A distribution network single-phase ground fault detection method and system of an adaptive fusion algorithm

Abstract

The application provides a distribution network single-phase grounding fault detection method and system of an adaptive fusion algorithm, relates to the technical field of distribution network grounding fault detection methods, and comprises the following steps: dynamically tracking the waveform of a target distribution network, comparing the zero-sequence voltage mutation value with the dynamic zero-sequence voltage mutation starting value, performing adaptive zero-sequence voltage mutation detection, and judging whether a zero-sequence voltage anomaly occurs; if a zero-sequence voltage anomaly occurs, tracing back to the original sampling data in the waveform, identifying the waveform mutation position from the original sampling data, demarcating a dynamic data window based on the waveform mutation position, and synchronously acquiring the alternating current channel data in the dynamic data window from the instantaneous value waveform; through an adaptive fusion algorithm, the alternating current channel data in the dynamic data window is subjected to grounding fault type identification, and the single-phase grounding fault detection result of the target distribution network is obtained. Through the dynamic zero-sequence voltage mutation starting value and the dynamic data window, the application improves the accuracy and robustness of the distribution network single-phase grounding fault detection.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of power distribution network grounding fault detection methods, and in particular to a power distribution network single-phase grounding fault detection method and system based on an adaptive fusion algorithm. BACKGROUND

[0002] For small current grounding faults, due to different neutral grounding modes and complex combined fault types, a single processing algorithm cannot meet all grounding fault judgments. According to statistics, most of the substations that supply power to the power distribution network adopt the neutral point non-grounding mode, and one-third adopt the arc suppression coil grounding mode. For the power distribution line of the neutral point non-grounding mode, since its steady-state angle can fully represent the grounding position characteristics, the steady-state power angle method is the best small current grounding algorithm. For the arc suppression coil grounding mode, a transient grounding algorithm is usually used to judge the grounding fault. Nowadays, many places propose that the power distribution network equipment should adapt to the neutral point grounding mode, and further requirements are put forward for the accuracy of the grounding fault. The original single grounding algorithm mode cannot meet the market demand. SUMMARY

[0003] In order to solve the above problems, the application provides a power distribution network single-phase grounding fault detection method and system based on an adaptive fusion algorithm, which improves the accuracy and robustness of the power distribution network single-phase grounding fault detection by using a dynamic zero sequence voltage mutation starting value and a dynamic data window.

[0004] According to some embodiments, the application adopts the following technical scheme:

[0005] A power distribution network single-phase grounding fault detection method based on an adaptive fusion algorithm, comprising:

[0006] Dynamically tracking the waveform of the target power distribution network, wherein the waveform comprises original sampling data and alternating current channel data, and the alternating current channel data comprises zero sequence voltage, phase current and zero sequence current;

[0007] Based on the zero sequence voltage, a zero sequence voltage mutation value is calculated in real time, adaptive zero sequence voltage mutation detection is performed by comparing the zero sequence voltage mutation value with a dynamic zero sequence voltage mutation starting value, and it is judged whether a zero sequence voltage anomaly occurs;

[0008] If the zero sequence voltage anomaly occurs, the original sampling data in the waveform is traced back, the waveform mutation position is identified therefrom, a dynamic data window is demarcated based on the waveform mutation position, and the alternating current channel data in the dynamic data window is synchronously acquired from the instantaneous value waveform;

[0009] Through an adaptive fusion algorithm, the alternating current channel data in the dynamic data window is identified in terms of the grounding fault type, and a single-phase grounding fault detection result of the target power distribution network is obtained.

[0010] According to some embodiments, the application adopts the following technical scheme:

[0011] A distribution network single-phase ground fault detection system of an adaptive fusion algorithm comprises:

[0012] A waveform tracking module is configured to dynamically track a waveform of a target distribution network, the waveform comprising original sampling data and alternating current channel data, and the alternating current channel data comprising zero sequence voltage, phase current and zero sequence current.

[0013] A mutation detection module is configured to calculate a zero sequence voltage mutation value in real time based on the zero sequence voltage, and perform adaptive zero sequence voltage mutation detection by comparing the zero sequence voltage mutation value with a dynamic zero sequence voltage mutation starting value, and determine whether a zero sequence voltage anomaly occurs.

[0014] A data acquisition module is configured to trace the original sampling data in the waveform when the zero sequence voltage anomaly occurs, identify a waveform mutation position therefrom, and demarcate a dynamic data window based on the waveform mutation position, and synchronously acquire alternating current channel data in the dynamic data window from the instantaneous value waveform.

[0015] A fault identification module is configured to identify a ground fault type of the alternating current channel data in the dynamic data window by an adaptive fusion algorithm, and obtain a single-phase ground fault detection result of the target distribution network.

[0016] According to some embodiments, the present application adopts the following technical solutions:

[0017] A computer program product comprises a computer program, which, when executed by a processor, implements the distribution network single-phase ground fault detection method of the adaptive fusion algorithm.

[0018] According to some embodiments, the present application adopts the following technical solutions:

[0019] A non-transitory computer readable storage medium is used to store computer instructions, which, when executed by a processor, implement the distribution network single-phase ground fault detection method of the adaptive fusion algorithm.

[0020] According to some embodiments, the present application adopts the following technical solutions:

[0021] An electronic device comprises a processor, a memory and a computer program; the processor is connected with the memory, and the computer program is stored in the memory; when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the distribution network single-phase ground fault detection method of the adaptive fusion algorithm.

[0022] Compared with the prior art, the present application has the following beneficial effects:

[0023] (1) For transient algorithm, obtaining correct fault transient waveform is the key to affect the correct identification of ground fault, and due to the imbalance degree of the line itself, the zero sequence voltage value before the occurrence of ground fault cannot be determined; the existing algorithm sets the zero sequence voltage starting mutation fixed value, which cannot meet the ground fault identification under all imbalance degrees, so the dynamic zero sequence voltage mutation starting value is adopted in the application, the ground fault detection link is adaptively started, and the ground fault identification under all imbalance degrees is met.

[0024] (2) For transient algorithm, high frequency component is an extremely important feature in transient data window, if high frequency component occupies the main body, at this time, the fixed length data window (half wave or quarter cycle) cannot accurately reflect the high frequency component feature at the fault moment, therefore, the flexible data window identification is adopted in the application, the dynamic data window length scheme is used, and the transient high frequency component interference caused by the fixed length data window is reduced.

[0025] (3) The application adopts multi-sub-algorithm fusion, the sub-algorithms include commonly used zero sequence power direction method, vector superposition power direction method, three-phase unbalanced mutation method and other sub-algorithms, the number of algorithms can be expanded in the future, and the reliable result is given by fusing each algorithm through weighted average of "reliability". BRIEF DESCRIPTION OF DRAWINGS

[0026] The accompanying drawings, which form a part of this specification, are included to provide a further understanding of the application and are incorporated herein for explanation.

[0027] Figure 1 The method flowchart of example 1. DETAILED DESCRIPTION

[0028] The application will be further described below in conjunction with the drawings and examples.

[0029] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0030] It should be noted that the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the exemplary embodiments according to the application. As used herein, the singular form is intended to include the plural form unless the context clearly indicates otherwise, and furthermore, it should be understood that when the terms "comprise" and / or "comprises" are used in the specification, there is a feature, step, operation, device, component and / or combination thereof.

[0031] Example 1

[0032] The adaptive fusion algorithm-based single-phase grounding fault detection method of the power distribution network in an embodiment of the present application comprises the following steps:

[0033] Step 1: Dynamically tracking the waveform of the target power distribution network, wherein the waveform comprises original sampling data and alternating current channel data, and the alternating current channel data comprises zero sequence voltage, phase current and zero sequence current.

[0034] Step 2: Based on the zero sequence voltage, calculating the zero sequence voltage mutation value in real time, and through the comparison between the zero sequence voltage mutation value and the dynamic zero sequence voltage mutation starting value, performing adaptive zero sequence voltage mutation detection to determine whether the zero sequence voltage is abnormal.

[0035] Step 3: If the zero sequence voltage is abnormal, tracing back to the original sampling data in the waveform to identify the waveform mutation position, and based on the waveform mutation position, defining a dynamic data window and synchronously acquiring the alternating current channel data in the dynamic data window from the instantaneous value waveform.

[0036] Step 4: Through the adaptive fusion algorithm, identifying the type of grounding fault of the alternating current channel data in the dynamic data window to obtain the single-phase grounding fault detection result of the target power distribution network.

[0037] As an embodiment, the adaptive fusion algorithm-based single-phase grounding fault detection method of the power distribution network comprises four links: the adaptive zero sequence voltage mutation detection link, the grounding fault detection link, the grounding algorithm fault identification link and the identification result weighted fusion link, as shown in the figure. Figure 1 The specific implementation process is as follows:

[0038] Step S1, starting the adaptive zero sequence voltage mutation detection link

[0039] The dynamic data tracking method is adopted, the dynamic zero sequence voltage mutation starting value is set according to the historical zero sequence voltage data of the line, it is determined whether the zero sequence voltage mutation value is greater than the dynamic zero sequence voltage mutation starting value, if yes, step S2 is entered, if not, the zero sequence voltage mutation value is continuously tracked, and the specific process is as follows:

[0040] S1.1 calculating the zero sequence voltage fundamental value of each cycle As a data source, the zero sequence voltage fundamental value is subjected to first-order low-pass filtering processing to increase the data smoothness, taking 50HZ as an example, 1 cycle = 20ms, based on the zero sequence voltage fundamental value and the zero sequence voltage data of the previous cycle, the zero sequence voltage data of the current cycle is calculated, which is expressed by the formula as follows:

[0041]

[0042] Among them, is the calculated real-time zero sequence voltage fundamental value, is the zero sequence voltage data of the nth cycle after first-order low-pass filtering, assuming that the nth cycle is the current cycle, is the zero sequence voltage data of the (n-1)th cycle, i.e., historical zero sequence voltage data, is a filter coefficient; the filter coefficient can be adjusted and the coefficient will affect the starting sensitivity.

[0043] S1.2, based on the zero sequence voltage data of the previous cycle, set the dynamic zero sequence voltage mutation starting value of the current cycle:

[0044]

[0045] wherein, is the dynamic zero sequence voltage mutation starting value, is a mutation starting coefficient; the mutation starting coefficient sets a default value according to actual line fault, grounding true type experiment fault, etc.

[0046] S1.3, determine whether the zero sequence voltage mutation value is greater than the dynamic zero sequence voltage mutation starting value:

[0047] When , it indicates that the zero sequence voltage is abnormal, and enters the grounding fault detection link.

[0048] Step S2, start the grounding fault detection link

[0049] Trace back N cycle data to find the waveform mutation position, and synchronously obtain zero sequence voltage data and other AC channel data through a dynamic data window.

[0050] Specifically, when the zero sequence voltage mutation value is greater than the dynamic zero sequence voltage mutation starting value, first, trace back N cycle data (4 cycle data can be selected here) and determine the waveform mutation position therefrom, then scan the waveforms from the waveform mutation position forward and backward to confirm the fault packet width, so as to form a dynamic data window, and synchronously obtain zero sequence voltage data and other AC channel data in the dynamic data window, wherein the other AC channel data includes phase current, zero sequence current, including the following steps:

[0051] (1) calculate the original sampling data in the traced back data area (algorithm starting 4 cycles) point by point, calculate the whole cycle difference data, and the calculation formula is as follows:

[0052]

[0053] wherein, is the whole cycle difference sampling point data (hereinafter referred to as difference data), is the original sampling data, The number of sampling points per cycle.

[0054] Here And The difference is:

[0055] The zero sequence voltage fundamental or the zero sequence voltage data derived from the fundamental calculated by the update period is 20ms; The data is collected by the analog-to-digital converter AD, and the update period depends on the sampling frequency. The common sampling frequency is 128 points per cycle (20ms), that is, the update period is 156.25 microseconds.

[0056] The former fundamental is calculated by FFT from the actual sampling data of 20ms, which reflects the overall trend of zero sequence voltage amplitude (used for starting), and the latter reflects the transient (very short time) data characteristics of zero sequence voltage (used for fine feature analysis).

[0057] (2) Point-by-point scanning differential data, checking its relationship with dynamic starting setting value Determine the waveform mutation position, specifically:

[0058] First, in order to prevent misjudgment caused by external reasons (such as electromagnetic interference), it is required that the mutation of two consecutive sampling points, which can be expressed by the formula:

[0059]

[0060]

[0061] Among them, The zero sequence voltage fundamental value to AD sampling value conversion coefficient.

[0062] Then, the differential data is further differentiated, which is actually a second-order whole-wave differential, which reflects the change speed of the data, that is, the change rate of the change rate. Since it is alternating current, the mutation has polarity, so through the following formula, it is ensured that the mutation direction of the two consecutive points is consistent, that is, the mutation polarity is consistent, that is, either both increase or both decrease:

[0063]

[0064] (3) Reduce the influence of noise zero drift to record the mutation point position n, and find the positions p and q respectively forward and backward, reduce the influence of noise zero drift, and the search condition is:

[0065]

[0066]

[0067] Among them, Fine voltage for secondary mutual inductor

[0068] And in order to prevent too small data window cause misjudgment, requires:

[0069]

[0070] Finally get dynamic data window [p, q].

[0071] (4) select the dynamic data window [p, q] in the AC channel data, input each transient ground algorithm.

[0072] Step S3, each ground fault identification link algorithm

[0073] The zero sequence voltage data and other AC channel data obtained by the dynamic data window are input into each ground algorithm, and each ground algorithm outputs a result containing algorithm reliability and representing the type of ground fault; each ground algorithm includes: zero sequence power direction method, vector superposition power direction method, and three-phase unbalance mutation method.

[0074] Here, the zero sequence power direction method is taken as an example for illustration:

[0075] S3.1, input the data of the dynamic data window, including: zero sequence voltage, phase current, and zero sequence current;

[0076] S3.2, extend the dynamic data window length to the whole cycle length, and zero-fill the extended area data;

[0077] S3.3, calculate the phase difference (0~180°) of the zero sequence voltage and the zero sequence current in the dynamic data window;

[0078] S3.4, normalize the phase difference to (-1, 1) using the following formula:

[0079]

[0080] Where, angle is the phase difference of the zero sequence voltage and the zero sequence current, represents the normalized output of the algorithm, i.e. the result containing algorithm reliability and representing the type of ground fault, represents the ground within the 100% reliability boundary, represents the ground outside the 100% reliability boundary, when near 0, it means that the data is unreliable, when the absolute value is near 0.5, it means that the data is relatively reliable.

[0081] Other transient algorithms also input the same dynamic data window data, and adopt similar processing methods to output each algorithm with a result of -1 to 1. The final algorithm result is weighted and averaged.

[0082] Step S4, each identification result weighting fusion link

[0083] The result output by each grounding algorithm The weighted average is performed to output the final result with the fusion algorithm reliability and representing the grounding fault type , which is expressed by a formula as

[0084]

[0085] Wherein, m is the number of grounding algorithms, If the positive number represents an in-bounds grounding fault, If the negative number represents an out-of-bounds grounding fault.

[0086] Here, if each algorithm identifies the waveform as its reliable feature, a higher reliability result (near +1 or near -1) is given; if each algorithm identifies it as an unreliable feature, a lower reliability result (near 0) is given. Through the final weighted average, the result with low reliability will be automatically ignored. Through this processing method, each algorithm can fully play its role, for example, for arc grounding fault, the three-phase current unbalance method has a very high recognition rate, and the algorithm gives an absolute value almost equal to 1 reliability, and other algorithms cannot give a higher reliability data (the result is in a fuzzy state, and the reliability is between -0.5~+0.5), and the final weighted average is collapsed to the three-phase current unbalance method result with high reliability, and a correct result is given.

[0087] Embodiment 2

[0088] In an embodiment of the present application, a single-phase grounding fault detection system of a distribution network adaptive fusion algorithm is provided, comprising:

[0089] The waveform tracking module is configured to dynamically track the waveform of the target distribution network, wherein the waveform includes original sampling data and alternating current channel data, and the alternating current channel data includes zero sequence voltage, phase current and zero sequence current.

[0090] The mutation detection module is configured to calculate the zero sequence voltage mutation value in real time based on the zero sequence voltage, and perform adaptive zero sequence voltage mutation detection by comparing the zero sequence voltage mutation value with the dynamic zero sequence voltage mutation starting value, to determine whether the zero sequence voltage anomaly occurs.

[0091] The data acquisition module is configured to trace the original sampling data in the waveform when the zero sequence voltage anomaly occurs, identify the waveform mutation position therefrom, and demarcate a dynamic data window based on the waveform mutation position, to synchronously acquire the alternating current channel data in the dynamic data window from the instantaneous value waveform.

[0092] The fault identification module is configured to identify the ground fault type of the alternating current channel data in the dynamic data window through an adaptive fusion algorithm to obtain a single-phase ground fault detection result of the target distribution network.

[0093] Embodiment 3

[0094] In an embodiment of the present application, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the single-phase ground fault detection method of the distribution network according to the adaptive fusion algorithm.

[0095] Embodiment 4

[0096] In an embodiment of the present application, a non-transitory computer readable storage medium is provided, which is used to store computer instructions, and when the computer instructions are executed by a processor, the single-phase ground fault detection method of the distribution network according to the adaptive fusion algorithm is implemented.

[0097] Embodiment 5

[0098] In an embodiment of the present application, an electronic device is provided, comprising a processor, a memory and a computer program; wherein the processor is connected with the memory, and the computer program is stored in the memory; when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the single-phase ground fault detection method of the distribution network according to the adaptive fusion algorithm.

[0099] The present application is described with reference to flowcharts and / or block diagrams of the method, device (system) and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks. Figure 1 The devices that implement the functions specified in one or more flows and / or blocks.

[0100] These computer program instructions can also be loaded into a computer or other programmable data processing device to cause a series of operation steps to be performed on the computer or other programmable data processing device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable data processing device provide a process for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks. Figure 1 The steps that implement the functions specified in one or more flows and / or blocks.

[0101] The above describes the specific embodiments of the present application in conjunction with the drawings, but is not a limitation on the scope of protection of the present application. Those skilled in the art should understand that various modifications or variations made by those skilled in the art on the basis of the technical solutions of the present application without creative labor are still within the scope of protection of the present application.

Claims

1. A method for detecting single-phase grounding faults in distribution networks using an adaptive fusion algorithm, characterized in that, include: The waveform of the target distribution network is dynamically tracked. The waveform includes raw sampled data and AC channel data. The AC channel data includes zero-sequence voltage, phase current, and zero-sequence current. Based on the zero-sequence voltage, the zero-sequence voltage abrupt change value is calculated in real time. Specifically, based on the fundamental value of the zero-sequence voltage, the zero-sequence voltage data is calculated, expressed by the formula: in, The real-time zero-sequence voltage fundamental value is calculated. This is the zero-sequence voltage data of the nth cycle after first-order low-pass filtering. This is the zero-sequence voltage data for the (n-1)th cycle. Here are the filter coefficients; based on historical zero-sequence voltage data, the dynamic zero-sequence voltage abrupt change start-up setpoint is calculated, expressed by the formula: ， in, The setpoint for dynamic zero-sequence voltage mutation start-up, The mutation initiation coefficient; By comparing the zero-sequence voltage mutation value with the dynamic zero-sequence voltage mutation start-up setpoint, adaptive zero-sequence voltage mutation detection is performed to determine whether a zero-sequence voltage anomaly has occurred. When a zero-sequence voltage anomaly occurs, the original sampled data in the waveform is traced back to identify the waveform abrupt change position. Based on the waveform abrupt change position, a dynamic data window is defined. Specifically, the waveforms that satisfy the zero-sequence voltage abrupt change value being greater than the dynamic zero-sequence voltage abrupt change start-up value are scanned forward and backward from the waveform abrupt change position to confirm the fault packet width, thereby forming a dynamic data window. The AC channel data in the dynamic data window is synchronously obtained from the instantaneous value waveform. An adaptive fusion algorithm is used to identify the ground fault type in the AC channel data of the dynamic data window, thereby obtaining the single-phase ground fault detection results of the target distribution network.

2. The method for detecting single-phase grounding faults in distribution networks using an adaptive fusion algorithm as described in claim 1, characterized in that, The zero-sequence voltage mutation value is the fundamental value of the zero-sequence voltage. Zero-sequence voltage data of the (n-1)th cycle The difference.

3. The method for detecting single-phase grounding faults in distribution networks using an adaptive fusion algorithm as described in claim 1, characterized in that, The method of identifying ground fault types in the AC channel data within the dynamic data window using an adaptive fusion algorithm is as follows: The AC channel data is input into several grounding algorithms, and each grounding algorithm outputs a result that includes the algorithm's reliability and characterizes the grounding fault type; The results from each grounding algorithm are weighted and averaged to obtain the final fault detection result that reflects the reliability of the fusion algorithm and characterizes the grounding fault type.

4. The method for detecting single-phase grounding faults in distribution networks using an adaptive fusion algorithm as described in claim 3, characterized in that, The grounding algorithms include, but are not limited to, the zero-sequence power direction method, the vector superposition power direction method, and the three-phase unbalance mutation method.

5. A distribution network single-phase grounding fault detection system based on an adaptive fusion algorithm, employing a distribution network single-phase grounding fault detection method based on an adaptive fusion algorithm as described in any one of claims 1-4, characterized in that, include: The waveform tracking module is configured to dynamically track the waveform of the target distribution network, the waveform including raw sampled data and AC channel data, the AC channel data including zero-sequence voltage, phase current, and zero-sequence current; The mutation detection module is configured to: calculate the zero-sequence voltage mutation value in real time based on the zero-sequence voltage, and perform adaptive zero-sequence voltage mutation detection by comparing the zero-sequence voltage mutation value with the dynamic zero-sequence voltage mutation start-up setpoint to determine whether a zero-sequence voltage anomaly has occurred. The data acquisition module is configured to: trace the original sampled data in the waveform when a zero-sequence voltage anomaly occurs, identify the waveform change position, define a dynamic data window based on the waveform change position, and synchronously acquire AC channel data in the dynamic data window from the instantaneous waveform. The fault identification module is configured to identify the grounding fault type of the AC channel data in the dynamic data window through an adaptive fusion algorithm, and obtain the single-phase grounding fault detection result of the target distribution network.

6. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the single-phase grounding fault detection method for distribution networks according to any one of claims 1-4, which is an adaptive fusion algorithm.

7. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium is used to store computer instructions, which, when executed by a processor, implement a distribution network single-phase grounding fault detection method according to any one of claims 1-4.

8. An electronic device, characterized in that, include: The device includes a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to perform a distribution network single-phase grounding fault detection method that implements an adaptive fusion algorithm as described in any one of claims 1-4.

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