Series fault arc detection and positioning method and system, terminal and medium

Abstract

The invention discloses a series fault arc detection and positioning method, and discloses a system, a terminal and a medium with the series fault arc detection and positioning method, and the series fault arc detection and positioning method comprises a measurement part and a data preprocessing and decision part. The measuring part is used for collecting current and voltage data, the data preprocessing part is used for analyzing in a time domain, dividing signals into independent periods, constructing current and voltage arrays and extracting arc fault features of the current and voltage signals, and the decision making part is used for carrying out classified detection on single-period signals by using a random forest classifier. Arc fault diagnosis is carried out by adopting a scoring system; and realizing arc fault positioning (line selection) based on an MVC50 feature vector and a k-nearest neighbor algorithm. High-accuracy detection and high-precision fault positioning of the low-voltage series fault arc are realized, and the safety and reliability of a power grid are improved.

Description

Technical Field

[0001] The present invention relates to the field of detection technologies in the power system field, and particularly relates to a series fault arc detection and location method and system. Background Art

[0002] Arc fault is a serious problem in low-voltage power grids and an important inducement for household fires. For the 220V / 50Hz alternating current for civil use behind the meter, the characteristics of parallel arc and grounding arc faults are obvious and easy to detect. However, affected by the dynamic arc resistance and load characteristics, when a low-voltage AC series arc fault occurs, the arc current characteristics under different load types vary greatly, such as the degree of waveform distortion and the length of the "zero rest period" at zero crossing, etc., all of which are different, increasing the difficulty of arc fault detection and location. Summary of the Invention

[0003] The present invention aims to solve at least one of the technical problems existing in the prior art. For this purpose, the present invention proposes a series fault arc detection and location method, which can be applied to arc detection in low-voltage AC household networks. The method includes a measurement part, data preprocessing, and a decision part; the measurement part is used for data acquisition of current and voltage, the data preprocessing part analyzes in the time domain, divides the signal into separate cycles, constructs current and voltage arrays, extracts the arc fault characteristics of the current and voltage signals, the decision part uses a random forest classifier to classify and detect single-cycle signals, and uses a scoring system for arc fault diagnosis; based on the MVC 50 feature vector and the k-nearest neighbor algorithm to achieve the location (fault line selection) of arc faults. High-accuracy detection of low-voltage series fault arcs and high-precision fault location are realized, improving the safety and reliability of the power grid.

[0004] The present invention also proposes a system with the above-mentioned series fault arc detection and location.

[0005] The series fault arc detection and location method according to the first aspect embodiment of the present invention is characterized by including the following steps:

[0006] Obtain the original signal of the main power line;

[0007] Perform low-pass filtering on the original signal to obtain a processed signal;

[0008] Obtain the arc fault characteristics in the processed signal;

[0009] Based on the feature vector containing the arc fault characteristics, apply it to a random forest classifier, and detect arc faults based on the random forest classifier. If a fault is detected within a single cycle, increment the fault parameter by one, and if no fault is detected, decrement the fault parameter by one. If the fault parameter is greater than a predetermined value, it is considered that there is a fault in the line;

[0010] When a fault occurs, calculate the recognition feature vector of the arc fault;

[0011] Based on the recognition feature vector, locate the arc fault.

[0012] According to the series fault arc detection and location method of the embodiments of the present invention, it has at least the following beneficial effects: The method provided by the present invention includes a measurement part, a data preprocessing part, and a decision part; the measurement part is used for data acquisition of current and voltage, the data preprocessing part analyzes in the time domain, divides the signal into separate cycles, constructs current and voltage arrays, extracts the arc fault characteristics of the current and voltage signals, and the decision part uses a random forest classifier to classify and detect single-cycle signals, and uses a scoring system for arc fault diagnosis; based on the MVC 50 feature vector and the k-nearest neighbor algorithm to achieve the location (line selection) of the arc fault. The high-accuracy detection and high-precision fault location of low-voltage series fault arcs are realized, and the safety and reliability of the power grid are improved.

[0013] According to some embodiments of the present invention, in the step of performing low-pass filtering on the original signal to obtain a processed signal, the collected voltage signal and current signal are respectively converted into a voltage two-dimensional array U and a current two-dimensional array I, where:

[0014] U k =[u 1,k , ……, u m,k , ……, u M,k

[0015] I k =[i 1,k , ……, i m,k , ……, i M,k .

[0016] According to some embodiments of the present invention, there are 12 arc fault characteristics, which are six pairs of characteristics corresponding to the current two-dimensional array I and the voltage two-dimensional array U respectively. Among them, the current two-dimensional array includes:

[0017] Sum of differences between adjacent cycles of array I:

[0018]

[0019] Sum of absolute values of adjacent cycles of array I:

[0020]

[0021] Maximum Euclidean distance:

[0022] ​

[0023] Euclidean distance ED:

[0024]

[0025] Maximum single-sample distance MSSD:

[0026] MSSD I (k)=max(|I k+1 (m)-I k (m)|),m∈<1,2,...,M>

[0027] Maximum slip difference MSD:

[0028]

[0029] Replace the current two-dimensional array I in the above features with the voltage two-dimensional array U, then the remaining six arc fault features are obtained.

[0030] According to some embodiments of the present invention, the minimum value of the fault parameter is 0 and the maximum value is 50. When the minimum value has been reached, it will no longer decrease, and when the maximum value has been reached, it will no longer increase.

[0031] According to some embodiments of the present invention, the random forest classifier divides the collected data into a training set and a test set; the training set has 7 sequences. In 6 sequences, only one device is connected to the faulty line. During the seventh run, when connected to the faulty line, at most 6 devices are working; in the test set, the test set contains sequences of pairs, groups of three, groups of four, groups of five, and all 6 groups of devices.

[0032] According to some embodiments of the present invention, the process of calculating the recognition feature vector of the arc fault includes:

[0033] For each period k in which the random forest classifier detects an arc fault, the value of the fault parameter ALS increases, and the vector ΔI k is calculated: ΔI k =I k -I k-1 , and is stored in the VC array in the i-th column, where i is equal to k modulo 50; if no arc fault is detected, the value of the fault parameter ALS is decreased, and a zero vector is stored in the i-th column of the VC array;

[0034] Only calculate the MVC k feature vector for the ΔI 50 vector during the period when an arc fault is detected, and calculate the MVC 50 feature vector from the VC array and the fault parameter ALS:

[0035]

[0036] Reduce the number of attributes in the MVC 50 from 5000 to 50 in the vector, calculate the average value for every 100 values to obtain the MVC 50-mean :

[0037]

[0038] According to some embodiments of the present invention, in the step of locating the arc fault based on the recognition feature vector and the monitoring data, the k-nearest neighbor algorithm is used, where k = 6; the features for classification are the average value and the maximum value of the MVC 50-mean of the average value and the maximum value.

[0039] A series fault arc detection and location system according to an embodiment of the second aspect of the present invention, characterized by comprising:

[0040] A signal acquisition module capable of acquiring the original signal of the main power line;

[0041] A data processing module capable of performing low-pass filtering on the original signal to obtain a processed signal;

[0042] A feature extraction module capable of acquiring the arc fault features in the processed signal;

[0043] A fault detection module capable of applying a feature vector containing the arc fault features to a random forest classifier, and detecting the arc fault based on the random forest classifier. If a fault is detected within a single cycle, the fault parameter is incremented by one, and if no fault is detected, the fault parameter is decremented by one. If the fault parameter is greater than a predetermined value, it is considered that there is a fault in the line;

[0044] A feature vector calculation module capable of calculating the recognition feature vector of the arc fault when a fault occurs;

[0045] A fault location module capable of locating the arc fault based on the recognition feature vector.

[0046] A terminal according to an embodiment of the third aspect of the present invention, the terminal comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the above-mentioned series fault arc detection and location method is implemented.

[0047] A computer-readable storage medium provided according to an embodiment of the fourth aspect of the present application, the medium storing computer-executable instructions for executing the above-mentioned series fault arc detection and location method.

[0048] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present invention. Description of the Drawings

[0049] The above and / or additional aspects and advantages of the present invention will become apparent and be readily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0050] Figure 1 is a schematic diagram of the steps of the series fault arc detection and location method according to an embodiment of the present invention;

[0051] Figure 2 is a structural block diagram of the series fault arc detection and location system according to an embodiment of the present invention. Detailed Description of the Embodiments

[0052] Embodiments of the present invention will be described in detail below. Examples of the embodiments are shown in the drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0053] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by terms such as up, down, front, back, left, right, etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention.

[0054] In the description of the present invention, the meaning of "a number of" is one or more, the meaning of "a plurality of" is two or more, and understandings such as "greater than", "less than", "exceeding", etc. do not include the present number, and understandings such as "above", "below", "within", etc. include the present number. If there is a description of "first", "second", etc., it is only for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features or implicitly indicating the sequence relationship of the indicated technical features.

[0055] In the description of the present invention, unless otherwise clearly defined, terms such as "arrangement", "installation", "connection", etc. should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above terms in the present invention in combination with the specific content of the technical solution.

[0056] Referring to Figure 1 , embodiments of the present application provide a series fault arc detection and location method, including:

[0057] Step S100, obtaining the original signal of the main power line.

[0058] Obtain the voltage u of the main power line using a transformer total (t), and obtain the current i of the main power line using a current transformer total (t). The sampling frequency fs = 250 kHz, for a single 50 Hz current and voltage cycle, a single cycle contains M = 5000 samples.

[0059] Step S200: Perform low-pass filtering on the original signal to obtain a processed signal.

[0060] Perform low-pass filtering on the original voltage signal u total (t), with a cut-off frequency of 70 Hz, to eliminate the influence of harmonics higher than the fundamental component. The start of each cycle is the time instant when the voltage fundamental value crosses from zero to a positive value. Therefore, regardless of the interference in a specific power grid, the start of each cycle is the same.

[0061] Convert the acquired signals u total (t), i total (t) into 2D arrays U and I respectively.

[0062] U k = [u 1,k , ……, u m,k , ……, u M,k

[0063] I k = [i 1,k , ……, i m,k , ……, i M,k

[0064] Step S300: Obtain the arc fault characteristics in the processed signal.

[0065] Calculate the following signal characteristics for each signal cycle. The feature vector contains 12, with 6 pairs of features for voltage and current signals. Calculate for the current and voltage signals as follows:

[0066] a. Sum of differences between adjacent cycles of array I:

[0067]

[0068] b. Sum of absolute values of adjacent cycles of array I

[0069]

[0070] c. Maximum Euclidean distance

[0071]

[0072] d. Euclidean distance ED ​​

[0073]

[0074] e. Maximum Single-Sample Distance (MSSD)

[0075] MSSD I (k) = max(|I k+1 (m) - I k (m)|), m ∈ <1, 2,..., M> (5)

[0076] f. Maximum Slip Difference (MSD)

[0077]

[0078] Replacing the current two-dimensional array I in the above formulas (1) to (6) with the voltage two-dimensional array U, the remaining six arc fault features can be obtained.

[0079] Step S400: Based on the feature vector containing the arc fault features, apply it to a random forest classifier, and detect the arc fault based on the random forest classifier. If a fault is detected within a single cycle, increment the fault parameter by one; if no fault is detected, decrement the fault parameter by one. If the fault parameter is greater than a predetermined value, it is considered that there is a fault in the line.

[0080] Based on the feature vector composed of 12 fault arc features, apply it to a random forest classifier with the number of training trees n = 25. Use the random forest classifier to detect a series of arc faults. If an arc fault is detected within the cycle, the value of the ALS parameter is incremented by 1, and the maximum value cannot exceed 50; if no arc fault is detected within the cycle, the value of the ALS parameter is decremented by 1, and the minimum value is 0. If more than 6 arc fault cycles (ALS >= 7) are detected within the last second, there is an arc fault in the line.

[0081] Among them, for the random forest classifier, the collected data is divided into two data sets: the training set and the test set. The training set has 7 sequences. In 6 sequences, only one device is connected to the faulty line. During the seventh run, when connected to the faulty line, at most 6 devices are working; in the test set, the test set contains sequences of pairs, groups of three, groups of four, groups of five, and all 6 groups of devices.

[0082] Step S500: When a fault occurs, calculate the recognition feature vector of the arc fault.

[0083] For each cycle k in which the random forest classifier detects an arc fault, the value of ALS (the number of arc fault cycles detected in the last second) is incremented, and the vector ΔI is calculated k : ΔI k = I k - Ik-1 It is stored in the VC array of the i-th column, where i is equal to k modulo 50; if no arc fault is detected, the ALS value is decremented, and a 0 vector is stored in the i-th column of the VC array.

[0084] Only calculate MVC for ΔI during the period when an arc fault is detected k Vector to calculate MVC 50 Feature vector, calculate MVC from the VC array and ALS 50 Feature vector.

[0085]

[0086] Reduce the number of attributes in the MVC 50 vector from 5000 (the number of samples in one period) to 50, calculate the average value of every 100 values to obtain MVC 50-mean .

[0087]

[0088] Step S600, based on the identified feature vector, locate the arc fault.

[0089] The location of the series arc fault is achieved by identifying the equipment powered by the faulty line. For the identification (line selection) of the faulty line, the k-nearest neighbor algorithm is used, with k = 6. The features for classification are based on MVC 50-mean , and the other two features are the average value and the maximum value of MVC 50-mean .

[0090] An embodiment of another aspect of the present application provides a series fault arc detection and location system, as Figure 2 shown. This system 20 includes:

[0091] A signal acquisition module 201 capable of acquiring the original signal of the main power line;

[0092] A data processing module 202 capable of performing low-pass filtering on the original signal to obtain a processed signal;

[0093] A feature extraction module 203 capable of acquiring the arc fault features in the processed signal;

[0094] A fault detection module 204 capable of applying a feature vector containing the arc fault features to a random forest classifier and detecting the arc fault based on the random forest classifier. If a fault is detected within a single period, the fault parameter is incremented by one; if no fault is detected, the fault parameter is decremented by one. If the fault parameter is greater than a predetermined value, it is considered that there is a fault in the line;

[0095] The eigenvector calculation module 205 is capable of calculating the recognition eigenvector of the arc fault when a fault occurs.

[0096] The fault location module 206 is capable of locating the arc fault based on the recognition eigenvector.

[0097] The embodiment of the present application includes a measurement part, data preprocessing, and a decision part; the measurement part is used for data acquisition of current and voltage, the data preprocessing part analyzes in the time domain, divides the signal into separate cycles, constructs current and voltage arrays, extracts the arc fault characteristics of the current and voltage signals, the decision part uses a random forest classifier to classify and detect single-cycle signals, and uses a scoring system for arc fault diagnosis; the arc fault is located (line selection) based on the MVC50 eigenvector and the k-nearest neighbor algorithm. High-accuracy detection and high-precision fault location of low-voltage series fault arcs are achieved, improving the safety and reliability of the power grid.

[0098] The application program's lag detection device in this embodiment can execute the series fault arc detection and location method provided by the embodiment of the present application, and its implementation principle is similar and will not be elaborated here.

[0099] Another embodiment of the present application provides a terminal, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, it realizes the above-mentioned series fault arc detection and location method.

[0100] Specifically, the processor can be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logical blocks, modules, and circuits described in combination with the disclosure of the present application. The processor can also be a combination that realizes computing functions, such as a combination including one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0101] Specifically, the processor is connected to the memory through a bus, and the bus can include a path for transmitting information. The bus can be a PCI bus or an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc.

[0102] The memory can be a ROM or other types of static storage devices that can store static information and instructions, a RAM or other types of dynamic storage devices that can store information and instructions, or an EEPROM, a CD-ROM, or other optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0103] Optionally, the memory is used to store the code of the computer program for executing the solution of this application, and is controlled by the processor for execution. The processor is used to execute the application program code stored in the memory to implement Figure 2 the functions of the series fault arc detection and location system provided by the illustrated embodiment.

[0104] Another embodiment of this application provides a computer-readable storage medium storing computer-executable instructions for executing the above-mentioned Figure 1 series fault arc detection and location method shown.

[0105] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0106] Those of ordinary skill in the art will understand that all or some of the steps and systems disclosed above can be implemented as software, firmware, hardware, and their appropriate combinations. Some physical components or all physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or can be implemented as hardware, or can be implemented as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include a computer storage medium (or non-transitory medium) and a communication medium (or transitory medium). As is well known to those of ordinary skill in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, as is well known to those of ordinary skill in the art, communication media typically includes computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transmission mechanism, and can include any information delivery medium.

[0107] The above is a specific description of the preferred embodiment of the present application. However, the present application is not limited to the above embodiments. Those skilled in the art can make various equivalent deformations or substitutions without departing from the spirit of the present application, and these equivalent deformations or substitutions are all included within the scope defined by the claims of the present application.

Claims

1. A method for detecting and locating a series fault arc, characterized in that: The following steps are involved: Get the original signal of the main power line; Performing low-pass filtering on the original signal to obtain a processed signal; obtaining arc fault characteristics in the processed signal; Based on the feature vector containing the arc fault feature, a random forest classifier is applied, and the arc fault is detected based on the random forest classifier, if a fault is detected within a single cycle, a fault parameter is increased by one, if no fault is detected, the fault parameter is decreased by one, and if the fault parameter is greater than a predetermined value, it is considered that there is a fault in the line; When a fault occurs, the identification feature vector of the arc fault is calculated; The arc fault is located based on the identification feature vector.

2. The method according to claim 1, characterized in that In the step of performing low-pass filtering on the original signal to obtain a processed signal, the collected voltage signal and current signal are also converted into a voltage two-dimensional array U and a current two-dimensional array I, respectively, wherein: IN k =[in 1,k ,……,in m,k ,……,in M,k ] I k =[i 1,k ,……,i m,k ,……,i M,k ]。 3. The method according to claim 2, characterized in that There are 12 arc fault features, with six pairs of features corresponding to the current two-dimensional array I and the voltage two-dimensional array U, respectively. The current two-dimensional array includes: The sum of the differences between adjacent periods of array I: The sum of the absolute values ​​of adjacent periods of array I: Maximum Euclidean distance: Euclidean distance ED: Maximum single sample distance MSSD: MSSD I (k)=max(I k+1 (m)-I k (m)|),m∈<1,2,...,M> Maximum slip difference MSD: By replacing the current two-dimensional array I in the above characteristics with the voltage two-dimensional array U, the remaining six arc fault characteristics are obtained.

4. The method according to claim 1, characterized in that: The minimum value of the fault parameter is 0, and the maximum value is 50. When the minimum value is reached, the fault parameter will not decrease any more, and when the maximum value is reached, the fault parameter will not increase any more.

5. The method according to claim 1, characterized in that The random forest classifier divides the collected data into a training set and a test set; the training set has 7 sequences, in 6 sequences, only one device is connected to the fault line, and during the seventh run, when connected to the fault line, a maximum of 6 devices work; in the test set, the test set contains sequences of pairs, three groups, four groups, five groups, and all 6 groups of devices.

6. The method according to claim 1, characterized in that The process of calculating the identification feature vector of the arc fault includes: For each cycle k in which the random forest classifier detects an arc fault, the value of the fault parameter ALS is incremented and the vector ΔI is calculated. k :ΔI k =I k -I k-1 , stored in the VC array of the i-th column, where i is equal to k modulo 50; if no arc fault is detected, the value of the fault parameter ALS is decremented and a 0 vector is stored in the i-th column of the VC array; Only the ΔI in the period when the arc fault is detected is k Vector Calculation MVC 50 Eigenvector, calculate MVC from VC array and fault parameter ALS 50 Eigenvectors: MVC 50 The number of attributes in the vector is reduced from 5000 to 50, and the average of every 100 values ​​is calculated to get the MVC 50-mean :

7. The method according to claim 1, characterized in that In the step of locating the arc fault based on the identification feature vector and the monitoring data, a k-nearest neighbor algorithm is used, where k=6; the feature used for classification is MVC 50-mean The average and maximum values ​​of .

8. A series fault arc detection and location system, characterized in that: include: A signal acquisition module capable of acquiring the original signal of the main power line; A data processing module, capable of performing low-pass filtering on the original signal to obtain a processed signal; A feature extraction module capable of acquiring arc fault features in the processed signal; A fault detection module, which can be applied to a random forest classifier based on a feature vector containing the arc fault feature, and detects the arc fault based on the random forest classifier, and if a fault is detected within a single cycle, a fault parameter is increased by one, and if no fault is detected, the fault parameter is decreased by one, and if the fault parameter is greater than a predetermined value, it is considered that there is a fault in the line; A feature vector calculation module is capable of calculating the identification feature vector of the arc fault when a fault occurs; The fault location module can locate the arc fault based on the identification feature vector.

9. A terminal, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the method according to any one of claims 1 to 7.

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