An adaptive topology recognition method based on autocorrelation characteristics

Abstract

The present invention discloses an adaptive topology identification method based on autocorrelation characteristics, comprising the following steps: a substation intelligent terminal initiates a topology identification process, determines a characteristic current signal, and broadcasts it to all devices in the substation; an identification device begins to collect current values ​​on the power line and calculates the amplitude of the same-frequency noise; a sending device sends a characteristic current signal with a specific frequency, amplitude, and characteristic code information; the identification device identifies and determines the collected current values ​​using an identification algorithm based on the autocorrelation characteristics, and generates an identification result; the substation intelligent terminal obtains the identification result of the identification device and generates a substation topology relationship diagram based on the identification result. The present invention solves the problem of how to accurately and quickly identify the topology of a distribution substation and output a correct topology relationship diagram.

Description

Technical Field

[0001] The present invention relates to the technical field of substation topology identification, and in particular to an adaptive topology identification method based on autocorrelation characteristics. Background Art

[0002] Electric power planning explicitly calls for improving the intelligence of distribution networks, accelerating the realization of "measurable, observable, and controllable" distribution networks, and promoting the construction of distribution network automation with fault self-healing as the goal. To achieve this goal, it is first necessary to clarify the physical and electrical relationships between equipment at all levels of the substation and form a complete substation topology diagram. However, with complex on-site wiring, numerous devices, and the constant addition of new equipment, manual maintenance inevitably leads to errors and cannot be updated at any time. To ensure the accuracy and real-time nature of substation topology relationships, there is an urgent need for an automatic substation topology recognition device with fast recognition speed and high recognition rate.

[0003] Currently, due to the immaturity of big data analysis methods, rapid identification is difficult and accuracy cannot be guaranteed. Furthermore, electrical signal distortion methods require significant signal distortion, which can affect power supply quality and pose safety risks. Therefore, these two automatic topology identification methods are not widely used. In recent years, topology identification has primarily relied on methods for identifying substation topology relationships based on characteristic currents. The technology for transmitting characteristic currents has become mature, requiring only attention to heat generation and power dissipation during power supply periods. However, the accuracy of identifying characteristic signals remains to be improved. This is particularly true in non-residential areas, where high noise signals can lead to false alarms and missed alarms, resulting in inaccurate topological relationships. To prevent the characteristic current from being overwhelmed by the grid's background current, multiple filtering schemes have been developed. However, these filtering schemes target noise at different frequencies, particularly 50Hz power frequency signals. Filtering cannot eliminate co-frequency noise with the characteristic current signal. The presence of co-frequency noise inevitably alters the characteristic current signature code signal. In this case, conventional binary discrimination methods may fail to identify the characteristic current signal, resulting in missed alarms. The most critical step in identifying substation topology based on characteristic currents is accurately extracting information at specific frequencies from the sampled current and determining whether the extracted information is consistent with the characteristic code information carried by the characteristic current, thereby confirming whether the characteristic current signal has been received. However, the current in the power grid is constantly changing, and the amplitude of the fluctuation can be far greater than the signal amplitude of the characteristic current. The current noise is large, and the presence of co-frequency noise at the same frequency as the characteristic current cannot be ruled out. This co-frequency noise, superimposed on the characteristic current signal, may cause the characteristic code information of the characteristic current to change, making it impossible to accurately extract and identify the characteristic current signal, and thus, unable to complete substation topology identification and form a correct topology relationship diagram. Summary of the Invention

[0004] The main purpose of the present invention is to provide an adaptive topology identification method based on autocorrelation characteristics, aiming to solve the problem of how to accurately and quickly identify the topology of a distribution station area and output a correct topology relationship diagram.

[0005] To achieve the above object, the present invention provides an adaptive topology identification method based on autocorrelation characteristics, comprising the following steps:

[0006] S1. The intelligent terminal in the substation initiates the topology identification process, determines the characteristic current signal and broadcasts it to all devices in the substation;

[0007] S2. The recognition device starts to collect the current value on the power line and calculates the amplitude of the same-frequency noise;

[0008] S3, the sending device sends a characteristic current signal with a specific frequency, amplitude and characteristic code information;

[0009] S4, the identification device identifies and determines the collected current value based on the identification algorithm of the autocorrelation characteristic, and generates an identification result;

[0010] S5. The intelligent terminal in the substation area obtains the recognition result of the recognition device and generates a substation area topology relationship diagram according to the recognition result.

[0011] In one preferred solution, in step S2, the identification device starts to collect the current on the power line and calculates the amplitude of the same-frequency noise, specifically:

[0012] The identification device collects the current value on the power line at a first sampling rate, and calculates, based on the current value, the amplitude of the same-frequency noise of the current value and the characteristic current signal.

[0013] In one preferred solution, the amplitude of the same-frequency noise is:

[0014]

[0015] Where F is the amplitude of the same-frequency noise, H is the number of cycles within the sampling time, and X1(i) and X2(i) are the frequency domain amplitudes of the characteristic frequency points within the i-th cycle.

[0016] In one preferred solution, the characteristic current signal adopts OOK modulation;

[0017] If the bit of the characteristic code information of the characteristic current signal is 1, signal modulation is performed within the bit width duration; if the bit of the characteristic code information of the characteristic current signal is 0, no modulation is performed.

[0018] In one preferred solution, in step S4, the identification device identifies and determines the collected current value based on the identification algorithm of the autocorrelation characteristic and generates an identification result. The specific steps are:

[0019] S41, the identification device stores the current value in a processor, and performs a cycle sliding DFT process on the current value;

[0020] S42, sequentially caching the processing results in S41 to obtain a DFT data sequence;

[0021] S43, calculating the mean of the DFT data sequence, and performing data differential conversion on the DFT data sequence;

[0022] S44, symbolizing the data after the differential conversion;

[0023] S45, forming a local correlation sequence based on the characteristic code information and the transmission frequency of the characteristic current signal, correlating and accumulating the symbolized DFT data sequence with the local correlation sequence to obtain a correlation value;

[0024] S46. Determine whether the collected current value is a characteristic current signal according to the correlation value.

[0025] In one preferred solution, in step S41, the identification device stores the current value in a processor and performs a cycle sliding DFT process on the current value, specifically:

[0026] A circular buffer buf_1 is constructed in the processor, and the collected current values ​​are sequentially stored in the circular buffer buf_1; after each first number of sampling points, the data in the circular buffer buf_1 is subjected to DFT processing, and its frequency domain component is calculated.

[0027] In one preferred solution, the frequency domain component is:

[0028]

[0029] Among them, X(k) is the frequency domain component, k is the characteristic frequency point, N is the number of sampling points, n is the sampling point sequence number, and x(n) is the value of the nth point.

[0030] In one preferred solution, in step S42, the processing results in step S41 are sequentially cached to obtain a DFT data sequence, specifically:

[0031] A circular buffer buf_2 with M data units is constructed in the processor, and the results of the DFT processing are sequentially stored in the circular buffer buf_2 to obtain a DFT data sequence. The DFT data sequence is:

[0032] data_fft(i),i=1,2,3...N

[0033] Wherein, data_fft(i) is the DFT data sequence.

[0034] In one preferred solution, the mean of the DFT data sequence is calculated in step S43, and the DFT data sequence is subjected to data differential conversion, specifically:

[0035] Extract the data of the first M / 2 data units in the circular buffer buf_2 and calculate the mean; the mean is:

[0036]

[0037] Among them, mean_data_fft is the mean;

[0038] Perform data differential conversion on the DFT data sequence stored in the circular buffer buf_2, specifically:

[0039] diff_data_fft(i)=data_fft(i)-mean_data_fft

[0040] Wherein, diff_data_fft(i) is the DFT data after differential conversion.

[0041] One of the preferred solutions is to symbolize the data after the differential conversion in step S44, specifically:

[0042] sign_data_fft(i)=sign(diff_data_fft(i))

[0043] Among them, sign_data_fft(i) is the symbolized DFT data sequence, and sign(x) is the sign function.

[0044] In the above technical solution of the present invention, the adaptive topology identification method based on autocorrelation characteristics includes the following steps: the intelligent terminal in the substation initiates the topology identification process, determines the characteristic current signal and broadcasts it to all devices in the substation; the identification device starts to collect the current value on the power line and calculates the amplitude of the same-frequency noise; the sending device sends a characteristic current signal with a specific frequency, amplitude and characteristic code information; the identification device identifies and determines the collected current value based on the identification algorithm of the autocorrelation characteristics and generates an identification result; the intelligent terminal in the substation obtains the identification result of the identification device and generates a substation topology relationship diagram based on the identification result. The present invention solves the problem of how to accurately and quickly perform distribution substation topology identification and output a correct topology relationship diagram.

[0045] In the present invention, the traditional binary judgment method is abandoned, and the characteristic code information of the characteristic current signal is used to form a local correlation sequence. The correlation between the sampling signal and the local characteristic signal correlation sequence is used to determine whether the characteristic current signal is collected. This reduces the interference of the same-frequency noise on the recognition result and increases the recognition success rate. Even if some code elements are flipped due to interference, they can still be recognized.

[0046] In the present invention, the average value of the data of the first M / 2 data units in the circular buffer buf_2 is used as the judgment threshold, the first half of the characteristic code information of the characteristic current signal is "AA", which is expressed in binary as "10101010", wherein the number of bits "0" and "1" is equal, and the average value of this part is used as the judgment standard for "0" and "1", that is, the present invention does not use a certain amplitude as the standard for judging whether each bit is 0 or 1, but uses the average value of the first 8 bits as the judgment threshold, which can effectively avoid the problem of inaccurate judgment caused by noise differences in different areas or at different times. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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 the structures shown in these drawings without paying any creative work.

[0048] Figure 1 A schematic diagram of a process of an adaptive topology identification method based on autocorrelation characteristics according to an embodiment of the present invention;

[0049] Figure 2 This is a first schematic diagram of step S4 of an embodiment of the present invention;

[0050] Figure 3 This is a second schematic diagram of step S4 of an embodiment of the present invention.

[0051] The realization of the objectives, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0053] It should be noted that all directional indications (such as up, down, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0054] In addition, the terms "first," "second," and so on, used in this disclosure are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, features specified as "first" or "second" may explicitly or implicitly include at least one of these features.

[0055] Moreover, the technical solutions between the various embodiments of the present invention may be combined with each other, but this must be based on the fact that ordinary technicians in this field can implement them. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0056] See also Figure 1-Figure 3 According to one aspect of the present invention, the present invention provides an adaptive topology recognition method based on autocorrelation characteristics, comprising the following steps:

[0057] S1. The intelligent terminal in the substation initiates the topology identification process, determines the characteristic current signal and broadcasts it to all devices in the substation;

[0058] S2. The recognition device starts to collect the current value on the power line and calculates the amplitude of the same-frequency noise;

[0059] S3, the sending device sends a characteristic current signal with a specific frequency, amplitude and characteristic code information;

[0060] S4, the identification device identifies and determines the collected current value based on the identification algorithm of the autocorrelation characteristic, and generates an identification result;

[0061] S5. The intelligent terminal in the substation area obtains the recognition result of the recognition device and generates a substation area topology relationship diagram according to the recognition result.

[0062] Specifically, in this embodiment, the identification device in step S2 starts to collect the current on the power line and calculates the amplitude of the same-frequency noise, specifically:

[0063] The identification device collects the current value on the power line at a first sampling rate, and calculates the amplitude of the same-frequency noise at the same frequency as the characteristic current signal based on the current value; the first sampling rate is 6.4 kbps, and the first sampling rate can be adjusted according to the processor ADC used. The present invention does not specifically limit it and can be set as needed; the identification device collects the current value on the power line at a sampling rate of 6.4 kbps, and performs a same-frequency noise evaluation based on the current value, that is, calculates the amplitude of the same-frequency noise at the same frequency as the characteristic signal current. The same-frequency noise amplitude is the average value of the same-frequency noise amplitude calculated by the identification device based on the collected current value over a period of continuous sampling. The same-frequency noise amplitude is:

[0064]

[0065] Where F is the amplitude of the same-frequency noise, H is the number of cycles within the sampling time, and X1(i) and X2(i) are the frequency domain amplitudes of the characteristic frequency points within the i-th cycle.

[0066] Specifically, in this embodiment, after receiving the instruction of the topology identification process issued by the substation intelligent terminal, the sending device sends it in sequence. Only after confirming that the previous sending device has completed the sending of the characteristic current signal will it notify the next sending device to send, wherein the sending time interval between every two sending devices is 30s; in the present invention, the center frequency of the characteristic current signal is 833.3Hz, the effective value of the amplitude is 0.4A, the characteristic code information is 0xAAE9, and the characteristic code bit width is 600ms, that is, 30 cycles of the industrial frequency signal. The present invention does not make specific limitations and can be set according to specific needs; the characteristic current signal is modulated using OOK modulation; if the bit of the characteristic code information of the characteristic current signal is 1, signal modulation is performed within the bit width duration; if the bit of the characteristic code information of the characteristic current signal is 0, no modulation is performed.

[0067] Specifically, in this embodiment, the identification device in step S4 identifies and determines the collected current value based on the identification algorithm of the autocorrelation characteristic and generates an identification result. The specific steps are:

[0068] S41, one-cycle sliding DFT: the identification device stores the current value in a processor, and performs one-cycle sliding DFT processing on the current value;

[0069] S42, data caching: caching the processing results in S41 in sequence to obtain a DFT data sequence;

[0070] S43, data differential conversion: calculating the mean of the DFT data sequence, and performing data differential conversion on the DFT data sequence;

[0071] S44, data symbolization: symbolizing the data after differential conversion;

[0072] S45, correlation accumulation: forming a local correlation sequence based on the characteristic code information and the transmission frequency of the characteristic current signal, and correlating and accumulating the symbolized DFT data sequence with the local correlation sequence to obtain a correlation value;

[0073] S46. Determination: Determine whether the collected current value is a characteristic current signal according to the correlation value.

[0074] Specifically, in this embodiment, the identification device in step S41 stores the current value in a processor and performs a sliding DFT process on the current value per cycle, specifically: constructing a circular buffer buf_1 in the processor, the circular buffer buf_1 is provided with N data units, wherein N=128*30=3840, the sampling points per cycle are 128, and the number of cycles is 30. The present invention does not make specific limitations and can be set as needed; the collected current values ​​are sequentially stored in each data unit in the circular buffer buf_1; after each interval of the first number of sampling points, the data in the circular buffer buf_1 is subjected to DFT processing and its frequency domain component is calculated; the first number of sampling points is 128, and each interval of 128 After sampling points, the data in the circular buffer buf_1 is extracted for DFT processing, specifically, the first characteristic frequency point and the second characteristic frequency point are extracted for DFT processing, and their frequency domain components are calculated; since the characteristic current signal is superimposed on the 50Hz industrial frequency current signal, the frequency domain components of the characteristic current are mainly concentrated in the first characteristic frequency point and the second characteristic frequency point, therefore, the first characteristic frequency point and the second characteristic frequency point are extracted for DFT processing to calculate their frequency domain components. In the present invention, the center frequency of the characteristic current signal is 833.3Hz, and the first characteristic frequency and the second characteristic frequency are 833.3+50Hz and 833.3-50Hz, respectively. The present invention does not make specific limitations and can be set according to needs.

[0075] Specifically, in this embodiment, the frequency domain component is:

[0076]

[0077] Among them, X(k) is the frequency domain component, k is the characteristic frequency point, N is the number of sampling points, n is the sampling point sequence number, and x(n) is the value of the nth point.

[0078] Specifically, in this embodiment, in step S42, the processing results in step S41 are sequentially cached to obtain a DFT data sequence, specifically:

[0079] A circular buffer buf_2 with M data units is constructed in the processor, and the results after DFT processing are sequentially stored at the end of the queue of the circular buffer buf_2 to obtain a DFT data sequence. The DFT data sequence is:

[0080] data_fft(i),i=1,2,3...N

[0081] Wherein, data_fft(i) is the DFT data sequence.

[0082] Specifically, in this embodiment, the mean of the DFT data sequence is calculated in step S43, and the DFT data sequence is subjected to data differential conversion, specifically as follows:

[0083] Extract the data of the first M / 2 data units in the circular buffer buf_2 and perform mean calculation, which is the judgment threshold for data search and conversion; according to the characteristic code signal of the characteristic current signal being 0xAAE9, the first half of the characteristic sequence is "AA", which is converted into binary "10101010", where the number of data "0" and "1" is equal. Using this partial mean as the judgment standard for "0" and "1" can effectively avoid the problem of inaccurate judgment caused by noise differences in different stations or at different times;

[0084] The mean is:

[0085]

[0086] Among them, mean_data_fft is the mean;

[0087] Perform data differential conversion on the DFT data sequence stored in the circular buffer buf_2, specifically:

[0088] diff_data_fft(i)=data_fft(i)-mean_data_fft

[0089] Wherein, diff_data_fft(i) is the DFT data after differential conversion.

[0090] Specifically, in this embodiment, the data after the differential conversion is symbolized in step S44, specifically:

[0091] sign_data_fft(i)=sign(diff_data_fft(i))

[0092] Among them, sign_data_fft(i) is the symbolized DFT data sequence, sign(x) is the sign function; the sign function

[0093] Specifically, in this embodiment, in step S45, a local correlation sequence is formed according to the characteristic code information and the transmission frequency of the characteristic current signal, specifically:

[0094] A circular buffer buf_3 is constructed within the processor. The circular buffer buf_3 is provided with M data units. The circular buffer buf_3 stores a local correlation sequence formed according to the characteristic code information of the characteristic current and the transmission frequency. In the present invention, the characteristic code information is "0xAAE9" and the transmission frequency is 30. M=16*30=480, wherein the frequency is 30 and the binary digit of the characteristic code information is 16. Then, the local correlation sequence is:

[0095]

[0096] Among them, S_local is the local correlation sequence, C is the transmission frequency,

[0097] Specifically, in this embodiment, in step S45, the symbolized DFT data sequence is correlated and accumulated with the local correlation sequence to obtain a correlation value, which is specifically:

[0098]

[0099] Among them, sum_data_fft is the relevant value.

[0100] Specifically, in this embodiment, the step S46 determines whether the collected current value is a characteristic current signal according to the correlation value, specifically:

[0101] The processor determines the correlation decision threshold value based on the amplitude of the characteristic signal and the amplitude of the same-frequency noise. The greater the noise, the greater the difference between the sampled signal and the original characteristic current signal. In this case, the decision threshold value should be appropriately lowered. The decision threshold value is:

[0102]

[0103] Wherein, Thr(x) is the decision threshold value, and x is the ratio of the amplitude of the same-frequency noise to the effective value of the characteristic current signal.

[0104] When the correlation value sum_data_fft obtained by correlation accumulation is greater than Thr(x)*480, it is judged that the characteristic current signal is identified, otherwise, the characteristic current signal is not identified; since the present invention calculates once per cycle sliding, there may be a situation where the correlation of multiple consecutive points exceeds the threshold, so in the next current value cycle after the characteristic current signal is identified, it is considered to be the same topology identification result, and the time interval for different sending devices to send characteristic current signals is also required to be greater than one signal cycle. In the present invention, the signal cycle is 16*30*20=9600ms, where the time per cycle is 20ms; that is, the time interval for different sending devices to send characteristic current signals needs to be greater than 9600ms. The present invention does not make specific limitations and can be set according to needs.

[0105] Specifically, in this embodiment, the present invention determines whether the characteristic current signal is collected based on the correlation between the current value on the collected power line and the local characteristic current signal. Since the substation voltage signal is attenuated, it is not suitable for substation topology identification based on the voltage value.

[0106] Specifically, in this embodiment, after all sending devices in the substation complete the sending of characteristic current signals, the substation intelligent terminal notifies all identification devices to upload the identification information through the sending devices and summarize it to the substation intelligent terminal. The substation intelligent terminal can generate a substation topology relationship diagram based on the identification information.

[0107] In order to facilitate the understanding of the relevant terms of the present invention, an explanation is given here:

[0108] Substation topology relationship: the physical electrical relationship between devices at each layer in the substation.

[0109] Characteristic current signal: a current signal of a specific frequency carrying a characteristic code injected into the power line.

[0110] Transmitting device: A device that realizes the function of transmitting (injecting) characteristic current signals.

[0111] Identification device: A device that has the function of sampling characteristic current signals and can extract characteristic code information at specific frequency points.

[0112] Co-frequency noise: refers to the current signal amplitude of a specific frequency on the power line when no characteristic current signal is injected.

[0113] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. All equivalent structural transformations made using the contents of the present description and drawings under the inventive concept of the present invention, or direct / indirect applications in other related technical fields, are included in the patent protection scope of the present invention.

Claims

1. An adaptive topology recognition method based on autocorrelation characteristics, characterized in that: The following steps are involved: S1. The intelligent terminal in the substation initiates the topology identification process, determines the characteristic current signal and broadcasts it to all devices in the substation; S2. The recognition device starts to collect the current value on the power line and calculates the amplitude of the same-frequency noise; S3, the sending device sends a characteristic current signal with a specific frequency, amplitude and characteristic code information; S4. The identification device identifies and determines the collected current value based on the identification algorithm of the autocorrelation characteristic and generates an identification result. The specific steps are: S41, per-cycle sliding DFT: the identification device stores the current value in a processor, and performs per-cycle sliding DFT processing on the current value; S42, data caching: caching the processing results in S41 in sequence to obtain a DFT data sequence; S43, data differential conversion: calculating the mean of the DFT data sequence, and performing data differential conversion on the DFT data sequence; S44, data symbolization: symbolizing the data after differential conversion; S45, correlation accumulation: forming a local correlation sequence according to the characteristic code information and the transmission frequency of the characteristic current signal, and correlating and accumulating the symbolized DFT data sequence with the local correlation sequence to obtain a correlation value; S46, judging: judging whether the collected current value is a characteristic current signal according to the correlation value; S5. The intelligent terminal in the substation area obtains the recognition result of the recognition device and generates a substation area topology relationship diagram according to the recognition result.

2. The method for adaptive topology recognition based on autocorrelation characteristics according to claim 1, characterized in that: In step S2, the identification device starts to collect the current on the power line and calculates the amplitude of the same-frequency noise, specifically: The identification device collects the current value on the power line at a first sampling rate, and calculates, based on the current value, the amplitude of the same-frequency noise of the current value and the characteristic current signal.

3. The method for adaptive topology recognition based on autocorrelation characteristics according to claim 2, characterized in that: The same frequency noise amplitude is: Where F is the amplitude of the same-frequency noise, H is the number of cycles within the sampling time, and X1(i) and X2(i) are the frequency domain amplitudes of the characteristic frequency points within the i-th cycle.

4. The method for adaptive topology recognition based on autocorrelation characteristics according to claim 1, characterized in that: The characteristic current signal adopts OOK modulation mode; If the bit of the characteristic code information of the characteristic current signal is 1, signal modulation is performed within the bit width duration; if the bit of the characteristic code information of the characteristic current signal is 0, no modulation is performed.

5. The method for adaptive topology recognition based on autocorrelation characteristics according to claim 1, characterized in that: In step S41, the identification device stores the current value in a processor and performs a cycle sliding DFT process on the current value, specifically: A circular buffer buf_1 is constructed in the processor, and the collected current values ​​are sequentially stored in the circular buffer buf_1; after each first number of sampling points, the data in the circular buffer buf_1 is subjected to DFT processing, and its frequency domain component is calculated.

6. The method for adaptive topology recognition based on autocorrelation characteristics according to claim 5, characterized in that: The frequency domain components are: Among them, X(k) is the frequency domain component, k is the characteristic frequency point, N is the number of sampling points, n is the sampling point sequence number, and x(n) is the value of the nth point.

7. The method for adaptive topology recognition based on autocorrelation characteristics according to claim 6, characterized in that: In step S42, the processing results in step S41 are sequentially cached to obtain a DFT data sequence, specifically: A circular buffer buf_2 with M data units is constructed in the processor, and the results of the DFT processing are sequentially stored in the circular buffer buf_2 to obtain a DFT data sequence. The DFT data sequence is: data_fft(i),i=1,2,3...N Wherein, data_fft(i) is the DFT data sequence.

8. The method for adaptive topology recognition based on autocorrelation characteristics according to claim 7, characterized in that: In step S43, the mean of the DFT data sequence is calculated, and the DFT data sequence is subjected to data differential conversion, specifically: Extract the data of the first M / 2 data units in the circular buffer buf_2 and calculate the mean; the mean is: Among them, mean_data_fft is the mean; Perform data differential conversion on the DFT data sequence stored in the circular buffer buf_2, specifically: diff_data_fft(i)=data_fft(i)-mean_data_fft Wherein, diff_data_fft(i) is the DFT data after differential conversion.

9. The method for adaptive topology recognition based on autocorrelation characteristics according to claim 8, characterized in that: In step S44, the data after the differential conversion is symbolized, specifically: sign_data_fft(i)=sign(diff_data_fft(i)) Among them, sign_data_fft(i) is the symbolized DFT data sequence, and sign(x) is the sign function.

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