Micro-grid edge control system

Abstract

The invention discloses a microgrid edge control system. In order to solve the problems that under the condition that the data volume is large, the credibility of data cannot be guaranteed, error analysis is easy to occur, and when the edge equipment is controlled after the analysis of the edge equipment is completed, other edge equipment can be influenced and cannot work normally. According to the method, the obtained edge equipment information is subjected to confidence evaluation, so that the validity of the data is ensured. And the correlation between the edge equipment and other equipment is judged, so that the coordination between the abnormal edge equipment and the normal working edge equipment can be coordinated when the abnormal edge equipment is controlled.

Description

Technical Field

[0001] The invention belongs to the technical field of power grid management and relates to a microgrid edge control system. Background Art

[0002] A smart microgrid is a smaller, decentralized, independent system that integrates distributed power sources, energy storage devices, energy conversion devices, related loads, and monitoring and protection devices into a small power generation and distribution system. It is an autonomous system capable of self-control, protection, and management, and can operate with an external power grid or in isolation.

[0003] However, the amount of data obtained by the microgrid from the edge devices is very large. When the data volume is large, the credibility of the data cannot be guaranteed, and it is easy to make errors in analysis. When the edge device is controlled after the analysis is completed, it will affect other edge devices, causing other edge devices to fail to work properly. Summary of the invention

[0004] In order to solve the problems existing in the background technology, the present invention proposes a microgrid edge control system.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] Data acquisition module, which collects data from various devices in the microgrid and monitors the status of the microgrid;

[0007] Data transmission module, which communicates between edge devices and microgrid edge control system;

[0008] The data processing module analyzes the credibility of the edge device data collected by the data acquisition module, performs abnormal analysis on the trusted information of the edge devices collected by the data acquisition module, and analyzes the correlation between the edge device to be controlled and other edge devices;

[0009] A data storage module stores the historical records of information processed by the data processing module;

[0010] The device control module controls the abnormal edge device and multiple edge devices related to the abnormal edge device according to the processing result of the data processing module.

[0011] Furthermore, the data collected by the data collection module includes:

[0012] Information about the edge devices of the microgrid, including the voltage, current, power, power consumption and number of the edge devices;

[0013] The operating status of the microgrid, including the working conditions of the power supply, load and energy storage system.

[0014] Furthermore, the specific method of analyzing the microgrid anomaly by the data processing module is:

[0015] Design evaluation mechanisms to assess the reliability of data and information collected by edge devices and determine the confidence level of the information;

[0016] Extract the edge device information and the trusted data after the edge device information confidence evaluation analysis, perform data analysis in the data processing module, judge the abnormal data during the operation of the microgrid, if abnormal data occurs, mark the edge device corresponding to the source of the abnormal data, and formulate corresponding control strategies.

[0017] Furthermore, the confidence calculation method of the data processing module is:

[0018] The user first customizes one or more indicators for evaluating the credibility of the data. The credibility indicators correspond to the attributes of the edge device data acquired by the data acquisition module. The attributes include attribute information such as time and spatial distance.

[0019] Based on the user-defined credibility index, the credibility is judged by combining the edge device data attributes with the credibility index corresponding data. The system sets an initial threshold to exclude low-credibility data and calculates whether each data is credible by combining the credibility of the edge device and the system threshold.

[0020] The credibility of data obtained from multiple data sources is integrated according to the user's preset weights to form a comprehensive confidence assessment.

[0021] Furthermore, the control mode of the device control module is:

[0022] By controlling the abnormal edge devices, the data changes of other edge devices are obtained, and the correlation of the abnormal edge devices is calculated. The specific formula is:

[0023] H = -∑P(x)lgfn(P(x))

[0024] Where P(x) is the value of the abnormal edge device P when the change value of the abnormal edge device P is x, fn(P(x)) is the impact of the change of the value of P(x) on the fn edge device, H is the correlation value between the abnormal edge device P and the fn edge device, and n is the number of other edge devices;

[0025] Extract correlation values ​​of multiple edge devices and abnormal edge devices, set a correlation threshold, select devices whose correlation values ​​with abnormal edge devices are greater than the correlation threshold, obtain multiple related edge devices, and control the multiple related edge devices and abnormal edge devices through distributed collaborative control.

[0026] Furthermore, the data processing module can optimize learning according to the operating status of the microgrid, and the specific method is:

[0027] Analyze the correlation of all devices in the microgrid and detect all related edge devices of any edge device;

[0028] Dynamically adjust the microgrid control strategy according to different system requirements issued by users.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] The present invention adds data confidence verification to ensure data credibility. The present invention also adds correlation calculation for edge devices, which can coordinate the coordination between abnormal edge devices and normally working edge devices when controlling abnormal edge devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a system structure architecture diagram of the present invention. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] like Figure 1 As shown, the technical solution adopted by the present invention is as follows: a microgrid edge control system, comprising:

[0034] The data acquisition module collects data from various devices in the microgrid and monitors the status of the microgrid.

[0035] Data transmission module, which communicates between edge devices and microgrid edge control system.

[0036] The data processing module analyzes the credibility of the edge device data collected by the data acquisition module, performs anomaly analysis on the trusted information of the edge devices collected by the data acquisition module, and analyzes the correlation between the edge devices to be controlled and other devices.

[0037] The data storage module stores the historical records of information processed by the data processing module.

[0038] The device control module controls the abnormal edge device and multiple edge devices related to the abnormal edge device according to the processing result of the data processing module.

[0039] The data collected by the data acquisition module includes information about the edge devices of the microgrid and the overall operating status information of the microgrid.

[0040] The information of the edge devices of the microgrid includes the voltage, current, power, power consumption and number of the edge devices.

[0041] The overall operating status information of the microgrid includes information such as the working conditions of the power supply, load and energy storage system.

[0042] When analyzing microgrid anomalies, the data processing module must first verify the confidence of the data obtained by the data acquisition module to ensure that the information obtained by the data acquisition module is true and valid, and avoid bad data from having a negative impact on microgrid control.

[0043] The specific method of performing confidence lack recognition on the data acquired by the data acquisition module is:

[0044] Design evaluation mechanisms to assess the reliability of data and information collected by edge devices and determine the confidence level of the information.

[0045] The user first customizes one or more indicators for evaluating the credibility of the data. The credibility indicators correspond to the attributes of the edge device data acquired by the data acquisition module, and the attributes include attribute information such as time and spatial distance.

[0046] Before the first credibility assessment, users need to set credibility indicators and credibility weights. Based on the user-defined credibility indicators, the system combines the edge device data attributes with the credibility indicator corresponding data to make credibility judgments. The system sets an initial threshold to exclude low-credibility data and calculates whether each data is credible based on the credibility of the edge device and the system threshold.

[0047] The credibility of data obtained from multiple data sources is integrated according to the user's preset weights to form a comprehensive confidence assessment. Comprehensive assessment based on user-preset weights can fully reflect whether a data is authentic and reliable, reducing the chance of misjudgment.

[0048] After extracting the edge device information and the trusted data of the edge device information confidence evaluation analysis, the data analysis is performed in the data processing module, and the analysis result is obtained by comparing any normal value range with the value of the existing edge device information.

[0049] Determine abnormal data during the operation of the microgrid. If abnormal data occurs, mark the edge device corresponding to the source of the abnormal data, formulate corresponding control strategies, and adjust the operating status of the edge device.

[0050] After confirming the control strategy of the edge device, first analyze the correlation between the abnormal edge device and other devices. By controlling the abnormal edge device, the data changes of other edge devices are obtained, and the correlation of the abnormal edge device is calculated. The specific formula is:

[0051] H = -∑P(x)lgfn(P(x))

[0052] Where P(x) is the value of the abnormal edge device P when the change value of the abnormal edge device P is x, fn(P(x)) is the impact of the change of the P(x) value on the fn edge device, H is the correlation value between the P edge device and the fn edge device, and n is the number of other edge devices.

[0053] Extract the correlation values ​​of multiple edge devices and abnormal edge devices, set the correlation threshold, select the devices whose correlation values ​​with the abnormal edge devices are greater than the correlation threshold, obtain multiple related edge devices, and control the multiple related edge devices and abnormal edge devices through distributed collaborative control. When controlling the abnormal edge device and multiple related abnormal edge devices, the coordination between the abnormal edge device and the normally working edge device can be coordinated.

[0054] The data processing module can optimize learning according to the microgrid operating status. The specific method is:

[0055] The correlation of all devices in the microgrid is analyzed to detect all related edge devices of any edge device.

[0056] According to different system requirements issued by users, the microgrid control strategy is dynamically adjusted, which enables the microgrid to actively adjust related equipment when adjusting the system mode, thus improving the durability of the entire system.

[0057] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A microgrid edge control system, characterized in that: Included are: Data acquisition module, which collects data from various devices in the microgrid and monitors the status of the microgrid; Data transmission module, which communicates between edge devices and microgrid edge control system; The data processing module analyzes the credibility of the edge device data collected by the data acquisition module, performs abnormal analysis on the trusted information of the edge devices collected by the data acquisition module, and analyzes the correlation between the edge device to be controlled and other edge devices; A data storage module stores the historical records of information processed by the data processing module; The device control module controls the abnormal edge device and multiple edge devices related to the abnormal edge device according to the processing result of the data processing module.

2. A microgrid edge control system according to claim 1, characterized in that ,The data collected by the data collection module include: Information about the edge devices of the microgrid, including the voltage, current, power, power consumption and number of the edge devices; The overall operating status information of the microgrid, including the working conditions of the power supply, load and energy storage system.

3. A microgrid edge control system according to claim 1, characterized in that ,The specific way in which the data processing module analyzes the ,abnormality of the microgrid is as follows: Design an evaluation mechanism to assess the credibility of data and information collected by edge devices and determine the confidence level of the information; Extract edge device information and trusted data after edge device information confidence evaluation and analysis, and perform data analysis in the data processing module; Determine abnormal data during the operation of the microgrid. If abnormal data occurs, mark the edge device corresponding to the source of the abnormal data and formulate corresponding control strategies.

4. A microgrid edge control system according to claim 3, characterized in that ,The confidence calculation method of the data processing module is: The user first customizes one or more indicators for evaluating the credibility of the data. The credibility indicators correspond to the attributes of the edge device data acquired by the data acquisition module, and the attributes include time attribute information and spatial distance attribute information; Based on the user-defined credibility index, the credibility is judged by combining the edge device data attributes with the credibility index corresponding data. The system sets an initial threshold to exclude low-credibility data and calculates whether each data is credible by combining the credibility of the edge device and the system threshold. The credibility of data obtained from multiple data sources is integrated according to the user's preset weights to form a comprehensive confidence assessment.

5. A microgrid edge control system according to claim 1, characterized in that ,The control mode of the device control module is: By controlling the abnormal edge devices, the data changes of other edge devices are obtained, and the correlation of the abnormal edge devices is calculated. The specific formula is: H = -∑P(x)lgfn(P(x)) Where P(x) is the value of the abnormal edge device P when the change value of the abnormal edge device P is x, fn(P(x)) is the impact of the change of the value of P(x) on the fn edge device, H is the correlation value between the abnormal edge device P and the fn edge device, and n is the number of other edge devices; Extract correlation values ​​of multiple edge devices and abnormal edge devices, set a correlation threshold, select devices whose correlation values ​​with abnormal edge devices are greater than the correlation threshold, obtain multiple related edge devices, and control the multiple related edge devices and abnormal edge devices through distributed collaborative control.

6. A microgrid edge control system according to claim 1, characterized in that ,The data processing module can optimize learning according to the operation status of the microgrid. The specific method is: Analyze the correlation of all devices in the microgrid and detect all related edge devices of any edge device; Dynamically adjust the microgrid control strategy according to different system requirements issued by users.

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