A method, device and equipment for power grid station area identification and a storage medium

Abstract

The application belongs to the technical field of power line communication, and discloses a power grid station area identification method, device, equipment and storage medium, the method comprises the following steps: issuing a synchronous clock instruction to all transformer terminal and user electric meter in the target area; at the synchronous zero-crossing moment, each transformer terminal injects a preset characteristic signal sequence into the line to which it belongs at the same time, and the user electric meter in the target area synchronously collects the voltage data of the line to which it belongs; each user electric meter extracts continuous zero-crossing noise time blocks from the voltage data; calculate the signal-to-noise ratio of each zero-crossing noise time block, and screen out effective zero-crossing noise time blocks; match the effective zero-crossing noise time blocks with the preset characteristic signal sequence injected by each transformer terminal to determine the station area to which the user electric meter belongs. The method has stronger anti-interference ability, and can effectively improve the station area identification accuracy and success rate in complex power grid environment. The power grid station area identification device, equipment and storage medium disclosed by the application have the same advantages.

Description

Technical Field

[0001] This invention belongs to the field of power line communication technology, and in particular relates to a method, apparatus, device and storage medium for identifying power grid transformer areas. Background Technology

[0002] In a power system, the power supply area of ​​a distribution transformer (referred to as a "transformer") is called a transformer zone. Accurately and efficiently identifying and determining the affiliation between user meters and transformers (i.e., transformer zone identification) is the foundation for line loss management, load control, fault location, and smart electricity consumption.

[0003] In existing technologies, transformer substation identification methods mainly include the following two types. The first is the power line carrier communication method. Specifically, under the command of the master station, the transformer terminal sends a characteristic signal to the meter through the power line. The meter receives and responds, thereby establishing a connection. However, in complex power grid environments, this method suffers from significant signal attenuation and strong noise interference, easily leading to identification failure or misjudgment. The second is the zero-crossing detection method, which uses the zero-crossing point of the voltage waveform as a time reference and identifies the transformer substation by analyzing the time difference characteristics of the zero-crossing points between the transformer side and the user side. However, this traditional zero-crossing detection method is prone to distortion or jitter at the zero-crossing point under complex operating conditions such as load fluctuations, harmonic pollution, or three-phase imbalance, resulting in blurred features and a significant decrease in identification accuracy. Therefore, the above-mentioned existing technologies have low identification accuracy and unstable success rate in complex power grid environments, and there is an urgent need for a more effective power grid transformer substation identification method that can resist interference and extract stable features. In recent years, a transformer substation identification method based on zero-crossing NTB has also emerged. This method identifies the transformer substation by matching the zero-crossing NTB sequences of the transformer and the meter. However, this method can only eliminate a single abnormal data node and cannot filter out the situation where the entire NTB block is overwhelmed by strong noise. In complex power grid environments, there are still problems such as high mismatch rate and unstable identification success rate. Summary of the Invention

[0004] To address the aforementioned issues, this invention provides a method, apparatus, device, and storage medium for identifying power grid distribution areas. It leverages the time-domain focusing advantage of zero-crossing NTBs and the quality filtering capability of SNR to reliably extract and identify weak feature signals in noisy environments. The quality of the filtered effective feature data is significantly improved, and the probability of false matching is greatly reduced. Correct identification can be achieved as long as a sufficient number of high-SNR NTBs are successfully matched within the statistical period. Therefore, it has stronger anti-interference capabilities and can effectively improve the accuracy and success rate of distribution area identification in complex power grid environments.

[0005] The present invention provides a method for identifying power grid substation areas, comprising:

[0006] Send a synchronization clock command to all transformer terminals and user meters within the target area;

[0007] At the zero-crossing moment of synchronization, each transformer terminal simultaneously injects a preset characteristic signal sequence into its corresponding line. The preset characteristic signal sequence is a Gold code. At the same time, the user meters in the target area synchronously collect the voltage data of their respective lines. The voltage data includes the preset characteristic signal sequence.

[0008] Each user meter extracts multiple consecutive zero-crossing noise time blocks from the voltage data it collects;

[0009] Calculate the signal-to-noise ratio (SNR) of each zero-crossing noise time block and filter out valid zero-crossing noise time blocks with an SNR greater than a preset threshold.

[0010] The effective zero-crossing noise time block is matched with the preset feature signal sequence injected into each of the transformer terminals to determine the transformer area to which the user's meter belongs.

[0011] Each of the user meters extracts multiple consecutive zero-crossing noise time blocks from the voltage data it collects, including:

[0012] Each of the user's electricity meters performs zero-crossing detection on the collected voltage data;

[0013] Before and after each detected zero crossing, a time window of data of fixed duration is extracted, and the time window data is a zero-crossing noise time block;

[0014] Multiple consecutive zero-crossing noise time blocks are extracted to form the original feature data set.

[0015] Preferably, in the above-mentioned method for identifying power grid substations, the step of calculating the signal-to-noise ratio (SNR) of each zero-crossing noise time block and filtering out valid zero-crossing noise time blocks with an SNR greater than a preset threshold includes:

[0016] The preset feature signal sequence is used as a reference signal;

[0017] Subtract the reference signal from the zero-crossing noise time block to obtain the residual noise;

[0018] Calculate the average power P of the reference signal. signal and the average power P of the residual noise noise ;

[0019] According to the formula Calculate the signal-to-noise ratio (SNR) of the zero-crossing noise time block, where SNR is the signal-to-noise ratio.

[0020] All zero-crossing noise time blocks with an SNR higher than the preset signal-to-noise ratio threshold are selected to form an effective feature data set.

[0021] Preferably, in the above-mentioned method for identifying power grid distribution areas, the step of matching the effective zero-crossing noise time block with the preset feature signal sequence injected into each of the transformer terminals to determine the distribution area to which the user's meter belongs includes:

[0022] The correlation between the effective zero-crossing noise time block and the preset feature signal sequence injected into each of the transformer terminals is calculated, and the transformer terminal with the highest number of successful matches is determined to be the transformer area to which the user's electricity meter belongs.

[0023] The present invention provides a device for identifying power grid substation areas, comprising:

[0024] The synchronization clock command issuing unit is used to issue synchronization clock commands to all transformer terminals and user meters within the target area;

[0025] A preset feature signal transmission unit is used to inject a preset feature signal sequence into the line to which each transformer terminal belongs at the zero crossing time of synchronization. The preset feature signal sequence is a Gold code. At the same time, the user electricity meters in the target area synchronously collect the voltage data of their respective lines. The voltage data includes the preset feature signal sequence.

[0026] A zero-crossing noise time block extraction unit is used to extract multiple consecutive zero-crossing noise time blocks from the voltage data collected by each user's electricity meter.

[0027] The signal-to-noise ratio (SNR) calculation unit is used to calculate the SNR of each zero-crossing noise time block and filter out valid zero-crossing noise time blocks that are greater than a preset SNR threshold.

[0028] The transformer area determination unit is used to match the effective zero-crossing noise time block with the preset characteristic signal sequence injected into each transformer terminal to determine the transformer area to which the user's meter belongs.

[0029] The zero-crossing noise time block extraction unit includes:

[0030] A zero-crossing detection component is used to perform zero-crossing detection on the collected voltage data using each of the user's electricity meters;

[0031] The zero-crossing noise time block interception component is used to intercept a time window of data of a fixed duration before and after each detected zero-crossing point, wherein the time window data is a zero-crossing noise time block;

[0032] The continuous zero-crossing noise time block extraction component is used to extract multiple consecutive zero-crossing noise time blocks to form an original feature data set.

[0033] Preferably, in the above-mentioned power grid area identification device, the signal-to-noise ratio calculation unit includes:

[0034] A reference signal determination component is used to use the preset feature signal sequence as a reference signal;

[0035] A residual noise determination component is used to subtract the reference signal from the zero-crossing noise time block to obtain the residual noise;

[0036] An average power calculation unit is used to calculate the average power P of the reference signal. signal and the average power P of the residual noise noise ;

[0037] Signal-to-noise ratio calculation component, used to calculate the signal-to-noise ratio according to the formula Calculate the signal-to-noise ratio (SNR) of the zero-crossing noise time block, where SNR is the signal-to-noise ratio.

[0038] The filtering component is used to filter out all zero-crossing noise time blocks whose SNR is higher than the preset signal-to-noise ratio threshold, thus forming a valid feature data set.

[0039] Preferably, in the above-mentioned power grid area identification device, the area determination unit is specifically used to perform correlation calculation between the effective zero-crossing noise time block and the preset feature signal sequence injected by each of the transformer terminals, and determine the transformer terminal with the highest number of successful matches as the area to which the user's electricity meter belongs.

[0040] The present invention provides a device for identifying power grid substation areas, comprising:

[0041] Memory, used to store computer programs;

[0042] A processor, configured to implement the steps of the method for identifying power grid substations as described in any of the preceding claims when executing the computer program.

[0043] The present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the power grid area identification method as described in any of the above claims.

[0044] As described above, the method for identifying power grid distribution areas provided by this invention includes: issuing synchronization clock commands to all transformer terminals and user meters within the target area; at the zero-crossing moment of synchronization, each transformer terminal simultaneously injects a preset characteristic signal sequence into its corresponding line, the preset characteristic signal sequence being a Gold code; simultaneously, the user meters within the target area synchronously collect voltage data of their respective lines, the voltage data including the preset characteristic signal sequence; each user meter extracts multiple consecutive zero-crossing noise time blocks from the voltage data it collects; calculating the signal-to-noise ratio (SNR) of each zero-crossing noise time block, and filtering out valid blocks with an SNR greater than a preset threshold. The method involves matching the effective zero-crossing noise time block with the preset feature signal sequence injected into each of the transformer terminals to determine the transformer area to which the user's meter belongs. Therefore, the beneficial effect of this method is that it can utilize the time-domain focusing advantage of zero-crossing noise time blocks and the quality filtering capability of signal frequency (SNR) to reliably extract and identify weak feature signals in strong noise environments. The quality of the filtered effective feature data is significantly improved, and the probability of false matching is greatly reduced. As long as a sufficient number of high-SNR NTBs are successfully matched within the statistical period, correct identification can be achieved. Therefore, it has stronger anti-interference capabilities and can effectively improve the accuracy and success rate of transformer area identification in complex power grid environments. The above-mentioned power grid transformer area identification device, equipment, and storage medium provided by this invention have the same advantages. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0046] Figure 1 This is a schematic diagram of an embodiment of a method for identifying power grid substations provided by the present invention;

[0047] Figure 2 A schematic diagram illustrating an embodiment of a power grid area identification device provided by the present invention;

[0048] Figure 3 A schematic diagram illustrating an embodiment of a power grid area identification device provided by the present invention;

[0049] Figure 4 A schematic diagram of the system architecture and signal flow provided as an example of the present invention;

[0050] Figure 5 This is a flowchart illustrating the processing method between the transformer terminal and the user's electricity meter.

[0051] Figure 6 This is a schematic diagram illustrating the principles of zero-crossing NTB extraction and SNR calculation.

[0052] The annotations in the attached figures are explained as follows:

[0053] Synchronous clock command issuing unit 21, preset characteristic signal transmission unit 22, zero-crossing noise time block extraction unit 23, signal-to-noise ratio calculation unit 24, transformer area determination unit 25, memory 401, processor 402, master station system 101, transformer area A terminal 102, transformer area B terminal 103, user meter 104, communication network 106, power line 107, zero-crossing point 301, zero-crossing NTB 302, preset characteristic signal sequence 303, residual noise 304. Detailed Implementation

[0054] The core of this invention is to provide a method, apparatus, device, and storage medium for identifying power grid distribution areas. It leverages the time-domain focusing advantage of zero-crossing NTBs and the quality filtering capability of SNR to reliably extract and identify weak feature signals in noisy environments. The quality of the filtered effective feature data is significantly improved, and the probability of false matching is greatly reduced. Correct identification can be achieved as long as a sufficient number of high-SNR NTBs are successfully matched within the statistical period. Therefore, it has stronger anti-interference capabilities and can effectively improve the accuracy and success rate of distribution area identification in complex power grid environments. It is applicable to situations involving shared high voltage and construction site lines, accurately and reliably identifying the power grid distribution area to which the user's meter belongs. Here, shared high voltage refers to construction work within the high-voltage line protection zone, and construction site lines refer to temporary overhead cable lines at construction sites that are parallel, intersecting, or too close to high-voltage lines.

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] An implementation example of the method for identifying power grid substations provided by this invention. Figure 1 As shown, Figure 1 This is a schematic diagram of an embodiment of a method for identifying power grid substations provided by the present invention. The method may include the following steps:

[0057] S1: Send a synchronization clock command to all transformer terminals and user meters within the target area.

[0058] It should be noted that the main station or concentrator of the transformer area identification can send a high-precision synchronous clock calibration command to all transformer monitoring terminals, distribution transformer measurement and control units, and smart meters of each user within the target identification area through power line carrier, low-power wireless, and other power communication channels. The command carries a Beidou or GPS reference timestamp and unified timing parameters, triggering all terminals and meters to complete clock synchronization calibration and lock a unified time base. This ensures strict time consistency in subsequent transformer area characteristic signal transmission, electrical quantity synchronous acquisition, and time series data comparison, eliminating misjudgments of user-transformer relationships and topology identification distortions caused by clock deviations. This lays the foundation for subsequent transformer area attribution determination based on synchronous time series characteristics.

[0059] S2: At the zero-crossing moment of synchronization, each transformer terminal simultaneously injects a preset characteristic signal sequence into its corresponding line. The preset characteristic signal sequence is a Gold code. At the same time, the user meters in the target area synchronously collect the voltage data of their respective lines. The voltage data includes the preset characteristic signal sequence.

[0060] It should be noted that after completing the network-wide clock synchronization calibration, using the unified and synchronized voltage zero-crossing moment as the trigger reference, each transformer terminal in the target area simultaneously injects a unique and distinguishable preset characteristic signal sequence into its respective distribution line according to a preset timing sequence. This signal sequence is superimposed on the power frequency voltage waveform and transmitted along the transformer area line. Simultaneously, all user meters in the target area synchronously start collecting data at the same zero-crossing moment, continuously collecting line voltage data at their connection point. The collected voltage data includes the power frequency component and the preset characteristic signal sequence injected by each transformer, thereby achieving strict timing synchronization between the injection of the preset characteristic signal sequence and the voltage data collection, providing original synchronized data for subsequent signal identification and transformer area attribution determination. In this step, actively injecting a unique preset characteristic signal sequence distinguishes it from the existing technology's method of "collecting the original NTB noise characteristics of the power grid." In this embodiment, the transformer actively injects a unique and distinguishable preset characteristic signal sequence into the line, fundamentally solving the problem of unstable original characteristics.

[0061] S3: Each user's electricity meter extracts multiple consecutive zero-crossing noise time blocks from the voltage data it collects.

[0062] It should be noted that after the synchronous acquisition of line voltage data is completed, each user's meter, based on the continuous voltage waveform data it has collected, uses the voltage zero-crossing moment as the time reference point and, according to the preset time window length and data truncation rules, filters and extracts multiple continuous and interconnected zero-crossing noise time blocks from the voltage signal. These time blocks, centered on the voltage zero-crossing point, contain time-domain noise segments formed by voltage fluctuations, high-frequency disturbances, and superposition of characteristic signals within the corresponding time period. By truncating multiple consecutive zero-crossing time blocks, the characteristic signal components and transformer-specific noise characteristics during line transmission are fully preserved, providing stable and reliable local analysis samples for subsequent feature sequence matching and noise feature comparison.

[0063] S4: Calculate the signal-to-noise ratio (SNR) of each zero-crossing noise time block and filter out the valid zero-crossing noise time blocks that are greater than the preset SNR threshold.

[0064] This step involves feature enhancement. For each extracted zero-crossing noise time block (NTB), the signal-to-noise ratio (SNR) is calculated. For each extracted continuous zero-crossing noise time block, each user's meter calculates the corresponding SNR based on the effective feature signals within the block. By quantifying and analyzing the signal energy distribution and noise interference intensity of each time block, the SNR value for each time period is obtained. The calculated SNR is then compared one by one with a preset SNR threshold. Invalid time blocks with low SNR, susceptibility to interference, or unclear features are eliminated, retaining only valid zero-crossing noise time blocks with an SNR greater than the preset threshold, clear feature signals, and high reliability. This reduces interference in subsequent signal matching processes, thereby improving the accuracy and stability of transformer area identification. It is evident that this SNR-based full NTB block screening method differs from the existing filtering method of "removing a single abnormal data node." This embodiment calculates the SNR for the entire NTB block, directly eliminating invalid NTB blocks completely submerged by strong noise, thus significantly reducing the probability of mismatches.

[0065] S5: Match the effective zero-crossing noise time block with the preset characteristic signal sequence injected into each transformer terminal to determine the transformer area to which the user's meter belongs.

[0066] Specifically, after selecting valid zero-crossing noise time blocks, each user's meter sequentially compares the retained valid zero-crossing noise time blocks with the preset characteristic signal sequences pre-injected into the lines of each transformer terminal within the target area using time-domain waveform comparison, correlation coefficient calculation, or feature encoding matching. By analyzing the similarity between the amplitude, phase, and timing pattern of the characteristic signals carried in the valid noise time blocks and each preset sequence, it determines which transformer terminal's preset characteristic signal sequence has the highest matching degree. Based on the matching result, the power supply area to which the user's meter belongs is uniquely determined, achieving accurate identification of the user-transformer relationship. It is evident that this statistical matching method using multiple valid NTBs differs from the single-matching and scoring methods of existing technologies. This embodiment determines the transformer area by statistically analyzing the number of successful matches of multiple high SNR valid NTBs. Even if individual NTBs are interfered with, it does not affect the final result, thus significantly improving the robustness of the identification.

[0067] Extracting multiple consecutive zero-crossing noise time blocks from the voltage data collected by each user's electricity meter may include the following steps:

[0068] Each user's electricity meter performs zero-crossing detection on the collected voltage data. Specifically, after completing the synchronous acquisition of line voltage data, each user's electricity meter first performs zero-crossing detection processing on the acquired continuous voltage sampling sequence. By judging the alternating jump time of the instantaneous voltage value from negative to positive or from positive to negative in real time, it accurately locates each zero-crossing position of the voltage waveform and marks the corresponding sampling point number and timestamp, providing a unified and reliable timing reference for subsequent extraction of continuous zero-crossing noise time blocks.

[0069] Before and after each detected zero-crossing point, a fixed-duration time window of data is extracted. This time window data is a zero-crossing noise time block. Specifically, after the accurate detection of voltage zero-crossing points is completed, each user's meter takes each detected zero-crossing point as the center time and, according to the fixed time length preset by the system, symmetrically extracts data from the time period before and after the zero-crossing point to obtain a continuous voltage sampling data including the zero-crossing point. The electrical data within this fixed-duration time window constitutes a zero-crossing noise time block for subsequent analysis.

[0070] Multiple consecutive zero-crossing noise time blocks are extracted to form the original feature data set. Specifically, based on generating individual zero-crossing noise time blocks one by one, each user's meter selects and extracts multiple adjacent and consecutively arranged zero-crossing noise time blocks according to the order of the occurrence of the voltage zero crossing point. These time block data with coherent time sequence and complete features are combined in an orderly manner to form the original feature data set used for subsequent signal-to-noise ratio calculation and feature matching, providing sufficient and continuous analysis samples for subsequent identification of transformer area feature signals.

[0071] As described above, the method for identifying power grid distribution areas provided by this invention includes: issuing synchronization clock commands to all transformer terminals and user meters within the target area; at the zero-crossing moment of synchronization, each transformer terminal simultaneously injects a preset characteristic signal sequence into its corresponding line, while user meters within the target area simultaneously collect voltage data of their respective lines, the voltage data including the preset characteristic signal sequence; each user meter extracts multiple consecutive zero-crossing noise time blocks from its collected voltage data; calculating the signal-to-noise ratio of each zero-crossing noise time block, and selecting effective zero-crossing noise times greater than a preset signal-to-noise ratio threshold. The method involves matching the effective zero-crossing noise time block with the preset feature signal sequence injected into each transformer terminal to determine the transformer area to which the user's meter belongs. Therefore, this method can utilize the time-domain focusing advantage of zero-crossing NTBs and the quality screening capability of SNR to achieve reliable extraction and identification of weak feature signals in a strong noise environment. The quality of the selected effective feature data is significantly improved, and the probability of mismatch is greatly reduced. As long as enough high SNR NTBs are successfully matched within the statistical period, correct identification can be completed. Therefore, the anti-interference capability is stronger, and it can effectively improve the accuracy and success rate of transformer area identification in complex power grid environments.

[0072] In another specific embodiment of the above-mentioned method for identifying power grid distribution areas, calculating the signal-to-noise ratio (SNR) of each zero-crossing noise time block and filtering out effective zero-crossing noise time blocks with an SNR greater than a preset SNR threshold may include the following steps: using a preset feature signal sequence as a reference signal; subtracting the reference signal from the zero-crossing noise time block to obtain residual noise; and calculating the average power P of the reference signal. signal and the average power P of residual noise noise According to the formula Calculate the signal-to-noise ratio (SNR) of the zero-noise time blocks, where SNR is the signal-to-noise ratio; select all zero-noise time blocks with SNR higher than the preset SNR threshold to form an effective feature data set.

[0073] Specifically, when calculating the signal-to-noise ratio (SNR) for each zero-crossing noise time block, a pre-injected characteristic signal sequence from each transformer terminal is used as a standard reference signal. This reference signal must be aligned with the zero-crossing noise time block in time, have the same data length, and share the same data starting point. In practice, this reference signal and the zero-crossing noise time block are precisely aligned on the time axis beforehand, and the data length must be consistent before subtraction is performed. This ensures the accuracy and effectiveness of the residual noise calculation results. Specifically, this reference signal and the corresponding zero-crossing noise... The sampled data within the acoustic time block are interpolated, and the reference signal component is removed to obtain residual noise containing only interference components. Then, the mean of the squared amplitude of the reference signal is calculated to obtain the average signal power. At the same time, the mean square of the residual noise data is calculated to obtain the average noise power. Then, the signal-to-noise ratio (SNR) of each zero-crossing noise time block is calculated according to the formula. Finally, the calculated SNR is compared with the preset SNR threshold, and time blocks with a SNR higher than the preset SNR threshold and reliable feature signal quality are selected. These high-quality and effective zero-crossing noise time blocks are combined to form an effective feature data set for substation matching.

[0074] In another specific embodiment of the above-mentioned method for identifying power grid distribution areas, matching the effective zero-crossing noise time block with the preset feature signal sequence injected by each transformer terminal to determine the distribution area to which the user's electricity meter belongs may include: calculating the correlation between the effective zero-crossing noise time block and the preset feature signal sequence injected by each transformer terminal, and determining the transformer terminal with the highest number of successful matches as the distribution area to which the user's electricity meter belongs. Specifically, in the actual application scenario of power user distribution area identification, one of the core technical aspects is to accurately match the effective zero-crossing noise time block with the preset feature signal sequence injected by each transformer terminal, thereby accurately locating the distribution area to which the user's electricity meter belongs. In specific implementation, the collected noise signal of the user's electricity meter needs to be preprocessed first to filter out time segments containing effective zero-crossing features and remove interference noise to ensure data validity; subsequently, for the preset feature signal sequence injected by the transformer terminal corresponding to each distribution area, the correlation coefficient between the effective zero-crossing noise time block and each preset feature signal sequence is calculated respectively, and the degree of matching is reflected by quantifying the strength of the correlation. After completing the correlation calculation of all transformer terminals, the number of successful matches between the user's electricity meter and the preset characteristic signal sequence of each transformer terminal is counted. Finally, the transformer terminal with the highest number of successful matches is determined as the actual transformer area to which the user's electricity meter belongs. This achieves accurate identification of the transformer area to which the user's electricity meter belongs, providing reliable data support for transformer area management, fault diagnosis, load allocation and other work in the power system.

[0075] The method provided by this invention has four advantages. First, it employs a dual screening process, resulting in strong anti-interference capabilities. Specifically, it first utilizes the zero-crossing NTB extraction method to focus the analysis on the region near the zero-crossing point, where the signal is relatively stable and features are easily captured, avoiding complex interference from other parts of the voltage waveform. Then, it introduces SNR calculation and threshold screening to effectively eliminate invalid data with blurred features due to excessive noise, retaining only high-quality feature fragments for matching, thus improving feature reliability from the source. Second, it boasts high recognition accuracy. The quality of the effective feature data set screened by SNR is significantly higher than the original data. Based on this, feature matching greatly reduces the probability of mismatches, maintaining extremely high recognition accuracy even in complex power grid environments such as load changes and harmonic interference. Third, it offers a high recognition success rate. This method does not rely on a single zero-crossing time difference but utilizes the presence of the coded signal in the NTB for multiple statistical decisions. Even if the feature signal of individual cycles is submerged in noise, as long as a sufficient number of high-SNR NTBs are successfully matched within the statistical period, correct recognition can be achieved, greatly improving the robustness and success rate of recognition. Finally, it is easy to implement. This method does not require modification of existing hardware equipment. It only requires upgrading the software algorithm in the firmware of the transformer terminal and the meter. The implementation cost is low, so it is easy to promote and apply on a large scale.

[0076] An embodiment of the power grid area identification device provided by the present invention is as follows: Figure 2 As shown, Figure 2 This is a schematic diagram of an embodiment of a power grid area identification device provided by the present invention. The device may include:

[0077] The synchronization clock command issuing unit 21 is used to issue synchronization clock commands to all transformer terminals and user meters within the target area. It should be noted that the transformer identification master station or concentrator can issue high-precision synchronization clock calibration commands to all transformer monitoring terminals, distribution transformer measurement and control units, and user smart meters within the target identification area through power line carrier, low-power wireless, and other power communication channels. The commands carry Beidou or GPS reference timestamps and unified timing parameters, triggering all terminals and meters to complete clock synchronization calibration and lock a unified time base. This ensures strict time consistency in subsequent transformer characteristic signal transmission, electrical quantity synchronous acquisition, and time series data comparison, eliminating misjudgments of user-transformer relationships and topology identification distortions caused by clock deviations, and laying the foundation for subsequent transformer affiliation determination based on synchronization time series characteristics.

[0078] The preset characteristic signal transmission unit 22 is used to simultaneously inject a preset characteristic signal sequence into the corresponding line of each transformer terminal at the synchronous zero-crossing moment. This preset characteristic signal sequence is a Gold code. Simultaneously, user meters within the target area synchronously collect voltage data from their respective lines. The voltage data includes the preset characteristic signal sequence. It should be noted that after completing the network-wide clock synchronization calibration, using the unified synchronous voltage zero-crossing moment as the trigger reference, each transformer terminal within the target area simultaneously injects a unique and distinguishable preset characteristic signal sequence into its respective distribution line according to a preset timing sequence. This signal sequence is superimposed on the power frequency voltage waveform and transmitted along the transformer area lines. At the same time, the target... All user meters within the designated area synchronously start collecting data at the same zero-crossing moment, continuously collecting line voltage data at their connection points. The collected voltage data includes power frequency components and preset characteristic signal sequences injected by each transformer, thereby achieving strict time synchronization between characteristic signal injection and voltage data collection. This provides original synchronized data for subsequent signal identification and transformer area attribution determination. The active injection of a unique preset characteristic signal sequence here is different from the existing technology of "collecting the original NTB noise characteristics of the power grid". In this embodiment, the transformer actively injects a unique and distinguishable preset characteristic signal sequence into the line, which fundamentally solves the problem of unstable original characteristics.

[0079] The zero-crossing noise time block extraction unit 23 is used to extract multiple continuous zero-crossing noise time blocks from the voltage data collected by each user's meter. It should be noted that after the synchronous acquisition of line voltage data is completed, each user's meter, based on its own collected continuous voltage waveform data, uses the voltage zero-crossing moment as the time reference point and selects and extracts multiple continuous and interconnected zero-crossing noise time blocks from the voltage signal according to the preset time window length and data truncation rules. These time blocks are centered on the voltage zero-crossing point and include time-domain noise segments formed by voltage fluctuations, high-frequency disturbances, and superposition of characteristic signals within the corresponding time period. By truncating multiple continuous zero-crossing time blocks, the characteristic signal components and transformer-specific noise characteristics during line transmission are fully preserved, providing stable and reliable local analysis samples for subsequent feature sequence matching and noise feature comparison.

[0080] The signal-to-noise ratio (SNR) calculation unit 24 is used to calculate the SNR of each zero-crossing noise time block and filter out valid zero-crossing noise time blocks with a preset SNR threshold. In other words, the SNR is calculated for each extracted zero-crossing noise time block, i.e., the zero-crossing NTB. For each extracted continuous zero-crossing noise time block, each user's meter calculates the corresponding SNR based on the effective characteristic signal within the block. By quantitatively analyzing the signal energy distribution and noise interference intensity of each time block, the SNR value for each time period is obtained. Then, the calculated SNR is compared one by one with the preset SNR threshold. By comparing and eliminating invalid time blocks with low signal-to-noise ratios (SNR), those susceptible to interference, or those lacking clear features, only valid zero-crossing noise time blocks with SNRs greater than a preset threshold, clear feature signals, and high reliability are retained. This reduces interference in subsequent signal matching processes, thereby improving the accuracy and stability of station identification. It is evident that this SNR-based full NTB block screening method differs from the existing filtering method of "removing a single abnormal data node." The solution in this embodiment calculates the SNR of the entire NTB block and directly removes invalid NTB blocks completely submerged by strong noise, thus significantly reducing the probability of mismatches.

[0081] The transformer substation determination unit 25 is used to match the effective zero-crossing noise time blocks with the preset feature signal sequences injected by each transformer terminal to determine the transformer substation to which the user's meter belongs. Specifically, after selecting the effective zero-crossing noise time blocks, each user's meter sequentially compares the retained effective zero-crossing noise time blocks with the preset feature signal sequences injected by each transformer terminal in the target area in the time domain waveform, calculates the correlation coefficient, or matches the feature codes. By analyzing the similarity between the amplitude, phase, and timing pattern of the feature signals carried in the effective noise time blocks and each preset sequence, it is determined which transformer terminal's feature signal sequence has the highest matching degree. Then, based on the matching result, the power supply transformer substation to which the user's meter belongs is uniquely determined, realizing accurate identification of the user-transformer relationship. It can be seen that this matching statistical determination method of multiple effective NTBs is different from the single matching and scoring method of the existing technology. The scheme of this embodiment determines the transformer substation by counting the number of successful matches of multiple high SNR effective NTBs. Even if individual NTBs are interfered with, it will not affect the final result, thereby greatly improving the robustness of identification.

[0082] The aforementioned zero-crossing noise time block extraction unit 23 may include:

[0083] The zero-crossing detection component is used to perform zero-crossing detection on the collected voltage data using each user's electricity meter. Specifically, after each user's electricity meter completes the synchronous acquisition of line voltage data, it first performs zero-crossing detection processing on the acquired continuous voltage sampling sequence. By judging the alternating jump time of the instantaneous voltage value from negative to positive or from positive to negative in real time, it accurately locates each zero-crossing position of the voltage waveform and marks the corresponding sampling point number and timestamp, providing a unified and reliable timing reference for subsequent extraction of continuous zero-crossing noise time blocks.

[0084] The zero-crossing noise time block interception component is used to intercept a fixed-duration time window of data before and after each detected zero-crossing point. The time window data is a zero-crossing noise time block. Specifically, after the accurate detection of voltage zero-crossing points is completed, each user's meter takes each detected zero-crossing point as the center time and performs symmetrical interception to the time period before and after the zero-crossing point according to the fixed time length preset by the system. This obtains a continuous voltage sampling data including the zero-crossing point. The electrical data within the fixed-duration time window obtained by interception constitutes a zero-crossing noise time block for subsequent analysis.

[0085] The continuous zero-crossing noise time block extraction component is used to extract multiple consecutive zero-crossing noise time blocks to form an original feature data set. Specifically, based on generating individual zero-crossing noise time blocks one by one, each user's meter selects and extracts multiple adjacent and consecutively arranged zero-crossing noise time blocks according to the order of the occurrence of the voltage zero crossing point. These time block data with coherent time sequence and complete features are combined in an orderly manner to form an original feature data set for subsequent signal-to-noise ratio calculation and feature matching, providing sufficient and continuous analysis samples for subsequent identification of transformer area feature signals.

[0086] The aforementioned device can leverage the time-domain focusing advantage of zero-crossing NTBs and the quality screening capability of SNRs to reliably extract and identify weak feature signals in noisy environments. The quality of the selected effective feature data is significantly improved, and the probability of false matching is greatly reduced. As long as enough high-SNR NTBs are successfully matched within the statistical period, correct identification can be achieved. Therefore, it has stronger anti-interference capabilities and can effectively improve the accuracy and success rate of transformer substation identification in complex power grid environments.

[0087] In another specific embodiment of the aforementioned power grid area identification device, the signal-to-noise ratio calculation unit may include:

[0088] The reference signal determination component uses a preset characteristic signal sequence as a reference signal. This reference signal must be aligned with the zero-crossing noise time block in time, have the same data length, and share the same data starting point. Specifically, in implementing this scheme, the reference signal and the zero-crossing noise time block are precisely aligned on the time axis beforehand, and the data lengths must be consistent before performing the subtraction operation. This ensures the accuracy and effectiveness of the residual noise calculation result. The residual noise determination component subtracts the reference signal from the zero-crossing noise time block to obtain the residual noise. The average power calculation component calculates the average power P of the reference signal. signal and the average power P of residual noise noise Signal-to-noise ratio calculation component, used to calculate the signal-to-noise ratio according to the formula. Calculate the signal-to-noise ratio (SNR) of the zero-noise time blocks, where SNR is the signal-to-noise ratio; a filtering component is used to filter out all zero-noise time blocks with SNR higher than a preset SNR threshold, forming a valid feature data set.

[0089] Specifically, when calculating the signal-to-noise ratio (SNR) for each zero-crossing noise time block, the preset characteristic signal sequence pre-injected by each transformer terminal is first used as the standard reference signal. The difference between the reference signal and the sampled data in the corresponding zero-crossing noise time block is calculated, and the reference signal component is removed to obtain the residual noise containing only interference components. Then, the mean value of the square of the amplitude of the reference signal is calculated to obtain the average signal power. At the same time, the mean square value of the residual noise data is calculated to obtain the average noise power. Then, the SNR of each zero-crossing noise time block is calculated one by one according to the formula. Finally, the calculated SNR is compared with the preset SNR threshold, and time blocks with a SNR higher than the preset SNR threshold and reliable characteristic signal quality are selected. These high-quality and effective zero-crossing noise time blocks together form an effective characteristic data set for transformer area matching.

[0090] In another specific embodiment of the aforementioned power grid distribution area identification device, the distribution area determination unit is specifically used to calculate the correlation between the effective zero-crossing noise time block and the preset feature signal sequence injected into each distribution transformer terminal, and to determine the distribution area to which the user's electricity meter belongs by the distribution transformer terminal with the highest number of successful matches. Specifically, in the actual application scenario of power user distribution area identification, one of the core technical aspects is to accurately match the effective zero-crossing noise time block with the preset feature signal sequence injected into each distribution transformer terminal, thereby accurately locating the distribution area to which the user's electricity meter belongs. In specific implementation, the collected noise signal of the user's electricity meter needs to be preprocessed first to filter out time segments containing effective zero-crossing features and remove interference noise to ensure data validity; subsequently, for the preset feature signal sequence injected into the distribution transformer terminal corresponding to each distribution area, the correlation coefficient between the effective zero-crossing noise time block and each preset feature signal sequence is calculated respectively, and the degree of matching is reflected by quantifying the strength of the correlation. After completing the correlation calculation of all transformer terminals, the number of successful matches between the user's electricity meter and the preset characteristic signal sequence of each transformer terminal is counted. Finally, the transformer terminal with the highest number of successful matches is determined as the actual transformer area to which the user's electricity meter belongs. This achieves accurate identification of the transformer area to which the user's electricity meter belongs, providing reliable data support for transformer area management, fault diagnosis, load allocation and other work in the power system.

[0091] An example implementation of the device for identifying power grid substations provided by this invention. Figure 3 As shown, Figure 3 This is a schematic diagram of an embodiment of a power grid area identification device provided by the present invention. The device may include:

[0092] The memory 401 is used to store a computer program; the processor 402 is used to execute the computer program to implement the steps of the method for identifying power grid substations as described above.

[0093] In an embodiment of the computer-readable storage medium provided by the present invention, a computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the steps of the method for identifying power grid substations as described in any of the above embodiments.

[0094] The following example illustrates the execution process of the above scheme in detail:

[0095] refer to Figure 4 , Figure 4The system architecture and signal flow diagram provided for this invention includes a master station system 101, transformer substation A terminal 102 and transformer substation B terminal 103, and a user meter 104. The master station system 101 communicates with transformer substation A terminal 102, transformer substation B terminal 103, and user meter 104 through a communication network 106 (such as a public wireless network / private network). The power line 107 is the transmission medium for a preset characteristic signal sequence.

[0096] Combination Figure 5 and Figure 6 , Figure 5 This is a flowchart illustrating the processing method between the transformer terminal and the user's electricity meter. Figure 6 The diagram illustrates the principles of zero-crossing NTB extraction and SNR calculation. The execution flow can be as follows:

[0097] Step 1: Synchronous transmission and acquisition of characteristic signals:

[0098] Step 201 (Master Station Side): The master station system broadcasts a precise synchronization clock signal to all transformer terminals (102, 103) and user meters (104), and issues an instruction to start the identification process.

[0099] Step 202 (Transformer Terminal Side): After receiving the instruction, each transformer terminal waits for the next voltage zero-crossing point. At the precise synchronous zero-crossing moment, a preset characteristic signal sequence is injected into the power grid through a signal coupling device. This preset characteristic signal sequence can be a Gold code, a specific frequency FSK signal, or other coded signals with good autocorrelation characteristics. Among them, Gold code is a type of pseudo-random sequence widely used in communication systems such as Code Division Multiple Access (CDMA), synchronization, and encryption. Its advantage lies in its excellent autocorrelation and cross-correlation characteristics, which enable good separation when multiple users' sequences are transmitted simultaneously in the same channel.

[0100] Step 2: Zero-crossing noise time block (NTB) extraction:

[0101] Step 203 (User meter side): All user meters start at the above synchronization time and continuously and synchronously acquire voltage waveform data for a certain duration (e.g., 2 seconds) at a high sampling rate (e.g., 6.4kHz).

[0102] Step 204 (User meter side): Each user meter independently executes the voltage waveform zero-crossing detection algorithm to locate the zero-crossing point of the voltage waveform from negative to positive.

[0103] Step 205 (User's meter side): For example Figure 6As shown, taking each zero-crossing point 301 as the center, a period of time is intercepted before and after (for example, a total of 10ms) to form a continuous zero-crossing NTB302 (zero-crossing noise time block). In this way, N (for example, 100) zero-crossing NTB302s are intercepted continuously.

[0104] Step 3: Signal-to-Noise Ratio (SNR) Calculation and Feature Enhancement:

[0105] Step 206 (User Meter Side): For each zero-crossing NTB 302, calculate its signal-to-noise ratio (SNR). Specifically: Use the preset characteristic signal sequence 303 injected from the transformer terminal side as a reference signal, subtract the reference signal from the NTB data to obtain residual noise 304, calculate the average power of the reference signal and the average power of the residual noise respectively, and then calculate the SNR according to the aforementioned calculation formula. Set a preset SNR threshold (e.g., 3dB), and only retain zero-crossing NTBs with an SNR higher than the preset SNR threshold to filter out valid data, thereby forming a valid feature dataset.

[0106] Step 4: Feature matching and transformer area identification:

[0107] Step 207 (User meter side): Perform cross-correlation calculation on each zero-crossing NTB in the effective feature dataset and the corresponding preset feature signal sequence of each transformer terminal, match them, find the sequence with the largest cross-correlation peak, and record a successful match.

[0108] Step 208 (Meter Side): Count the number of successful matches between transformer terminal 102 in area A and transformer terminal 103 in area B. For example, the results may be: a user's meter successfully matched the sequence with transformer terminal 102 in area A 85 times and with transformer terminal 103 in area B 2 times.

[0109] Step 209 (User meter side): Determine that the user meter belongs to the transformer terminal with the highest number of successful matches, i.e., transformer area A. The meter can directly report the transformer area identification result to the main station.

[0110] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for identifying power grid distribution areas, characterized in that, include: Send a synchronization clock command to all transformer terminals and user meters within the target area; At the zero-crossing moment of synchronization, each transformer terminal simultaneously injects a preset characteristic signal sequence into its corresponding line. The preset characteristic signal sequence is a Gold code. At the same time, the user meters in the target area synchronously collect the voltage data of their respective lines. The voltage data includes the preset characteristic signal sequence. Each user meter extracts multiple consecutive zero-crossing noise time blocks from the voltage data it collects; Calculate the signal-to-noise ratio (SNR) of each zero-crossing noise time block and filter out valid zero-crossing noise time blocks with an SNR greater than a preset threshold. The effective zero-crossing noise time block is matched with the preset feature signal sequence injected into each of the transformer terminals to determine the transformer area to which the user's meter belongs. Each of the user meters extracts multiple consecutive zero-crossing noise time blocks from the voltage data it collects, including: Each of the user's electricity meters performs zero-crossing detection on the collected voltage data; Before and after each detected zero crossing, a time window of data of fixed duration is extracted, and the time window data is a zero-crossing noise time block; Multiple consecutive zero-crossing noise time blocks are extracted to form the original feature data set.

2. The method for identifying power grid distribution areas according to claim 1, characterized in that, The step of calculating the signal-to-noise ratio (SNR) of each zero-crossing noise time block and filtering out valid zero-crossing noise time blocks with an SNR greater than a preset threshold includes: The preset feature signal sequence is used as a reference signal; Subtract the reference signal from the zero-crossing noise time block to obtain the residual noise; Calculate the average power P of the reference signal. signal and the average power P of the residual noise noise ; According to the formula Calculate the signal-to-noise ratio (SNR) of the zero-crossing noise time block, where SNR is the signal-to-noise ratio. All zero-crossing noise time blocks with an SNR higher than the preset signal-to-noise ratio threshold are selected to form an effective feature data set.

3. The method for identifying power grid distribution areas according to claim 1, characterized in that, The step of matching the effective zero-crossing noise time block with the preset characteristic signal sequence injected into each of the transformer terminals to determine the transformer area to which the user's meter belongs includes: The correlation between the effective zero-crossing noise time block and the preset feature signal sequence injected into each of the transformer terminals is calculated, and the transformer terminal with the highest number of successful matches is determined to be the transformer area to which the user's electricity meter belongs.

4. A device for identifying power grid distribution areas, characterized in that, include: The synchronization clock command issuing unit is used to issue synchronization clock commands to all transformer terminals and user meters within the target area; A preset feature signal transmission unit is used to inject a preset feature signal sequence into the line to which each transformer terminal belongs at the zero crossing time of synchronization. The preset feature signal sequence is a Gold code. At the same time, the user electricity meters in the target area synchronously collect the voltage data of their respective lines. The voltage data includes the preset feature signal sequence. A zero-crossing noise time block extraction unit is used to extract multiple consecutive zero-crossing noise time blocks from the voltage data collected by each user's electricity meter. The signal-to-noise ratio (SNR) calculation unit is used to calculate the SNR of each zero-crossing noise time block and filter out valid zero-crossing noise time blocks that are greater than a preset SNR threshold. The transformer area determination unit is used to match the effective zero-crossing noise time block with the preset characteristic signal sequence injected into each transformer terminal to determine the transformer area to which the user's meter belongs. The zero-crossing noise time block extraction unit includes: A zero-crossing detection component is used to perform zero-crossing detection on the collected voltage data using each of the user's electricity meters; The zero-crossing noise time block interception component is used to intercept a time window of data of a fixed duration before and after each detected zero-crossing point, wherein the time window data is a zero-crossing noise time block; The continuous zero-crossing noise time block extraction component is used to extract multiple consecutive zero-crossing noise time blocks to form an original feature data set.

5. The device for identifying power grid distribution areas according to claim 4, characterized in that, The signal-to-noise ratio calculation unit includes: A reference signal determination component is used to use the preset feature signal sequence as a reference signal; A residual noise determination component is used to subtract the reference signal from the zero-crossing noise time block to obtain the residual noise; An average power calculation unit is used to calculate the average power P of the reference signal. signal and the average power P of the residual noise noise ; Signal-to-noise ratio calculation component, used to calculate the signal-to-noise ratio according to the formula Calculate the signal-to-noise ratio (SNR) of the zero-crossing noise time block, where SNR is the signal-to-noise ratio. The filtering component is used to filter out all zero-crossing noise time blocks whose SNR is higher than the preset signal-to-noise ratio threshold, thus forming a valid feature data set.

6. The device for identifying power grid distribution areas according to claim 4, characterized in that, The transformer area determination unit is specifically used to perform correlation calculation between the effective zero-crossing noise time block and the preset feature signal sequence injected into each transformer terminal, and to determine the transformer terminal with the highest number of successful matches as the transformer area to which the user's electricity meter belongs.

7. A device for identifying power grid distribution areas, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the steps of the method for identifying power grid substations as described in any one of claims 1 to 3 when executing the computer program.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method for identifying power grid substations as described in any one of claims 1 to 3.

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