State sensing method, device and equipment of low-voltage power distribution network and storage medium

Abstract

The invention discloses a state sensing method, device and equipment for a low-voltage power distribution network and a storage medium, and relates to the technical field of electric power Internet of Things, and the method comprises the steps: carrying out the investigation of a deployment place of the low-voltage power distribution network, so as to determine the monitoring parameter information and safety demand information of a power grid; determining a sensor deployment scheme based on the monitoring parameter information and the security demand information; after deployment is completed according to the scheme, standardization processing is carried out on power grid operation data collected based on the deployed sensor so as to determine processed data; obtaining a current state evaluation result and an alarm triggering judgment result based on a preset machine learning model, a preset alarm mechanism and the processed data; and if the alarm triggering judgment result shows that the power grid alarm operation is currently triggered, determining a current state sensing result based on a historical fault library, the state evaluation result and a preset sensing function optimization rule. The expandability, accuracy and reliability of state sensing of the low-voltage distribution network can be improved.

Description

Technical Field

[0001] This invention relates to the field of power Internet of Things (IoT) technology, and in particular to a method, apparatus, equipment, and storage medium for state sensing of low-voltage distribution networks. Background Technology

[0002] Currently, existing solutions for condition monitoring of low-voltage distribution networks suffer from insufficient scalability of their functional modules. When the scale of the low-voltage distribution network changes or new monitoring needs arise, they struggle to adapt and upgrade quickly, failing to meet diverse monitoring scenarios. Furthermore, the selection of sensors often fails to adequately consider the environmental characteristics and parameter range of the monitored targets, resulting in sensors with insufficient accuracy, weak anti-interference capabilities, and a tendency to produce data errors. Simultaneously, the determination of sensor installation nodes and quantities relies heavily on manual experience, lacking scientific basis. This leads to incomplete and inaccurate data collection of subsequent power grid operation data, failing to provide a reliable data foundation for subsequent analysis. Summary of the Invention

[0003] In view of this, the purpose of this invention is to provide a state sensing method, apparatus, device, and storage medium for low-voltage distribution networks, which can improve the scalability, accuracy, and reliability of state sensing in low-voltage distribution networks, thereby enhancing the scenario adaptability and user experience of state sensing, and ensuring the safe and stable operation of low-voltage distribution networks. The specific solution is as follows:

[0004] Firstly, this application provides a state-sensing method for low-voltage distribution networks, including:

[0005] By conducting a survey of the deployment sites of the low-voltage distribution network, and based on the survey results, the monitoring parameters and safety requirements of the low-voltage distribution network are determined.

[0006] Based on the monitoring parameter information and the safety requirement information, sensors are selected and deployed to determine the sensor deployment scheme;

[0007] After the sensor deployment scheme is completed, based on the deployed sensors, the power grid operation data corresponding to the low-voltage distribution network is collected, and the power grid operation data is standardized to determine the processed data.

[0008] Based on the preset machine learning model and the processed data, the current state assessment result is obtained, and combined with the preset alarm mechanism, the current alarm trigger judgment result is determined.

[0009] If the alarm triggering judgment result indicates that a power grid alarm operation is currently triggered, then the current state perception result of the low-voltage distribution network is determined based on the historical fault database, the state assessment result, and the preset perception function optimization rules.

[0010] Optionally, the step of conducting a survey of the deployment sites of the low-voltage distribution network and determining the monitoring parameters and security requirements of the low-voltage distribution network based on the survey results includes:

[0011] Environmental surveys and user visits were conducted at the deployment sites of the low-voltage distribution network to determine the survey information; the survey information included power grid operation scenario information and key power grid monitoring information.

[0012] The survey information is analyzed for monitoring constraints to determine the safety requirements of the low-voltage distribution network.

[0013] Based on the aforementioned safety requirements information, the monitoring parameters of the low-voltage distribution network are determined.

[0014] Optionally, the step of selecting and deploying sensors based on the monitoring parameter information and the safety requirement information to determine the sensor deployment scheme includes:

[0015] Based on the survey results and historical power grid data, node parameter information for each monitoring node in the low-voltage distribution network is obtained; the node parameter information includes temperature, humidity, and electromagnetic interference intensity.

[0016] The voltage, current, and harmonic range of each monitoring node are analyzed to determine the node analysis results;

[0017] Based on the node analysis results, node parameter information, monitoring parameter information, and safety requirement information, a sensor selection is made that combines environmental adaptability requirements and monitoring accuracy requirements to determine the sensor selection result.

[0018] Based on the sensor selection results, the spatial layout information of the substation corresponding to the low-voltage distribution network, and the line topology, a sensor deployment scheme is determined; the sensor deployment scheme includes the sensor installation location and the number of monitoring nodes covered by the sensor installation location.

[0019] Optionally, based on the deployed sensors, the process of collecting grid operation data corresponding to the low-voltage distribution network and standardizing the grid operation data to determine the processed data includes:

[0020] Based on the deployed sensors, node data of each monitoring node in the low-voltage distribution network is collected; the node data includes voltage, current, temperature, humidity and equipment status at the node;

[0021] The current power grid operation data is determined based on the node data of each monitoring node;

[0022] The power grid operation data is processed using a sliding window-based filtering algorithm and a preset fault value detection mechanism to determine the fault value processing result.

[0023] Based on a preset time reference, the processing results of the fault values ​​are uniformly aligned to determine the data alignment result;

[0024] Based on a preset data format, the data alignment result is converted to determine the format conversion result;

[0025] The key fields of the format conversion result are extracted to determine the field extraction results;

[0026] Based on the results extracted from the aforementioned fields, the initial dataset is determined;

[0027] The initial dataset is filtered based on preset data quality verification rules to determine the processed data.

[0028] Optionally, the step of obtaining the current state evaluation result based on the preset machine learning model and the processed data, and determining the current alarm trigger judgment result in conjunction with the preset alarm mechanism, includes:

[0029] Acquire a pre-trained machine learning model based on unsupervised learning algorithms, pre-defined clustering analysis rules, and historical power grid operation data;

[0030] Based on the processed data and the preset machine learning model, the current state assessment result of the low-voltage distribution network is obtained;

[0031] Determine whether the values ​​of each parameter in the processed data are greater than the corresponding preset alarm threshold, and obtain the threshold determination result;

[0032] Based on the threshold judgment result, it is determined whether the corresponding alarm operation is triggered, so as to determine the alarm trigger judgment result.

[0033] Optionally, if the alarm triggering judgment result indicates that a power grid alarm operation is currently triggered, then based on the historical fault database, the state assessment result, and the preset sensing function optimization rules, the current state sensing result of the low-voltage distribution network is determined, including:

[0034] If the alarm triggering judgment result indicates that a power grid alarm operation has been triggered, then alarm information is generated based on the alarm triggering judgment result to determine the initial alarm information; the initial alarm information includes the fault time point, fault type, location of related equipment, fault severity information, and fault handling suggestion information;

[0035] Based on the fault type and the location of the relevant equipment, a pattern match is performed on the historical fault database to determine the pattern match result;

[0036] Based on the pattern matching results, knowledge graph technology, and the initial alarm information, a standard handling plan is determined.

[0037] Based on the standard handling plan, the current state assessment results of the low-voltage distribution network, topology information, load information, and environmental information, the plan is adjusted to determine the target fault handling plan.

[0038] Based on the target fault handling plan, the state assessment results, and the preset sensing function optimization rules, the current state sensing results of the low-voltage distribution network are determined.

[0039] Optionally, determining the current state perception result of the low-voltage distribution network based on the target fault handling plan, the state assessment result, and the preset sensing function optimization rules includes:

[0040] Based on the fault time point and the fault type, a comparison is made item by item with the actual operation and maintenance data to determine the comparison result;

[0041] Based on the comparison results, state-aware optimization information is determined;

[0042] Based on the state awareness optimization information, the target fault handling plan, and the state assessment results, the current state awareness result of the low-voltage distribution network is determined.

[0043] Secondly, this application provides a state sensing device for a low-voltage distribution network, comprising:

[0044] The monitoring parameter determination module is used to conduct a survey of the deployment sites of the low-voltage distribution network and, based on the survey results, determine the monitoring parameter information and safety requirement information of the low-voltage distribution network.

[0045] The deployment scheme determination module is used to select and deploy sensors based on the monitoring parameter information and the safety requirement information, so as to determine the sensor deployment scheme;

[0046] The power grid data processing module is used to collect power grid operation data corresponding to the low-voltage distribution network based on the deployed sensors after the deployment is completed according to the sensor deployment scheme, and to standardize the power grid operation data to determine the processed data.

[0047] The model processing module is used to obtain the current state evaluation result based on the preset machine learning model and the processed data, and to determine the current alarm trigger judgment result in combination with the preset alarm mechanism.

[0048] The perception result determination module is used to determine the current state perception result of the low-voltage distribution network based on the historical fault database, the state assessment result, and the preset perception function optimization rules if the alarm trigger judgment result indicates that a power grid alarm operation is currently triggered.

[0049] Thirdly, this application provides an electronic device, comprising:

[0050] Memory, used to store computer programs;

[0051] A processor is used to execute the computer program to implement the steps of the aforementioned low-voltage distribution network state sensing method.

[0052] Fourthly, this application provides a computer-readable storage medium for storing a computer program, which, when executed by a processor, implements the steps of the aforementioned low-voltage distribution network state sensing method.

[0053] As can be seen, in this application, by conducting a survey of the deployment sites of the low-voltage distribution network and based on the survey results, the monitoring parameter information and safety requirement information of the low-voltage distribution network are determined; based on the monitoring parameter information and the safety requirement information, sensors are selected and deployed to determine a sensor deployment scheme; after the deployment is completed according to the sensor deployment scheme, based on the deployed sensors, the corresponding power grid operation data of the low-voltage distribution network is collected, and the power grid operation data is standardized to determine the processed data; based on a preset machine learning model and the processed data, the current state assessment result is obtained, and combined with a preset alarm mechanism, the current alarm trigger judgment result is determined; if the alarm trigger judgment result indicates that a power grid alarm operation is triggered, the current state perception result of the low-voltage distribution network is determined based on the historical fault database, the state assessment result, and the preset perception function optimization rules. In other words, this application first determines monitoring parameters and safety requirements by surveying the deployment locations of the low-voltage distribution network. Based on this information, a sensor deployment plan is then determined. After sensor deployment, grid operation data corresponding to the low-voltage distribution network is collected and standardized to determine the processed data. Then, based on a preset machine learning model and the processed data, the current state assessment result is obtained. When a preset alarm mechanism is used to determine when a grid alarm operation is triggered, the current state perception result of the low-voltage distribution network is determined by combining historical fault databases and preset perception function optimization rules. This improves the scalability, accuracy, and reliability of low-voltage distribution network state perception, thereby enhancing the scenario adaptability and user experience of state perception and ensuring the safe and stable operation of the low-voltage distribution network. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0055] Figure 1 A flowchart of a state sensing method for a low-voltage distribution network provided in this application;

[0056] Figure 2 A schematic diagram of the structure of a state sensing device for a low-voltage distribution network provided in this application;

[0057] Figure 3 This application provides a structural diagram of an electronic device. Detailed Implementation

[0058] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] Currently, existing solutions for condition monitoring of low-voltage distribution networks suffer from insufficient scalability of their functional modules. When the scale of the low-voltage distribution network changes or new monitoring needs arise, they struggle to adapt and upgrade quickly, failing to meet diverse monitoring scenarios. Furthermore, the selection of sensors often fails to adequately consider the environmental characteristics and parameter range of the monitored targets, resulting in sensors with insufficient accuracy, weak anti-interference capabilities, and a tendency to produce data errors. Simultaneously, the determination of sensor installation nodes and quantities relies heavily on manual experience, lacking scientific basis. This leads to incomplete and inaccurate data collection of subsequent power grid operation data, failing to provide a reliable data foundation for subsequent analysis.

[0060] To this end, this application provides a state awareness scheme for low-voltage distribution networks, which can improve the scalability, accuracy and reliability of state awareness in low-voltage distribution networks, thereby improving the scenario adaptability and user experience of state awareness, and ensuring the safe and stable operation of low-voltage distribution networks.

[0061] See Figure 1 As shown in the figure, an embodiment of the present invention discloses a state sensing method for a low-voltage distribution network, including:

[0062] Step S11: Conduct a survey of the deployment sites of the low-voltage distribution network, and based on the survey results, determine the monitoring parameter information and safety requirements information of the low-voltage distribution network.

[0063] In this embodiment, firstly, through on-site surveys and communication with users, the monitoring parameters and safety requirements of the low-voltage distribution network are clarified. Specifically, environmental surveys and user visits are conducted at the deployment sites of the low-voltage distribution network to determine survey information. This survey information includes power grid operation scenario information and key power grid monitoring information. Monitoring constraints are analyzed based on the survey information to determine the safety requirements of the low-voltage distribution network. Based on these safety requirements, the monitoring parameters of the low-voltage distribution network are determined. Simultaneously, the survey information can be used to design a highly integrated and scalable system architecture by analyzing functional and non-functional requirements, resulting in a state-aware system architecture diagram for the low-voltage distribution network.

[0064] Understandably, in this embodiment, the deployment sites of the low-voltage distribution network are accessed through on-site visits and in-depth communication with users to obtain information on the actual operating scenarios and key monitoring points of the low-voltage distribution network. Then, by analyzing the user's requirements for real-time monitoring, fault early warning, and data traceability, and considering the system's stability and scalability requirements, key design constraints are extracted. These business requirements are then transformed into technical parameters, and a highly integrated architecture supporting multi-protocol access and edge computing is designed. This architecture is divided into perception, network, and application layer modules, resulting in a clearly layered and standardized system architecture diagram and technical specifications. This significantly improves the system's relevance and practicality, ensuring a clearly layered and standardized system implementation, and laying a solid foundation for the intelligent monitoring and management of low-voltage distribution networks.

[0065] Step S12: Based on the monitoring parameter information and the safety requirement information, select and deploy sensors to determine the sensor deployment scheme.

[0066] In this embodiment, by analyzing the environmental characteristics and parameter range of the monitored target, high-precision, interference-resistant sensors are selected, and the sensor installation nodes and quantities are determined to obtain a deployment plan document. Specifically: based on the survey results and historical power grid data, node parameter information of each monitoring node in the low-voltage distribution network is obtained; the node parameter information includes temperature, humidity, and electromagnetic interference intensity; the voltage, current, and harmonic range of each monitoring node are analyzed to determine the node analysis results; based on the node analysis results, the node parameter information, the monitoring parameter information, and the safety requirement information, sensors are selected that combine environmental adaptability requirements and monitoring accuracy requirements to determine the sensor selection results; based on the sensor selection results, the spatial layout information of the substation corresponding to the low-voltage distribution network, and the line topology, a sensor deployment plan is determined; the sensor deployment plan includes the sensor installation locations and the number of monitoring nodes covered by the sensor installation locations.

[0067] It is important to understand that in this embodiment, on-site environmental surveys and historical data backtracking are first employed to accurately obtain parameters such as temperature, humidity, and electromagnetic interference intensity at key nodes of the low-voltage distribution network, providing fundamental data support for monitoring. Then, using these parameters, the voltage, current, and harmonic range of the monitoring targets at different nodes are analyzed, and sensor accuracy and response speed are extracted. By combining environmental adaptability requirements with monitoring accuracy requirements, sensors with anti-interference capabilities are selected to ensure high accuracy and strong anti-interference capabilities. Next, based on the spatial layout of the substation and the line topology, the sensor installation nodes and coverage quantity are determined, resulting in a deployment plan document including an equipment list and installation diagrams. In this way, by scientifically planning the sensor installation nodes and quantity based on the substation layout and line topology, a detailed deployment plan is formed, effectively improving the accuracy and reliability of low-voltage distribution network monitoring and providing strong support for intelligent operation and maintenance.

[0068] Step S13: After completing the deployment according to the sensor deployment scheme, based on the deployed sensors, collect the power grid operation data corresponding to the low-voltage distribution network, and perform standardization processing on the power grid operation data to determine the processed data.

[0069] In this embodiment, after determining the sensor deployment scheme, sensors are deployed in the low-voltage distribution network according to the scheme. Then, electrical and non-electrical data are acquired in real time through the obtained sensor network. Data cleaning algorithms are used to remove noise, and timestamps and formats are unified to obtain a standardized structured dataset. Specifically: based on the deployed sensors, node data of each monitoring node in the low-voltage distribution network is collected; the node data includes voltage, current, temperature, humidity, and equipment status at the node; based on the node data of each monitoring node, the current power grid operation data is determined; using a sliding window-based filtering algorithm and a preset fault value detection mechanism, fault value processing is performed on the power grid operation data to determine the fault value processing result; based on a preset time base, the fault value processing result is uniformly aligned to determine the data alignment result; based on a preset data format, the data alignment result is format converted to determine the format conversion result; key fields are extracted from the format conversion result to determine the field extraction result; based on the field extraction result, an initial dataset is determined; based on preset data quality verification rules, the initial dataset is filtered to determine the processed data.

[0070] It is important to understand that in this embodiment, a sensor network deployed at low-voltage distribution network nodes first acquires real-time data on voltage, current, temperature, humidity, and equipment status, providing a rich information source for monitoring. Then, a sliding window-based filtering algorithm and outlier detection mechanism are used to remove impulse noise and data jitter during data acquisition. Multi-source heterogeneous data are aligned according to a unified time base and converted into a preset CSV format (Comma Separated Values), providing another rich information source for monitoring. Finally, key fields are extracted to form a standardized data model, and invalid records are filtered through data quality verification rules to obtain a structured dataset with time consistency and field integrity—the processed data. This significantly improves data quality and processing efficiency, providing reliable data support for the intelligent management of low-voltage distribution networks.

[0071] The filtering algorithm can be represented by the following formula:

[0072] .

[0073] In the formula, For the output signal at the index The value at that location, For the input signal at the index Return to original value, The radius of the window, represented by the index. Take from both the left and right Neighboring points, The total number of samples in the window. To view the window from arrive All input values Sum.

[0074] Step S14: Based on the preset machine learning model and the processed data, obtain the current state evaluation result, and combine it with the preset alarm mechanism to determine the current alarm trigger judgment result.

[0075] In this embodiment, after determining the processed data, historical data is analyzed using a machine learning model to extract features of normal and abnormal operating modes. The processed data obtained in real time is input into the model to determine the state, and a threshold is used to determine whether an alarm is triggered. Specifically: a pre-trained machine learning model based on an unsupervised learning algorithm, preset clustering analysis rules, and historical power grid operating data is obtained; based on the processed data and the pre-trained machine learning model, the current state assessment result of the low-voltage distribution network is obtained; it is determined whether the values ​​of various parameters in the processed data are greater than the corresponding preset alarm thresholds, based on the obtained threshold judgment result; based on the threshold judgment result, it is determined whether the corresponding alarm operation is triggered, thus determining the alarm trigger judgment result.

[0076] It is important to understand that in this embodiment, historical operating data of the low-voltage distribution network is first collected, and unsupervised learning algorithms are used for cluster analysis to effectively extract the normal range characteristics and abnormal change patterns of voltage fluctuations and load rates, providing a foundation for condition assessment. Then, using this extracted data, a deep learning-based condition assessment model is constructed. Real-time collected and processed electrical and non-electrical data are input into the model for real-time calculations to ensure monitoring accuracy. Simultaneously, by judging whether the various parameter values ​​in the processed data, i.e., the monitoring indicators, exceed preset safety thresholds, it is determined whether to trigger an alarm operation. It is understood that if the thresholds are exceeded, an alarm is automatically triggered; otherwise, it is not.

[0077] The formula for cluster analysis is as follows:

[0078] .

[0079] In the formula, To measure a specific component, The sum of weighted combination terms of all components is used as the normalization benchmark. For all possible Combinatorial summation. Where, , , , This is an index of the attributes sensed by the sensor. Indicates the first One dimension, Indicates the first One dimension, For time t, the value of attribute a after quantization and transformation. Let be the quantized and transformed value of attribute b at time t. For any pair of attributes at time t The converted value, For any pair of attributes at time t The converted value.

[0080] Step S15: If the alarm trigger judgment result indicates that a power grid alarm operation is currently triggered, then the current state perception result of the low-voltage distribution network is determined based on the historical fault database, the state assessment result, and the preset perception function optimization rules.

[0081] In this embodiment, an alarm is triggered when the threshold is exceeded, and alarm information is obtained. The alarm information is associated with the historical fault database, the processing flow and resource requirements are matched, and a dynamic emergency plan is generated by combining the real-time network status. By comparing the actual operation and maintenance results with the system prediction data, the identification accuracy and response time are quantified, and the improvement requirements are transformed into functional module upgrade plans to obtain an optimized perception and security alarm solution. Specifically: If the alarm triggering judgment result indicates that a power grid alarm operation is currently triggered, then alarm information is generated based on the alarm triggering judgment result to determine initial alarm information; the initial alarm information includes the fault time point, fault type, location of related equipment, fault severity information, and fault handling suggestion information; based on the fault type and the location of related equipment, pattern matching is performed on the historical fault database to determine the pattern matching result; based on the pattern matching result, knowledge graph technology, and the initial alarm information, a standard handling plan is determined; based on the standard handling plan, the current state assessment result of the low-voltage distribution network, topology information, load information, and environmental information, the plan is adjusted to determine the target fault handling plan; based on the target fault handling plan, the state assessment result, and preset sensing function optimization rules, the current state sensing result of the low-voltage distribution network is determined.

[0082] Understandably, in this embodiment, when the monitored indicators exceed the preset safety threshold, an alarm mechanism is automatically triggered and the anomaly type is marked. The anomaly time point, equipment location, and severity information are integrated to obtain structured alarm information including handling suggestions, enabling rapid response. This significantly improves the fault early warning capability and operation and maintenance efficiency of the low-voltage distribution network.

[0083] It's important to understand that regarding the determination of emergency response plans, i.e., handling plans, in this embodiment, a historical fault database is first established and typical fault characteristics are extracted. The anomaly types and equipment location data in real-time alarm information are then matched with the fault database to accurately pinpoint the fault type and location, providing a basis for rapid handling. Next, knowledge graph technology is used to associate fault handling processes, required spare parts lists, and personnel skill requirements to obtain standardized handling plans, improving handling efficiency. Then, combining the current distribution network topology, load levels, and meteorological parameters, handling priorities and resource scheduling paths are dynamically adjusted. The optimized operation steps, safety protection measures, and coordination mechanisms are integrated to obtain an executable emergency response plan and resource allocation list for the current fault scenario. This effectively shortens fault recovery time, reduces operation and maintenance costs, and enhances system resilience.

[0084] Furthermore, after determining the target fault handling plan, the fault time point and fault type are compared item by item with actual operation and maintenance data to determine the comparison results. Based on the comparison results, state perception optimization information is determined. Based on the state perception optimization information, the target fault handling plan, and the state assessment results, the current state perception result of the low-voltage distribution network is determined. That is, firstly, by collecting data on fault handling time and false alarm frequency in actual operation and maintenance, the system's predicted fault type and alarm time are compared item by item to accurately assess system performance. Then, an error analysis algorithm is used to quantify the deviation values ​​of identification accuracy and response time indicators, extract functional modules in the system whose deviation exceeds the threshold, and combine the needs of operation and maintenance personnel to transform the improvement directions of hardware performance enhancement and algorithm optimization into specific upgrade tasks to ensure the targeted nature of the improvement. Then, a development plan is formulated through modular design principles, clarifying priorities and resource investment, resulting in a low-voltage distribution network state perception and safety alarm scheme with expanded functions and optimized performance, effectively improving the overall system efficiency and operation and maintenance efficiency.

[0085] In summary, the solution proposed in this embodiment solves the problems existing in related solutions, such as poor system architecture integration and scalability, unreasonable sensor deployment, suboptimal data processing, inaccurate status judgment, inflexible emergency plans, and lack of effective system optimization basis, which make it difficult to ensure the safe and stable operation of low-voltage power distribution networks.

[0086] Therefore, this application first determines monitoring parameters and safety requirements by surveying the deployment locations of the low-voltage distribution network. Based on this information, a sensor deployment plan is then determined. After sensor deployment, grid operation data corresponding to the low-voltage distribution network is collected and standardized to determine the processed data. Then, based on a preset machine learning model and the processed data, the current state assessment result is obtained. When a preset alarm mechanism is used to determine when a grid alarm operation is triggered, the current state perception result of the low-voltage distribution network is determined by combining historical fault databases and preset perception function optimization rules. This improves the scalability, accuracy, and reliability of low-voltage distribution network state perception, thereby enhancing the scenario adaptability and user experience of state perception and ensuring the safe and stable operation of the low-voltage distribution network.

[0087] See Figure 2 As shown in the figure, this application also discloses a state sensing device for a low-voltage distribution network, comprising:

[0088] The monitoring parameter determination module 11 is used to conduct a survey of the deployment location of the low-voltage distribution network and, based on the survey results, determine the monitoring parameter information and safety requirement information of the low-voltage distribution network.

[0089] The deployment scheme determination module 12 is used to select and deploy sensors based on the monitoring parameter information and the safety requirement information, so as to determine the sensor deployment scheme;

[0090] The power grid data processing module 13 is used to collect power grid operation data corresponding to the low-voltage distribution network based on the deployed sensors after the deployment is completed according to the sensor deployment scheme, and to perform standardized processing on the power grid operation data to determine the processed data.

[0091] The model processing module 14 is used to obtain the current state evaluation result based on the preset machine learning model and the processed data, and to determine the current alarm trigger judgment result in combination with the preset alarm mechanism.

[0092] The perception result determination module 15 is used to determine the current state perception result of the low-voltage distribution network based on the historical fault database, the state assessment result, and the preset perception function optimization rules if the alarm trigger judgment result indicates that a power grid alarm operation is currently triggered.

[0093] In some specific embodiments, the monitoring parameter determination module 11 may specifically include:

[0094] The survey unit is used to conduct environmental surveys and user visits at the deployment sites of low-voltage distribution networks to determine survey information; the survey information includes power grid operation scenario information and key power grid monitoring information.

[0095] The constraint analysis unit is used to analyze the survey information to monitor constraints in order to determine the safety requirements of the low-voltage distribution network.

[0096] The parameter determination unit is used to determine the monitoring parameter information of the low-voltage distribution network based on the safety requirement information.

[0097] In some specific embodiments, the deployment scheme determination module 12 may specifically include:

[0098] The node information acquisition unit is used to obtain node parameter information of each monitoring node in the low-voltage distribution network based on the survey results and historical power grid data; the node parameter information includes temperature, humidity and electromagnetic interference intensity.

[0099] The node analysis unit is used to analyze the voltage, current, and harmonic range of each monitoring node to determine the node analysis results.

[0100] The sensor selection unit is used to select sensors based on the node analysis results, the node parameter information, the monitoring parameter information, and the safety requirement information, combining environmental adaptability requirements and monitoring accuracy requirements, so as to determine the sensor selection result;

[0101] The deployment scheme determination unit is used to determine the sensor deployment scheme based on the sensor selection results, the spatial layout information of the substation corresponding to the low-voltage distribution network, and the line topology; the sensor deployment scheme includes the sensor installation location and the number of monitoring nodes covered by the sensor installation location.

[0102] In some specific embodiments, the power grid data processing module 13 may specifically include:

[0103] The data acquisition unit is used to collect node data of each monitoring node in the low-voltage distribution network based on the deployed sensors; the node data includes voltage, current, temperature, humidity and equipment status at the node;

[0104] The operation data determination unit is used to determine the current power grid operation data based on the node data of each of the monitoring nodes;

[0105] The fault value processing unit is used to process the power grid operation data using a sliding window-based filtering algorithm and a preset fault value detection mechanism to determine the fault value processing result.

[0106] The alignment unit is used to uniformly align the fault value processing results based on a preset time base to determine the data alignment result.

[0107] A conversion unit is used to perform format conversion on the data alignment result based on a preset data format, so as to determine the format conversion result;

[0108] The extraction unit is used to extract key fields from the format conversion result to determine the field extraction result;

[0109] A dataset determination unit is used to determine an initial dataset based on the results extracted from the fields.

[0110] The filtering unit is used to filter the initial dataset based on preset data quality verification rules in order to determine the processed data.

[0111] In some specific embodiments, the model processing module 14 may specifically include:

[0112] The model acquisition unit is used to acquire a pre-trained machine learning model based on unsupervised learning algorithms, preset clustering analysis rules, and historical power grid operation data.

[0113] A status assessment unit is used to obtain the current status assessment result of the low-voltage distribution network based on the processed data and the preset machine learning model.

[0114] A threshold judgment unit is used to determine whether the values ​​of various parameters in the processed data are greater than the corresponding preset alarm thresholds, so as to obtain the threshold judgment result;

[0115] The alarm judgment unit is used to determine whether the corresponding alarm operation is triggered based on the threshold judgment result, so as to determine the alarm trigger judgment result.

[0116] In some specific embodiments, the perception result determination module 15 may specifically include:

[0117] An information generation unit is used to generate alarm information based on the alarm triggering judgment result if the alarm triggering judgment result indicates that a power grid alarm operation is currently triggered, so as to determine the initial alarm information; the initial alarm information includes the fault time point, fault type, location of related equipment, fault severity information, and fault handling suggestion information;

[0118] The pattern matching unit is used to perform pattern matching on the historical fault database based on the fault type and the location of the related equipment to determine the pattern matching result;

[0119] The handling plan determination unit is used to determine a standard handling plan based on the pattern matching results, knowledge graph technology, and the initial alarm information;

[0120] The scheme adjustment unit is used to adjust the scheme based on the standard handling scheme, the current state assessment results of the low-voltage distribution network, topology information, load information and environmental information, so as to determine the target fault handling scheme;

[0121] The perception result determination unit is used to determine the current state perception result of the low-voltage distribution network based on the target fault handling plan, the state assessment result, and the preset perception function optimization rules.

[0122] In some specific embodiments, the perception result determination unit may specifically include:

[0123] The item-by-item comparison subunit is used to compare the fault time point and the fault type with the actual operation and maintenance data item by item to determine the comparison result.

[0124] An optimization determination subunit is used to determine state-aware optimization information based on the comparison results;

[0125] The result determination subunit is used to determine the current state perception result of the low-voltage distribution network based on the state perception optimization information, the target fault handling plan, and the state assessment result.

[0126] Furthermore, embodiments of this application also disclose an electronic device, Figure 3This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application.

[0127] Figure 3 This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of this application. Specifically, the electronic device 20 may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the low-voltage distribution network state sensing method disclosed in any of the foregoing embodiments. Furthermore, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0128] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0129] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0130] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the low-voltage distribution network state sensing method executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include computer programs capable of performing other specific tasks.

[0131] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned low-voltage distribution network state awareness method. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0132] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0133] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0134] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0135] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0136] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A state sensing method for a low-voltage distribution network, characterized in that, include: By conducting a survey of the deployment sites of the low-voltage distribution network, and based on the survey results, the monitoring parameters and safety requirements of the low-voltage distribution network are determined. Based on the monitoring parameter information and the safety requirement information, sensors are selected and deployed to determine the sensor deployment scheme; After the sensor deployment scheme is completed, based on the deployed sensors, the power grid operation data corresponding to the low-voltage distribution network is collected, and the power grid operation data is standardized to determine the processed data. Based on the preset machine learning model and the processed data, the current state assessment result is obtained, and combined with the preset alarm mechanism, the current alarm trigger judgment result is determined. If the alarm triggering judgment result indicates that a power grid alarm operation is currently triggered, then the current state perception result of the low-voltage distribution network is determined based on the historical fault database, the state assessment result, and the preset perception function optimization rules.

2. The state sensing method for low-voltage distribution networks according to claim 1, characterized in that, The process involves surveying the deployment sites of the low-voltage distribution network and, based on the survey results, determining the monitoring parameters and security requirements of the low-voltage distribution network, including: Environmental surveys and user visits were conducted at the deployment sites of the low-voltage distribution network to determine the survey information; the survey information included power grid operation scenario information and key power grid monitoring information. The survey information is analyzed for monitoring constraints to determine the safety requirements of the low-voltage distribution network. Based on the aforementioned safety requirements information, the monitoring parameters of the low-voltage distribution network are determined.

3. The state sensing method for low-voltage distribution networks according to claim 1, characterized in that, The process of selecting and deploying sensors based on the monitoring parameter information and the safety requirement information to determine the sensor deployment scheme includes: Based on the survey results and historical power grid data, node parameter information for each monitoring node in the low-voltage distribution network is obtained; the node parameter information includes temperature, humidity, and electromagnetic interference intensity. The voltage, current, and harmonic range of each monitoring node are analyzed to determine the node analysis results; Based on the node analysis results, the node parameter information, the monitoring parameter information, and the safety requirement information, a sensor selection is made that combines environmental adaptability requirements and monitoring accuracy requirements to determine the sensor selection result. Based on the sensor selection results, the spatial layout information of the substation corresponding to the low-voltage distribution network, and the line topology, a sensor deployment scheme is determined; the sensor deployment scheme includes the sensor installation location and the number of monitoring nodes covered by the sensor installation location.

4. The state sensing method for low-voltage distribution networks according to claim 3, characterized in that, Based on the deployed sensors, the system collects grid operation data corresponding to the low-voltage distribution network and performs standardization processing on the grid operation data to determine the processed data, including: Based on the deployed sensors, node data of each monitoring node in the low-voltage distribution network is collected; the node data includes voltage, current, temperature, humidity and equipment status at the node; The current power grid operation data is determined based on the node data of each monitoring node; The power grid operation data is processed using a sliding window-based filtering algorithm and a preset fault value detection mechanism to determine the fault value processing result. Based on a preset time reference, the processing results of the fault values ​​are uniformly aligned to determine the data alignment result; Based on a preset data format, the data alignment result is converted to determine the format conversion result; The key fields of the format conversion result are extracted to determine the field extraction results; Based on the results extracted from the aforementioned fields, the initial dataset is determined; The initial dataset is filtered based on preset data quality verification rules to determine the processed data.

5. The state sensing method for low-voltage distribution networks according to claim 1, characterized in that, The current state assessment result is obtained based on the preset machine learning model and the processed data, and the current alarm trigger judgment result is determined in conjunction with the preset alarm mechanism, including: Acquire a pre-trained machine learning model based on unsupervised learning algorithms, pre-defined clustering analysis rules, and historical power grid operation data; Based on the processed data and the preset machine learning model, the current state assessment result of the low-voltage distribution network is obtained; Determine whether the values ​​of each parameter in the processed data are greater than the corresponding preset alarm threshold, and obtain the threshold determination result; Based on the threshold judgment result, it is determined whether the corresponding alarm operation is triggered, so as to determine the alarm trigger judgment result.

6. The state sensing method for low-voltage distribution networks according to any one of claims 1 to 5, characterized in that, If the alarm triggering judgment result indicates that a power grid alarm operation is currently triggered, then based on the historical fault database, the state assessment result, and the preset sensing function optimization rules, the current state sensing result of the low-voltage distribution network is determined, including: If the alarm triggering judgment result indicates that a power grid alarm operation has been triggered, then alarm information is generated based on the alarm triggering judgment result to determine the initial alarm information; the initial alarm information includes the fault time point, fault type, location of related equipment, fault severity information, and fault handling suggestion information; Based on the fault type and the location of the relevant equipment, a pattern match is performed on the historical fault database to determine the pattern match result; Based on the pattern matching results, knowledge graph technology, and the initial alarm information, a standard handling plan is determined. Based on the standard handling plan, the current state assessment results of the low-voltage distribution network, topology information, load information, and environmental information, the plan is adjusted to determine the target fault handling plan. Based on the target fault handling plan, the state assessment results, and the preset sensing function optimization rules, the current state sensing results of the low-voltage distribution network are determined.

7. The state sensing method for low-voltage distribution networks according to claim 6, characterized in that, The determination of the current state perception result of the low-voltage distribution network based on the target fault handling plan, the state assessment result, and the preset sensing function optimization rules includes: Based on the fault time point and the fault type, a comparison is made item by item with the actual operation and maintenance data to determine the comparison result; Based on the comparison results, state-aware optimization information is determined; Based on the state awareness optimization information, the target fault handling plan, and the state assessment results, the current state awareness result of the low-voltage distribution network is determined.

8. A state sensing device for a low-voltage distribution network, characterized in that, include: The monitoring parameter determination module is used to conduct a survey of the deployment sites of the low-voltage distribution network and, based on the survey results, determine the monitoring parameter information and safety requirement information of the low-voltage distribution network. The deployment scheme determination module is used to select and deploy sensors based on the monitoring parameter information and the safety requirement information, so as to determine the sensor deployment scheme; The power grid data processing module is used to collect power grid operation data corresponding to the low-voltage distribution network based on the deployed sensors after the deployment is completed according to the sensor deployment scheme, and to standardize the power grid operation data to determine the processed data. The model processing module is used to obtain the current state evaluation result based on the preset machine learning model and the processed data, and to determine the current alarm trigger judgment result in combination with the preset alarm mechanism. The perception result determination module is used to determine the current state perception result of the low-voltage distribution network based on the historical fault database, the state assessment result, and the preset perception function optimization rules if the alarm trigger judgment result indicates that a power grid alarm operation is currently triggered.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the state sensing method for a low-voltage distribution network as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Used to store a computer program, which, when executed by a processor, implements the state-aware method for low-voltage distribution networks as described in any one of claims 1 to 7.

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