Intelligent online line fault monitoring method and system

Abstract

The invention provides a line fault intelligent online monitoring method and system, and belongs to the technical field of power line fault monitoring. The intelligent on-line monitoring method for the line fault comprises the following steps: acquiring historical monitoring data of the line fault; analyzing and determining fault frequency data of each power line branch in a preset sample time according to the line fault historical monitoring data; screening and determining a to-be-analyzed power line branch according to the fault frequency data; based on a rule engine, calculating and determining fault measurement data of the to-be-analyzed power line branch according to the historical line fault monitoring data; through the method, the power line fault monitoring cost can be reduced.

Description

Technical Field

[0001] The present application relates to the field of power line fault monitoring, and in particular to an intelligent online monitoring method and system for line faults. Background Art

[0002] Online monitoring of line faults is an important technology in the power system, which is used to detect and diagnose the operating status of power lines in real time, so as to discover and locate faults in time, reduce the losses caused by faults, and ensure the stable operation of the power system. At present, the fault monitoring of power lines is carried out by setting up fault monitoring terminals in the power grid area, and a fault monitoring system is running in the fault monitoring terminals. Fault monitoring devices are set at the nodes of each power line branch in the power grid area. When any two fault monitoring devices show a fault, it indicates that the power line branch between the two fault monitoring devices has a fault. However, when using this method to perform online fault monitoring on circuit lines, it is necessary to perform simultaneous opening or closing operations on all fault monitoring devices in the power grid area, which greatly increases the monitoring cost of the circuit lines. Summary of the invention

[0003] In order to solve the above technical problems, the present application provides a method and system for intelligent online monitoring of line faults.

[0004] In a first aspect, the present application provides a method for intelligent online monitoring of line faults. The method comprises:

[0005] Obtain historical monitoring data of line faults;

[0006] Determine the fault frequency data of each power line branch within a preset sample time according to the line fault historical monitoring data analysis;

[0007] Screening and determining the power line branch to be analyzed according to the fault frequency data;

[0008] Based on the rule engine, the fault measurement data of the power line branch to be analyzed is calculated and determined according to the line fault history monitoring data.

[0009] Further, the rule engine-based method of calculating and determining the fault measurement data of the power line branch to be analyzed according to the line fault history monitoring data includes:

[0010] Obtaining fault frequency data of a preset number of sampling time periods of the power line branch to be analyzed according to the line fault history monitoring data;

[0011] Based on the rule engine, the fault measurement data of the power line branch to be analyzed is calculated and determined according to the fault frequency data;

[0012] The fault measurement data includes a fault interval duration; the fault interval duration is the duration between the time when the most recent fault occurs on the power line branch to be analyzed and the time when the next fault occurs.

[0013] Furthermore, the method for calculating the fault interval duration includes:

[0014]

[0015] In the formula, T represents the fault interval time, Indicates the fault frequency data of the most recent sampling period. and Respectively represent the lower and upper limits of the preset frequency range, represents the preset interval duration, z takes the minimum value of x and y, and v represents the expected frequency change speed of pre-acquisition.

[0016] Furthermore, the method for calculating the expected frequency change speed includes:

[0017]

[0018] In the formula, Indicates the average frequency change rate reflected by the fault frequency data in the nearly preset number of sampling time periods. is the speed influence coefficient.

[0019] Furthermore, the method for obtaining the average frequency change speed includes:

[0020] Assume that the preset number of sampling time periods is n, i takes the value of 1-n, representing the 1st to nth sampling time periods, and the fault frequency data of the i-th sampling time period is The time interval between any two adjacent sampling time periods is ,but , where the smaller i is, the closer it is to the current moment.

[0021] Furthermore, the calculation method of the speed influence coefficient includes:

[0022] Assume that there are m sampling time periods within the preset time period before the current moment, and the fault frequency data of the jth sampling time period is , the smaller j is, the closer it is to the current moment;

[0023] A frequency change speed is calculated based on the frequency data of each two adjacent faults. There are m-1 frequency change speeds in total, and the jth frequency change speed is , the latest frequency change speed is ,but , -1;

[0024] judge Are the signs of the same?

[0025] If so, then ,otherwise , where p is greater than 0 The number of q is not greater than 0 The number of , a and b are both preset positive real numbers.

[0026] Further, the screening and determining of the power line branch to be analyzed according to the fault frequency data includes:

[0027] Based on the preset screening rules, determining the measurement label of each power line branch according to the fault frequency data and the preset frequency threshold range data;

[0028] The preset frequency threshold range data includes a threshold range lower limit value and a threshold range upper limit value;

[0029] When the fault frequency value is less than the lower limit of the threshold range, the measurement tag of the power line branch is a safe power line branch;

[0030] When the fault frequency is greater than the upper limit of the threshold range, the measurement label of the power line branch is a dangerous power line branch;

[0031] When the fault frequency value is not greater than the upper limit value of the threshold range and not less than the lower limit value of the threshold range, the measurement tag of the power line branch is the power line branch to be analyzed.

[0032] In a second aspect, the present application provides a line fault intelligent online monitoring system, the system comprising:

[0033] An acquisition module is used to acquire historical monitoring data of line faults;

[0034] An analysis module, configured to determine the fault frequency data of each power line branch within a preset sample time according to the line fault history monitoring data;

[0035] A screening module, used for screening and determining the power line branch to be analyzed according to the fault frequency data;

[0036] The calculation module is used to calculate and determine the fault measurement data of the power line branch to be analyzed based on the rule engine and the line fault history monitoring data.

[0037] Furthermore, the calculation module is further configured to calculate and determine the fault measurement data of the power line branch to be analyzed based on the rule engine according to the line fault history monitoring data, including:

[0038] Obtaining fault frequency data of a preset number of sampling time periods of the power line branch to be analyzed according to the line fault history monitoring data;

[0039] Based on the rule engine, the fault measurement data of the power line branch to be analyzed is calculated and determined according to the fault frequency data;

[0040] The fault measurement data includes a fault interval duration; the fault interval duration is the duration between the time when the most recent fault occurs on the power line branch to be analyzed and the time when the next fault occurs.

[0041] Furthermore, the calculation module is further configured such that the method for calculating the fault interval duration includes:

[0042]

[0043] In the formula, T represents the fault interval time, Indicates the fault frequency data of the most recent sampling period. and Respectively represent the lower and upper limits of the preset frequency range, represents the preset interval duration, z takes the minimum value of x and y, and v represents the expected frequency change speed of pre-acquisition.

[0044] It should be understood that the contents described in the Summary of the Invention are not intended to limit the key or important features of the embodiments of the present application, nor are they intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0046] Figure 1 A flow chart of an intelligent online monitoring method for line faults in an embodiment of the present application is shown;

[0047] Figure 2 A block diagram of an intelligent online monitoring system for line faults in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0049] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0050] The present application provides a method and system for intelligent online monitoring of line faults, which can reduce the monitoring cost of power lines.

[0051] In a first aspect, the present application provides a method for intelligent online monitoring of line faults. Figure 1 The specific steps of the method are as follows.

[0052] Step S110: Acquire historical line fault monitoring data.

[0053] In an embodiment of the present application, fault monitoring devices are provided at both ends of the power line branch, and the line fault history monitoring data includes the historical monitoring data of the fault monitoring device, that is, the historical fault conditions of each power line branch can be reflected through the line fault history monitoring data, including the number of faults occurring in each power line branch within a preset sample time; it can be understood that the power grid area mentioned in the present application includes multiple power line branches, and the power line between every two fault monitoring devices and the power line that does not include other fault monitoring devices between the two fault monitoring devices is a power line branch.

[0054] Step S120: Analyze and determine the fault frequency data of each power line branch within a preset sample time according to the line fault history monitoring data.

[0055] It can be understood that the fault frequency data here can be obtained based on the number of faults within a preset sample time divided by the sample time length value.

[0056] Step S130: Screening and determining the power line branches to be analyzed according to the fault frequency data.

[0057] In an embodiment of the present application, determining the power line branch to be analyzed based on the fault frequency data screening specifically includes: based on a preset screening rule, determining the measurement label of each power line branch according to the fault frequency data and the preset frequency threshold range data; the preset frequency threshold range data includes a threshold range lower limit value and a threshold range upper limit value; when the fault frequency value is less than the threshold range lower limit value, the measurement label of the power line branch is a safe power line branch; when the fault frequency is greater than the threshold range upper limit value, the measurement label of the power line branch is a dangerous power line branch; when the fault frequency value is not greater than the threshold range upper limit value and not less than the threshold range lower limit value, the measurement label of the power line branch is a power line branch to be analyzed.

[0058] It can be understood that in the embodiment of the present application, each power line branch is divided into different types of power line branches by different frequencies; wherein, a safe power line branch indicates that the fault monitoring device at both ends of the branch does not need to be turned on within a preset safety time period; a dangerous power line branch indicates that the fault monitoring device at both ends of the branch needs to be turned on all the time within a preset danger time period; the preset safety time period and the preset danger time period here can be set by oneself.

[0059] Step S140: Based on the rule engine, the fault measurement data of the power line branch to be analyzed is calculated and determined according to the line fault history monitoring data.

[0060] In an embodiment of the present application, the rule-based engine-based method for calculating and determining the fault measurement data of the power line branch to be analyzed based on the line fault history monitoring data specifically includes: obtaining the fault frequency data of a preset number of sampling time periods of the power line branch to be analyzed based on the line fault history monitoring data; based on the rule engine, calculating and determining the fault measurement data of the power line branch to be analyzed based on the fault frequency data; the fault measurement data includes a fault interval duration; the fault interval duration is the duration between the time when the most recent fault of the power line branch to be analyzed occurs and the time when the next fault occurs.

[0061] Furthermore, the method for calculating the fault interval duration specifically includes:

[0062]

[0063] In the formula, T represents the fault interval time, Indicates the fault frequency data of the most recent sampling period. and Respectively represent the lower and upper limits of the preset frequency range, represents the preset interval duration, z takes the minimum value of x and y, and v represents the expected frequency change speed of pre-acquisition.

[0064] The method for calculating the expected frequency change speed specifically includes:

[0065]

[0066] In the formula, Indicates the average frequency change rate reflected by the fault frequency data in the nearly preset number of sampling time periods. is the speed influence coefficient.

[0067] The method for obtaining the average frequency change speed specifically includes: assuming that the preset number of sampling time periods is n, i takes a value of 1-n, representing the 1st to nth sampling time periods, and the fault frequency data of the i-th sampling time period is The time interval between any two adjacent sampling time periods is ,but , where the smaller i is, the closer it is to the current moment.

[0068] The calculation method of the speed influence coefficient specifically includes: assuming that there are m sampling time periods within a preset time period before the current moment, and the fault frequency data of the jth sampling time period is , the smaller j is, the closer it is to the current moment;

[0069] A frequency change speed is calculated based on the frequency data of each two adjacent faults. There are m-1 frequency change speeds in total, and the jth frequency change speed is , the latest frequency change speed is ,but , -1;

[0070] judge Are the signs of the same?

[0071] If so, then ,otherwise , where p is greater than 0 The number of q is not greater than 0 The number of , a and b are both preset positive real numbers.

[0072] It can be understood that the above method can realize the measurement of the working time of each fault monitoring device in the power grid area, thereby reducing the monitoring cost of the power grid area.

[0073] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the embodiments of the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0074] In the second aspect, the present application provides an intelligent online monitoring system for line faults. Figure 2 As shown, the system includes an acquisition module 210 for acquiring line fault history monitoring data; an analysis module 220 for determining the fault frequency data of each power line branch within a preset sample time based on the analysis of the line fault history monitoring data; a screening module 230 for screening and determining the power line branch to be analyzed based on the fault frequency data; and a calculation module 240 for calculating and determining the fault measurement data of the power line branch to be analyzed based on the line fault history monitoring data based on a rule engine.

[0075] Among them, the screening module 230 is further configured to screen and determine the power line branch to be analyzed according to the fault frequency data, including: based on the preset screening rules, determining the measurement label of each power line branch according to the fault frequency data and the preset frequency threshold range data; the preset frequency threshold range data includes a threshold range lower limit value and a threshold range upper limit value; when the fault frequency value is less than the threshold range lower limit value, the measurement label of the power line branch is a safe power line branch; when the fault frequency is greater than the threshold range upper limit value, the measurement label of the power line branch is a dangerous power line branch; when the fault frequency value is not greater than the threshold range upper limit value and not less than the threshold range lower limit value, the measurement label of the power line branch is a power line branch to be analyzed.

[0076] Wherein, the calculation module 240 is further configured as follows: based on the rule engine, the fault measurement data of the power line branch to be analyzed is calculated and determined according to the line fault history monitoring data, including: obtaining the fault frequency data of a preset number of sampling time periods of the power line branch to be analyzed according to the line fault history monitoring data; based on the rule engine, the fault measurement data of the power line branch to be analyzed is calculated and determined according to the fault frequency data; the fault measurement data includes the fault interval duration; the fault interval duration is the duration between the time when the power line branch to be analyzed last failed and the time when the next failure occurs.

[0077] The calculation module 240 is further configured to calculate the fault interval duration by:

[0078]

[0079] In the formula, T represents the fault interval time, Indicates the fault frequency data of the most recent sampling period. and Respectively represent the lower and upper limits of the preset frequency range, represents the preset interval duration, z takes the minimum value of x and y, and v represents the expected frequency change speed of pre-acquisition.

[0080] The method for calculating the expected frequency change speed specifically includes:

[0081]

[0082] In the formula, Indicates the average frequency change rate reflected by the fault frequency data in the nearly preset number of sampling time periods. is the speed influence coefficient.

[0083] The method for obtaining the average frequency change speed specifically includes: assuming that the preset number of sampling time periods is n, i takes a value of 1-n, representing the 1st to nth sampling time periods, and the fault frequency data of the i-th sampling time period is The time interval between any two adjacent sampling time periods is ,but , where the smaller i is, the closer it is to the current moment.

[0084] The calculation method of the speed influence coefficient specifically includes: assuming that there are m sampling time periods within a preset time period before the current moment, and the fault frequency data of the jth sampling time period is , the smaller j is, the closer it is to the current moment;

[0085] A frequency change speed is calculated based on the frequency data of each two adjacent faults. There are m-1 frequency change speeds in total, and the jth frequency change speed is , the latest frequency change speed is ,but , -1;

[0086] judge Are the signs of the same?

[0087] If so, then ,otherwise , where p is greater than 0 The number of q is not greater than 0 The number of , a and b are both preset positive real numbers.

[0088] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described device can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0089] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

Claims

1. A method for intelligent online monitoring of line faults, characterized in that: include: Obtain historical monitoring data of line faults; Determine the fault frequency data of each power line branch within a preset sample time according to the line fault historical monitoring data analysis; Screening and determining the power line branch to be analyzed according to the fault frequency data; Based on the rule engine, the fault measurement data of the power line branch to be analyzed is calculated and determined according to the line fault history monitoring data.

2. The intelligent online monitoring method for line faults according to claim 1 is characterized in that: The method of calculating and determining the fault measurement data of the power line branch to be analyzed based on the rule engine and the line fault history monitoring data includes: Obtaining fault frequency data of a preset number of sampling time periods of the power line branch to be analyzed according to the line fault history monitoring data; Based on the rule engine, the fault measurement data of the power line branch to be analyzed is calculated and determined according to the fault frequency data; The fault measurement data includes a fault interval duration; the fault interval duration is the duration between the time when the most recent fault of the power line branch to be analyzed occurs and the time when the next fault occurs.

3. The intelligent online monitoring method for line faults according to claim 2 is characterized in that: The method for calculating the fault interval time includes: In the formula, T represents the fault interval time, Indicates the fault frequency data of the most recent sampling period. and Respectively represent the lower and upper limits of the preset frequency range, represents the preset interval duration, z takes the minimum value of x and y, and v represents the expected frequency change speed of pre-acquisition.

4. The intelligent online monitoring method for line faults according to claim 3 is characterized in that: The method for calculating the expected frequency change speed includes: In the formula, Indicates the average frequency change rate reflected by the fault frequency data in the nearly preset number of sampling time periods. is the speed influence coefficient.

5. The intelligent online monitoring method for line faults according to claim 4 is characterized in that: The method for obtaining the average frequency change speed includes: Assume that the preset number of sampling time periods is n, i takes the value of 1-n, representing the 1st to nth sampling time periods, and the fault frequency data of the i-th sampling time period is The time interval between any two adjacent sampling time periods is ,but , where smaller i means closer to the current moment.

6. The intelligent online monitoring method for line faults according to claim 5 is characterized in that: The calculation method of the speed influence coefficient includes: Assume that there are m sampling time periods within the preset time period before the current moment, and the fault frequency data of the jth sampling time period is , the smaller j is, the closer it is to the current moment; A frequency change speed is calculated based on the frequency data of each two adjacent faults. There are m-1 frequency change speeds in total, and the jth frequency change speed is , the latest frequency change speed is ,but , -1; judge Are the signs of the same? If so, then ,otherwise , where p is greater than 0 The number of q is not greater than 0 The number of , a and b are both preset positive real numbers.

7. The intelligent online monitoring method for line faults according to claim 1, characterized in that: The step of screening and determining the power line branch to be analyzed according to the fault frequency data comprises: Based on the preset screening rules, determining the measurement label of each power line branch according to the fault frequency data and the preset frequency threshold range data; The preset frequency threshold range data includes a threshold range lower limit value and a threshold range upper limit value; When the fault frequency value is less than the lower limit of the threshold range, the measurement tag of the power line branch is a safe power line branch; When the fault frequency is greater than the upper limit of the threshold range, the measurement label of the power line branch is a dangerous power line branch; When the fault frequency value is not greater than the upper limit value of the threshold range and not less than the lower limit value of the threshold range, the measurement tag of the power line branch is the power line branch to be analyzed.

8. An intelligent online monitoring system for line faults, characterized in that: include: An acquisition module (210), used to acquire line fault historical monitoring data; An analysis module (220) is used to determine the fault frequency data of each power line branch within a preset sample time according to the line fault history monitoring data analysis; A screening module (230), configured to screen and determine a power line branch to be analyzed according to the fault frequency data; The calculation module (240) is used to calculate and determine the fault measurement data of the power line branch to be analyzed based on the rule engine and the line fault history monitoring data.

9. The intelligent online monitoring system for line faults according to claim 8, characterized in that: The calculation module (240) is further configured to calculate and determine the fault measurement data of the power line branch to be analyzed based on the rule engine according to the line fault history monitoring data, including: Obtaining fault frequency data of a preset number of sampling time periods of the power line branch to be analyzed according to the line fault history monitoring data; Based on the rule engine, the fault measurement data of the power line branch to be analyzed is calculated and determined according to the fault frequency data; The fault measurement data includes a fault interval duration; the fault interval duration is the duration between the time when the most recent fault of the power line branch to be analyzed occurs and the time when the next fault occurs.

10. The intelligent online monitoring system for line faults according to claim 9, characterized in that: The calculation module (240) is further configured such that the method for calculating the fault interval duration includes: In the formula, T represents the fault interval time, Indicates the fault frequency data of the most recent sampling period. and Respectively represent the lower and upper limits of the preset frequency range, represents the preset interval duration, z takes the minimum value of x and y, and v represents the expected frequency change speed of pre-acquisition.

Images