A method for identifying the same source of voltage sag and related equipment

Abstract

The present disclosure provides a method and related equipment for identifying the same source of voltage sags. The method processes the data to be identified as the same source, corresponding to the voltage sag fault waveform recorded by an intelligent power distribution terminal, at a unified sampling rate. The S transform is used to extract the sag data segments from the data to be identified as the same source. The Gram angular field matrix is ​​obtained by sequentially using a preset transformer transfer matrix and time-series two-dimensional graphical processing. The voltage sag similarity is obtained through image matching. The voltage sag similarity is compared with a preset same-source identification threshold to obtain an effective voltage sag same-source identification result. The present disclosure uses the voltage sag fault waveform as a reference, extracts the sag data segments through the S transform, and performs image matching using the Gram angular field matrix. This preserves the characteristics of the voltage sag fault waveform, can obtain true voltage sag similarity, and thus achieves effective voltage sag same-source identification.

Description

Technical Field

[0001] The present disclosure relates to the field of electric power technology, and in particular to a method for identifying the same source of voltage sags and related equipment. Background Art

[0002] With the steady progress of smart grid construction, the power grid is gradually transforming towards intelligence, informationization, and service-oriented development. During this transformation, the real-time perception and rational analysis of grid faults have become key to building smart grids. Grid faults primarily refer to power quality issues, primarily voltage sags. Therefore, smart distribution terminals are required to monitor grid voltage and other data. Currently, due to the increasing density of smart distribution terminals, a single voltage sag event will be recorded by different smart distribution terminals. Therefore, to analyze and study a specific voltage sag event, it is necessary to study the identification of voltage sag sources.

[0003] Voltage sag source identification combines voltage sag waveforms recorded by different intelligent distribution terminals for the same fault source. This allows for analysis of the voltage sag's propagation range and pinpointing its source, improving the efficiency of voltage sag analysis and management, and enabling a comprehensive assessment of the power grid.

[0004] Therefore, how to effectively identify the homology of voltage sags has become a technical problem that researchers in this field urgently need to solve. Summary of the Invention

[0005] In view of the above problems, the present disclosure provides a method and related equipment for identifying the same source of voltage sags to overcome or at least partially solve the above problems. The technical solution is as follows:

[0006] A method for identifying the same source of voltage sags, the method comprising:

[0007] Obtaining first homologous identification data corresponding to at least two smart power distribution terminals, wherein the first homologous identification data includes three-phase alternating current voltage data corresponding to a voltage sag fault waveform recorded by the smart power distribution terminal for a target power grid;

[0008] Performing unified sampling rate processing on each of the first data to be identified as homologous to the source, and obtaining second data to be identified as homologous to the source corresponding to each of the first data to be identified as homologous to the source;

[0009] Obtaining a temporary dip data segment in each of the second data to be identified as being of the same source using S transformation;

[0010] Determining a reference data segment and a non-reference data segment in each of the dip data segments;

[0011] constructing a benchmark data set based on the benchmark data segment using a preset transformer transfer matrix;

[0012] Performing time-series two-dimensional graphical processing on the reference data set and the non-reference data set constructed from the non-reference data segments, respectively, to obtain a first Gram angular field matrix corresponding to the reference data set and a second Gram angular field matrix corresponding to the non-reference data set;

[0013] performing image matching using the first Gram's angle field matrix and the second Gram's angle field matrix to obtain voltage sag similarity;

[0014] The voltage sag similarity is compared with a preset homologous identification threshold to obtain a voltage sag homologous identification result.

[0015] Optionally, performing unified sampling rate processing on each of the first data to be identified as homologous to the source to obtain the second data to be identified as homologous to the source corresponding to each of the first data to be identified as homologous to the source, includes:

[0016] respectively determining the number of sampling points of each of the first data to be identified from the same source in the same sampling period;

[0017] According to the preset sampling selection conditions, determining the number of reference sampling points among the number of sampling points;

[0018] According to the reference number of sampling points, the sampling rates of the first data to be identified as being from the same source are unified to obtain the second data to be identified as being from the same source corresponding to the first data to be identified as being from the same source.

[0019] Optionally, the step of obtaining the temporary dip data segment in each of the second data to be identified as homologous to the source by using S transformation includes:

[0020] Using S-transform to respectively determine the voltage sag start time and the voltage sag end time in each of the second to-be-identified homologous data;

[0021] For any of the second data to be identified as being from the same source, extract the voltage sag data segment in the data to be identified as being from the same source according to the voltage sag start time and the voltage sag end time corresponding to the data to be identified as being from the same source.

[0022] Optionally, determining a reference data segment and a non-reference data segment in each of the temporary dip data segments includes:

[0023] According to a preset reference data segment determination condition, a reference data segment is determined in each of the temporary dip data segments, and the temporary dip data segments other than the reference data segment are determined as non-reference data segments.

[0024] Optionally, the preset transformer transfer matrix includes at least one transfer matrix related to a transformer transfer characteristic, and constructing a reference data set based on the reference data segment using the preset transformer transfer matrix includes:

[0025] The reference data segments are multiplied by the respective transfer matrices in the preset transformer transfer matrices to obtain a reference data set.

[0026] Optionally, performing time-series two-dimensional graphical processing on the reference dataset and the non-reference dataset constructed from each of the non-reference data segments to obtain a first Gram angular field matrix corresponding to the reference dataset and a second Gram angular field matrix corresponding to the non-reference dataset includes:

[0027] performing data normalization and scaling on the benchmark dataset and the non-benchmark dataset constructed from the non-benchmark data segments, respectively, to obtain benchmark scaled data corresponding to the benchmark dataset and non-benchmark scaled data corresponding to the non-benchmark dataset;

[0028] Converting the reference scaling data and the non-reference scaling data from a Cartesian coordinate system to a polar coordinate system to obtain a reference mapping angle corresponding to the reference scaling data and a non-reference mapping angle corresponding to the non-reference scaling data;

[0029] Gram angle field conversion is performed using the reference mapping angle and the non-reference mapping angle respectively to obtain a first Gram angle field matrix corresponding to the reference data set and a second Gram angle field matrix corresponding to the non-reference data set.

[0030] Optionally, performing image matching using the first Gram's angle field matrix and the second Gram's angle field matrix to obtain voltage sag similarity includes:

[0031] Using a perceptual hash algorithm, respectively calculating a first hash fingerprint sequence of the first Gram's angular field matrix and a second hash fingerprint sequence of the second Gram's angular field matrix;

[0032] The voltage sag similarity is calculated based on the first hash fingerprint sequence and the second hash fingerprint sequence using a Euclidean norm.

[0033] A device for identifying the same source of a voltage sag comprises: a first unit for obtaining data to be identified as the same source, a second unit for obtaining data to be identified as the same source, a unit for obtaining a sag data segment, a unit for processing the sag data segment, a unit for constructing a reference data set, a unit for obtaining a Gram angle field matrix, a unit for obtaining a voltage sag similarity, and a unit for obtaining a voltage sag same source identification result.

[0034] The first to-be-identified-source data obtaining unit is configured to obtain first to-be-identified-source data corresponding to at least two smart power distribution terminals, wherein the first to-be-identified-source data includes three-phase alternating current voltage data corresponding to a voltage sag fault waveform recorded by the smart power distribution terminal for the target power grid;

[0035] The second data to be identified as homologous to the source obtaining unit is configured to perform uniform sampling rate processing on each of the first data to be identified as homologous to the source, and obtain the second data to be identified as homologous to the source corresponding to each of the first data to be identified as homologous to the source;

[0036] The temporary dip data segment obtaining unit is configured to obtain the temporary dip data segment in each of the second data to be identified as being of the same source by using S transformation;

[0037] The temporary dip data segment processing unit is configured to determine a reference data segment and a non-reference data segment in each temporary dip data segment;

[0038] The benchmark data set construction unit is configured to construct a benchmark data set based on the benchmark data segment using a preset transformer transfer matrix;

[0039] The Gram angular field matrix obtaining unit is configured to perform time-series two-dimensional graphical processing on the reference data set and the non-reference data set constructed from the non-reference data segments, respectively, to obtain a first Gram angular field matrix corresponding to the reference data set and a second Gram angular field matrix corresponding to the non-reference data set;

[0040] The voltage sag similarity obtaining unit is configured to perform image matching using the first Gram's angle field matrix and the second Gram's angle field matrix to obtain voltage sag similarity;

[0041] The voltage sag homologous identification result obtaining unit is configured to compare the voltage sag similarity with a preset homologous identification threshold to obtain a voltage sag homologous identification result.

[0042] A computer-readable storage medium stores a program thereon, wherein when the program is executed by a processor, any of the above-mentioned methods for identifying the same source of voltage sag is implemented.

[0043] An electronic device comprising at least one processor, at least one memory connected to the processor, and a bus; wherein the processor and the memory communicate with each other via the bus; and the processor is configured to call program instructions in the memory to execute any of the above-described methods for identifying the same source of voltage sags.

[0044] By means of the above technical solution, the present disclosure provides a method for identifying the same source of voltage sag and related equipment, the method comprising: obtaining first data to be identified from the same source corresponding to at least two intelligent power distribution terminals, wherein the first data to be identified from the same source include voltage data of three-phase AC power corresponding to the voltage sag fault waveform recorded by the intelligent power distribution terminal for the target power grid; performing unified sampling rate processing on each first data to be identified from the same source to obtain second data to be identified from the same source corresponding to each first data to be identified from the same source; obtaining a sag data segment in each second data to be identified from the same source by using S transformation; and Determine a benchmark data segment and a non-benchmark data segment; use a preset transformer transfer matrix to construct a benchmark data set based on the benchmark data segment; perform time-series two-dimensional graphical processing on the benchmark data set and the non-benchmark data set constructed by each non-benchmark data segment, respectively, to obtain a first Gram angular field matrix corresponding to the benchmark data set and a second Gram angular field matrix corresponding to the non-benchmark data set; use the first Gram angular field matrix and the second Gram angular field matrix to perform image matching to obtain voltage sag similarity; compare the voltage sag similarity with a preset homology identification threshold to obtain a voltage sag homology identification result. The present disclosure uniformly samples the data to be identified as homology corresponding to the voltage sag fault waveform recorded by the intelligent distribution terminal, uses S-transformation to extract the sag data segment from the data to be identified as homology, sequentially uses a preset transformer transfer matrix and time-series two-dimensional graphical processing to obtain the Gram angular field matrix, obtains the voltage sag similarity through image matching, compares the voltage sag similarity with a preset homology identification threshold, and obtains an effective voltage sag homology identification result. The present invention takes the voltage sag fault waveform as the standard, uses S-transform to extract the sag data segment, and performs image matching through the Gram angle field matrix, thereby retaining the voltage sag fault waveform characteristics, being able to obtain the true voltage sag similarity, and thus realizing effective voltage sag homology identification.

[0045] The above description is only an overview of the technical solution of the present disclosure. In order to more clearly understand the technical means of the present disclosure, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present disclosure more obvious and easy to understand, the specific implementation methods of the present disclosure are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present disclosure. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0047] Figure 1 A schematic diagram showing a flow chart of an implementation of a method for identifying the same source of a voltage sag provided by an embodiment of the present disclosure;

[0048] Figure 2 A schematic flow chart showing another implementation of the method for identifying the same source of voltage sags provided by an embodiment of the present disclosure is shown;

[0049] Figure 3 A schematic flow chart showing another implementation of the method for identifying the same source of voltage sags provided by an embodiment of the present disclosure is shown;

[0050] Figure 4 A schematic flow chart showing another implementation of the method for identifying the same source of voltage sags provided by an embodiment of the present disclosure is shown;

[0051] Figure 5 A schematic flow chart showing another implementation of the method for identifying the same source of voltage sags provided by an embodiment of the present disclosure is shown;

[0052] Figure 6 A schematic flow chart showing another implementation of the method for identifying the same source of voltage sags provided by an embodiment of the present disclosure is shown;

[0053] Figure 7 A schematic flow chart showing another implementation of the method for identifying the same source of voltage sags provided by an embodiment of the present disclosure is shown;

[0054] Figure 8 A schematic structural diagram of a voltage sag homologous identification device provided by an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0055] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0056] Currently, identifying the source of voltage sags is a relatively new research topic in the field of voltage sags. The following challenges exist in the research on identifying the source of voltage sags: the sampling rate of smart distribution terminals, the timing problem of smart distribution terminals, and the influence of transformer transfer characteristics, making it impossible to directly identify the source using monitoring data.

[0057] (1) Sampling rate impact. Since different intelligent power distribution terminals may be produced by different manufacturers, the sampling rates of the terminals may be different, which in turn makes the length of the recorded data different. Therefore, it is not possible to directly identify the source directly based on the data length.

[0058] (2) The impact of timing errors. Due to the cost constraints of intelligent distribution terminals, some intelligent distribution terminals still use local clocks, which will cause timing errors ranging from milliseconds to minutes, making it difficult to directly identify the same source through recording time.

[0059] (3) Influence of transformer transfer characteristics. In actual power grids, after a voltage sag passes through transformers with different connection methods, the amplitude and phase angle of the voltage sag will change to varying degrees, which may cause the type of voltage sag to change. In other words, it is difficult to directly identify the source of the voltage sag based on the waveform characteristics of the voltage sag.

[0060] like Figure 1 FIG. 1 is a flow chart of an implementation of a method for identifying a common source of a voltage sag according to an embodiment of the present disclosure. The method for identifying a common source of a voltage sag may include:

[0061] S100. Obtain first homologous identification data corresponding to at least two smart power distribution terminals, wherein the first homologous identification data includes three-phase alternating current voltage data corresponding to a voltage sag fault waveform recorded by the smart power distribution terminal for a target power grid.

[0062] The intelligent power distribution terminal can be a terminal device that collects real-time grid operation data, detects and identifies faults, and monitors the operating conditions of distribution switches. It is understood that the intelligent power distribution terminal can record the voltage waveform of the target power grid and identify voltage sag fault waveforms. It is understood that the voltage sag fault waveform corresponds to the voltage data of phases A, B, and C of the three-phase AC power.

[0063] Optionally, the embodiment of the present disclosure can obtain voltage sag fault waveforms recorded by different intelligent power distribution terminals in the same recording period, and determine the three-phase AC voltage data corresponding to the voltage sag fault waveform as the data to be identified as the same source.

[0064] The embodiment of the present disclosure takes the voltage sag fault waveform as a starting point, which can avoid the loss of waveform features of the voltage sag fault waveform and is conducive to obtaining a true homologous identification result.

[0065] S200 , performing unified sampling rate processing on each first data to be identified as homologous, and obtaining second data to be identified as homologous corresponding to each first data to be identified as homologous.

[0066] In actual situations, since the sampling rates of different smart distribution terminals may be different, the lengths of the data to be identified as the same source of different smart distribution terminals are different. Therefore, the embodiment of the present disclosure can unify the sampling rates of each data to be identified as the same source, so that the lengths of the data to be identified as the same source corresponding to different smart distribution terminals are the same, so as to perform same source identification.

[0067] Optional, based on Figure 1 The method shown, such as Figure 2 As shown, a flowchart of another implementation of the method for identifying the same source of voltage sags provided by an embodiment of the present disclosure is shown, where step S200 may include:

[0068] S210: Determine the number of sampling points of each first data to be identified as being from the same source in the same sampling period.

[0069] S220: Determine the number of reference sampling points among the number of sampling points according to the preset sampling selection conditions.

[0070] S230 , unifying the sampling rates of the first data to be identified as homologous to the source according to the number of reference sampling points, and obtaining the second data to be identified as homologous to the source corresponding to the first data to be identified as homologous to the source.

[0071] Optionally, the preset sampling selection condition may be to determine the sampling point number with the smallest value among the sampling point numbers as the reference sampling point number. The disclosed embodiment may downsample the first data to be identified as homologous to the source, except for the first data to be identified as homologous to the source corresponding to the reference sampling point number, based on the reference sampling point number, thereby unifying the sampling rates of the first data to be identified as homologous to the source, and obtaining second data to be identified as homologous to the source of the same length.

[0072] Optionally, the preset sampling selection condition may be to determine the sampling point with the largest value among all sampling points as the base sampling point. The disclosed embodiment may upsample the first data to be identified as homologous to the source, except for the first data to be identified as homologous to the source, based on the base sampling point, thereby unifying the sampling rates of the first data to be identified as homologous to the source, and obtaining second data to be identified as homologous to the source of the same length.

[0073] S300: Obtain a temporary drop data segment in each second data to be identified as being of the same source by using S transformation.

[0074] Among them, S-transformation is a combination of short-time Fourier transform and continuous wavelet transform. It can obtain localized time-frequency information like short-time Fourier transform, and at the same time, it can have different frequency resolutions like wavelet transform by using a time window with variable length and width.

[0075] Optional, based on Figure 1 The method shown, such as Figure 3 As shown, a flowchart of another implementation of the method for identifying the same source of voltage sags provided by an embodiment of the present disclosure is shown, step S300 may include:

[0076] S310 , using S transformation to respectively determine the voltage sag start time and the voltage sag end time in each second data to be identified as the same source.

[0077] The S transformation may include the following process: Assuming that the sampling time interval of the second data h(t) to be identified is t and the total time length is T, the number of sampling points is Then the discrete S transform of the second data to be identified as homologous can be expressed as:

[0078]

[0079] Among them, k is the time sampling point of the continuous time domain signal of the second data to be identified as the same source h(t), which is an integer with a value range of 0 to N-1; m is the summation sequence number, which is an integer with a value range of 0 to N-1; n is the frequency sampling point, which is an integer with a value range of 0 to N-1.

[0080] When n=0,

[0081]

[0082] The result of the change of S is a two-dimensional matrix. The row vectors of this two-dimensional matrix represent the amplitude and phase information of the signal at different times at a certain frequency, and the column vectors of this two-dimensional matrix represent the amplitude and phase information of the signal at different frequency components at a certain time. The result is obtained by taking the modulus values ​​of each two-dimensional matrix:

[0083]

[0084] S320 , for any second data to be identified as being from the same source: extracting a voltage sag data segment from the second data to be identified as being from the same source according to the voltage sag start time and the voltage sag end time corresponding to the second data to be identified as being from the same source.

[0085] The embodiment of the present disclosure uses S transformation to extract the temporary drop data segment from the second data to be identified as homologous. Since the amount of the temporary drop data segment is smaller than the second data to be identified as homologous, the computational complexity of homologous identification can be reduced, thereby improving the efficiency of homologous identification.

[0086] S400: Determine a reference data segment and a non-reference data segment in each dip data segment.

[0087] Optional, based on Figure 1 The method shown, such as Figure 4 As shown, a flowchart of another implementation of the method for identifying the same source of voltage sags provided by an embodiment of the present disclosure is provided. Step S400 may include:

[0088] S410 , according to a preset reference data segment determination condition, determine a reference data segment in each dip data segment, and determine other dip data segments except the reference data segment as non-reference data segments.

[0089] Optionally, the preset reference data segment determination condition may be to randomly select a temporary dip data segment from each temporary dip data segment as the reference data segment.

[0090] Optionally, the preset reference data segment determination condition may be to use the temporary sag data segment corresponding to the target intelligent power distribution terminal as the reference data segment.

[0091] S500: Using a preset transformer transfer matrix, a benchmark data set is constructed based on the benchmark data segment.

[0092] Optionally, the preset transformer transfer matrix includes at least one transfer matrix related to transformer transfer characteristics. Because the reference data segment and the non-reference data segment may pass through different transformer combinations, the transfer characteristics of each transformer may be different, and the corresponding transformer transfer matrix may also be different. Therefore, it is necessary to process the reference data segment in conjunction with the transformer transfer matrix.

[0093] Optional, based on Figure 1 The method shown, such as Figure 5 FIG. 5 is a flow chart of another embodiment of a method for identifying a voltage sag from the same source according to an embodiment of the present disclosure. Step S500 may include:

[0094] S510 : Multiply the reference data segments by each transfer matrix in the preset transformer transfer matrix to obtain a reference data set.

[0095] For ease of understanding, an example is given here: Assuming that T1 to T8 represent the transfer matrices corresponding to the possible transformer transfer characteristics, and the benchmark data segment is S1″, then S1″ is multiplied by T1 to T8 to obtain the benchmark data set (S1″ -1 ,S1″ -2 ,...,S1″ -8 ), where S1″ -1 The waveform showing the reference data segment S1″ passing through the transfer matrix T1.

[0096] According to the transfer characteristics of the transformer, the transformer connection methods can be divided into the first, second and third categories:

[0097] Type 1 (YNyn): This type of transformer has both high-voltage and low-voltage sides grounded. Neither the phase voltage nor the line voltage changes when passing through this type of transformer. That is, both the line voltage and phase voltage transfer matrices are unit matrices, and the sag type on the high-voltage and low-voltage sides is the same. The corresponding transfer matrix T1 is:

[0098]

[0099] Type II (Yyn, YNy, Yy): This type of transformer does not transmit zero-sequence components, so the line voltage sag transfer matrix is ​​the unit matrix. For the phase voltage, the sag type does not change after passing through this type of transformer, only the sag amplitude changes. The corresponding transfer matrix T2 is:

[0100]

[0101] Category 3 (YD, YNd, Dyn): The low-voltage side phase angle of this type of transformer is 30° ahead of the high-voltage side, which is equivalent to the conversion of phase voltage to line voltage, that is, T 3P-N =T 3P-P , the corresponding transfer matrix T3 is:

[0102]

[0103] The calculation formula for the reference data segment transmitted through the multi-stage transformer is:

[0104]

[0105] Among them, T x =T1×T2×T3…T8, U a 、U b 、U c is the three-phase voltage without transformer transmission, U A 、U B 、U C is the three-phase voltage after transmission through the transformer, and Tx represents the product of the transformer transfer matrix that the voltage sag passes through from the fault location to the monitoring point terminal. According to the different combinations of the three types of transformers, assuming there are m type I, n type II, and p type III transformers (m, n, p∈N), i is an integer multiple of 6, the transmission rules through the multi-stage transformer are shown in Table 1:

[0106] Table 1

[0107]

[0108] S600 , performing time-series two-dimensional graphical processing on the benchmark dataset and the non-benchmark dataset constructed from each non-benchmark data segment, respectively, to obtain a first Gram angular field matrix corresponding to the benchmark dataset and a second Gram angular field matrix corresponding to the non-benchmark dataset.

[0109] Among them, the Gramian Angular Field (GAF) converts the one-dimensional time series in the Cartesian coordinate system into a polar coordinate system, and then uses trigonometric functions to generate the GAF matrix.

[0110] In the embodiment of the present disclosure, coordinate transformation can be performed on the reference data set and the non-standard data set to obtain a first Gram angular field matrix and a second Gram angular field matrix.

[0111] Among them, the first Gram angular field matrix can be:

[0112]

[0113] Where G1 is any element G 1-ij represents the GAF of the j-th phase of the benchmark dataset after passing through the transformer transfer matrix i.

[0114] The second Gram angular field matrix can be:

[0115]

[0116] Where G2 any element G k-j represents the GAF of the jth phase of the non-benchmark dataset.

[0117] Optional, based on Figure 1 The method shown, such as Figure 6 FIG. 5 is a flow chart of another embodiment of a method for identifying a voltage sag from the same source according to an embodiment of the present disclosure. Step S600 may include:

[0118] S610 , performing data normalization and scaling on the benchmark dataset and the non-benchmark dataset constructed from each non-benchmark data segment, respectively, to obtain benchmark scaled data corresponding to the benchmark dataset and non-benchmark scaled data corresponding to the non-benchmark dataset.

[0119] S620 , respectively converting the reference scaling data and the non-reference scaling data from the Cartesian coordinate system to the polar coordinate system to obtain a reference mapping angle corresponding to the reference scaling data and a non-reference mapping angle corresponding to the non-reference scaling data.

[0120] S630 , performing Gram angle field conversion using the reference mapping angle and the non-reference mapping angle respectively to obtain a first Gram angle field matrix corresponding to the reference data set and a second Gram angle field matrix corresponding to the non-reference data set.

[0121] For ease of understanding, the process of two-dimensional graphical processing of time series is explained here by taking an example: Assume that a one-dimensional time series data is X = {x1, x2, ...x n}, the corresponding time T={t1,t2,…t n}, the one-dimensional time series data is normalized and scaled to [-1,1], and the formula is:

[0122]

[0123] Then, the inverse cosine function is monotonically decreasing in [-1, 1] (the mapping relationship is strictly one-to-one), and the scaled observation value is Mapping to angle The timestamp t in the time series i The mapping is radius r, and its formula is:

[0124]

[0125] Then perform Gram angle field transformation, and the obtained Gram angle field matrix is:

[0126]

[0127] The disclosed embodiment converts a one-dimensional time series into a two-dimensional image through the Gram angle field, thereby avoiding the influence of the resolution of the image generating device while retaining the time characteristics, so as to better perform homology identification.

[0128] S700 , performing image matching using the first Gram angular field matrix and the second Gram angular field matrix to obtain voltage sag similarity.

[0129] The embodiment of the present disclosure can use the perceptual hash algorithm to calculate the Gram angular field matrix to obtain a 64-bit binary hash fingerprint sequence, and then use the Euclidean norm (L2 norm) to calculate the similarity between two hash fingerprint sequences.

[0130] Optional, based on Figure 1 The method shown, such as Figure 7 FIG. 8 is a flow chart of another embodiment of a method for identifying a voltage sag from the same source provided by an embodiment of the present disclosure. Step S700 may include:

[0131] S710 , using a perceptual hash algorithm, respectively calculating a first hash fingerprint sequence of the first Gram's angular field matrix and a second hash fingerprint sequence of the second Gram's angular field matrix.

[0132] The implementation process of the perceptual hash algorithm includes:

[0133] 1. Grayscale. Read the image and convert the two-dimensional image matrix X N*N Grayscale is performed to remove the influence of factors such as image color, and matrix M is used X express.

[0134] 2. Image scaling. Scale the grayscale image matrix MX to a 32*32 matrix I X .

[0135] 3. DCT transformation. The scaled matrix I X Perform DCT discrete cosine transform to form matrix D X (DX is also a 32*32 matrix).

[0136] 4. Feature extraction. Intercept matrix D X The 8*8 part in the upper left corner forms the matrix Q X , where Q X (i, j) represents the corresponding element D in the matrix after discrete cosine transform X The value of (i∈[1,8],j∈[1,8]).

[0137] 5. Update element values. Calculate Q X The mean value of the matrix u X , traverse all elements and use the formula:

[0138]

[0139] Optimize element values ​​and generate an 8*8 hash fingerprint map P X .

[0140]

[0141] 6. Hash fingerprint sequence generation: Convert the hash fingerprint image into a string of 64-bit binary hash values.

[0142] S720 , using the Euclidean norm, calculate the voltage sag similarity based on the first hash fingerprint sequence and the second hash fingerprint sequence.

[0143] To facilitate understanding of Euclidean norm calculation, an example is given here: Assuming that the first hash fingerprint sequence is h1 = [h1(1), h1(2), ... h1(L)], and the second hash fingerprint sequence is h2 = [h2(1), h2(2), ... h2(L)], the Euclidean norm calculation formula is:

[0144]

[0145] S800: Compare the voltage sag similarity with a preset homologous identification threshold to obtain a voltage sag homologous identification result.

[0146] To facilitate understanding, here is an example:

[0147]

[0148] Among them, h i-1j Indicates that the hash value of the non-reference data segment i and the reference data segment are T j The number of different elements in the hash fingerprint sequence after the transformer transfer matrix. And h i-1j It contains the elements of phases A, B, and C. It satisfies:

[0149]

[0150] In order to better express the similarity, h is converted into i-1j Convert to [0, 1]:

[0151]

[0152] Pick The minimum value of each row of elements is:

[0153]

[0154] In L i-1min (i∈(2,3,…n))(where L i-1min When all elements in the matrix (a 3*1 matrix) are smaller than the preset homology identification threshold, the non-reference data segment i is determined to be homologous to the reference data; otherwise, the non-reference data segment i is determined to be different from the reference data.

[0155] The present disclosure provides a method for identifying the same source of voltage sags. The method performs unified sampling rate processing on the data to be identified as the same source, corresponding to the voltage sag fault waveform recorded by the intelligent distribution terminal, and uses S-transformation to extract the sag data segments from the data to be identified as the same source. The method then sequentially uses a preset transformer transfer matrix and time-series two-dimensional graphical processing to obtain a Gram angular field matrix. The method then obtains voltage sag similarity through image matching, and compares the voltage sag similarity with a preset same source identification threshold to obtain an effective voltage sag same source identification result. The method uses the voltage sag fault waveform as the basis, uses S-transformation to extract the sag data segments, and performs image matching using the Gram angular field matrix. This method retains the characteristics of the voltage sag fault waveform, can obtain true voltage sag similarity, and thus achieves effective voltage sag same source identification.

[0156] The present disclosure can not only perform homology identification on normal recording data of intelligent distribution equipment, but also perform homology identification on incomplete recording data, and can also identify a piece of recording data containing multiple sag times, and has good applicability.

[0157] Although the operations are depicted in a particular order, this should not be understood as requiring that the operations be performed in the particular order shown or in a sequential order.Multitasking and parallel processing may be advantageous under certain circumstances.

[0158] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0159] Corresponding to the above method embodiment, the embodiment of the present disclosure also provides a device for identifying the same source of voltage sag, the structure of which is as follows: Figure 8 As shown, it may include: a first data acquisition unit 100 for homologous identification, a second data acquisition unit 200 for homologous identification, a sag data segment acquisition unit 300, a sag data segment processing unit 400, a reference data set construction unit 500, a Gram angular field matrix acquisition unit 600, a voltage sag similarity acquisition unit 700, and a voltage sag homologous identification result acquisition unit 800.

[0160] The first homologous identification data obtaining unit 100 is used to obtain the first homologous identification data corresponding to at least two smart distribution terminals, wherein the first homologous identification data includes the three-phase AC voltage data corresponding to the voltage sag fault waveform recorded by the smart distribution terminal for the target power grid.

[0161] The second data to be identified as homologous to the source obtaining unit 200 is configured to perform uniform sampling rate processing on each of the first data to be identified as homologous to the source, and obtain the second data to be identified as homologous to the source corresponding to each of the first data to be identified as homologous to the source.

[0162] The temporary dip data segment obtaining unit 300 is configured to obtain the temporary dip data segment in each second data to be identified as being of the same source by using S-transformation.

[0163] The dip data segment processing unit 400 is configured to determine a reference data segment and a non-reference data segment in each dip data segment.

[0164] The benchmark data set construction unit 500 is configured to construct a benchmark data set based on the benchmark data segments using a preset transformer transfer matrix.

[0165] The Gram angular field matrix obtaining unit 600 is used to perform time-series two-dimensional graphical processing on the reference data set and the non-reference data set constructed by each non-reference data segment, respectively, to obtain a first Gram angular field matrix corresponding to the reference data set and a second Gram angular field matrix corresponding to the non-reference data set.

[0166] The voltage sag similarity obtaining unit 700 is configured to perform image matching using the first Gram's angle field matrix and the second Gram's angle field matrix to obtain the voltage sag similarity.

[0167] The voltage sag homologous identification result obtaining unit 800 is configured to compare the voltage sag similarity with a preset homologous identification threshold to obtain a voltage sag homologous identification result.

[0168] Optionally, the second data to be identified as homologous to the source obtaining unit 200 is specifically used to respectively determine the number of sampling points of each first data to be identified as homologous to the source in the same sampling period; determine the reference number of sampling points among each sampling point according to preset sampling selection conditions; unify the sampling rate of each first data to be identified as homologous to the source according to the reference number of sampling points, and obtain the second data to be identified as homologous to the source corresponding to each first data to be identified as homologous to the source.

[0169] Optionally, the voltage sag data segment obtaining unit 300 is specifically configured to use S transform to respectively determine the voltage sag start time and the voltage sag end time in each second data to be identified from the same source; for any second data to be identified from the same source: extract the voltage sag data segment in the second data to be identified from the same source according to the voltage sag start time and the voltage sag end time corresponding to the second data to be identified from the same source.

[0170] Optionally, the dip data segment processing unit 400 is specifically configured to determine a reference data segment in each dip data segment according to a preset reference data segment determination condition, and determine other dip data segments except the reference data segment as non-reference data segments.

[0171] Optionally, the preset transformer transfer matrix includes at least one transfer matrix related to the transformer transfer characteristic. The reference data set construction unit 500 is specifically configured to multiply the reference data segment by each transfer matrix in the preset transformer transfer matrix to obtain a reference data set.

[0172] Optionally, the Gram angular field matrix obtaining unit 600 includes: a first obtaining subunit, a second obtaining subunit, and a third obtaining subunit.

[0173] A first obtaining subunit is configured to perform data normalization and scaling on the benchmark dataset and the non-benchmark dataset constructed from each non-benchmark data segment, respectively, to obtain benchmark scaled data corresponding to the benchmark dataset and non-benchmark scaled data corresponding to the non-benchmark dataset;

[0174] a second obtaining subunit, configured to convert the reference scaled data and the non-reference scaled data from a Cartesian coordinate system to a polar coordinate system, respectively, to obtain a reference mapping angle corresponding to the reference scaled data and a non-reference mapping angle corresponding to the non-reference scaled data;

[0175] The third obtaining subunit is used to perform Gram angle field conversion using the reference mapping angle and the non-reference mapping angle respectively to obtain a first Gram angle field matrix corresponding to the reference data set and a second Gram angle field matrix corresponding to the non-reference data set.

[0176] Optionally, the voltage sag similarity obtaining unit 700 is specifically used to use the perceptual hash algorithm to calculate the first hash fingerprint sequence of the first Gram's angular field matrix and the second hash fingerprint sequence of the second Gram's angular field matrix respectively; and use the Euclidean norm to calculate the voltage sag similarity based on the first hash fingerprint sequence and the second hash fingerprint sequence.

[0177] The present disclosure provides a device for identifying the same source of voltage sags. The device processes the data to be identified as the same source, corresponding to the voltage sag fault waveform recorded by an intelligent power distribution terminal, at a unified sampling rate. The device then uses an S-transform to extract the sag data segments from the data to be identified as the same source. The device then sequentially uses a preset transformer transfer matrix and two-dimensional time-series graphical processing to obtain a Gram angular field matrix. The device then obtains voltage sag similarity through image matching, and compares the voltage sag similarity with a preset same-source identification threshold to obtain an effective voltage sag same-source identification result. The device uses the voltage sag fault waveform as a reference, extracts the sag data segments through an S-transform, and performs image matching using the Gram angular field matrix. This method preserves the characteristics of the voltage sag fault waveform, obtains true voltage sag similarity, and thus achieves effective voltage sag same-source identification.

[0178] Regarding the apparatus in the above embodiment, the specific manner in which each unit performs operations has been described in detail in the embodiment of the method, and will not be elaborated on here.

[0179] The voltage sag homology identification device includes a processor and a memory. The first to-be-identified-homologous-data obtaining unit 100, the second to-be-identified-homologous-data obtaining unit 200, the sag data segment obtaining unit 300, the sag data segment processing unit 400, the reference data set construction unit 500, the Gram angular field matrix obtaining unit 600, the voltage sag similarity obtaining unit 700, and the voltage sag homology identification result obtaining unit 800 are all stored in the memory as program units. The processor executes the program units stored in the memory to implement corresponding functions.

[0180] The processor includes a kernel, which retrieves the corresponding program unit from memory. One or more kernels can be configured. By adjusting kernel parameters, the voltage sag fault waveform is used as the basis, and the sag data segments are extracted using the S transform. Image matching is performed using the Gram angle field matrix, preserving the characteristics of the voltage sag fault waveform. This allows for accurate voltage sag similarity and effective identification of the same source of the voltage sag.

[0181] An embodiment of the present disclosure provides a computer-readable storage medium having a program stored thereon. When the program is executed by a processor, the method for identifying the same source of voltage sag is implemented.

[0182] An embodiment of the present disclosure provides a processor, which is used to run a program, wherein the method for identifying the same source of voltage sag is executed when the program is run.

[0183] An embodiment of the present disclosure provides an electronic device comprising at least one processor, at least one memory device connected to the processor, and a bus. The processor and the memory device communicate with each other via the bus. The processor is configured to invoke program instructions stored in the memory device to execute the aforementioned method for identifying the same source of a voltage sag. The electronic device herein may be a server, a PC, a PAD, a mobile phone, or the like.

[0184] The present disclosure also provides a computer program product, which, when executed on an electronic device, is suitable for executing the steps of the method for initializing the same-source identification of voltage sags.

[0185] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, apparatuses, electronic devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable device to produce a machine, so that the instructions executed by the processor of the computer or other programmable device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0186] In a typical configuration, an electronic device includes one or more processors (CPUs), a memory, and a bus. The electronic device may also include an input / output interface, a network interface, and the like.

[0187] Memory may include non-permanent memory in a computer-readable medium, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory includes at least one memory chip. Memory is an example of a computer-readable medium.

[0188] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0189] In the description of the present disclosure, it should be understood that if the terms "up", "down", "front", "back", "left" and "right" are used to indicate directions or positional relationships, they are based on the directions or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the positions or elements referred to must have a specific direction, be constructed and operate in a specific direction. Therefore, they should not be understood as limitations of the present disclosure.

[0190] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. It should also be noted that the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, commodity, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, commodity, or device comprising the element.

[0191] Those skilled in the art will appreciate that embodiments of the present disclosure may be provided as methods, systems, or computer program products. Thus, the present disclosure may take the form of a fully hardware embodiment, a fully software embodiment, or an embodiment combining software and hardware. Furthermore, the present disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0192] The above are merely examples of the present disclosure and are not intended to limit the present disclosure. Various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present disclosure are intended to be included within the scope of the claims of the present disclosure.

Claims

1. A method for identifying the same source of voltage sags, characterized in that: The method comprises: Obtaining first homologous identification data corresponding to at least two smart power distribution terminals, wherein the first homologous identification data includes three-phase alternating current voltage data corresponding to a voltage sag fault waveform recorded by the smart power distribution terminal for a target power grid; Performing unified sampling rate processing on each of the first data to be identified as homologous to the source, and obtaining second data to be identified as homologous to the source corresponding to each of the first data to be identified as homologous to the source; Obtaining a temporary dip data segment in each of the second data to be identified as being of the same source using S transformation; Determining a reference data segment and a non-reference data segment in each of the dip data segments; constructing a benchmark data set based on the benchmark data segment using a preset transformer transfer matrix; Performing time-series two-dimensional graphical processing on the reference data set and the non-reference data set constructed from the non-reference data segments, respectively, to obtain a first Gram angular field matrix corresponding to the reference data set and a second Gram angular field matrix corresponding to the non-reference data set; performing image matching using the first Gram's angle field matrix and the second Gram's angle field matrix to obtain voltage sag similarity; Comparing the voltage sag similarity with a preset homology identification threshold to obtain a voltage sag homology identification result; The preset transformer transfer matrix includes at least one transfer matrix related to the transformer transfer characteristics, and the use of the preset transformer transfer matrix to construct a reference data set based on the reference data segment includes: The reference data segments are multiplied by the respective transfer matrices in the preset transformer transfer matrices to obtain a reference data set.

2. The method according to claim 1, characterized in that The performing unified sampling rate processing on each of the first data to be identified as homologous to the source to obtain the second data to be identified as homologous to the source corresponding to each of the first data to be identified as homologous to the source, comprises: respectively determining the number of sampling points of each of the first data to be identified from the same source in the same sampling period; According to the preset sampling selection conditions, determining the number of reference sampling points among the number of sampling points; According to the reference number of sampling points, the sampling rates of the first data to be identified as being from the same source are unified to obtain the second data to be identified as being from the same source corresponding to the first data to be identified as being from the same source.

3. The method according to claim 1, characterized in that The step of using S transformation to obtain the temporary dip data segment in each of the second data to be identified as being of the same source includes: Using S-transform to respectively determine the voltage sag start time and the voltage sag end time in each of the second to-be-identified homologous data; For any of the second data to be identified as being from the same source, extract the voltage sag data segment in the data to be identified as being from the same source according to the voltage sag start time and the voltage sag end time corresponding to the data to be identified as being from the same source.

4. The method according to claim 1, wherein The determining of the reference data segment and the non-reference data segment in each of the temporary dip data segments includes: According to a preset reference data segment determination condition, a reference data segment is determined in each of the temporary dip data segments, and the temporary dip data segments other than the reference data segment are determined as non-reference data segments.

5. The method according to claim 1, characterized in that The performing time-series two-dimensional graphical processing on the reference data set and the non-reference data set constructed from the non-reference data segments to obtain a first Gram angular field matrix corresponding to the reference data set and a second Gram angular field matrix corresponding to the non-reference data set includes: performing data normalization and scaling on the benchmark dataset and the non-benchmark dataset constructed from the non-benchmark data segments, respectively, to obtain benchmark scaled data corresponding to the benchmark dataset and non-benchmark scaled data corresponding to the non-benchmark dataset; Converting the reference scaling data and the non-reference scaling data from a Cartesian coordinate system to a polar coordinate system to obtain a reference mapping angle corresponding to the reference scaling data and a non-reference mapping angle corresponding to the non-reference scaling data; Gram angle field conversion is performed using the reference mapping angle and the non-reference mapping angle respectively to obtain a first Gram angle field matrix corresponding to the reference data set and a second Gram angle field matrix corresponding to the non-reference data set.

6. The method according to claim 1, characterized in that The performing image matching using the first Gram's angle field matrix and the second Gram's angle field matrix to obtain voltage sag similarity includes: Using a perceptual hash algorithm, respectively calculating a first hash fingerprint sequence of the first Gram's angular field matrix and a second hash fingerprint sequence of the second Gram's angular field matrix; The voltage sag similarity is calculated based on the first hash fingerprint sequence and the second hash fingerprint sequence using a Euclidean norm.

7. A device for identifying the same source of voltage sag, characterized in that: include: a first unit for obtaining data to be identified as homologous, a second unit for obtaining data to be identified as homologous, a unit for obtaining temporary sag data segments, a unit for processing temporary sag data segments, a unit for constructing a reference data set, a unit for obtaining a Gram angle field matrix, a unit for obtaining voltage sag similarity, and a unit for obtaining a result of obtaining a voltage sag homologous identification, The first to-be-identified-source data obtaining unit is configured to obtain first to-be-identified-source data corresponding to at least two smart power distribution terminals, wherein the first to-be-identified-source data includes three-phase alternating current voltage data corresponding to a voltage sag fault waveform recorded by the smart power distribution terminal for the target power grid; The second data to be identified as homologous to the source obtaining unit is configured to perform uniform sampling rate processing on each of the first data to be identified as homologous to the source, and obtain the second data to be identified as homologous to the source corresponding to each of the first data to be identified as homologous to the source; The temporary dip data segment obtaining unit is configured to obtain the temporary dip data segment in each of the second data to be identified as being of the same source by using S transformation; The temporary dip data segment processing unit is configured to determine a reference data segment and a non-reference data segment in each temporary dip data segment; The benchmark data set construction unit is configured to construct a benchmark data set based on the benchmark data segment using a preset transformer transfer matrix; The Gram angular field matrix obtaining unit is configured to perform time-series two-dimensional graphical processing on the reference data set and the non-reference data set constructed from the non-reference data segments, respectively, to obtain a first Gram angular field matrix corresponding to the reference data set and a second Gram angular field matrix corresponding to the non-reference data set; The voltage sag similarity obtaining unit is configured to perform image matching using the first Gram's angle field matrix and the second Gram's angle field matrix to obtain voltage sag similarity; The voltage sag homology identification result obtaining unit is configured to compare the voltage sag similarity with a preset homology identification threshold to obtain a voltage sag homology identification result; The preset transformer transfer matrix includes at least one transfer matrix related to the transformer transfer characteristics, and the benchmark data set construction unit is specifically used to multiply the benchmark data segment by each of the transfer matrices in the preset transformer transfer matrix to obtain a benchmark data set.

8. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the method for identifying the same source of voltage sags according to any one of claims 1 to 6 is implemented.

9. An electronic device comprising at least one processor, and at least one memory and bus connected to the processor; wherein: The processor and the memory communicate with each other via the bus; The processor is configured to call program instructions in the memory to execute the voltage sag homologous identification method according to any one of claims 1 to 6.

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