Power frequency phase detection method and system based on double-cycle interpolation algorithm

Abstract

The invention belongs to but not limited to the technical field of phase detection, and discloses a power frequency phase detection method and system based on a double-cycle interpolation algorithm, which accurately calculates the frequency of a power grid through sampling point signals of two complete cycles, then performs linear interpolation based on the frequency, and finally calculates the phase of the signals through discrete Fourier transform. Therefore, the problems of large signal phase calculation result error and low speed at present are solved. According to the method, the power grid frequency is accurately calculated by ingeniously utilizing the double-cycle sampling signal, linear interpolation and discrete Fourier transform are carried out on the basis of the calculated frequency, so that the amplitude and the phase of the signal are calculated, and the calculation error caused by frequency fluctuation in the traditional discrete Fourier transform is optimized. According to the algorithm adopted by the invention, the defect of insufficient precision of other methods is overcome; compared with a traditional interpolation method, the linear interpolation method is simpler, and the calculation speed is greatly increased.

Description

Technical Field

[0001] The present invention belongs to but is not limited to the field of phase detection technology, and in particular relates to a power frequency phase detection method and system based on a dual-cycle interpolation algorithm. Background Art

[0002] With the continuous acceleration of urbanization and the increasing demand for electricity, high-voltage cables are widely used in urban transmission and distribution networks. The reliable operation of high-voltage cables is a vital part of power grid safety. Although cable main insulation failures rarely occur, once a failure occurs, it will bring huge economic losses. The dielectric loss tangent can characterize the insulation state of the cable main insulation, such as insulation moisture, aging and deterioration. Since the dielectric loss of the main insulation of the high-voltage cable is relatively small, it is about 0.06% under normal circumstances. Therefore, this detection method has high requirements on the phase calculation accuracy of the measured voltage and leakage current signals. Therefore, it is of great theoretical and engineering significance to design an algorithm with high calculation accuracy and fast calculation speed to calculate the power frequency phase of the cable voltage signal and the cable leakage current signal.

[0003] The rated frequency of the domestic power grid is not always 50Hz, and the allowable deviation of the power grid frequency is ±0.2Hz. In addition, due to the interference of harmonics and various noises, the traditional power frequency phase algorithm cannot accurately calculate the amplitude and phase of the voltage and current signals, and even has a large error.

[0004] The following are the main methods currently used to calculate signal phase:

[0005] A. Discrete Fourier Transform (DFT)

[0006] DFT can convert discrete sampled signals into the frequency domain and provide the amplitude and phase information of each frequency component. The phase calculated by DFT can reflect the relative position of each sampling point in the time domain signal. Currently, FFT (Fast Fourier Transform) has become the mainstream for calculating phase. Although the calculation speed of FFT has been greatly improved, the problem that frequency fluctuations will cause large errors in the results has not been solved. Therefore, methods such as windowing and interpolation are used to improve the calculation accuracy.

[0007] B. Windowed DFT Algorithm

[0008] The windowed DFT algorithm still cannot solve the problem that frequency fluctuations will cause large errors in the results.

[0009] C. Interpolation method:

[0010] For discretely sampled signals, interpolation can be used to estimate the value of the signal between the sampling points, and then the phase is calculated. Interpolation can make the signal continuous in the time domain, which is convenient for subsequent phase calculations. Traditional interpolation uses various interpolation functions (such as linear, polynomial interpolation, Newton interpolation, cubic spline interpolation, and neighbor method interpolation) to retain most of the signal information without losing it.

[0011] D. Autocorrelation function method

[0012] The corresponding autocorrelation function is calculated by discretely sampling the signal, and the autocorrelation properties of the function, especially the main peak position of the autocorrelation function, are analyzed to estimate the phase information of the sampled signal. However, the correlation function method is very sensitive to the harmonic components in the collected signal, which will reduce the measurement accuracy.

[0013] The existing technology has the following technical problems in the industrial application of high-voltage cable power frequency phase detection:

[0014] 1. Errors caused by frequency fluctuations

[0015] The operating frequency of the domestic power grid is allowed to fluctuate within the range of ±0.2Hz, but the existing power frequency phase detection method based on discrete Fourier transform (DFT) and fast Fourier transform (FFT) has weak error correction capability when the frequency deviates from the standard value, and cannot effectively cope with frequency fluctuations, resulting in insufficient phase calculation accuracy during cable insulation monitoring. This problem is particularly significant because changes in cable insulation status are usually detected through accurate phase difference and amplitude calculations.

[0016] 2. Harmonic and noise interference

[0017] Power frequency signals are often accompanied by harmonics and various noises, especially the strong electromagnetic interference introduced by industrial equipment and environment around high-voltage cables. These harmonics and noises will significantly affect the frequency distribution of the signal, resulting in large deviations in the phase and amplitude obtained by DFT or windowed DFT. The current algorithm cannot effectively filter out these interferences, which in turn affects the accuracy of phase detection.

[0018] 3. Limitations of interpolation algorithms

[0019] Although the interpolation algorithm can improve the accuracy of phase calculation by compensating for discrete signals, existing interpolation algorithms, such as linear, polynomial, and cubic spline, still have a certain loss of accuracy, especially when the grid frequency fluctuates or the signal contains noise, the interpolation result is still inaccurate. In addition, the calculation speed of the interpolation algorithm is slow and cannot meet the needs of real-time monitoring of the grid and real-time evaluation of cable status.

[0020] 4. Sensitivity of the autocorrelation function method

[0021] Although the autocorrelation function method can extract the phase information of the signal, it is very sensitive to harmonic components, especially in power frequency signals containing a large amount of harmonics and noise, the phase calculation accuracy of the autocorrelation function method is greatly reduced. In addition, the autocorrelation method has insufficient suppression ability for high-frequency noise and is easily interfered, resulting in increased errors.

[0022] 5. The trade-off between real-time performance and accuracy of existing algorithms

[0023] Although FFT speeds up phase calculation, errors caused by frequency fluctuations and noise still exist, making it difficult to ensure high accuracy and high efficiency at the same time. Existing methods usually require a compromise between accuracy and calculation speed, and cannot meet the high-precision requirements of real-time status monitoring of high-voltage cables.

[0024] In the industrial application of high-voltage cable power frequency phase detection, the existing technology has problems such as large frequency fluctuation error, serious noise interference, insufficient interpolation accuracy, and excessive sensitivity of the autocorrelation function, making it difficult to achieve high-precision and fast phase calculation. Therefore, a new algorithm is urgently needed that can effectively deal with frequency fluctuations and noise interference, and improve the calculation speed while ensuring the accuracy of phase detection to meet the needs of real-time monitoring. This will provide more reliable technical support for the safe operation of high-voltage cables and the stability of power grids. Summary of the invention

[0025] In view of the problems existing in the prior art, the present invention provides a power frequency phase detection method and system based on a dual power frequency cycle interpolation algorithm, which accurately calculates the grid frequency through the sampling point signals of two power frequency cycles (40ms), and then performs linear interpolation based on the corrected grid frequency. Finally, the fundamental frequency phase of the signal is calculated through discrete Fourier transform, thereby solving the problems of large errors and slow calculation speed in the current signal phase calculation results.

[0026] The present invention is implemented as follows: a power frequency phase detection method based on a dual-cycle interpolation algorithm, comprising:

[0027] S1, assuming the sampling frequency is f s , continuously collect two cycles of discrete signals to obtain a discrete sequence X; under normal circumstances, the power grid frequency fluctuates by ±0.2Hz. In order to still be able to take two complete cycle sampling points at the lowest frequency of 49.8Hz, the number of sampling points N = (int)(nT0×2*1.005), where nT0 is the number of sampling points in one power frequency cycle (T = 0.02s), and the sampling frequency f s =nT0×50, sampling time interval

[0028] S2, using the full cycle angle difference method, calculate the actual frequency f of the power grid. It is known that f0 = 50Hz, and the number of sampling points in the full cycle is nT = nT0; using the DFT algorithm, calculate the phase θ1 of the first power frequency cycle of the signal and the phase angle θ2 of the second power frequency cycle of the signal, using the formula Correct the grid frequency f; if the grid frequency calculation accuracy does not meet the requirements, set f0 = f, Calculate the grid frequency f again; after two calculations, the grid frequency calculation accuracy meets the calculation accuracy requirements;

[0029] S3, through linear interpolation, obtain the signal Y after the whole cycle interpolation, the number of signal whole cycle interpolation points is nT0. k}, where k = 0, 1, 2, ... k ..., nT0-1}, interpolation time interval Time series t = 0, dt, 2 × dt, 3 × dt, ... (nT0-1) * dt; use linear interpolation to perform linear interpolation on the sampling points. The linear interpolation formula is:

[0030]

[0031] The more interpolation points there are, the more complete information of the signal is retained; where t = k*dt, Through nT0 cycles, the interpolated signal Y can be calculated;

[0032] S4, calculating the amplitude and phase of the interpolation signal Y by discrete Fourier transform (DFT).

[0033] Furthermore, S1 mainly consists of the following two sub-steps:

[0034] S101: The number of sampling points in one power frequency cycle (T = 20ms) is nT0, and the sampling frequency is f s =nT0×50;

[0035] S102: At sampling frequency f s N=(int)(nT0×2*1.005) points are collected continuously to ensure that the discrete sequence X of two consecutive cycles can be completely collected even at the lowest frequency of the power grid of 49.8 Hz.

[0036] Furthermore, S2 specifically includes:

[0037] S201: The principle of solving the phase is to perform discrete Fourier transform (DFT) on the nT0 sampling point signals. After Fourier transform, the sum of the real part is r, the sum of the imaginary part is v, and the effective value is Phase Pass get;

[0038] S202: First, solve the phases θ1 and θ2 of the first and second power frequency cycles of the signal at f0 = 50 Hz, using the formula To perform frequency correction and obtain the actual frequency value f;

[0039] S23: If the actual grid frequency still needs to be corrected, let f0 = f, S22 is repeated to calculate the actual grid frequency f again.

[0040] Furthermore, S3 specifically includes:

[0041] S301: The number of interpolation points in the whole cycle is nT0, and the interpolated signal Y={Y k}, where k = 0, 1, 2, ... k ..., nT0-1}, the time interval of the interpolation points Time series t=0,dt,2×dt,3×dt,...(nT0-1)*dt;

[0042] S302: The linear interpolation formula is:

[0043]

[0044] Use linear interpolation to perform linear interpolation on the sampling points.

[0045] S303: where t = k*dt, After nT0 cycles, nT0 interpolation point data Y are finally obtained;

[0046] Further, S4 specifically includes: repeating S201 through the interpolated nT0 signals Y to obtain the effective value and phase of the final sampling signal.

[0047] Another object of the present invention is to provide a power frequency phase detection system based on a dual-cycle interpolation algorithm for realizing the power frequency phase detection method based on a dual-cycle interpolation algorithm, comprising:

[0048] Discrete sequence acquisition module, used to assume that the sampling frequency is f s , continuously collect two cycles of discrete signals to obtain a discrete sequence X; under normal circumstances, the power grid frequency fluctuates by ±0.2Hz. In order to still be able to take two complete cycle sampling points at the lowest frequency of 49.8Hz, the number of sampling points N = (int)(nT0×2*1.005), where nT0 is the number of sampling points in the power frequency cycle (T = 0.02s), and the sampling frequency f s =nT0×50, sampling time interval

[0049] The actual frequency calculation module of the power grid uses the whole cycle angle difference method to calculate the actual frequency f of the power grid. It is known that f0 = 50Hz and the number of sampling points in the whole cycle is nT = nT0. The DFT algorithm is used to calculate the phase θ1 of the first power frequency cycle of the signal and the phase angle θ2 of the second power frequency cycle of the signal. The formula Correct the grid frequency f; if the calculation accuracy of the grid frequency f does not meet the requirements, set f0 = f, Calculate the grid frequency f again; after two calculations, the grid frequency calculation accuracy generally meets the calculation accuracy requirements;

[0050] The linear interpolation module is used to obtain the signal Y after the whole cycle interpolation through linear interpolation. The number of signal whole cycle interpolation points is nT0; Y = {Y k}, where k = 0, 1, 2, ... k ..., nT0-1}, interpolation time interval Time series t = 0, dt, 2 × dt, 3 × dt, ... (nT0-1) * dt; use linear interpolation to perform linear interpolation on the sampling points. The linear interpolation formula is:

[0051]

[0052] where t = k*dt, Through nT0 cycles, the interpolated signal Y can be calculated;

[0053] The difference signal amplitude and phase calculation module is used to calculate the amplitude and phase of the interpolation signal Y through discrete Fourier transform (DFT).

[0054] Another object of the present invention is to provide a computer device, the computer device includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the power frequency phase detection method based on the dual-cycle interpolation algorithm.

[0055] Another object of the present invention is to provide a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of the power frequency phase detection method based on a dual-cycle interpolation algorithm.

[0056] Another object of the present invention is to provide an information data processing terminal, which includes the industrial frequency phase detection system based on the dual-cycle interpolation algorithm.

[0057] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0058] First, compared with the prior art, the advantages of the present invention are:

[0059] (A) The present invention provides a signal phase calculation method based on a dual-period interpolation algorithm, which cleverly uses a dual-period sampling signal to accurately calculate the grid frequency, and then performs linear interpolation and discrete Fourier transform based on the calculated frequency to calculate the amplitude and phase of the signal, thereby optimizing the calculation error of the traditional discrete Fourier transform caused by fluctuations in the power frequency and harmonic size.

[0060] (B) The algorithm used in the present invention overcomes the shortcomings of other methods that are insufficient in accuracy;

[0061] (C) Compared with the traditional sinc interpolation method, this linear interpolation method is simpler and has a significantly improved calculation speed. This algorithm only needs to interpolate within one power frequency cycle, and the calculation efficiency is O(n), while the sinc interpolation method has a calculation efficiency of O(n). 2 ).

[0062] Second, the expected benefits and commercial value of the technical solution of the present invention after transformation are: it can be used in situations with high requirements for phase calculation accuracy, especially for online monitoring of small dielectric loss of the main insulation of high-voltage cables.

[0063] The technical solution of the present invention fills the technical gap in the industry at home and abroad, and solves the influence of power grid frequency fluctuation and harmonic size on the signal phase angle calculation accuracy and calculation speed.

[0064] The technical solution of the present invention solves the technical problem that people have been eager to solve but have never succeeded in solving, improves the calculation accuracy and speed of the signal phase angle, and solves the problem of accurate signal calculation through frequency correction and linear interpolation.

[0065] Third, the technical solution of the present invention solves several key problems in the prior art in industrial applications and has achieved significant technical progress, which is specifically reflected in the following aspects:

[0066] 1) Problems with existing technologies

[0067] Insufficient power frequency phase detection accuracy: Traditional power frequency phase detection methods are prone to large detection errors when the grid frequency fluctuates or there is noise interference, especially when the grid frequency deviates from the standard value (50Hz or 60Hz), the accuracy is difficult to guarantee.

[0068] Inaccurate frequency correction: In the prior art, the correction of the grid frequency mostly relies on a simple frequency detection algorithm, which makes it difficult to make quick and accurate corrections when the frequency fluctuates slightly, resulting in low accuracy in phase detection.

[0069] Limitations of interpolation methods: Conventional signal interpolation methods cannot fully retain the complete information of the signal, resulting in large phase and amplitude errors of the interpolated signal, thus affecting the final phase detection result.

[0070] 2) Technological progress and innovation

[0071] The dual-cycle interpolation algorithm improves detection accuracy: By introducing a dual-cycle interpolation algorithm, the present invention can perform refined interpolation based on the sampled signal, retain the complete information of the signal, and significantly improve the accuracy of power frequency phase detection. Compared with the traditional single-cycle interpolation method, this technology can more effectively capture signal changes, especially when the frequency fluctuates slightly, it can still provide accurate phase detection.

[0072] Accurate frequency correction mechanism: This invention uses the DFT (discrete Fourier transform) calculation of the dual-periodic signal and the angle difference method to perform frequency correction, and repeatedly iterates the grid frequency through a nonlinear optimization algorithm to ensure high-precision phase detection under frequency offset conditions. Compared with traditional frequency correction methods, this solution can better cope with dynamic changes in grid frequency and ensure the stability of phase detection.

[0073] Combination of linear interpolation and Fourier transform: By interpolating the sampled signal through linear interpolation and calculating the amplitude and phase of the signal with Fourier transform, more interpolation points can be provided under limited sampling points, and more signal feature information can be retained. This method overcomes the information loss problem of existing interpolation methods and further improves the accuracy of signal amplitude and phase calculation.

[0074] 3) Significant technological progress

[0075] The detection accuracy and stability are greatly improved: By introducing a dual-cycle interpolation algorithm and an accurate frequency correction mechanism, the present invention greatly improves the accuracy and stability of power frequency phase detection, and can still provide reliable detection results, especially when the grid frequency fluctuates or there is noise interference.

[0076] Enhanced real-time monitoring capability: This technical solution can perform real-time phase and frequency detection in a dynamic power grid environment. It is suitable for complex power grid monitoring systems, provides more accurate data support for power systems, and enhances the real-time response capability of power dispatching and control systems.

[0077] Wider industrial applicability: This method is not only suitable for phase detection in traditional power systems, but can also be applied to scenarios such as smart grids and power quality monitoring, and has broad industrial application prospects.

[0078] In summary, the present invention effectively solves the problems of low phase detection accuracy and inaccurate frequency correction in the prior art through technical means such as dual-period interpolation algorithm, precise frequency correction and linear interpolation, and significantly improves the accuracy, stability and applicability of industrial frequency phase detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 It is a schematic diagram of the technical solution provided by an embodiment of the present invention;

[0080] Figure 2 It is a flow chart of a power frequency phase detection method based on a dual-cycle interpolation algorithm provided by an embodiment of the present invention;

[0081] Figure 3 is a linear interpolation principle diagram provided by an embodiment of the present invention;

[0082] Figure 4 It is a structural diagram of an industrial frequency phase detection system based on a dual-cycle interpolation algorithm provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0083] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0084] like Figure 1 , Figure 2 As shown, the power frequency phase detection method based on the dual-cycle interpolation algorithm provided in the embodiment of the present invention includes:

[0085] S1, assuming the sampling frequency is f s , continuously collect two cycles of discrete signals to obtain a discrete sequence X; under normal circumstances, the power grid frequency fluctuates by ±0.2Hz. In order to still be able to take two complete cycle sampling points at the lowest frequency of 49.8Hz, the number of sampling points N = (int)(nT0×2*1.005), where nT0 is the number of sampling points in the power frequency cycle (T = 0.02s), and the sampling frequency f s =nT0×50,

[0086] S2, using the whole cycle angle difference method, calculate the actual frequency f of the power grid, let f0 = 50Hz, the number of sampling points in the whole cycle is nT0; use the DFT algorithm to calculate the phase θ1 of the first power frequency cycle of the signal and the phase angle θ2 of the second power frequency cycle of the signal, using the formula Correct the grid frequency f; if the grid frequency calculation accuracy does not meet the requirements, set f0 = f, Calculate the grid correction frequency f again; after two calculations, the grid frequency calculation accuracy generally meets the calculation accuracy requirements;

[0087] S3, through linear interpolation, obtain the signal Y after the whole cycle interpolation, the number of signal whole cycle interpolation points is nT0. k}, where k = 0, 1, 2, ... k ..., nT0-1}, interpolation time interval Time series t = 0, dt, 2 × dt, 3 × dt, ... (nT0-1) * dt; use linear interpolation to perform linear interpolation on the sampling points. The linear interpolation formula is:

[0088]

[0089] where t = k*dt, Through nT0 cycles, the interpolated signal Y can be calculated;

[0090] S4, calculating the amplitude and phase of the interpolation signal Y by discrete Fourier transform (DFT).

[0091] Furthermore, S1 mainly consists of the following two sub-steps:

[0092] S101: The number of sampling points in one power frequency cycle (T = 20ms) is nT0. At this time, the sampling frequency is f s =nT0×50;

[0093] S102: At sampling frequency f s N=(int)(nT0×2*1.005) points are collected continuously to ensure that the discrete sequence X of two consecutive cycles can be completely collected even at the lowest frequency of the power grid of 49.8 Hz.

[0094] Furthermore, S2 specifically includes:

[0095] S201: The principle of solving the phase is to perform discrete Fourier transform (DFT) on the nT0 sampling point signals. After Fourier transform, the sum of the real part is r, the sum of the imaginary part is v, and the effective value is Phase Pass get;

[0096] S202: First, solve the phases θ1 and θ2 of the first and second power frequency cycles of the signal at f0 = 50 Hz, using the formula To correct the frequency, get the frequency value f;

[0097] S23: Let f0 = f, Repeat S22 to calculate and obtain the grid frequency f again.

[0098] Furthermore, S3 specifically includes:

[0099] S301: The number of interpolation points in the whole cycle is nT0, and the interpolated signal Y={Y k}, where k = 0, 1, 2, ... k ..., nT0-1}, the time interval of the interpolation points Time series t=0,dt,2×dt,3×dt,...(nT0-1)*dt;

[0100] S302: The linear interpolation formula is:

[0101]

[0102] like Figure 3 As shown, linear interpolation is used to perform linear interpolation on the sampling points.

[0103] S303: where t = k*dt, Through nT0 cycles, nT0 interpolation point data Y are finally obtained;

[0104] Further, S4 specifically includes: after interpolation, nT0 signals Y repeat S201 to obtain the effective value and phase of the final sampling signal.

[0105] like Figure 4 As shown, the power frequency phase detection system based on the dual-cycle interpolation algorithm includes:

[0106] Discrete sequence acquisition module, used to assume that the sampling frequency is f s , continuously collect two cycles of discrete signals to obtain a discrete sequence X; under normal circumstances, the power grid frequency fluctuates by ±0.2Hz. In order to still be able to take two complete cycle sampling points at the lowest frequency of 49.8Hz, the number of sampling points N = (int)(nT0×2*1.005), where nT0 is the number of sampling points in the power frequency cycle (T = 0.02s), and the sampling frequency f s =nT0×50, sampling time interval

[0107] The actual frequency calculation module of the power grid uses the whole cycle angle difference method to calculate the actual frequency f of the power grid. It is known that f0 = 50Hz and the number of sampling points in the whole cycle is nT = nT0. The DFT algorithm is used to calculate the phase θ1 of the first power frequency cycle of the signal and the phase angle θ2 of the second power frequency cycle of the signal. The formula Correct the grid frequency f; if the grid frequency calculation accuracy does not meet the requirements, set f0 = f, Calculate the grid frequency f again; after two calculations, the grid frequency calculation accuracy generally meets the calculation accuracy requirements;

[0108] The linear interpolation module is used to obtain the signal Y after the whole cycle interpolation through linear interpolation. The number of signal whole cycle interpolation points is nT0; Y = {Y k}, where k = 0, 1, 2, ... k ..., nT0-1}, interpolation time interval Time series t = 0, dt, 2 × dt, 3 × dt, ... (nT0-1) * dt; use linear interpolation to perform linear interpolation on the sampling points. The linear interpolation formula is:

[0109]

[0110] where t = k*dt, Through nT0 cycles, the interpolated signal Y can be calculated;

[0111] The difference signal amplitude and phase calculation module is used to calculate the amplitude and phase of the interpolation signal Y through discrete Fourier transform (DFT).

[0112] The signal used below is a pure sinusoidal signal, φ=0.0005, without harmonics and white noise;

[0113] Table 1. Comparison of the accuracy of the DFT phase calculation method and the patented phase calculation method for grid frequency f

[0114]

[0115] Table 2. Comparison of the accuracy of the DFT phase calculation method and the patented phase calculation method based on the number of sampling points nT0 in the whole cycle

[0116]

[0117]

[0118] Table 3 Comparison of the accuracy of the classic interpolation phase calculation method and the patented phase calculation method when f = 49.8Hz

[0119]

[0120] Table 4 Comparison of calculation time between the classic interpolation phase calculation method and the patented phase calculation method when f = 49.8 Hz

[0121]

[0122] Table 5. Comparison of the accuracy of the classic interpolation phase calculation method and the patented phase calculation method when nT0=4000 and the number of interpolation points nI in the whole cycle

[0123]

[0124]

[0125] An application embodiment of the present invention provides a computer device, which includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor executes the steps of an industrial frequency phase detection method based on a dual-cycle interpolation algorithm.

[0126] An application embodiment of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of a power frequency phase detection method based on a dual-cycle interpolation algorithm.

[0127] An application embodiment of the present invention provides an information data processing terminal, which includes an industrial frequency phase detection system based on a dual-cycle interpolation algorithm.

[0128] The following are two industrial application examples of the power frequency phase detection method based on the dual-cycle interpolation algorithm:

[0129] 1) Smart Grid Frequency and Phase Monitoring System

[0130] In smart grids, the frequency and phase stability of the grid are crucial to the safe and stable operation of power transmission. This invention can be applied to the phase detection and frequency monitoring system of smart grids. By accurately detecting the power phase and frequency of the grid, abnormal fluctuations of the grid can be monitored and identified in a timely manner. Since it adopts a dual-cycle interpolation algorithm, the frequency detection accuracy can be improved, especially when there is a slight fluctuation in the grid frequency, it can quickly correct and maintain system stability, provide accurate phase information for the power dispatching center, and assist in realizing refined grid control.

[0131] 2) Power quality analysis equipment

[0132] Power quality detection equipment is widely used in industrial manufacturing, data centers and other scenarios with high power requirements. This invention can be integrated into a power quality analyzer for high-precision power frequency phase detection to ensure the stability of the equipment power supply. By improving the detection accuracy of frequency and phase through a dual-cycle interpolation algorithm, it can more effectively monitor frequency deviations, voltage fluctuations and other problems in the power supply, help users discover power quality problems, avoid equipment failures due to power grid fluctuations, and improve the safety and stability of equipment operation.

[0133] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. It can be understood by a person of ordinary skill in the art that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium. The device and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0134] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A power frequency phase detection method based on a dual-cycle interpolation algorithm, characterized in that: By continuously collecting two-cycle discrete signals and performing double-cycle interpolation, the signal is processed using a linear interpolation formula and discrete Fourier transform (DFT), thereby accurately calculating the amplitude and phase of the signal. The core innovation of the present invention is to correct the power grid frequency through a double-cycle interpolation algorithm, calculate the signal phase in combination with DFT, and use a linear interpolation method to retain more signal information, thereby improving the accuracy and stability of power frequency phase detection.

2. The power frequency phase detection method based on the dual-cycle interpolation algorithm according to claim 1, characterized in that: include: S1, assuming the sampling frequency is f s , continuously collect two periods of discrete signals to obtain a discrete sequence X; The number of sampling points N = (int)(nT0×2*1.005), where nT0 is the number of sampling points in the power frequency cycle, and the sampling frequency f s =nT0×50, sampling time interval S2, using the whole cycle angle difference method, calculate the actual frequency f of the power grid. It is known that f0 = 50Hz and the number of sampling points in the whole cycle is nT = nT0. Use the DFT algorithm to calculate the phase θ1 of the first power frequency cycle of the signal and the phase angle θ2 of the second power frequency cycle of the signal. Use the formula Correct the grid frequency f; if the grid frequency calculation accuracy does not meet the requirements, set f0 = f, Calculate the grid frequency f again; after two calculations, the grid frequency calculation accuracy generally meets the calculation accuracy requirements; S3, through linear interpolation, obtain the signal Y after the whole cycle interpolation, the number of signal whole cycle interpolation points is nT0. k }, where k = 0, 1, 2, ... k ..., nT0-1}, interpolation time interval Time series t = 0, dt, 2 × dt, 3 × dt, ... (nT0-1) * dt; use linear interpolation to perform linear interpolation on the sampling points. The linear interpolation formula is: where t = k*dt, Through nT0 cycles, the interpolated signal Y can be calculated; S4, calculating the amplitude and phase of the interpolation signal Y by discrete Fourier transform.

3. The power frequency phase detection method based on the dual-cycle interpolation algorithm according to claim 1, characterized in that: S1 mainly consists of the following two sub-steps: S101: The number of sampling points in one power frequency cycle is nT0, and the sampling frequency is f s =nT0×50; S102: At sampling frequency f s N=(int)(nT0×2*1.005) points are collected continuously to ensure that the discrete sequence X of two consecutive cycles can be completely collected even at the lowest frequency of the power grid of 49.8 Hz.

4. The power frequency phase detection method based on the dual-cycle interpolation algorithm according to claim 1, characterized in that: S2 specifically includes: S201: The principle of solving the phase is to perform discrete Fourier transform (DFT) on the nT0 sampling point signals. After Fourier transform, the sum of the real part is r, the sum of the imaginary part is v, and the effective value is Phase Pass get; S202: First, find the phases θ1 and θ2 of the first and second cycles of the signal at f0 = 50 Hz, using the formula To correct the frequency, get the frequency value f; S23: Let f0 = f, Repeat S22 to calculate and obtain the grid frequency f again.

5. The power frequency phase detection method based on the dual-cycle interpolation algorithm according to claim 1, characterized in that: S3 specifically includes: S301: The number of interpolation points in the whole cycle is nT0, and the interpolated signal Y={Y k }, where k = 0, 1, 2, ... k ..., nT0-1}, the time interval of the interpolation points Time series t=0,dt,2×dt,3×dt,...(nT0-1)*dt; S302: The linear interpolation formula is: Using linear interpolation, the sampling points are linearly interpolated. The more interpolation points there are, the more complete information of the signal is retained. S303: where t = k*dt, Through nT0 loops, nT0 interpolation point data Y are finally obtained.

6. The power frequency phase detection method based on the dual-cycle interpolation algorithm according to claim 1, characterized in that: S4 specifically includes: after interpolation of nT0 signals Y, repeating S201 to obtain the effective value and phase of the final sampling signal.

7. A power frequency phase detection system based on a double-cycle interpolation algorithm for implementing the power frequency phase detection method based on a double-cycle interpolation algorithm as claimed in any one of claims 1 to 6, characterized in that: include: Discrete sequence acquisition module, used to assume that the sampling frequency is f s , continuously collect two cycles of discrete signals to obtain a discrete sequence X; in order to still be able to take two complete cycle sampling points at the lowest frequency of 49.8Hz, the number of sampling points N = (int)(nT0×2*1.005), where nT0 is the number of sampling points in the power frequency cycle, and the sampling frequency f s =nT0×50, sampling time interval The actual frequency calculation module of the power grid is used to calculate the actual frequency f of the power grid using the angle difference method. It is known that f0 = 50Hz and the number of sampling points in the whole cycle is nT = nT0. The DFT algorithm is used to calculate the phase θ1 of the first power frequency cycle of the signal and the phase angle θ2 of the second power frequency cycle of the signal. The formula Correct the grid frequency f; if the grid frequency calculation accuracy does not meet the requirements, set f0 = f, Calculate the grid frequency f again; after two calculations, the grid frequency calculation accuracy generally meets the calculation accuracy requirements; The linear interpolation module is used to obtain the signal Y after the whole cycle interpolation through linear interpolation. The number of signal whole cycle interpolation points is nT0; Y = {Y k }, where k = 0, 1, 2, ... k ..., nT0-1}, interpolation time interval Time series t = 0, dt, 2 × dt, 3 × dt, ... (nT0-1) * dt; use linear interpolation to perform linear interpolation on the sampling points. The linear interpolation formula is: where t = k*dt, Through nT0 cycles, the interpolated signal Y can be calculated; The interpolation signal amplitude and phase calculation module is used to calculate the amplitude and phase of the interpolation signal Y through discrete Fourier transform (DFT).

8. A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the power frequency phase detection method based on a dual-cycle interpolation algorithm as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the power frequency phase detection method based on a dual-cycle interpolation algorithm as claimed in any one of claims 1 to 6.

10. An information data processing terminal, comprising the industrial frequency phase detection system based on the double-cycle interpolation algorithm as claimed in claim 7.

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