is reduced are solved.

The invention relates to a transformer area topology identification method and device based on a carrier communication network, and a concentrator. The method comprises the following steps: determining electric energy meter nodes in the same phase line and the same court and electric energy meter nodes in a cluster unit of the lowest level in a plurality of cluster units; performing hierarchical identification on the plurality of cluster units by adopting a carrier communication network to obtain hierarchies of the plurality of cluster units; according to the electric quantity data sent by the monitoring device corresponding to each branch box cluster unit in the plurality of cluster units and the electric quantity data sent by the monitoring devices corresponding to the superior power distribution room unit and the subordinate plurality of meter box units of each branch box cluster unit, determining an outgoing line and incoming line corresponding relation of the branch box cluster units in the plurality of cluster units, and forming a transformer area topological graph. By adopting the method, the transformer area topology identification can be carried out based on the carrier communication network, so that the situation that signal receiving devices are additionally arranged on branch boxes and electric meter boxes in a power distribution network is avoided, and the cost of transformer area topology identification is further reduced.

The invention discloses a distribution network dynamic topology identification and self-healing control method and device, and the method comprises the steps: collecting operation data of a distribution network, carrying out the time synchronization processing, data quality evaluation and data restoration, and obtaining a high-quality network state data set; based on the data set, performing dynamic topology identification by adopting a path improved by a relaxation method and a forest enhancement algorithm to obtain a dynamic network topology model; constructing a backpack network model by adopting a cyclic data generation method, and evaluating distributed photovoltaic output and load conditions; network abnormity is monitored in real time, and a machine learning algorithm is adopted to assist in judging fault types, positions and influence ranges; power supply reliability and economy are comprehensively considered, and a multi-objective optimization algorithm is adopted to generate a switch operation sequence; switching operation is executed, a temporary micro-grid is formed or a power supply path is adjusted, and the execution effect is monitored and evaluated in real time. According to the method, the dynamic topology of the high-proportion distributed photovoltaic power distribution network can be accurately identified, and fault positioning and efficient self-healing control are realized.

The invention discloses a graph contrast learning identification method for the low-voltage transformer area topology of a power distribution network. The method comprises the following steps: data acquisition and standardization; constructing a feature extraction module; constructing a time sequence encoder, a voltage correlation module, a time alignment module and a power mode module; semi-supervision and measurement combined training: carrying out graph contrast learning based on attributes, and carrying out supervision training on transformer attribution and meter box attribution by adopting cross entropy loss; and topology identification and reasoning output: inputting time sequence data of a user and a transformer under the condition of zero sample of a new transformer area, and outputting a user-transformer attribution probability and a meter box attribution probability through a feature extraction and classifier. According to the method, physical interpretable features such as voltage correlation and time alignment are introduced and are combined with time sequence coding and metric learning, so that the accuracy and stability of topology identification of the low-voltage transformer area are remarkably improved under the condition of small samples.

The invention relates to the technical field of power grid topology data updating, in particular to a power distribution network topology identification self-iteration updating method, which comprises the following steps of: acquiring a multi-layer topology structure of a power distribution network, adopting a random walk algorithm and considering the influence of edges and weight information of the edges on walk; performing multi-step migration on nodes in the same-layer power distribution network topological structure in the multi-layer topological structure of the power distribution network, calculating access probabilities among different nodes in a simulation process, and marking node pairs with the access probabilities exceeding a preset access probability threshold as potential associated nodes in the same layer; and analyzing relevance among nodes in the cross-layer power distribution network topological structure according to the same-layer potential relevance nodes, and generating same-layer and cross-layer potential relevance node information. According to the method, the problem that the incidence relation between the nodes in a complex network is difficult to accurately capture is solved by extracting the nodes, the paths and the weight information of the nodes in the multi-layer topological structure of the power distribution network and determining the potential correlation between the nodes in combination with a random walk algorithm.

The invention relates to the technical field of power distribution network bidirectional power flow coordination control, and discloses a power distribution network bidirectional power flow coordination control method based on V2G aggregation, and the method comprises the following steps: S1, constructing a logic energy storage unit composed of a plurality of V2G charging piles and vehicles supporting reverse discharge; s2, aggregating the logic energy storage units into virtual power plant nodes which can be dispatched by a power grid through an information communication technology (ICT); and S3, bidirectional power flow sensing and real-time state estimation: utilizing an intelligent electric meter, a sensor and a topology identification algorithm. According to the method, a logic energy storage unit composed of a plurality of V2G charging piles and vehicles supporting reverse discharging is constructed, the logic energy storage unit is aggregated into a virtual power plant node capable of being dispatched by a power grid through an information communication technology (ICT), and specific charging and discharging actions are decomposed and executed, so that dispersed V2G resources can be effectively integrated, and the V2G resources can be effectively integrated. And collaborative optimization with operation states such as voltage and power flow of the power distribution network is carried out.

The invention relates to the technical field of power distribution networks, in particular to a low-voltage transformer area topology recognition method and system adopting a graph diffusion model, equipment and a medium, and the method mainly comprises the steps that a topology recognition model is established, target time sequence characteristics are input into the topology recognition model, and the target time sequence characteristics are input into the low-voltage transformer area topology recognition model. And implicitly identifying node pairs with synchronous fluctuation through a topology identification model, outputting node features fusing neighbor information, predicting a connection probability between nodes based on the node features, and outputting a new transformer area topology structure. Different from a traditional method which depends on a specific transformer area rule or needs a large amount of manual adjustment, the graph neural network trained by the scheme aims at learning a general electrical characteristic-topological structure mapping rule and denoising logic. The model is not limited to a specific structure of training data, can be effectively generalized and applied to new courts with large topological structure differences, and solves the problems that a traditional method is poor in adaptability and weak in expansibility.

The invention discloses a transformer area topology identification method and system capable of being deployed in a concentrator, equipment and a medium, and the method comprises the steps: solving an initial virtual impedance matrix based on historical electrical quantity data of a transformer area, and taking the minimum deviation between an optimization result and the initial matrix as an optimization target; and taking symmetry constraint, boundary constraint and collinear constraint as constraint conditions, and performing iterative solution to obtain an optimized virtual impedance matrix, so that the optimized virtual impedance matrix is closest to an original matrix on the basis of conforming to the actual topology, noise interference can be eliminated, the topology identification precision is improved, the method can dynamically adapt to a transformer area change scene, and the method is suitable for being applied to a transformer area. The method has high robustness, the unstructured virtual impedance matrix can be fitted into a unique solution conforming to physical topology, fuzziness of a traditional method is avoided, the whole recognition process only depends on historical electrical quantity data of the transformer area, no initial topology information is needed, and the high-precision transformer area topology can be constructed from zero.

The invention discloses a structure three-dimensional displacement monitoring method and device based on a monocular camera and a concentric annulus, and relates to the field of computer vision and structure health monitoring, a concentric annulus target is fixed at a to-be-monitored structure monitoring point, and the monocular camera carries out positioning and lens parameter adjustment; shooting a checkerboard image and calibrating the checkerboard image by adopting a camera calibration algorithm to obtain a distortion parameter and an internal reference matrix; collecting a video sequence containing a structure displacement process of the target; image distortion correction is carried out based on the distortion parameters and the internal reference matrix, a target is identified through a target detection algorithm, and target parameters are calculated through least square circle fitting; realizing multi-target tracking by a target point topology identification algorithm based on Y-X threshold sorting; calculating scale factors according to target parameters and actual physical sizes, and calculating in-plane orthogonal direction displacement and out-of-plane displacement to form three-dimensional displacement information of the structure; according to the invention, the monocular camera is adopted to realize the accurate measurement of the three-dimensional displacement of the structure and reduce the complexity and calibration difficulty of the system.

The invention discloses a bidirectional reversible conversion method and system between a peptide molecule SMILES and a sequence expression. The core innovation lies in that a new sequence description syntax is defined to retain information of a polypeptide special bond and specific modification of amino acid; a main chain atom index and adjacency traversal topology identification algorithm is adopted, and end group and topology integrated detection and coding are carried out; a residue recognition algorithm for main chain cutting and template library matching is compatible with any standard or non-standard amino acid residues, an extensible end group library / monomer template library and an automatic increment mechanism, and automatic recognition and sequence annotation of S-S disulfide bonds; the invention relates to a high-fidelity assembly algorithm of HELM anchor points and topology aware cyclic peptide processing. The method solves the problems of incapability of supporting a complex polypeptide topological structure, poor reversibility, insufficient expansibility of a monomer library and the like in the prior art, can be widely applied to scenes of quantitative structure-activity relationship model construction, large-scale polypeptide data cleaning and the like, and has remarkable practicability and innovativeness.

The invention belongs to the technical field of primary and secondary fusion in the power industry, and particularly relates to an electromagnetic type primary and secondary fusion device and method, the device comprises a standard pole-mounted circuit breaker, a fusion PT and a fusion FTU, and the method comprises a method of using the electromagnetic type primary and secondary fusion device to carry out graph model checking, distributed FA and precise positioning. Real-time topology identification of a 10kV line is realized based on a medium-voltage carrier communication system, distributed FA is realized by adopting a medium-voltage carrier low-delay peer-to-peer communication technology, and accurate fault positioning is realized by adopting a double-end traveling wave positioning technology.

The invention discloses a low-frequency load shedding cooperative control method, system and device based on GOOSE communication and a readable storage medium, and the method specifically comprises the steps: calling a topology analysis service to confirm that a home terminal is in a receiving end power grid, sending a state query request to an adjacent terminal, and receiving an action signal of the adjacent terminal through GOOSE communication; based on the dynamic load shedding sequence, executing load shedding operation; a marginal cost function of each terminal is calculated, distributed consensus verification is carried out in combination with a block chain technology, and then load shedding parameters are optimized, issued and updated; and after load shedding is completed, a fault section is positioned through residual voltage detection and direction discrimination, a personalized reconstruction scheme is generated by combining topology identification and fault type matching, and power supply of a non-fault section is recovered within a preset distance. According to the method, quick response, cooperative control, accurate load shedding, intelligent optimization and quick fault recovery of low-frequency load shedding are realized, and the frequency stability and the power supply reliability of a power system in a new energy high-proportion access scene are remarkably improved.

The invention discloses a power distribution network topology identification method based on ARAM graph convolution, and the method comprises the steps: 1, taking lines in a power distribution network as nodes, taking the lines with power connection between the lines as edges, and abstracting the power distribution network as a line graph structure; 2, generating various types of topological structures according to the basic topological structure of the power distribution network system, and establishing a data set by taking electrical quantities such as voltage difference of a power distribution network line as node characteristics of a line graph and taking an on-off state vector of the line as a label; 3, constructing an ARMA-based graph convolutional network model for power distribution network topology identification; 4, training an ARMA-based graph convolutional network model and storing the trained model; and 5, collecting real-time measurement data of the power distribution network, and inputting the normalized data into the trained optimal ARMA graph convolutional network model to carry out real-time identification on the topology of the power distribution network. According to the method, accurate and real-time topology identification can be realized in a large-scale power distribution network with uncertain scenes such as distributed renewable power supply fluctuation.

The invention discloses a topology identification method based on intelligent measurement data of a low-voltage transformer area. According to the technical scheme, the topology identification method comprises the steps that 1, a master station issues a timing command to an intelligent measurement terminal and an electric meter; step 2, the master station issues a transformer area electric meter file to the intelligent measurement terminal, configures an acquisition task and issues the acquisition task to the intelligent measurement terminal; step 3, establishing an electric quantity sum linear regression model based on a Lasso algorithm by using daily frozen electric quantity data, and converting the identification problem of the transformer area user-transformer relation into regression coefficient solving; step 4, on the basis of user change relation identification, performing dimension reduction on historical voltage data by using a t-SNE algorithm to improve the identification efficiency of the model; and step 5, clustering the voltage data after dimension reduction by using agglomerated hierarchical clustering, identifying a user phase and a meter box to which the user belongs, and completing identification of a topological structure. According to the method, the power grid topology information of the transformer area can be automatically identified, the efficiency and the accuracy of topology identification are improved, and the method has far-reaching significance for construction of an intelligent power grid.

The invention provides a one-master multi-slave type high-speed serial communication and synchronous control method and system and a medium, and the method comprises the steps: firstly, resetting slave stations based on an initialization instruction, and sequentially transmitting equipment type query instructions based on a physical connection sequence, thereby completing the topology recognition of the slave stations; then, aggregated data frames are periodically constructed, a frame header is written into a master station status word, a frame body is formed by splicing slave station data packets according to a station number sequence, and a frame tail is a check code; and finally, the slave station analyzes the aggregated data frame based on the station number, executes the task, and feeds back a task execution result according to a physical inverted sequence. In addition, when a new slave station is accessed, the master station switches a status word and triggers the whole system to be reinitialized, and the new slave station is dynamically incorporated into the topology; according to the invention, the expansibility, the real-time performance and the reliability of multi-node synchronous control in an industrial scene are comprehensively improved.