Intelligent power grid line short circuit fault diagnosis method based on electrical parameters

Abstract

The invention relates to the technical field of electrical variable measurement, in particular to a smart grid line short-circuit fault diagnosis method based on electrical parameters. The method comprises the following steps: acquiring electrical parameter data on a 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 in the short-circuit fault analysis time window. According to the embodiment of the invention, the target duration of the short-circuit fault analysis time window of the power grid line can be adjusted according to the short-circuit diagnosis misjudgment coefficient, so that short-circuit fault misjudgment caused by short-time current sudden increase when large-scale equipment is started is reduced, and the accuracy and reliability of short-circuit fault diagnosis of the power grid line are improved.

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: 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.

[0005] 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: Obtaining, according to the electrical parameter data, a first average current mutation degree per unit time of the power grid line; The significance level of the current mutation of the power grid line is obtained according to the first average current mutation level.

[0006] Optionally, obtaining 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.

[0007] Optionally, obtaining 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.

[0008] Optionally, obtaining a first average current mutation degree per unit time of the power grid line according to the current mutation degree at the extreme point 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.

[0009] Optionally, obtaining a significance level of a current mutation of the power grid line according to the first average current mutation level 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.

[0010] Optionally, obtaining a short-circuit diagnosis misjudgment coefficient of the power grid line according to the significance of the current mutation includes: Obtaining, based on the electrical parameter data, a third average current mutation degree of the power grid line over the previous b days; The short-circuit diagnosis misjudgment coefficient of the power grid line is obtained according to the third average current mutation degree, the current mutation significance degree and the first average current mutation degree in the previous b days.

[0011] Optionally, obtaining the short-circuit diagnosis misjudgment coefficient of the power grid line according to the third average current mutation degree, the current mutation significance degree, and the first average current mutation degree in the previous b days includes: Taking any line as the target line and any time as the target time, calculate the difference in the degree of current mutation on the target line between the most recent day and the target time b days before 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 at the target time on the target line on the most recent day and the target time on the previous b days as the single moment misjudgment value at the target time on the most recent day on the target line; The sum of the single-time misjudgment values ​​at all times of the most recent day on the target line is taken as the single-day misjudgment value; The average of the single-day misjudgment values ​​of all days on the target line compared with the most recent day is calculated as the short-circuit diagnosis misjudgment coefficient of the power grid line.

[0012] Optionally, obtaining the short-circuit fault analysis time window of the power grid line according to the short-circuit diagnosis misjudgment coefficient includes: Obtaining a basic duration of a short-circuit fault analysis time window for the power grid line; According to the basic duration and the short-circuit diagnosis misjudgment coefficient, a target duration of the short-circuit fault analysis time window of the power grid line is obtained.

[0013] Optionally, the performing short-circuit fault diagnosis on the power grid line within the short-circuit fault analysis time window includes: Detecting the current of the power grid line within a target duration of the short-circuit fault analysis time window; When the current of the power grid line exceeds twice the rated current, it is determined that a short circuit fault occurs on the power grid line.

[0014] In summary, the disclosed embodiments provide a method for diagnosing short-circuit faults in smart grid lines based on electrical parameters. The method comprises: obtaining electrical parameter data on the grid line; obtaining a short-circuit fault analysis time window for the grid line based on the electrical parameter data; and performing short-circuit fault diagnosis on the grid line within the short-circuit fault analysis time window. The disclosed embodiments can adjust the target duration of the short-circuit fault analysis time window for the grid line based on the short-circuit diagnosis misjudgment coefficient, thereby reducing short-circuit fault misjudgments caused by short-term current surges during the startup of large equipment and improving the accuracy and reliability of grid line short-circuit fault diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the implementation scheme of the present disclosure, the following will briefly introduce the drawings required for use in the implementation scheme. It should be understood that the drawings only show certain implementation schemes of the present disclosure and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on the drawings without paying any creative work.

[0016] Figure 1 The present invention is a flowchart showing a method for diagnosing short-circuit faults in a smart grid line based on electrical parameters according to an exemplary embodiment.

[0017] Figure 2 The present invention is a flowchart showing a method for obtaining a short-circuit fault analysis time window of a power grid line according to an exemplary embodiment.

[0018] Figure 3 The present invention is a flowchart showing a method for obtaining a short-circuit diagnosis misjudgment coefficient of a power grid line according to an exemplary embodiment.

[0019] Figure 4 The present invention is a flowchart showing a method for obtaining a first average current mutation degree per unit time of a power grid line according to an exemplary embodiment.

[0020] Figure 5 The present invention is a flowchart showing a method for obtaining the degree of current mutation at an extreme point of a power grid line according to an exemplary embodiment.

[0021] Figure 6 This is a flow chart showing another method for obtaining a first average current mutation degree per unit time of a power grid line according to an exemplary embodiment.

[0022] Figure 7 The present invention is a flowchart showing a method for obtaining the significance of a sudden change in current of a power grid line according to an exemplary embodiment.

[0023] Figure 8 The flowchart of another method for obtaining a short-circuit diagnosis misjudgment coefficient of a power grid line is shown according to an exemplary embodiment.

[0024] Figure 9 The flowchart of another method for obtaining a short-circuit fault analysis time window of a power grid line is shown according to an exemplary embodiment.

[0025] Figure 10 The present invention is a flowchart showing a method for diagnosing a short-circuit fault on a power grid line within a short-circuit fault analysis time window according to an exemplary embodiment. DETAILED DESCRIPTION

[0026] In order to clearly illustrate the technical features of this solution, the present disclosure is described in detail below through specific implementation methods and in conjunction with the accompanying drawings.

[0027] The following describes embodiments of the present disclosure in more detail with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0028] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0029] As used herein, the term "including" and its variations are open-ended, i.e., "including 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," and the term "some embodiments" means "at least some embodiments." Other terms are defined in the following description.

[0030] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0031] It should be noted that the modifiers "a" and "a plurality" mentioned in this disclosure are illustrative and non-restrictive. Those skilled in the art will understand that, unless the context clearly indicates otherwise, they should be understood as "one or more." In the description of this disclosure, unless otherwise specified, "a plurality" refers to two or more than two, and other quantifiers are used similarly. "At least one item," "one or more items," or similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, "at least one item a" can refer to any number of a's. For another example, "one or more items a, b, and c" can refer to: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural. "And / or" describes an associative relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can refer to: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural.

[0032] Although operations or steps are described in a particular order in the drawings in the embodiments of the present disclosure, this should not be understood as requiring that these operations or steps be performed in the particular order shown or in a serial order, or that all of the operations or steps shown be performed to obtain a desired result. In the embodiments of the present disclosure, these operations or steps may be performed serially; these operations or steps may also be performed in parallel; or some of these operations or steps may be performed.

[0033] At the same time, it is understood that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) must comply with the requirements of relevant laws, regulations and relevant provisions. The present disclosure is described below in conjunction with specific embodiments.

[0034] Figure 1 FIG. 1 is a flow chart showing a method for diagnosing short-circuit faults in a smart grid based on electrical parameters according to an exemplary embodiment. Figure 1 As shown, the embodiment of the present disclosure provides a method for diagnosing short-circuit faults in a smart grid line based on electrical parameters, and the method may include the following steps: In step S1, electrical parameter data on the power grid line is obtained.

[0035] In this step, electrical parameter data on the power grid line is acquired. Exemplarily, the electrical parameter data may include current data or voltage data.

[0036] In step S2, the significance of the current mutation of the power grid line is obtained according to the mutation degree of the current data of the power grid line per unit time; the short-circuit diagnosis misjudgment coefficient of the power grid line is obtained according to the significance of the current mutation; and the short-circuit fault analysis time window of the power grid line is obtained according to the short-circuit diagnosis misjudgment coefficient.

[0037] In this step, a short-circuit fault analysis time window for the power grid is obtained based on the electrical parameter data. For example, a short-circuit diagnosis misjudgment coefficient for the power grid can be obtained based on the electrical parameter data, and then the short-circuit fault analysis time window for the power grid can be obtained based on the short-circuit diagnosis misjudgment coefficient. The short-circuit diagnosis misjudgment coefficient is caused by a sudden increase in circuit load and current caused by the startup of large, high-power equipment in the power grid, which can lead to a sudden increase in circuit load and thus a misjudgment of a short circuit.

[0038] In step S3, short-circuit fault diagnosis is performed on the power grid line within the short-circuit fault analysis time window.

[0039] In this step, a short-circuit fault diagnosis is performed on the power grid within the short-circuit fault analysis time window. For example, the current of the power grid can be detected within the target duration of the short-circuit fault analysis time window, and if the current of the power grid exceeds twice the rated current, it is determined that a short-circuit fault has occurred in the power grid.

[0040] In summary, the disclosed embodiments provide a method for diagnosing short-circuit faults in smart grid lines based on electrical parameters. The method comprises: obtaining electrical parameter data on the grid line; obtaining a short-circuit fault analysis time window for the grid line based on the electrical parameter data; and performing short-circuit fault diagnosis on the grid line within the short-circuit fault analysis time window. The disclosed embodiments can adjust the target duration of the short-circuit fault analysis time window for the grid line based on the short-circuit diagnosis misjudgment coefficient, thereby reducing short-circuit fault misjudgments caused by short-term current surges during the startup of large equipment and improving the accuracy and reliability of grid line short-circuit fault diagnosis.

[0041] Figure 2 FIG. 1 is a flow chart showing a method for obtaining a short-circuit fault analysis time window of a power grid line according to an exemplary embodiment. Figure 2 As shown, obtaining the short-circuit fault analysis time window of the power grid line according to the electrical parameter data may include the following steps: In step S21, a short-circuit diagnosis misjudgment coefficient of the power grid line is obtained according to the electrical parameter data.

[0042] In this step, the short-circuit diagnosis error coefficient of the power grid is obtained based on the electrical parameter data. For example, a first average current mutation degree per unit time of the power grid can be obtained based on the electrical parameter data. The significance of the current mutation of the power grid can then be obtained based on the first average current mutation degree. Finally, the short-circuit diagnosis error coefficient of the power grid can be obtained based on the significance of the current mutation.

[0043] In step S22, a short-circuit fault analysis time window of the power grid line is obtained according to the short-circuit diagnosis misjudgment coefficient.

[0044] In this step, a short-circuit fault analysis time window for the power grid line is obtained based on the short-circuit diagnosis misjudgment coefficient. For example, a base duration of the short-circuit fault analysis time window for the power grid line can be first obtained, and then a target duration of the short-circuit fault analysis time window for the power grid line can be obtained based on the base duration and the short-circuit diagnosis misjudgment coefficient.

[0045] Figure 3 FIG. 1 is a flow chart showing a method for obtaining a short-circuit diagnosis error coefficient of a power grid line according to an exemplary embodiment. Figure 3As shown, obtaining the short-circuit diagnosis misjudgment coefficient of the power grid line according to the electrical parameter data may include the following steps: In step S211, a first average current mutation degree per unit time of the power grid line is obtained according to the electrical parameter data.

[0046] In this step, a first average current mutation degree per unit time of the power grid is obtained based on the electrical parameter data. For example, the current mutation degree at the extreme point of the power grid can be first obtained based on the electrical parameter data, and then the first average current mutation degree per unit time of the power grid can be obtained based on the current mutation degree at the extreme point.

[0047] In step S212, the current mutation significance of the power grid line is obtained according to the first average current mutation degree.

[0048] In this step, the significance of the current mutation of the power grid line is obtained based on the first average current mutation degree. For example, a second average current mutation degree within a fixed period including a unit time can be obtained based on the electrical parameter data, and then the significance of the current mutation of the power grid line can be obtained based on the first and second average current mutation degrees.

[0049] In step S213, the short-circuit diagnosis misjudgment coefficient of the power grid line is obtained according to the significance of the current mutation.

[0050] In this step, the short-circuit diagnosis error coefficient of the power grid line is obtained based on the current mutation significance. For example, the third average current mutation degree of the power grid line for the previous b days can be obtained based on the electrical parameter data. The short-circuit diagnosis error coefficient of the power grid line can then be obtained based on the third average current mutation degree, the current mutation significance, and the first average current mutation degree for the previous b days.

[0051] Figure 4 FIG. 1 is a flow chart showing a method for obtaining a first average current mutation degree per unit time of a power grid line according to an exemplary embodiment. Figure 4 As shown, obtaining the first average current mutation degree of the power grid line per unit time may include the following steps: In step S2111, the degree of current mutation at the extreme point of the power grid line is obtained according to the electrical parameter data.

[0052] In this step, the degree of current mutation at the extreme point of the power grid is obtained based on the electrical parameter data. For example, the average current data of each collection point within a unit time of the power grid for the most recent day can be obtained, and the degree of current mutation at the extreme point of the power grid can be determined based on the average current data of each collection point.

[0053] In step S2112, a first average current mutation degree per unit time of the power grid line is obtained according to the current mutation degree of the extreme point.

[0054] In this step, a first average current mutation degree per unit time of the power grid line is obtained based on the current mutation degree at the extreme value points. For example, a first average of the current mutation degrees of all extreme value points in the power grid line can be obtained, and then a second average of the current mutation degrees at the extreme value points that are greater than or equal to the first average is used as the first average current mutation degree per unit time of the power grid line.

[0055] Figure 5 FIG. 1 is a flow chart showing a method for obtaining the degree of current mutation at an extreme point of a power grid line according to an exemplary embodiment. Figure 5 As shown, obtaining the degree of current mutation at the extreme point of the power grid line may include the following steps: In step S21111, average current data of each collection point within a unit time of the power grid line in the most recent day is obtained.

[0056] In this step, the average current data of each collection point within a unit time of the power grid line in the most recent day is obtained. For example, the unit time may be 1 hour.

[0057] The startup of large-load equipment may have different impacts on different lines, but on the same line, the startup of large-load equipment in different time periods may be relatively consistent. This is because the line's load conditions, operating mode, and the overall load level of the system may change regularly or periodically in different time periods. For example, during the high-load period of the day, the startup and shutdown of large-load equipment may have a more significant impact on the line, and the impact on the same line may remain relatively consistent during the high-load period; while during the low-load period of the night, the impact may be relatively small, and the impact on the same line may remain relatively consistent during the low-load period. Although the load conditions of the line may vary in different time periods, on the same line, the impact of the startup of large-load equipment on the line may remain relatively consistent to a certain extent. Therefore, analysis can be carried out based on hourly current data.

[0058] For each line, the current data for each hour in the most recent day is obtained on an hourly basis. Because the start and stop of large equipment can cause sudden changes in current, the extreme points of the current data within each hour are marked for subsequent analysis.

[0059] To clarify, the current waveform of a line is typically derived by integrating current data collected by sensors at various locations. Therefore, hourly current data is typically obtained by averaging the hourly average current value at each collection point, taking the hourly average current value at that collection point as the current value for that collection point. This provides the line's current data for the entire hour.

[0060] In step S21112, 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.

[0061] In this step, 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. , which can include the following formula: Formula 1 in, is the degree of current mutation at the extreme point of the power grid line, which means the degree of current mutation at the nth extreme point on the i-th line within the j-th hour of the most recent day. is the current change between the current unit time and the previous unit time of the nth extreme point, indicating the instantaneous change of current at the moment of the extreme point. M represents the current data of the previous M unit times based on the nth extreme point. represents the current value at the mth unit moment before the nth extreme point, σ is the standard deviation, is the standard deviation of the current values ​​at M unit moments before the nth extreme point.

[0062] Formula 1 analyzes the nth extreme point on the i-th line at the j-th hour of the most recent day, and compares the current data at the time of the extreme point with the current data in the previous time period to reflect the sudden change of the current fluctuation at the nth extreme point; It reflects the instantaneous change of current at the moment of the extreme point. The larger the value is, the greater the possibility of the current mutation caused by the start and stop of large equipment in the line at the current moment is. This is reflected in the relatively consistent state of current data before the current moment arrives. Because the local current in the line is relatively consistent when not disturbed, the more consistent the current data of multiple unit moments selected before the current moment arrives, the smaller the corresponding standard deviation, indicating that the mutation corresponding to the extreme point is stronger, that is, the impact brought by the startup of large equipment may be greater.

[0063] Figure 6 FIG. 1 is a flow chart of another method for obtaining the first average current mutation degree per unit time of a power grid line according to an exemplary embodiment. Figure 6As shown, obtaining the first average current mutation degree per unit time of the power grid line according to the current mutation degree at the extreme point may include the following steps: In step S21121, a first average value of the degree of current mutation of all the extreme points in the power grid line is obtained.

[0064] In this step, a first mean value of the degree of current mutation at all extreme points in the power grid line is obtained.

[0065] In step S21122, a second mean value of the extreme point current mutation degree that 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.

[0066] In this step, the second mean of the extreme point current mutation degree 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. The first average current mutation degree Indicates the degree of current mutation on the i-th line within the j-th hour of the most recent day.

[0067] Formula 1 can be used to obtain the degree of current mutation fluctuation at different extreme points on the i-th line within the j-th hour of the most recent day. The current mutations generated by different extreme points are not entirely caused by the start and stop of large equipment. They may also be small current mutations caused by factors such as temperature and humidity under normal circumstances. Therefore, the determination of the degree of current mutation on the i-th line within the j-th hour of the most recent day cannot be completely determined by the degree of current mutation corresponding to all extreme points. Therefore, the average current mutation degree (first mean) of all extreme points on the i-th line within the j-th hour of the most recent day is used as a threshold, and the current mutation degree of extreme points greater than or equal to the threshold is screened out, and the average of the current mutation degrees of the screened extreme points is used as the current mutation degree on the i-th line within the j-th hour of the most recent day. Here, the first average current mutation degree can be used to determine the degree of current mutation. To express.

[0068] Figure 7 FIG. 1 is a flow chart showing a method for obtaining the degree of current mutation of a power grid line according to an exemplary embodiment. Figure 7 As shown, obtaining the current mutation significance of the power grid line according to the first average current mutation degree may include the following steps: In step S2121, a second average current mutation degree within a fixed time period including the unit time is obtained according to the electrical parameter data.

[0069] In this step, the second average current mutation degree within a fixed period of N hours including the unit time (1 hour) is obtained according to the electrical parameter data. For example, the fixed period of N hours can be N / 2 hours before and after the current moment, for a total of N hours (for example, N can be 4 hours). The first average current mutation degree can be calculated within N hours. The average of the sum of the values ​​is used to obtain the second average current mutation degree .

[0070] Given that each line faces different load conditions at different times of the day, such as high load pressure during the day and low load pressure at night, the impact of the start and stop of large equipment on the line can be analyzed based on the relative consistency of the degree of current mutation of each line within a fixed time period (N hours) every hour in the most recent day. The greater the relative consistency, the more likely it is that different large equipment starts and stops will affect it every hour within the fixed time period.

[0071] In step S2122, 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.

[0072] In this step, according to the first average current mutation degree and the second average current mutation degree , obtain the significance of the current mutation of the power grid line For example, the current of the power grid line is significantly changed. It can be obtained by the following formula: Formula 2 in, is the current mutation significance, which indicates the significance of the current mutation on the i-th line at the j-th hour of the last day. is the second average current mutation degree, which represents the average current mutation degree based on the fixed time period (within N hours) of the current i-th line in the j-th hour of the most recent day. is the first average current mutation degree, which indicates the current mutation degree of the i-th line in the j-th hour of the last day. It represents the degree of current mutation of the i-th line at the k-th hour in the fixed time period (N hours) at the j-th hour of the most recent day. It is processed by the normalization function.

[0073] In formula 2, the second average current mutation degree To reflect the degree of influence caused by the start and stop of large equipment in the corresponding fixed time period, The larger the value, the more likely it is that there are frequent starts and stops of different large-scale equipment in the current stage; By comparing the degree of current mutation at each moment in the fixed time period at the current moment with the degree of current mutation at the current moment, it is determined whether there is a significant difference at the current moment in the fixed time period. The smaller the ratio, the more significant the difference between the current moment and other moments in the fixed time period, and the more prominent the current moment is within the fixed time range. The larger the value is, the greater the impact of the start and stop of large equipment is on the fixed time period at the current moment. The smaller the value, the greater the impact of the start and stop of large equipment in the fixed time period at the current moment. The larger the ratio of the two, the greater the significance of the degree of current mutation at the current moment, that is, The bigger.

[0074] Figure 8 FIG. 1 is a flow chart of another method for obtaining a short-circuit diagnosis error coefficient of a power grid line according to an exemplary embodiment. Figure 8 As shown, obtaining the short-circuit diagnosis misjudgment coefficient of the power grid line according to the significance of the current mutation may include the following steps: 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.

[0075] In this step, the third average current mutation degree of the power grid line in the previous b days is obtained based on the electrical parameter data. For example, the third average current mutation degree of the power grid line in the previous b days is The method for obtaining the first average current mutation degree can refer to the above The method of obtaining is not described here.

[0076] 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 significance degree and the first average current mutation degree.

[0077] Taking any line as the target line and any time as the target time, the difference value of the current mutation degree on the target line between the target time of the most recent day and the target time of the previous b days is calculated as the current mutation difference value; negative correlation mapping is performed on the current mutation difference value, and the product of the result value after the negative correlation mapping and the significant expression degree of the current mutation degree on the target line between the target time of the most recent day and the target time of the previous b days is used as the single-moment misjudgment value of the target line at the target time of the most recent day; the sum of the single-moment misjudgment values ​​of all times on the target line in the most recent day is used as the single-day misjudgment value; and the average of the single-day misjudgment values ​​of all days on the target line compared with the most recent day is calculated as the short-circuit diagnosis misjudgment coefficient of the power grid line.

[0078] In this step, the third average current mutation degree of the previous b days is calculated. , the significance of current mutation and the first average current mutation degree , obtain the short-circuit diagnosis misjudgment coefficient of the power grid line For example, the short-circuit diagnosis error coefficient of the power grid line is It can be obtained by the following formula: Formula 3 in, is the short-circuit diagnosis misjudgment coefficient, B represents the number of days compared with the most recent day, and J represents the Jth moment (hour) of the most recent day. Indicates the degree of significant manifestation of the current mutation degree on the i-th line in the j-th hour of the last day, Indicates the degree of current mutation on the i-th line at the j-th hour of the last day, It represents the degree of sudden change of the current in the jth hour on the bth day before the most recent day on the i-th line. It is processed by the normalization function.

[0079] Formula 3 determines the short circuit diagnosis misjudgment coefficient of the current i-th line by comparing the data of the most recent day with the data of the previous B days. ,Will As a weight, the larger it is, the more attention is paid to the comparative performance based on different days at the current moment. It indicates the similarity of the degree of current mutation based on different days at the current moment. The larger the similarity, the more consistent the influence of short-circuit misjudgment of large equipment on the current moment. Finally, after comparing B days, the average value is taken as the degree of short-circuit misjudgment of the i-th line affected by the start and stop of large equipment (short-circuit diagnosis misjudgment coefficient ).

[0080] Figure 9 FIG. 1 is a flow chart of another method for obtaining a short-circuit fault analysis time window of a power grid line according to an exemplary embodiment. Figure 9 As shown, obtaining the short-circuit fault analysis time window of the power grid line according to the short-circuit diagnosis misjudgment coefficient may include the following steps: In step S221 , a basic duration of a short-circuit fault analysis time window of the power grid line is obtained.

[0081] In this step, a basic duration t of the short-circuit fault analysis time window of the power grid line is obtained. The basic duration t is a priori data, that is, empirical data.

[0082] In step S222, a target duration of a 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.

[0083] In this step, according to the basic time t and the short circuit diagnosis misjudgment coefficient , obtain the target duration of the short-circuit fault analysis time window of the power grid line For example, the target duration of the short-circuit fault analysis time window of the power grid line is It can be obtained by the following formula: Formula 4 in, is the target duration, which indicates the target time window length for short-circuit fault analysis of the i-th line. t is the basic duration of the short-circuit fault analysis time window (which can be 4 seconds). is the short-circuit diagnosis misjudgment coefficient, which indicates the degree of short-circuit misjudgment of the i-th line affected by the start-up and shutdown of large equipment.

[0084] In summary, the disclosed embodiments provide a method for diagnosing short-circuit faults in smart grid lines based on electrical parameters. The method comprises: obtaining electrical parameter data on the grid line; obtaining a short-circuit fault analysis time window for the grid line based on the electrical parameter data; and performing short-circuit fault diagnosis on the grid line within the short-circuit fault analysis time window. The disclosed embodiments can adjust the target duration of the short-circuit fault analysis time window for the grid line based on the short-circuit diagnosis misjudgment coefficient, thereby reducing short-circuit fault misjudgments caused by short-term current surges during the startup of large equipment and improving the accuracy and reliability of grid line short-circuit fault diagnosis.

[0085] Figure 10 FIG. 1 is a flow chart showing a method for diagnosing a short-circuit fault on a power grid line within a short-circuit fault analysis time window according to an exemplary embodiment. Figure 10 As shown, the short-circuit fault diagnosis of the power grid line within the short-circuit fault analysis time window may include the following steps: In step S31 , the current of the power grid line is detected within the target duration of the short-circuit fault analysis time window.

[0086] In this step, the target duration of the short-circuit fault analysis time window is The current of the power grid line is detected.

[0087] In step S32 , when the current of the power grid line exceeds twice the rated current, it is determined that a short circuit fault occurs in the power grid line.

[0088] In this step, the target duration of the short-circuit fault analysis time window is If the current of the power grid line exceeds twice the rated current, it is determined that a short circuit fault occurs in the power grid line. It should be noted that twice is set based on empirical values ​​in the embodiment of the present invention, and the multiple can also be set by the implementer according to actual conditions.

[0089] The present disclosure also provides a computer-readable storage medium having computer program instructions stored thereon. When the program instructions are executed by a processor, the steps of the smart grid line short circuit fault diagnosis method based on electrical parameters provided by the present disclosure are implemented.

[0090] In another exemplary embodiment, a computer program product is also provided, which includes a computer program that can be executed by a programmable electronic device, and the computer program has a code portion for executing the above-mentioned smart grid line short circuit fault diagnosis method based on electrical parameters when executed by the programmable electronic device.

[0091] The above-described embodiments merely represent several implementation methods of the present disclosure. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present disclosure. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the scope of the present disclosure, all of which fall within the scope of protection of the present disclosure.

Claims

1. A smart grid line short circuit fault diagnosis method 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.

2. The method for diagnosing short-circuit faults in a smart grid line based on electrical parameters according to claim 1, characterized in that: The obtaining, based on the degree of mutation of current data of the power grid line within a unit time, a significance degree of the current mutation of the power grid line includes: Obtaining, according to the electrical parameter data, a first average current mutation degree per unit time of the power grid line; The significance level of the current mutation of the power grid line is obtained according to the first average current mutation level.

3. The method for diagnosing short-circuit faults in a smart grid line based on electrical parameters according to claim 2, characterized in that: 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.

4. The method for diagnosing short-circuit faults in a smart grid line based on electrical parameters according to claim 3, characterized in that: 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.

5. The method for diagnosing short-circuit faults in a smart grid line based on electrical parameters according to claim 3, characterized in that: 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.

6. The method for diagnosing short-circuit faults in a smart grid line based on electrical parameters according to claim 2, characterized in that: 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.

7. The method for diagnosing short-circuit faults in a smart grid line based on electrical parameters according to claim 2, characterized in that: The obtaining of a short-circuit diagnosis misjudgment coefficient of the power grid line according to the significance of the current mutation includes: Obtaining, based on the electrical parameter data, a third average current mutation degree of the power grid line over the previous b days; The short-circuit diagnosis misjudgment coefficient of the power grid line is obtained according to the third average current mutation degree, the current mutation significance degree and the first average current mutation degree in the previous b days.

8. The method for diagnosing short-circuit faults in a smart grid line based on electrical parameters according to claim 7, characterized in that: The obtaining of the short-circuit diagnosis misjudgment coefficient of the power grid line according to the third average current mutation degree, the current mutation significance degree, and the first average current mutation degree in the previous b days includes: Taking any line as the target line and any time as the target time, calculate the difference in the degree of current mutation on the target line between the most recent day and the target time b days before 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 at the target time on the target line on the most recent day and the target time on the previous b days as the single moment misjudgment value at the target time on the most recent day on the target line; The sum of the single-time misjudgment values ​​at all times on the target line in the most recent day is taken as the single-day misjudgment value; The average of the single-day misjudgment values ​​of all days on the target line compared with the most recent day is calculated as the short-circuit diagnosis misjudgment coefficient of the power grid line.

9. The method for diagnosing short-circuit faults in a smart grid line based on electrical parameters according to claim 2, characterized in that: The obtaining of the short-circuit fault analysis time window of the power grid line according to the short-circuit diagnosis misjudgment coefficient includes: Obtaining a basic duration of a short-circuit fault analysis time window for the power grid line; According to the basic duration and the short-circuit diagnosis misjudgment coefficient, a target duration of the short-circuit fault analysis time window of the power grid line is obtained.

10. The method for diagnosing short-circuit faults in a smart grid based on electrical parameters according to claim 1, characterized in that: The performing short-circuit fault diagnosis on the power grid line within the short-circuit fault analysis time window includes: Detecting the current of the power grid line within a target duration of the short-circuit fault analysis time window; When the current of the power grid line exceeds twice the rated current, it is determined that a short circuit fault occurs on the power grid line.

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