A method for identifying abnormal discharge of a line

Abstract

The present invention relates to the field of smart grid technology, and in particular to a method for identifying line discharge anomalies, which solves the problems brought about by related technologies in the entire process of identifying line discharge defects. The present invention provides a method for identifying line discharge anomalies, comprising: analyzing a historical database and data under normal line conditions to obtain a reference time segment and a range of judgment standard values ​​for identifying discharge anomalies; when the line data meets certain conditions, finding a set of data to be determined consisting of data in matching time segments to be analyzed; analyzing the data in the set of data to be determined by a discharge anomaly trend judgment model to obtain an identification result of whether the line has a discharge defect. The beneficial effects of the present invention: the identification result of whether the line has a discharge defect is obtained by analyzing the line data, which is not subject to the limitations of the existing technology in the entire process of line defect identification, and increases the accuracy of defect warning.

Description

Technical Field

[0001] The present invention relates to the technical field of smart grids and is used to determine whether a line has insulation defects by analyzing line data. Specifically, the present invention relates to a method for identifying abnormal discharge in a line. Background Art

[0002] With the growing demand for electricity in the national economy and residents' lives, reliable and stable operation during power transmission has become a key focus in power production.

[0003] To ensure that insulation defects in transmission lines, whether caused by internal or external factors, can be identified in a timely manner before they cause failures, thereby providing early warning, current technical personnel in this field mainly rely on on-site inspections by humans or drones, using ultraviolet instruments to locate discharge points on the line. However, due to the influence of on-site terrain conditions, the experience of inspectors, and the visual concealment of discharge itself, it is difficult to timely identify weak or sporadic discharge phenomena on transmission lines. Furthermore, due to the limited monitoring range, the above monitoring method is also difficult to monitor the entire length of the line. On the other hand, technical personnel in this field also use solutions to add dedicated monitoring equipment to the line to monitor the relevant line status in real time. However, due to the limitations of the monitoring scheme itself, this solution can only identify periodic discharge phenomena that match the power frequency. Such regular discharge conditions are rare, and most discharge defects are non-periodic. Therefore, this solution can only be applied to specific needs of specific line structures. Moreover, the monitoring results are affected by the interference of environmental magnetic fields and noise, which limits the scope of application and requires the corresponding hardware, installation, commissioning, and maintenance costs. Summary of the Invention

[0004] In order to solve various problems existing in the prior art, the present invention aims to provide a method for identifying abnormal line discharge, and solve the problems brought about by the related art in the whole process of identifying line discharge defects.

[0005] To achieve the above technical objectives, a method for identifying abnormal line discharge is proposed, comprising:

[0006] The reference time segment is obtained by extracting the time points before and after the predetermined time length of the peak time point when the discharge on the line to be analyzed occurs from the historical database;

[0007] The predetermined time length is in the range of 10% to 50% of the reciprocal of the line operating frequency;

[0008] When the line data does not exceed the predetermined threshold, all data collected on the line and within the above-mentioned reference time segment are extracted, and all data within the same time segment are summed to obtain a number of judgment standard values, where the number of judgment standard values ​​is consistent with the number of time segments; the maximum and minimum values ​​of the above-mentioned judgment standard values ​​are extracted to finally obtain the judgment standard value range;

[0009] The number of the above-mentioned judgment standard values ​​and the number of the above-mentioned time segments are determined by the collection time length set when collecting data on the line.

[0010] When the line data exceeds the predetermined threshold, the time period in which the data collected on the line when the data exceeds the predetermined threshold matches the reference time period is extracted, and the data within the same time period are summed up. The summed values ​​of the maximum values ​​that are greater than the judgment standard value range are then extracted, and the sums are sorted according to the time sequence of the time periods to which they belong, thereby obtaining a set of data to be judged;

[0011] The data collected on the line that exceeds the predetermined threshold and does not match the reference time period is determined to be false trigger data, that is, irrelevant data generated by environmental magnetic field and noise interference, and is excluded;

[0012] The predetermined threshold value ranges from 10mA to 30mA.

[0013] The above data set to be determined is analyzed by the discharge abnormality trend determination model to obtain the identification result.

[0014] Optionally, the abnormal discharge trend determination model includes:

[0015] For the data in the above-mentioned data set to be determined, two data are randomly extracted from the other data except the last data; if the data with the earlier time in the two data is numerically smaller than the data with the later time, it is determined that there is a discharge abnormality in the circuit, and the result of the discharge defect in the circuit is obtained.

[0016] Optionally, the abnormal discharge trend determination model includes:

[0017] From the data in the above-mentioned data set to be determined, two data are randomly extracted; if the absolute value of the difference between the values ​​of the two data is less than or equal to a first predetermined difference, it is determined that the circuit has a discharge abnormality, and a result that the circuit has a discharge defect is obtained;

[0018] The first predetermined difference value ranges from 0 to 0.1 times the maximum value of the two data.

[0019] Optionally, the abnormal discharge trend determination model includes:

[0020] For the data in the above-mentioned data set to be determined, two data are randomly extracted; of the above-mentioned two data, the data with an earlier time is the earlier data, and the data with a later time is the later data; one data between the first data in the above-mentioned data set to be determined and the said first data, including both end points, is randomly extracted to obtain the earlier reference data; two or more consecutive data between the said second data and the last data in the above-mentioned data set to be determined, including both end points, are randomly extracted to obtain the later reference data group; if the absolute value of the difference between the corresponding data in the said later reference data group and the numerical value of the said earlier reference data is greater than the numerical value corresponding to the corresponding data in the said later reference data group in the second predetermined difference value group, it is determined that the circuit has a discharge abnormality, and a result of the circuit having a discharge defect is obtained;

[0021] The value range of the second predetermined difference value group is greater than or equal to half of the corresponding data in the post-reference data group.

[0022] Optionally, the abnormal discharge trend determination model includes:

[0023] For the data of the above-mentioned data set to be determined, two data separated by a predetermined interval are arbitrarily extracted; of the above-mentioned two data, the data closer in time is the front data, and the data closer in time is the back data; extract two or more consecutive data between the first data of the above-mentioned data set to be determined and the front data, including the two end points, which are closest to the above-mentioned front data, to obtain a continuous front reference data group; extract two or more consecutive values ​​between the above-mentioned back data and the last data in the above-mentioned data set to be determined, including the two end points, which are closest to the above-mentioned back data, to obtain a continuous back reference value group; the number of data in the above-mentioned continuous front reference data group is consistent with the number of data in the above-mentioned continuous back reference data group; if the absolute value of the difference between the values ​​of the above-mentioned continuous front reference data group and the above-mentioned continuous back reference data group when the corresponding relationship is satisfied is less than or equal to a third predetermined difference, it is determined that there is a discharge abnormality in the circuit, and a result of a discharge defect in the circuit is obtained;

[0024] The predetermined interval value ranges from one quarter to three quarters of the reciprocal of the line operating frequency;

[0025] The third predetermined difference value ranges from 0 to 0.1 times the maximum value of all data in the continuous previous reference data set and the continuous subsequent reference data set.

[0026] If the above-mentioned data set to be determined is analyzed by any mathematical model of the above-mentioned discharge abnormality trend determination model, and it is concluded that the line has a discharge defect, the line will be included in the key observation lines and further targeted measures will be implemented.

[0027] If the data in the data set to be determined after analysis do not satisfy the abnormal discharge trend determination model, the data in the data set to be determined will be stored in a historical database. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0029] Figure 1 This is a full flow chart in an embodiment of the present invention.

[0030] Figure 2 FIG. 1 is a schematic diagram of a reference time segment in an embodiment of the present invention.

[0031] Figure 3 4 is a corresponding relationship diagram of Q in a time segment in an embodiment of the present invention.

[0032] Figure 4 Schematic diagram of Q value trend change of one of the mathematical models of the abnormal discharge trend determination model in an embodiment of the present invention.

[0033] Figure 5 Schematic diagram of Q value trend change of the second mathematical model of the abnormal discharge trend determination model in an embodiment of the present invention.

[0034] Figure 6 Schematic diagram of Q value trend change of the third mathematical model of the abnormal discharge trend determination model in an embodiment of the present invention.

[0035] Figure 7 Schematic diagram of Q value trend change of the fourth mathematical model of the abnormal discharge trend determination model in an embodiment of the present invention.

[0036] Figure 8 The figure is a brief flow chart of a method for identifying abnormal line discharge in an embodiment of the present invention. DETAILED DESCRIPTION

[0037] The following describes the embodiments of the present disclosure in detail with reference to the accompanying drawings:

[0038] The following describes the implementation methods of the present disclosure through specific concrete examples. Those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific implementation methods, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present disclosure.

[0039] This embodiment can be applied to various devices, for example, single-chip microcomputer modules or devices with computing capabilities. For example, it can be applied to various devices that collect line data in real time on power transmission lines, or to devices that simply perform data analysis anywhere. Alternatively, it can be applied to devices running on various operating systems, such as professional test equipment, mobile phones, computers, and tablet computers.

[0040] The parameters selected in this embodiment will be adjusted accordingly according to different power transmission levels and different operating frequencies. Therefore, this embodiment can be applied to power transmission lines with different power transmission levels and different operating frequencies.

[0041] like Figure 1 A method for identifying abnormal discharge of a line is shown, comprising:

[0042] The reference time segment is obtained by extracting the time points before and after the predetermined time length of the peak time point when the discharge occurs on the line to be analyzed from the historical database, such as Figure 2 The time period shown is [t1, t2];

[0043] The range of the above-mentioned predetermined time length is related to the line operating frequency f 运行频率 , and the line voltage level, the value range is 0.1×1 / f 运行频率 to 0.5×1 / f 运行频率 For example, when the line operating frequency is 50 Hz, the predetermined time length ranges from 2 ms to 10 ms. Since the present invention analyzes the data of each time segment individually, that is, the data within the half cycle of the line electrical parameter waveform is analyzed individually, the present invention is applicable to transmission lines operating at different operating frequencies.

[0044] When the line data does not exceed the predetermined threshold, all data collected on the line and within the reference time segment are extracted, and all data within the same time segment are summed up to obtain several judgment standard values, where the number of judgment standard values ​​is consistent with the number of time segments. The corresponding relationship of Q in a time segment is as follows: Figure 3 As shown; extract the maximum and minimum values ​​of the above-mentioned judgment standard values, and finally obtain the judgment standard value range Q 标 , satisfying the formula:

[0045] Q 标 ∈[a, b],

[0046] in,

[0047] a is the minimum value of several judgment standard values ​​summed up in the above several time periods,

[0048] b is the maximum value of several judgment standard values ​​summed up in the above several time periods;

[0049] The maximum number of data collected in a time period i max It is derived from the following formula:

[0050] i max =(t2-t1)f-1;

[0051] in,

[0052] t1 and t2 are the time points at the front and back ends of a time segment.

[0053] f is the data sampling frequency;

[0054] The number of the above-mentioned judgment standard values ​​and the number of the above-mentioned time segments are determined by the acquisition time length T set when collecting data on the line, and satisfy the formula:

[0055] N=T / (1 / f 运行频率 );

[0056] in,

[0057] N is the number of judgment standard values;

[0058] T is the collection time length set when collecting data on the line;

[0059] f 运行频率 is the line operating frequency;

[0060] When the line data exceeds the predetermined threshold, extract the time period in which the data collected on the line when the data exceeds the predetermined threshold matches the reference time period, sum the data within the same time period, extract the maximum sum value that is greater than the judgment standard value range, and sort the sum values ​​according to the time sequence of the time periods to which they belong, to obtain a data set to be judged;

[0061] The data collected on the line that exceeds the predetermined threshold and does not match the reference time period is determined to be false trigger data, that is, irrelevant data generated by environmental magnetic field and noise interference, and is excluded;

[0062] The predetermined threshold value ranges from 10mA to 30mA. As the voltage level of the transmission line increases, the value will gradually increase, but generally remains within the above range.

[0063] The above data set to be determined is analyzed by the discharge abnormality trend determination model to obtain the identification result.

[0064] The aforementioned data set to be determined is composed of the summed values ​​of data from several time periods. The application of the present invention is not affected by whether the discharge pattern is periodic or not. Therefore, the present invention is applicable not only to periodic and non-periodic discharge patterns; it can filter suspected discharge time periods using the aforementioned reference time periods and then perform trend analysis on the filtered data.

[0065] The aforementioned abnormal discharge trend determination model is a mathematical analysis model. This embodiment lists four examples. Their purpose is to perform trend analysis on data extracted using a specific method to determine whether a circuit has a discharge defect. Therefore, the scope of patent protection for the aforementioned identification method is not limited to the aforementioned examples and should be extended to other mathematical models for trend analysis.

[0066] One of the mathematical models of the above-mentioned discharge abnormality identification model is as follows:

[0067] For the data in the above data set to be determined, arbitrarily extract two data Q from the data other than the last data 2l , Q 2m ; If the formula is satisfied:

[0068] Q 2l 2m ,(2l<2m<2i);

[0069] in,

[0070] 2i is the label of the last data in this group of data;

[0071] The Q value trend changes as follows​ Figure 4 As shown, it is determined that there is a discharge abnormality in the circuit, and a result is obtained that there is a discharge defect in the circuit;

[0072] The second mathematical model of the above-mentioned abnormal discharge recognition model is as follows:

[0073] For the data of the above data set to be determined, arbitrarily extract two data Q 2l , Q 2m ; If the formula is satisfied:

[0074] |Q 2l -Q 2m |≤c,(2l<2m≤2i);

[0075] in,

[0076] 2i is the label of the last data in this group of data;

[0077] The value range of c is 0≤c≤0.1max(Q 2l ,Q 2m );

[0078] The Q value trend changes as follows Figure 5 As shown, it is determined that there is a discharge abnormality in the circuit, and it is concluded that there is a discharge defect in the circuit.

[0079] The third mathematical model of the above-mentioned abnormal discharge recognition model is as follows:

[0080] For the data of the above data set to be determined, arbitrarily extract two data Q 2l , Q 2m , the data earlier in time is the previous data Q 2l , the data at the later time is the later data Q 2m ; Arbitrarily extract the first data Q in the above data set to be determined 21 Previous data Q 2l The data between the two end points is used to obtain the previous reference data Q 2a ; Arbitrarily extract the above-mentioned data set to be determined Q 2m To the last data Q 2i The continuous posterior benchmark data set [Q 2b ,Q2b′…]; if the formula is satisfied:

[0081] |Q 2b -Q 2a |≥d,|Q 2b′ -Q 2a |≥d′,…(2a<2l<2m<2b<2b′<…≤ 2i);

[0082] in,

[0083] 2i is the label of the last data in this group of data;

[0084] The value range of d, d′… is d≥0.5Q 2b ,d′≥0.5Q 2b′ ,…;

[0085] The Q value trend changes as follows Figure 6 As shown, it is determined that there is a discharge abnormality in the circuit, and it is concluded that there is a discharge defect in the circuit.

[0086] The fourth mathematical model of the above-mentioned abnormal discharge recognition model is as follows:

[0087] For the data of the above-mentioned data set to be determined, two data Q separated by a predetermined interval value t are randomly extracted. 2x , Q 2y , the data earlier in time is the previous data Q 2x The data at the later time is the later data Q 2y ; Arbitrarily extract the first data Q of the above-mentioned data set to be determined 21 Previous data Q 2x The data Q closest to the two end points is included 2x 2 or more consecutive data are used to obtain the continuous previous benchmark data set {Q 2l ,Q 2m ,Q 2a ,Q 2b ,…}; arbitrarily extract the final data Q of the above-mentioned data set to be determined 2y To the last data Q 2i The data Q closest to the two end points is included 2y 2 or more consecutive data, and obtain the continuous reference value set {Q 2l′ ,Q 2m′ ,Q 2a′ ,Q 2b′ ,…}; if the formula is satisfied:

[0088] |Q 2l -Q 2l′ |≤e,|Q 2m -Q 2m′ |≤e,|Q 2a -Q 2a′ |≤e,|Q 2b -Q 2b′ |≤e,… (2l<2m<2a<2b<2l′<2m′<2a′<2b′≤2i);

[0089] in,

[0090] 2i is the label of the last data in this group of data;

[0091] The value range of e is 0≤e≤0.1max(Q 2l ,Q 2m ,Q 2a ,Q 2b ,…);

[0092] The Q value trend changes as follows Figure 7 As shown, it is determined that there is a discharge abnormality in the circuit, and a result is obtained that there is a discharge defect in the circuit;

[0093] The value of the above-mentioned predetermined interval value t is related to the line operating frequency f, and the value range is 1 / 4f 运行频率 <t<3 / 4f 运行频率 For example, when the line operating frequency is 50Hz, the t value range is 5ms. <t<15ms。

[0094] If the above-mentioned data set to be determined is analyzed by any mathematical model of the above-mentioned discharge abnormality trend determination model, and it is concluded that the line has a discharge defect, the line will be included in the key observation lines and further targeted measures will be implemented.

[0095] Furthermore, the above four mathematical models all perform mathematical trend analysis on the extracted data set that can reflect the line discharge defect, thereby determining whether the line has a discharge defect. However, the above mathematical trend analysis can also be implemented using other mathematical models. For example, three data points can be randomly extracted from the data set to be determined, and the relationship between the three data points can be analyzed to determine whether they meet the Q 2x 2y 2z ,(2x<2y<2z≤2i) mathematical model, it is still possible to analyze whether there is a discharge defect in the circuit.

[0096] If the data in the data set to be determined after analysis do not satisfy the abnormal discharge trend determination model, the data in the data set to be determined will be stored in a historical database.

[0097] ​​From the above embodiments and mathematical models, it can be seen that the present invention performs qualitative analysis based on time periods with high discharge probabilities in historical data as reference time periods. The data collected under normal circumstances that match the data in the time periods with high discharge probabilities are summed to obtain a reference standard value range for the corresponding time period. The collected triggered discharge data is filtered, and trigger values ​​that are not in the reference time period are determined to be false trigger data. The collected non-false trigger discharge data is then summed within the corresponding time period to screen out a set of data to be determined that exceeds the judgment standard value range. The identified abnormal discharge trend determination model is then used to determine the Q value change trend, thereby obtaining an identification result, providing a prompt for the line operation status, and playing a key early warning role.

[0098] Because the data analysis process of the present invention eliminates the need for on-site inspections, provides clear operational instructions, and defines a clear range of values ​​in the analysis, it eliminates the need for additional equipment. Instead, the analysis is based on data from the entire line. Furthermore, the data analysis eliminates irrelevant data generated by environmental magnetic fields and noise interference. Therefore, the present invention frees the entire process of identifying line discharge defects from the limitations of on-site inspections, personnel experience, the visual concealment of discharges themselves, monitoring range, and the overall cost of dedicated monitoring equipment. It effectively reduces the impact of environmental magnetic fields and noise interference, solves the problems associated with related technologies in identifying line discharge defects, and achieves corresponding results.

[0099] In the description of the present invention, it should be understood that the terms "middle", "length", "upper", "lower", "front", "back", "vertical", "horizontal", "inner", "outer", "radial", "circumferential", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0100] In the present invention, unless otherwise expressly specified or limited, a first feature "on" a second feature may mean that the first and second features are in direct contact, or that the first and second features are in indirect contact through an intermediary. "Multiple" means at least two, such as two or three, unless otherwise expressly specified or limited.

[0101] In the present invention, unless otherwise specified or limited, the terms "installed," "connected," "connect," "fixed," etc. should be understood in a broad sense. For example, they can refer to fixed connection, detachable connection, or integration; mechanical connection, electrical connection, or communication; direct connection or indirect connection through an intermediate medium; internal communication between two elements or interaction between two elements, unless otherwise specified. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0102] The above is only for explaining the embodiments of the present invention and is not intended to limit the present invention. For those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present invention without creative work should be included in the scope of protection of the present invention.

Claims

1. A method for identifying abnormal discharge of a line, characterized in that: include: The reference time segment is obtained by extracting the time points before and after the predetermined time length of the peak time point when the discharge on the line to be analyzed occurs from the historical database; When the line data does not exceed a predetermined threshold, all data collected on the line and within the reference time segment are extracted, all data within the same time segment are summed to obtain a plurality of judgment standard values, where the number of judgment standard values ​​is consistent with the number of time segments; the maximum and minimum values ​​of the plurality of judgment standard values ​​are extracted to obtain a judgment standard value range; When the line data exceeds the predetermined threshold, all data collected on the line in the time period when the data exceeding the predetermined threshold matches the reference time period are extracted, and after summing the data in the same time period, the summed values ​​of the maximum values ​​that are greater than the judgment standard value range are extracted, and the sums are sorted according to the time sequence of the time periods to which they belong, to obtain a data set to be judged; The data set to be determined is analyzed by using a discharge abnormality trend determination model to obtain a recognition result.

2. The method for identifying abnormal line discharge according to claim 1, characterized in that: The abnormal discharge trend determination model randomly extracts two data from the data set to be determined, excluding the last data; if the data with the earlier time period is numerically smaller than the data with the later time period, it is determined that the circuit has an abnormal discharge, and a discharge defect is obtained.

3. The method for identifying abnormal line discharge according to claim 1, characterized in that: The abnormal discharge trend determination model randomly extracts two data from the data set to be determined; if the absolute value of the difference between the values ​​of the two data is less than or equal to a first predetermined difference, it is determined that the circuit has a discharge abnormality, and a result that the circuit has a discharge defect is obtained.

4. The method for identifying abnormal line discharge according to claim 1, characterized in that: The abnormal discharge trend determination model is to arbitrarily extract two data from the data set to be determined; of the two data, the data closer in time is the front data, and the data closer in time is the back data; Arbitrarily extract one data between the first data and the front data in the data set to be determined, including both end points, to obtain the front reference data; arbitrarily extract two or more consecutive data between the rear data and the last data in the data set to be determined, including both end points, to obtain a continuous rear reference data group; if the absolute value of the difference between the corresponding data of the continuous rear reference data group and the numerical value of the front reference data is greater than the numerical value corresponding to the corresponding data of the continuous rear reference data group in the second predetermined difference group, it is determined that there is a discharge abnormality in the circuit, and a result is obtained that there is a discharge defect in the circuit.

5. The method for identifying abnormal line discharge according to claim 4, characterized in that: The discharge abnormality trend judgment model is to arbitrarily extract two data separated by a predetermined interval value in the data set to be judged; among the two data, the data earlier in time is the front data, and the data later in time is the back data; extract two or more consecutive data including the two end points closest to the front data between the first data in the data set to be judged and the front data, and obtain a continuous front reference data group; extract two or more consecutive values ​​including the two end points closest to the rear data between the rear data and the last data in the data set to be judged, and obtain a continuous rear reference value group; the number of data in the continuous front reference data group is consistent with that in the continuous rear reference data group; if the absolute value of the difference between the values ​​of the continuous front reference data group and the continuous rear reference data group under the corresponding relationship is less than or equal to the third predetermined difference, it is determined that the circuit has a discharge abnormality, and a result is obtained that the circuit has a discharge defect.

6. A method for identifying abnormal line discharge according to any one of claims 1 to 5, characterized in that: The identification method further includes storing the data in the to-be-determined data set in a history database if none of the data in the to-be-determined data set that are analyzed satisfy the abnormal discharge trend determination model.

7. The method for identifying abnormal line discharge according to claim 1, characterized in that: The data collected on the line that exceeds the predetermined threshold and does not match the reference time period is determined to be false trigger data, that is, irrelevant data generated by environmental magnetic field and noise interference, and is excluded.

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