Power distribution network electric energy quality early warning method based on frequency domain characteristic correlation analysis algorithm

Abstract

The invention relates to the technical field of power distribution network electric energy quality analysis, in particular to a power distribution network electric energy quality early warning method based on a frequency domain feature correlation analysis algorithm, and the method comprises the steps: firstly obtaining a bus voltage waveform and a plurality of feeder line current waveforms; frequency domain feature extraction is carried out on the bus voltage waveform and the plurality of feeder line current waveforms respectively, and a plurality of obtained frequency domain features are constructed into a first frequency domain queue; dimension raising is carried out on the first frequency domain queue, an obtained second frequency domain queue is input into a linear early warning classification equation, and equation output is obtained; and finally, outputting an electric energy quality early warning message according to a numerical value interval in which the equation is output. According to the method, due to the fact that the linear classification equation is adopted for numeralization and classification, electric energy quality anomalies which are not prone to being found can be found, and due to the fact that the equation is a linear equation, few samples are adopted for construction, and the construction process is simple, the method is suitable for the power distribution network and convenient to achieve and popularize.

Description

Technical Field

[0001] The present invention relates to the technical field of power quality analysis of distribution networks, and in particular to a power quality early warning method for distribution networks based on a frequency domain feature correlation analysis algorithm. Background Art

[0002] Power Quality refers to the quality of electric energy in the power system. Ideal electric energy should be a perfectly symmetrical sine wave. Some factors can cause the waveform to deviate from the symmetrical sine, which results in power quality problems. Strictly speaking, the main indicators for measuring power quality are voltage, frequency and waveform. In a general sense, it refers to high-quality power supply, including voltage quality, current quality, power supply quality and power quality. On the one hand, we study what factors can cause power quality problems, on the other hand, we study what problems these factors can cause, and finally, we study how to eliminate these factors so that the electric energy can be as close to a sine wave as possible.

[0003] Power quality problems can be defined as: deviations in voltage, current or frequency that cause electrical equipment to malfunction or not work properly. They include frequency deviation, voltage deviation, voltage fluctuation and flicker, three-phase imbalance, instantaneous or transient overvoltage, waveform distortion (harmonics), voltage sag, interruption, swell and power supply continuity.

[0004] As can be seen from the above, power quality includes many aspects, and analyzing the power quality problems is the key to distribution network regulation. At present, the analysis of power quality is mostly done through mathematical analysis. The main problem with this method is that it is not easy to detect relatively early and comprehensive power quality anomalies and power quality problems.

[0005] Based on this, it is necessary to develop and design a distribution network power quality early warning method based on frequency domain feature correlation analysis algorithm. Summary of the invention

[0006] The embodiment of the present invention provides a distribution network power quality early warning method based on a frequency domain feature correlation analysis algorithm, which is used to solve the problem that power quality anomalies are not easy to be discovered in the prior art.

[0007] In a first aspect, an embodiment of the present invention provides a distribution network power quality early warning method based on a frequency domain feature correlation analysis algorithm, comprising:

[0008] Obtaining a bus voltage waveform and multiple feeder current waveforms, wherein each feeder current corresponds to a feeder, and a distributed power source or a load is connected to the bus through the feeder;

[0009] Extracting frequency domain features from the bus voltage waveform and the multiple feeder current waveforms respectively, and constructing the obtained multiple frequency domain features into a first frequency domain queue;

[0010] The first frequency domain queue is dimensionally upgraded, and the obtained second frequency domain queue is input into a linear early warning classification equation to obtain an equation output, wherein the linear early warning classification equation is constructed according to a plurality of sample pairs, and the sample pairs include frequency domain queue samples and power quality anomalies;

[0011] According to the numerical range in which the output of the equation is located, a power quality warning message is output.

[0012] In a possible implementation, frequency domain feature extraction is performed on the bus voltage waveform and the multiple feeder current waveforms respectively, and the obtained multiple frequency domain features are constructed into a first frequency domain queue, including:

[0013] Get the fundamental frequency;

[0014] Sequentially extract waveforms from the bus voltage waveform and the multiple feeder current waveforms as waveforms to be processed;

[0015] Extracting the frequency domain value of the first frequency of the waveform to be processed according to the fundamental frequency, and adding the obtained frequency domain value to the first frequency domain queue;

[0016] If the traversal of the bus voltage waveform and the multiple feeder current waveforms is not completed, jump to the step of sequentially taking waveforms from the bus voltage waveform and the multiple feeder current waveforms as waveforms to be processed;

[0017] Otherwise, if the first frequency does not reach the frequency domain extraction limit, the first frequency is adjusted, the waveform extraction position is initialized, and the process jumps to the step of extracting waveforms from the bus voltage waveform and the multiple feeder current waveforms in sequence as the waveforms to be processed.

[0018] In a possible implementation, extracting the frequency domain value of the first frequency of the waveform to be processed according to the fundamental frequency includes:

[0019] The frequency domain value of the first frequency of the waveform to be processed is extracted according to the first formula and the fundamental frequency, wherein the first formula is:

[0020]

[0021] Wherein, WF(k) is the kth eigenvalue of the waveform to be processed, wnMax is the total number of data after the waveform to be processed is discretized, DW(wn) is the wnth data after the waveform to be processed is discretized, e is a natural constant, j is an imaginary unit, ω0 is the fundamental frequency, PN is the number of data corresponding to the period length of the discrete waveform to be processed corresponding to the basic frequency, and π is the pi.

[0022] In a possible implementation manner, the second frequency domain queue obtained by performing dimension upgrading on the first frequency domain queue includes:

[0023] The first frequency domain queue is dimensionally upgraded according to the second formula to obtain a second frequency domain queue, wherein the second formula is:

[0024]

[0025] Where Qx(xm) is the xmth element of the second frequency domain queue, w xm is the xmth first coefficient, QF(Fm) is the Fmth element of the first frequency domain queue, int() is the integer function, and mod() is the remainder function.

[0026] In a possible implementation, constructing the linear early warning classification equation according to multiple sample pairs includes:

[0027] Acquire multiple sample pairs, wherein each sample pair includes a frequency domain queue sample and a power quality anomaly, and the dimension of the frequency domain queue sample is the same as the dimension of the second frequency domain queue;

[0028] Dividing the plurality of sample pairs into a plurality of sample groups according to the power quality abnormality, wherein each sample group corresponds to a power quality abnormality;

[0029] Substituting the frequency domain queue samples of the multiple sample pairs into the linear equation respectively to obtain multiple first results, wherein each first result corresponds to the frequency domain queue sample of a sample pair;

[0030] sorting the plurality of first results, and dividing the sorted results into a plurality of sorting intervals according to the number of samples in the sample group, wherein each sorting interval corresponds to a sample group;

[0031] If there are differences between the multiple sorting intervals and the multiple sample groups, adjust the multiple coefficients of the linear equation according to the multiple differences and jump to the step of substituting the frequency domain queue samples of the multiple sample pairs into the linear equation respectively to obtain multiple first results, wherein the difference is the difference between the first result that does not belong to the sorting interval and the median of the interval to which it belongs;

[0032] Otherwise, the linear equation is used as the linear early warning classification equation.

[0033] In one possible implementation, the linear equation is:

[0034]

[0035] Where Result is the output of the linear equation, wx xm is the xmth second coefficient, SQx(xm) is the xmth element of the input frequency domain queue, and xmMax is the total number of elements in the input frequency domain queue.

[0036] In a possible implementation, adjusting the multiple coefficients of the linear equation according to the multiple differences includes:

[0037] The multiple coefficients of the linear equation are adjusted according to a third formula, wherein the third formula is:

[0038]

[0039] In the formula, is the adjusted xmth coefficient, is the xmth coefficient before adjustment, Bias bm is the bmth difference value, and bmMax is the total number of difference values.

[0040] In a second aspect, an embodiment of the present invention provides an operation and maintenance device for an electric power metering device, which is used to implement the distribution network power quality early warning method based on the frequency domain feature correlation analysis algorithm as described in the first aspect or any possible implementation method of the first aspect above, and the electric power metering device operation and maintenance device includes:

[0041] A waveform acquisition module, used to acquire a bus voltage waveform and multiple feeder current waveforms, wherein each feeder current corresponds to a feeder, and a distributed power source or a load is connected to the bus through the feeder;

[0042] A frequency domain feature extraction module, used to extract frequency domain features from the bus voltage waveform and the multiple feeder current waveforms, respectively, and construct the obtained multiple frequency domain features into a first frequency domain queue;

[0043] An association analysis module, used for performing dimension upgrading on the first frequency domain queue and inputting the obtained second frequency domain queue into a linear early warning classification equation to obtain an equation output, wherein the linear early warning classification equation is constructed according to a plurality of sample pairs, the sample pairs including frequency domain queue samples and power quality anomalies;

[0044] as well as,

[0045] The early warning module is used to output a power quality early warning message according to the numerical interval in which the equation output is located.

[0046] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, it implements the steps of the method described in the first aspect or any possible implementation method of the first aspect.

[0047] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in the first aspect or any possible implementation of the first aspect are implemented.

[0048] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0049] The embodiment of the present invention discloses a distribution network power quality warning method based on a frequency domain feature association analysis algorithm, which first obtains a bus voltage waveform and multiple feeder current waveforms, wherein each feeder current corresponds to a feeder, and a distributed power source or load is connected to the bus through the feeder; then, the bus voltage waveform and the multiple feeder current waveforms are subjected to frequency domain feature extraction, and the multiple frequency domain features obtained are constructed into a first frequency domain queue; then, the first frequency domain queue is dimensionally upgraded, and the obtained second frequency domain queue is input into a linear warning classification equation to obtain an equation output, wherein the linear warning classification equation is constructed according to multiple sample pairs, and the sample pairs include frequency domain queue samples and power quality anomalies; finally, according to the numerical interval in which the equation output is located, a power quality warning message is output. The implementation mode of the present invention performs frequency domain analysis based on the bus voltage waveform and feeder current waveform of the distribution network, and then determines the abnormality of the distribution network through the correlation analysis method. Since the linear classification equation is used for digitization and classification, it is possible to discover power quality abnormalities that are not easy to be found. Since the equation is a linear equation, the number of samples used in the construction is small and the construction process is relatively simple. Therefore, it is suitable for distribution networks and is easy to implement and promote. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0051] Figure 1 It is a flow chart of a power quality early warning method for a distribution network based on a frequency domain feature correlation analysis algorithm provided by an embodiment of the present invention;

[0052] Figure 2 It is a principle diagram of the linear early warning classification equation construction process provided by an embodiment of the present invention;

[0053] Figure 3 It is a functional block diagram of the power metering equipment operation and maintenance device provided by an embodiment of the present invention;

[0054] Figure 4 It is a functional block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, and methods are omitted so as not to obscure the description of the present invention with unnecessary details.

[0056] In order to make the purpose, technical solutions and advantages of the present invention more clear, a specific implementation method will be described below in conjunction with the accompanying drawings.

[0057] The following is a detailed description of an embodiment of the present invention. This example is implemented based on the technical solution of the present invention, and provides a detailed implementation method and a specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0058] Figure 1 A flow chart of a distribution network power quality early warning method based on a frequency domain feature correlation analysis algorithm provided in an embodiment of the present invention.

[0059] like Figure 1 As shown, it shows a flow chart of the implementation of the distribution network power quality early warning method based on the frequency domain feature correlation analysis algorithm provided by the embodiment of the present invention, which is described in detail as follows:

[0060] In step 101, a bus voltage waveform and a plurality of feeder current waveforms are obtained, wherein each feeder current corresponds to a feeder, and a distributed power source or a load is connected to the bus through the feeder.

[0061] Exemplarily, one application scenario of the embodiment of the present invention is that the main power supply is connected to multiple feeders through a bus, and the feeders are connected to distributed power supplies and / or loads. By obtaining the voltage waveform of the bus and the current waveform of the feeder, and extracting the frequency domain features of the waveforms, the waveforms with abnormalities are analyzed by the correlation analysis method. With regard to the waveform frequency domain feature extraction, the present invention is discussed in detail from the following steps.

[0062] In step 102, frequency domain features are extracted from the bus voltage waveform and the multiple feeder current waveforms, respectively, and the multiple frequency domain features obtained are constructed into a first frequency domain queue.

[0063] In some implementations, the extracting frequency domain features of the bus voltage waveform and the multiple feeder current waveforms respectively, and constructing the obtained multiple frequency domain features into a first frequency domain queue includes:

[0064] Get the fundamental frequency;

[0065] Sequentially extract waveforms from the bus voltage waveform and the multiple feeder current waveforms as waveforms to be processed;

[0066] Extracting the frequency domain value of the first frequency of the waveform to be processed according to the fundamental frequency, and adding the obtained frequency domain value to the first frequency domain queue;

[0067] If the traversal of the bus voltage waveform and the multiple feeder current waveforms is not completed, jump to the step of sequentially taking waveforms from the bus voltage waveform and the multiple feeder current waveforms as waveforms to be processed;

[0068] Otherwise, if the first frequency does not reach the frequency domain extraction limit, the first frequency is adjusted, the waveform extraction position is initialized, and the process jumps to the step of extracting waveforms from the bus voltage waveform and the multiple feeder current waveforms in sequence as the waveforms to be processed.

[0069] In some implementations, extracting the frequency domain value of the first frequency of the waveform to be processed according to the fundamental frequency includes:

[0070] The frequency domain value of the first frequency of the waveform to be processed is extracted according to the first formula and the fundamental frequency, wherein the first formula is:

[0071]

[0072] Wherein, WF(k) is the kth eigenvalue of the waveform to be processed, wnMax is the total number of data after the waveform to be processed is discretized, DW(wn) is the wnth data after the waveform to be processed is discretized, e is a natural constant, j is an imaginary unit, ω0 is the fundamental frequency, PN is the number of data corresponding to the period length of the discrete waveform to be processed corresponding to the basic frequency, and π is the pi.

[0073] Exemplarily, the embodiment of the present invention sequentially extracts waveforms from the bus voltage waveform and multiple feeder current waveforms each time, then extracts the frequency domain value of the first frequency of the waveform to be processed according to the fundamental frequency, and adds the obtained frequency domain value to the first frequency domain queue;

[0074] Specifically, in terms of frequency domain value extraction, the embodiment of the present invention applies the first formula:

[0075]

[0076] Wherein, WF(k) is the kth eigenvalue of the waveform to be processed, wnMax is the total number of data after the waveform to be processed is discretized, DW(wn) is the wnth data after the waveform to be processed is discretized, e is a natural constant, j is an imaginary unit, ω0 is the fundamental frequency, PN is the number of data corresponding to the period length of the discrete waveform to be processed corresponding to the basic frequency, and π is the pi.

[0077] Next, the waveform extraction situation is analyzed. If the traversal of the bus voltage waveform and multiple feeder current waveforms is not completed, jump to the step of extracting waveforms from the bus voltage waveform and multiple feeder current waveforms in sequence; otherwise, if the first frequency does not reach the frequency domain extraction limit, adjust the first frequency, initialize the waveform extraction position, and jump to the step of extracting waveforms from the bus voltage waveform and multiple feeder current waveforms in sequence. Repeat this operation. When the bus voltage waveform and multiple feeder current waveforms are traversed multiple times and different frequency domain values ​​are obtained after each traversal, the construction of the first frequency domain queue is completed.

[0078] In step 103, the first frequency domain queue is dimensionally upgraded, and the obtained second frequency domain queue is input into a linear warning classification equation to obtain an equation output, wherein the linear warning classification equation is constructed based on multiple sample pairs, and the sample pairs include frequency domain queue samples and power quality anomalies.

[0079] In some implementations, the second frequency domain queue obtained by performing dimension upgrading on the first frequency domain queue includes:

[0080] The first frequency domain queue is dimensionally upgraded according to the second formula to obtain a second frequency domain queue, wherein the second formula is:

[0081]

[0082] Where Qx(xm) is the xmth element of the second frequency domain queue, w xm is the xmth first coefficient, QF(Fm) is the Fmth element of the first frequency domain queue, int() is the integer function, and mod() is the remainder function.

[0083] In some implementations, constructing the linear early warning classification equation based on multiple sample pairs includes:

[0084] Acquire multiple sample pairs, wherein each sample pair includes a frequency domain queue sample and a power quality anomaly, and the dimension of the frequency domain queue sample is the same as the dimension of the second frequency domain queue;

[0085] Dividing the plurality of sample pairs into a plurality of sample groups according to the power quality abnormality, wherein each sample group corresponds to a power quality abnormality;

[0086] Substituting the frequency domain queue samples of the multiple sample pairs into the linear equation respectively to obtain multiple first results, wherein each first result corresponds to the frequency domain queue sample of a sample pair;

[0087] sorting the plurality of first results, and dividing the sorted results into a plurality of sorting intervals according to the number of samples in the sample group, wherein each sorting interval corresponds to a sample group;

[0088] If there are differences between the multiple sorting intervals and the multiple sample groups, adjust the multiple coefficients of the linear equation according to the multiple differences and jump to the step of substituting the frequency domain queue samples of the multiple sample pairs into the linear equation respectively to obtain multiple first results, wherein the difference is the difference between the first result that does not belong to the sorting interval and the median of the interval to which it belongs;

[0089] Otherwise, the linear equation is used as the linear early warning classification equation.

[0090] In some embodiments, the linear equation is:

[0091]

[0092] Where Result is the output of the linear equation, wx xm is the xmth second coefficient, SQx(xm) is the xmth element of the input frequency domain queue, and xmMax is the total number of elements in the input frequency domain queue.

[0093] In some implementations, adjusting the plurality of coefficients of the linear equation according to the plurality of differences comprises:

[0094] The multiple coefficients of the linear equation are adjusted according to a third formula, wherein the third formula is:

[0095]

[0096] In the formula, is the adjusted xmth coefficient, is the xmth coefficient before adjustment, Bias bm is the bmth difference value, and bmMax is the total number of difference values.

[0097] For example, Figure 2As shown, the sample pair includes a frequency domain queue sample 202 and a power quality anomaly 201, and the two are in a corresponding relationship. In other words, some waveform combinations (bus voltage waveform and multiple feeder voltage waveforms) correspond to the power quality anomaly 201 (for example, corresponding to waveform distortion), and the frequency domain queue extracted from the waveform is converted by dimensionality increase to obtain the frequency domain queue sample 202. In other words, the frequency domain queue sample 202 is the same as the process of dimensionality increase of the first frequency domain queue.

[0098] In terms of dimensionality conversion, the second formula is used in one application scenario:

[0099]

[0100] Where Qx(xm) is the xmth element of the second frequency domain queue, w xm is the xmth first coefficient, QF(Fm) is the Fmth element of the first frequency domain queue, int() is the integer function, and mod() is the remainder function.

[0101] We know that these frequency domain queue samples 202 can be clustered into multiple classes, but in fact, it is quite difficult to cluster the frequency domain queue samples 202 into classes corresponding to the power quality anomaly 201 through an algorithm.

[0102] Therefore, an embodiment of the present invention provides a linear equation 203 to convert the frequency domain queue samples 202 into numerical values:

[0103]

[0104] Where Result is the output of the linear equation, wx xm is the xmth second coefficient, SQx(xm) is the xmth element of the input frequency domain queue, and xmMax is the total number of elements in the input frequency domain queue.

[0105] Inputting the frequency domain queue sample 202 into the above equation 203, the result value 204 is obtained. Then, after arranging the values ​​204, the sample pairs of the same power quality anomaly 201 are constructed into a sample group 205, and the values ​​204 are arranged and divided into the same intervals as the sample group 205. According to the correspondence, there are some values ​​204 in these intervals that should not belong to the interval ( Figure 2 The values ​​204 that do not belong to the interval and the median of the interval that should be divided are the difference values.

[0106] For example, Figure 2In the example, the top interval is 60-160, and the value 204 (filled square) that does not belong to the interval is 160. The interval that should be divided should be the bottom interval, which is 500-900, and the middle value of the interval is 700. Then the difference value of the value 204 that does not belong to the interval is 700-160=540. Similarly, the value 204 (filled square) that does not belong to the interval in the bottom interval is 900. The interval that should be divided should be the top interval, and the middle value is 110. Then the difference value is 110-900=-790.

[0107] After checking all the difference values, the multiple coefficients of the linear equation can be adjusted according to the third formula, where the third formula is:

[0108]

[0109] In the formula, is the adjusted xmth coefficient, is the xmth coefficient before adjustment, Bias bm is the bmth difference value, and bmMax is the total number of difference values.

[0110] After the adjustment is completed, the frequency domain queue sample 202 is input into the above equation 203, and the above steps are repeated until the difference value reaches an acceptable level, or there is no difference value. At this time, the above linear equation is used as a linear early warning classification equation, and the interval corresponding to the linear early warning classification equation is used as a classification interval.

[0111] Inputting the second frequency domain queue obtained by dimensionality upgrading into the linear early warning classification equation, we get the output of the equation.

[0112] In step 104, a power quality warning message is output according to the numerical interval in which the equation output is located.

[0113] Exemplarily, the output of the equation is compared with the interval corresponding to the linear early warning classification equation as the classification interval. When it falls into one of the intervals, it indicates that there is an abnormality in power quality, and the abnormality is output.

[0114] The present invention is based on the distribution network power quality early warning method implementation method of the frequency domain feature association analysis algorithm, which first obtains the bus voltage waveform and multiple feeder current waveforms, wherein each feeder current corresponds to a feeder, and the distributed power source or load is connected to the bus through the feeder; then the bus voltage waveform and the multiple feeder current waveforms are respectively subjected to frequency domain feature extraction, and the obtained multiple frequency domain features are constructed into a first frequency domain queue; then the first frequency domain queue is dimensionally upgraded, and the obtained second frequency domain queue is input into the linear early warning classification equation to obtain the equation output, wherein the linear early warning classification equation is constructed according to multiple sample pairs, and the sample pairs include frequency domain queue samples and power quality anomalies; finally, according to the numerical interval where the equation output is located, the power quality early warning message is output. The implementation method of the present invention performs frequency domain analysis based on the bus voltage waveform and the feeder current waveform of the distribution network, and then determines the anomaly of the distribution network through the association analysis method. Since the linear classification equation is used for digitization and classification, the power quality anomaly that is not easy to be found can be found. Since the equation is a linear equation, the samples used for construction are small, and the construction process is relatively simple, so it is suitable for the distribution network and is easy to implement and promote.

[0115] It should be understood that the size of the serial numbers of the steps in the above implementation does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the implementation method of the present invention.

[0116] The following is an implementation of the device of the present invention. For details not described in detail, reference may be made to the corresponding method implementation described above.

[0117] Figure 3 is a functional block diagram of the power metering equipment operation and maintenance device provided by the embodiment of the present invention, referring to Figure 3 The power metering equipment operation and maintenance device includes: a waveform acquisition module 301, a frequency domain feature extraction module 302, a correlation analysis module 303 and an early warning module 304, wherein:

[0118] A waveform acquisition module 301 is used to acquire a bus voltage waveform and multiple feeder current waveforms, wherein each feeder current corresponds to a feeder, and a distributed power source or load is connected to the bus through the feeder;

[0119] A frequency domain feature extraction module 302 is used to extract frequency domain features from the bus voltage waveform and the multiple feeder current waveforms, respectively, and construct the obtained multiple frequency domain features into a first frequency domain queue;

[0120] The association analysis module 303 is used to upgrade the first frequency domain queue and input the obtained second frequency domain queue into a linear early warning classification equation to obtain an equation output, wherein the linear early warning classification equation is constructed according to a plurality of sample pairs, the sample pairs including frequency domain queue samples and power quality anomalies;

[0121] The warning module 304 is used to output a power quality warning message according to the numerical interval in which the equation output is located.

[0122] Figure 4 is a functional block diagram of an electronic device provided by an embodiment of the present invention. Figure 4 As shown, the electronic device 4 of this embodiment includes: a processor 400 and a memory 401, wherein the memory 401 stores a computer program 402 that can be run on the processor 400. When the processor 400 executes the computer program 402, the above-mentioned distribution network power quality early warning method based on the frequency domain feature correlation analysis algorithm and the steps in the embodiment are implemented, for example Figure 1 Steps 101 to 104 are shown.

[0123] Exemplarily, the computer program 402 may be divided into one or more modules / units, and the one or more modules / units are stored in the memory 401 and executed by the processor 400 to implement the present invention.

[0124] The electronic device 4 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The electronic device 4 may include, but is not limited to, a processor 400 and a memory 401. Those skilled in the art will appreciate that Figure 4 It is only an example of the electronic device 4 and does not constitute a limitation of the electronic device 4. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device 4 may also include input and output devices, network access devices, buses, etc.

[0125] The processor 400 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0126] The memory 401 may be an internal storage unit of the electronic device 4, such as a hard disk or memory of the electronic device 4. The memory 401 may also be an external storage device of the electronic device 4, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 4. Further, the memory 401 may also include both an internal storage unit and an external storage device of the electronic device 4. The memory 401 is used to store the computer program 402 and other programs and data required by the electronic device 4. The memory 401 may also be used to temporarily store data that has been output or is to be output.

[0127] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the implementation method can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method implementation method, which will not be repeated here.

[0128] In the above-mentioned embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0129] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0130] In the embodiments provided by the present invention, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0131] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0132] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0133] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned implementation method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned various methods and device implementation methods. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium.

[0134] The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A distribution network power quality early warning method based on frequency domain feature correlation analysis algorithm, characterized in that: include: Obtaining a bus voltage waveform and multiple feeder current waveforms, wherein each feeder current corresponds to a feeder, and a distributed power source or a load is connected to the bus through the feeder; Extracting frequency domain features from the bus voltage waveform and the multiple feeder current waveforms respectively, and constructing the obtained multiple frequency domain features into a first frequency domain queue; The first frequency domain queue is dimensionally upgraded, and the obtained second frequency domain queue is input into a linear early warning classification equation to obtain an equation output, wherein the linear early warning classification equation is constructed according to a plurality of sample pairs, and the sample pairs include frequency domain queue samples and power quality anomalies; According to the numerical range in which the output of the equation is located, a power quality warning message is output.

2. The power quality early warning method for distribution network based on frequency domain feature correlation analysis algorithm according to claim 1 is characterized in that: The extracting frequency domain features of the bus voltage waveform and the multiple feeder current waveforms respectively, and constructing the obtained multiple frequency domain features into a first frequency domain queue, comprises: Get the fundamental frequency; Sequentially extract waveforms from the bus voltage waveform and the multiple feeder current waveforms as waveforms to be processed; Extracting the frequency domain value of the first frequency of the waveform to be processed according to the fundamental frequency, and adding the obtained frequency domain value to the first frequency domain queue; If the traversal of the bus voltage waveform and the multiple feeder current waveforms is not completed, jump to the step of sequentially taking waveforms from the bus voltage waveform and the multiple feeder current waveforms as waveforms to be processed; Otherwise, if the first frequency does not reach the frequency domain extraction limit, the first frequency is adjusted, the waveform extraction position is initialized, and the step of jumping to the step of sequentially extracting waveforms from the bus voltage waveform and the multiple feeder current waveforms as the waveforms to be processed.

3. The power quality early warning method for distribution network based on frequency domain feature correlation analysis algorithm according to claim 2 is characterized in that: The step of extracting the frequency domain value of the first frequency of the waveform to be processed according to the fundamental frequency includes: The frequency domain value of the first frequency of the waveform to be processed is extracted according to the first formula and the fundamental frequency, wherein the first formula is: Wherein, WF(k) is the kth eigenvalue of the waveform to be processed, wnMax is the total number of data after the waveform to be processed is discretized, DW(wn) is the wnth data after the waveform to be processed is discretized, e is a natural constant, j is an imaginary unit, ω0 is the fundamental frequency, PN is the number of data corresponding to the period length of the discrete waveform to be processed corresponding to the basic frequency, and π is the pi.

4. The power quality early warning method for distribution network based on frequency domain feature correlation analysis algorithm according to claim 1 is characterized in that: The second frequency domain queue obtained by performing dimension upgrading on the first frequency domain queue includes: The first frequency domain queue is dimensionally upgraded according to the second formula to obtain a second frequency domain queue, wherein the second formula is: Where Qx(xm) is the xmth element of the second frequency domain queue, w xm is the xmth first coefficient, QF(Fm) is the Fmth element of the first frequency domain queue, int() is the integer function, and mod() is the remainder function.

5. The power quality early warning method for distribution network based on frequency domain feature correlation analysis algorithm according to claim 1 is characterized in that: The step of constructing the linear early warning classification equation according to multiple sample pairs includes: Acquire multiple sample pairs, wherein each sample pair includes a frequency domain queue sample and a power quality anomaly, and the dimension of the frequency domain queue sample is the same as the dimension of the second frequency domain queue; Dividing the plurality of sample pairs into a plurality of sample groups according to the power quality abnormality, wherein each sample group corresponds to a power quality abnormality; Substituting the frequency domain queue samples of the multiple sample pairs into the linear equation respectively to obtain multiple first results, wherein each first result corresponds to the frequency domain queue sample of a sample pair; sorting the plurality of first results, and dividing the sorted results into a plurality of sorting intervals according to the number of samples in the sample group, wherein each sorting interval corresponds to a sample group; If there are differences between the multiple sorting intervals and the multiple sample groups, adjust the multiple coefficients of the linear equation according to the multiple differences and jump to the step of substituting the frequency domain queue samples of the multiple sample pairs into the linear equation respectively to obtain multiple first results, wherein the difference is the difference between the first result that does not belong to the sorting interval and the median of the interval to which it belongs; Otherwise, the linear equation is used as the linear early warning classification equation.

6. The power quality early warning method for distribution network based on frequency domain feature correlation analysis algorithm according to claim 5 is characterized in that: The linear equation is: Where Result is the output of the linear equation, wx xm is the xmth second coefficient, SQx(xm) is the xmth element of the input frequency domain queue, and xmMax is the total number of elements in the input frequency domain queue.

7. The power quality early warning method for distribution network based on frequency domain feature correlation analysis algorithm according to any one of claims 1 to 6, characterized in that: The step of adjusting the plurality of coefficients of the linear equation according to the plurality of differences comprises: The multiple coefficients of the linear equation are adjusted according to a third formula, wherein the third formula is: In the formula, is the adjusted xmth coefficient, is the xmth coefficient before adjustment, Bias bm is the bmth difference value, and bmMax is the total number of difference values.

8. An operation and maintenance device for electric power metering equipment, characterized in that: For implementing the power quality early warning method for distribution network based on frequency domain feature correlation analysis algorithm as described in any one of claims 1 to 7, the power metering equipment operation and maintenance device comprises: A waveform acquisition module, used to acquire a bus voltage waveform and multiple feeder current waveforms, wherein each feeder current corresponds to a feeder, and a distributed power source or a load is connected to the bus through the feeder; A frequency domain feature extraction module, used to extract frequency domain features from the bus voltage waveform and the multiple feeder current waveforms, respectively, and construct the obtained multiple frequency domain features into a first frequency domain queue; An association analysis module, used for performing dimension upgrading on the first frequency domain queue and inputting the obtained second frequency domain queue into a linear early warning classification equation to obtain an equation output, wherein the linear early warning classification equation is constructed according to a plurality of sample pairs, the sample pairs including frequency domain queue samples and power quality anomalies; as well as, The early warning module is used to output a power quality early warning message according to the numerical interval in which the equation output is located.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method as claimed in any one of claims 1 to 7 are implemented.

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