Source end maintenance-based microgrid edge device state monitoring method and system

Abstract

The invention relates to the technical field of micro-grids, in particular to a micro-grid edge device state monitoring method and system based on source end maintenance, and the method comprises the following steps: S10, building an initial source end topology model which is built based on edge devices of a micro-grid; s20, collecting operation data of edge equipment at the source end of the micro-grid, remotely monitoring the real-time operation state of the edge equipment at the source end, and obtaining a monitoring result of the edge equipment at the source end; s30, on the basis of the remote monitoring result of the source end edge device, early warning is carried out on the source end edge device, and early warning information is pushed; s40, synchronizing the initial source end topology model and the monitoring result and the early warning result of the edge device, and updating the source end topology model; and S50, after the source end topology model is updated, the general control section synchronously performs state analysis on the monitoring result, and verifies the early warning information pushed by the source end. Through the arrangement, the real-time performance and the accuracy of monitoring the running state of the micro-grid are improved.

Description

Technical Field

[0001] The present invention relates to the field of microgrid technology, and in particular to a method and system for monitoring the status of microgrid edge devices based on source-end maintenance. Background Art

[0002] A microgrid is a small, localized power network that is usually composed of distributed energy resources (such as solar energy, wind energy, energy storage systems, etc.) and loads. In a microgrid, the "source end" usually refers to the source of electricity generation, such as solar panels, wind turbines, micro gas turbines and other types of power generation equipment. It is the starting point of power supply in a microgrid, and its working status and output characteristics have an important impact on the overall operation and performance of the microgrid. The "edge devices" in a microgrid usually refer to various devices located at the edge of the network, such as sensors and smart meters, inverters and charging controllers, and load control devices.

[0003] In the prior art, in order to reflect the operating status of the microgrid, it is mostly achieved through the microgrid topology modeling method. Microgrid topology modeling is an abstract representation of the interconnection and functional relationship between the various components of the microgrid. However, traditional microgrid topology modeling methods often rely on centralized architectures, and the efficiency of information collection and processing is low. In addition, when processing a large amount of scattered edge device data, this method is prone to data delays, losses or inaccuracies, resulting in untimely updates of the topology model and failure to accurately reflect the actual operating status of the microgrid. Summary of the invention

[0004] In view of at least one of the above technical problems, the present invention provides a microgrid edge device status monitoring method and system based on source-end maintenance, which improves the monitoring method to improve the real-time and accuracy of microgrid operating status monitoring.

[0005] According to a first aspect of the present invention, a method for monitoring the state of a microgrid edge device based on source maintenance is provided, comprising the following steps:

[0006] S10: Establishing an initial source end topology model, where the initial source end topology model is established based on edge devices of the microgrid;

[0007] S20: Collect the operating data of the edge device at the source end of the microgrid, remotely monitor the real-time operating status of the edge device at the source end, and obtain the monitoring results of the edge device at the source end;

[0008] S30: Based on the remote monitoring result of the source edge device, the source edge device is warned and the warning information is pushed;

[0009] S40: Synchronize the initial source end topology model and the monitoring results and warning results of the edge device, and update the source end topology model;

[0010] S50: After the source end topology model is updated, the main control section synchronously performs status analysis on the monitoring results and verifies the warning information pushed by the source end.

[0011] In some embodiments of the present invention, in step S10, the following steps are further included:

[0012] S11: The source end of the microgrid is abstracted as a node, the line and connection relationship are abstracted as an edge, and a topological structure diagram is constructed;

[0013] S12: Based on the constructed topological structure diagram, the microgrid is divided into multiple sub-areas from the source end, a local topological model is established for each sub-area, and corresponding attributes are assigned to each node and edge to form a local topological model;

[0014] S13: Integrate all local demolding models into the initial source end topology model, and measure the modeling accuracy.

[0015] In some embodiments of the present invention, in step S12, the corresponding attributes include voltage level, power capacity and impedance, and the modeling formula of the local topology model is:

[0016]

[0017] Among them, T m represents the local topological model, represents the source node set, represents the source edge set, Indicates the source node attributes, represents the source edge attribute, and r is the modeling function.

[0018] In some embodiments of the present invention, in step S13, when measuring the modeling accuracy, the formula for measuring the modeling accuracy is:

[0019]

[0020] Among them, M a Indicates the modeling accuracy, R s Indicates the source resolution. Indicates the accuracy of source parameters. Indicates the source connection accuracy, s is the coefficient, and u, v, and w are exponents.

[0021] In some embodiments of the present invention, in step S20, the calculation formula of the monitoring result of the source edge device is:

[0022] S=a*R s+b*E s +c*D ps +d*D is

[0023] Among them, S represents the monitoring result; R s Indicates the source gateway operation data; E s Indicates source end environment information data; D ps Indicates the defect law at the source end, D is It represents the source defect index, and a, b, c, and d are weight coefficients.

[0024] In some embodiments of the present invention, in step S30, when the source edge device is warned, the warning is implemented by a deep learning algorithm, and the warning formula is:

[0025]

[0026] Among them, W represents the warning result, S represents the monitoring result, Indicates the historical maintenance information of the source device. Indicates source device defect information. Represents the source device positioning and navigation information, m, n, p, q are weight coefficients, and f is the deep learning algorithm function;

[0027] The warning information includes warning message push, defect elimination demand release and emergency repair reminder. When the warning information is pushed, the push algorithm formula of the warning information is:

[0028] P = g(W*h)

[0029] Among them, P represents the push strategy, W represents the warning result, h is the adjustment coefficient, and g is the push function.

[0030] In some embodiments of the present invention, in step S40, the following steps are further included:

[0031] S41: When the edge device status changes or a new edge device is deployed, the source terminal actively initiates a model update push and informs the master control terminal. After confirmation, the master control terminal synchronizes the initial source terminal topology model and uses an incremental update algorithm. The formula of the incremental update algorithm is:

[0032]

[0033] Among them, D u represents the updated topology model, Indicates source change events. Represents the model before the source end, represents the source-side update logic, and t is the update function;

[0034] S42: The calculation formula for synchronization time is:

[0035]

[0036] Where T represents the synchronization time, Indicates the amount of data at the source end. Indicates network quality, k is the coefficient, x and y are exponents;

[0037] S43: Verify and evaluate the synchronized initial source end topology model.

[0038] In some embodiments of the present invention, in step S43, the formula for checking the evaluation is:

[0039]

[0040] Wherein, V represents the verification result; M s Indicates the source model; D s represents the source data; m is the verification evaluation function.

[0041] In some embodiments of the present invention, in step S50, when performing status analysis, the monitoring result is judged based on the historical monitoring result, and the judgment formula is:

[0042]

[0043] Among them, S d Indicates the status detection result; Indicates source-side perception information; Indicates historical measurement information of the source end; Represents the real-time measurement information of the source end; n is the state analysis function.

[0044] According to a second aspect of the present invention, a microgrid edge device status monitoring system based on source-end maintenance is also provided, which runs a microgrid edge device status monitoring method based on source-end maintenance as described in any one of the first aspects.

[0045] The beneficial effects of the present invention are as follows: the present invention improves the above method, integrates edge computing and deep learning energy gateway state monitoring and early warning technology, improves real-time performance and accuracy, and achieves more efficient and accurate state perception and early warning. At the same time, the synchronization mechanism is set to ensure the timely update and consistency of key models. The fast switching and flexible configuration technology that adapts to the changeable state of the microgrid enhances the adaptability and efficiency of remote management. Task processing and configuration adjustment are optimized according to the characteristics of the microgrid, which improves the responsiveness and operating efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0047] Figure 1 It is a flowchart of the steps of a method for monitoring the state of a microgrid edge device based on source-end maintenance in an embodiment of the present invention;

[0048] Figure 2 The figure is a flowchart of the steps of the method for constructing a topology structure in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0050] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only and do not represent the only implementation method.

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0052] like Figure 1 The microgrid edge device status monitoring method based on source-side maintenance shown includes the following steps:

[0053] S10: Establish an initial source end topology model, which is established based on the edge devices of the microgrid; in an embodiment of the present invention, unlike the traditional global model construction method, it is based on the edge devices of the microgrid. Through this setting, the accuracy and flexibility of the initial source topology model can be improved.

[0054] S20: Collect the operating data of the edge device at the source end of the microgrid, remotely monitor the real-time operating status of the edge device at the source end, and obtain the monitoring results of the edge device at the source end;

[0055] S30: Based on the remote monitoring result of the source edge device, the source edge device is warned and the warning information is pushed; in the embodiment of the present invention, the topology model is dynamically adjusted through real-time monitoring and warning information, thereby enhancing the adaptability to complex system changes;

[0056] S40: Synchronize the initial source topology model and the monitoring results and warning results of the edge devices, and update the source topology model; dynamically adjust the topology model by introducing test data to make it reflect the actual status and operation changes of the equipment. Through the synchronous update method, the dynamic adaptability of the model can be enhanced to adapt to the characteristics of frequent changes in the status of edge devices.

[0057] S50: After the source end topology model is updated, the main control section synchronously performs status analysis on the monitoring results and verifies the warning information pushed by the source end.

[0058] In the above embodiments, by integrating edge computing and deep learning energy gateway state monitoring and early warning technology, the real-time performance and accuracy are improved, and more efficient and accurate state perception and early warning are achieved. At the same time, the synchronization mechanism is set to ensure the timely update and consistency of key models. The fast switching and flexible configuration technology that adapts to the changing state of the microgrid enhances the adaptability and efficiency of remote management. Task processing and configuration adjustment are optimized according to the characteristics of the microgrid, which improves the system's responsiveness and operating efficiency.

[0059] Based on the above examples, please refer to Figure 2 In an embodiment of the present invention, in step S10, the following steps are also included:

[0060] S11: The source end of the microgrid is abstracted as a node, the lines and connection relationships are abstracted as edges, and a topological structure diagram is constructed; the modeling difficulty of the microgrid topology model is reduced by abstracting the nodes and edges, so that the complex physical network can be described by a simple mathematical model.

[0061] S12: Based on the constructed topological structure diagram, the microgrid is divided into multiple sub-areas from the source end, a local topological model is established for each sub-area, and corresponding attributes are assigned to each node and edge to form a local topological model; by dividing the microgrid from the source end into multiple sub-areas and establishing a local topological model for each sub-area, it helps to reduce complexity and facilitate independent analysis and optimization of local areas.

[0062] S13: Integrate all local demolding models into an initial source end topology model, and measure the modeling accuracy. In the embodiment of the present invention, the integration between local models provides flexibility for subsequent topology update and expansion.

[0063] In some embodiments of the present invention, in step S12, the corresponding attributes include voltage level, power capacity and impedance, and the modeling formula of the local topology model is:

[0064]

[0065] Among them, T m represents the local topological model, represents the source node set, represents the source edge set, Indicates the source node attributes, Represents the source edge attribute, and r is the modeling function. By establishing the above formula, the set of nodes and edges is the weighted sum of their attributes, which quantifies the local topological model and helps to associate the topological structure with the actual physical attributes.

[0066] In the embodiment of the present invention, in step S13, when measuring the modeling accuracy, the formula for measuring the modeling accuracy is:

[0067]

[0068] Among them, M a Indicates the modeling accuracy, R s Indicates the source resolution. Indicates the accuracy of source parameters. Indicates the source connection accuracy, s is the coefficient, and u, v, and w are exponents. The accuracy measurement formula integrates resolution, parameter accuracy, and connection accuracy, and the modeling results are more objective through the quantification of accuracy.

[0069] In an embodiment of the present invention, in step S20, the calculation formula of the monitoring result of the source edge device is:

[0070] S=a*R s +b*E s +c*D ps +d*D is

[0071] Among them, S represents the monitoring result; R s Indicates the source gateway operation data; E s Indicates source end environment information data; D ps Indicates the defect law at the source end, D is It represents the source defect index, and a, b, c, and d are weight coefficients. It integrates multiple influencing factors through weighted summation to generate a unified detection result. By considering the gateway operation status, environmental information, and defect analysis results, the monitoring result is comprehensive.

[0072] In an embodiment of the present invention, in step S30, when the source edge device is warned, the warning is implemented by a deep learning algorithm, and the warning formula is:

[0073]

[0074] Among them, W represents the warning result, S represents the monitoring result, Indicates the historical maintenance information of the source device. Indicates source device defect information. Represents the source device positioning and navigation information, m, n, p, q are weight coefficients, and f is the deep learning algorithm function;

[0075] Warning information includes warning message push, defect elimination demand release and emergency repair reminder. When pushing warning information, the push algorithm formula of warning information is:

[0076] P = g(W*h)

[0077] Wherein, P represents the push strategy, W represents the warning result, h is the adjustment coefficient, and g is the push function. In the above embodiment, the warning is performed through the deep learning algorithm, which can improve the intelligence and accuracy of monitoring, and the push algorithm converts the warning information into a targeted action strategy, thereby improving the efficiency of information transmission.

[0078] In an embodiment of the present invention, in step S40, the following steps are also included:

[0079] S41: When the edge device status changes or a new edge device is deployed, the source end actively initiates the model update push and informs the master control end. After confirmation, the master control end synchronizes the initial source end topology model and uses an incremental update algorithm. The formula of the incremental update algorithm is:

[0080]

[0081] Among them, D u represents the updated topology model, Indicates source change events. Represents the model before the source end, represents the source-side update logic, and t is the update function;

[0082] S42: The calculation formula for synchronization time is:

[0083]

[0084] Where T represents the synchronization time, Indicates the amount of data at the source end. Indicates network quality, k is the coefficient, x and y are exponents;

[0085] S43: Check and evaluate the initial source end topology model after synchronization. In the embodiment of the present invention, the consumption of system resources by model update is reduced through the incremental update algorithm, the update efficiency is optimized, the timeliness of the synchronization process is ensured through the synchronization time calculation formula considering the quality of the network and the amount of data, the accuracy of the update model is ensured through the twisted evaluation, and the error risk is reduced.

[0086] Furthermore, in step S43, the formula for checking and evaluating is:

[0087] V=m(∑(M s *D s ) / (M s +D s ))

[0088] Wherein, V represents the verification result; M s Indicates the source model; D s Represents source data; m is the verification evaluation function. The consistency between the model and the data is ensured by weight allocation and weighted mean calculation of the verification evaluation formula.

[0089] In the embodiment of the present invention, in step S50, when performing state analysis, the monitoring result is judged based on the historical monitoring result, and the judgment formula is:

[0090]

[0091] Among them, S d Indicates the status detection result; Indicates source-side perception information; Indicates historical measurement information of the source end; Indicates the real-time measurement information of the source end; n is the state analysis function. In the state judgment formula of the present invention, by integrating historical and real-time monitoring data, a more comprehensive and dynamic device state evaluation is provided, and by effectively combining historical experience with current perception results, multi-dimensional data support is provided for device state judgment.

[0092] In an embodiment of the present invention, a microgrid edge device status monitoring system based on source-end maintenance is also provided, which runs any of the above-mentioned microgrid edge device status monitoring methods based on source-end maintenance.

[0093] Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for monitoring the state of a microgrid edge device based on source-side maintenance, characterized in that: The following steps are involved: S10: Establishing an initial source end topology model, where the initial source end topology model is established based on edge devices of the microgrid; S20: Collect the operating data of the edge device at the source end of the microgrid, remotely monitor the real-time operating status of the edge device at the source end, and obtain the monitoring results of the edge device at the source end; S30: Based on the remote monitoring result of the source edge device, the source edge device is warned and the warning information is pushed; S40: Synchronize the initial source end topology model and the monitoring results and warning results of the edge device, and update the source end topology model; S50: After the source end topology model is updated, the main control section synchronously performs status analysis on the monitoring results and verifies the warning information pushed by the source end.

2. The microgrid edge device status monitoring method based on source end maintenance according to claim 1 is characterized in that: In step S10, the following steps are also included: S11: The source end of the microgrid is abstracted as a node, the line and connection relationship are abstracted as an edge, and a topological structure diagram is constructed; S12: Based on the constructed topological structure diagram, the microgrid is divided into multiple sub-areas from the source end, a local topological model is established for each sub-area, and corresponding attributes are assigned to each node and edge to form a local topological model; S13: Integrate all local demolding models into the initial source end topology model, and measure the modeling accuracy.

3. The microgrid edge device status monitoring method based on source end maintenance according to claim 2 is characterized in that: In step S12, the corresponding attributes include voltage level, power capacity and impedance, and the modeling formula of the local topology model is: Among them, T m represents the local topological model, represents the source node set, represents the source edge set, Indicates the source node attributes, represents the source edge attribute, and r is the modeling function.

4. The microgrid edge device status monitoring method based on source end maintenance according to claim 2 is characterized in that: In step S13, when measuring the modeling accuracy, the formula for measuring the modeling accuracy is: Among them, M a Indicates the modeling accuracy, R s Indicates the source resolution. Indicates the accuracy of source parameters. Indicates the source connection accuracy, s is the coefficient, and u, v, and w are exponents.

5. The microgrid edge device status monitoring method based on source end maintenance according to claim 1 is characterized in that: In step S20, the calculation formula of the monitoring result of the source edge device is: S=a*R s +b*E s +c*D ps +d*D is Among them, S represents the monitoring result; R s Indicates the source gateway operation data; E s Indicates source end environment information data; D ps Indicates the source defect law, D is It represents the source defect index, and a, b, c, and d are weight coefficients.

6. The microgrid edge device status monitoring method based on source end maintenance according to claim 1 is characterized in that: In step S30, when the source edge device is warned, the warning is realized by a deep learning algorithm, and the warning formula is: Among them, W represents the warning result, S represents the monitoring result, Indicates the historical maintenance information of the source device. Indicates source device defect information. Represents the source device positioning and navigation information, m, n, p, q are weight coefficients, and f is the deep learning algorithm function; The warning information includes warning message push, defect elimination demand release and emergency repair reminder. When the warning information is pushed, the push algorithm formula of the warning information is: P = g(W*h) Among them, P represents the push strategy, W represents the warning result, h is the adjustment coefficient, and g is the push function.

7. The microgrid edge device status monitoring method based on source end maintenance according to claim 1 is characterized in that: In step S40, the following steps are also included: S41: When the edge device status changes or a new edge device is deployed, the source terminal actively initiates a model update push and informs the master control terminal. After confirmation, the master control terminal synchronizes the initial source terminal topology model and uses an incremental update algorithm. The formula of the incremental update algorithm is: Among them, D u represents the updated topology model, Indicates source change events. Represents the model before the source end, represents the source-side update logic, and t is the update function; S42: The calculation formula for synchronization time is: Where T represents the synchronization time, Indicates the amount of data at the source end. Indicates network quality, k is the coefficient, x and y are exponents; S43: Verify and evaluate the synchronized initial source end topology model.

8. The microgrid edge device status monitoring method based on source end maintenance according to claim 7 is characterized in that: In step S43, the formula for checking the evaluation is: Wherein, V represents the verification result; M s Indicates the source model; D s represents the source data; m is the verification evaluation function.

9. The microgrid edge device status monitoring method based on source end maintenance according to claim 1 is characterized in that: In step S50, when performing status analysis, the monitoring results are judged based on the historical monitoring results, and the judgment formula is: Among them, S d Indicates the status detection result; Indicates source-side perception information; Indicates historical measurement information of the source end; Represents the real-time measurement information of the source end; n is the state analysis function.

10. A microgrid edge device status monitoring system based on source end maintenance, characterized in that: The system runs the microgrid edge device status monitoring method based on source-end maintenance as described in any one of claims 1 to 9.

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