Power system information processing method based on power big data

Abstract

The invention relates to the technical field of electric power data processing, and discloses an electric power big data-based electric power system information processing method, which comprises the following steps of S1, acquiring data; s2, data classification; s3, comparing the data; s4, data analysis; s5, risk level pre-judgment; s6, emergency processing and early warning; s7, collecting feedback information; and S8, big data processing. According to the electric power system information processing method based on the electric power big data, a normal electric power system data two-dimensional change function model is established, so that the fault data condition of an electric power transmission node in an electric power system can be more intuitively and quickly distinguished when the data information of the electric power system is compared and processed; and the corresponding potential safety hazard result in the cloud database is directly fed back to the corresponding power transmission node, so that related operation and maintenance personnel can be timely and effectively notified, the fault and potential safety hazard transmission time is shortened, the operation and maintenance efficiency of the power system is improved, and normal operation and power supply of the power system are ensured.

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 electric energy production and consumption system consisting of power generation, transmission, transformation, distribution and consumption. Its function is to convert primary energy from nature into electric energy through power generation devices, and then supply electric energy to various users through transmission, transformation and distribution. The power system also has corresponding information and control systems at various links and levels to measure, regulate, control, protect, communicate and dispatch the production process of electric energy to ensure that users obtain safe, economical and high-quality electric energy.

[0003] According to the disclosed method and system for risk prediction of power system 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 according to each initial state probability set and the corresponding state transformation network diagram, and the risk warning information is determined and output according to 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 time, 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 object, 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: collect the operation data of each power transportation node of the power system, and collect the weather forecast information of each power transportation node of the power system through big data;

[0008] S2, data classification: classify and group the data collected in S1, and classify the data of each power transportation node of 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 preset alarm threshold, the power system alarm program is triggered, and risk warning information 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 processing work at the relevant locations within the processing range of the power transportation node according to the risk warning information;

[0013] S7, collecting feedback information: when performing safety hazard inspection and power system maintenance in S6, uploading the operation location and maintenance location information, and simultaneously uploading the corresponding safety hazard information;

[0014] S8, big data processing: The data uploaded in S7 is classified and saved through the power system database, and the types of safety hazards are classified.

[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 node control terminals and alarm terminals, and the power system is divided into operation and maintenance scopes according to regions, and each of the power transportation nodes has a coordinate information label, the content of the coordinate information label includes a power system mark X, a power system operation and maintenance scope mark Y and a power transportation node mark Z, and the coordinate information label 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 and vertical axes 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 transportation node is calculated and compared separately, and when grouping information, the same items of the coordinate information tags of the power transportation 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 transportation 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 cope with emergencies.

[0019] Preferably, when comparing risk fault data in S4, a corresponding curve change function is obtained by using big data based on the change curve formed in the two-dimensional rectangular coordinate system obtained from the collected data of the power transportation node, 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, 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 the relevant risk data.

[0020] Preferably, 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 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 a change curve obtained by big data based on the current information data collected by the power transportation node and a 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 circumstances 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 relevant safety hazard issues 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 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 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.

[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 during subsequent safety hazard investigation.

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

[0025] 1. 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 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, through secondary calculation, accurately collects the data information of the corresponding power transportation nodes as judgment and evaluation materials, 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. The power system information processing method based on power big data adds corresponding differentiated data change curves to the risk warning information, so that the power system operation and maintenance personnel can promptly deal with or prevent possible dangerous situations according to the current progress of potential safety hazards, thereby facilitating the 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 It is a schematic diagram of the steps of power system information processing of the present invention;

[0029] Figure 2 It 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 method for processing power system information based on power big data, comprising the following steps:

[0031] S1. Data collection: collect the operation data of each power transportation node of the power system, and collect the weather forecast information of each power transportation node of the power system through big data;

[0032] S2, data classification: classify and group the data collected in S1, and classify the data of each power transportation node of 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 preset alarm threshold, the power system alarm program is triggered, and risk warning information 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 processing work at the relevant locations within the processing range of the power transportation node according to the risk warning information;

[0037] S7, collecting feedback information: when performing safety hazard inspection and power system maintenance in S6, uploading the operation location and maintenance location information, and simultaneously uploading the corresponding safety hazard information;

[0038] S8, big data processing: The data uploaded in S7 is classified and saved through the power system database, and the types of safety hazards are classified.

[0039] In one embodiment of the present invention, the power system includes a cloud database and a power transmission node, the number of power transmission nodes is set to 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 divides the operation and maintenance scope according to the region, and each power transportation node has a coordinate information label, the coordinate information label content composition includes the power system belonging mark X, the power system operation and maintenance scope mark Y and the power transportation node mark Z, and its coordinate information label 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 the possible dangers in the power operation and maintenance process, and reduce the loss in the power system. At the same time, when the collected data is classified and grouped in S2, the data of each power transportation node is calculated and compared separately, and when grouping information, the same items of the coordinate information label of the power transportation node are used as the screening mark for grouping, so that big data can analyze and compare the relevant data in the entire power system according to the specific items of the coordinate information label corresponding to the power transportation node, thereby improving the convenience of information call, reducing data errors, and making big data analysis more intuitive and fast.

[0040] Furthermore, 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 the subsequent time according to the change curve of the current data, so as to timely discover the potential safety hazards in the power system, and then deal with and eliminate them in time. 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 related operation and maintenance safety hazards in the current power transportation node according to the changes in the external environment, and can more intuitively judge the possibility of risk occurrence according to the corresponding change curve, thereby improving the response capability of the power system to cope with 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 from 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 then 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 the 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, so as to facilitate 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 the relevant operation and maintenance work, and the identity information of the operation and maintenance personnel is uploaded synchronously, so that the handling status and progress of the safety hazards of the power transportation node can be timely understood in the power system, and at the same time, the risk warning information issued in S6 is fed back and the accuracy of the problem is confirmed, so that the power system big data can provide more reference information for the accuracy of the 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 safety hazards of this type in the subsequent safety hazard investigation process.

[0045] The above generally describes the present invention in detail, but it is obvious to a person skilled in the art that some modifications or improvements can be made to 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 method for processing power system information based on power big data, characterized in that: The following steps are involved: S1. Data collection: collect the operation data of each power transportation node of the power system, and collect the weather forecast information of each power transportation node of the power system through big data; S2, data classification: classify and group the data collected in S1, and classify the data of each power transportation node of 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 and the risk failure data are compared to obtain the difference results; S5, risk level prediction: if the difference result obtained in S4 exceeds the preset alarm threshold, the power system alarm program is triggered, and risk warning information 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 processing work at the relevant locations within the processing range of the power transportation node according to the risk warning information; S7, collecting feedback information: when performing safety hazard inspection and power system maintenance in S6, uploading the operation location and maintenance location information, and simultaneously uploading the corresponding safety hazard information; S8, big data processing: The data uploaded in S7 is classified and saved through the power system database, and the types of safety hazards are classified.

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 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 node control terminals and alarm terminals. The power system is divided into operation and maintenance scopes according to regions, and each of the power transportation nodes has a coordinate information label. The content of the coordinate information label includes a power system mark X, a power system operation and maintenance scope mark Y, and a power transportation node mark Z. The coordinate information label 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 and vertical axes 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 is 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 the information, the same 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 is 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 is characterized in that: When comparing risk fault data in S4, a corresponding curve change function is obtained by using big data to form a change curve in a two-dimensional rectangular coordinate system obtained based on the collected data of the power transportation node, and the compared data is substituted into the change function to compare the corresponding parameter value and the data value corresponding to the data itself and calculate, thereby obtaining a differential 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 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 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 a change curve obtained by big data based on the current information data collected by the power transportation node and a 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.

Images