CN117977586A - Power failure early warning method and device based on three-dimensional digital twin model and electronic equipment - Google Patents
Power failure early warning method and device based on three-dimensional digital twin model and electronic equipment Download PDFInfo
- Publication number
- CN117977586A CN117977586A CN202410390611.5A CN202410390611A CN117977586A CN 117977586 A CN117977586 A CN 117977586A CN 202410390611 A CN202410390611 A CN 202410390611A CN 117977586 A CN117977586 A CN 117977586A
- Authority
- CN
- China
- Prior art keywords
- power
- node
- updated
- information
- digital twin
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 39
- 230000002159 abnormal effect Effects 0.000 claims abstract description 18
- 238000012545 processing Methods 0.000 claims abstract description 17
- 230000000007 visual effect Effects 0.000 claims abstract description 12
- 238000012546 transfer Methods 0.000 claims description 49
- 230000005611 electricity Effects 0.000 claims description 27
- 238000004088 simulation Methods 0.000 claims description 13
- 238000004590 computer program Methods 0.000 claims description 10
- 238000010276 construction Methods 0.000 claims description 9
- 230000004044 response Effects 0.000 claims description 8
- 238000001514 detection method Methods 0.000 claims description 5
- 239000013598 vector Substances 0.000 description 20
- 230000006870 function Effects 0.000 description 7
- 208000003443 Unconsciousness Diseases 0.000 description 6
- 238000004422 calculation algorithm Methods 0.000 description 6
- 238000004891 communication Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 6
- 239000011159 matrix material Substances 0.000 description 6
- 230000008878 coupling Effects 0.000 description 5
- 238000010168 coupling process Methods 0.000 description 5
- 238000005859 coupling reaction Methods 0.000 description 5
- 238000004141 dimensional analysis Methods 0.000 description 5
- 230000003111 delayed effect Effects 0.000 description 4
- 238000000605 extraction Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 230000003044 adaptive effect Effects 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000007499 fusion processing Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000009499 grossing Methods 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 238000012098 association analyses Methods 0.000 description 1
- 230000002457 bidirectional effect Effects 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 238000010219 correlation analysis Methods 0.000 description 1
- 238000013481 data capture Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
Landscapes
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The embodiment of the invention discloses a power failure early warning method, a device and electronic equipment based on a three-dimensional digital twin model. One embodiment of the method comprises the following steps: acquiring actual operation data of a power system; updating actual operation data into a pre-constructed three-dimensional digital twin model to obtain an updated digital twin model, wherein the updated digital twin model comprises updated system operation data; updating a pre-constructed power node network in the updated digital twin model based on the updated system operation data to obtain an updated power node network; performing abnormal outage analysis processing on the updated power node network to obtain an outage analysis result; and responding to the fact that the outage analysis result meets the preset risk condition, sending the outage analysis result to a pre-constructed visual interface for display, and executing outage early warning operation by utilizing the outage analysis result. The implementation mode can accurately perform power failure early warning in time.
Description
Technical Field
The embodiment of the disclosure relates to the field of power system safety, in particular to a power failure early warning method and device based on a three-dimensional digital twin model and electronic equipment.
Background
As the demand for electricity increases, so too does the factors that lead to a power outage. At present, when power failure early warning is carried out, the mode generally adopted is as follows: and carrying out power outage analysis by combining historical power utilization data of each power utilization unit so as to carry out power outage early warning.
However, in practice, when the power outage early warning is performed in the above manner, the following technical problems often exist:
firstly, the problem of power consumption areas and power consumption lines is not considered, power failure analysis is comprehensively carried out on different power consumption areas, and large coupling exists, so that the actual coverage area of power failure is difficult to detect, and the error of the area for power failure early warning is large;
Secondly, power outage analysis is performed without combining the power utilization structure relations of the power utilization units, so that potential association relations of power outage factors among the power utilization units are difficult to discover, data integrity for power outage analysis is insufficient, a power outage area is difficult to predict in time, and power outage early warning is difficult to perform in time.
In the process of solving the second technical problem by adopting the technical scheme, the following technical problem III is often accompanied: even if the structural relation among power utilization units or users is introduced, the data which can be used for power failure analysis is difficult to effectively discover, on the contrary, the power failure analysis is difficult to be timely carried out due to the fact that the more related data of the introduced structural relation, and the power failure early warning time is delayed.
The above information disclosed in this background section is only for enhancement of understanding of the background of the inventive concept and, therefore, may contain information that does not form the prior art that is already known to those of ordinary skill in the art in this country.
Disclosure of Invention
The disclosure is in part intended to introduce concepts in a simplified form that are further described below in the detailed description. The disclosure is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Some embodiments of the present disclosure propose a power outage early warning method, apparatus and electronic device based on a three-dimensional digital twin model to solve one or more of the technical problems mentioned in the background section above.
In a first aspect, some embodiments of the present disclosure provide a power outage early warning method based on a three-dimensional digital twin model, the method comprising: acquiring actual operation data of a power system; updating the actual operation data into a pre-constructed three-dimensional digital twin model to obtain an updated digital twin model, wherein the updated digital twin model comprises updated system operation data; updating a pre-constructed power node network in the updated digital twin model based on the updated system operation data to obtain an updated power node network; performing abnormal outage analysis processing on the updated power node network to obtain an outage analysis result; and responding to the fact that the power outage analysis result meets the preset risk condition, sending the power outage analysis result to a pre-constructed visual interface for display, and executing power outage early warning operation by utilizing the power outage analysis result.
In a second aspect, some embodiments of the present disclosure provide a power outage early warning device based on a three-dimensional digital twin model, the device comprising: an acquisition unit configured to acquire actual operation data of the power system; the first updating unit is configured to update the actual operation data into a pre-constructed three-dimensional digital twin model to obtain an updated digital twin model, wherein the updated digital twin model comprises updated system operation data; a second updating unit configured to update a pre-built power node network in the updated digital twin model based on the updated system operation data, to obtain an updated power node network; the abnormal power outage analysis unit is configured to perform abnormal power outage analysis processing on the updated power node network to obtain a power outage analysis result; and the sending and early warning unit is configured to send the outage analysis result to a pre-built visual interface for display in response to determining that the outage analysis result meets a preset risk condition, and execute outage early warning operation by using the outage analysis result.
In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors causes the one or more processors to implement the method described in any of the implementations of the first aspect above.
In a fourth aspect, some embodiments of the present disclosure provide a computer readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.
The above embodiments of the present disclosure have the following advantageous effects: by the power outage early warning method based on the three-dimensional digital twin model, the accuracy of the area aimed at by the power outage early warning can be improved. Specifically, the reason why the error of the area for which the power outage warning is aimed is large is that: the problem of power consumption area and power consumption line is not considered, and the power failure analysis is comprehensively carried out on different power consumption areas, so that the power failure actual coverage area is difficult to detect because of large coupling. Based on the power failure early warning method based on the three-dimensional digital twin model, disclosed by some embodiments of the invention, introduces a pre-built three-dimensional digital twin model so as to simulate the actual operation scene of the circuit. Meanwhile, the actual operation data of the power system is obtained in real time and updated into the three-dimensional digital twin model for synchronous operation with an actual scene. Next, the power node network in the three-dimensional digital twin model is updated with the actual operational data. Here, too, since the power node network is constructed in advance, it can be used to distinguish between different power usage units and power usage circuits. Thus, outage analysis is facilitated based on the power node network variations. Then, by the abnormal power outage analysis, a power outage analysis result can be obtained. Therefore, the coupling of the comprehensive power failure analysis of different power utilization areas can be greatly relieved. Therefore, the accuracy of the outage analysis is improved, and the area corresponding to the outage analysis result is further accurately obtained. And finally, responding to the fact that the power outage analysis result meets the preset risk condition, sending the power outage analysis result to a pre-constructed visual interface for display, and executing power outage early warning operation by utilizing the power outage analysis result. Furthermore, the power failure early warning can be accurately carried out on the area with the power failure risk.
Drawings
The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale.
FIG. 1 is a flow chart of some embodiments of a power outage early warning method based on a three-dimensional digital twin model according to the present disclosure;
FIG. 2 is a schematic structural view of some embodiments of a power outage early warning device based on a three-dimensional digital twinning model according to the present disclosure;
fig. 3 is a schematic structural diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.
It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings. Embodiments of the present disclosure and features of embodiments may be combined with each other without conflict.
It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.
It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.
The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.
Operations such as collection, storage, and use of personal information (e.g., user electricity information) of a user referred to in the present disclosure, and the like, until the corresponding operations are performed, the relevant organization or individual is up to the end to include developing personal information security impact assessment, fulfilling informed obligations to the personal information body, and obtaining authorized consent of the personal information body in advance.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
FIG. 1 illustrates a flow 100 of some embodiments of a three-dimensional digital twinning model-based outage early warning method according to the present disclosure. The power failure early warning method based on the three-dimensional digital twin model comprises the following steps:
Step 101, acquiring actual operation data of a power system.
In some embodiments, the execution subject of the outage early warning method based on the three-dimensional digital twin model may acquire actual operation data of the power system in a wired manner or a wireless manner. The power system may refer to each power unit. The actual operating data may be actual electricity consumption of each user.
It should be noted that the wireless connection may include, but is not limited to, 3G/4G connection, wiFi connection, bluetooth connection, wiMAX connection, zigbee connection, UWB (ultra wideband) connection, and other now known or later developed wireless connection.
And 102, updating actual operation data into a pre-constructed three-dimensional digital twin model to obtain an updated digital twin model.
In some embodiments, the execution entity may update the actual operation data to a pre-constructed three-dimensional digital twin model, to obtain an updated digital twin model. Wherein, the updated digital twin model may include updated system operation data.
In some alternative implementations of some embodiments, the actual operating data may include, but is not limited to, at least one of: actual current information, actual voltage information, actual load information, actual temperature information, and the like. The three-dimensional digital twin model may include a sensor data acquisition system. The three-dimensional digital twin model may further include various information corresponding to actual operation data: model current information, model voltage information, model load information, and model temperature information. And the executing body updating the actual operation data into a pre-constructed three-dimensional digital twin model to obtain an updated digital twin model, which may include the following steps:
And taking the actual current information, the actual voltage information, the actual load information and the actual temperature information which are included in the actual operation data as data acquired by a sensor data acquisition system so as to update the model current information, the model voltage information, the model load information and the model temperature information which are included in the three-dimensional digital twin model. The updating can be to take the actual current information, the actual voltage information, the actual load information and the actual temperature information as the latest real-time data to correspondingly replace the model current information, the model voltage information, the model load information and the model temperature information in the three-dimensional digital twin model. The sensor data acquisition system may be a system for analog acquisition of electricity usage by individual users and electricity usage units. Second, the actual current information, the actual voltage information, the actual load information, and the actual temperature information may each represent actual data of power consumption of each user. For example, the actual load information may include an electrical load per electricity usage unit. The actual temperature information may then include a temperature value corresponding to the environment in which each user is located. Here, the temperature value may be measured by a temperature sensor installed in the electricity meter box or may be acquired from a weather site.
Alternatively, the three-dimensional digital twin model may be constructed by:
First, integrating a sensor data acquisition system in a pre-acquired three-dimensional high-precision map model. The integrated sensor data acquisition system can be a module for adding acquired data in the three-dimensional high-precision map model. The sensor data acquisition system can be used for acquiring power information in an actual scene, so that the power information can be used as real-time power consumption information of each user in the three-dimensional high-precision map model, and the actual power consumption data can be sent to each user power consumption position and historical power consumption data can be saved.
And secondly, acquiring a power information set by using the sensor data acquisition system. Wherein, each of the above-mentioned power information sets may include, but is not limited to, at least one of: user information, electricity usage information, spatial location information, and circuit information. Here, each power information may correspond to one power usage unit or power usage data of a user. The user information may include a unique identification of the user. The electricity usage information may include electricity usage data of the user. Such as power usage, voltage, etc. The spatial location information may be location coordinates and addresses of the user in the three-dimensional high-precision map model. The circuit information may be temperature information of the circuit.
And thirdly, adding power utilization nodes corresponding to the space position information included in each power information in the power information set into the three-dimensional high-precision map model. The three-dimensional map model is constructed according to an actual map scene. The three-dimensional map model may include simulated terrain and simulated buildings for simulating an actual map. Thus, a scene corresponding to the location coordinates and address of each user may be included in the three-dimensional high-precision map model. The electricity utilization node can be established at each user position coordinate in the three-dimensional high-precision map model.
And step four, adding the user information and the power consumption information included in each piece of power information in the power information set to the corresponding power consumption node to obtain a constructed power consumption node set. Wherein each post-construction electricity node in the set of post-construction electricity nodes may include a unique electricity identification. Each post-construction electricity node in the post-construction electricity node set is located at a corresponding coordinate position in the three-dimensional map model.
Fifthly, constructing a simulation circuit among the constructed power utilization nodes according to circuit information included in each power information in the power information set, and obtaining a constructed simulation circuit set. Wherein each post-construction simulation circuit in the set of post-construction simulation circuits may include corresponding circuit information. Each circuit information may characterize the operation of the circuit between two post-construction power nodes. Therefore, the simulation circuit can be arranged between the power utilization nodes according to the actual circuit trend.
In practice, if a section of circuit is a high-voltage circuit, the circuit information may further include data such as arc weight, length, crossing clearance distance, etc. of the electric wire.
And sixthly, determining that the three-dimensional digital twin model is constructed in response to determining that the sensor data acquisition system, the constructed power consumption node set and the constructed simulation circuit set are constructed. The data in each power consumption node and the simulation circuit in the three-dimensional digital twin model are updated in real time based on the data acquired by the sensor data acquisition system.
And step 103, updating the pre-constructed power node network in the updated digital twin model based on the updated system operation data to obtain an updated power node network.
In some embodiments, the executing entity may update the pre-constructed power node network in the updated digital twin model based on the updated system operation data to obtain an updated power node network.
Alternatively, the above power node network may be constructed by:
And the first step is to carry out grouping processing on each constructed electricity consumption node in the constructed electricity consumption node set in the three-dimensional digital twin model so as to generate a main power node group, a primary power transfer node group, a secondary power transfer node group, a tertiary power transfer node group and a power user node group. Wherein, the grouping can be performed according to a preset power consumption node level. The power master node group may characterize a unit or power generation equipment for generating power. The primary power transfer node may be a first level of power transfer equipment. The powered user node may represent a location node of a particular user. In addition, for each constructed power consumption node, a corresponding node identifier can be set after grouping.
As an example, the preset power node level may be divided into five levels. For example, level 1 is the power master node, corresponding to the node of the power plant. The level 2 is a level one power transfer node and corresponds to total power equipment for a power supply area. The level 3 is a secondary power transfer node and can correspond to the total power equipment of the cell. The level 4 is a three-level power transfer node, and can correspond to the total power equipment of a certain unit of a cell. The level 5 is a power consumer node, and can correspond to a specific power device of a certain consumer.
As another example, the node identification may be set according to the node level. For example, the node identification of a certain power master node is 10000. Then, the primary power transfer node, the secondary power transfer node, the tertiary power transfer node, and the power consumer node connected to the power master node may be: 11000. 11100, 11110, 11111. Thus, correspondence may be determined from the node identification for the associated node.
And secondly, selecting a matched primary power transfer node from the primary power transfer node groups for each power master node in the power master node groups so as to construct a primary power node network. The primary power node network is a star network constructed by taking a power main node as a main node. And secondly, the matched nodes can be determined through the node identifiers corresponding to the nodes. Here, each power master node is a master node of a primary power node network, and the matched primary power transfer nodes are associated as slave nodes, so as to construct the primary power node network.
And thirdly, selecting a matched secondary power transfer node from the secondary power transfer node groups for each primary power transfer node in the primary power transfer node groups so as to construct a secondary power node network. The primary power node network is a star network constructed by taking a primary power transfer node as a main node. Here, each primary power transfer node is a primary node of a secondary power node network, and the secondary power transfer nodes that match each other are associated with each other as secondary nodes, so as to construct the secondary power node network.
Fourth, for each secondary power transfer node in the secondary power transfer node group, selecting a matched tertiary power transfer node from the tertiary power transfer node group, and selecting a power consumer node corresponding to the matched tertiary power transfer node from the power consumer node group to construct a tertiary power node network. The three-level power node network is a tree network constructed by taking two-level power transfer nodes as main nodes, each matched three-level power transfer node is a child node, and each corresponding power user node is a leaf node under the child node.
And fifthly, integrating a power fluctuation detection system in the primary power node network, the secondary power node network and the tertiary power node network to complete construction of the power node network, wherein the power fluctuation detection system can be used for detecting power fluctuation data of a circuit between two adjacent nodes. Each power fluctuation data is used to characterize the power fluctuation of the circuit between one node and the previous node.
In some optional implementations of some embodiments, the executing entity updates a pre-constructed power node network in the updated digital twin model based on updated system operation data, to obtain an updated power node network, and may include the following steps:
And the first step is to update the updated system operation data to each node in the power node network, and determine the power fluctuation data between every two adjacent nodes to obtain an updated primary power node network, an updated secondary power node network and an updated tertiary power node network. The updated system operation data may include data corresponding to each node and transmission circuit data corresponding to each node. Thus, the updated system operation data can be distributed to the corresponding nodes to complete the update. Second, historical power fluctuation data for the circuit between every two adjacent nodes can be obtained. Here, the historical power fluctuation data may include data of a current fluctuation curve, a voltage fluctuation curve, and the like. Then, the voltage value and the current value in the circuit transmission data corresponding to the section of circuit can be respectively updated into a current fluctuation curve and a voltage fluctuation curve to obtain power fluctuation data. Therefore, the data of each node in the post-update primary power node network, the post-update secondary power node network and the post-update tertiary power node network and the power fluctuation data corresponding to the circuit are updated.
And a second step of determining the updated primary power node network, the updated secondary power node network, and the updated tertiary power node network as updated power node networks.
The steps 102-103 and related matters serve as an invention point of the embodiments of the present disclosure, and the second technical problem mentioned in the background art is solved, namely "power outage analysis is not performed in combination with the power utilization structural relation of each power utilization unit, so that it is difficult to discover the potential association relation of power outage factors between the power utilization units, and the data integrity for power outage analysis is insufficient, so that it is difficult to predict a power outage area in time, and further, it is difficult to perform power outage early warning in time. Factors that lead to difficulty in timely power outage early warning are often as follows: the power outage analysis is not carried out by combining the power utilization structure relation of each power utilization unit, so that potential association relation of power outage factors among the power utilization units is difficult to discover, the data integrity for the power outage analysis is insufficient, and a power outage area is difficult to predict in time. If the above factors are solved, the power failure early warning can be timely carried out. To achieve this, first, individual electricity usage units can be mapped into the same space by a three-dimensional digital twin model constructed. Then, by constructing the electricity usage node, it can be used to introduce the positional and spatial relationships of the electricity usage units. Here, in order to further extract the structural relationship between the nodes, the power main node group, the primary power transfer node group, the secondary power transfer node group, the tertiary power transfer node group, and the power consumer node group are divided according to the actual conditions of the nodes. Then, in consideration of the fact that there is a correlation in most cases of power outage, a node network capable of representing the power utilization structure relationship, namely a primary power node network, the secondary power node network and the tertiary power node network, is constructed among the nodes with the defined spatial relationship according to the power utilization direction of the power utilization units. Therefore, according to the electricity utilization structure relationship, the data capture of the power failure condition is facilitated, and the potential relationship of the power failure factors among the electricity utilization points is extracted. Thus, it can be used to further improve the data integrity for outage analysis. Furthermore, the power failure area is convenient to predict in time, so that the power failure early warning can be performed in time.
And 104, carrying out abnormal outage analysis processing on the updated power node network to obtain an outage analysis result.
In some embodiments, the executing body may perform abnormal outage analysis processing on the updated power node network to obtain an outage analysis result. The outage analysis result can be used for representing information of whether an outage area exists in the power grid area or not.
In practice, the technical problems accompanying the above-mentioned second technical problems are considered to be solved. Facing technical problems three: even if the structural relation among power utilization units or users is introduced, the data which can be used for power failure analysis is difficult to effectively discover, on the contrary, the power failure analysis is difficult to be timely carried out due to the fact that the more related data of the introduced structural relation, and the power failure early warning time is delayed. When the abnormal power failure analysis is performed, the following solutions can be adopted in combination with the owned technical solutions.
In some optional implementations of some embodiments, the executing body performs an abnormal outage analysis process on the updated power node network to obtain an outage analysis result, and may include the following steps:
the method comprises the steps of firstly, acquiring a historical power fluctuation data set and a historical power outage data set of each node in a power node network after corresponding updating, wherein the historical power fluctuation data set and the historical power outage data set are detected by a power fluctuation detection system. Wherein, each of the historical power outage data in the historical power outage data set may include power outage data corresponding to one node. Second, the historical power fluctuation data may include a current fluctuation curve, a voltage fluctuation curve of one node.
As an example, the historical outage data may include current, voltage, power load, temperature values of the node at the time of the outage.
Second, for each leaf node and child node in the updated primary power node network, the updated secondary power node network, and the updated tertiary power node network, the following single-dimensional analysis step is performed:
And step one, determining a power failure probability value group of the Shan Weizi node by using historical power fluctuation data, actual current information, actual voltage information, actual load information and actual temperature information corresponding to the child node. Wherein, each Shan Weizi node outage probability value in the Shan Weizi node outage probability value group represents the outage probability value corresponding to different outage risk attributes respectively. Second, first, a current ripple curve, a current average value in a voltage ripple curve, and a voltage average value included in the historical power ripple data may be determined. Then, the current value, the voltage value, the power load and the temperature value in the actual current information, the actual voltage information, the actual load information and the actual temperature information can be combined into a child node single-dimension feature vector. And finally, inputting the child node single-dimensional feature vector into a preset single-dimensional analysis model to generate Shan Weizi node outage probability value sets. Here, the different blackout risk attributes may include, but are not limited to, at least one of: voltage, current, temperature, equipment load, etc. In addition, the number of Shan Weizi node outage probability values in the Shan Weizi node outage probability value set may be the same as the dimensions of the child node single-dimensional feature vector.
As an example, the above-described single-dimensional analytical model may include, but is not limited to, at least one of: linear regression, autoregressive models (AR, autoregressive Model), autoregressive filters, long-short-time memory networks, bidirectional long-short-time memory networks, and the like.
And secondly, determining Shan Weishe a child node power failure probability value group by using historical power fluctuation data, actual current information, actual voltage information, actual load information and actual temperature information corresponding to the leaf nodes. The leaf node single-dimensional feature vector can be generated in the mode. Then, the leaf node single-dimensional feature vector generated in the above manner can be input into the single-dimensional analysis model to generate Shan Weishe child node outage probability value sets.
Third, for each updated three-level power node network in the updated power node networks, performing the following multidimensional analysis steps:
And step one, constructing a multidimensional data matrix by utilizing the historical power fluctuation data, the actual current information, the actual voltage information, the actual load information and the actual temperature information corresponding to each node in the updated three-level power node network. First, for each node in the respective nodes, a node feature vector may be generated in the above-described manner of constructing the feature vector. The individual node feature vectors may then be combined into a multi-dimensional data matrix in the order of node identification.
And secondly, carrying out multidimensional outage analysis on the multidimensional data matrix to generate a multidimensional child node outage probability value group and a multidimensional leaf node outage probability value group corresponding to each node. The multidimensional data matrix can be input into a preset multidimensional analysis model to generate a multidimensional child node outage probability value group and a multidimensional leaf node outage probability value group corresponding to each node. Here, the multidimensional data matrix includes data corresponding to each node in the three-level power node network after updating, so that the generated data also corresponds to each node respectively. Each multi-dimensional child node outage probability value set may characterize outage probability values corresponding to different attributes of one child node. Each multidimensional leaf node outage probability value set may characterize outage probability values corresponding to different attributes of a leaf node.
And fourthly, carrying out fusion processing on each single-dimensional leaf node power failure probability value group and the corresponding multi-dimensional leaf node power failure probability value group to generate a fused leaf node power failure probability value group. The fusion process may be: and weighting and summing probability values belonging to the same attribute in the Shan Weishe child node power failure probability value group and the multidimensional leaf node power failure probability value group corresponding to the same leaf node according to preset weights to generate a fused leaf node power failure probability value group.
And fifthly, carrying out fusion processing on each Shan Weizi-node power failure probability value group and the corresponding multidimensional sub-node power failure probability value group to generate a fused sub-node power failure probability value group. The power failure probability value sets of Shan Weizi nodes corresponding to the same sub-node and probability values belonging to the same attribute in the power failure probability value sets of the multidimensional sub-nodes can be weighted and summed according to preset weights to generate the fused power failure probability value sets of the sub-nodes.
And a sixth step of determining risk association weights corresponding to each node in the updated primary power node network, the updated secondary power node network and the updated tertiary power node network based on the historical power outage data set, and obtaining a risk association weight set. And taking current, voltage, power load and temperature value which are included in the historical power outage data corresponding to each node as power outage data vectors. Then, the power outage data vectors of each node are arranged into a power outage data matrix according to a time sequence within a preset time period. And then determining risk association weights corresponding to each node in the updated primary power node network, the updated secondary power node network and the updated tertiary power node network by using a preset node weight association algorithm.
As an example, the above-described node weight association algorithm may include, but is not limited to, at least one of: a multivariate adaptive regression spline (Multivariate Adaptive Regression Splines), a local scatter smoothing estimate (Locally Estimated Scatterplot Smoothing), and the like.
In practice, a certain power failure condition of a certain node is considered to cause a certain range of power failure, so that the degree of correlation between the nodes in the power failure can be extracted through a node weight correlation algorithm, and the power failure area can be determined conveniently.
And seventh, constructing each generated fused leaf node outage probability value group into a leaf node outage probability value vector to obtain a leaf node outage probability value vector set. The power failure probability values of the fused leaf nodes in the power failure probability value group of the fused leaf nodes can be combined into a leaf node power failure probability value vector according to the attribute sequence.
And eighth step, constructing each generated fused sub-node outage probability value group into a sub-node outage probability value vector, and obtaining a sub-node outage probability value vector set. The power failure probability values of the fused child nodes in the power failure probability value group of the fused child nodes can be combined into a child node power failure probability value vector according to the attribute sequence.
And a ninth step of performing regional risk association processing on each leaf node and child node in the updated primary power node network, the updated secondary power node network and the updated tertiary power node network based on the risk association weight set, the leaf node outage probability value vector set and the child node outage probability value vector set, and obtaining a regional risk association node information set. Wherein each regional risk associated node information in the regional risk associated node information set may include information characterizing nodes and inter-node circuits where a risk association exists. And secondly, inputting the risk association weight set, the leaf node outage probability value vector set and the child node outage probability value vector set into a preset risk association analysis model, and carrying out regional risk association processing on each leaf node and child node in the updated primary power node network, the updated secondary power node network and the updated tertiary power node network to obtain a regional risk association node information set. Wherein, each regional risk associated node information in the regional risk associated node information set may include a plurality of associated node identifications having blackout risks. Here, each region risk association node information may characterize a region (i.e., including a plurality of associated nodes) where there is a risk of outage.
As an example, the risk correlation analysis model may include, but is not limited to, at least one of: spectral clustering algorithm, hierarchical clustering algorithm, self-organizing map (SOM) algorithm.
And tenth, determining the fused child node power outage probability value group, the fused leaf node power outage probability value group and the regional risk associated node information set as power outage analysis results. And if the regional risk associated node information set in the outage analysis result is not null, representing that the outage risk region exists. And if the power failure risk area is empty, the power failure risk area is represented.
And step 105, in response to determining that the outage analysis result meets the preset risk condition, sending the outage analysis result to a pre-constructed visual interface for display, and executing outage early warning operation by utilizing the outage analysis result.
In some embodiments, the executing entity may send the outage analysis result to a pre-constructed visual interface for display in response to determining that the outage analysis result meets a preset risk condition, and execute an outage early warning operation using the outage analysis result. The preset risk condition may be that the regional risk associated node information set included in the outage analysis result is not empty. Second, the visual interface may be used to present a three-dimensional high-precision map model and its various nodes and circuits between nodes in the model. Meanwhile, the corresponding power failure probability value can be displayed on each node and each circuit. Different colors may be displayed for the nodes and circuits that are at risk of outage for prompting. And finally, power failure risk early warning information can be sent to the region corresponding to the risk associated node information of each region. In addition, if the regional risk associated node information set included in the outage analysis result is empty, but there is a fused child node outage probability value or a fused leaf node outage probability value exceeding the preset outage threshold, the outage early warning prompt can be sent out for protection maintenance by aiming at the node corresponding to the fused child node outage probability value or the fused leaf node outage probability value exceeding the preset outage threshold alone.
Optionally, the executing body may further execute the following steps:
And in response to determining that the outage analysis result does not meet the preset risk condition, acquiring actual operation data of the power system again, so as to generate the outage analysis result based on the three-dimensional digital twin model. The actual operation data of the power system is acquired again, and the actual operation data can be used as the latest data of power failure early warning for circulation. And simultaneously, the power failure analysis result is regenerated through the implementation modes. Therefore, the power failure real-time early warning based on the three-dimensional digital twin model is realized.
The steps 104-105 and related content serve as an invention point of the embodiments of the present disclosure, and solve the third "even if a structural relationship between power units or users is introduced, it is difficult to effectively discover data that can be used for power outage analysis, but on the contrary, due to the fact that the more related data of the introduced structural relationship are, it is difficult to perform power outage analysis in time, and the timing of power outage early warning is delayed". The factors that lead to delayed power outage warning are often as follows: even if the structural relation between the power utilization units or the users is introduced, the data which can be used for power outage analysis is difficult to effectively discover, but the power outage analysis is difficult to timely perform due to the fact that the related data of the introduced structural relation are more. If the above factors are solved, the time for delaying the power failure early warning can be avoided. To achieve this, first, in order to further determine the outage probability according to the electricity consumption structural relationship, hierarchical extraction is performed through a single-dimensional analysis step and a multi-dimensional analysis step, respectively. Here, an adaptive extraction scheme is performed for different network configurations. Therefore, the situation that the data feature extraction is difficult caused when the networks with different structures are subjected to feature extraction simultaneously is avoided. Then, in order to further predict a blackout area caused by a blackout factor, a historical blackout data set is introduced to serve as a data reference to determine risk association weights corresponding to each node in the post-update primary power node network, the post-update secondary power node network and the post-update tertiary power node network, in consideration of a higher connecting effect of blackouts in the power utilization structural relation network. Thus, the method can be used for representing the degree of correlation between the nodes in the power failure. Therefore, the areas with power outage risk can be further determined through the area risk association processing. Thereby, the coverage of the power outage risk is further refined. And furthermore, the power failure early warning is convenient to timely carry out.
The above embodiments of the present disclosure have the following advantageous effects: by the power outage early warning method based on the three-dimensional digital twin model, the accuracy of the area aimed at by the power outage early warning can be improved. Specifically, the reason why the error of the area for which the power outage warning is aimed is large is that: the problem of power consumption area and power consumption line is not considered, and the power failure analysis is comprehensively carried out on different power consumption areas, so that the power failure actual coverage area is difficult to detect because of large coupling. Based on the power failure early warning method based on the three-dimensional digital twin model, disclosed by some embodiments of the invention, introduces a pre-built three-dimensional digital twin model so as to simulate the actual operation scene of the circuit. Meanwhile, the actual operation data of the power system is obtained in real time and updated into the three-dimensional digital twin model for synchronous operation with an actual scene. Next, the power node network in the three-dimensional digital twin model is updated with the actual operational data. Here, too, since the power node network is constructed in advance, it can be used to distinguish between different power usage units and power usage circuits. Thus, outage analysis is facilitated based on the power node network variations. Then, by the abnormal power outage analysis, a power outage analysis result can be obtained. Therefore, the coupling of the comprehensive power failure analysis of different power utilization areas can be greatly relieved. Therefore, the accuracy of the outage analysis is improved, and the area corresponding to the outage analysis result is further accurately obtained. And finally, responding to the fact that the power outage analysis result meets the preset risk condition, sending the power outage analysis result to a pre-constructed visual interface for display, and executing power outage early warning operation by utilizing the power outage analysis result. Furthermore, the power failure early warning can be accurately carried out on the area with the power failure risk.
With further reference to fig. 2, as an implementation of the method shown in the foregoing drawings, the present disclosure provides some embodiments of a power outage early warning device based on a three-dimensional digital twin model, where the embodiments of the device correspond to those shown in fig. 1, and the power outage early warning device based on the three-dimensional digital twin model may be specifically applied to various electronic devices.
As shown in fig. 2, a power outage early warning device 200 based on a three-dimensional digital twin model according to some embodiments includes: an acquisition unit 201, a first updating unit 202, a second updating unit 203, an abnormal power outage analysis unit 204, and a transmission and early warning unit 205. Wherein the acquisition unit 201 is configured to acquire actual operation data of the power system; a first updating unit 202 configured to update the actual operation data into a pre-constructed three-dimensional digital twin model to obtain an updated digital twin model, where the updated digital twin model includes updated system operation data; a second updating unit 203 configured to update a pre-constructed power node network in the above-described updated digital twin model based on the updated system operation data, to obtain an updated power node network; an abnormal power outage analysis unit 204 configured to perform abnormal power outage analysis processing on the updated power node network to obtain a power outage analysis result; and the sending and early warning unit 205 is configured to send the outage analysis result to a pre-constructed visual interface for display in response to determining that the outage analysis result meets a preset risk condition, and perform an outage early warning operation by using the outage analysis result.
It will be appreciated that the elements described in the apparatus 200 correspond to the various steps in the method described with reference to fig. 1. Thus, the operations, features and resulting benefits described above for the method are equally applicable to the apparatus 200 and the units contained therein, and are not described in detail herein.
Referring now to fig. 3, a schematic diagram of an electronic device 300 suitable for use in implementing some embodiments of the present disclosure is shown. The electronic device shown in fig. 3 is merely an example and should not impose any limitations on the functionality and scope of use of embodiments of the present disclosure.
As shown in fig. 3, the electronic device 300 may include a processing means 301 (e.g., a central processing unit, a graphics processor, etc.) that may perform various suitable actions and processes in accordance with a program stored in a Read Only Memory (ROM) 302 or a program loaded from a storage means 308 into a Random Access Memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic apparatus 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.
In general, the following devices may be connected to the I/O interface 305: input devices 306 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 307 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 308 including, for example, magnetic tape, hard disk, etc.; and communication means 309. The communication means 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. While fig. 3 shows an electronic device 300 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead. Each block shown in fig. 3 may represent one device or a plurality of devices as needed.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via communications device 309, or from storage device 308, or from ROM 302. The above-described functions defined in the methods of some embodiments of the present disclosure are performed when the computer program is executed by the processing means 301.
It should be noted that, in some embodiments of the present disclosure, the computer readable medium may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, the computer-readable signal medium may comprise a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (Hyper Text Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.
The computer readable medium may be embodied in the apparatus; or may exist alone without being incorporated into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring actual operation data of a power system; updating the actual operation data into a pre-constructed three-dimensional digital twin model to obtain an updated digital twin model, wherein the updated digital twin model comprises updated system operation data; updating a pre-constructed power node network in the updated digital twin model based on the updated system operation data to obtain an updated power node network; performing abnormal outage analysis processing on the updated power node network to obtain an outage analysis result; and responding to the fact that the power outage analysis result meets the preset risk condition, sending the power outage analysis result to a pre-constructed visual interface for display, and executing power outage early warning operation by utilizing the power outage analysis result.
Computer program code for carrying out operations for some embodiments of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in some embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. The described units may also be provided in a processor, for example, described as: a processor comprising: the system comprises an acquisition unit, a first updating unit, a second updating unit, an abnormal power failure analysis unit and a sending and early warning unit. The names of these units do not constitute a limitation of the unit itself in some cases, for example, the acquisition unit may also be described as "acquiring actual operation data of the power system".
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above technical features, but encompasses other technical features formed by any combination of the above technical features or their equivalents without departing from the spirit of the invention. Such as the above-described features, are mutually substituted with (but not limited to) the features having similar functions disclosed in the embodiments of the present disclosure.
Claims (9)
1. A power failure early warning method based on a three-dimensional digital twin model comprises the following steps:
acquiring actual operation data of a power system;
Updating the actual operation data into a pre-constructed three-dimensional digital twin model to obtain an updated digital twin model, wherein the updated digital twin model comprises updated system operation data;
updating a pre-constructed power node network in the updated digital twin model based on the updated system operation data to obtain an updated power node network;
performing abnormal outage analysis processing on the updated power node network to obtain an outage analysis result;
and responding to the fact that the power outage analysis result meets a preset risk condition, sending the power outage analysis result to a pre-constructed visual interface for display, and executing power outage early warning operation by utilizing the power outage analysis result.
2. The method of claim 1, wherein the method further comprises:
And responding to the fact that the power outage analysis result does not meet the preset risk condition, acquiring actual operation data of the power system again, and generating the power outage analysis result based on the three-dimensional digital twin model.
3. The method of claim 1, wherein the actual operational data comprises at least one of: the three-dimensional digital twin model comprises a sensor data acquisition system, and further comprises various information corresponding to actual operation data: model current information, model voltage information, model load information and model temperature information; and
The step of updating the actual operation data into a pre-constructed three-dimensional digital twin model to obtain an updated digital twin model comprises the following steps:
and taking the actual current information, the actual voltage information, the actual load information and the actual temperature information which are included in the actual operation data as data acquired by a sensor data acquisition system so as to update the model current information, the model voltage information, the model load information and the model temperature information which are included in the three-dimensional digital twin model, wherein the updating is to take the actual current information, the actual voltage information, the actual load information and the actual temperature information as latest real-time data to correspondingly replace the model current information, the model voltage information, the model load information and the model temperature information in the three-dimensional digital twin model.
4. A method according to claim 3, wherein the three-dimensional digital twin model is constructed by:
integrating a sensor data acquisition system in the pre-acquired three-dimensional high-precision map model;
Acquiring a set of power information with the sensor data acquisition system, wherein each power information in the set of power information comprises at least one of: user information, electricity information, spatial location information, and circuit information;
Adding power utilization nodes corresponding to space position information included in each piece of power information in the power information set into the three-dimensional high-precision map model, wherein the three-dimensional high-precision map model is constructed according to an actual map scene, and the three-dimensional high-precision map model comprises simulated terrain and simulated buildings for simulating an actual map;
Adding user information and power consumption information included in each piece of power information in the power information set to corresponding power consumption nodes to obtain a constructed power consumption node set, wherein each constructed power consumption node in the constructed power consumption node set comprises a unique power consumption identifier, and each constructed power consumption node in the constructed power consumption node set is located at a corresponding coordinate position in the three-dimensional high-precision map model;
Constructing a simulation circuit among the constructed power utilization nodes according to circuit information included in each power information in the power information set to obtain a constructed simulation circuit set, wherein each constructed simulation circuit in the constructed simulation circuit set comprises corresponding circuit information, and each circuit information represents the circuit operation condition between two constructed power utilization nodes;
and in response to determining that the sensor data acquisition system, the built electricity consumption node set and the built simulation circuit set are built, determining that the three-dimensional digital twin model is built, wherein data in each electricity consumption node and the simulation circuit in the three-dimensional digital twin model are updated in real time based on the data acquired by the sensor data acquisition system.
5. The method of claim 4, wherein the power node network is constructed by:
Grouping each constructed power utilization node in the constructed power utilization node set in the three-dimensional digital twin model to generate a power main node group, a primary power transfer node group, a secondary power transfer node group, a tertiary power transfer node group and a power user node group;
For each power master node in the power master node group, selecting a matched primary power transfer node from the primary power transfer node group to construct a primary power node network, wherein the primary power node network is a star network constructed by taking the power master node as a master node;
for each primary power transfer node in the primary power transfer node group, selecting a matched secondary power transfer node from the secondary power transfer node group to construct a secondary power node network, wherein the primary power node network is a star network constructed by taking the primary power transfer node as a main node;
For each secondary power transfer node in the secondary power transfer node group, selecting a matched tertiary power transfer node from the tertiary power transfer node group and selecting a power consumer node corresponding to the matched tertiary power transfer node from the power consumer node group to construct a tertiary power node network, wherein the tertiary power node network is a tree network constructed by taking the secondary power transfer node as a main node, each matched tertiary power transfer node is a child node, and each corresponding power consumer node is a leaf node under the child node;
And integrating a power fluctuation detection system in the primary power node network, the secondary power node network and the tertiary power node network to complete construction of the power node network, wherein the power fluctuation detection system is used for detecting power fluctuation data of a circuit between two adjacent nodes, and each power fluctuation data is used for representing power fluctuation conditions of the circuit between one node and the previous node.
6. The method of claim 5, wherein updating the pre-built power node network in the updated digital twin model based on the updated system operational data results in an updated power node network, comprising:
updating the updated system operation data to each node in the power node network, and determining power fluctuation data between every two adjacent nodes to obtain an updated primary power node network, an updated secondary power node network and an updated tertiary power node network;
and determining the updated primary power node network, the updated secondary power node network and the updated tertiary power node network as updated power node networks.
7. A power outage early warning device based on a three-dimensional digital twin model, comprising:
An acquisition unit configured to acquire actual operation data of the power system;
The first updating unit is configured to update the actual operation data into a pre-constructed three-dimensional digital twin model to obtain an updated digital twin model, wherein the updated digital twin model comprises updated system operation data;
a second updating unit configured to update a pre-built power node network in the updated digital twin model based on the updated system operation data, to obtain an updated power node network;
the abnormal power outage analysis unit is configured to perform abnormal power outage analysis processing on the updated power node network to obtain a power outage analysis result;
And the sending and early warning unit is configured to send the outage analysis result to a pre-built visual interface for display in response to determining that the outage analysis result meets a preset risk condition, and execute outage early warning operation by utilizing the outage analysis result.
8. An electronic device, comprising:
one or more processors;
A storage device having one or more programs stored thereon,
When executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-6.
9. A computer readable medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the method of any of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410390611.5A CN117977586A (en) | 2024-04-02 | 2024-04-02 | Power failure early warning method and device based on three-dimensional digital twin model and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410390611.5A CN117977586A (en) | 2024-04-02 | 2024-04-02 | Power failure early warning method and device based on three-dimensional digital twin model and electronic equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117977586A true CN117977586A (en) | 2024-05-03 |
Family
ID=90866084
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410390611.5A Pending CN117977586A (en) | 2024-04-02 | 2024-04-02 | Power failure early warning method and device based on three-dimensional digital twin model and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117977586A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115597659A (en) * | 2022-09-21 | 2023-01-13 | 山东锐翊电力工程有限公司（Cn） | Intelligent safety management and control method for transformer substation |
| CN117154739A (en) * | 2023-10-30 | 2023-12-01 | 国网江苏省电力有限公司南通供电分公司 | User power distribution control method and system according to power utilization characteristics |
| CN117411011A (en) * | 2023-09-12 | 2024-01-16 | 百色新铝电力有限公司 | Flexible power load regulating system of multiple power generation systems |
| CN117574624A (en) * | 2023-11-09 | 2024-02-20 | 北京天易数智科技有限公司 | Transformer state monitoring and early warning method based on digital twin technology |
| WO2024052888A2 (en) * | 2022-09-10 | 2024-03-14 | Strong Force Ee Portfolio 2022, Llc | Ai-based energy edge platform, systems, and methods |
-
2024
- 2024-04-02 CN CN202410390611.5A patent/CN117977586A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024052888A2 (en) * | 2022-09-10 | 2024-03-14 | Strong Force Ee Portfolio 2022, Llc | Ai-based energy edge platform, systems, and methods |
| CN115597659A (en) * | 2022-09-21 | 2023-01-13 | 山东锐翊电力工程有限公司（Cn） | Intelligent safety management and control method for transformer substation |
| CN117411011A (en) * | 2023-09-12 | 2024-01-16 | 百色新铝电力有限公司 | Flexible power load regulating system of multiple power generation systems |
| CN117154739A (en) * | 2023-10-30 | 2023-12-01 | 国网江苏省电力有限公司南通供电分公司 | User power distribution control method and system according to power utilization characteristics |
| CN117574624A (en) * | 2023-11-09 | 2024-02-20 | 北京天易数智科技有限公司 | Transformer state monitoring and early warning method based on digital twin technology |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Sanchez-Hidalgo et al. | A survey on visual data representation for smart grids control and monitoring | |
| CN110471979B (en) | Power grid information display simulation system based on multi-source simulation data aggregation and mining | |
| CN109344476B (en) | CIM model-based power distribution network single line diagram line branch structure generation method and device | |
| US9625887B2 (en) | Contingency screening in multi-control area electrical power systems using coordinated DC power flow | |
| Deng et al. | An assessment method for traffic state vulnerability based on a cloud model for urban road network traffic systems | |
| CN109408603A (en) | A kind of platform area topology drawing drawing method based on big data | |
| CN114880884B (en) | Specialized modeling analysis method and system based on digital energy system | |
| Peng et al. | Reliability analysis in interdependent smart grid systems | |
| CN111159897B (en) | Target optimization method and device based on system modeling application | |
| CN115954933A (en) | Power grid digital twin system construction method and device, electronic equipment and storage medium | |
| CN106921702A (en) | It is a kind of based on service-oriented power distribution network information physical system | |
| Chen et al. | [Retracted] The Construction of a Smart City Energy Efficiency Management System Oriented to the Mobile Data Aggregation of the Internet of Things | |
| Ali et al. | Modeling synthetic power distribution network and datasets with industrial validation | |
| Ryabko et al. | Graph model of Fog Computing system. | |
| CN114968981A (en) | Big data platform system based on power grid data and data processing method thereof | |
| Wang et al. | Intelligent power grid monitoring and management strategy using 3D model visual computation with deep learning | |
| CN206021354U (en) | One kind is based on BIM and VR smart cities pipeline coordination platform | |
| He et al. | Management and real-time monitoring of interconnected energy hubs using digital twin: Machine learning based approach | |
| CN111208384A (en) | Smart power grid management system | |
| CN116738867B (en) | Unmanned aerial vehicle defense simulation method and system based on machine learning | |
| CN117977586A (en) | Power failure early warning method and device based on three-dimensional digital twin model and electronic equipment | |
| Wang et al. | Multi-stage resilient operation strategy of urban electric–gas system against rainstorms | |
| Ma et al. | Service coverage optimization for facility location: considering line-of-sight coverage in continuous demand space | |
| CN114139762A (en) | Maintenance path planning method and system for power equipment based on intelligent safety helmet | |
| CN104463434B (en) | Data processing method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |