Intelligent distribution network phase checking method and system based on GPS clock synchronization

Abstract

The application discloses an intelligent distribution network phase checking method and system based on GPS clock synchronization, and relates to the field of power system distribution network operation and maintenance. The intelligent distribution network phase checking method and system based on GPS clock synchronization comprises a phase checking source reference unit, a non-contact phase checking terminal and a cloud server system. The intelligent distribution network phase checking method and system based on GPS clock synchronization continuously collects three-phase voltage waveforms through the phase checking source reference unit, adds time labels through GPS clock synchronization, and forms reference waves through cloud real-time synchronization. The non-contact phase checking terminal collects three-phase voltage waveforms of a measurement end, adds time labels through GPS clock synchronization, packs data and uploads the data to the cloud, and then the cloud server system uses GPS clock to intercept voltage waveforms of the same period to check the phase, analyzes the phase difference, generates a feedback signal according to the analysis result and a phase difference threshold value, and realizes single-person non-contact operation, high safety, high accuracy and the ability to save the phase checking structure.

Description

Technical Field

[0001] The present invention relates to the technical field of power system distribution network operation and maintenance, and in particular to an intelligent distribution network phase verification method and system based on GPS clock synchronization. Background Art

[0002] The core purpose of distribution network phase verification is to verify the phase consistency between connected power systems or equipment, ensuring safe and reliable system operation during paralleling, loop closing, or power transmission, and avoiding accidents caused by phase misalignment. Specific functions include:

[0003] 1. Prevent short-circuit faults and equipment damage. If the phases or phase sequences are inconsistent and the systems are operated in parallel, a huge voltage difference will be generated between the systems, forming a circulating current or short-circuit current, which may burn out transformers, generators, cables and other equipment.

[0004] 2. Avoid the risk of asynchronous parallel connection. Power phase difference can lead to asynchronous parallel connection, causing system shock oscillation, which can cause mechanical damage to equipment or grid instability in severe cases.

[0005] 3. Eliminate abnormal operation of equipment. Phase sequence error may cause the motor to reverse or cause other three-phase electrical equipment to malfunction.

[0006] The main application scenarios include: before the initial commissioning of new equipment, restoration of power supply after maintenance, parallel operation of multiple power sources, or after modification or relocation of interconnected operation lines;

[0007] The current distribution network phase checking process mainly relies on real-time human operations. The existing distribution network phase checking methods have the following main problems:

[0008] 1. Traditional nuclear phase requires simultaneous operation at both ends, which is labor-intensive and has distance limitations.

[0009] 2. The nuclear phase process requires direct contact with live equipment, which poses a safety hazard;

[0010] 3. The results of nuclear phase analysis rely on manual judgment, which is prone to errors;

[0011] 4. Lack of a unified time base makes it difficult to ensure measurement accuracy;

[0012] 5. Nuclear data cannot be saved, making it difficult to manage and trace;

[0013] In order to solve the above problems, a smart distribution network phase verification method and system based on GPS clock synchronization are provided. Summary of the Invention

[0014] In view of the deficiencies of the prior art, the application provides an intelligent distribution network phase comparison method and system based on GPS clock synchronization, and solves the problems of the prior art, such as the need for simultaneous operation of both ends in traditional phase comparison, high labor cost, distance limitation, the need for direct contact with live equipment in the phase comparison process, safety hazards, the dependence of phase comparison results on manual judgment, the possibility of errors, the lack of a unified time reference, the difficulty in ensuring measurement accuracy, and the inability to save phase comparison data for convenient management and tracing.

[0015] To achieve the above object, the application is implemented by the following technical scheme: an intelligent distribution network phase comparison method based on GPS clock synchronization, comprising the following steps:

[0016] S1, continuously collecting three-phase voltage waveforms, adding time labels through GPS clock synchronization, cloud real-time synchronization, and forming a reference wave;

[0017] S2, collecting three-phase voltage waveforms at a measurement end, adding time labels through GPS clock synchronization, and uploading data to the cloud;

[0018] S3, the cloud server uses GPS clock to intercept voltage waveforms at the same period for phase comparison and analyzes the phase difference;

[0019] S4, generating a feedback signal according to the analysis result and the phase difference threshold.

[0020] Preferably, in S2, the cloud server phase comparison process is as follows:

[0021] S21, time synchronization verification, using GPS clock to synchronize the time of waveform interception;

[0022] S22, waveform pretreatment, filtering and removing impurities from the waveform;

[0023] S23, phase difference calculation, using FFT acceleration and cross-correlation method to calculate the phase difference of two waveforms;

[0024] S24, phase sequence determination, determining the phase sequence according to the phase difference calculation result;

[0025] At the same time, continuous abnormality monitoring is performed throughout the process.

[0026] Preferably, the calculation formula of time synchronization verification is as follows:

[0027]

[0028] Wherein represents the reference waveform timestamp; represents the to-be-measured waveform timestamp;

[0029] The time error threshold of the reference waveform timestamp and the to-be-measured waveform timestamp is set to ≤1 μs.

[0030] like , then the time synchronization is determined to be valid. If , it is judged that time synchronization fails, and the timestamp waveform is re-captured.

[0031] Preferably, the waveform preprocessing process includes the following steps:

[0032] S221, use IIR bandpass filter to remove the waveform that exceeds the threshold range. The formula is:

[0033]

[0034] in , is the filter coefficient, The filter output value at the current time t, n is the discrete time index, k is the forward coefficient index, m is the feedback coefficient index, Represents the historical input signal, is the historical output value;

[0035] S222, wavelet denoising, extracts effective power frequency signal from noise through multi-scale analysis, and its formula is:

[0036] ;

[0037] in Represents the signal after power frequency filtering, represents the wavelet basis function, j is the scale parameter, k is the translation parameter, is the adaptive threshold;

[0038] Its threshold function is:

[0039]

[0040] in represents the wavelet coefficients, is the threshold, is a symbolic function;

[0041] S223, amplitude normalization, the formula is:

[0042]

[0043] in represents the normalized signal, represents the input signal, represents the time window, Represents the peak absolute value operator; by inputting the original waveform data , and then set the filter frequency threshold range to effectively eliminate invalid waves and interference waves, providing accurate signal data for subsequent phase difference calculation.

[0044] Preferably, in the phase difference calculation process, FFT acceleration and cross-correlation method are used, and the specific calculation process includes:

[0045] S231, FFT transformation, perform fast FFT transformation on the reference waveform and the measured waveform respectively, and convert the time domain signal into frequency domain representation. The formula can be expressed as:

[0046] ,

[0047] in Indicates the reference wave The FFT transformation result is: Indicates the wave to be measured FFT transformation result of

[0048] S232, calculate the cross power spectrum, multiply the FFT result of the reference waveform by the FFT conjugate complex number of the measured waveform to obtain the cross power spectrum, the formula is:

[0049]

[0050] in represents the cross power spectrum of the reference wave and the wave to be measured, Represents the conjugate of the FFT transform result of the wave to be measured;

[0051] S233, calculate the cross-correlation function, perform inverse fast FFT transformation on the cross power spectrum, and obtain the cross-correlation function in the time domain, whose formula is:

[0052]

[0053] in is the cross-correlation function, which indicates the similarity of two signals at different delays, and IFFT is the inverse fast Fourier transform;

[0054] S234, peak detection, find the maximum point in the cross-correlation function, and then use parabolic interpolation to perform sub-pixel precise positioning near the peak. The parabolic interpolation is expressed as:

[0055]

[0056] in represents the delay time corresponding to the maximum value of the cross-correlation function;

[0057] The interpolation formula is:

[0058]

[0059] wherein represents the accurate phase difference time, represents the peak time, , and respectively represent the complex response function values at the peak time, a time point before the peak time and a time point after the peak time;

[0060] S235, phase difference calculation, according to the accurate measured time delay value, combined with the system power frequency, the phase difference value is calculated, and finally the phase difference is normalized to obtain the final phase difference result, and the formula is:

[0061]

[0062] wherein N represents the waveform frequency represents the accurate delay time obtained by parabolic interpolation, and N represents the wave frequency value.

[0063] Preferably, in the process of phase sequence determination, the phase difference and the signal-to-noise ratio SNR are input, and then the dynamic tolerance calculation is carried out, which is represented as:

[0064]

[0065] wherein represents the tolerance, represents the signal-to-noise ratio;

[0066] The phase sequence matching rule is:

[0067]

[0068] According to the phase difference, different phase sequences are output.

[0069] Preferably, in the abnormality monitoring process, the quality problem of the measurement data is identified, which includes total harmonic distortion rate detection, signal strength detection and waveform correlation detection, and the calculation methods are as follows:

[0070] The total harmonic distortion calculation formula is:

[0071]

[0072] wherein V1 is the fundamental voltage amplitude, is the hth harmonic voltage amplitude, and h is the highest harmonic number considered;

[0073] The signal strength calculation formula is:

[0074]

[0075] By setting the threshold, if , it is judged that the signal is too weak;

[0076] The waveform correlation calculation formula is:

[0077]

[0078] in is the autocorrelation function, The precise delay time between waveforms is determined by setting the threshold. , it is judged as waveform mismatch.

[0079] Preferably, an intelligent distribution network phase checking system based on GPS clock synchronization includes a phase checking source reference unit, a contactless phase checking terminal and a cloud server system;

[0080] The nuclear phase source reference unit includes a GPS clock synchronization module, a three-phase voltage acquisition unit, a data processing unit, a communication module and a power supply module;

[0081] The contactless phase terminal includes a contactless voltage sensor, a GPS clock synchronization module, a signal processing unit, a communication module, a three-color indicator light and an insulating rod installation interface;

[0082] The cloud server system includes a data access layer, a core processing layer and a business application layer.

[0083] Preferably, the data access layer includes a communication interface, a data verification module and a timestamp alignment module;

[0084] The core processing layer includes a waveform preprocessing module, a phase difference calculation engine, a phase sequence determination module and an anomaly monitoring module;

[0085] The business application layer includes a real-time result push module, a nuclear phase database, a visual monitoring platform and a report generation system.

[0086] Preferably, the phase checking source reference unit and the contactless phase checking terminal respectively use communication modules to transmit data with the cloud server system, thereby realizing real-time uploading of the reference wave and the measurement wave. The cloud server system aligns the measurement wave and the reference wave through the GPS clock for phase checking. The cloud server generates a phase sequence from the phase checking result and feeds it back to the contactless phase checking terminal through the real-time result push module, and displays the phase sequence through a three-color indicator light.

[0087] The beneficial effects are as follows:

[0088] 1. This smart distribution network phase verification method based on GPS clock synchronization sets up a continuous three-phase voltage waveform acquisition scheme. In this scheme, waveform acquisition is performed at a fixed frequency by setting an interval time. GPS clock synchronization technology is used to anchor the acquired waveform data according to the GPS clock data at the time of acquisition, thereby adding a time tag to the data. The acquired data is then uploaded to the cloud in real time to form a reference wave. This reference wave is the actual three-phase voltage waveform of the entire power grid system, providing a basic reference for subsequent measured and acquired three-phase voltage waveforms. The operator then collects the three-phase voltage waveform of the target area or equipment. This data is the measured data. Similarly, GPS clock synchronization technology is used to add a time tag to the data. The collected data is then uploaded to the cloud in real time to form a data packet to be analyzed. By setting up a cloud server, the voltage waveforms of the same period are intercepted for phase verification and phase difference analysis. A feedback signal is generated based on the analysis results and the phase difference threshold and fed back to the operator. Only one person is required to operate this method, and it is a non-contact measurement with low difficulty and high safety. It uses system intelligent judgment and uses GPS clocks for precise data alignment, resulting in high accuracy.

[0089] 2. The intelligent distribution network phase checking system based on GPS clock synchronization includes a phase checking source reference unit, a contactless phase checking terminal and a cloud server system. The phase checking source reference unit and the contactless phase checking terminal respectively use communication modules to transmit data to the cloud server system, thereby realizing real-time uploading of the reference wave and the measurement wave. The cloud server system performs phase checking by aligning the measurement wave and the reference wave through the GPS clock. The cloud server generates a phase sequence based on the phase checking result and feeds it back to the contactless phase checking terminal through the real-time result push module, and displays the phase sequence through the three-color indicator light. In actual use, the current phase sequence can be directly judged by the light and status of the three-color light. It is convenient and fast, and supports the use of multiple types of terminal equipment. The cloud can process multiple tests simultaneously, and use the phase checking database and report generation system to store all waveforms and related calculation results, and can generate phase checking reports at any time. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] 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 these drawings without paying any creative work.

[0091] Figure 1 This is the overall flow chart of the nuclear phase method of the present invention;

[0092] Figure 2 This is a flowchart of the cloud server core phase of the present invention;

[0093] Figure 3 This is the overall functional framework diagram of the nuclear phase system of the present invention;

[0094] Figure 4 This is a functional architecture diagram of the cloud server system of the present invention;

[0095] Figure 5 This is a functional architecture diagram of the nuclear phase source reference unit of the present invention;

[0096] Figure 6 This is a functional architecture diagram of the contactless core phase terminal of the present invention;

[0097] Figure 7 This is a schematic diagram of the operation flow of the nuclear phase source reference unit of the present invention;

[0098] Figure 8 This is a schematic diagram of the non-contact nuclear phase operation process of the present invention;

[0099] Figure 9 Schematic diagram of the cloud server system operation flow of the present invention. DETAILED DESCRIPTION

[0100] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0101] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0102] Embodiment 1: The embodiment of the present invention discloses a smart distribution network phase verification method based on GPS clock synchronization. Figure 1-2 As shown, the following steps are included:

[0103] S1. Continuously collect three-phase voltage waveforms, synchronize them with GPS clocks, add time tags, and synchronize them in real time with the cloud to form a reference wave.

[0104] S2: Collect the three-phase voltage waveform at the measuring end, synchronize it with the GPS clock, add a time tag, and package the data and upload it to the cloud;

[0105] S3. The cloud server uses the GPS clock to intercept the voltage waveform in the same period for phase verification and analyze the phase difference.

[0106] S4. Generate a feedback signal according to the analysis result and the phase difference threshold.

[0107] In this method, the main implementation principle is to set up a continuous three-phase voltage waveform acquisition scheme. In this scheme, waveform acquisition is performed at a fixed frequency by setting an interval time. GPS clock synchronization technology is used to anchor the acquired waveform data according to the GPS clock data at the time of acquisition, thereby adding a time tag to the data. The acquired data is then uploaded to the cloud in real time to form a reference wave. This reference wave is the actual three-phase voltage waveform of the entire power grid system, providing a basic reference for the subsequent measured and acquired three-phase voltage waveform.

[0108] The operator then collects the three-phase voltage waveforms of the target area or equipment. This data is actually measured. Similarly, GPS clock synchronization technology is used to add time tags to the data. The collected data is then uploaded to the cloud in real time to form a data package for analysis.

[0109] The most critical thing is to set up a cloud server, which parses the real-time data packet to obtain the voltage waveform of the data packet, and then parses the waveform data of the reference wave corresponding to the time tag according to the time tag of the waveform to complete the time synchronization verification;

[0110] After completing the time synchronization check, waveform preprocessing is required for the reference wave and the voltage waveform to be measured. This process needs to be carried out in steps. First, an IIR bandpass filter is used to remove waveforms that exceed the threshold range, thereby retaining the waveform segments within the target frequency range to eliminate excessive data errors. Then, waveform noise reduction technology is used to extract the effective power frequency signal from the noise through multi-scale analysis. Finally, the extracted waveform data is amplitude normalized to effectively eliminate invalid waves and interference waves, providing accurate signal data for subsequent phase difference calculation.

[0111] After completing the waveform processing, the effective waveform data of the reference wave and the waveform to be measured are obtained, and then the phase difference between the two waveforms is calculated. In this embodiment, the FFT acceleration and cross-correlation method are used to calculate the waveform, and then the phase sequence is matched according to the phase difference. In this embodiment, the phase sequence matching scheme adopted is to input the phase difference. And signal-to-noise ratio SNR, and then perform dynamic tolerance calculation to finally get the corresponding phase sequence, and then use the phase sequence judgment module according to the phase difference The phase sequence is judged and finally different phase sequences are converted into different signal light control signals which are fed back to the terminal device for display.

[0112] In S2, the cloud server core phase process is as follows;

[0113] S21, time synchronization check, using the GPS clock to determine the waveform capture time for synchronization;

[0114] S22, waveform preprocessing, filtering and removing impurities from the waveform;

[0115] S23, phase difference calculation, using FFT acceleration and cross-correlation method to calculate the phase difference between the two waveforms;

[0116] S24, phase sequence determination, determining the phase sequence based on the phase difference calculation result;

[0117] At the same time, continuous abnormal monitoring is carried out throughout the process.

[0118] The calculation formula for time synchronization verification is:

[0119]

[0120] in Indicates the timestamp of the reference waveform; Indicates the timestamp of the waveform to be measured;

[0121] By setting the time error threshold between the reference waveform timestamp and the waveform to be measured timestamp to ≤1μs;

[0122] like , then the time synchronization is determined to be valid. If , it is judged that time synchronization fails, and the timestamp waveform is re-captured.

[0123] The waveform preprocessing process includes the following steps:

[0124] S221, use IIR bandpass filter to remove the waveform that exceeds the threshold range. The formula is:

[0125]

[0126] in , is the filter coefficient, The filter output value at the current time t, n is the discrete time index, k is the forward coefficient index, m is the feedback coefficient index, Represents the historical input signal, is the historical output value;

[0127] S222, wavelet denoising, extracts effective power frequency signal from noise through multi-scale analysis, and its formula is:

[0128] ;

[0129] in Represents the signal after power frequency filtering, represents the wavelet basis function, j is the scale parameter, k is the translation parameter, is the adaptive threshold;

[0130] Its threshold function is:

[0131]

[0132] in represents the wavelet coefficients, is the threshold, is a symbolic function;

[0133] S223, amplitude normalization, the formula is:

[0134]

[0135] in represents the normalized signal, represents the input signal, represents the time window, Represents the peak absolute value operator; by inputting the original waveform data , and then set the filter frequency threshold range to effectively eliminate invalid waves and interference waves, providing accurate signal data for subsequent phase difference calculation.

[0136] In the phase difference calculation process, FFT acceleration and cross-correlation method are used. The specific calculation process includes:

[0137] S231, FFT transformation, perform fast FFT transformation on the reference waveform and the measured waveform respectively, and convert the time domain signal into frequency domain representation. The formula can be expressed as:

[0138] ,

[0139] in Indicates the reference wave The FFT transformation result is: Indicates the wave to be measured FFT transformation result of

[0140] S232, calculate the cross power spectrum, multiply the FFT result of the reference waveform by the FFT conjugate complex number of the measured waveform to obtain the cross power spectrum, the formula is:

[0141]

[0142] in represents the cross power spectrum of the reference wave and the wave to be measured, Represents the conjugate of the FFT transform result of the wave to be measured;

[0143] S233, calculate the cross-correlation function, perform inverse fast FFT transformation on the cross power spectrum, and obtain the cross-correlation function in the time domain, whose formula is:

[0144]

[0145] in is the cross-correlation function, which indicates the similarity of two signals at different delays, and IFFT is the inverse fast Fourier transform;

[0146] S234, peak detection, find the maximum point in the cross-correlation function, and then use parabolic interpolation to perform sub-pixel precise positioning near the peak. The parabolic interpolation is expressed as:

[0147]

[0148] in represents the delay time corresponding to the maximum value of the cross-correlation function;

[0149] The interpolation formula is:

[0150]

[0151] in Indicates the precise phase difference time, Indicates the peak time, 、 and represent the complex response function values ​​at the peak time, the time point before the peak time, and the time point after the peak time, respectively.

[0152] S235, phase difference calculation, based on the accurately measured time delay value, combined with the system power frequency, calculate the phase difference value, and finally normalize the phase difference to obtain the final phase difference result, the formula is:

[0153]

[0154] Where N represents the waveform frequency, It represents the exact delay time obtained by parabolic interpolation, and N represents the wave frequency value.

[0155] In the process of phase sequence determination, the phase difference is input And the signal-to-noise ratio SNR, and then the dynamic tolerance calculation is performed, which is expressed as:

[0156]

[0157] in Indicates tolerance, represents the signal-to-noise ratio;

[0158] The phase sequence matching rules are:

[0159]

[0160] According to the phase difference, different phase sequences are outputted.

[0161] In the abnormal monitoring process, the quality problems of the measurement data are identified, which includes total harmonic distortion rate detection, signal strength detection and waveform correlation detection, and the calculation methods are as follows respectively;

[0162] The total harmonic distortion rate calculation formula is:

[0163]

[0164] Wherein V1 is the fundamental voltage amplitude, is the hth harmonic voltage amplitude, and h is the highest harmonic number considered;

[0165] The signal strength calculation formula is:

[0166]

[0167] By setting a threshold, if , it is judged that the signal is too weak;

[0168] The waveform correlation calculation formula is:

[0169]

[0170] Wherein is the autocorrelation function, is the accurate delay time between waveforms, and if , it is judged that the waveform is mismatched.

[0171] Working principle; in this method, the main scheme implementation principle is to set a continuous three-phase voltage waveform acquisition scheme, in which the waveform is collected at fixed frequency by setting interval time, and the collected waveform data is anchored according to the GPS clock data at the time of collection by using GPS clock synchronization technology, so as to add time label to the data, then the collected data is uploaded to the cloud in real time, so as to form the reference wave, which is the actual waveform of the three-phase voltage of the whole power grid system, thereby providing a basic reference for the subsequent measured three-phase voltage waveform;

[0172] Then the operator collects the three-phase voltage waveform of the target area or equipment, which is the measured data, and the data is labeled with time by using GPS clock synchronization technology, and then the collected data is uploaded to the cloud in real time to form a data package to be analyzed;

[0173] Most crucially, by setting the cloud server, the cloud server parses the real-time data packet, obtains the voltage waveform of the data packet, and then parses the waveform data of the reference wave corresponding to the time label according to the time label corresponding to the waveform, so as to complete the time synchronization verification;

[0174] In this process, according to the time synchronization verification formula, as follows:

[0175]

[0176] The specific value of the time difference between the two is calculated , and then the threshold value is set for judgment and analysis. In this embodiment, the time error threshold of the reference waveform timestamp and the to-be-measured waveform timestamp is ≤1μs. If , it is judged that the time synchronization is valid, and if , it is judged that the time synchronization is invalid. When the timestamp is judged to be invalid, the system re-intercepts the timestamp waveform to perform waveband interception.

[0177] It should be noted that the threshold value is an example in this embodiment. For application scenarios with high precision requirements for time error accuracy, the threshold value can be adjusted for design and application. The smaller the threshold value, the more accurate the phase sequence difference obtained by analysis, and more accurate data calculation and waveform interception are also required.

[0178] After completing the time synchronization verification, the reference wave and the to-be-measured voltage waveform need to be preprocessed. This process needs to be carried out in steps. First, an IIR band-pass filter is used to remove waveforms exceeding the threshold range, thereby retaining waveforms in the target frequency range, so as to eliminate large data errors. Then, a waveform denoising technology is used to extract effective power frequency signals from noise through multi-scale analysis. Finally, the amplitude of the extracted waveform data is normalized, thereby effectively eliminating invalid waves and interference waves, and providing accurate signal data for subsequent phase difference calculation.

[0179] After completing the waveform processing, the effective waveform data in the reference wave and the to-be-measured waveform are obtained, and the phase difference of the two waveforms is calculated. In this embodiment, FFT acceleration and cross-correlation method are used for waveform calculation, and then phase sequence matching is performed according to the phase difference. In this embodiment, the phase sequence matching scheme adopted is to input the phase difference and the signal-to-noise ratio SNR, and then perform dynamic tolerance calculation, where the dynamic tolerance can be represented as tolerance,

[0180] The phase sequence matching rule is:

[0181]

[0182] and output the corresponding phase sequence.

[0183] During the entire calculation and output process, the time difference, total harmonic distortion rate detection, signal strength detection and waveform correlation detection are performed to improve the accuracy of phase difference calculation. During the entire calculation process, if the GPS time intercept deviation between the two terminals is greater than 1us, an error will be directly reported; if the calculated waveform distortion rate THD is greater than 5%, it will be considered invalid; when the measured voltage is less than 50% of the rated value, a "weak signal" warning will be triggered.

[0184] As follows, specific values ​​will be substituted into the simulation calculation;

[0185] make;

[0186] System frequency is 50Hz

[0187] The sampling rate is 10kHz, which is 10,000 points per second.

[0188] The sampling time is 0.1 seconds, and a total of 1000 points are obtained.

[0189] Reference wave; $V {\text{ref}}(t)= \sin(2\pi \times 50 \times t)$;

[0190] Measured wave: $V {\{meas}}(t)=\sin(2\pi \times 50 \times t + \frac{\pi}{6})$ (lag 30°);

[0191] Noise; add 20dB Gaussian white noise;

[0192]

[0193] After processing, the reference wave and the wave to be measured are obtained, and then the data are subjected to FFT transformation, cross-power spectrum calculation, cross-correlation function calculation, peak detection and phase difference calculation, and finally the following data can be obtained;

[0194]

[0195] The actual phase difference was finally measured to be 29.98°, with an error of 0.02°. Using the time delay as a reference value for comparison, the actual error rate was 0.07%.

[0196] As an implementation, to achieve optimal results, time synchronization verification requires that the timestamp error between the reference waveform and the waveform under test does not exceed 1μs. However, in some application scenarios, GPS signals are susceptible to interference or complete loss indoors, in tunnels, or in inclement weather, which can lead to frequent time synchronization failures. Therefore, this embodiment introduces a multi-source time synchronization redundancy mechanism. This mechanism utilizes the existing GPS clock module of the phase-checking source reference unit and the contactless phase-checking terminal as the primary time source, while simultaneously activating a backup time source: for example, integrating the Network Time Protocol (NTP) and a local, highly stable oscillator via the communication module. Specifically, during the time synchronization verification step, the system automatically detects GPS signal quality. If the signal is weak or interrupted, it switches to the NTP server time or local oscillator for compensation. Because the data processing unit (phase-checking source reference unit) and the signal processing unit (contactless phase-checking terminal) already have data verification capabilities, they can be expanded to monitor signal strength in real time and execute switching logic. For example, when the GPS error exceeds a threshold, the NTP timestamp is used for alignment. If the network is unavailable, the local oscillator compensates for drift based on historical GPS data, ensuring reliability of Δt ≤ 1μs. This algorithm upgrade is achieved by modifying the timestamp alignment module (data access layer) of the cloud server. The communication module of the phase source reference unit can directly access the NTP server, while the GPS clock module of the contactless phase terminal is embedded with local oscillator logic. The cloud server system displays the time source status in real time through the visual monitoring platform of the business application layer, facilitating intervention by operations and maintenance personnel. In this embodiment, the system can still maintain high-precision phase correction in the event of GPS failure, reducing the frequency of waveform re-capture and improving overall efficiency. At the same time, the scope of application of this technology is expanded. For example, in remote areas or electromagnetic interference environments, it ensures that phase difference calculation is not affected by time errors and meets the needs of dynamic power distribution networks.

[0197] Furthermore, in some embodiments, measurement data must be packaged and uploaded to the cloud for analysis to generate feedback signals. However, in distribution network operations and maintenance, network latency or excessive cloud load can introduce significant processing delays, impacting the real-time nature of feedback. This drawback stems from the centralized cloud architecture, where all data processing is performed in the cloud, while the contactless phase-checking terminal is solely responsible for data collection. When phase-checking operations are frequent or the data volume is large (e.g., multiple terminals concurrently), the cloud's core processing layer may become overloaded, resulting in delayed phase difference analysis.

[0198] Therefore, as an implementation method, edge computing optimization is implemented, offloading some preprocessing and analysis tasks to the contactless phase-checking terminal, leveraging its existing signal processing unit to reduce the burden on the cloud. Specifically, the terminal performs preliminary waveform preprocessing and rapid phase estimation before data upload, while the cloud is solely responsible for final verification and complex calculations. For example, the signal processing unit of the contactless phase-checking terminal performs the following algorithmic processing: after waveform acquisition, it first performs local filtering to remove impurities and uses a simplified FFT to calculate preliminary phase differences. Because this unit already supports signal processing, it can embed a lightweight cross-correlation algorithm to estimate phases in real time and temporarily store the results. Simultaneously, the terminal uploads compressed data packets to the cloud via the communication module. The cloud server system optimizes the core processing layer, prioritizing high-priority tasks and rapidly verifying the terminal's estimates using the phase difference calculation engine. This reduces the amount of uploaded data and cloud load, ensuring timely delivery of feedback signals via the real-time result push module. In low-bandwidth environments, the terminal can provide immediate phase notifications, while the cloud only performs final confirmation. This reduces latency in the S3-S4 process, enhances phase-checking efficiency, and supports high-concurrency scenarios.

[0199] Embodiment 2: The embodiment of the present invention discloses an intelligent distribution network phase correction system based on GPS clock synchronization. Figure 1-9 As shown, including;

[0200] A phase checking source reference unit includes a GPS clock synchronization module, a three-phase voltage acquisition unit, a data processing unit, a communication module, and a power supply module. The phase checking source reference unit is used to perform step S1 of the phase checking method by periodically acquiring three-phase voltage waveforms, synchronizing with the GPS clock, adding time tags, packaging the data into a reference wave data packet, and then uploading it to the server system. In this embodiment, the sampling interval is 10 seconds, that is, sampling is performed every 10 seconds during the entire process, and the sampling time is synchronized by the GPS clock.

[0201] A non-contact phase verification terminal includes a non-contact voltage sensor, a GPS clock synchronization module, a signal processing unit, a communication module, a three-color indicator light, and an insulating rod mounting interface. The non-contact phase verification terminal is used to perform step S2 of the phase verification method by starting the device, then performing GPS clock synchronization, and then placing the non-contact voltage sensor close to any phase conductor to collect waveforms for more than 10 seconds, adding time tags to the collected waveforms, and then packaging the data and uploading it to a server system.

[0202] The cloud server system includes a data access layer, a core processing layer and a business application layer. The data access layer includes a communication interface, a data verification module and a timestamp alignment module; the core processing layer includes a waveform preprocessing module, a phase difference calculation engine, a phase sequence determination module and an anomaly monitoring module; the business application layer includes a real-time result push module, a nuclear phase database, a visual monitoring platform and a report generation system.

[0203] The cloud server system is used to execute step S3 of the phase verification method. The cloud server system first parses and verifies the data packets of the reference wave and the waveform to be measured through the data verification module; then aligns the timestamps of the reference wave and the waveform to be measured through the timestamp alignment module, and sets the time error threshold of the timestamp of the reference waveform and the timestamp of the waveform to be measured to ≤1μs. If the time difference between the two is , it is judged that time synchronization fails, and the data is discarded and an error message is given about synchronization failure. , then it is determined that time synchronization is valid, and then proceed to the next step.

[0204] The waveform preprocessing module then intercepts the waveform segments of the reference wave and the wave to be measured, and then preprocesses the waveform. The preprocessing process mainly uses existing filtering technology. First, an IIR bandpass filter is used to remove waveforms that exceed the threshold range, thereby retaining the waveform segments within the target frequency range to eliminate excessive data errors. Then, waveform noise reduction technology is used to extract effective power frequency signals from the noise through multi-scale analysis. Finally, the extracted waveform data is amplitude normalized to effectively eliminate invalid waves and interference waves.

[0205] At this time, the phase difference calculation engine is used to calculate the phase difference between the reference wave and the wave to be measured, and the FFT acceleration and cross-correlation method are used to calculate the waveform, and finally the phase difference is obtained. ;

[0206] Then the phase sequence determination module is used to determine the phase difference Perform phase sequence judgment. In this embodiment, basic phase sequence judgment rules are written;

[0207]

[0208] Finally, step S4 in the phase checking method is completed through the cooperation of the cloud server system and the contactless phase checking terminal, and data interaction is completed with the contactless phase checking terminal through the real-time result push module in the cloud server system, and different phase sequences are converted into different signal light control signals. The three-color indicator lights in the contactless phase checking terminal include green, red and yellow lights. In this embodiment, by converting phase A into a green light control signal; converting phase B into a red light output signal; and converting phase C into a yellow light control signal, the unknown phase is set to a three-color light flashing signal. In actual use, the current phase sequence can be directly judged by the light and status of the three-color light, which is convenient and fast.

[0209] The phase check source reference unit and the contactless phase check terminal respectively use communication modules to transmit data with the cloud server system, thereby realizing real-time uploading of the reference wave and the measurement wave. The cloud server system aligns the measurement wave and the reference wave through the GPS clock for phase check. The cloud server generates a phase sequence from the phase check result through the real-time result push module and feeds it back to the contactless phase check terminal, and displays the phase sequence through a three-color indicator light.

[0210] The technical advantages of this nuclear phase system include:

[0211] 1. Safety and efficiency;

[0212] Non-contact measurement

[0213] Single-person operation, using non-contact sensor detection, rapid cloud detection results, and easy-to-understand result display;

[0214] 2. Accuracy guarantee;

[0215] Time synchronization error ≤ 1us; phase error ≤ 0.018°; cross-correlation algorithm has strong noise resistance and adapts to on-site electromagnetic interference;

[0216] 3. Scalability;

[0217] Supports the use of multiple types of terminal devices, and the cloud can process multiple tests simultaneously;

[0218] Terminal data traceability: Utilizing the phase analysis database and report generation system, all waveforms and related calculation results can be stored, and phase analysis reports can be generated at any time.

[0219] As follows, a specific operation example is used to demonstrate;

[0220] The test environment is: 10kV line section switch core phase;

[0221] 1. The nuclear phase source is installed at the outgoing line switch of the substation and continuously uploads the A-phase reference wave;

[0222] 2. The operation and maintenance personnel hold the terminal close to the section switch conductor;

[0223] 3. The terminal displays a green light within 10 seconds → confirming that the phase is A and no adjustment is required;

[0224] 4. If the light is red → the phase is B, you need to adjust the switch phase sequence connection;

[0225] This system is suitable for scenarios requiring frequent phase checking, such as ring network cabinets and overhead line insulation renovation.

[0226] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0227] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A smart distribution network phase verification method based on GPS clock synchronization, characterized in that: The following steps are involved: S1. Continuously collect three-phase voltage waveforms, synchronize them with GPS clocks, add time tags, and synchronize them in real time with the cloud to form a reference wave. S2: Collect the three-phase voltage waveform at the measuring end, synchronize it with the GPS clock, add a time tag, and package the data and upload it to the cloud; S3. The cloud server uses the GPS clock to intercept the voltage waveform in the same period for phase verification and analyze the phase difference. S4. Generate a feedback signal based on the analysis result and the phase difference threshold; In S2, the cloud server core phase process is as follows: S21, time synchronization check, using the GPS clock to determine the waveform capture time for synchronization; S22, waveform preprocessing, filtering and removing impurities from the waveform; S23, phase difference calculation, using FFT acceleration and cross-correlation method to calculate the phase difference between the two waveforms; S24, phase sequence determination, determining the phase sequence based on the phase difference calculation result; At the same time, continuous abnormal monitoring is carried out throughout the process; In the phase difference calculation process, FFT acceleration and cross-correlation method are used. The specific calculation process includes: S231, FFT transformation, perform fast FFT transformation on the reference waveform and the measured waveform respectively, and convert the time domain signal into frequency domain representation. The formula can be expressed as: ， in Indicates the reference wave The FFT transformation result is: Indicates the wave to be measured FFT transformation result of S232, calculate the cross power spectrum, multiply the FFT result of the reference waveform by the FFT conjugate complex number of the measured waveform to obtain the cross power spectrum, the formula is: in represents the cross power spectrum of the reference wave and the wave to be measured, Represents the conjugate of the FFT transform result of the wave to be measured; S233, calculate the cross-correlation function, perform inverse fast FFT transformation on the cross power spectrum, and obtain the cross-correlation function in the time domain, whose formula is: in is the cross-correlation function, which indicates the similarity of two signals at different delays, and IFFT is the inverse fast Fourier transform; S234, peak detection, find the maximum point in the cross-correlation function, and then use parabolic interpolation to perform sub-pixel precise positioning near the peak. The parabolic interpolation is expressed as: in represents the delay time corresponding to the maximum value of the cross-correlation function; The interpolation formula is: in Indicates the precise phase difference time, Indicates the peak time, 、 and represent the complex response function values ​​at the peak time, the time point before the peak time, and the time point after the peak time, respectively; S235, phase difference calculation, based on the accurately measured time delay value, combined with the system power frequency, calculate the phase difference value, and finally normalize the phase difference to obtain the final phase difference result, the formula is: Where N represents the waveform frequency, It represents the exact delay time obtained by parabolic interpolation, and N represents the wave frequency value.

2. The method for intelligent distribution network phase verification based on GPS clock synchronization according to claim 1, characterized in that: During the time synchronization verification process, Indicates the timestamp of the reference waveform; Indicates the timestamp of the waveform to be measured; the calculation formula for time synchronization verification is: By calculation and The difference is , and set the time error threshold between the reference waveform timestamp and the waveform to be measured timestamp to ≤1μs. , then the time synchronization is determined to be valid. If , it is judged that time synchronization fails, and the timestamp waveform is re-captured.

3. The method for intelligent distribution network phase verification based on GPS clock synchronization according to claim 1, characterized in that: The waveform preprocessing process includes the following steps: S221, use IIR bandpass filter to remove the waveform that exceeds the threshold range. The formula is: in , is the filter coefficient, The filter output value at the current time t, n is the discrete time index, k is the forward coefficient index, m is the feedback coefficient index, Represents the historical input signal, is the historical output value; S222, wavelet denoising, extracts effective power frequency signal from noise through multi-scale analysis, and its formula is: in Represents the signal after power frequency filtering, represents the wavelet basis function, j is the scale parameter, k is the translation parameter, is the adaptive threshold; Its threshold function is: in represents the wavelet coefficients, is the threshold, is a symbolic function; S223, amplitude normalization, the formula is: in represents the normalized signal, represents the input signal, represents the time window, Represents the peak absolute value operator; by inputting the original waveform data , and then set the filter frequency threshold range to effectively eliminate invalid waves and interference waves, providing accurate signal data for subsequent phase difference calculation.

4. The method for intelligent distribution network phase verification based on GPS clock synchronization according to claim 1, characterized in that: During the phase sequence determination process, the phase difference is input And the signal-to-noise ratio SNR, and then the dynamic tolerance calculation is performed, which is expressed as: ； in Indicates tolerance, represents the signal-to-noise ratio; The phase sequence matching rules are: In this way, different phase sequences are output according to the phase difference.

5. The method for intelligent distribution network phase verification based on GPS clock synchronization according to claim 1, characterized in that: The abnormality monitoring process is used to identify the quality problems of the measurement data, which includes total harmonic distortion rate detection, signal strength detection and waveform correlation detection, and the calculation methods are as follows: The total harmonic distortion calculation formula is: Where V1 is the fundamental voltage amplitude, is the hth harmonic voltage amplitude, H is the highest harmonic order considered; The signal strength calculation formula is: By setting the threshold, if , it is judged that the signal is too weak; The waveform correlation calculation formula is: in is the autocorrelation function, The precise delay time between waveforms is determined by setting the threshold. , it is judged as waveform mismatch.

6. An intelligent distribution network phase verification system based on GPS clock synchronization, used to execute the intelligent distribution network phase verification method based on GPS clock synchronization according to any one of claims 1 to 5, characterized in that: Including phase source reference unit, contactless phase terminal and cloud server system: The nuclear phase source reference unit includes a GPS clock synchronization module, a three-phase voltage acquisition unit, a data processing unit, a communication module and a power supply module; The contactless phase terminal includes a contactless voltage sensor, a GPS clock synchronization module, a signal processing unit, a communication module, a three-color indicator light and an insulating rod installation interface; The cloud server system includes a data access layer, a core processing layer and a business application layer.

7. The intelligent distribution network phase checking system based on GPS clock synchronization according to claim 6, characterized in that: The data access layer includes a communication interface, a data verification module and a timestamp alignment module; The core processing layer includes a waveform preprocessing module, a phase difference calculation engine, a phase sequence determination module and an anomaly monitoring module; The business application layer includes a real-time result push module, a nuclear phase database, a visual monitoring platform and a report generation system.

8. The intelligent distribution network phase checking system based on GPS clock synchronization according to claim 7, characterized in that: The phase checking source reference unit and the contactless phase checking terminal respectively use communication modules to transmit data with the cloud server system, thereby realizing real-time uploading of the reference wave and the measurement wave. The cloud server system aligns the measurement wave and the reference wave through the GPS clock for phase checking. The cloud server generates a phase sequence from the phase checking result and feeds it back to the contactless phase checking terminal through the real-time result push module, and displays the phase sequence through a three-color indicator light.

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