Risk early warning system based on power grid digital platform

Abstract

The invention relates to the field of power grids, in particular to a risk early warning system based on a power grid digital platform, which comprises a data acquisition module for acquiring running state data of a power grid in real time; the data processing module is used for cleaning, integrating and standardizing the collected data and deeply mining and analyzing the processed data; the operation state evaluation module is used for evaluating the operation state of the power grid by applying a big data analysis and machine learning algorithm; and the risk early warning module is used for carrying out early warning on possible risks of the power grid according to a result of the operation state evaluation module. According to the invention, real-time monitoring and early warning of power grid operation risks are realized, and the safety and stability of power grid operation are improved; and big data analysis and a machine learning algorithm are adopted, so that the accuracy and reliability of risk early warning are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grids, and in particular to a risk early warning system based on a power grid digital platform. Background Art

[0002] As the scale of power grids continues to expand and their complexity increases, the risks in power grid operations are becoming increasingly complex and diverse.

[0003] Traditional power grid risk warning methods often rely on manual inspections and empirical judgments, and are unable to provide timely and accurate warnings of potential risks during power grid operation.

[0004] Therefore, those skilled in the art provide a risk warning system based on a power grid digital platform to solve the problems raised in the above background technology. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a risk early warning system based on a power grid digital platform.

[0006] include

[0007] Data acquisition module: collects the operation status data of the power grid in real time;

[0008] Data processing module: cleans, integrates and standardizes the collected data, and conducts in-depth mining and analysis on the processed data;

[0009] Operation status assessment module: Use big data analysis and machine learning algorithms to assess the operation status of the power grid;

[0010] Risk warning module: Based on the results of the operation status assessment module, this module warns of possible risks in the power grid.

[0011] Preferably, the data acquisition module includes a sensor module, a data acquisition frequency module, and a data transmission module;

[0012] The sensor module includes a voltage sensor, a current sensor, a frequency sensor and a temperature sensor, wherein the voltage sensor is used to detect the voltage of the power grid, the current sensor is used to detect the current of the power grid, the frequency sensor is used to detect the frequency of the power grid, and the temperature sensor is used to detect the temperature of the power grid;

[0013] The data acquisition frequency module sets a suitable data acquisition frequency according to the characteristics and requirements of the power grid operation to ensure the integrity and timeliness of the data;

[0014] The data transmission module adopts efficient data transmission protocol and communication technology to ensure that the collected data can be transmitted to the data processing module in real time and accurately.

[0015] Preferably, the data processing module includes data cleaning, data integration, and data analysis;

[0016] The data cleaning utilizes data cleaning technology to remove noise and outliers;

[0017] The data integration is used to integrate the data collected by the sensor modules to form a unified power grid operation status data set;

[0018] The data analysis uses big data analysis and machine learning algorithms to conduct in-depth mining and analysis on the data after data integration and processing, and extract key features that describe the operating status of the power grid.

[0019] Preferably, the operation status assessment module includes assessment model construction, model training and optimization;

[0020] The evaluation model construction: constructing a power grid operation status evaluation model based on historical data and expert knowledge;

[0021] Model training and optimization: Use historical data and real-time data to train and optimize the evaluation model.

[0022] Preferably, the power grid operation status assessment model includes voltage stability and equipment health status, and performs a comprehensive evaluation on the operation status of the power grid.

[0023] Preferably, the risk warning module includes risk threshold setting, warning strategy formulation, and warning feedback and optimization;

[0024] The risk threshold is set according to the characteristics and needs of the power grid operation, and a reasonable risk threshold is set; when the power grid operation status exceeds the threshold, a risk warning is triggered;

[0025] The early warning strategy is formulated according to different risk types and levels to formulate corresponding early warning strategies.

[0026] Early warning feedback and optimization: Continuously optimize and improve the early warning strategy based on the actual effects and feedback of the early warning.

[0027] Preferably, the warning strategy includes a sending method of the warning signal, a receiving object, and processing suggestions.

[0028] Technical effects and advantages of the present invention:

[0029] Real-time monitoring and early warning of power grid operation risks are achieved, improving the safety and stability of power grid operation.

[0030] The use of big data analysis and machine learning algorithms has improved the accuracy and reliability of risk warnings. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is an overall structural block diagram of the risk early warning system based on the power grid digital platform provided in the embodiment of the present application;

[0032] Figure 2 It is a structural block diagram of a data acquisition module in a risk early warning system based on a power grid digital platform provided in an embodiment of the present application;

[0033] Figure 3 It is a structural block diagram of a processing module in a risk warning system based on a power grid digital platform provided in an embodiment of the present application;

[0034] Figure 4 It is a structural block diagram of an operation status assessment module in a risk early warning system based on a power grid digital platform provided in an embodiment of the present application;

[0035] Figure 5 It is a structural block diagram of a risk warning module in a risk warning system based on a power grid digital platform provided in an embodiment of the present application; DETAILED DESCRIPTION

[0036] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. The embodiments of the present invention are provided for the purpose of illustration and description, and are not intended to be exhaustive or to limit the present invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention, and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.

[0037] Example 1

[0038] See also Figures 1 to 5 In this embodiment, a risk early warning system based on a power grid digital platform is provided, including

[0039] Data acquisition module: collects the operation status data of the power grid in real time;

[0040] The data acquisition module includes a sensor module, a data acquisition frequency module, and a data transmission module;

[0041] The sensor module includes a voltage sensor, a current sensor, a frequency sensor and a temperature sensor. The voltage sensor is used to detect the voltage of the power grid, the current sensor is used to detect the current of the power grid, the frequency sensor is used to detect the frequency of the power grid, and the temperature sensor is used to detect the temperature of the power grid to ensure the accuracy and real-time performance of the data.

[0042] The data acquisition frequency module sets a suitable data acquisition frequency according to the characteristics and requirements of the power grid operation to ensure the integrity and timeliness of the data;

[0043] The data transmission module adopts efficient data transmission protocol and communication technology to ensure that the collected data can be transmitted to the data processing module in real time and accurately.

[0044] Data processing module: cleans, integrates and standardizes the collected data to eliminate noise and outliers, improve data quality, conducts in-depth mining and analysis on the processed data, and extracts key features that describe the operating status of the power grid;

[0045] The data processing module includes data cleaning, data integration, and data analysis;

[0046] The data cleaning utilizes data cleaning technology to remove noise and outliers and improve data quality;

[0047] The data integration is used to integrate the data collected by the sensor modules to form a unified power grid operation status data set.

[0048] The data analysis uses big data analysis and machine learning algorithms to conduct in-depth mining and analysis of the data after data integration and processing, and extract key features that describe the operating status of the power grid.

[0049] Operation status assessment module: Use big data analysis and machine learning algorithms to assess the operation status of the power grid;

[0050] The operation status assessment module includes assessment model construction, model training and optimization;

[0051] The evaluation model construction: based on historical data and expert knowledge, a power grid operation status evaluation model is constructed, and the power grid operation status evaluation model includes voltage stability and equipment health status, and comprehensively evaluates the operation status of the power grid;

[0052] Model training and optimization: Use historical data and real-time data to train and optimize the evaluation model to improve the accuracy and reliability of the evaluation.

[0053] Risk warning module: Based on the results of the operation status assessment module, this module warns of possible risks in the power grid.

[0054] The risk warning module includes risk threshold setting, warning strategy formulation, and warning feedback and optimization;

[0055] The risk threshold setting is based on the characteristics and needs of the power grid operation, and a reasonable risk threshold is set. When the power grid operation status exceeds the threshold, a risk warning is triggered;

[0056] The early warning strategy is formulated according to different risk types and levels, and corresponding early warning strategies are formulated. The early warning strategy includes the sending method of the early warning signal, the recipient, and the handling suggestions;

[0057] Early warning feedback and optimization: Based on the actual effects and feedback of the early warning, the early warning strategy is continuously optimized and improved to improve the accuracy and practicality of the early warning.

[0058] Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field and related fields without creative work should fall within the scope of protection of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention are implemented according to the conventional means in the field unless otherwise specified and limited.

Claims

1. The risk early warning system based on the power grid digital platform is characterized by: include Data acquisition module: collects the operation status data of the power grid in real time; Data processing module: cleans, integrates and standardizes the collected data, and conducts in-depth mining and analysis on the processed data; Operation status assessment module: Use big data analysis and machine learning algorithms to assess the operation status of the power grid; Risk warning module: Based on the results of the operation status assessment module, this module warns of possible risks in the power grid.

2. The risk early warning system based on the power grid digital platform according to claim 1 is characterized in that: The data acquisition module includes a sensor module, a data acquisition frequency module, and a data transmission module; The sensor module includes a voltage sensor, a current sensor, a frequency sensor and a temperature sensor, wherein the voltage sensor is used to detect the voltage of the power grid, the current sensor is used to detect the current of the power grid, the frequency sensor is used to detect the frequency of the power grid, and the temperature sensor is used to detect the temperature of the power grid; The data acquisition frequency module sets a suitable data acquisition frequency according to the characteristics and requirements of the power grid operation to ensure the integrity and timeliness of the data; The data transmission module adopts efficient data transmission protocol and communication technology to ensure that the collected data can be transmitted to the data processing module in real time and accurately.

3. The risk early warning system based on the power grid digital platform according to claim 1 is characterized in that: The data processing module includes data cleaning, data integration, and data analysis; The data cleaning utilizes data cleaning technology to remove noise and outliers; The data integration is used to integrate the data collected by the sensor modules to form a unified power grid operation status data set; The data analysis uses big data analysis and machine learning algorithms to conduct in-depth mining and analysis on the data after data integration and processing, and extract key features that describe the operating status of the power grid.

4. The risk early warning system based on the power grid digital platform according to claim 1 is characterized in that: The operation status assessment module includes assessment model construction, model training and optimization; The evaluation model construction: constructing a power grid operation status evaluation model based on historical data and expert knowledge; Model training and optimization: Use historical data and real-time data to train and optimize the evaluation model.

5. The risk early warning system based on the power grid digital platform according to claim 4 is characterized in that: The power grid operation status assessment model includes voltage stability and equipment health status, and comprehensively evaluates the operation status of the power grid.

6. The risk early warning system based on the power grid digital platform according to claim 1 is characterized in that: The risk warning module includes risk threshold setting, warning strategy formulation, and warning feedback and optimization; The risk threshold is set to set a reasonable risk threshold according to the characteristics and needs of power grid operation; When the grid operation status exceeds the threshold, a risk warning is triggered; The early warning strategy is formulated according to different risk types and levels to formulate corresponding early warning strategies. Early warning feedback and optimization: Continuously optimize and improve the early warning strategy based on the actual effects and feedback of the early warning.

7. The risk early warning system based on the power grid digital platform according to claim 6 is characterized in that: The early warning strategy includes the sending method of the early warning signal, the recipients, and the processing suggestions.

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