CN115616364A - Fault arc detection method, device, equipment and storage medium - Google Patents
Fault arc detection method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention relates to the technical field of arc detection, in particular to a method, a device, equipment and a storage medium for detecting fault arc, wherein the method comprises the following steps: collecting a current signal of a loop to be tested where a load is located, and acquiring a plurality of wavelet coefficients according to the current signal; preprocessing the wavelet coefficient, and extracting at least two fault indication characteristic values from the preprocessed data; and carrying out fault arc detection on the loop to be detected based on the at least two fault indication characteristic values. According to the invention, the wavelet coefficient is obtained through the current signal of the loop to be detected, at least two fault indication characteristics are extracted according to the wavelet coefficient, and fault arc detection is carried out on the loop to be detected through the fault indication characteristics.
Description
Technical Field
The invention relates to the technical field of arc detection, in particular to a fault arc detection method, a fault arc detection device, fault arc detection equipment and a storage medium.
Background
At present, the central temperature of the electric arc can reach 5000K to 15000K, and once a breakdown point occurs, the electric arc frequently occurs, and the fault electric arc caused by factors such as damage, aging and connection looseness of a power distribution system circuit can cause local high temperature, so that an electrical fire and even explosion are very easy to cause, and the detection of the fault electric arc is particularly important.
Most of the existing fault arc detectors detect fault arcs based on characteristic vector thresholds, and due to the lack of characteristic vectors with high discrimination, the actual detection effect is poor and the accuracy is low.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a fault arc detection method, a fault arc detection device, fault arc detection equipment and a storage medium, and aims to solve the technical problem that in the prior art, the detection accuracy of a fault arc detection mode through a feature vector threshold is low.
To achieve the above object, the present invention provides a method for detecting a fault arc, the method comprising the steps of:
collecting a current signal of a loop to be tested where a load is located, and acquiring a plurality of wavelet coefficients according to the current signal;
preprocessing the wavelet coefficient, and extracting at least two fault indication characteristic values from the preprocessed data;
and carrying out fault arc detection on the loop to be detected based on the at least two fault indication characteristic values.
Optionally, the step of preprocessing the wavelet coefficients and extracting at least two fault indication characteristic values from the preprocessed data includes:
preprocessing the wavelet coefficients to obtain the number of the wavelet coefficients, the quarter quantiles of the wavelet coefficients and the average value of the wavelet coefficients;
obtaining a first fault indication characteristic value and a second fault indication characteristic value according to the wavelet coefficients, the number of the wavelet coefficients, the quarter quantiles of the wavelet coefficients and the wavelet coefficient average value;
correspondingly, the step of detecting the fault arc of the loop to be detected based on the at least two fault indication characteristic values includes:
and carrying out fault arc detection on the loop to be detected based on the first fault indication characteristic value and the second fault indication characteristic value.
Optionally, the step of obtaining a first fault indication characteristic value and a second fault indication characteristic value according to the wavelet coefficients, the number of the wavelet coefficients, the quartile of the wavelet coefficients, and the wavelet coefficient average value includes:
obtaining a first fault indication characteristic value according to the wavelet coefficient, the number of the wavelet coefficients and a quarter fraction of the wavelet coefficient;
and obtaining a second fault indication characteristic value according to the wavelet coefficient, the wavelet coefficient quantity and the wavelet coefficient average value.
Optionally, the step of obtaining a first fault indication characteristic value according to the wavelet coefficient, the number of wavelet coefficients and a quarter fraction of the wavelet coefficient includes:
obtaining a first fault indication characteristic value through a first preset processing formula according to the wavelet coefficients, the number of the wavelet coefficients and the quarter quantiles of the wavelet coefficients;
wherein the first preset processing formula is as follows:
in the formula (I), the compound is shown in the specification,
a characteristic value is indicated for the first fault,
for the number of the wavelet coefficients,
is four of the wavelet coefficientA fraction of the number of bits, and,
is the wavelet coefficient.
Optionally, the step of obtaining a second failure indication feature value according to the wavelet coefficient, the number of wavelet coefficients and the wavelet coefficient average value includes:
obtaining a second fault indication characteristic value through a second preset processing formula according to the wavelet coefficients, the wavelet coefficient quantity and the wavelet coefficient average value;
wherein the second preset processing formula is:
in the formula (I), the compound is shown in the specification,
a characteristic value is indicated for the second fault,
for the number of the wavelet coefficients,
is the average value of the wavelet coefficients and is,
is the wavelet coefficient.
Optionally, the step of performing fault arc detection on the loop to be detected based on the first fault indication characteristic value and the second fault indication characteristic value includes:
comparing the first fault indication characteristic value with a first preset threshold value, and comparing the second fault indication characteristic value with a second preset threshold value;
and when the first fault indication characteristic value does not exceed the first preset threshold value and the second fault indication characteristic value does not exceed the second preset threshold value, judging that the fault electric arc occurs in the loop to be detected.
Optionally, the step of acquiring a current signal of a loop to be measured where the load is located, and obtaining a plurality of wavelet coefficients according to the current signal includes:
collecting a current signal of a loop to be tested where a load is located;
and performing wavelet decomposition on the current signal according to a preset number of layers to obtain wavelet coefficients corresponding to the layers.
In addition, to achieve the above object, the present invention also provides a fault arc detecting apparatus, including:
the signal acquisition module is used for acquiring a current signal of a loop to be detected where a load is located and acquiring a plurality of wavelet coefficients according to the current signal;
the coefficient processing module is used for preprocessing the wavelet coefficient and extracting at least two fault indication characteristic values from the preprocessed data;
and the arc detection module is used for carrying out fault arc detection on the loop to be detected based on the at least two fault indication characteristic values.
Further, to achieve the above object, the present invention also proposes a fault arc detecting apparatus, comprising: a memory, a processor and a fault arc detection program stored on the memory and executable on the processor, the fault arc detection program configured to implement the steps of the fault arc detection method as described above.
Furthermore, to achieve the above object, the present invention also provides a storage medium having a fault arc detection program stored thereon, wherein the fault arc detection program is executed by a processor to implement the steps of the fault arc detection method as described above.
The method comprises the steps of acquiring a current signal of a loop to be tested where a load is located, and acquiring a plurality of wavelet coefficients according to the current signal; preprocessing the wavelet coefficient, and extracting at least two fault indication characteristic values from the preprocessed data; and carrying out fault arc detection on the loop to be detected based on the at least two fault indication characteristic values. According to the invention, the wavelet coefficient is obtained through the current signal of the loop to be detected, at least two fault indication characteristics are extracted according to the wavelet coefficient, and fault arc detection is carried out on the loop to be detected through the fault indication characteristics.
Drawings
FIG. 1 is a schematic diagram of a fault arc detection device for a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a fault arc detection method according to a first embodiment of the present invention;
FIG. 3 is a schematic flow chart of a fault arc detection method according to a second embodiment of the present invention;
FIG. 4 is a diagram of a comparison of a first fault indication characteristic under a resistive load and a normal parameter under a normal condition in a second embodiment of a method for detecting a fault arc in accordance with the present invention;
FIG. 5 is a comparison of a first fault indication characteristic under resistive-inductive load and a normal parameter under normal condition in a second embodiment of a fault arc detection method of the present invention;
FIG. 6 is a comparison of a first fault indication characteristic under a non-linear load and a normal parameter under a normal state in a second embodiment of a fault arc detection method according to the present invention;
FIG. 7 is a diagram illustrating a comparison of a second fault indication characteristic under resistive load with a normal parameter under normal condition in a second embodiment of a method for fault arc detection in accordance with the present invention;
FIG. 8 is a comparison of a second fault indication characteristic under resistive load and normal parameters under normal conditions for a second embodiment of a method of arc fault detection in accordance with the present invention;
FIG. 9 is a graph comparing a second fault indication characteristic under a non-linear load with a normal parameter under a normal state in a second embodiment of the fault arc detection method of the present invention;
fig. 10 is a block diagram showing the structure of the arc fault detection apparatus according to the first embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a fault arc detection device in a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the fault arc detecting apparatus may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (Wi-Fi) interface). The Memory 1005 may be a high-speed Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in fig. 1 does not constitute a limitation of the fault arc detection device and may include more or fewer components than shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a storage medium, may include therein an operating system, a network communication module, a user interface module, and a fault arc detection program.
In the arc fault detection apparatus shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 of the arc fault detection device of the present invention may be provided in the arc fault detection device, which calls the arc fault detection program stored in the memory 1005 through the processor 1001 and executes the arc fault detection method provided by the embodiment of the present invention.
An embodiment of the present invention provides a method for detecting a fault arc, and referring to fig. 2, fig. 2 is a schematic flowchart of a first embodiment of the method for detecting a fault arc according to the present invention.
In this embodiment, the fault arc detection method includes the following steps:
step S10: the method comprises the steps of collecting current signals of a loop to be tested where a load is located, and obtaining a plurality of wavelet coefficients according to the current signals.
It should be noted that the method of the present embodiment may be applied to a scenario in which a fault arc is detected for a load, or other scenarios in which a fault arc needs to be detected. The main body of the implementation of the present embodiment may be a fault arc detection device with data processing, network communication, and program running functions, such as a fault arc detector, or other devices capable of implementing the same or similar functions. The present embodiment and the following embodiments will be specifically described with reference to the above-described arc fault detection device (hereinafter, simply referred to as a device).
It is understood that the load may be a resistive load, a resistive-inductive load, or a non-linear load, the resistive load may refer to a load with a single resistor, the inductive load may refer to a combined load formed by connecting a resistor and an inductor in series, and the non-linear load may refer to a load with a rectifying device, such as a computer, a high-power supply, and the like, which is not limited in this embodiment.
It should be understood that the loop to be tested may be a loop to which the load is connected, the number of loads in the loop to be tested is not limited in this embodiment, the apparatus may directly acquire a current signal in the loop to be tested, and a current transformer may be further disposed in the loop to be tested, and the apparatus acquires the current signal in the loop to be tested through the current transformer.
It should be noted that the wavelet coefficient may be a linear combination of a series of wavelet functions with different scales and different time shifts, where each term of the wavelet coefficient is the wavelet coefficient, and the current signal is represented by the device through wavelet transform, that is, the current signal is expanded according to a certain wavelet function cluster.
It is emphasized that the specific number of the wavelet coefficients can be set according to the actual situation.
Further, the specific flow of wavelet transform is as follows: collecting a current signal of a loop to be tested where a load is located; and performing wavelet decomposition on the current signal according to a preset number of layers to obtain wavelet coefficients corresponding to the layers.
It can be understood that the preset number of layers can be set according to actual conditions, the wavelet decomposition can decompose a current signal into a low-frequency part and a high-frequency part to further realize one-layer decomposition, the wavelet decomposition is performed on the high-frequency part on the basis of the first-layer decomposition to obtain the low-frequency part and the high-frequency part of the second layer, the second-layer decomposition is performed on the high-frequency part of the second layer to decompose the high-frequency part of the second layer into the low-frequency part and the high-frequency part of the third layer, and so on, in this embodiment, the preset number of layers can be set to three layers, and further wavelet coefficients corresponding to the three layers can be obtained.
In a specific implementation, the device can collect a current signal of a loop where a load is located, and perform wavelet decomposition on the current signal with a preset number of layers to obtain wavelet coefficients corresponding to each layer.
Step S20: and preprocessing the wavelet coefficient, and extracting at least two fault indication characteristic values from the preprocessed data.
The preprocessing may be processing such as summing up wavelet coefficients of the respective layers.
It is to be understood that the fault indication features may be features for reflecting characteristics of the current signal, and the number of the fault indication features may be two or more, and it should be emphasized that the larger the number of the fault indication features is, the more accurate the detection result is, and the two fault indication features are adopted for explanation in this embodiment.
It should be understood that, if the preset number of layers is three, the apparatus may select wavelet coefficients of any two layers from the three layers to obtain two fault indication features, and if the preset number of layers is other layers, for example five layers, the apparatus may select wavelet coefficients of any one layer as one fault indication feature, and then select any subsequent layer as another fault indication feature, for example select wavelet coefficients of a second layer as one fault indication feature, and then select wavelet coefficients of any one layer between the second layer and the fifth layer as another fault indication feature.
In a specific implementation, the above apparatus may perform preprocessing on the obtained wavelet coefficients of each layer, and extract at least two fault indication features from the preprocessed wavelet coefficients.
Step S30: and carrying out fault arc detection on the loop to be detected based on the at least two fault indication characteristic values.
Further, in order to make the extracted fault indication feature more accurate, in the present embodiment, the step S20 includes:
step S21: and preprocessing the wavelet coefficients to obtain the number of the wavelet coefficients, the quarter quantiles of the wavelet coefficients and the wavelet coefficient average value.
It should be noted that, in this embodiment, a concept of a first quartile is introduced, where the quartile is one of the statistical quartiles, that is, all data are arranged from small to large and divided into quarters, and data at three division points are the quartiles.
It can be understood that the first quartile can be the 25 th% of the wavelet coefficients arranged from small to large.
It should be understood that, the concept of the variation coefficient is also introduced in the present embodiment, and the variation coefficient may be that when the discrete degrees of the two sets of data need to be compared, if the measurement scales of the two sets of data are different greatly, or the data dimensions are different, it is not appropriate to directly use the standard deviation for comparison, and at this time, the influence of the measurement scales and dimensions can be eliminated through the variation coefficient.
Step S22: and obtaining a first fault indication characteristic value and a second fault indication characteristic value according to the wavelet coefficients, the number of the wavelet coefficients, the quarter quantiles of the wavelet coefficients and the wavelet coefficient average value.
Accordingly, the step S30 includes:
step S30': and carrying out fault arc detection on the loop to be detected based on the first fault indication characteristic value and the second fault indication characteristic value.
In a specific implementation, the apparatus may pre-process the wavelet coefficients based on a concept of the first quartile and a concept of the variation coefficient to obtain the number of the wavelet coefficients, the quartile of the wavelet coefficients, and an average value of the wavelet coefficients; then obtaining a first fault indication characteristic value and a second fault indication characteristic value according to the wavelet coefficient, the number of the wavelet coefficients, the quarter quantile of the wavelet coefficient and the wavelet coefficient average value; and finally, carrying out fault arc detection on the to-be-detected loop through the first fault indication characteristic value and the second fault indication characteristic value.
The device of the embodiment can collect the current signal of the loop where the load is positioned, and carry out wavelet decomposition on the current signal with preset layers to obtain wavelet coefficients corresponding to each layer; preprocessing the wavelet coefficients based on the concept of the first quartile and the concept of the variation coefficient to obtain the number of the wavelet coefficients, the quartile of the wavelet coefficients and the average value of the wavelet coefficients; then obtaining a first fault indication characteristic value and a second fault indication characteristic value according to the wavelet coefficient, the number of the wavelet coefficients, the quarter quantile of the wavelet coefficient and the wavelet coefficient average value; finally, fault arc detection is carried out on the loop to be detected through the first fault indication characteristic value and the second fault indication characteristic value; compared with the existing characteristic vector threshold value, the fault arc detection is carried out on the loop to be detected based on the wavelet coefficient in the embodiment, the fault indication characteristic region degree is higher, and the accuracy of the detection result is improved.
Referring to fig. 3, fig. 3 is a schematic flow chart of a fault arc detection method according to a second embodiment of the present invention.
In order to obtain accurate first and second fault indication characteristics, as shown in fig. 2, based on the first embodiment, in this embodiment, the step S22 includes:
step S221: obtaining a first fault indication characteristic value according to the wavelet coefficient, the number of the wavelet coefficients and a quarter fraction of the wavelet coefficient;
specifically, the step S221 includes: obtaining a first fault indication characteristic value through a first preset processing formula according to the wavelet coefficients, the number of the wavelet coefficients and the quarter quantiles of the wavelet coefficients;
wherein the first preset processing formula is as follows:
in the formula (I), the compound is shown in the specification,
a characteristic value is indicated for the first fault,
for the number of the wavelet coefficients,
is a quarter-fraction of the wavelet coefficients,
is the wavelet coefficient.
In a specific implementation, the apparatus may obtain the first fault indication characteristic value according to the wavelet coefficient, the number of wavelet coefficients, and the quarter-digit number of the wavelet coefficient through the first preset formula.
Step S222: and obtaining a second fault indication characteristic value according to the wavelet coefficient, the wavelet coefficient quantity and the wavelet coefficient average value.
Specifically, the step S222 includes:
obtaining a second fault indication characteristic value through a second preset processing formula according to the wavelet coefficients, the wavelet coefficient quantity and the wavelet coefficient average value;
wherein the second preset processing formula is as follows:
in the formula (I), the compound is shown in the specification,
a characteristic value is indicated for the second fault,
for the number of the wavelet coefficients,
is the average value of the wavelet coefficients and is,
is the wavelet coefficient.
In a specific implementation, the apparatus may obtain the second fault indication feature value through a second preset processing formula according to the wavelet coefficient, the number of wavelet coefficients, and the wavelet coefficient average value.
Further, in order to perform the fault arc detection by the first fault indication characteristic value and the second fault indication characteristic value, in this embodiment, the step S30' includes:
comparing the first fault indication characteristic value with a first preset threshold value, and comparing the second fault indication characteristic value with a second preset threshold value; and when the first fault indication characteristic value does not exceed the first preset threshold value and the second fault indication characteristic value does not exceed the second preset threshold value, judging that the fault electric arc occurs in the loop to be detected.
It should be noted that, the first preset threshold and the second preset threshold may be set by themselves according to actual situations, and this embodiment is not limited.
It should be emphasized that, in this embodiment, only one of the first fault indication characteristic value and the second fault indication characteristic value may be selected to perform the fault arc detection, but the first fault indication characteristic value and the second fault indication characteristic value are combined to further improve the detection accuracy.
It can be understood that, in the fault indication feature in the embodiment, the two fault indication features may be fused by using a data fusion algorithm in a modern signal processing technology or by adding other fault indication features, a fusion vector criterion is proposed, and the detection of the fault arc may also be realized based on the threshold monitoring of the fusion vector criterion.
It should be understood that the other fault indication features described above may be based on fault indication features obtained by a form factor, a division factor, a warp factor, a pulse factor, etc., and the present embodiment is not limited thereto.
Further, for convenience of understanding, reference is made to fig. 4 to 9 for explanation, and fig. 4 is a comparison graph of a first fault indication characteristic value under a resistive load and a normal parameter under a normal state in the second embodiment of the fault arc detection method according to the present invention; FIG. 5 is a comparison of a first fault indication characteristic under resistive-inductive load and a normal parameter under normal condition in a second embodiment of a fault arc detection method of the present invention; FIG. 6 is a comparison of a first fault indication characteristic under a non-linear load and a normal parameter under a normal state according to a second embodiment of the method for detecting a fault arc of the present invention; the abscissa in fig. 4 to 6 represents the test group, the ordinate represents the parameter value corresponding to the first fault indication characteristic, i.e. the first fault indication characteristic value, normal in the graph represents the curve corresponding to the normal parameter in the normal state, abnormal represents the curve corresponding to the first fault indication characteristic value, and Threshold represents the first preset Threshold value; as can be seen from fig. 4 to 6, the first failure indication characteristic values are all below 8.
FIG. 7 is a diagram illustrating a comparison of a second fault indication characteristic under resistive load with a normal parameter under normal condition in a second embodiment of a method for fault arc detection in accordance with the present invention; FIG. 8 is a comparison of a second fault indication characteristic under resistive-inductive loading and normal parameters under normal conditions for a fault arc detection method according to a second embodiment of the present invention; FIG. 9 is a graph showing the comparison between the second fault indication characteristic under the nonlinear load and the normal parameter under the normal state in the second embodiment of the method for detecting a fault arc in accordance with the present invention; in fig. 7 to 9, the abscissa represents the test group, and the ordinate represents the parameter value corresponding to the second fault indicating characteristic, i.e., the second fault indicating characteristic value, in which normal represents the curve corresponding to the normal parameter in the normal state, abnormal represents the curve corresponding to the second fault indicating characteristic value, and Threshold represents the above-mentioned second preset Threshold value; as can be seen from fig. 7 to 9, the second failure indication characteristic values are all 15 or less.
The device of the embodiment can obtain a first fault indication characteristic value through the first preset formula according to the wavelet coefficient, the number of the wavelet coefficients and the quarter digit of the wavelet coefficient; the equipment can obtain a second fault indication characteristic value through a second preset processing formula according to the wavelet coefficient, the quantity of the wavelet coefficient and the average value of the wavelet coefficient; comparing the first fault indication characteristic value with a first preset threshold value, and comparing the second fault indication characteristic value with a second preset threshold value; and when the first fault indication characteristic value does not exceed the first preset threshold value and the second fault indication characteristic value does not exceed the second preset threshold value, judging that the fault electric arc occurs in the loop to be detected.
Furthermore, an embodiment of the present invention further provides a storage medium, where a fault arc detection program is stored, and the fault arc detection program, when executed by a processor, implements the steps of the fault arc detection method as described above.
In addition, referring to fig. 10, fig. 10 is a block diagram of a first embodiment of the arc fault detection apparatus according to the present invention, and the embodiment of the present invention further provides an arc fault detection apparatus, including:
the signal acquisition module 601 is used for acquiring a current signal of a loop to be detected where a load is located and acquiring a plurality of wavelet coefficients according to the current signal;
a coefficient processing module 602, configured to preprocess the wavelet coefficient, and extract at least two fault indication characteristic values from the preprocessed data;
and an arc detection module 603, configured to perform fault arc detection on the to-be-detected loop based on the at least two fault indication feature values.
The device of the embodiment can collect the current signal of the loop where the load is positioned, and carry out wavelet decomposition on the current signal with preset layers to obtain wavelet coefficients corresponding to each layer; preprocessing the wavelet coefficients based on the concept of the first quartile and the concept of the variation coefficient to obtain the number of the wavelet coefficients, the quartile of the wavelet coefficients and the average value of the wavelet coefficients; then obtaining a first fault indication characteristic value and a second fault indication characteristic value according to the wavelet coefficient, the number of the wavelet coefficients, the quarter quantile of the wavelet coefficient and the wavelet coefficient average value; finally, fault arc detection is carried out on the loop to be detected through the first fault indication characteristic value and the second fault indication characteristic value; compared with the existing characteristic vector threshold value, the fault arc detection is carried out on the loop to be detected based on the wavelet coefficient in the embodiment, the fault indication characteristic discrimination is higher, and the accuracy of the detection result is improved.
Other embodiments or specific implementation manners of the fault arc detection device of the present invention may refer to the above method embodiments, and are not described herein again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrases "comprising one of 8230; \8230;" 8230; "does not exclude the presence of additional like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are only for description, and do not represent the advantages and disadvantages of the embodiments. Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention or portions thereof contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium (such as a rom/ram, a magnetic disk, and an optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, or a network device) to execute the methods according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A method of fault arc detection, the method comprising the steps of:
collecting a current signal of a loop to be tested where a load is located, and acquiring a plurality of wavelet coefficients according to the current signal;
preprocessing the wavelet coefficient, and extracting at least two fault indication characteristic values from the preprocessed data;
and carrying out fault arc detection on the loop to be detected based on the at least two fault indication characteristic values.
2. The method for arc fault detection according to claim 1, wherein said step of preprocessing said wavelet coefficients and extracting at least two fault indication feature values from the preprocessed data comprises:
preprocessing the wavelet coefficients to obtain the number of the wavelet coefficients, the quarter quantiles of the wavelet coefficients and the average value of the wavelet coefficients;
obtaining a first fault indication characteristic value and a second fault indication characteristic value according to the wavelet coefficients, the number of the wavelet coefficients, the quarter quantiles of the wavelet coefficients and the wavelet coefficient average value;
correspondingly, the step of performing fault arc detection on the loop to be detected based on the at least two fault indication characteristic values includes:
and carrying out fault arc detection on the loop to be detected based on the first fault indication characteristic value and the second fault indication characteristic value.
3. The method of fault arc detection as in claim 2, wherein said step of obtaining a first fault indication characteristic value and a second fault indication characteristic value based on said wavelet coefficients, said number of wavelet coefficients, a quartile of said wavelet coefficients, and said wavelet coefficient average comprises:
obtaining a first fault indication characteristic value according to the wavelet coefficient, the number of the wavelet coefficients and the quarter fraction of the wavelet coefficient;
and obtaining a second fault indication characteristic value according to the wavelet coefficient, the wavelet coefficient quantity and the wavelet coefficient average value.
4. The method of fault arc detection as claimed in claim 3 wherein said step of obtaining a first fault indication characteristic value based on said wavelet coefficients, said number of wavelet coefficients and a quarter fraction of said wavelet coefficients comprises:
obtaining a first fault indication characteristic value through a first preset processing formula according to the wavelet coefficients, the number of the wavelet coefficients and the quarter quantiles of the wavelet coefficients;
wherein the first preset processing formula is:
in the formula (I), the compound is shown in the specification,
a characteristic value is indicated for the first fault,
for the number of the wavelet coefficients,
is a quarter-fraction of the wavelet coefficients,
is the wavelet coefficient.
5. The method of claim 3, wherein said step of obtaining a second fault indication characteristic value based on said wavelet coefficients, said number of wavelet coefficients and said average of wavelet coefficients comprises:
obtaining a second fault indication characteristic value through a second preset processing formula according to the wavelet coefficients, the wavelet coefficient quantity and the wavelet coefficient average value;
wherein the second preset processing formula is as follows:
in the formula (I), the compound is shown in the specification,
a characteristic value is indicated for the second fault,
for the number of the wavelet coefficients,
is the average value of the wavelet coefficients and is,
is the wavelet coefficient.
6. The fault arc detection method according to any one of claims 2 to 5, wherein the step of performing fault arc detection on the loop under test based on the first fault indication characteristic value and the second fault indication characteristic value comprises:
comparing the first fault indication characteristic value with a first preset threshold value, and comparing the second fault indication characteristic value with a second preset threshold value;
and when the first fault indication characteristic value does not exceed the first preset threshold value and the second fault indication characteristic value does not exceed the second preset threshold value, judging that the fault electric arc occurs in the loop to be detected.
7. The method for detecting a fault arc according to claim 1, wherein the step of collecting a current signal of a loop to be detected in which a load is located and obtaining a plurality of wavelet coefficients according to the current signal comprises:
collecting a current signal of a loop to be tested where a load is located;
and performing wavelet decomposition on the current signal according to a preset number of layers to obtain wavelet coefficients corresponding to the layers.
8. A fault arc detection device, characterized in that the device comprises:
the signal acquisition module is used for acquiring a current signal of a loop to be detected where a load is located and acquiring a plurality of wavelet coefficients according to the current signal;
the coefficient processing module is used for preprocessing the wavelet coefficient and extracting at least two fault indication characteristic values from the preprocessed data;
and the arc detection module is used for carrying out fault arc detection on the loop to be detected based on the at least two fault indication characteristic values.
9. A fault arc detection device, characterized in that the device comprises: memory, a processor and a fault arc detection program stored on the memory and executable on the processor, the fault arc detection program being configured to implement the steps of the fault arc detection method according to any of claims 1 to 7.
10. A storage medium, characterized in that the storage medium has stored thereon a fault arc detection program which, when executed by a processor, carries out the steps of the fault arc detection method according to any one of claims 1 to 7.
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