CN118074073A - Distribution network differential protection system and method based on 5G communication - Google Patents
Distribution network differential protection system and method based on 5G communication Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02H—EMERGENCY PROTECTIVE CIRCUIT ARRANGEMENTS
- H02H7/00—Emergency protective circuit arrangements specially adapted for specific types of electric machines or apparatus or for sectionalised protection of cable or line systems, and effecting automatic switching in the event of an undesired change from normal working conditions
- H02H7/26—Sectionalised protection of cable or line systems, e.g. for disconnecting a section on which a short-circuit, earth fault, or arc discharge has occured
- H02H7/261—Sectionalised protection of cable or line systems, e.g. for disconnecting a section on which a short-circuit, earth fault, or arc discharge has occured involving signal transmission between at least two stations
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02H—EMERGENCY PROTECTIVE CIRCUIT ARRANGEMENTS
- H02H1/00—Details of emergency protective circuit arrangements
- H02H1/0007—Details of emergency protective circuit arrangements concerning the detecting means
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02H—EMERGENCY PROTECTIVE CIRCUIT ARRANGEMENTS
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Abstract
The invention provides a distribution network differential protection system and method based on 5G communication, which relate to the technical field of relay protection and comprise the following steps: the data acquisition module is used for acquiring power parameters at two ends of the N groups of distribution network lines, wherein the power parameters comprise differential current data DL and differential voltage data DY; the data preprocessing module is used for processing the acquired N groups of differential current data DL and N groups of differential voltage data DY. According to the invention, the differential current data DL and the differential voltage data DY at two ends of the line are collected to generate the comprehensive index ZS of the distribution network line, and the fault risk level model is constructed, so that the power grid can be judged to be in an abnormal operation state at the moment, the level of the power grid fault can be directly judged, the level of the power grid fault is processed in a targeted manner, the problem that all power grid faults are subjected to power failure processing is avoided, the reliability of power supply of the power grid is ensured, and the power consumption experience of a user is improved.
Description
Technical Field
The invention relates to the technical field of relay protection, in particular to a distribution network differential protection system and method based on 5G communication.
Background
The distribution network differential protection system based on 5G communication is essentially to apply 5G communication technology to differential protection of a power system. The 5G communication technology has the characteristics of high speed, large bandwidth, low delay and multiple connections, and becomes an effective tool for realizing real-time, rapid and accurate differential protection of the power distribution network. Modern power grid protection systems are increasingly dependent on advanced communication technologies, especially in terms of smart grid and distribution automation. The 5G communication technology can optimize the data acquisition and transmission process of the power distribution network, providing higher data transmission rate and lower data delay. This is critical to achieving differential protection in real time. In particular, 5G communication can transmit current and voltage information across the line in real time to a differential protection device that will calculate differential current and voltage and trigger a protection action when an anomaly is detected, isolating the faulty section to prevent further expansion of the fault.
In the prior art, the 5G communication-based distribution network differential protection equipment and method provided by the publication number CN115621974A comprises a timing module, a 5G communication module, an identification module, an information acquisition module, an information processing module and a protection module; the 5G communication module is used for communication among the modules; the identification module is used for identifying equipment information on two sides of the distribution network; the information acquisition module is used for timing and sampling power grid information by the timing module; the information processing module is used for processing according to the information acquired by the information acquisition module; the protection module is used for judging according to the information processed by the information processing module, and carrying out power-off processing and alarming when the power grid is judged to be in an abnormal operation state. The invention adopts 5G communication, reduces the resource consumption, overcomes the defect of time delay jitter in 5G communication based on a current correlation coefficient algorithm, solves the problems of misoperation and refusal operation accidents easily caused by traditional protection setting, and ensures the reliability of distribution network protection.
But there are also the following disadvantages: according to the statement, the power grid can be judged to be in an abnormal operation state only according to the collected information, the grade of the power grid fault is not judged directly, corresponding strategies are executed according to the grade of the fault, power-off processing is carried out on all faults, one cut is carried out, the power consumption of a user is seriously affected, the reliability of power supply of the power grid is reduced, and the grade of the power grid fault and the processing strategy aiming at the grade of the power grid fault are not distinguished.
The above information disclosed in the background section is only for enhancement of understanding of the background of the disclosure and therefore it may include information that does not form the prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
The invention aims to provide a distribution network differential protection system and method based on 5G communication, which are used for solving the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions:
A 5G communication based distribution network differential protection system comprising:
The data acquisition module is used for acquiring power parameters at two ends of the N groups of distribution network lines, wherein the power parameters comprise differential current data DL and differential voltage data DY;
The preprocessing module is used for processing the acquired N groups of differential current data DL and the N groups of differential voltage data DY to generate an average number of the differential current data DL and an average number of the differential voltage data DY;
The data processing module is used for carrying out dimensionless processing on the average of the differential current data DL and the average of the differential voltage data DY, carrying out correlation analysis, generating a comprehensive index ZS of the distribution network line, analyzing the comprehensive index ZS of the distribution network line, generating a fault risk level model for evaluating the fault risk level of the distribution network line, and verifying the fault risk level model parameters through data interaction between a 5G network and a database to generate a fault risk level evaluation model;
the data analysis module is used for analyzing the comprehensive index ZS of the distribution network line through the fault risk level evaluation model, comparing the fault risk level coefficient with different level evaluation thresholds, judging the fault level of the distribution network line of the parameter, and sending different early warning signals according to the fault level;
And the execution module is used for executing different strategies according to the different early warning signals.
Further, the preprocessing module processes the collected N groups of differential current data DL and N groups of differential voltage data DY, where N is an integer greater than 1, and the generated set is as follows:
DL＝[DL1、DL2…DLi…DLN]
DY＝[DY1、DY2…DYi…DYN]
DL i is the data of the i-th differential current, and DY i is the data of the i-th differential voltage.
Further, the calculation formula according to which the average of the differential current data DL and the average of the differential voltage data DY are calculated is as follows:
further, the data processing module will And/>Carrying out dimensionless treatment, correlating each parameter to generate a comprehensive index ZS of the distribution network line according to the following formula:
Wherein, the parameter meaning is: alpha is the weight factor coefficient of the average number of differential currents, alpha is more than or equal to 0.2 and less than or equal to 0.4, beta is the weight factor coefficient of the average number of differential voltages, beta is more than or equal to 0.2 and less than or equal to 0.4, delta is the index factor of the average number of differential currents, delta is more than or equal to 2 and less than or equal to 4, epsilon is the index factor of the average number of differential voltages, epsilon is more than or equal to 2 and less than or equal to 4, and C1 is a constant correction coefficient.
Further, the data processing module analyzes the comprehensive index ZS of the distribution network line to generate a fault risk level model for evaluating the fault risk level of the distribution network line:
The fault risk level model comprises the following steps:
FDJPW＝γ+ρ*ZS+C2
Wherein, the parameter meaning is: ρ is the weight factor coefficient of the comprehensive index of the distribution network line, ρ is more than or equal to 0.2 and less than or equal to 0.5, FDJ PW is the fault risk level coefficient, γ is the unknown regression coefficient, also called constant term or offset, γ reflects the basic level of the fault risk level itself when the comprehensive index ZS of the distribution network line is not affected, and C2 is the constant correction coefficient.
6. The farmland weed detection system based on image recognition according to claim 5, wherein the fault risk level model parameters are verified through data interaction between the 5G network and the database, and a fault risk level evaluation model is generated:
FDJPW＝γ1+ρ*ZS+C2
wherein, gamma 1 is the verified gamma value.
Further, through data interaction between the 5G network and the database, the specific steps of verifying the fault risk level model parameters include:
The data analysis module analyzes and processes the reference differential current DLsc and the reference differential voltage DYsc to generate a reference comprehensive index ZSc of the distribution network line, brings the reference comprehensive index ZSc and the reference fault risk level FDJc into a fault risk level model, and calculates a regression coefficient gamma according to the following formula:
Further, the data analysis module compares the fault risk level model with different level evaluation thresholds, judges the fault level of the distribution network line of the parameter, sends out different early warning signals according to the fault level,
When 0.6FDJ < FDJpw is less than or equal to 0.9FDJ (FDJ is a fault risk level calibration threshold), the fault risk level model of the distribution network line is low risk, and a primary alarm is sent out;
When 0.3FDJ < FDJpw is less than or equal to 0.6FDJ, the fault risk level model of the distribution network line is a medium risk, and a secondary alarm is sent out;
when FDJpw is more than 0 and less than or equal to 0.3FDJ, the fault risk level model of the distribution network line is high risk, and three-level alarm is sent.
Further, the execution module is configured to execute different strategies according to different early warning signals, and specifically includes the following steps:
When a primary alarm is sent, preventive checking and maintenance work is required to ensure the normal operation of the circuit, so that the possibility of further upgrading the risk is reduced;
When a secondary alarm is sent, monitoring measures are required to be enhanced, more frequent inspection is performed or preventive maintenance is performed, so that the occurrence of faults is reduced;
when three-level alarms are sent out, the line fault risk is high, and immediate action is required to be taken, including emergency maintenance, power failure, fault region isolation and other emergency measures, so that the influence of faults on a power grid and a user is reduced.
A distribution network differential protection method based on 5G communication comprises the following steps:
S1, collecting power parameters at two ends of N groups of distribution network lines, wherein the power parameters comprise differential current data DL and differential voltage data DY;
s2, processing the acquired N groups of differential current data DL and N groups of differential voltage data DY to generate an average number of the differential current data DL and an average number of the differential voltage data DY;
S3, carrying out dimensionless processing on the average of the differential current data DL and the average of the differential voltage data DY, carrying out correlation analysis to generate a comprehensive index ZS of the distribution network line, analyzing the comprehensive index ZS of the distribution network line to generate a fault risk level model for evaluating the fault risk level of the distribution network line, and verifying parameters of the fault risk level model through data interaction between the 5G network and a database to generate a fault risk level evaluation model;
S4, analyzing the comprehensive index ZS of the distribution network line through a fault risk level evaluation model, generating a fault risk level coefficient, comparing the fault risk level coefficient with different level evaluation thresholds, judging the fault level of the distribution network line of the parameters, and sending different early warning signals according to the fault level;
s5, executing different strategies according to different early warning signals.
Compared with the prior art, the invention has the beneficial effects that:
The invention generates a comprehensive index ZS of the distribution network line by collecting differential current data DL and differential voltage data DY at two ends of the distribution network line, analyzes the comprehensive index ZS of the distribution network line, generates a fault risk level model for evaluating the fault risk level of the distribution network line, verifies the fault risk level model parameters through data interaction between a 5G network and a database, generates a fault risk level evaluation model, analyzes the comprehensive index ZS of the distribution network line through the fault risk level evaluation model, generates a fault risk level coefficient, compares the fault risk level coefficient with different level evaluation thresholds, judges the fault level of the distribution network line of the parameters, and sends different early warning signals according to the fault level. Therefore, by collecting differential current data DL and differential voltage data DY at two ends of a line, a comprehensive index ZS of a distribution network line is generated, and a fault risk level model is constructed, so that the power grid can be judged to be in an abnormal operation state at the moment, the level of the power grid fault can be directly judged, the level of the power grid fault is processed in a targeted manner, the problem that all power grid faults are subjected to power failure processing is avoided, the reliability of power supply of the power grid is guaranteed, and the power consumption experience of a user is improved.
Drawings
FIG. 1 is a block diagram of the modules of the present invention;
Fig. 2 is a flow chart of the method of the present invention.
Detailed Description
The present invention will be further described in detail with reference to specific embodiments in order to make the objects, technical solutions and advantages of the present invention more apparent.
It is to be noted that unless otherwise defined, technical or scientific terms used herein should be taken in a general sense as understood by one of ordinary skill in the art to which the present invention belongs. The terms "first," "second," and the like, as used herein, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "up", "down", "left", "right" and the like are used only to indicate a relative positional relationship, and when the absolute position of the object to be described is changed, the relative positional relationship may be changed accordingly.
Examples:
Referring to fig. 1 to 2, the present invention provides a technical solution:
a 5G communication-based distribution network differential protection system, as shown in fig. 1, includes:
The data acquisition module is used for acquiring power parameters at two ends of the N groups of distribution network lines, wherein the power parameters comprise differential current data DL and differential voltage data DY;
The data preprocessing module is used for processing the acquired N groups of differential current data DL and N groups of differential voltage data DY to generate an average number of the differential current data DL and an average number of the differential voltage data DY;
the data processing module is used for carrying out dimensionless processing on the average of the differential current data DL and the average of the differential voltage data DY, carrying out correlation analysis, generating a comprehensive index ZS of the distribution network line, analyzing the comprehensive index ZS of the distribution network line, generating a fault risk level model for evaluating the fault risk level of the distribution network line, verifying the fault risk level model parameters through data interaction between the 5G network and the database, and generating a fault risk level evaluation model;
The data analysis module is used for analyzing the comprehensive index ZS of the distribution network line through the fault risk level evaluation model, generating a fault risk level coefficient, comparing the fault risk level coefficient with different level evaluation thresholds, judging the fault level of the distribution network line of the parameters, and sending different early warning signals according to the fault level;
And the execution module is used for executing different strategies according to different early warning signals.
When the differential current data DL data at two ends of the distribution network line changes, the fault of the distribution network is indicated, and the following specific reasons are as follows:
line fault: the most common is a fault on the line, such as a short circuit or a ground fault. These faults can cause an imbalance in the current flow on the line, thereby causing a change in the differential current data DL.
Equipment failure: devices on the line, such as transformers, switching devices, etc., may also cause current anomalies if they fail, thereby affecting the differential current data DL.
Ground fault: a ground fault of the line may cause a change in the differential current data DL, since the ground fault may cause an imbalance of the current loop.
Therefore, it is important to measure the differential current data DL at two ends of the distribution network line through the current transformer, for example, the following effects can be achieved:
And (3) fault detection: the current transformer measures the change in differential current data DL so that the system can detect a fault on the line in time, such as a short circuit or a ground fault. This helps to improve the reliability of the system and reduce the impact of faults on the grid.
And (3) rapidly positioning a fault position: measurement of the differential current data DL can help the system locate the location of the fault quickly and accurately. By analyzing the phase and amplitude information of the differential current, a fault zone can be determined, which facilitates rapid action to repair the problem.
The protection action is as follows: the differential current data DL data measured by the current transformer is typically connected to a protection device, and upon detection of an abnormality, a corresponding protection action may be triggered, such as cutting off the fault section, preventing the fault from propagating.
Monitoring the state of the system in real time: the current transformer provides real-time monitoring of the system differential current data DL, which is helpful for operators to know the running state of the power grid. This allows predictive maintenance to be performed before problems occur, improving the usability and stability of the system.
And (3) false alarm is reduced: by accurately measuring the differential current data DL, the possibility of false alarms can be reduced, ensuring that protection actions are triggered only when a fault actually exists, avoiding unnecessary interventions and power failure.
When the differential voltage data DY at two ends of the distribution network line changes, the fault of the distribution network is indicated, and the following specific reasons are as follows:
Line fault: if a fault, such as a short circuit or a ground fault, occurs on the line, this may result in a change in the differential voltage data DY. Line faults cause unbalance in the current flow and thus affect the voltage across.
Equipment failure: devices in the distribution network, such as transformers, switching devices, etc., may cause voltage anomalies if they fail, thereby affecting the differential voltage data DY.
Therefore, it is important to measure the differential voltage data DY at two ends of the distribution network line through the voltage transformer, for example, the following effects can be produced:
and (3) fault detection: the voltage transformer measures the change of the differential voltage data DY, so that the system can timely detect faults on the line. The change in differential voltage data DY may indicate a line fault, such as a short circuit, a ground fault, etc.
And (3) rapidly positioning a fault position: by analyzing the variation of the differential voltage data DY, the system can be helped to quickly and accurately locate the position where the fault occurs. This helps to reduce the time for fault repair and improve the availability of the grid.
The protection action is as follows: the differential voltage data DY measured by the voltage transformer is typically connected to the protection device. Upon detection of an anomaly, a corresponding protective action, such as cutting off the faulty section, may be triggered, preventing the fault from expanding.
The safety of the power grid is improved: by timely detecting and processing faults, damage of the faults to power grid equipment and systems can be minimized, and the safety of a power grid is improved.
Monitoring the state of the system in real time: the voltage transformer provides real-time monitoring of system voltage, and helps operators to know the running state of the power grid. This allows predictive maintenance to be performed before problems occur, improving the usability and stability of the system.
And (3) false alarm is reduced: by accurately measuring the differential voltage data DY, the possibility of false alarm can be reduced, the protection action can be triggered only when a fault exists actually, and unnecessary power failure is avoided.
In summary, the collection of the differential current data DL and the differential voltage data DY at the two ends of the distribution network line plays an extremely important role in predicting the failure level of the distribution network line, and the following is a specific embodiment of the collection of the differential current data DL and the differential voltage data DY at the two ends of the distribution network line in this embodiment.
The data preprocessing module processes the acquired N groups of differential current data DL and N groups of differential voltage data DY, wherein N is an integer greater than 1, and the generated set is as follows:
DL＝[DL1、DL2…DLi…DLN]
DY＝[DY1、DY2…DYi…DYN]
DL i is the data of the i-th differential current, and DY i is the data of the i-th differential voltage.
The calculation formula on which the average of the differential current data DL and the average of the differential voltage data DY are based is as follows:
The data processing module will And/>Carrying out dimensionless treatment, correlating each parameter to generate a comprehensive index ZS of the distribution network line according to the following formula:
Wherein, the parameter meaning is: alpha is the weight factor coefficient of the average number of differential currents, alpha is more than or equal to 0.2 and less than or equal to 0.4, beta is the weight factor coefficient of the average number of differential voltages, beta is more than or equal to 0.2 and less than or equal to 0.4, delta is the index factor of the average number of differential currents, delta is more than or equal to 2 and less than or equal to 4, epsilon is the index factor of the average number of differential voltages, epsilon is more than or equal to 2 and less than or equal to 4, and C1 is a constant correction coefficient.
As can be seen from the above formula, whenAnd/>The higher the comprehensive index ZS of the distribution network line, the more the comprehensive index ZS of the distribution network line shows thatAnd ZS positive correlation, wherein the weight factor coefficient is used for balancing the duty ratio of each item of data in the formula, so that the accuracy of a calculation result is promoted.
The data processing module analyzes the comprehensive index ZS of the distribution network line and generates a fault risk level model for evaluating the fault risk level of the distribution network line:
The fault risk level model comprises the following steps:
FDJPW＝γ+ρ*ZS+C2
Wherein, the parameter meaning is: ρ is the weight factor coefficient of the comprehensive index of the distribution network line, ρ is more than or equal to 0.2 and less than or equal to 0.5, FDJ PW is the fault risk level coefficient, γ is the unknown regression coefficient, also called constant term or offset, γ reflects the basic level of the fault risk level itself when the comprehensive index ZS of the distribution network line is not affected, and C2 is the constant correction coefficient.
Through data interaction between the 5G network and the database, verifying fault risk level model parameters, and generating a fault risk level evaluation model:
FDJPW＝γ1+ρ*ZS+C2
wherein, gamma 1 is the verified gamma value.
The data includes known differential current DL and differential voltage DY, and through the disclosure of this implementation, the comprehensive index ZS and the fault risk level FDJpw of the known distribution network line can be determined, and the γ=γ 1 can be verified by introducing the fault risk level model, and possibly, the data of each group of verification γ is inconsistent, and the average value of multiple groups of γ can be obtained by using the formula of average.
Through the data interaction between the 5G network and the database, the specific steps for verifying the fault risk level model parameters comprise:
The data analysis module analyzes and processes the reference differential current DLsc and the reference differential voltage DYsc to generate a reference comprehensive index ZSc of the distribution network line, brings the reference comprehensive index ZSc and the reference fault risk level coefficient FDJc into a fault risk level model, and calculates a regression coefficient gamma according to the following formula:
the data analysis module compares the fault risk level coefficient with different level evaluation thresholds, judges the fault level of the distribution network line of the parameter, sends out different early warning signals according to the fault level,
When 0.6FDJ < FDJpw is less than or equal to 0.9FDJ (FDJ is a fault risk level calibration threshold), the fault risk level model of the distribution network line is low risk, and a primary alarm is sent out;
When 0.3FDJ < FDJpw is less than or equal to 0.6FDJ, the fault risk level model of the distribution network line is a medium risk, and a secondary alarm is sent out;
when FDJpw is more than 0 and less than or equal to 0.3FDJ, the fault risk level model of the distribution network line is high risk, and three-level alarm is sent.
The execution module is used for executing different strategies according to different early warning signals, and specifically comprises the following steps:
When a primary alarm is sent, preventive checking and maintenance work is required to ensure the normal operation of the circuit, so that the possibility of further upgrading the risk is reduced;
When a secondary alarm is sent, monitoring measures are required to be enhanced, more frequent inspection is performed or preventive maintenance is performed, so that the occurrence of faults is reduced;
when three-level alarms are sent out, the line fault risk is high, and immediate action is required to be taken, including emergency maintenance, power failure, fault region isolation and other emergency measures, so that the influence of faults on a power grid and a user is reduced.
It should be noted that, the high risk is higher than the medium risk, the medium risk is higher than the low risk, that is, the failure risk level of the distribution network line corresponding to the high risk is greater than the failure risk level of the distribution network line corresponding to the medium risk, and the failure risk level of the distribution network line corresponding to the medium risk is greater than the failure risk level of the distribution network line corresponding to the low risk.
A distribution network differential protection method based on 5G communication, as shown in figure 2, comprises the following steps:
S1, collecting power parameters at two ends of N groups of distribution network lines, wherein the power parameters comprise differential current data DL and differential voltage data DY;
s2, processing the acquired N groups of differential current data DL and N groups of differential voltage data DY to generate an average number of the differential current data DL and an average number of the differential voltage data DY;
S3, carrying out dimensionless processing on the average of the differential current data DL and the average of the differential voltage data DY, carrying out correlation analysis to generate a comprehensive index ZS of the distribution network line, analyzing the comprehensive index ZS of the distribution network line to generate a fault risk level model for evaluating the fault risk level of the distribution network line, and verifying parameters of the fault risk level model through data interaction between the 5G network and a database to generate a fault risk level evaluation model;
S4, analyzing the comprehensive index ZS of the distribution network line through a fault risk level evaluation model, generating a fault risk level coefficient, comparing the fault risk level coefficient with different level evaluation thresholds, judging the fault level of the distribution network line of the parameters, and sending different early warning signals according to the fault level;
s5, executing different strategies according to different early warning signals.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. Those of skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein can be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application.
Claims (10)
1. A distribution network differential protection system based on 5G communication, comprising:
The data acquisition module is used for acquiring power parameters at two ends of the N groups of distribution network lines, wherein the power parameters comprise differential current data DL and differential voltage data DY;
The preprocessing module is used for processing the acquired N groups of differential current data DL and the N groups of differential voltage data DY to generate an average number of the differential current data DL and an average number of the differential voltage data DY;
The data processing module is used for carrying out dimensionless processing on the average of the differential current data DL and the average of the differential voltage data DY, carrying out correlation analysis, generating a comprehensive index ZS of the distribution network line, analyzing the comprehensive index ZS of the distribution network line, generating a fault risk level model for evaluating the fault risk level of the distribution network line, verifying parameters of the fault risk level model through data interaction between a 5G network and a database, and generating a fault risk level evaluation model:
the data analysis module is used for analyzing the comprehensive index ZS of the distribution network line through the fault risk level evaluation model, comparing the fault risk level coefficient with different level evaluation thresholds, judging the fault level of the distribution network line of the parameter, and sending different early warning signals according to the fault level;
And the execution module is used for executing different strategies according to the different early warning signals.
2. The distribution network differential protection system based on 5G communication according to claim 1, wherein the preprocessing module processes the collected N groups of differential current data DL and N groups of differential voltage data DY, N is an integer greater than 1, and the generated set is as follows:
DL＝[DL1、DL2...DLi...DLN]
DY＝[DY1、DY2...DYi...DYN]
DL i is the data of the i-th differential current, and DY i is the data of the i-th differential voltage.
3. The farmland weed detection system based on image recognition according to claim 2, wherein the calculation formula according to which the average number of differential current data DL and the average number of differential voltage data DY are based is as follows:
4. A farmland weed detection system based on image recognition according to claim 3, wherein said data processing module is to And/>Carrying out dimensionless treatment, correlating each parameter to generate a comprehensive index ZS of the distribution network line according to the following formula:
Wherein, the parameter meaning is: alpha is the weight factor coefficient of the average number of differential currents, alpha is more than or equal to 0.2 and less than or equal to 0.4, beta is the weight factor coefficient of the average number of differential voltages, beta is more than or equal to 0.2 and less than or equal to 0.4, delta is the index factor of the average number of differential currents, delta is more than or equal to 2 and less than or equal to 4, epsilon is the index factor of the average number of differential voltages, epsilon is more than or equal to 2 and less than or equal to 4, and C1 is a constant correction coefficient.
5. The image recognition-based farmland weed detection system, according to claim 4, wherein the data processing module analyzes the comprehensive index ZS of the distribution network line to generate a fault risk level model for evaluating the fault risk level of the distribution network line:
The fault risk level model comprises the following steps:
FDJPW＝γ+ρ*ZS+C2
Wherein, the parameter meaning is: ρ is the weight factor coefficient of the comprehensive index of the distribution network line, ρ is more than or equal to 0.2 and less than or equal to 0.5, FDJ PW is the fault risk level coefficient, γ is the unknown regression coefficient, also called constant term or offset, γ reflects the basic level of the fault risk level itself when the comprehensive index ZS of the distribution network line is not affected, and C2 is the constant correction coefficient.
6. The farmland weed detection system based on image recognition according to claim 5, wherein the fault risk level model parameters are verified through data interaction between the 5G network and the database, and a fault risk level evaluation model is generated:
FDJPW＝γ1+ρ*ZS+C2
wherein, gamma 1 is the verified gamma value.
7. The image recognition-based farmland weed detection system according to claim 6, wherein the specific step of verifying the failure risk level model parameters through data interaction between the 5G network and the database comprises:
The data analysis module analyzes and processes the reference differential current DLsc and the reference differential voltage DYsc to generate a reference comprehensive index ZSc of the distribution network line, brings the reference comprehensive index ZSc and the reference fault risk level coefficient FDJc into a fault risk level model, and calculates a regression coefficient gamma according to the following formula:
8. The farmland weed detection system based on image recognition according to claim 7, wherein the data analysis module compares the failure risk level coefficient with different level evaluation thresholds, judges the failure level of the distribution network line of the parameters, sends out different early warning signals according to the failure level,
When FDJ is more than 0.6 and less than FDJpw and less than or equal to 0.9 (FDJ is a fault risk level calibration threshold), the fault risk level model of the distribution network line is low risk, and a primary alarm is sent out;
when FDJ is more than 0.3 and less than FDJpw and less than or equal to 0.6, the fault risk level model of the distribution network line is a medium risk, and a secondary alarm is sent out;
And when FD pw is more than 0 and less than or equal to 0.3FDJ, the fault risk level model of the distribution network line is high risk, and a three-level alarm is sent.
9. The farmland weed detection system based on image recognition according to claim 8, wherein the execution module is configured to execute different strategies according to different pre-warning signals, specifically as follows:
When a primary alarm is sent, preventive checking and maintenance work is required to ensure the normal operation of the circuit, so that the possibility of further upgrading the risk is reduced;
When a secondary alarm is sent, monitoring measures are required to be enhanced, more frequent inspection is performed or preventive maintenance is performed, so that the occurrence of faults is reduced;
when three-level alarms are sent out, the line fault risk is high, and immediate action is required to be taken, including emergency maintenance, power failure, fault region isolation and other emergency measures, so that the influence of faults on a power grid and a user is reduced.
10. A distribution network differential protection method based on 5G communication is characterized by comprising the following steps:
S1, collecting power parameters at two ends of N groups of distribution network lines, wherein the power parameters comprise differential current data DL and differential voltage data DY;
s2, processing the acquired N groups of differential current data DL and N groups of differential voltage data DY to generate an average number of the differential current data DL and an average number of the differential voltage data DY;
S3, carrying out dimensionless processing on the average of the differential current data DL and the average of the differential voltage data DY, carrying out correlation analysis to generate a comprehensive index ZS of the distribution network line, analyzing the comprehensive index ZS of the distribution network line to generate a fault risk level model for evaluating the fault risk level of the distribution network line, and verifying parameters of the fault risk level model through data interaction between the 5G network and a database to generate a fault risk level evaluation model;
S4, analyzing the comprehensive index ZS of the distribution network line through a fault risk level evaluation model, generating a fault risk level coefficient, comparing the fault risk level coefficient with different level evaluation thresholds, judging the fault level of the distribution network line of the parameters, and sending different early warning signals according to the fault level;
s5, executing different strategies according to different early warning signals.
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