Electric power environment operation control system based on Internet of Things
By introducing edge computing and six early warning centers into the power environment monitoring system, the problem of insufficient power environment monitoring delay and early warning capabilities in the existing technology is solved, and timely and accurate monitoring of the power environment is achieved.
Patent Information
- Application Number
- CN202411432348.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-14
- Publication Date
- 2025-02-11
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The existing technology has a time delay phenomenon in power environment monitoring, and it is impossible to early warning of the impact of the natural environment on power equipment, resulting in the inability to monitor the power environment in a timely and effective manner.
By introducing edge computing, the central processor and edge processor cooperate to reduce the delay caused by data processing; combined with six early warning centers, the equipment status coefficient of power equipment is warning and visualized through the equipment topology relationship.
It realizes timely early warning and monitoring of the power environment, reduces data processing delays, and improves the efficiency and accuracy of power environment monitoring.
Smart Images
Figure CN119415354A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the field of electric power environment monitoring, and relates to an electric power environment operation monitoring technology based on the Internet of Things, and specifically to an electric power environment operation control system based on the Internet of Things. Background Art
[0002] When the power system is transmitting electricity, some core power equipment is exposed to the natural environment or is in a closed environment for a long time. The natural environment will have a certain impact on the core power equipment, resulting in the power system being unable to operate stably for a long time.
[0003] When the existing technology monitors the power environment, the base station sends the collected power environment data to the local server, and the local server processes and analyzes the returned power environment data to realize the monitoring of the power environment; however, the existing technology has a time delay in the data processing process, and is unable to warn of the impact of the natural environment on the power equipment, and is unable to monitor the power environment in a timely and effective manner; therefore, there is an urgent need for a power environment operation control system based on the Internet of Things. Summary of the invention
[0004] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes an Internet of Things-based power environment operation control system to solve the technical problems that the prior art has time delays and is unable to provide early warnings for the power environment, resulting in the inability to monitor the power environment in a timely and effective manner. The present invention introduces edge computing, reduces the time delay caused by data processing through the cooperation of the central processing unit and the edge processor, combines six early warning centers to provide early warnings for the equipment status coefficients of power equipment, and solves the above problems through visual display of equipment topology relationships.
[0005] To achieve the above object, an embodiment of the first aspect of the present invention provides an Internet of Things-based power environment operation control system, including:
[0006] Edge processor: obtains time series warning data, analyzes and processes the time series warning data according to the environmental mapping relationship, and obtains the equipment status coefficient; it is also used to warn or adjust the power environment according to the equipment status coefficient;
[0007] Central Processing Unit: used to establish equipment topology relationships based on the relationships between power equipment; used to generate environmental mapping relationships; also used to obtain timing warning data based on environmental warning data and set cycles, and distribute the timing warning data to the corresponding edge processors; among them, environmental warning data is obtained through six major warning centers.
[0008] Preferably, the central processor combines with the power equipment to construct the device topology relationship, including:
[0009] Obtain the location of the power generation equipment and take the power generation equipment as the starting point;
[0010] Establish initial topological relationships with power equipment as nodes and transmission lines as connecting lines;
[0011] The initial topological relationship is modified based on the device uniqueness principle to obtain the device topological relationship; wherein the device uniqueness principle means that the power equipment only appears once in the device topological relationship.
[0012] Preferably, the environment mapping relationship includes a function mapping model and an intelligent evaluation model, and the environment mapping relationship is acquired through standard experimental data; wherein the standard experimental data includes target environment data and corresponding equipment state coefficients.
[0013] Preferably, the standard experimental data is obtained through a laboratory, including:
[0014] Obtain target environmental data that affects power equipment; the target environmental data is consistent with the data obtained by the six early warning centers;
[0015] Adjust the target environmental data within the planning scope and record the operating status of the corresponding power equipment;
[0016] Scoring the operating status of power equipment through expert scoring to obtain the equipment status coefficient;
[0017] Integrate the equipment status coefficient and the corresponding target environment data to obtain standard experimental data.
[0018] Preferably, the planning range refers to an area obtained with the power equipment as the center; wherein the planning range includes a circle and a rectangle.
[0019] Preferably, obtaining the function mapping model through the central processing unit includes:
[0020] The target environment data in the standard experimental data is extracted as the independent variable, and the equipment state coefficient is taken as the dependent variable. The functional relationship is established through the polynomial fitting method, and the functional relationship is marked as a function mapping model.
[0021] Preferably, the intelligent evaluation model is specifically an artificial intelligence model trained by standard experimental data; wherein the artificial intelligence model includes a deep convolutional neural network model and an RBF neural network model.
[0022] Preferably, after the environment mapping relationship is generated, the central processor sends the environment mapping relationship to the edge processor corresponding to the power equipment in real time or periodically.
[0023] Preferably, obtaining the time series warning data through the central processor includes:
[0024] Obtain environmental warning data through six early warning centers;
[0025] The GIS platform extracts the environmental warning data after data preprocessing in combination with the set cycle, and generates N vector maps of the monitoring area; each pixel in each vector map corresponds to an environmental data group, and the environmental data group corresponds to the environmental warning data of the six major warning centers;
[0026] N vector maps are stacked in time series to obtain a vector map, which is marked as time series warning data.
[0027] Preferably, the edge processor obtains the equipment status coefficient corresponding to the power equipment in combination with the time series warning data, including:
[0028] The edge processor intercepts the time series warning data according to the planning scope of the power equipment and generates target warning data;
[0029] The target warning data is matched and processed according to the environment mapping relationship, and the equipment state array of the power equipment is obtained through the environment mapping relationship and the target warning data; wherein the equipment state data includes N equipment state coefficients.
[0030] Preferably, the device status array is displayed on the device topology relationship.
[0031] Preferably, the central processor is communicatively connected to a plurality of edge processors.
[0032] Preferably, the electric power equipment includes a generator, a transformer and a transmission line.
[0033] Compared with the prior art, the present invention has the following beneficial effects:
[0034] 1. The present invention takes the power production equipment as the starting point and the power equipment as the node, and establishes the equipment topology relationship. The edge processor obtains the equipment status coefficient of the power equipment according to the updated environment mapping relationship and the timing warning data; the environment mapping relationship is established in advance according to the standard experimental data, and the delay caused by data processing is reduced through the cooperation of the central processor and the edge processor, and the power equipment status can be warned through the setting of the timing warning data.
[0035] 2. When acquiring standard experimental data, the present invention simulates the real environment in which the power equipment is located, and determines the corresponding equipment status coefficient through expert scoring, thereby ensuring the accuracy of the standard experimental data and laying a data foundation for the accuracy of the environmental mapping relationship.
[0036] 3. The present invention establishes time series warning data based on six warning centers and set period extraction. The equipment status coefficient can be accurately obtained through the mapping relationship between the time series warning data and the environment, further ensuring the accuracy of the power environment warning. BRIEF DESCRIPTION OF THE DRAWINGS
[0037] Figure 1 It is a schematic diagram of the working steps of the present invention. DETAILED DESCRIPTION
[0038] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than 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.
[0039] During the process of transmitting electricity in the power system, some power equipment is exposed to the natural environment, such as transmission lines. When the existing technology monitors the power environment, the base station sends the collected power environment data to the local server, and the local server processes and analyzes the returned power environment data to realize the monitoring of the power environment; Obviously, in the data processing process, the existing technology has a relatively serious time delay phenomenon, and it is impossible to warn the impact of the natural environment on the power equipment, and it is impossible to monitor the power environment in a timely and effective manner; this application introduces edge computing, reduces the delay caused by data processing through the cooperation of the central processing unit and the edge processor, combines the six early warning centers to warn the equipment status coefficient of the power equipment, and solves the above problems through visual display of the equipment topology relationship.
[0040] See also Figure 1 , this application provides an electric power environment operation control system based on the Internet of Things, including:
[0041] Edge processor: obtains time series warning data, analyzes and processes the time series warning data according to the environmental mapping relationship, and obtains the equipment status coefficient; it is also used to warn or adjust the power environment according to the equipment status coefficient;
[0042] Central Processing Unit: used to establish equipment topology relationships based on the relationships between power equipment; used to generate environmental mapping relationships; also used to obtain timing warning data based on environmental warning data and set cycles, and distribute the timing warning data to the corresponding edge processors.
[0043] It is worth noting that environmental warning data is obtained through six major warning centers, which include the Ice Warning Center, Wildfire Warning Center, Dancing Warning Center, Lightning Warning Center, Typhoon Warning Center and Geological Disaster Warning Center.
[0044] It can be understood that the central processor is communicatively connected to several edge processors, and the power equipment mainly includes equipment that is easily affected by natural disasters, such as transmission lines, transformers, etc.
[0045] The edge processor can be associated with only one power device or with multiple power devices at the same time. The specific number of associated power devices is set according to the processing capacity of the edge processor.
[0046] In one embodiment, the central processor constructs the device topology relationship in combination with the power device, including:
[0047] Obtain the location of the power generation equipment and take the power generation equipment as the starting point;
[0048] Establish initial topological relationships with power equipment as nodes and transmission lines as connecting lines;
[0049] The initial topological relationship is modified based on the device uniqueness principle to obtain the device topological relationship.
[0050] In this embodiment, the purpose of establishing the device topology relationship is to facilitate better monitoring of the power equipment in the monitoring area and to be able to more intuitively observe the status of the power equipment in the monitoring area.
[0051] The device uniqueness principle means that the power equipment only appears once in the device topology relationship, that is, it is ensured that the same power equipment only serves as a node once in the device topology relationship, ensuring the simplicity of the device topology relationship.
[0052] In one embodiment, the environment mapping relationship includes a function mapping model and an intelligent evaluation model, and the environment mapping relationship is obtained through standard experimental data; wherein the standard experimental data includes target environment data and corresponding equipment state coefficients.
[0053] The standard experimental data in this embodiment are obtained through the laboratory, including:
[0054] Obtain target environmental data that affects power equipment;
[0055] Adjust the target environmental data within the planning scope and record the operating status of the corresponding power equipment;
[0056] Scoring the operating status of power equipment through expert scoring to obtain the equipment status coefficient;
[0057] Integrate the equipment status coefficient and the corresponding target environment data to obtain standard experimental data.
[0058] In this embodiment, the target environment data is consistent with the data content obtained by the six early warning centers. The target environment data is combined to simulate the situation of the power equipment in the natural environment, and then the power equipment is scored according to its performance to generate standard experimental data.
[0059] It is worth noting that the planning range of the power equipment in this embodiment refers to a circular area with the power equipment as the center and the set distance as the radius; wherein the set distance is obtained by manual setting or according to the working range of the power equipment. In other embodiments, the planning range may also be a rectangle or other irregular shapes.
[0060] Adjust the target environmental data within the planning range. For example, adjust the wind force within the planning range from level 0 to level 17, obtain the average wind force within the planning range as the target environmental data, and then score the status of the power equipment to obtain the equipment status coefficient.
[0061] In a specific embodiment, obtaining the function mapping model by the central processor includes:
[0062] The target environment data in the standard experimental data is extracted as the independent variable, and the equipment state coefficient is taken as the dependent variable. The functional relationship is established through the polynomial fitting method, and the functional relationship is marked as a function mapping model.
[0063] In another specific embodiment, the central processing unit obtains the intelligent evaluation model through the artificial intelligence model, including:
[0064] Divide the standard experimental data into training set, test set and verification set;
[0065] Constructing an artificial intelligence model; wherein the artificial intelligence model includes a deep convolutional neural network model and an RBF neural network model;
[0066] The artificial intelligence model is trained through training sets, test sets and verification sets, and the trained artificial intelligence model is marked as an intelligent evaluation model.
[0067] In one embodiment, after the environment mapping relationship is generated, the central processor sends the environment mapping relationship to the edge processor corresponding to the power device in real time or periodically.
[0068] The timing in this embodiment specifically refers to the generation period of the environment mapping relationship. When the generation or update period of the environment mapping relationship is fixed, the generation or update period is the timing period.
[0069] It is worth noting that the central processor will generate an environmental mapping relationship for each power device or each type of power device based on standard experimental data, and send the environmental mapping relationship to the corresponding edge processor in a timely manner.
[0070] In one embodiment, obtaining time series warning data through the central processor includes:
[0071] Obtain environmental warning data through six early warning centers;
[0072] The GIS platform extracts the environmental warning data after data preprocessing in combination with the set cycle, and generates N vector maps of the monitoring area; each pixel in each vector map corresponds to an environmental data group, and the environmental data group corresponds to the environmental warning data of the six major warning centers; the GIS platform includes ArcGIS, ENVI and other software and related software;
[0073] N vector maps are stacked in time series to obtain a vector map, which is marked as time series warning data.
[0074] The set period in this embodiment can be understood as the monitoring time of the monitoring area. For example, if an early warning is required for the power equipment in the monitoring area for the next five days and a status needs to be reported every day, the set period is 1 day and the corresponding N should be 5.
[0075] In a specific embodiment, the edge processor obtains the device status coefficient corresponding to the power device in combination with the time series warning data, including:
[0076] The edge processor intercepts the time series warning data according to the planning scope of the power equipment and generates target warning data;
[0077] The target warning data is matched and processed according to the environment mapping relationship, and the equipment state array of the power equipment is obtained through the environment mapping relationship and the target warning data; wherein the equipment state data includes N equipment state coefficients.
[0078] In this embodiment, matching processing is performed on the target warning data according to the environment mapping relationship, which specifically refers to converting the target warning data into data that can be read and used by the environment mapping relationship, and also includes processing such as calculating the average value.
[0079] It is worth noting that this application will also display the device status array on the device topology relationship. For example, when an early warning appears in the power equipment on the second day, the power equipment on the device topology relationship will be marked in red. If an abnormality appears in the power equipment on the third day, it will be marked in orange...; providing data support for staff to handle in a timely manner.
[0080] Working principle of the present invention:
[0081] The location of the power production equipment is obtained, and the power production equipment is used as the starting point; the initial topological relationship is established with the power equipment as the node and the transmission line as the connecting line; the initial topological relationship is corrected based on the principle of equipment uniqueness to obtain the equipment topological relationship.
[0082] The central processor obtains the environmental mapping relationship in combination with the standard experimental data. After the environmental mapping relationship is generated, the central processor sends the environmental mapping relationship to the edge processor corresponding to the power equipment in real time or at a fixed time.
[0083] The edge processor obtains the timing warning data, analyzes and processes the timing warning data according to the environmental mapping relationship, and obtains the equipment status coefficient; the equipment status coefficient is visualized and converted, and the power environment is warned or adjusted according to the equipment status coefficient.
[0084] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.
Claims
1. The power environment operation control system based on the Internet of Things is characterized by: include: Edge processor: obtains time series warning data, analyzes and processes the time series warning data according to the environmental mapping relationship, and obtains the device status coefficient; It is also used to warn or adjust the power environment according to the equipment status coefficient; Central Processing Unit: used to establish equipment topology relationships based on the relationships between power equipment; used to generate environmental mapping relationships; also used to obtain timing warning data based on environmental warning data and set cycles, and distribute the timing warning data to the corresponding edge processors; among them, environmental warning data is obtained through six major warning centers.
2. The power environment operation control system based on the Internet of Things according to claim 1 is characterized in that: The central processor combines the power equipment to build the equipment topology relationship, including: Obtain the location of the power generation equipment and take the power generation equipment as the starting point; Establish initial topological relationships with power equipment as nodes and transmission lines as connecting lines; The initial topological relationship is modified based on the device uniqueness principle to obtain the device topological relationship.
3. The power environment operation control system based on the Internet of Things according to claim 1 is characterized in that: The environment mapping relationship includes a function mapping model and an intelligent evaluation model, and the environment mapping relationship is obtained through standard experimental data; wherein the standard experimental data includes target environment data and corresponding equipment state coefficients.
4. The power environment operation control system based on the Internet of Things according to claim 3 is characterized in that: The standard experimental data are obtained through the laboratory, including: Obtain target environmental data that affects power equipment; the target environmental data is consistent with the data obtained by the six early warning centers; Adjust the target environmental data within the planning scope and record the operating status of the corresponding power equipment; Scoring the operating status of power equipment through expert scoring to obtain the equipment status coefficient; Integrate the equipment status coefficient and the corresponding target environment data to obtain standard experimental data.
5. The power environment operation control system based on the Internet of Things according to claim 3 is characterized in that: Acquiring a function mapping model through the central processor includes: The target environment data in the standard experimental data is extracted as the independent variable, and the equipment state coefficient is taken as the dependent variable. The functional relationship is established through the polynomial fitting method, and the functional relationship is marked as a function mapping model.
6. The power environment operation control system based on the Internet of Things according to claim 1 is characterized in that: After the environmental mapping relationship is generated, the central processor sends the environmental mapping relationship to the edge processor corresponding to the power equipment in real time or periodically.
7. The power environment operation control system based on the Internet of Things according to claim 1 is characterized in that: Acquiring time series warning data through the central processor includes: Obtain environmental warning data through six early warning centers; The GIS platform extracts the environmental warning data after data preprocessing in combination with the set cycle, and generates N vector maps of the monitoring area; each pixel in each vector map corresponds to an environmental data group, and the environmental data group corresponds to the environmental warning data of the six major warning centers; the value of N is determined according to the set cycle; N vector maps are stacked in time series to obtain a vector map, which is marked as time series warning data.
8. The power environment operation control system based on the Internet of Things according to claim 1 is characterized in that: The edge processor combines the time series warning data to obtain the equipment status coefficient corresponding to the power equipment, including: The edge processor intercepts the time series warning data according to the planning scope of the power equipment and generates target warning data; The target warning data is matched and processed according to the environment mapping relationship, and the equipment state array of the power equipment is obtained through the environment mapping relationship and the target warning data; wherein the equipment state data includes N equipment state coefficients.