A power system information processing method based on power big data

Abstract

The present invention relates to the field of power data processing technology, and discloses a power system information processing method based on power big data, including S1, data collection; S2, data classification; S3, data comparison; S4, data analysis; S5, risk level prediction; S6, emergency response warning; S7, collection of feedback information; S8, big data processing. The power system information processing method based on power big data establishes a two-dimensional change function model of normal power system data, so that when comparing the data information of the power system, it can more intuitively and quickly identify the fault data of the power transportation node in the power system, and directly feedback the corresponding safety hazard results in the cloud database to the corresponding power transportation node, so as to timely and effectively notify the relevant operation and maintenance personnel, reduce the time for communicating faults and safety hazards, improve the operation and maintenance efficiency of the power system, and ensure the normal operation and power supply of the power system.

Description

Technical Field

[0001] The present invention relates to the technical field of power data processing, and in particular to a power system information processing method based on power big data. Background Art

[0002] The power system is an electrical energy production and consumption system consisting of power generation, transmission, transformation, distribution, and consumption. Its function is to convert natural primary energy into electrical energy through power generation devices, and then supply this energy to users through transmission, transformation, and distribution. The power system also has corresponding information and control systems at various stages and levels to measure, regulate, control, protect, communicate, and dispatch the power production process to ensure that users receive safe, economical, and high-quality electricity.

[0003] According to the disclosed method and system for power system risk prediction based on big data analysis (Announcement No.: CN117495114B), in the above application, a preset tracing algorithm is used to determine the combined risk prediction sequence of the power system based on each initial state probability set and the corresponding each state transformation network diagram, and risk warning information is determined and output based on the combined risk prediction sequence, thereby solving the technical problem of how to perform efficient and accurate risk prediction for the power system.

[0004] However, in the actual operation of the above-mentioned system information processing method, due to the large coverage of the power system and the different environments in which the power system is located, different locations in the entire power system may face different risks when the external environment changes. If they cannot be handled and avoided in a timely manner, it will have an adverse impact on the normal operation and power supply of the power system. In view of this, we propose a power system information processing method based on power big data. Summary of the Invention

[0005] The purpose of the present invention is to provide a power system information processing method based on power big data to solve the problems raised in the above background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for processing power system information based on power big data, comprising the following steps:

[0007] S1. Data collection: Collecting operational data of each power transportation node in the power system, and collecting weather forecast information at each power transportation node in the power system through big data;

[0008] S2. Data classification: classify and group the data collected in S1 and categorize the data of each power transportation node in the power system;

[0009] S3, data comparison: collect the past operation and risk failure data of each power transportation node through big data, and compare it with the data corresponding to each power transportation node classified in S2;

[0010] S4. Data analysis: Using the past operation data and risk failure data of each power transportation node in the power system as a comparison, the data of the corresponding power transportation node collected in S1 is compared with the risk failure data to obtain the difference results;

[0011] S5. Risk level prediction: If the difference result obtained in S4 exceeds the pre-set alarm threshold, the power system alarm program is triggered, and a risk warning message is sent to the corresponding power transportation node of the power system, reminding the corresponding power transportation node of the power system to start safety hazard investigation and protection;

[0012] S6. Emergency processing warning: After receiving the risk warning information, the power transportation node of the power system will conduct safety hazard inspection and power system maintenance work at the relevant locations within the processing range of the power transportation node according to the risk warning information;

[0013] S7. Collect feedback information: When performing safety hazard inspections and power system maintenance in S6, upload the work location and maintenance location information, and simultaneously upload the corresponding safety hazard information;

[0014] S8, big data processing: Classify and save the data uploaded in S7 through the power system database, and classify the types of safety hazards.

[0015] Preferably, the power system includes a cloud database and power transmission nodes, the number of the power transmission nodes is set in several groups, and the several groups of power transmission nodes are connected by transmission lines, the power transmission nodes include a node control terminal and an alarm terminal, and the power system is divided into operation and maintenance ranges according to regions, and each of the power transportation nodes has a coordinate information tag, the content of the coordinate information tag includes a power system mark X, a power system operation and maintenance range mark Y and a power transportation node mark Z, and the coordinate information tag is recorded as (X, Y, Z), so that the power system can quickly locate a specific power transportation node, so as to respond promptly and quickly to possible dangerous situations in the power operation and maintenance process, thereby reducing losses in the power system.

[0016] Preferably, the data collection content of the power transportation node of the power system in S1 includes the current value, voltage value, frequency value and power factor of the line of the current power transportation node, and the value collection range of the above data is within the current data collection time to the previous 3 days, and the power system uses big data to establish a two-dimensional plane rectangular coordinate system with time-data as the horizontal axis and vertical axis for the above data, and forms a change curve through the above data, so as to facilitate big data to analyze and calculate the operation and maintenance status of the power system in subsequent time according to the change curve of the current data, so as to timely discover possible safety hazards in the power system, and then deal with and eliminate them in time.

[0017] Preferably, when the collected data is classified and grouped in S2, the data of each power transport node is calculated and compared separately, and when grouping information, the same items of the coordinate information tags of the power transport nodes are used as screening marks for grouping, so that big data can be analyzed and compared with relevant data in the entire power system according to the specific items of the coordinate information tags corresponding to the power transport nodes, thereby improving the convenience of information retrieval, reducing data errors, and making big data analysis more intuitive and quick.

[0018] Preferably, the past operation and risk failure data collected through big data in S3 include the ambient temperature and humidity data of the corresponding power transportation node, and also include the change curve obtained from the current value, voltage value, frequency value and power factor data of the line of the power transportation node before the past risk failure data, so that big data can analyze the possibility of related operation and maintenance safety hazards in the current power transportation node according to changes in the external environment, and according to the corresponding change curve, the possibility of risk occurrence can be judged more intuitively, thereby improving the response capability of the power system to emergencies.

[0019] Preferably, when comparing risk fault data in S4, a corresponding curve change function is obtained by using a change curve formed in a two-dimensional rectangular coordinate system obtained based on the collected data of the power transportation node through big data, and the compared data is substituted into the change function to compare the corresponding parameter values ​​and the data values ​​corresponding to the data itself and calculate, thereby obtaining a differential change value. By substituting the curve change function and the corresponding numerical value into the calculation, an intuitive comparison of the data is achieved, which facilitates rapid calculation of big data and obtains relatively accurate results, so as to ensure the accuracy and efficiency of the judgment of the entire power system on relevant risk data.

[0020] Preferably, if the difference between the difference result data obtained in S4 and the normal data is greater than 20%, the alarm threshold is reached, and before starting the power system alarm program, the power system big data needs to re-collect the relevant data of the corresponding power transportation node and perform a secondary calculation, and compare the secondary calculation result with the normal data. If the difference value of the comparison result is greater than 20%, the power system alarm program is directly triggered. If the difference value of the comparison result is less than 20% and greater than 0%, the power system alarm program is not triggered, and the power system sends a data abnormality reminder to the power transportation node, so that the accuracy of the data information of the power transportation node can be verified, false alarms can be avoided, and the operation and maintenance efficiency of the power system can be improved.

[0021] Preferably, the risk warning information issued in S5 includes the difference results obtained in S4, and the risk warning information also includes a comparison between the change curve obtained by big data based on the current information data collected by the power transportation node and the change curve of normal data, so that the operation and maintenance personnel of the power transportation node can more intuitively and accurately understand the specific situation of the safety hazards within the scope of operation and maintenance of the power transportation node, thereby facilitating the operation and maintenance personnel to timely check and avoid risks and ensure the safe operation of the power system.

[0022] Preferably, the feedback information in S7 includes the content of the relevant safety hazard issues checked by the operation and maintenance personnel for the power transportation node and the progress of the relevant operation and maintenance work, and the identity information of the operation and maintenance personnel is uploaded simultaneously, so that the power system can timely understand the handling status and progress of the safety hazard issues of the power transportation node, and at the same time, feedback is provided on the risk warning information issued in S6 and the accuracy of the problem is confirmed, so that the power system big data can provide more reference information for the accuracy of problem judgment during the operation of the power system, so as to improve the accuracy of information judgment in the subsequent power system problem investigation process.

[0023] Preferably, during the big data processing in S8, the feedback information uploaded in S7 is marked and classified according to the power system operation risk category and saved in the cloud database of the power system, so as to facilitate the retrieval and comparison of data when comparing the safety hazard risks of this type in the subsequent safety hazard investigation process.

[0024] Compared with the existing technology, the present invention provides a power system information processing method based on power big data, which has the following beneficial effects:

[0025] 1. This power system information processing method based on power big data establishes a two-dimensional change function model of normal power system data, so that when comparing the data information of the power system, it can more intuitively and quickly identify the fault data of the power transportation node in the power system, and directly feedback the corresponding safety hazard results in the cloud database to the corresponding power transportation node, so as to promptly and effectively inform the relevant operation and maintenance personnel, reduce the time for communicating faults and safety hazards, improve the operation and maintenance efficiency of the power system, and ensure the normal operation and power supply of the power system.

[0026] 2. The power system information processing method based on power big data accurately collects the data information of the corresponding power transportation nodes as judgment and evaluation materials through secondary calculation, so that when there are differences in the data, it can accurately judge whether there are safety hazards in the power system, thereby verifying the accuracy of the data information of the power transportation nodes, avoiding false alarms, and improving the operation and maintenance efficiency of the power system.

[0027] 3. This power system information processing method based on power big data adds corresponding differentiated data change curves to risk warning information, so that power system operation and maintenance personnel can promptly deal with or prevent possible dangerous situations according to the current progress of safety hazards, thereby facilitating operation and maintenance personnel to timely check and avoid risks and ensure the safe operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a schematic diagram of the power system information processing steps of the present invention;

[0029] Figure 2 This is a schematic diagram of the power system information processing flow of the present invention. DETAILED DESCRIPTION

[0030] like Figure 1-Figure 2 As shown, the present invention provides a technical solution: a power system information processing method based on power big data, comprising the following steps:

[0031] S1. Data collection: Collecting operational data of each power transportation node in the power system, and collecting weather forecast information at each power transportation node in the power system through big data;

[0032] S2. Data classification: classify and group the data collected in S1 and categorize the data of each power transportation node in the power system;

[0033] S3, data comparison: collect the past operation and risk failure data of each power transportation node through big data, and compare it with the data corresponding to each power transportation node classified in S2;

[0034] S4. Data analysis: Using the past operation data and risk failure data of each power transportation node in the power system as a comparison, the data of the corresponding power transportation node collected in S1 is compared with the risk failure data to obtain the difference results;

[0035] S5. Risk level prediction: If the difference result obtained in S4 exceeds the pre-set alarm threshold, the power system alarm program is triggered, and a risk warning message is sent to the corresponding power transportation node of the power system, reminding the corresponding power transportation node of the power system to start safety hazard investigation and protection;

[0036] S6. Emergency processing warning: After receiving the risk warning information, the power transportation node of the power system will conduct safety hazard inspection and power system maintenance work at the relevant locations within the processing range of the power transportation node according to the risk warning information;

[0037] S7. Collect feedback information: When performing safety hazard inspections and power system maintenance in S6, upload the work location and maintenance location information, and simultaneously upload the corresponding safety hazard information;

[0038] S8, big data processing: Classify and save the data uploaded in S7 through the power system database, and classify the types of safety hazards.

[0039] In one embodiment of the present invention, a power system includes a cloud database and power transmission nodes. The number of power transmission nodes is set in several groups, and the power transmission nodes are connected by transmission lines. The power transmission nodes include a node control terminal and an alarm terminal. The power system is divided into operation and maintenance ranges according to regions, and each power transmission node has a coordinate information tag. The coordinate information tag content includes a power system belonging mark X, a power system operation and maintenance range mark Y, and a power transmission node mark Z. The coordinate information tag is recorded as (X, Y, Z), so that the power system can quickly locate a specific power transmission node to respond promptly and quickly to possible dangerous situations during the power operation and maintenance process, thereby reducing losses in the power system. At the same time, when classifying and grouping the collected data in S2, the data of each power transmission node is calculated and compared separately, and when grouping information, the similar items of the coordinate information tags of the power transmission nodes are used as filtering marks for grouping. Therefore, big data can be analyzed and compared based on the specific items of the coordinate information tags corresponding to the power transmission nodes. This improves the convenience of information retrieval, reduces data errors, and makes big data analysis more intuitive and quick.

[0040] Furthermore, the data collected from the power transportation nodes of the power system in S1 includes the current value, voltage value, frequency value, and power factor of the line of the current power transportation node, and the value collection range of the above data is within the current data collection time to the previous 3 days. The power system uses big data to establish a two-dimensional plane rectangular coordinate system with time-data as the horizontal and vertical axes for the above data, and forms a change curve based on the above data, so as to facilitate big data to analyze and calculate the operation and maintenance status of the power system in the subsequent time based on the change curve of the current data, so as to timely discover possible safety hazards in the power system, and then promptly deal with and eliminate them. Specifically, the past operation and risk failure data collected by big data in S3 include the ambient temperature and humidity data of the corresponding power transportation node, and also include the change curve obtained by the current value, voltage value, frequency value, and power factor data of the line of the power transportation node before the past risk failure data, so that big data can analyze the possibility of relevant operation and maintenance safety hazards at the current power transportation node according to changes in the external environment, and can more intuitively judge the possibility of risk occurrence based on the corresponding change curve, thereby improving the power system's response capability to emergencies.

[0041] In addition, when comparing risk fault data in S4, the corresponding curve change function is obtained by using the change curve formed in the two-dimensional rectangular coordinate system obtained based on the collected data of the power transportation node through big data, and the compared data is substituted into the change function to compare the corresponding parameter values ​​and the data values ​​corresponding to the data itself and calculate to obtain the difference change value. By substituting the curve change function and the corresponding numerical value into the calculation, an intuitive comparison of the data is achieved, which facilitates rapid calculation of big data and obtains relatively accurate results, so as to ensure the accuracy and efficiency of the judgment of the entire power system on the relevant risk data.

[0042] In an embodiment of the present invention, if the difference value between the difference result data obtained in S4 and the normal data is greater than 20%, the alarm threshold is reached, and before starting the power system alarm program, the power system big data needs to re-collect the relevant data of the corresponding power transportation node and perform a secondary calculation, and compare the secondary calculation result with the normal data. If the difference value of the comparison result is greater than 20%, the power system alarm program is directly triggered. If the difference value of the comparison result is less than 20% and greater than 0, the power system alarm program is not triggered, and the power system sends a data abnormality reminder to the power transportation node through the power system, so that the accuracy of the data information of the power transportation node can be verified, false alarms can be avoided, and the operation and maintenance efficiency of the power system can be improved.

[0043] In the present invention, the risk warning information issued in S5 includes the difference results obtained in S4, and the risk warning information also includes a comparison of the change curve obtained by big data based on the current information data collected by the power transportation node and the change curve of normal data, so that the operation and maintenance personnel of the power transportation node can more intuitively and accurately understand the specific situation of the safety hazards within the scope of operation and maintenance of the power transportation node, thereby facilitating the operation and maintenance personnel to timely check and avoid risks and ensure the safe operation of the power system. Furthermore, the feedback information in S7 includes the content of the relevant safety hazards checked by the operation and maintenance personnel for the power transportation node and the progress of related operation and maintenance work, and the identity information of the operation and maintenance personnel is uploaded synchronously, so that the power system can timely understand the handling status and progress of the safety hazards of the power transportation node, and at the same time feedback and confirm the accuracy of the risk warning information issued in S6, so that the power system big data can provide more reference information for the accuracy of problems judged during the operation of the power system, so as to improve the accuracy of information judgment in the subsequent power system problem troubleshooting process.

[0044] It is worth noting that during the big data processing in S8, the feedback information uploaded in S7 is marked and classified according to the power system operation risk category and saved in the cloud database of the power system, so as to facilitate the retrieval and comparison of data when comparing the risks of this type of safety hazards in the subsequent safety hazard investigation process.

[0045] The above generally describes the present invention in detail. However, it is obvious to those skilled in the art that modifications or improvements may be made based on the present invention. Therefore, modifications or improvements that do not depart from the spirit of the present invention are within the scope of protection of the present invention.

Claims

1. A power system information processing method based on power big data, characterized by: The following steps are involved: S1. Data collection: Collecting operational data of each power transportation node in the power system, and collecting weather forecast information at each power transportation node in the power system through big data; S2. Data classification: classify and group the data collected in S1 and categorize the data of each power transportation node in the power system; S3, data comparison: collect the past operation and risk failure data of each power transportation node through big data, and compare it with the data corresponding to each power transportation node classified in S2; S4. Data analysis: Using the past operation data and risk failure data of each power transportation node in the power system as a comparison, the data of the corresponding power transportation node collected in S1 is compared with the risk failure data to obtain the difference results; S5. Risk level prediction: If the difference result obtained in S4 exceeds the pre-set alarm threshold, the power system alarm program is triggered, and a risk warning message is sent to the corresponding power transportation node of the power system, reminding the corresponding power transportation node of the power system to start safety hazard investigation and protection; S6. Emergency processing warning: After receiving the risk warning information, the power transportation node of the power system will conduct safety hazard inspection and power system maintenance work at the relevant locations within the processing range of the power transportation node according to the risk warning information; S7. Collect feedback information: When performing safety hazard inspections and power system maintenance in S6, upload the work location and maintenance location information, and simultaneously upload the corresponding safety hazard information; S8, big data processing: Classify and save the data uploaded in S7 through the power system database, and classify the types of safety hazards.

2. The method for processing power system information based on power big data according to claim 1, characterized in that: The power system includes a cloud database and power transmission nodes. The number of power transmission nodes is set in several groups, and the several groups of power transmission nodes are connected by transmission lines. The power transmission nodes include a node control terminal and an alarm terminal. The power system is divided into operation and maintenance ranges according to regions, and each of the power transportation nodes has a coordinate information tag. The content of the coordinate information tag includes a power system belonging mark X, a power system operation and maintenance range mark Y, and a power transportation node mark Z. The coordinate information tag is recorded as (X, Y, Z).

3. The method for processing power system information based on power big data according to claim 1, characterized in that: The data collection content of the power transportation node of the power system in S1 includes the current value, voltage value, frequency value and power factor of the line of the current power transportation node, and the value collection range of the above data is within the current data collection time to the previous 3 days, and the power system uses big data to establish a two-dimensional plane rectangular coordinate system with time-data as the horizontal axis and vertical axis for the above data, and forms a change curve through the above data.

4. The method for processing power system information based on power big data according to claim 2, characterized in that: When the collected data is classified and grouped in S2, the data of each power transport node is calculated and compared separately, and when grouping information, similar items of the coordinate information tags of the power transport nodes are used as screening marks for grouping.

5. The method for processing power system information based on power big data according to claim 3, characterized in that: The past operation and risk failure data collected through big data in S3 include the ambient temperature and humidity data of the corresponding power transportation node, and also include the change curve obtained by the current value, voltage value, frequency value and power factor data of the line of the power transportation node before the past risk failure data.

6. The method for processing power system information based on power big data according to claim 5, characterized in that: When comparing risk fault data in S4, a corresponding curve change function is obtained by using a change curve formed in a two-dimensional rectangular coordinate system obtained based on the collected data of the power transportation node through big data, and the compared data is substituted into the change function, and the corresponding parameter values ​​and the data values ​​corresponding to the data themselves are compared and calculated to obtain a difference change value.

7. The method for processing power system information based on power big data according to claim 1, characterized in that: If the difference between the difference result data obtained in S4 and the normal data is greater than 20%, the alarm threshold is reached, and before starting the power system alarm program, the power system big data needs to re-collect the relevant data of the corresponding power transportation node and perform a secondary calculation, and compare the secondary calculation result with the normal data. If the difference value of the comparison result is greater than 20%, the power system alarm program is directly triggered. If the difference value of the comparison result is less than 20% and greater than 0%, the power system alarm program is not triggered, and a data abnormality reminder is issued to the power transportation node through the power system.

8. The method for processing power system information based on power big data according to claim 1, characterized in that: The risk warning information issued in S5 includes the difference result obtained in S4, and the risk warning information also includes a comparison between the change curve obtained by big data based on the current information data collected by the power transportation node and the change curve of normal data.

9. The method for processing power system information based on power big data according to claim 1, characterized in that: The feedback information in S7 includes the content of the relevant safety hazards checked by the operation and maintenance personnel for the power transportation node and the progress of the relevant operation and maintenance work, and the identity information of the operation and maintenance personnel is uploaded simultaneously. At the same time, the risk warning information released in S6 is fed back and the accuracy of the problem is confirmed.

10. The method for processing power system information based on power big data according to claim 1, characterized in that: During the big data processing in S8, the feedback information uploaded in S7 is marked and classified according to the power system operation risk category and stored in the cloud database of the power system.

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