Voltage measurement method based on pulse coding and related equipment

Abstract

The invention discloses a voltage measurement method based on pulse coding and related equipment, relates to the technical field of voltage measurement, and solves the problem of low voltage measurement precision. The method comprises the following steps: generating a pseudo-random coding sequence with sparse time domain, and modulating a pilot frequency reference signal based on the pseudo-random coding sequence to obtain a low-frequency pulse modulation wave; performing pulse power amplification on the low-frequency pulse modulation wave, and migrating a signal frequency band to a high-frequency range to obtain a high-frequency pulse train; and after the high-frequency pulse train is injected into the measured line, acquiring a coupling signal, and extracting a characteristic component corresponding to the pseudo-random coding sequence from the coupling signal so as to invert the voltage value of the measured line. According to the invention, the pilot frequency reference signal based on pulse code modulation is adopted to replace a continuous sine wave, so that the power consumption of signal injection can be reduced; and the corresponding characteristic component in the coupling signal is identified through the pseudo-random coding sequence, so that interference of a strong electromagnetic signal can be avoided, and the voltage measurement precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of voltage measurement, and in particular to a voltage measurement method based on pulse coding and related equipment. Background Art

[0002] Voltage is a fundamental parameter in electronic measurement, and voltage measurement is fundamental to electronic measurement. In the field of voltage measurement, continuous sine wave injection is a commonly used measurement method. This method continuously injects a sine wave signal into the target system, measures the attenuation of the injected signal, and uses a specific algorithm to infer the voltage value. This measurement principle is based on the fact that signal attenuation occurs during transmission due to voltage changes. By accurately calculating this attenuation, the target voltage can be determined.

[0003] However, due to the need to continuously inject sinusoidal signals, the system is in an operating state for a long time, resulting in a significant increase in system power consumption. High power consumption not only accelerates the loss of device batteries and shortens device life, but also greatly limits the use scenarios and operating time of some portable devices that rely on batteries or are located in remote areas where batteries are difficult to replace. At the same time, excessive power consumption will also generate a large amount of heat, affecting the stability and service life of the electronic components inside the device, increasing the maintenance cost and failure risk of the equipment. In addition, continuous signal injection may also cause unnecessary interference to the target system, affecting the normal operation of the system. Therefore, how to reduce system power consumption while ensuring measurement accuracy has become a technical problem that needs to be solved urgently in this field.

[0004] In view of this, a voltage measurement method based on pulse coding and related equipment are needed. Summary of the Invention

[0005] To address the problem of high power consumption during voltage measurement in the prior art, the present invention provides a voltage measurement method and related equipment based on pulse coding, which can reduce power consumption during voltage measurement. The specific technical solution is as follows:

[0006] In a first aspect, an embodiment of the present application provides a voltage measurement method based on pulse coding, comprising:

[0007] Generate a pseudo-random code sequence that is sparse in the time domain, and modulate an heterodyne reference signal based on the pseudo-random code sequence to obtain a low-frequency pulse modulation wave; perform pulse power amplification on the low-frequency pulse modulation wave, and migrate the signal frequency band of the low-frequency pulse modulation wave to a high-frequency range to obtain a high-frequency pulse group; inject the high-frequency pulse group into the line under test; obtain a coupled signal from the line under test, and extract a characteristic component corresponding to the pseudo-random code sequence from the coupled signal; wherein the coupled signal is a signal obtained by coupling the high-frequency pulse group with the power frequency voltage signal of the line under test; and invert the voltage value of the line under test based on the characteristic component.

[0008] Preferably, the generation of a time-domain sparse pseudo-random coding sequence includes: generating a non-periodic initial pulse sequence; performing time-domain sparse processing on the initial pulse sequence so that the duty cycle of the initial pulse sequence is less than or equal to a preset first threshold; and distributing the sparsely processed initial pulse sequence to N discrete frequency points to obtain the pseudo-random coding sequence, where N is an integer greater than or equal to 3.

[0009] Preferably, extracting the characteristic component corresponding to the pseudo-random code sequence from the coupling signal includes: calculating, through a preset sliding window, a mutual correlation coefficient between the coupling signal within the sliding window and a preset template; wherein the preset template is a time-frequency feature corresponding to the pseudo-random code sequence; and extracting the characteristic component of the coupling signal within the sliding window when the mutual correlation coefficient is greater than a preset second threshold.

[0010] Preferably, the extraction of the characteristic component of the coupled signal within the sliding window includes: updating a weight matrix based on the coupled signal within the sliding window through a recursive least squares algorithm, wherein the updated weight matrix is used to compensate for the channel distortion and noise influence of the corresponding coupled signal during the propagation process when the characteristic component is extracted next time; inverse filtering the coupled signal within the sliding window based on the weight matrix before updating to obtain an inverse filtered signal; and calculating the characteristics of the inverse filtered signal as the characteristic component.

[0011] Preferably, inverting the voltage value of the measured circuit based on the characteristic component includes: obtaining the voltage value of the measured circuit based on a preset "voltage-characteristic component" relationship curve and the characteristic component.

[0012] Preferably, inverting the voltage value of the measured line based on the characteristic component includes: inputting the characteristic component into a preset third-order Volterra series model to obtain an intermediate voltage value output by the third-order Volterra series model; wherein the third-order Volterra series model is used to describe the corresponding relationship between the characteristic component and the voltage value of the measured line, and the parameters of the third-order Volterra series model are optimized by a quantum particle swarm optimization algorithm; and correcting the intermediate voltage value by a preset Kalman filter to obtain the voltage value of the measured line.

[0013] Preferably, obtaining the coupled signal from the measured line includes: after injecting the high-frequency pulse group into the measured line, obtaining the coupled signal from the measured line after N power frequency cycles, where the value range of N is [0.5, 2].

[0014] In a second aspect, an embodiment of the present application provides a voltage measurement system based on pulse coding, which is applied to the method according to the first aspect, and the system includes:

[0015] A modulation module is used to generate a pseudo-random code sequence that is sparse in the time domain, and modulate an inter-frequency reference signal based on the pseudo-random code sequence to obtain a low-frequency pulse modulation wave;

[0016] an amplifying module, for amplifying the pulse power of the low-frequency pulse modulation wave and migrating the signal frequency band of the low-frequency pulse modulation wave to a high-frequency range to obtain a high-frequency pulse group;

[0017] An injection module is used to inject the high-frequency pulse group into the line under test;

[0018] an extraction module, configured to obtain a coupled signal from the line under test and extract a characteristic component corresponding to the pseudo-random code sequence from the coupled signal; wherein the coupled signal is a signal obtained by coupling the high-frequency pulse group with the power frequency voltage signal of the line under test;

[0019] An inversion module is used to invert the voltage value of the measured line based on the characteristic component.

[0020] In a third aspect, an embodiment of the present application provides a computing device, comprising: a memory for storing a program; and a processor for loading the program to execute the method described in the first aspect.

[0021] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the method as described in the first aspect.

[0022] Compared with the prior art, the beneficial effects of the present invention are as follows: by adopting a different-frequency reference signal based on pulse code modulation instead of a continuous sine wave, the power consumption of signal injection can be reduced; and by identifying the corresponding characteristic components in the coupled signal through a pseudo-random coding sequence, the interference of strong electromagnetic signals can be avoided, thereby improving the accuracy of voltage measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly describes the drawings required for the specific embodiments or the description of the prior art. Similar elements or parts are generally identified by similar reference numerals throughout the drawings. Elements or parts in the drawings are not necessarily drawn to scale.

[0024] Figure 1 A schematic flow chart of a voltage measurement method based on pulse coding provided in an embodiment of the present application;

[0025] Figure 2 A schematic diagram of the structure of a voltage measurement system based on pulse coding provided in an embodiment of the present application;

[0026] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0028] It will be understood that when used in this specification and the appended claims, the terms “comprises” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0029] It should also be understood that the terms used in the present specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0030] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0031] In order to solve the problem of high power consumption during voltage measurement in traditional methods, the present invention provides a voltage measurement method based on pulse coding and related equipment, which can reduce power consumption during voltage measurement.

[0032] See also Figure 1 , Figure 1 The present invention provides a flow chart of a voltage measurement method based on pulse coding, which is applied to a computing device; Figure 1 As shown, the method includes:

[0033] Step 101: A computing device generates a time-domain sparse pseudo-random code sequence, and modulates an inter-frequency reference signal based on the pseudo-random code sequence to obtain a low-frequency pulse modulation wave.

[0034] Among them, the computing device can be a computing module or control module arranged on the line under test, specifically a field programmable gate array (FPGA), a central processing unit (CPU), an application-specific integrated circuit (ASIC) or a digital signal processor (DSP), which directly performs operations related to voltage measurement; it can also be a server, personal computer or tablet computer and other smart terminals independent of the line under test, which obtains the operating parameters of the line under test through wired or wireless communication with the monitoring equipment on the line under test, and performs operations related to voltage measurement by instructing the device, unit or equipment directly connected to the line under test.

[0035] Among them, the pseudo-random coding sequence is a binary sequence generated by a specific algorithm or circuit, which locally exhibits characteristics similar to a random sequence. For example, the appearance of its elements (usually 0 and 1) has a certain randomness, and the distribution of 0 and 1 in the sequence is relatively uniform. Unlike a true random sequence, the pseudo-random coding sequence is generated by a certain algorithm or rule. Therefore, by modulating the heterofrequency reference signal through the pseudo-random coding sequence, the pseudo-random coding sequence can be used as the "fingerprint" of the heterofrequency reference signal, so that the computing device can more easily identify the fragment related to the heterofrequency reference signal from the coupled signal.

[0036] "Time-domain sparsity" means that the non-zero elements 1 in the pseudo-random code sequence are relatively sparsely distributed in the time domain. In other words, along the pseudo-random code sequence's time axis, 1s appear less frequently, while a large number of elements are zero. This reduces the number of transmitted pulses in the modulated signal, while ensuring sufficient fingerprint recognition, thereby lowering the power consumption of signal injection.

[0037] The inter-frequency reference signal is a reference signal with a different frequency than the voltage signal under test, and typically also with a different frequency than the harmonics of the voltage signal under test. This distinguishes the inter-frequency reference signal from interference components in the measured signal, effectively suppressing the impact of interference on the measurement results. The computing device can calculate the voltage value of the measured line by obtaining parameters and parameter changes of one or more of the coupled signal, the power frequency voltage signal, and the inter-frequency reference signal in the measured line.

[0038] Preferably, the computing device can generate a non-periodic initial pulse sequence; perform time domain sparsification processing on the initial pulse sequence so that the duty cycle of the initial pulse sequence is less than or equal to a preset first threshold; and distribute the initial pulse sequence after sparsification processing to N discrete frequency points to obtain the pseudo-random coding sequence, where N is an integer greater than or equal to 3.

[0039] The computing device may iteratively generate a non-periodic initial pulse sequence based on the Lorenz chaotic sequence equation. The Lorenz chaotic sequence equation includes:

[0040]

[0041] Where σ, ρ, and β are system parameters, σ = 10, ρ = 28, and β = 8 / 3; x, y, and z are related variables of the initial pulse sequence, and t is time.

[0042] The computing device may perform an XOR operation on the initial pulse sequence and the Gold code to randomize the pulse intervals, thereby making the time domain of the initial pulse sequence sparse.

[0043] Specifically, the computing device can select two different m-sequences of the same length. If their periodic correlation function is a three-valued function, then a preferred pair of these two m-sequences can be used to form a Gold code through a shift modulo-2 addition operation; then, each binary bit in the pulse sequence is XORed with the binary bit at the corresponding position in the Gold code.

[0044] Specifically, the Gold code is a pseudo-random code composed of 0s and 1s, and the m-sequence is the abbreviation of the longest linear feedback shift register sequence.

[0045] By reducing the duty cycle of the initial pulse sequence below the first threshold, the power consumption of the signal injection can be reduced.

[0046] The computing device may distribute the initial pulse sequence after sparse processing to N discrete frequency points through orthogonal frequency division multiplexing.

[0047] For example, the computing device can distribute the initial pulse sequence after the sparsification process to three frequency points: 4.8 MHz, 5.0 MHz, and 5.2 MHz, with a frequency interval of 200 kHz. The multi-frequency design maintains signal integrity even under slight frequency offsets of the measured line, improving the algorithm's ability to resist frequency offsets.

[0048] Step 102: The computing device performs pulse power amplification on the low-frequency pulse modulation wave, and migrates the signal frequency band of the low-frequency pulse modulation wave to a high-frequency range to obtain a high-frequency pulse group.

[0049] Among them, the power of low-frequency pulse modulation waves is relatively small and can easily be submerged in the interference signal of the line under test; computing equipment can increase the power of low-frequency pulse modulation waves through pulse power amplification, making them have stronger energy and facilitating subsequent identification.

[0050] Low-frequency signals may be subject to significant interference during transmission and are not suitable for efficient transmission over certain channels (such as wireless channels). By migrating these signals to higher frequencies, we can leverage the characteristics of high-frequency signals, such as relatively low propagation loss in space and the ability to carry more information, thereby achieving more efficient signal transmission and processing.

[0051] Among them, the computing device can achieve pulse power amplification through the switched capacitor array structure, completing energy storage and release within the 10-100ns time window; then the baseband signal is moved to the 1-10MHz frequency band through the mixer, and the interval between each sub-band is not less than 200kHz.

[0052] It can be understood that the heterodyne reference signal modulated by pseudo-random code is essentially a low-frequency continuous wave plus a code modulation envelope. After pulse power amplification, the signal is converted into a high-frequency pulse group.

[0053] Specifically, the switched capacitor array releases stored energy in an extremely short time (ns level), truncating the continuous wave into discrete pulses, which are then multiplied by the mixer with the local oscillator signal to achieve spectrum shifting; then, bandpass filtering is used to retain the target frequency band (e.g., 5MHz±50kHz), which can suppress spurious components.

[0054] Step 103: The computing device injects the high-frequency pulse group into the circuit under test.

[0055] The circuit under test refers to the circuit portion whose voltage needs to be measured. It can be a section of a circuit in various electrical equipment, power systems, or electronic circuits. Examples include transmission and distribution lines in power systems, power lines in electrical equipment, or signal lines in electronic circuits.

[0056] The computing device can inject the pulse signal into the measured line through a high-voltage coupling capacitor (C=100pF~1nF). The injection point can be selected at the secondary side of the voltage transformer PT / current transformer CT or the grading ring of the line insulator.

[0057] Step 104: The computing device obtains a coupled signal from the measured line, and extracts a characteristic component corresponding to the pseudo-random code sequence from the coupled signal.

[0058] The coupled signal is a signal obtained by coupling the high-frequency pulse group with the power frequency voltage signal of the measured line.

[0059] Preferably, obtaining the coupled signal from the measured line includes: after injecting the high-frequency pulse group into the measured line, obtaining the coupled signal from the measured line after N power frequency cycles, where the value range of N is [0.5, 2].

[0060] After injecting the high-frequency pulse group into the measured circuit, the computing device can delay the start of measurement by N power frequency cycles when detecting that the power frequency crosses zero. By delaying the acquisition of the coupled signal, the transient interference period caused by the switching operation of the signal injection can be avoided.

[0061] Preferably, the computing device can calculate the mutual correlation coefficient between the coupled signal within the sliding window and a preset template through a preset sliding window; wherein the preset template is the time-frequency feature corresponding to the pseudo-random coding sequence; when the mutual correlation coefficient is greater than a preset second threshold, the characteristic component of the coupled signal within the sliding window is extracted.

[0062] The coupled signal is an actual signal including power frequency voltage, noise, interference and injected high-frequency pulse group; the preset template is the time-frequency characteristics of the pseudo-random coding sequence in a pre-stored ideal state that is not affected by the channel.

[0063] Specifically, time-frequency features include time domain features and frequency domain features. Time domain features include ideal pulse waveform (width, rising edge, etc.), which can be obtained by computing equipment through transmission circuit simulation or actual calibration measurement; frequency domain features include signal spectrum envelope (main lobe, side lobe structure), which can be extracted by computing equipment through Fourier transform and stored in amplitude-frequency / phase-frequency response.

[0064] For example, before executing the method of this embodiment, the computing device may generate all possible pseudo-random code sequences, record the time-frequency characteristics of the ideal transmitted signal for each code through hardware-in-the-loop simulation, and store them in a preset template library. When executing the method of this embodiment, only the preset template library is required for signal injection, without the need for real-time generation of the code sequence.

[0065] The computing device may only calculate the similarity between the coupled signal and the preset template corresponding to the injected high-frequency pulse group, or may calculate the similarity between the coupled signal and all preset templates.

[0066] It is understandable that even if the transmitter coding sequence is known, the signal will experience multiple distortions during the coupled transmission process. The use of full template matching can allow a certain code element error rate and make comprehensive judgments based on multi-template similarity to improve the robustness against pulse loss / distortion. At the same time, it can detect unauthorized injection signals and resist malicious signal injection attacks.

[0067] The calculation formula of the mutual correlation coefficient includes:

[0068]

[0069] Among them, s i represents the value of the coupled signal at the i-th sampling point, t i Indicates the value of the preset template at the i-th sampling point, represents the mean of the coupled signal segments, Represents the mean of the preset template fragment, and N is the total number of sampling points, that is, the sliding window length.

[0070] Exemplarily, the computing device can calculate the similarity between the coupled signal and the template segment by segment through a preset sliding window. When a high correlation coefficient is detected, that is, NCC is greater than 0.85, the coupled signal in the corresponding sliding window can be determined to be a valid signal segment, and the characteristic component of the valid signal segment can be extracted to invert the voltage value of the measured line.

[0071] Preferably, the computing device can update the weight matrix based on the coupling signal in the sliding window through a recursive least squares algorithm, and the updated weight matrix is used to compensate for the channel distortion and noise influence of the corresponding coupling signal during the propagation process when the characteristic component is extracted next time; the coupling signal in the sliding window is inversely filtered based on the weight matrix before the update to obtain an inverse filtered signal; and the characteristics of the inverse filtered signal are calculated as the characteristic component.

[0072] The computing device may initialize the weight matrix W0 and the forgetting factor λ, which is used to balance the weights of new and old data and control the weight decay rate of historical data. For example, the forgetting factor λ is 0.98.

[0073] For each identified valid signal segment, that is, a coupled signal within the sliding window whose correlation coefficient with the preset template is greater than a second threshold, the following operation can be performed:

[0074]

[0075] Wherein, W(n) is the iteration matrix at the nth iteration, which represents the estimated weight of the signal features at the nth iteration and is used for the feature component extraction for the n+1th time; x(n) is the effective signal segment when extracting the feature components for the nth time.

[0076] The weight matrix W(n) is updated iteratively and dynamically tracks channel characteristics (such as line impedance changes and temperature drift), which can optimize the accuracy of signal feature extraction.

[0077] Among them, the calculation formula of inverse filtering includes:

[0078]

[0079] Among them, s(t) represents the effective signal segment, W -1 (f) represents the frequency domain inverse of the weight matrix, H channel (f) represents the channel frequency response, and y(t) is the inverse filtered signal. FFT is the fast Fourier transform, and IFFT is the inverse fast Fourier transform.

[0080] Among them, the characteristic component is a quantitative parameter extracted from the inverse filtered signal, which is used to characterize the key characteristics of the signal. The characteristic component includes the time domain characteristics, frequency domain characteristics and statistical characteristics of the inverse filtered signal. Exemplarily, the computing device can obtain time domain characteristics including pulse peak value, rise time, overshoot rate, and pulse width through peak detection and derivative analysis; obtain frequency domain characteristics including main lobe energy, side lobe attenuation ratio, and harmonic distortion through FFT spectrum analysis; and obtain statistical characteristics including kurtosis, entropy value, and autocorrelation peak side lobe ratio through high-order moment calculation or entropy value algorithm.

[0081] Step 105: The computing device inverts the voltage value of the measured circuit based on the characteristic component.

[0082] Preferably, the computing device obtains the voltage value of the measured circuit based on a preset "voltage-characteristic component" relationship curve and the characteristic component.

[0083] Among them, the computing device can obtain a voltage-characteristic component curve based on experimental data fitting through experimental calibration; after the characteristic component is calculated, the corresponding voltage value can be found from the voltage-characteristic component curve based on the characteristic component.

[0084] Preferably, the computing device may input the characteristic component into a preset third-order Volterra series model to obtain an intermediate voltage value output by the third-order Volterra series model; and correct the intermediate voltage value through a preset Kalman filter to obtain the voltage value of the measured circuit.

[0085] The third-order Volterra series model is used to describe the corresponding relationship between the characteristic component and the voltage value of the measured circuit, and the parameters of the third-order Volterra series model are optimized by a quantum particle swarm optimization algorithm.

[0086] Among them, the computing device can first map the characteristic components to the interval [-1,1] to eliminate the dimensional difference; then compensate for the signal transmission delay through the cross-correlation algorithm to complete the preprocessing of the characteristic components; and then input the preprocessed characteristic components into the third-order Volterra series model.

[0087] The expression of the third-order Volterra series model includes:

[0088]

[0089] Among them, h1(i), h2(i,j), and h3(i,j,l) are the first-order, second-order, and third-order kernel functions of the third-order Volterra series model, respectively; x(k) is the input signal value at discrete time point k, and V(k) is the intermediate voltage value predicted by the model at time point k; M is the memory length, which is specifically the number of historical time steps of the input signal considered by the model; i, j, and l are different time points.

[0090] The first-order kernel function can capture linear responses (such as signal attenuation and time delay) and determine the linearity error of the basic range; the second-order kernel function can characterize quadratic nonlinear effects (such as harmonic distortion and intermodulation interference) and suppress measurement deviations introduced by grid harmonics; the third-order kernel function can describe higher-order nonlinear effects (such as saturation characteristics and cross-modulation) and improve measurement stability under extreme working conditions (such as lightning strikes); the memory length can control the dynamic response range of the model and avoid excessively long redundant calculations.

[0091] Among them, the state equation of the Kalman filter includes:

[0092] V final (k)=V model (k)+K(k)·[Z(k)-H·V model (k)];

[0093] Among them, V model (k) is the intermediate voltage value corresponding to time point k, V final(k) is the voltage value of the measured line at time point k, Z(k) is the auxiliary sensor observation value at time point k, K(k) is the Kalman gain at time point k, and H is the observation matrix.

[0094] In an embodiment of the present application, by adopting a different-frequency reference signal based on pulse code modulation instead of a continuous sine wave, the power consumption of signal injection can be reduced; and by identifying the corresponding characteristic components in the coupled signal through a pseudo-random coding sequence, interference from strong electromagnetic signals can be avoided and the accuracy of voltage measurement can be improved.

[0095] The above describes the method part provided by the embodiment of the present application. The following describes the system part provided by the embodiment of the present application.

[0096] See also Figure 2 , Figure 2 A schematic diagram of a voltage measurement system based on pulse coding is provided in an embodiment of the present application, as shown in FIG. Figure 2 As shown, the system 20 includes:

[0097] The modulation module 201 is used to generate a pseudo-random code sequence with sparse time domain, and modulate the different-frequency reference signal based on the pseudo-random code sequence to obtain a low-frequency pulse modulation wave;

[0098] an amplifying module 202 for amplifying the pulse power of the low-frequency pulse modulation wave and migrating the signal frequency band of the low-frequency pulse modulation wave to a high-frequency range to obtain a high-frequency pulse group;

[0099] The injection module 203 is used to inject the high-frequency pulse group into the line under test;

[0100] Extraction module 204, configured to obtain a coupled signal from the line under test, and extract a characteristic component corresponding to the pseudo-random code sequence from the coupled signal; wherein the coupled signal is a signal obtained by coupling the high-frequency pulse group with the power frequency voltage signal of the line under test;

[0101] The inversion module 205 is configured to invert the voltage value of the measured line based on the characteristic component.

[0102] Preferably, the modulation module 201 is specifically used to generate a non-periodic initial pulse sequence; perform time domain sparse processing on the initial pulse sequence so that the duty cycle of the initial pulse sequence is less than or equal to a preset first threshold; and distribute the initial pulse sequence after sparse processing to N discrete frequency points to obtain the pseudo-random coding sequence, where N is an integer greater than or equal to 3.

[0103] Preferably, the extraction module 204 is specifically used to calculate the mutual correlation coefficient between the coupled signal in the sliding window and a preset template through a preset sliding window; wherein the preset template is the time-frequency feature corresponding to the pseudo-random coding sequence; when the mutual correlation coefficient is greater than a preset second threshold, extract the characteristic component of the coupled signal in the sliding window.

[0104] Preferably, the extraction module 204 is specifically used to update the weight matrix based on the coupling signal in the sliding window through a recursive least squares algorithm, and the updated weight matrix is used to compensate for the channel distortion and noise influence of the corresponding coupling signal during the propagation process when the characteristic component is extracted next time; based on the weight matrix before the update, the coupling signal in the sliding window is inversely filtered to obtain an inverse filtered signal; and the characteristics of the inverse filtered signal are calculated as the characteristic component.

[0105] Preferably, the inversion module 205 is specifically configured to obtain the voltage value of the measured line based on a preset “voltage-characteristic component” relationship curve and the characteristic component.

[0106] Preferably, the inversion module 205 is specifically used to input the characteristic component into a preset third-order Volterra series model to obtain an intermediate voltage value output by the third-order Volterra series model; wherein the third-order Volterra series model is used to describe the corresponding relationship between the characteristic component and the voltage value of the measured line, and the parameters of the third-order Volterra series model are optimized by a quantum particle swarm optimization algorithm; the intermediate voltage value is corrected by a preset Kalman filter to obtain the voltage value of the measured line.

[0107] Preferably, the extraction module 204 is specifically configured to obtain the coupled signal from the measured line after injecting the high-frequency pulse group into the measured line and after N power frequency cycles, where the value range of N is [0.5, 2].

[0108] The voltage measurement system based on pulse coding provided in the embodiment of the present application can be understood by referring to the corresponding content of the aforementioned method embodiment part, and will not be repeated here.

[0109] like Figure 3 As shown, Figure 3 A possible logical structure diagram of a computing device provided in an embodiment of the present application. The computing device 300 includes: a processor 301, a communication interface 302, a memory 303, and a bus 304. The processor 301, the communication interface 302, and the memory 303 are interconnected via the bus 304. In the embodiment of the present application, the processor 301 is used to control and manage the actions of the computing device 300. For example, the processor 301 is used to execute Figure 1The steps in the embodiments and / or other processes for the technology described herein. The communication interface 302 is used to support the computing device 300 to communicate. The memory 303 is used to store program codes and data of the computing device 300.

[0110] Among them, the processor 301 can be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a transistor logic device, a hardware component or any combination thereof. It can implement or execute the various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a digital signal processor and a microprocessor, and so on. The bus 304 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0111] In another embodiment of the present application, a computer-readable storage medium is further provided, wherein the computer-readable storage medium includes instructions, which, when executed on a computer, causes the computer to execute the above-mentioned Figure 1 The method described in the embodiment.

[0112] Those skilled in the art will appreciate that the units of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0113] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0114] In the several embodiments provided in the embodiments of the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0115] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0116] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0117] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, magnetic disk or optical disk, and other media that can store program codes.

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and description of the present invention.

Claims

1. A voltage measurement method based on pulse coding, characterized in that: include: Generate a pseudo-random code sequence that is sparse in the time domain, and modulate an inter-frequency reference signal based on the pseudo-random code sequence to obtain a low-frequency pulse modulation wave; Performing pulse power amplification on the low-frequency pulse modulation wave, and migrating the signal frequency band of the low-frequency pulse modulation wave to a high-frequency range to obtain a high-frequency pulse group; injecting the high-frequency pulse group into the line under test; Acquire a coupled signal from the line under test, and extract a characteristic component corresponding to the pseudo-random code sequence from the coupled signal; wherein the coupled signal is a signal obtained by coupling the high-frequency pulse group with the power frequency voltage signal of the line under test; The voltage value of the measured line is inverted based on the characteristic component.

2. The method according to claim 1, characterized in that Generating a time-domain sparse pseudo-random code sequence comprises: generating a non-periodic initial pulse train; Performing time-domain sparse processing on the initial pulse sequence so that a duty cycle of the initial pulse sequence is less than or equal to a preset first threshold; The initial pulse sequence after the sparse processing is distributed to N discrete frequency points to obtain the pseudo-random code sequence, where N is an integer greater than or equal to 3.

3. The method according to claim 1, characterized in that The extracting the characteristic component corresponding to the pseudo-random code sequence from the coupled signal includes: Calculating the mutual correlation coefficient between the coupling signal within the sliding window and a preset template through a preset sliding window; wherein the preset template is the time-frequency feature corresponding to the pseudo-random code sequence; When the cross-correlation coefficient is greater than a preset second threshold, a characteristic component of the coupling signal within the sliding window is extracted.

4. The method according to claim 3, characterized in that The extracting the characteristic component of the coupling signal within the sliding window includes: updating a weight matrix based on the coupled signal within the sliding window using a recursive least squares algorithm, wherein the updated weight matrix is used to compensate for channel distortion and noise effects of the coupled signal during propagation when extracting the characteristic component next time; Performing inverse filtering on the coupled signal within the sliding window based on the weight matrix before updating to obtain an inverse filtered signal; The feature of the inverse filtered signal is calculated as the feature component.

5. The method according to any one of claims 1 to 4, characterized in that The inverting the voltage value of the measured line based on the characteristic component includes: Based on a preset "voltage-characteristic component" relationship curve and the characteristic component, the voltage value of the measured circuit is obtained.

6. The method according to any one of claims 1 to 4, characterized in that The inverting the voltage value of the measured line based on the characteristic component includes: Inputting the characteristic component into a preset third-order Volterra series model to obtain an intermediate voltage value output by the third-order Volterra series model; wherein the third-order Volterra series model is used to describe the corresponding relationship between the characteristic component and the voltage value of the measured circuit, and the parameters of the third-order Volterra series model are optimized using a quantum particle swarm optimization algorithm; The intermediate voltage value is corrected by a preset Kalman filter to obtain the voltage value of the measured circuit.

7. The method according to any one of claims 1 to 4, characterized in that The obtaining of a coupled signal from the measured circuit includes: After the high-frequency pulse group is injected into the measured line, the coupled signal is obtained from the measured line after N power frequency cycles, where the value range of N is [0.5, 2].

8. A voltage measurement system based on pulse coding, characterized in that: The method according to any one of claims 1 to 7, wherein the system comprises: A modulation module is used to generate a pseudo-random code sequence that is sparse in the time domain, and modulate an inter-frequency reference signal based on the pseudo-random code sequence to obtain a low-frequency pulse modulation wave; an amplification module, configured to amplify the pulse power of the low-frequency pulse modulation wave and migrate the signal frequency band of the low-frequency pulse modulation wave to a high-frequency range to obtain a high-frequency pulse group; An injection module, used for injecting the high-frequency pulse group into the line under test; an extraction module, configured to obtain a coupled signal from the line under test, and extract a characteristic component corresponding to the pseudo-random code sequence from the coupled signal; wherein the coupled signal is a signal obtained by coupling the high-frequency pulse group with the power frequency voltage signal of the line under test; An inversion module is used to invert the voltage value of the measured line based on the characteristic component.

9. A computing device, characterized in that include: Memory, used to store programs; A processor, configured to load the program to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

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