Fault arc detection method, system and detector based on adaptive threshold

Abstract

The application discloses a fault arc detection method and system based on an adaptive threshold value and a detector. The method comprises the following steps: acquiring a line current signal; extracting at least two fault indication features from the line current signal; if all the at least two fault indication features satisfy preset conditions, it is determined that a fault arc occurs in the line; and if at least one of the at least two fault indication features does not satisfy the preset conditions, whether the fault arc occurs in the line is assisted in arbitration based on an adaptive feature threshold value. The application realizes adaptive feature threshold value, and improves the fault arc detection accuracy and reduces the number of missed reports and false reports to a certain extent.

Description

Technical Field

[0001] This invention relates to the field of arc detection technology, and specifically to a fault arc detection method, system and detector based on adaptive threshold. Background Technology

[0002] Electrical arcs caused by damage, aging, or loose connections in power distribution systems can generate localized high temperatures, easily leading to electrical fires or even explosions. The center temperature of an arc can reach 5000K to 15000K, and once a breakdown point is established, it can occur frequently. Therefore, electrical fires are generally quite serious, making it crucial to prevent them as much as possible.

[0003] To prevent fires caused by arc faults, arc fault detection technology is commonly used for arc detection and prevention. Currently, most arc fault detectors on the market employ a feature vector threshold monitoring method. However, the feature threshold set by the detector is generally a fixed threshold. If this threshold is set too high, false negatives are likely to occur during arc fault detection; if the threshold is set too low, false positives are likely to occur. Setting appropriate feature thresholds for various types of loads in actual power usage scenarios is obviously very difficult. Therefore, adopting an adaptive threshold strategy for the feature thresholds set by arc fault detectors is both appropriate and important.

[0004] For example, in related technologies, the patent application document with publication number CN112162172A establishes a periodic fluctuation index and a dynamic threshold, as well as a fault discrimination method and a dynamic threshold update algorithm, based on the statistical information of the virtual energy index; the scheme is based on the virtual energy index established by the maximum value, minimum value, mean and variance.

[0005] The multi-load circuit series fault arc detection method proposed in the patent application document with publication number CN111707908A extracts at least two fault indication features from the wavelet coefficients of the main circuit current signal; if at least two fault indication features meet the preset judgment conditions, it is determined that a fault arc has occurred in the multi-load circuit; this scheme describes the fault arc from the perspective of kurtosis / peak factor, etc., and cannot clearly distinguish weak arcs.

[0006] However, there is currently little research on adaptive feature thresholds in the field of fault arc detection, and even fewer results that can be practically applied. Summary of the Invention

[0007] The present invention aims to solve the problem that the feature threshold cannot be adaptive in existing fault arc detection methods based on threshold monitoring.

[0008] The present invention solves the above-mentioned technical problems through the following technical means:

[0009] In a first aspect, the present invention proposes a fault arc detection method based on an adaptive threshold, the method comprising:

[0010] Obtain the line current signal;

[0011] Extract at least two fault indication features from the line current signal;

[0012] If at least two of the fault indication features meet the preset conditions, it is determined that a fault arc has occurred in the line;

[0013] If at least one of the at least two fault indication features does not meet the preset condition, then an adaptive feature threshold is used to assist in arbitrating whether a fault arc has occurred in the circuit.

[0014] Furthermore, the extraction of at least two fault indication features from the line current signal includes:

[0015] The line current signal is decomposed using wavelet decomposition to obtain the wavelet coefficients of each layer;

[0016] The wavelet coefficients of each layer are processed to extract the first fault indication feature and the second fault indication feature.

[0017] Further, the processing of the wavelet coefficients at each layer to extract the first fault indication feature and the second fault indication feature includes:

[0018] Based on the wavelet coefficients of each layer, the first quartile concept is used for calculation. The first fault indication feature is obtained, where N is the number of wavelet coefficients, Q1 represents the 1 / 4 quantile of the wavelet coefficients, and x(i) is the wavelet coefficient;

[0019] Based on the wavelet coefficients of each layer, the concept of coefficient of variation (CV) is used to calculate... The second fault indication feature is obtained, where σ is the standard deviation of the wavelet coefficients and u is the average value of the wavelet coefficients.

[0020] Furthermore, after performing wavelet decomposition on the line current signal and obtaining the wavelet coefficients of each layer, the method further includes:

[0021] Based on the wavelet coefficients of each layer, the adaptive feature threshold is calculated, and the formula is expressed as follows:

[0022]

[0023] In the formula: Threshold is the adaptive feature threshold, MeanValue is the mean of the wavelet coefficients in each layer, Discrete Degree is the degree of dispersion of the wavelet coefficients in each layer, Q represents the initial threshold of the fault indication feature, and K represents the dimensionless custom coefficient.

[0024] Furthermore, the formulas for the mean of the wavelet coefficients and the dispersion of the wavelet coefficients are expressed as follows:

[0025]

[0026]

[0027] In the formula: x(i) are wavelet coefficients, and N is the number of wavelet coefficients.

[0028] Furthermore, the method also includes:

[0029] When Flag>K o hour,

[0030] When Flag≤K o hour,

[0031] Among them, the flag function K o For dimensionless custom coefficients.

[0032] Further, the step of determining that a fault arc has occurred in the line if at least two of the fault indication features meet preset conditions includes:

[0033] If at least two of the fault indication features are less than the set normal value, then a fault arc is determined to have occurred in the line.

[0034] Further, the step of determining whether a fault arc has occurred in the arbitration line based on an adaptive feature threshold if at least one of the at least two fault indication features does not meet a preset condition includes:

[0035] Based on the adaptive feature threshold, determine the auxiliary arbitration function. N AAF This indicates the number of auxiliary arbitration coefficients. i represents the i-th fault indication feature, Thh(i) represents the current adaptive threshold, and Q(i) represents the initial feature threshold;

[0036] If AAF <N AAF If so, it is determined that a fault arc has occurred in the line;

[0037] If AAF≥N AAF If so, it is determined that no fault arc has occurred in the line.

[0038] Secondly, the present invention proposes a fault arc detection system based on an adaptive threshold, the system comprising:

[0039] The acquisition module is used to acquire the line current signal;

[0040] The feature extraction module is used to extract at least two fault indication features from the line current signal;

[0041] The first determination module is used to determine that a fault arc has occurred in the line when at least two of the fault indication features meet the preset conditions.

[0042] The second determination module is used to determine whether a fault arc has occurred in the arbitration line based on an adaptive feature threshold when at least one of the at least two fault indication features does not meet a preset condition.

[0043] Thirdly, the present invention proposes a fault arc detector, the detector including a memory and a processor; wherein the processor reads executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement the fault arc detection method based on adaptive threshold as described above.

[0044] The advantages of this invention are:

[0045] (1) The present invention extracts at least two fault indication features from the line current signal, and determines that a fault arc has occurred in the line when at least two fault indication features meet the preset conditions; otherwise, it enables the auxiliary arbitration algorithm and combines the adaptive threshold to further determine whether a fault arc has occurred in the line; the feature threshold can be adaptive, which can improve the accuracy of fault arc detection to a certain extent and reduce the number of missed and false alarms.

[0046] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0047] Figure 1 This is a schematic flowchart of the fault arc detection method based on adaptive threshold proposed in an embodiment of the present invention;

[0048] Figure 2 This is a schematic diagram of the overall process of the fault arc detection method based on adaptive threshold proposed in the embodiments of the present invention;

[0049] Figure 3These are the normal and fault arc current waveforms of different loads in the embodiments of the present invention, wherein (a) is the current waveform of an incandescent lamp under normal operating conditions, (b) is the current waveform of an incandescent lamp under fault operating conditions, (c) is the current waveform of an air conditioner under normal operating conditions, (d) is the current waveform of an air conditioner under fault operating conditions, (e) is the current waveform of a computer under normal operating conditions, and (f) is the current waveform of a computer under fault operating conditions.

[0050] Figure 4 This is a schematic diagram of the fault arc detection system based on adaptive threshold proposed in an embodiment of the present invention. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] like Figures 1 to 2 As shown, the first embodiment of the present invention proposes a fault arc detection method based on an adaptive threshold, the method comprising the following steps:

[0053] S10. Obtain the line current signal;

[0054] It should be noted that after device initialization, when acquiring the real-time current signal of the device, the current signal in any load circuit can be obtained as the signal to be measured through an external circuit.

[0055] Furthermore, it is recommended to use equipment with a high sampling rate for signal acquisition under simulated experimental conditions. A high sampling rate and high precision can make the acquired signal contain more and more obvious signal features.

[0056] S20. Extract at least two fault indication features from the line current signal;

[0057] S30. If at least two of the fault indication features meet the preset conditions, it is determined that a fault arc has occurred in the line.

[0058] S40. If at least one of the at least two fault indication features does not meet the preset condition, then an adaptive feature threshold is used to assist in arbitrating whether a fault arc has occurred in the circuit.

[0059] This embodiment employs at least dual feature value monitoring. When at least one of the at least two fault indication features fails to meet the preset conditions, an adaptive threshold is used, and a secondary verification is performed using an auxiliary arbitration algorithm. This overcomes the problem that fault arc detection algorithms cannot simultaneously achieve high detection accuracy and a high number of false alarms and misreports. This simplifies the fault arc judgment process and ensures reliability. Furthermore, it is applicable to various power consumption scenarios, especially the series fault arc detection of different types of loads in ordinary residential homes.

[0060] In one embodiment, step S20, extracting at least two fault indication features from the line current signal, includes the following steps:

[0061] S21. Perform wavelet decomposition on the line current signal to obtain the wavelet coefficients of each layer;

[0062] S22. Process the wavelet coefficients of each layer to extract the first fault indication feature and the second fault indication feature.

[0063] In one embodiment, step S22: processing the wavelet coefficients of each layer to extract the first fault indication feature and the second fault indication feature includes the following steps:

[0064] S221. Based on the wavelet coefficients of each layer, the first quartile concept is used to calculate... The first fault indication feature is obtained, where N is the number of wavelet coefficients, Q1 represents the 1 / 4 quantile of the wavelet coefficients, and x(i) is the wavelet coefficient;

[0065] S222. Based on the wavelet coefficients of each layer, the concept of coefficient of variation (CV) is used to calculate... The second fault indication feature is obtained, where σ is the standard deviation of the wavelet coefficients. u is the average value of the wavelet coefficients.

[0066] It should be noted that this embodiment requires at least three levels of wavelet decomposition of the current signal to obtain at least three levels of wavelet detail coefficients. In practical applications, the signal can be decomposed into four or five levels, and the fault indication characteristics of the wavelet coefficients at any level can be obtained for arc detection.

[0067] It is understandable that the first fault indication feature can be obtained by extracting the first fault indication feature of the second-level wavelet coefficients, or by extracting the first fault indication feature of any level from the second to the fifth wavelet coefficients. The more first fault indication features extracted for each level of wavelet coefficient, the more sufficient and convincing the verification of the experimental results will be. Similarly, the same applies to other fault indication features.

[0068] In one embodiment, after step S21: performing wavelet decomposition on the line current signal and obtaining the wavelet coefficients of each layer, the method further includes the following steps:

[0069] Based on the wavelet coefficients of each layer, the adaptive feature threshold is calculated, and the formula is expressed as follows:

[0070]

[0071] In the formula: Threshold is the adaptive feature threshold, MeanValue is the mean of the wavelet coefficients in each layer, Discrete Degree is the degree of dispersion of the wavelet coefficients in each layer, Q represents the initial threshold of the fault indication feature, and K represents the dimensionless custom coefficient.

[0072] Since the current dispersion is significantly amplified when a fault arc occurs, this embodiment calculates the adaptive feature threshold in such a way that the feature threshold can be correlated with the current dispersion, so as to achieve adaptation to the current power environment.

[0073] Furthermore, the formulas for the mean of the wavelet coefficients and the dispersion of the wavelet coefficients are expressed as follows:

[0074]

[0075]

[0076] In the formula: x(i) are wavelet coefficients, and N is the number of wavelet coefficients.

[0077] In one embodiment, a flag function is defined. The flag function is used to further optimize the adjustment range of the adaptive threshold.

[0078] Take a dimensionless custom coefficient K o When Flag > K o hour, When Flag≤K o hour,

[0079] Since the current dispersion will be significantly amplified when a fault arc occurs, the probability of a fault arc is lower if the dispersion is low. By optimizing the adjustment range of the adaptive threshold, the threshold will change accordingly, and fault arcs can be identified more accurately.

[0080] It should be noted that in this embodiment, the values of the first fault indication feature and the second fault indication feature during a fault are both smaller than the empirical threshold during normal operation. Therefore, when the discrete degree Discrete Degree of the wavelet coefficients is larger, it indicates that the signal is more irregular at this time, and it is more likely that a faulty arc has occurred. So, the threshold should be larger, and the adaptive threshold should be proportional to the discrete degree. It can be understood that when the flag function Flag is greater than the custom coefficient K o it means that the discrete degree of the signal has reached a certain value.

[0081] It should be noted that the power grid frequency is 50 Hz, that is, 100 half - waves can be collected in 1 s. According to the national standard: the number of half - wave faults occurring within a unit time needs to be counted. If the number of half - waves with faulty arcs detected within 1 s is greater than or equal to 14, it is determined that a faulty arc has occurred; otherwise, it is determined that no faulty arc has occurred. Figure 3 The comparison diagrams of the current waveforms of incandescent lamps, air conditioners, and computers under normal conditions and during faults are given.

[0082] In one embodiment, the step S30: if at least two of the fault indication features meet the preset conditions, it is determined that a faulty arc has occurred in the line, including:

[0083] If at least two of the fault indication features are less than the threshold value in the normal working state of the device, it is determined that a faulty arc has occurred in the line.

[0084] It should be noted that in this embodiment, the empirical threshold value in the normal working state of the device is obtained through a large number of experiments and is used for comparison with the fault indication features.

[0085] In one embodiment, the step S40: if at least one of at least two of the fault indication features does not meet the preset conditions, it is determined whether a faulty arc has occurred in the line based on the adaptive feature threshold for auxiliary arbitration, including the following steps:

[0086] Based on the adaptive feature threshold, an auxiliary arbitration function is determined N AAF represents the number of auxiliary arbitration coefficients, i represents the i - th fault indication feature, Thh(i) represents the current adaptive threshold, and Q(i) represents the initial feature threshold;

[0087] If AAF < N, it is determined that a faulty arc has occurred in the line;

[0088] If AAF ≥ N, it is determined that no faulty arc has occurred in the line.

[0089] In this embodiment, the adaptive feature threshold is compared with the fault indication feature value. When at least two fault indication feature values ​​meet the preset conditions, it is determined that a fault arc has occurred in the line. If one of the fault indication features does not meet the preset conditions, an auxiliary arbitration algorithm is activated to further determine whether a fault arc has occurred in the line.

[0090] In addition, such as Figure 4 As shown, the second embodiment of the present invention proposes a fault arc detection system based on an adaptive threshold, the system comprising:

[0091] Acquisition module 10 is used to acquire line current signals;

[0092] Feature extraction module 20 is used to extract at least two fault indication features from the line current signal;

[0093] The first determination module 30 is used to determine that a fault arc has occurred in the line when at least two of the fault indication features meet the preset conditions.

[0094] The second determination module 40 is used to determine whether a fault arc has occurred in the arbitration line based on an adaptive feature threshold when at least one of the at least two fault indication features does not meet a preset condition.

[0095] This embodiment employs at least dual feature value monitoring. When at least one of the at least two fault indication features fails to meet the preset conditions, an adaptive threshold is used, and a secondary verification is performed using an auxiliary arbitration algorithm. This overcomes the problem that fault arc detection algorithms cannot simultaneously achieve high detection accuracy and a high number of false alarms and misreports, thereby simplifying the fault arc determination process and ensuring reliability.

[0096] In one embodiment, the feature extraction module 20 includes:

[0097] The wavelet decomposition unit is used to perform wavelet decomposition on the line current signal and obtain wavelet coefficients at each level.

[0098] The feature extraction unit is used to process the wavelet coefficients of each layer and extract the first fault indication feature and the second fault indication feature.

[0099] In one embodiment, the feature extraction unit is specifically used for:

[0100] Based on the wavelet coefficients of each layer, the first quartile concept is used for calculation. The first fault indication feature is obtained, where N is the number of wavelet coefficients, Q1 represents the 1 / 4 quantile of the wavelet coefficients, and x(i) is the wavelet coefficient;

[0101] Based on the wavelet coefficients of each layer, the concept of coefficient of variation (CV) is used to calculate... The second fault indication feature is obtained, where σ is the standard deviation of the wavelet coefficients and u is the average value of the wavelet coefficients.

[0102] In one embodiment, the system further includes an adaptive threshold calculation module, specifically used for:

[0103] Based on the wavelet coefficients of each layer, the adaptive feature threshold is calculated, and the formula is expressed as follows:

[0104]

[0105] In the formula: Threshold is the adaptive feature threshold, MeanValue is the mean of the wavelet coefficients in each layer, Discrete Degree is the degree of dispersion of the wavelet coefficients in each layer, Q represents the initial threshold of the fault indication feature, and K represents the dimensionless custom coefficient.

[0106] In one embodiment, the formulas for the mean of the wavelet coefficients and the dispersion of the wavelet coefficients are respectively expressed as follows:

[0107]

[0108]

[0109] In the formula: x(i) are wavelet coefficients, and N is the number of wavelet coefficients.

[0110] In one embodiment, the system further includes a threshold adjustment module, specifically used for:

[0111] When Flag>K o hour,

[0112] When Flag≤K o hour,

[0113] Among them, the flag function K o For dimensionless custom coefficients.

[0114] In one embodiment, the first determination module 30 is configured to:

[0115] If at least two of the fault indication features are less than the set normal value, then a fault arc is determined to have occurred in the line.

[0116] In one embodiment, the second determination module 40 is configured to:

[0117] Based on the adaptive feature threshold, determine the auxiliary arbitration function. N AAF This indicates the number of auxiliary arbitration coefficients. i represents the i-th fault indication feature, Thh(i) represents the current adaptive threshold, and Q(i) represents the initial feature threshold;

[0118] If AAF < N, it is determined that a faulty arc occurs in the line;

[0119] If AAF ≥ N, it is determined that no faulty arc occurs in the line.

[0120] It should be noted that for other embodiments or implementation methods of the faulty arc detection system based on adaptive threshold according to the present invention, reference can be made to the above-mentioned method embodiments, and details are not repeated here.

[0121] In a third aspect, the present invention provides a faulty arc detector, which includes a memory and a processor; wherein, the processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the faulty arc detection method based on adaptive threshold as described above.

[0122] The faulty arc detector provided in this embodiment adopts technical means combining at least double eigenvalue monitoring, adaptive threshold, wavelet decomposition and auxiliary arbitration algorithm, overcomes the problems of low accuracy, many missed alarms and false alarms in traditional algorithms during fault detection, and thus improves the reliability of detection; it is especially suitable for detecting series faulty arcs of different types of loads in various power usage scenarios, especially ordinary household residences.

[0123] In the description of this specification, the description referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0124] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" can explicitly or implicitly include at least one of such features. In the description of the present invention, "a plurality" means at least two, such as two, three, etc., unless otherwise specifically and clearly defined.

[0125] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method of adaptive threshold based fault arc detection, characterized in that, The method includes: Obtain the line current signal; Extracting at least two fault indication features from the line current signal includes performing wavelet decomposition on the line current signal to obtain wavelet coefficients at each level; processing the wavelet coefficients at each level to extract a first fault indication feature and a second fault indication feature. If at least two of the fault indication features meet the preset conditions, it is determined that a fault arc has occurred in the line; If at least one of the at least two fault indication features does not meet the preset condition, then an auxiliary arbitration function is determined based on the adaptive feature threshold to determine whether a fault arc has occurred in the arbitration circuit. , This indicates the number of auxiliary arbitration coefficients. , Indicates the first Fault indication characteristics, Indicates the current adaptive threshold. Indicates the initial feature threshold; like If so, it is determined that a fault arc has occurred in the line; like If so, it is determined that no fault arc has occurred in the line.

2. The fault arc detection method based on adaptive threshold as described in claim 1, characterized in that, The process of processing the wavelet coefficients at each layer to extract the first fault indication feature and the second fault indication feature includes: Based on the wavelet coefficients of each layer, the first quartile concept is used for calculation. The first fault indication characteristic is obtained, where, The number of wavelet coefficients. This represents the 1 / 4 quantile of the wavelet coefficients. These are wavelet coefficients; Based on the wavelet coefficients of each layer, the concept of coefficient of variation (CV) is used to calculate... The second fault indication characteristic is obtained, where, The standard deviation of the wavelet coefficients. This represents the average value of the wavelet coefficients.

3. The fault arc detection method based on adaptive threshold as described in claim 1, characterized in that, After performing wavelet decomposition on the line current signal and obtaining the wavelet coefficients of each layer, the method further includes: Based on the wavelet coefficients of each layer, the adaptive feature threshold is calculated, and the formula is expressed as follows: In the formula: The adaptive feature threshold, The mean value of the wavelet coefficients at each layer. Q represents the degree of dispersion of the wavelet coefficients in each layer, Q represents the initial threshold of the fault indication feature, and K represents the dimensionless custom coefficient.

4. The fault arc detection method based on adaptive threshold as described in claim 3, characterized in that, The formulas for the mean of the wavelet coefficients and the dispersion of the wavelet coefficients are as follows: In the formula: These are wavelet coefficients. This represents the number of wavelet coefficients.

5. The fault arc detection method based on adaptive threshold as described in claim 1, characterized in that, The method further includes: when hour, ; when hour, ; Among them, the flag function , For dimensionless custom coefficients.

6. The fault arc detection method based on adaptive threshold as described in claim 1, characterized in that, The step of determining that a fault arc has occurred in the line if at least two of the fault indication features meet the preset conditions includes: If at least two of the fault indication features are less than the set normal experience threshold, then a fault arc is determined to have occurred in the line.

7. A fault arc detection system based on adaptive threshold, characterized in that, The system includes: The acquisition module is used to acquire the line current signal; The feature extraction module is used to extract at least two fault indication features from the line current signal; The first determination module is used to determine that a fault arc has occurred in the line when at least two of the fault indication features meet the preset conditions. The second determination module is used to determine whether a fault arc has occurred in the arbitration line based on an adaptive feature threshold when at least one of the at least two fault indication features does not meet the preset condition. The feature extraction module includes: The wavelet decomposition unit is used to perform wavelet decomposition on the line current signal and obtain wavelet coefficients at each level. The feature extraction unit is used to process the wavelet coefficients of each layer and extract the first fault indication feature and the second fault indication feature. The second determination module is used to determine the auxiliary arbitration function based on the adaptive feature threshold. , This indicates the number of auxiliary arbitration coefficients. , Indicates the first Fault indication characteristics, Indicates the current adaptive threshold. Indicates the initial feature threshold; like If so, it is determined that a fault arc has occurred in the line; like If so, it is determined that no fault arc has occurred in the line.

8. A fault arc detector, characterized in that, The detector includes a memory and a processor; wherein the processor reads executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement the method as described in any one of claims 1-6.

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