Smart grid line short circuit fault diagnosis method based on electrical parameters

Abstract

The present disclosure relates to the technical field of electric variable measurement, and particularly relates to a smart grid line short-circuit fault diagnosis method based on electric parameters. The method comprises: acquiring electric parameter data on the power grid line; acquiring a short-circuit fault analysis time window of the power grid line according to the electric parameter data; and performing short-circuit fault diagnosis on the power grid line within the short-circuit fault analysis time window. The embodiment of the present disclosure can adjust the target length of the short-circuit fault analysis time window of the power grid line according to a short-circuit diagnosis misjudgment coefficient, thereby reducing short-circuit fault misjudgment caused by a short-time current surge when a large equipment is started, and improving the accuracy and reliability of the short-circuit fault diagnosis of the power grid line.

Description

Technical Field

[0001] The present disclosure belongs to the technical field of electrical variable measurement, and in particular relates to a method for diagnosing short-circuit faults in a smart grid line based on electrical parameters. Background Art

[0002] The development of smart grids has revolutionized the management and operation of power systems. Electrical parameter-based fault diagnosis methods leverage the vast amount of electrical parameter data in smart grid systems, combined with advanced data analysis and machine learning techniques, to achieve automated diagnosis and rapid location of line short-circuit faults. While these methods have made some technical progress, they still face challenges, such as complex grid topologies and diverse fault types. Therefore, further research and exploration are needed, combined with practical smart grid application scenarios, to promote technological innovation and development in this field and provide more reliable support for the safe and stable operation of smart grids.

[0003] When diagnosing short-circuit faults in smart grids based on electrical parameters, the traditional method typically involves analyzing the line current for short-circuit faults. When the current exceeds a preset threshold, the system determines a short circuit has occurred. However, this method can be affected by load variations, transient faults, or other factors. In particular, sudden changes in smart grid loads (such as when large load devices start up) can cause short-term electrical parameter anomalies that mimic short circuits. This can easily lead to misjudgments during smart grid short-circuit fault analysis, compromising the accuracy and reliability of short-circuit fault detection. Summary of the Invention

[0004] In order to solve the above problems, an embodiment of the present disclosure provides a method for diagnosing short-circuit faults in a smart grid line based on electrical parameters, the method comprising:

[0005] Acquiring electrical parameter data on the power grid line; the electrical parameter data includes current data;

[0006] Obtaining a significance level of the current mutation of the power grid line based on the degree of mutation of the current data per unit time of the power grid line; obtaining a short-circuit diagnosis misjudgment coefficient of the power grid line based on the significance level of the current mutation; and obtaining a short-circuit fault analysis time window of the power grid line based on the short-circuit diagnosis misjudgment coefficient;

[0007] Performing short-circuit fault diagnosis on the power grid line within the short-circuit fault analysis time window.

[0008] Optionally, obtaining a significance level of a current mutation of the power grid line according to a mutation level of current data of the power grid line within a unit time includes:

[0009] According to the electrical parameter data, a first average current mutation degree of the power grid line in a unit time is obtained;

[0010] According to the first average current mutation degree, a current mutation significance degree of the power grid line is obtained.

[0011] Optionally, the first average current mutation degree of the power grid line in a unit time is obtained by:

[0012] According to the electrical parameter data, an extreme point current mutation degree of the power grid line is obtained;

[0013] According to the extreme point current mutation degree, a first average current mutation degree of the power grid line in a unit time is obtained.

[0014] Optionally, the extreme point current mutation degree of the power grid line is obtained by:

[0015] The average current data of each collection point in a unit time of the power grid line in the last day is obtained;

[0016] According to the average current data of each collection point, the extreme point current mutation degree of the power grid line is determined.

[0017] Optionally, the first average current mutation degree of the power grid line in a unit time is obtained according to the extreme point current mutation degree by:

[0018] A first average of all the extreme point current mutation degrees of the power grid line is obtained;

[0019] A second average of the values greater than or equal to the first average in the extreme point current mutation degrees is taken as the first average current mutation degree of the power grid line in a unit time.

[0020] Optionally, the current mutation significance degree of the power grid line is obtained according to the first average current mutation degree by:

[0021] According to the electrical parameter data, a second average current mutation degree in a fixed time period containing the unit time is obtained;

[0022] According to the first average current mutation degree and the second average current mutation degree, the current mutation significance degree of the power grid line is obtained.

[0023] Optionally, the short-circuit diagnosis misjudgment coefficient of the power grid line is obtained according to the current mutation significance degree by:

[0024] According to the electrical parameter data, a third average current mutation degree of the power grid line in the last b days is obtained;

[0025] According to the third average current mutation degree of the previous b days, the current mutation significant degree and the first average current mutation degree, a short-circuit diagnosis false judgment coefficient of the power grid line is obtained.

[0026] Optionally, the obtaining of the short-circuit diagnosis false judgment coefficient of the power grid line according to the third average current mutation degree of the previous b days, the current mutation significant degree and the first average current mutation degree comprises:

[0027] Taking any line as a target line and any time as a target time, a difference value of the current mutation degree of the target line at the target time in the latest day and the target time in the previous b days is calculated as a current mutation difference value;

[0028] The current mutation difference value is negatively correlated mapped, and a product of a result value after the negatively correlated mapping and a significant performance degree of the current mutation degree of the target line at the target time in the latest day and the target time in the previous b days is taken as a single-time false judgment value of the target line at the target time in the latest day;

[0029] A sum value of single-time false judgment values of all times in the latest day of the target line is taken as a single-day false judgment value;

[0030] An average value of single-day false judgment values of all days compared with the latest day of the target line is calculated as a short-circuit diagnosis false judgment coefficient of the power grid line.

[0031] Optionally, the obtaining of the short-circuit fault analysis time window of the power grid line according to the short-circuit diagnosis false judgment coefficient comprises:

[0032] A basic duration of the short-circuit fault analysis time window of the power grid line is obtained;

[0033] According to the basic duration and the short-circuit diagnosis false judgment coefficient, a target duration of the short-circuit fault analysis time window of the power grid line is obtained.

[0034] Optionally, the short-circuit fault diagnosis of the power grid line in the short-circuit fault analysis time window comprises:

[0035] The current of the power grid line is detected in the target duration of the short-circuit fault analysis time window;

[0036] In a case where the current of the power grid line exceeds twice the rated current, it is determined that the power grid line has a short-circuit fault.

[0037] In summary, the embodiment of the present disclosure provides a smart grid line short-circuit fault diagnosis method based on electrical parameters, which comprises: acquiring electrical parameter data on the power grid line; acquiring a short-circuit fault analysis time window of the power grid line according to the electrical parameter data; and performing short-circuit fault diagnosis on the power grid line within the short-circuit fault analysis time window. The embodiment of the present disclosure can adjust the target length of the short-circuit fault analysis time window of the power grid line according to the short-circuit diagnosis misjudgment coefficient, thereby reducing the short-circuit fault misjudgment caused by the sudden increase of short-time current when large equipment starts, and improving the accuracy and reliability of the short-circuit fault diagnosis of the power grid line. BRIEF DESCRIPTION OF DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present disclosure, the drawings required to be used in the embodiments will be briefly introduced below. It should be understood that the drawings only show some of the embodiments of the present disclosure, and therefore should not be considered as limiting the scope. Other related drawings can also be obtained by the drawings without creative labor.

[0039] Figure 1 FIG. 1 is a flowchart of a smart grid line short-circuit fault diagnosis method based on electrical parameters according to an exemplary embodiment.

[0040] Figure 2 FIG. 2 is a flowchart of a method for acquiring a short-circuit fault analysis time window of a power grid line according to an exemplary embodiment.

[0041] Figure 3 FIG. 3 is a flowchart of a method for acquiring a short-circuit diagnosis misjudgment coefficient of a power grid line according to an exemplary embodiment.

[0042] Figure 4 FIG. 4 is a flowchart of a method for acquiring a first average current mutation degree in a unit time of a power grid line according to an exemplary embodiment.

[0043] Figure 5 FIG. 5 is a flowchart of a method for acquiring an extreme point current mutation degree of a power grid line according to an exemplary embodiment.

[0044] Figure 6 FIG. 6 is a flowchart of another method for acquiring a first average current mutation degree in a unit time of a power grid line according to an exemplary embodiment.

[0045] Figure 7 FIG. 7 is a flowchart of a method for acquiring a current mutation significance degree of a power grid line according to an exemplary embodiment.

[0046] Figure 8is a flowchart of yet another method of obtaining a short-circuit misdiagnosis coefficient for a power grid line according to an example embodiment.

[0047] Figure 9 is a flowchart of yet another method of obtaining a short-circuit fault analysis time window for a power grid line according to an example embodiment.

[0048] Figure 10 is a flowchart of a method of performing a short-circuit fault diagnosis for a power grid line within a short-circuit fault analysis time window according to an example embodiment. DETAILED DESCRIPTION

[0049] In order to clearly illustrate the technical features of the scheme, specific implementation manners will be described below in conjunction with the drawings.

[0050] Embodiments of the present disclosure will be described below in detail with reference to the accompanying drawings. Although some embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms, and should not be interpreted as being limited to the embodiments set forth herein, but rather, these embodiments are provided to more thoroughly and completely understand the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for exemplary purposes only, and are not intended to limit the scope of protection of the present disclosure.

[0051] It should be understood that each of the steps recited in the method embodiments of the present disclosure can be performed in different orders and / or in parallel. In addition, the method embodiments can include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0052] The term "comprising" and variations thereof as used herein are open-ended, that is, "comprising but not limited to." The term "based on" means "based, at least in part, on." The term "one embodiment" means "at least one embodiment." The term "another embodiment" means "at least one additional embodiment." The term "some embodiments" means "at least some embodiments." Related definitions are given below in the description of the other terms.

[0053] It should be noted that the concepts of "first", "second", etc. mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not intended to limit the order or interdependence of the functions performed by these devices, modules or units.

[0054] It should be noted that the modification of "one", "multiple" mentioned in the present disclosure is illustrative but not restrictive, and those skilled in the art should understand that "one" or "multiple" should be understood as "one or more" unless otherwise explicitly indicated in the context. In the description of the present disclosure, "multiple" refers to two or more than two, and other quantifiers are similar; "at least one", "one or more" or similar expressions refer to any combination of these items, including any combination of single or multiple items. For example, at least one a can represent any number of a; for example, one or more of a, b and c can represent: a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, c can be single or multiple; "and / or" is a description of the association relationship between the associated objects, which means that there can be three relationships, for example, A and / or B, which means that there are three cases: A exists alone, A and B exist together, and B exists alone, where A and B can be singular or plural.

[0055] Although the operations or steps are described in a specific order in the accompanying drawings in the embodiments of the present disclosure, it should not be understood as requiring the operations or steps to be performed in the specific order or serial order shown, or requiring all the operations or steps to be performed to obtain the desired results. In the embodiments of the present disclosure, the operations or steps can be performed in series; the operations or steps can also be performed in parallel; or a part of the operations or steps can be performed.

[0056] At the same time, it can be understood that the data involved in the technical solution (including but not limited to data itself, acquisition or use of data) should comply with the requirements of relevant laws, regulations and relevant provisions. The present disclosure will be described below in conjunction with specific embodiments.

[0057] Figure 1 is a flow chart of an intelligent power grid line short circuit fault diagnosis method based on electrical parameters according to an exemplary embodiment. As shown in Figure 1 The present disclosure provides an intelligent power grid line short circuit fault diagnosis method based on electrical parameters, which can include the following steps:

[0058] In step S1, the electrical parameter data on the power grid line is acquired.

[0059] In this step, the electrical parameter data on the power grid line is acquired. Exemplarily, the electrical parameter data can include current data or voltage data.

[0060] In step S2, a current mutation significance of the power grid line is obtained according to a mutation degree of current data of the power grid line in a unit time, a short-circuit diagnosis misjudgment coefficient of the power grid line is obtained according to the current mutation significance, and a short-circuit fault analysis time window of the power grid line is obtained according to the short-circuit diagnosis misjudgment coefficient.

[0061] In this step, a short-circuit fault analysis time window of the power grid line is obtained according to the electrical parameter data. For example, a short-circuit diagnosis misjudgment coefficient of the power grid line can be obtained according to the electrical parameter data, and a short-circuit fault analysis time window of the power grid line can be obtained according to the short-circuit diagnosis misjudgment coefficient. The short-circuit diagnosis misjudgment coefficient is caused by the sudden increase of circuit load due to the start of a large-power large-scale device in the power grid line, the sudden increase of current in the line, and the short-circuit misjudgment.

[0062] In step S3, short-circuit fault diagnosis is performed on the power grid line in the short-circuit fault analysis time window.

[0063] In this step, short-circuit fault diagnosis is performed on the power grid line in the short-circuit fault analysis time window. For example, the current of the power grid line can be detected in the target time length of the short-circuit fault analysis time window, and it is determined that the power grid line has a short-circuit fault when the current of the power grid line exceeds twice the rated current.

[0064] In summary, the embodiment of the present disclosure provides an intelligent power grid line short-circuit fault diagnosis method based on electrical parameters, which comprises: obtaining electrical parameter data of the power grid line; obtaining a short-circuit fault analysis time window of the power grid line according to the electrical parameter data; and performing short-circuit fault diagnosis on the power grid line in the short-circuit fault analysis time window. The embodiment of the present disclosure can adjust the target time length of the short-circuit fault analysis time window of the power grid line according to the short-circuit diagnosis misjudgment coefficient, thereby reducing the short-circuit fault misjudgment caused by the sudden increase of current for a short time when a large-scale device starts, and improving the accuracy and reliability of the short-circuit fault diagnosis of the power grid line.

[0065] Figure 2 is a flowchart of a method for obtaining a short-circuit fault analysis time window of a power grid line according to an exemplary embodiment. As shown in Figure 2 obtaining a short-circuit fault analysis time window of the power grid line according to the electrical parameter data can include the following steps:

[0066] In step S21, a short-circuit diagnosis misjudgment coefficient of the power grid line is obtained according to the electrical parameter data.

[0067] In this step, the short-circuit diagnosis misjudgment coefficient of the power grid line is obtained according to the electrical parameter data. For example, the first average current mutation degree of the power grid line within a unit time is obtained according to the electrical parameter data, then the current mutation significant degree of the power grid line is obtained according to the first average current mutation degree, and then the short-circuit diagnosis misjudgment coefficient of the power grid line is obtained according to the current mutation significant degree.

[0068] In step S22, the short-circuit fault analysis time window of the power grid line is obtained according to the short-circuit diagnosis misjudgment coefficient.

[0069] In this step, the short-circuit fault analysis time window of the power grid line is obtained according to the short-circuit diagnosis misjudgment coefficient. For example, the basic duration of the short-circuit fault analysis time window of the power grid line is obtained first, and then the target duration of the short-circuit fault analysis time window of the power grid line is obtained according to the basic duration and the short-circuit diagnosis misjudgment coefficient.

[0070] Figure 3 is a flowchart of a method for obtaining a short-circuit diagnosis misjudgment coefficient of a power grid line according to an example embodiment. As shown in Figure 3 The step of obtaining the short-circuit diagnosis misjudgment coefficient of the power grid line according to the electrical parameter data can include the following steps:

[0071] In step S211, the first average current mutation degree of the power grid line within a unit time is obtained according to the electrical parameter data.

[0072] In this step, the first average current mutation degree of the power grid line within a unit time is obtained according to the electrical parameter data. For example, the extreme point current mutation degree of the power grid line is obtained according to the electrical parameter data first, and then the first average current mutation degree of the power grid line within a unit time is obtained according to the extreme point current mutation degree.

[0073] In step S212, the current mutation significant degree of the power grid line is obtained according to the first average current mutation degree.

[0074] In this step, the current mutation significant degree of the power grid line is obtained according to the first average current mutation degree. For example, the second average current mutation degree within a fixed period of time containing a unit time is obtained according to the electrical parameter data first, and then the current mutation significant degree of the power grid line is obtained according to the first average current mutation degree and the second average current mutation degree.

[0075] In step S213, the short-circuit diagnosis misjudgment coefficient of the power grid line is obtained according to the current mutation significant degree.

[0076] In this step, the short-circuit diagnosis misjudgment coefficient of the power grid line is obtained according to the current mutation significant degree. For example, the third average current mutation degree of the power grid line in the past b days can be obtained according to the electrical parameter data, and then the short-circuit diagnosis misjudgment coefficient of the power grid line can be obtained according to the third average current mutation degree in the past b days, the current mutation significant degree, and the first average current mutation degree.

[0077] Figure 4 is a flowchart of a method for obtaining the first average current mutation degree of the power grid line per unit time according to an example embodiment. As shown in Figure 4 , the method for obtaining the first average current mutation degree of the power grid line per unit time can include the following steps:

[0078] In step S2111, the extreme point current mutation degree of the power grid line is obtained according to the electrical parameter data.

[0079] In this step, the extreme point current mutation degree of the power grid line is obtained according to the electrical parameter data. For example, the average current data of each collection point per unit time of the power grid line in the most recent day can be obtained, and then the extreme point current mutation degree of the power grid line can be determined according to the average current data of each collection point.

[0080] In step S2112, the first average current mutation degree of the power grid line per unit time is obtained according to the extreme point current mutation degree.

[0081] In this step, the first average current mutation degree of the power grid line per unit time is obtained according to the extreme point current mutation degree. For example, the first average of all extreme point current mutation degrees of the power grid line can be obtained, and then the second average of the values greater than or equal to the first average in the extreme point current mutation degree can be taken as the first average current mutation degree of the power grid line per unit time.

[0082] Figure 5 is a flowchart of a method for obtaining the extreme point current mutation degree of the power grid line according to an example embodiment. As shown in Figure 5 , the method for obtaining the extreme point current mutation degree of the power grid line can include the following steps:

[0083] In step S21111, the average current data of each collection point per unit time of the power grid line in the most recent day is obtained.

[0084] In this step, the average current data of each collection point per unit time of the power grid line in the most recent day is obtained. For example, the unit time can be 1 hour.

[0085] The start of large load equipment can have different effects on different lines, but the start of large load equipment in different time periods on the same line can be relatively consistent, because the load condition, operation mode of the line and the overall load level of the system can have certain regularity or periodicity in different time periods. For example, during the high load period of the day, the start and stop of large load equipment can have more significant effects on the line, and the same line can have relatively consistent effects during the high load period. During the low load period at night, the effects can be relatively small, and the same line can have relatively consistent effects during the low load period. Although the load condition of the line can vary in different time periods, the effect of the start of large load equipment on the line can remain relatively consistent to some extent on the same line. Therefore, analysis can be carried out based on hourly current data.

[0086] For each line, the current data of each hour in the last day is obtained. Because the start and stop of large equipment can cause sudden changes in current, the extreme points of the current data in each hour are marked for subsequent analysis.

[0087] In addition, the current waveform of a line is usually obtained by integrating current data obtained from different location point sensors. Therefore, the current data in each hour is generally obtained by averaging the data, taking the average current value of each collection point in an hour as the current value of the collection point, and then obtaining the current data of the line in an hour.

[0088] In step S21112, the extreme point current mutation degree of the power grid line is determined according to the average current data of each collection point.

[0089] In this step, the extreme point current mutation degree of the power grid line is determined according to the average current data of each collection point The formula can include the following formula:

[0090] Formula 1

[0091] wherein, is the extreme point current mutation degree of the power grid line, represents the current mutation degree of the nth extreme point in the jth hour of the last day on the ith line, is the current change amount of the nth extreme point at the current unit time and the previous unit time, represents the current instantaneous change amount at the time of the extreme point, and M represents the current data of the M unit times before the nth extreme point, represents the current value of the mth unit time before the nth extreme point, and σ is the standard deviation, The standard deviation of the current values of the M unit time before the nth extreme point.

[0092] Formula 1 analyzes the nth extreme point on the i-th line at the j-th hour of the latest day, and reflects the current fluctuation mutation of the nth extreme point by comparing the time point of the extreme point with the current data in the previous time period; wherein the current fluctuation mutation of the extreme point is reflected by The current instantaneous change amount at the time point of the extreme point is reflected, and the larger the value is, the more likely the current line is to suffer a large current mutation caused by the start and stop of large equipment; by The current data is relatively consistent before the current time, because the local current in the line is relatively consistent without interference, so the more consistent the current data of the selected multiple unit time before the current time is, the smaller the corresponding standard deviation is, and the stronger the mutation of the extreme point is, that is, the more likely the impact caused by the start of large equipment is.

[0093] Figure 6 is a flowchart of another method for obtaining a first average current mutation degree in a unit time of a power grid line according to an exemplary embodiment. As Figure 6 shown, obtaining the first average current mutation degree in a unit time of the power grid line according to the extreme point current mutation degree can include the following steps:

[0094] In step S21121, a first mean value of all extreme point current mutation degrees in the power grid line is obtained.

[0095] In this step, the first mean value of all extreme point current mutation degrees in the power grid line is obtained.

[0096] In step S21122, a second mean value of the values in the extreme point current mutation degree that are greater than or equal to the first mean value is obtained as the first average current mutation degree in a unit time of the power grid line.

[0097] In this step, the second mean value of the values in the extreme point current mutation degree that are greater than or equal to the first mean value is obtained as the first average current mutation degree in a unit time of the power grid line . The first average current mutation degree represents the current mutation degree of the i-th line at the j-th hour of the latest day.

[0098] The current mutation degree of the i-th line in the j-th hour of the latest day can be obtained by formula 1. The current mutation caused by different extreme points is not entirely caused by the start and stop of large equipment, but also may be a small current mutation caused by temperature, humidity and other factors under normal circumstances. Therefore, the current mutation degree of the i-th line in the j-th hour of the latest day cannot be determined by the current mutation degree of all extreme points. Therefore, the current mutation degree of the i-th line in the j-th hour of the latest day is obtained by taking the average current mutation degree (first average) based on all extreme points as a threshold, and the current mutation degree of the extreme point greater than or equal to the threshold is screened out. The average of the current mutation degree of the screened extreme point is taken as the current mutation degree of the i-th line in the j-th hour of the latest day. Here, the first average current mutation degree .

[0099] Figure 7 is a flow chart of a method for obtaining a current mutation degree of a power grid line according to an exemplary embodiment. As Figure 7 shown, the method for obtaining the current mutation degree of the power grid line according to the first average current mutation degree can include the following steps:

[0100] In step S2121, a second average current mutation degree within a fixed time period containing the unit time is obtained according to the electrical parameter data.

[0101] In this step, the second average current mutation degree within a fixed time period N hours containing the unit time (1 hour) is obtained according to the electrical parameter data . For example, the fixed time period N hours can be N / 2 hours before and after the current time, a total of N hours (for example, N can be 4 hours). The sum of the first average current mutation degree within N hours can be averaged to obtain the second average current mutation degree .

[0102] Each line faces different load conditions at different time stages of each day, such as high load pressure during the day and low load pressure at night. The influence of the start and stop of large equipment on each line can be analyzed according to the relative consistency of the current mutation degree of each line within the fixed time period (N hours) of each hour of the latest day. The greater the relative consistency, the more likely it is that different large equipment start and stop within the fixed time period of each hour.

[0103] In step S2122, the current mutation degree of the power grid line is obtained according to the first average current mutation degree and the second average current mutation degree.

[0104] In this step, the current mutation significance of the power grid line is obtained according to the first average current mutation degree and the second average current mutation degree . Exemplarily, the current mutation significance of the power grid line may be obtained by the following formula:

[0105] Formula 2

[0106] wherein, is the current mutation significance, representing the significant performance degree of the current mutation degree of the i-th line at the j-th hour of the latest day, is the second average current mutation degree, representing the average current mutation degree based on the fixed time stage (N hours) in which the i-th line is located at the j-th hour of the latest day, is the first average current mutation degree, representing the current mutation degree of the i-th line at the j-th hour of the latest day, represents the current mutation degree of the k-th hour in the fixed time stage (N hours) in which the i-th line is located at the j-th hour of the latest day, is normalized by a normalization function.

[0107] In formula 2, the second average current mutation degree is used to reflect the influence degree of the start and stop of large equipment in the corresponding fixed time stage, The greater the value is, the more frequent the start and stop of different large equipment in the current stage are; By comparing the current mutation degree of the current time with the current mutation degree of the current time in the fixed time stage in which the current time is located, it is determined whether there is obvious difference in the fixed time stage in which the current time is located. The smaller the ratio is, the more obvious the difference between the current time and other times in the fixed time stage is, and the stronger the prominence of the current time in the fixed time stage is. Therefore, The greater the value is, the greater the influence degree of the start and stop of large equipment in the fixed time stage in which the current time is located is, The smaller the value is, the greater the influence degree of the start and stop of large equipment in the fixed time stage in which the current time is located is, and the greater the ratio is, the greater the significant degree of the current mutation degree of the current time is, that is, The greater the value is.

[0108] Figure 8 is a flowchart of another method for obtaining the short-circuit diagnosis misjudgment coefficient of the power grid line according to an exemplary embodiment. As Figure 8 ​As shown, the step of obtaining the short-circuit diagnosis misjudgment coefficient of the power grid line according to the current mutation significant degree can include the following steps:

[0109] In step S2131, the third average current mutation degree of the power grid line in the previous b days is obtained according to the electrical parameter data.

[0110] In this step, the third average current mutation degree of the power grid line in the previous b days is obtained according to the electrical parameter data . For example, the third average current mutation degree of the power grid line in the previous b days can be obtained by referring to the method for obtaining the first average current mutation degree . Details are not described herein again.

[0111] In step S2132, the short-circuit diagnosis misjudgment coefficient of the power grid line is obtained according to the third average current mutation degree of the previous b days, the current mutation significant degree, and the first average current mutation degree.

[0112] Taking any line as a target line and any time as a target time, a difference value of the current mutation degree of the target line at the target time in the previous b days and the target time in the most recent day is calculated as a current mutation difference value; a negative correlation mapping is performed on the current mutation difference value, and a product of a result value after the negative correlation mapping and a significant performance degree of the current mutation degree of the target line at the target time in the previous b days and the target time in the most recent day is taken as a single-time misjudgment value of the target line at the target time in the most recent day; a sum value of single-time misjudgment values of all times of the target line in the most recent day is taken as a single-day misjudgment value; and a mean value of single-day misjudgment values of all days compared with the most recent day of the target line is calculated as a short-circuit diagnosis misjudgment coefficient of the power grid line.

[0113] In this step, the short-circuit diagnosis misjudgment coefficient of the power grid line is obtained according to the third average current mutation degree of the previous b days , the current mutation significant degree , and the first average current mutation degree . For example, the short-circuit diagnosis misjudgment coefficient of the power grid line can be obtained by the following formula:

[0114] Formula 3

[0115] wherein, is the short-circuit diagnosis misjudgment coefficient, B represents the number of days compared with the most recent day, J represents J times (hours) of the most recent day, represents a significant performance degree of the current mutation degree of the i-th line at the j-th hour in the most recent day, ​represents the degree of current abruptness of the jth hour of the most recent day on the ith line, represents the degree of current abruptness of the jth hour of the bth day before the most recent day on the ith line, is normalized.

[0116] Formula 3 determines the short-circuit diagnosis false alarm coefficient of the current ith line by comparing and analyzing the data of the most recent day with the data of the previous B days , and is taken as the weight, and the greater it is, the more attention is paid to the comparison performance based on different days at the current time, represents the similarity of the degree of current abruptness based on different days at the current time, and the greater it is, the more consistent the influence of the short-circuit false alarm of the large equipment based on different days at the current time; finally, after comparing B days, the average value is taken as the short-circuit false alarm degree (short-circuit diagnosis false alarm coefficient ) of the ith line affected by the start and stop of the large equipment.

[0117] Figure 9 is a flowchart of another method of acquiring a short-circuit fault analysis time window of a power grid line according to an exemplary embodiment. As Figure 9 shown, acquiring the short-circuit fault analysis time window of the power grid line according to the short-circuit diagnosis false alarm coefficient can include the following steps:

[0118] In step S221, the basic duration of the short-circuit fault analysis time window of the power grid line is acquired.

[0119] In this step, the basic duration t of the short-circuit fault analysis time window of the power grid line is acquired. The basic duration t is prior data, i.e., empirical data.

[0120] In step S222, the target duration of the short-circuit fault analysis time window of the power grid line is acquired according to the basic duration and the short-circuit diagnosis false alarm coefficient.

[0121] In this step, the target duration of the short-circuit fault analysis time window of the power grid line is acquired according to the basic duration t and the short-circuit diagnosis false alarm coefficient . Exemplarily, the target duration of the short-circuit fault analysis time window of the power grid line can be obtained by the following formula:

[0122] Formula 4

[0123] wherein, is the target duration, representing the target time window length of the short-circuit fault analysis of the ith line, and t is the basic duration of the short-circuit fault analysis time window (which can be 4 seconds), ​The short-circuit diagnosis misjudgment coefficient is used to represent the misjudgment degree of the short circuit of the ith line affected by the start and stop of the large equipment.

[0124] In summary, the method for diagnosing short-circuit faults of a smart grid line based on an electrical parameter provided by the embodiments of the present disclosure includes: acquiring electrical parameter data of the grid line; acquiring a short-circuit fault analysis time window of the grid line according to the electrical parameter data; and diagnosing short-circuit faults of the grid line in the short-circuit fault analysis time window. The embodiments of the present disclosure can adjust the target length of the short-circuit fault analysis time window of the grid line according to a short-circuit diagnosis misjudgment coefficient, thereby reducing misjudgment of short-circuit faults caused by a sudden increase in short-time current when a large equipment starts, and improving the accuracy and reliability of diagnosing short-circuit faults of the grid line.

[0125] Figure 10 FIG. 1 is a flowchart of a method for diagnosing short-circuit faults of a grid line in a short-circuit fault analysis time window according to an exemplary embodiment. As shown in FIG. 1, the method for diagnosing short-circuit faults of the grid line in the short-circuit fault analysis time window can include the following steps: Figure 10

[0126] In step S31, the current of the grid line is detected in the target length of the short-circuit fault analysis time window.

[0127] In this step, the current of the grid line is detected in the target length of the short-circuit fault analysis time window.

[0128] In step S32, it is determined that the grid line has a short-circuit fault when the current of the grid line exceeds twice the rated current.

[0129] In this step, it is determined that the grid line has a short-circuit fault when the current of the grid line exceeds twice the rated current in the target length of the short-circuit fault analysis time window. It should be noted that twice is a setting according to an empirical value by the embodiments of the present disclosure, and the multiple can also be set by the implementer according to the actual situation.

[0130] The present disclosure also provides a computer-readable storage medium having computer program instructions stored thereon, the program instructions being executed by a processor to implement the steps of the method for diagnosing short-circuit faults of a smart grid line based on an electrical parameter provided by the present disclosure.

[0131] ​​​In another exemplary embodiment, there is also provided a computer program product comprising a computer program capable of being executed by a programmable electronic device, the computer program having code portions for performing the above-described intelligent power grid line short circuit fault diagnostic method based on electrical parameters when executed by the programmable electronic device.

[0132] The above-described embodiments are merely illustrative of several embodiments of the present disclosure, which are described in a more specific and detailed manner, but should not be construed as limiting the scope of the present disclosure. It should be noted that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present disclosure, and these all belong to the protection scope of the present disclosure.

Claims

1. A method for diagnosing short circuit fault of a smart grid line based on electrical parameters, characterized in that, The method comprises: Acquiring electrical parameter data on the power grid line; the electrical parameter data includes current data; Obtaining a significance level of the current mutation of the power grid line based on a mutation level of the current data per unit time of the power grid line; obtaining a short-circuit diagnosis misjudgment coefficient of the power grid line based on the significance level of the current mutation; and obtaining a short-circuit fault analysis time window of the power grid line based on the short-circuit diagnosis misjudgment coefficient; Performing short-circuit fault diagnosis on the power grid line within the short-circuit fault analysis time window; The method for obtaining the degree of significance of the current mutation is as follows: obtaining a first average degree of current mutation per unit time of the power grid line according to the electrical parameter data; and obtaining a degree of significance of the current mutation of the power grid line according to the first average degree of current mutation; Among them, the method for obtaining the short-circuit diagnosis misjudgment coefficient is: according to the electrical parameter data, obtain the third average current mutation degree of the power grid line in the previous b days; take any line as the target line and any time as the target time, calculate the difference value of the current mutation degree on the target line between the most recent day and the target time of the previous b days, as the current mutation difference value; perform negative correlation mapping on the current mutation difference value, and multiply the result value after negative correlation mapping by the significant expression degree of the current mutation degree on the target line between the most recent day and the target time of the previous b days as the single-moment misjudgment value of the target line at the target time of the most recent day; take the sum of the single-moment misjudgment values ​​of all times on the target line in the most recent day as the single-day misjudgment value; calculate the average of the single-day misjudgment values ​​of all days on the target line compared with the most recent day as the short-circuit diagnosis misjudgment coefficient of the power grid line.

2. The method of claim 1, wherein, The obtaining of a first average current mutation degree per unit time of the power grid line includes: Obtaining a degree of current mutation at an extreme point of the power grid line according to the electrical parameter data; According to the current mutation degree at the extreme point, a first average current mutation degree per unit time of the power grid line is obtained.

3. The method of claim 2, wherein, The obtaining of the degree of current mutation at the extreme point of the power grid line includes: Obtaining average current data of each collection point within a unit time of the power grid line in the most recent day; The degree of current mutation at the extreme point of the power grid line is determined based on the average current data of each collection point.

4. The method of claim 2, wherein the method further comprises: The obtaining, according to the current mutation degree at the extreme point, a first average current mutation degree per unit time of the power grid line includes: Obtaining a first average value of the current mutation degree of all the extreme value points in the power grid line; A second mean value of the extreme point current mutation degree, which is greater than or equal to the first mean value, is used as the first average current mutation degree per unit time of the power grid line.

5. The method of claim 1, wherein, The obtaining, based on the first average current mutation degree, a current mutation significance degree of the power grid line includes: Obtaining, according to the electrical parameter data, a second average current mutation degree within a fixed time period including the unit time; The significance level of the current mutation of the power grid line is obtained according to the first average current mutation level and the second average current mutation level.

6. The electric parameter based smart grid line short circuit fault diagnostic method of claim 1, wherein, The short-circuit fault analysis time window of the power grid line is obtained according to the short-circuit diagnosis misjudgment coefficient, and the short-circuit fault analysis time window of the power grid line comprises: obtaining the basic duration of the short-circuit fault analysis time window of the power grid line; obtaining the target duration of the short-circuit fault analysis time window of the power grid line according to the basic duration and the short-circuit diagnosis misjudgment coefficient.

7. The electric-parameter-based smart grid line short circuit fault diagnostic method according to claim 1, characterized by, The short-circuit fault diagnosis of the power grid line in the short-circuit fault analysis time window comprises: detecting the current of the power grid line within the target duration of the short-circuit fault analysis time window; determining that the power grid line has a short-circuit fault in the case that the current of the power grid line exceeds twice the rated current.

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