Characteristic wave identification method and storage medium for user topology identification in distribution station area

Abstract

The present invention discloses a characteristic wave identification method and storage medium for user topology identification in a distribution station area. The circuit breaker side, as a signal sending end, generates harmonic current of a specific frequency by switching on and off the load. The signal receiving end on the low-voltage side of the transformer uses sliding discrete Fourier transform (DFT) for real-time extraction and decoding. In order to solve the influence of power grid noise and load fluctuation on the identification success rate, the measured signal is subjected to IIR digital filtering, and the amplitude of the three-phase current characteristic signal is subjected to least square fitting and other methods for signal decoding. The household-to-transformer relationship is determined by comparing with the characteristic binary information, and the phase to which it belongs is determined by comparing the amplitude of the three-phase current signal. The method has the characteristics of high accuracy, strong anti-interference, and small impact on the power grid. The present invention facilitates the business application system of the distribution station area to identify the user topology of the station area by calculation, and can quickly confirm the topology changes from the user meter to the collector and to the concentrator, so as to facilitate the refined management of the station area.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution automation, and in particular to a characteristic wave identification method for identifying user topology in a power distribution area. Background Art

[0002] At present, in order to timely monitor the changes of the power supply network topology for the low-voltage distribution network with known topology, the injection current method is mostly used to online identify and monitor the topological relationship of the low-voltage distribution network. C =833.3Hz, that is, the load on-off cycle is 1200us, the harmonic current frequency fed into the power grid is 783.3Hz and f2=883.3Hz. Therefore, the identification of the current characteristic wave can be determined by detecting whether the amplitude and timing of the 783.3Hz and 883.3Hz interharmonics of the line current meet the characteristics of the transmitted waveform parameters.

[0003] The overall architecture of the physical layer should be consistent with Figure 1 The sending component generates a characteristic current signal through OOK modulation and feeds it to the power line; the identification component detects the characteristic current signal, performs corresponding demodulation processing, and finally decodes the characteristic current signal on the power line to obtain data information.

[0004] In the 220V, 50Hz power frequency environment, the default frequency points of the extracted characteristic signal of the identification component are 783.3Hz and 883.3Hz, and the default characteristic code information is AAE9. At present, the usual method for identifying characteristic waves is to use discrete Fourier transform (DFT) to calculate the amplitude of the three-phase current characteristic signal regularly. When the characteristic wave is not applied, the signal amplitude is small, and the calculated signal is the signal noise floor. When the characteristic wave is applied, the signal amplitude will increase. If the change in the signal amplitude is greater than a threshold, it can be judged that it may be the starting position of the characteristic wave, and the code position is 1. The signal amplitude of the following 9.6S is temporarily stored and compared with the signal noise floor plus the threshold. If it is greater than the value, the code position is 1, and if it is less than the value, the code position is 0, and the decoding information is obtained. If the decoding information of the phase current is consistent with the applied characteristic code information, a characteristic code record is generated. Due to the complexity of the background current of the power grid, the influence of noise and load fluctuations leads to spectrum leakage and inaccurate extraction when directly extracted by DFT, thereby interfering with the extraction effect of the characteristic current signal. The characteristic wave is missed or misidentified, resulting in the failure of topology identification. Summary of the invention

[0005] The present invention proposes a characteristic wave identification method for user topology identification in a distribution station area, aiming to solve the problem that the traditional low-voltage station area topology identification efficiency is low-cost and the low-voltage station area topology cannot be accurately identified.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A characteristic wave identification method for user topology identification in a distribution station area is based on the circuit breaker side as a signal sending end generating harmonic current of a specific frequency by switching on and off the load, and the signal receiving end on the low-voltage side of the transformer uses sliding discrete Fourier transform for real-time extraction and decoding, including the following steps:

[0008] S1, continuously acquiring current waveform data of phase A, phase B and phase C in the three-phase power grid as the measured signal;

[0009] S2, filtering the measured signal to filter out interference signals outside the detection frequency point;

[0010] S3, using discrete Fourier transform to calculate the amplitude of the three-phase current characteristic signal;

[0011] S4, performing least square fitting on the amplitude of the three-phase current characteristic signal;

[0012] S5. Determine the code value of the three-phase current characteristic signal.

[0013] Furthermore, in step S2, an IIR digital filter is selected, and its filtering time domain equation is:

[0014] S1: y1(n)=x(n)+b(1)x(n-1)+b(2)x(n-2)-a(1)y1(n-1)-a(2)y1(n-2)

[0015] S2: y2(n)=y1(n)+b(1)y1(n-1)+b(2)y1(n-2)-a(1)y2(n-1)-a(2)y2(n-2)

[0016] S3: y3(n)=y2(n)+b(1)y2(n-1)+b(2)y2(n-2)-a(1)y3(n-1)-a(2)y3(n-2)

[0017] S4: y4(n)=y3(n)+b(1)y3(n-1)+b(2)y3(n-2)-a(1)y4(n-1)-a(2)y4(n-2)

[0018] S5: y5(n)=y4(n)+b(1)y4(n-1)+b(2)y4(n-2)-a(1)y5(n-1)-a(2)y5(n-2)

[0019] A 5-stage direct-connected IIR filter is constructed using a second-order IIR cascade.

[0020] Furthermore, the step S3 specifically includes:

[0021] For a discretely sampled grid current Its trigonometric Fourier series is:

[0022]

[0023] The signal is written as the DC component a0 and each harmonic cosine component a n , sine component b n The form of the sum is as follows:

[0024]

[0025]

[0026]

[0027] Here we choose rectangular window TM , take the sliding window size as T = 10*K, continuously perform sliding extraction on the time domain signal, and calculate the frequency domain current values ​​of 783.3Hz and 883.3Hz, that is:

[0028]

[0029] Furthermore, the step S4 specifically includes:

[0030] The slope of the fitted amplitude line is normalized, and the normalization equation is:

[0031]

[0032] k——fitting slope, kset——slope threshold, x——signal amplitude, set——signal amplitude threshold.

[0033] Furthermore, the step S5 comprises:

[0034] Sliding decoding is performed with 16 binary bits and 48 points as units, and each point is judged separately;

[0035] For the normalized signal count of a fixed time window, the code value is 1 if it exceeds a certain threshold, otherwise it is 0;

[0036] The extracted characteristic wave code value is subjected to fault tolerance processing. Compared with the standard code value, if the number of different code values ​​is less than the threshold, it is determined to be a characteristic wave and the relevant information is recorded. If the number of different code values ​​is greater than or equal to the threshold, it is determined to be an interference waveform.

[0037] On the other hand, the present invention discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0038] It can be seen from the above technical scheme that the purpose of the present invention is to provide a method for detecting characteristic current signals for low-voltage substation topology identification, which is suitable for a topology identification system with a current waveform as a characteristic signal carrier. The generated characteristic current signal is similar to user consumption. Under the condition of controllable power consumption, it does not affect carrier communication, and can accurately and effectively realize the low-voltage substation topology structure. It is particularly suitable for low-voltage substation topology identification, can effectively meet the requirements of power grid companies for low-voltage substation topology identification, and is easy to promote and use.

[0039] The present invention discloses a characteristic wave identification method for user topology identification in a distribution station area. The circuit breaker side, as a signal sending end, generates harmonic current of a specific frequency by switching on and off the load. The signal receiving end on the low-voltage side of the transformer uses sliding discrete Fourier transform (DFT) for real-time extraction and decoding. In order to solve the influence of grid noise and load fluctuation on the identification success rate, the measured signal is subjected to IIR digital filtering, and the amplitude of the three-phase current characteristic signal is subjected to least square fitting and other methods for signal decoding. The household-to-transformer relationship is determined by comparing with the characteristic binary information, and the phase to which it belongs is determined by comparing the amplitude of the three-phase current signal. The method has the characteristics of high accuracy, strong anti-interference, and small impact on the grid. The present invention facilitates the business application system of the distribution station area to identify the user topology of the station area by calculation, and can quickly confirm the topology changes from the user meter to the collector and to the concentrator, which is convenient for refined management of the station area.

[0040] Compared with the prior art, the present invention has the following advantages:

[0041] 1. Before calculating the characteristic wave amplitude, digital filtering is performed first, and an IIR digital filter with better frequency selection performance is used to reduce background noise interference and improve the accuracy of characteristic wave calculation;

[0042] 2. Perform the least squares fitting algorithm on the amplitude of the three-phase current characteristic signal to eliminate clutter interference;

[0043] 3. The signal decoding adopts sliding decoding, and adds signal counting and decoding fault tolerance processing to improve the characteristic wave recognition rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is the overall architecture diagram of the present invention;

[0045] Figure 2 It is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0047] like Figure 1 As shown, the characteristic wave identification method for user topology identification in a distribution station area described in this embodiment is based on the circuit breaker side as a signal sending end generating a harmonic current of a specific frequency by switching on and off the load, and the signal receiving end on the low-voltage side of the transformer uses a sliding discrete Fourier transform for real-time extraction and decoding, and includes the following steps:

[0048] Step 1: Acquire the signal: continuously acquire the current waveform data of phase A, phase B and phase C in the three-phase power grid as the measured signal;

[0049] Considering the accuracy requirement of extracting current characteristic signals, 100 points can be sampled per cycle and the signal amplitude can be calculated every 10 cycles.

[0050] Step 2: Digital filtering: filtering the measured signal to remove interference signals outside the detection frequency point;

[0051] There are many clutters at the end of the power grid, especially the 16th, 17th, and 18th harmonics, which have a greater impact on the characteristic wave. Frequency leakage will cause jitter in the DFT calculation amplitude, and a bandpass digital filter needs to be added. Since there is no requirement for the phase, an IIR digital filter with fewer orders and better frequency selection performance can be selected.

[0052] Filtering time domain equation:

[0053] S1: y1(n)=x(n)+b(1)x(n-1)+b(2)x(n-2)-a(1)y1(n-1)-a(2)y1(n-2)

[0054] S2: y2(n)=y1(n)+b(1)y1(n-1)+b(2)y1(n-2)-a(1)y2(n-1)-a(2)y2(n-2)

[0055] S3: y3(n)=y2(n)+b(1)y2(n-1)+b(2)y2(n-2)-a(1)y3(n-1)-a(2)y3(n-2)

[0056] S4: y4(n)=y3(n)+b(1)y3(n-1)+b(2)y3(n-2)-a(1)y4(n-1)-a(2)y4(n-2)

[0057] S5: y5(n)=y4(n)+b(1)y4(n-1)+b(2)y4(n-2)-a(1)y5(n-1)-a(2)y5(n-2)

[0058] Considering the computational stability of the IIR filter, a second-order IIR cascade form is used to construct a 5-stage direct-connected IIR filter.

[0059] Step 3: Calculate the amplitude - use discrete Fourier transform (DFT) to calculate the amplitude of the three-phase current characteristic signal;

[0060] Any periodic signal that satisfies the Dirichlet condition can be expanded into a Fourier series⑺. The current signal in the power grid is usually a periodic signal that satisfies the Dirichlet condition. For a discretely sampled power grid current Its trigonometric Fourier series is:

[0061]

[0062] The signal can be written as the DC component a0 and each harmonic cosine component a n , sine component b n The form of the sum is as follows:

[0063]

[0064]

[0065]

[0066] Here we choose rectangular window TM , take the sliding window size as T = 10*K, continuously perform sliding extraction on the time domain signal, and calculate the frequency domain current values ​​of 783.3Hz and 883.3Hz, that is:

[0067]

[0068] Step 4: Signal fitting: perform least square fitting on the amplitude of the three-phase current characteristic signal;

[0069] Since the duration of the code value symbol is 600ms, the amplitude symbol change obtained by DFT calculation is a low-high-low linear change, and the slope can be fitted by least squares linear fitting, and the slope change represents the symbol value. In order to avoid the influence of clutter signals on the slope, the slope of the fitted amplitude straight line is normalized, and the normalization equation is:

[0070] k——fitting slope, kset——slope threshold, x——signal amplitude, set——signal amplitude threshold

[0071] Step 5: Signal decoding - judging the code value of the signal;

[0072] Sliding decoding is performed with 16 binary bits and 48 points as units, and each point is judged separately.

[0073] For the normalized signal count of a fixed time window, the code value is 1 if it exceeds a certain threshold, otherwise it is 0.

[0074] The extracted characteristic wave code value is subjected to fault tolerance processing. Compared with the standard code value, if the number of different code values ​​is less than the threshold, it is determined to be a characteristic wave and the relevant information is recorded. If the number of different code values ​​is greater than or equal to the threshold, it is determined to be an interference waveform.

[0075] The following are specific instructions:

[0076] like Figure 2 As shown, the following steps are included:

[0077] Step 1: Acquire the signal: continuously acquire 10 cycles of 200Ms current waveform data of phase A, phase B and phase C in the three-phase power grid as the measured signal;

[0078] Step 2: Digital filtering: filtering the measured signal to remove interference signals outside the detection frequency point;

[0079] Select an IIR digital filter with fewer orders and better frequency selection performance.

[0080] Filtering time domain equation:

[0081] S1: y1(n)=x(n)+b(1)x(n-1)+b(2)x(n-2)-a(1)y1(n-1)-a(2)y1(n-2)

[0082] S2: y2(n)=y1(n)+b(1)y1(n-1)+b(2)y1(n-2)-a(1)y2(n-1)-a(2)y2(n-2)

[0083] S3: y3(n)=y2(n)+b(1)y2(n-1)+b(2)y2(n-2)-a(1)y3(n-1)-a(2)y3(n-2)

[0084] S4: y4(n)=y3(n)+b(1)y3(n-1)+b(2)y3(n-2)-a(1)y4(n-1)-a(2)y4(n-2)

[0085] S5: y5(n)=y4(n)+b(1)y4(n-1)+b(2)y4(n-2)-a(1)y5(n-1)-a(2)y5(n-2)

[0086] Step 3: Calculate the amplitude - use discrete Fourier transform (DFT) to calculate the amplitude of the three-phase current characteristic signal;

[0087] Here we choose rectangular window TM, take the sliding window size as T = 10*K, continuously perform sliding extraction on the time domain signal, and calculate the frequency domain current values ​​of 783.3Hz and 883.3Hz, that is:

[0088]

[0089] Step 4: Signal fitting: perform least square fitting on the amplitude of the three-phase current characteristic signal;

[0090] The slope is fitted using the least squares linear method, and the change in the slope represents the code value.

[0091] When the slope of the fitted amplitude line is greater than the set value or the amplitude is greater than the set value, the code value is 1, otherwise the code value is 0.

[0092] In order to avoid the influence of clutter signal on the slope, the slope of the fitted amplitude line is normalized, and the normalization equation is:

[0093]

[0094] k——fitting slope, kset——slope threshold, x——signal amplitude, set——signal amplitude threshold.

[0095] Step 5: Code element code value judgment: code element code value judgment of the three-phase current characteristic signal;

[0096] Signal decoding - calculating the code value of the signal;

[0097] Sliding decoding is performed with 16 binary bits and 48 points as units, and each point is judged separately.

[0098] For the normalized signal count of a fixed time window, the code value is 1 if it exceeds a certain threshold, otherwise it is 0.

[0099] Characteristic wave judgment——characteristic wave code value judgment;

[0100] The extracted characteristic wave code value is subjected to fault tolerance processing. Compared with the standard code value, if the number of different code values ​​is less than the threshold, it is determined to be a characteristic wave and the relevant information is recorded. If the number of different code values ​​is greater than or equal to the threshold, it is determined to be an interference waveform.

[0101] After adopting the algorithm of the embodiment of the present invention, the success rate of characteristic wave identification of the product is greatly improved, especially in the case of harmonic interference, which provides information support for the management of distribution transformers in the substation area, improves the lean management capability of the distribution network substation area, and makes useful exploration for the in-depth application of the electricity consumption information collection system in this field.

[0102] In summary, the present invention discloses a characteristic wave identification method for user topology identification in a distribution station area. The circuit breaker side, as a signal sending end, generates harmonic current of a specific frequency by switching on and off the load. The signal receiving end on the low-voltage side of the transformer uses a sliding discrete Fourier transform (DFT) for real-time extraction and decoding. The household-to-transformer relationship is determined by comparing with characteristic binary information, and the phase is determined by comparing the amplitudes of three-phase current signals. The method has the characteristics of high accuracy, strong anti-interference, and little impact on the power grid. The present invention facilitates the business application system of the distribution station area to identify the user topology of the station area through calculation, and can quickly confirm the topology changes from the user meter to the collector and to the concentrator, which is convenient for refined management of the station area.

[0103] Specifically, in order to accurately extract the characteristic current signal, an embodiment of the present invention proposes a new signal decoding algorithm based on sliding DFT. The measured signal is filtered by an IIR digital filter to filter out interference signals outside the detection frequency. Discrete Fourier Transform (DFT) is used to calculate the amplitude of the three-phase current characteristic signal after signal filtering. The amplitude of the three-phase current characteristic signal is fitted by least squares, which has good applicability. The normalized signal count of the fixed time window is processed, and the extracted characteristic wave code value is processed for fault tolerance, so as to improve the success rate of characteristic current signal decoding and reduce the bit error rate.

[0104] In yet another aspect, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of any of the above methods.

[0105] On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of any of the above methods.

[0106] In another embodiment provided by the present application, a computer program product including instructions is also provided, which, when executed on a computer, enables the computer to execute the steps of any method in the above embodiments.

[0107] It is understandable that the system provided by the embodiment of the present invention corresponds to the method provided by the embodiment of the present invention, and the explanation, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the above method.

[0108] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0109] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0110] The above 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, those 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. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A characteristic wave identification method for user topology identification in a distribution station area, based on the circuit breaker side as a signal sending end generating harmonic current of a specific frequency by switching on and off the load, and the signal receiving end on the low-voltage side of the transformer adopts sliding discrete Fourier transform for real-time extraction and decoding, characterized in that: The following steps are included: S1, continuously acquiring current waveform data of phase A, phase B and phase C in the three-phase power grid as the measured signal; S2, filtering the measured signal to filter out interference signals outside the detection frequency point; S3, using discrete Fourier transform to calculate the amplitude of the three-phase current characteristic signal; S4, performing least square fitting on the amplitude of the three-phase current characteristic signal; S5, judging the code element value of the three-phase current characteristic signal; In step S2, an IIR digital filter is selected, and its filtering time domain equation is: S1: y1(n)=x(n)+b(1)x(n-1)+b(2)x(n-2)-a(1)y1(n-1)-a(2)y1(n-2) S2: y2(n)=y1(n)+b(1)y1(n-1)+b(2)y1(n-2)-a(1)y2(n-1)-a(2)y2(n-2) S3: y3(n)=y2(n)+b(1)y2(n-1)+b(2)y2(n-2)-a(1)y3(n-1)-a(2)y3(n-2) S4: y4(n)=y3(n)+b(1)y3(n-1)+b(2)y3(n-2)-a(1)y4(n-1)-a(2)y4(n-2) S5: y5(n)=y4(n)+b(1)y4(n-1)+b(2)y4(n-2)-a(1)y5(n-1)-a(2)y5(n-2) In the above formula, x(n), x(n-1), x(n-2) represent the sequence data to be filtered, and y1(n), y1(n-1), y1(n-2)...y5(n), y5(n-1), y5(n-2) represent the 5-level filtering output sequence data; The coefficients a(1), a(2), b(1), and b(2) are the filter coefficients; A 5-stage direct-connected IIR filter is constructed using a second-order IIR cascade. The step S3 specifically includes: For a discretely sampled grid current (k), its Fourier series in trigonometric form is: (k) The signal is written as the DC component a0 and each harmonic cosine component a n , sine component b n The form of the sum is as follows: a0 a n b n is the amplitude of the DC component of the current; is the amplitude of the nth harmonic component of the current; Here we choose a rectangular window and take the sliding window size as T = 10*K, continuously slide the time domain signal to extract the current value of 783.3Hz and 883.3Hz in the frequency domain, that is: ； The step S4 specifically includes: The slope of the fitted amplitude line is normalized, and the normalization equation is: M indicates decoded output k is the fitting slope, kset is the slope threshold, x is the signal amplitude, and set is the signal amplitude threshold.

2. The characteristic wave identification method for user topology identification in a distribution station area according to claim 1 is characterized in that: The step S5 comprises: Sliding decoding is performed with 16 binary bits and 48 points as units, and each point is judged separately; For the normalized signal count of a fixed time window, if it exceeds the set threshold, the code value is 1, otherwise it is 0; The extracted characteristic wave code value is subjected to fault tolerance processing. Compared with the standard code value, if the number of different code values ​​is less than the threshold, it is determined to be a characteristic wave and the relevant information is recorded. If the number of different code values ​​is greater than or equal to the threshold, it is determined to be an interference waveform.

3. A computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method according to claim 1 or 2.

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