New energy wave band characteristic metering method and system based on dual-ADC range switching

Abstract

The invention discloses a new energy wave band characteristic metering method and system based on dual-ADC range switching, and relates to the technical field of new energy wave band characteristic metering, a dual-ADC parallel acquisition architecture is constructed, and wave band characteristic signals of new energy equipment are acquired based on the dual-ADC parallel acquisition architecture; performing timestamp synchronous correction processing on the acquired wave band characteristic signal to generate a global wave band characteristic signal; performing weighted fusion processing on the global band characteristic signal to generate a global band characteristic signal after weighted fusion processing; and executing a fusion compensation mechanism on the global wave band characteristic signal after the weighted fusion processing to obtain a target wave band characteristic signal. The device covers full-range wave band characteristic signal acquisition of new energy under an extreme working condition, considers the acquisition precision of high-amplitude fluctuation signals and extremely weak signals, improves the new energy wave band characteristic metering efficiency, reduces metering errors, is suitable for electric energy quality monitoring and protection in a new energy scene, and has the advantages of high reliability and low cost.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of new energy wave band characteristic measurement technology, and in particular to a new energy wave band characteristic measurement method and system based on double ADC range switching. BACKGROUND

[0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute the prior art.

[0003] With the acceleration of the global energy structure to low-carbon transformation, the installed capacity of new energy industry mainly based on wind power, photovoltaic power and hydropower is growing rapidly at an annual rate of 12.3%, which has greatly promoted the development of energy industry. However, the strong randomness, intermittency and volatility of new energy power generation will affect the quality of grid-connected power, especially in the measurement accuracy of new energy wave band characteristics. According to the statistical data, the grid scheduling deviation rate caused by measurement error in the new energy industry is as high as 7.8%, which is much higher than that of traditional thermal power industry of 1.2%.

[0004] In the traditional measurement method of new energy wave band characteristics, a single range analog-to-digital converter ADC is usually used to collect the wave band signals of new energy. However, this traditional single range analog-to-digital converter ADC is limited by its fixed range and dynamic range, and it is difficult to balance the collection accuracy of high amplitude fluctuation signals and extremely weak signals when dealing with new energy scenarios, resulting in insufficient dynamic response of measurement, easy signal loss or distortion, and large measurement error, which affects the power quality of grid-connected power. SUMMARY

[0005] In order to overcome the above-mentioned shortcomings of the prior art, the present application provides a new energy wave band characteristic measurement method and system based on double ADC range switching, which aims to obtain accurate wave band characteristic signals of new energy equipment to realize accurate scheduling of power grid and avoid scheduling error of power grid.

[0006] To achieve the above-mentioned purpose, one or more embodiments of the present application provide the following technical solutions: In a first aspect, the present application provides a new energy wave band characteristic measurement method based on double ADC range switching, comprising: constructing a double ADC parallel acquisition architecture, and collecting wave band characteristic signals of new energy equipment based on the double ADC parallel acquisition architecture; performing timestamp synchronization correction processing on the collected wave band characteristic signals to generate global wave band characteristic signals; performing weighted fusion processing on the global wave band characteristic signals to generate weighted fusion processed global wave band characteristic signals; A fusion compensation mechanism is performed on the global band characteristic signal after the weighted fusion processing to obtain a target band characteristic signal.

[0007] A further technical solution is to construct a dual ADC parallel acquisition architecture by deploying a high-range ADC and a low-range ADC in parallel; configuring the sampling range, sampling rate and resolution of the high-range ADC and the low-range ADC respectively; Alternatively, a hysteresis comparator is provided in the dual ADC parallel acquisition architecture.

[0008] A further technical solution is to collect the band characteristic signals of new energy equipment based on the dual ADC parallel acquisition architecture, specifically: triggering a low-range mode of the dual-ADC parallel acquisition architecture, and acquiring a sample band characteristic signal of a new energy device based on the low-range ADC; Calculating the signal amplitude change rate of the sample band characteristic signal based on a sliding window algorithm; Comparing the signal amplitude change rate with a preset range switching dynamic threshold, and determining whether to switch from the low-range mode to the high-range mode based on the comparison result; After the mode switching is completed, the subsequent band characteristic signals of the new energy equipment are collected based on the dual ADC parallel acquisition architecture; The calculated signal amplitude change rate is subjected to median filtering and Grubbs test to eliminate outliers.

[0009] A further technical solution is to lock the dual ADC parallel acquisition architecture into a high range mode when the signal amplitude change rate of the sample band characteristic signal is greater than a preset range switching dynamic threshold in a plurality of consecutive sampling periods; Alternatively, when the signal amplitude change rate of the sample band characteristic signal is less than half of the preset range switching dynamic threshold in several consecutive sampling periods, the dual ADC parallel acquisition architecture is locked to the low range mode.

[0010] A further technical solution is that generating the global band characteristic signal is specifically as follows: Configuring a programmable gate array to generate a pulse per second signal based on the programmable gate array; Analyze the sampling clock in the dual ADC parallel acquisition architecture and generate a global timestamp file; Dividing the collected band characteristic signal into a plurality of band characteristic signal blocks, performing sampling alignment processing on each of the plurality of band characteristic signal blocks based on the second pulse signal, and generating a reference timestamp; For the band characteristic signal block with a timestamp difference greater than a threshold value from the reference timestamp in the plurality of band characteristic signal blocks, a sub-sampling period compensation processing is performed based on a cubic spline interpolation algorithm; The global band characteristic signal is generated by splicing all the band characteristic signal blocks in the original order based on the global timestamp marker file.

[0011] In a further technical solution, the generating of the global band characteristic signal after the weighted fusion processing is specifically: The signal-to-noise ratios of the high-range signal component and the low-range signal component in the global band characteristic signal are calculated respectively; The weighted weight coefficients are determined based on the entropy weight method combined with the signal-to-noise ratios; The high-range signal component and the low-range signal component in the global band characteristic signal are weighted and fused based on the attention mechanism and the weighted weight coefficients, to generate the global band characteristic signal after the weighted fusion processing.

[0012] In a further technical solution, the acquiring of the target band characteristic signal is specifically: The extreme weak band signal in the global band characteristic signal after the weighted fusion processing is extracted based on a low-pass filter; The dynamic gain amplification processing is performed on the extreme weak band signal; The transient noise in the extreme weak band signal after the dynamic gain amplification processing is positioned and separated; The steady-state noise in the extreme weak band signal after the dynamic gain amplification processing is suppressed; The signal reconstruction is performed on the processed extreme weak band signal and the global band characteristic signal after the weighted fusion processing, to acquire the target band characteristic signal.

[0013] In a second aspect, the application provides a new energy band characteristic measurement system based on dual-ADC range switching, comprising: A signal parallel acquisition module configured to: construct a dual-ADC parallel acquisition architecture, and acquire the band characteristic signal of the new energy equipment based on the dual-ADC parallel acquisition architecture; A timestamp synchronization correction module configured to: perform timestamp synchronization correction processing on the acquired band characteristic signal, to generate a global band characteristic signal; A weighted fusion module configured to: perform weighted fusion processing on the global band characteristic signal, to generate a global band characteristic signal after the weighted fusion processing; A fusion compensation module configured to: perform a fusion compensation mechanism on the global band characteristic signal after the weighted fusion processing, to acquire a target band characteristic signal.

[0014] In a third aspect, the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the new energy band characteristic measurement method based on dual-ADC range switching according to the first aspect.

[0015] In a fourth aspect, the present application provides a computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the new energy band characteristic measurement method based on dual-ADC range switching according to the first aspect when executing the program.

[0016] The above one or more technical solutions have the following beneficial effects: The present application collects the band characteristic signal through the parallel acquisition architecture of dual-range ADC, and after the time stamp synchronization correction, weighted fusion, and noise reduction processing of the band characteristic signal, the accurate band characteristic signal of the new energy equipment, i.e., the target band characteristic signal, is obtained, and the power grid scheduling is performed based on the target band characteristic signal, thereby avoiding the deviation of the power grid scheduling.

[0017] The present application sets the parallel acquisition architecture of high / low dual-range ADC, realizes the automatic switching of the range through the calculation of the signal amplitude change rate and the dynamic threshold, covers the full-range band characteristic signal acquisition of the new energy under extreme working conditions, and simultaneously considers the acquisition accuracy of high-amplitude fluctuation signals and extremely weak signals; based on the time stamp synchronization correction and the weighted fusion processing, the error caused by the range switching is eliminated; through the execution of the fusion compensation mechanism, the instantaneous filtering and noise suppression technology are combined to improve the capture ability of the extremely weak signal, improve the new energy band characteristic measurement efficiency, reduce the measurement error, and be suitable for the power quality monitoring and protection under the new energy scene, and have the advantages of high reliability and low cost. BRIEF DESCRIPTION OF DRAWINGS

[0018] The drawings accompanying the specification of the present application form a part thereof and serve to provide further understanding of the present application, the illustrative embodiments of the present application and its description serve to explain the present application, and do not constitute an improper limitation of the present application.

[0019] Figure 1 is a flowchart of the new energy band characteristic measurement method based on dual-ADC range switching in the embodiment of the present application; Figure 2 is a flowchart of the acquisition of the band characteristic signal of the new energy equipment based on the dual-ADC parallel acquisition architecture in the embodiment of the present application; Figure 3 is a flowchart of the generation of the global band characteristic signal through the time stamp synchronization correction processing in the embodiment of the present application; Figure 4 is a flowchart of the sub-sampling period compensation processing based on the cubic spline interpolation algorithm in the embodiment of the present application; Figure 5 1 is a flow chart of performing weighted fusion processing to generate a global band characteristic signal after weighted fusion processing in an embodiment of the present invention; Figure 6 This is a flow chart of executing a fusion compensation mechanism to obtain target band characteristic signals in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0021] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0022] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0023] Example 1 like Figure 1 As shown, this embodiment discloses a new energy band characteristic measurement method based on dual ADC range switching, which includes the following steps: S1: Building a dual-ADC parallel acquisition architecture to collect band characteristic signals of new energy equipment based on the dual-ADC parallel acquisition architecture; In an optional implementation of this embodiment, a dual-ADC (analog-to-digital converter) parallel acquisition architecture is constructed by deploying a high-range ADC and a low-range ADC in parallel. Traditional methods for measuring renewable energy band characteristics typically use a single-range ADC to acquire renewable energy band signals. However, renewable energy band signals exhibit strong randomness, intermittency, and volatility, resulting in significant differences between peak and valley values ​​of their signal amplitudes. Therefore, when setting the acquisition range of a single-range ADC, if both peak and valley values ​​of the signal amplitude are included, the acquisition range will be too large, affecting acquisition accuracy and causing signal loss or distortion. If both peak and valley values ​​are not included, partial signal loss will occur, further impacting overall accuracy and leading to greater measurement errors, which in turn affects the power quality of grid-connected power. Therefore, in this embodiment, a dual-ADC parallel acquisition architecture is constructed, deploying both high-range and low-range ADCs in parallel. This architecture can cover the entire range of band characteristic signals while ensuring a certain level of acquisition accuracy.

[0024] The sampling range, sampling rate, and resolution of the high-range ADC and low-range ADC are configured separately. Specifically, the sampling range of the high-range ADC covers ±10V to ±1000V, and the sampling range of the low-range ADC covers ±1mV to ±50V.

[0025] Here, the sampling range of the high-range ADC is larger, basically covering the entire range of the band characteristic signals of new energy equipment, while the sampling range of the low-range ADC is concentrated in the range where most components of the band characteristic signals are located.

[0026] Furthermore, the sampling rate of the high-range ADC is configured to be 2MS / s and the resolution is 12 bits, and the sampling rate of the low-range ADC is configured to be 10MS / s and the resolution is 16 bits. That is, the low-range ADC has a higher sampling rate and a higher resolution, which is more convenient for collecting signals in a range with higher signal density.

[0027] Acquiring the band characteristic signals of new energy devices based on a dual-ADC parallel acquisition architecture includes the following steps: S101: triggering the low-range mode of the dual-ADC parallel acquisition architecture to acquire the sample band characteristic signal of the new energy device based on the low-range ADC; The default acquisition mode of the dual-ADC parallel acquisition architecture is low-range mode. Therefore, in the initial state, the low-range mode is triggered, and the low-range ADC in the dual-ADC parallel acquisition architecture collects the sample band characteristic signals of the new energy device. This acquisition of the sample band characteristic signals provides data support for subsequent determination of whether a high-range ADC or a low-range ADC is needed for signal acquisition.

[0028] S102: Calculating the signal amplitude change rate of the sample band characteristic signal based on a sliding window algorithm; Based on the non-overlapping window strategy, the sliding window parameters are set, including the window width and the window sliding step size, and the signal amplitude change rate of the sample band characteristic signal is calculated in each window. The calculation formula is:

[0029] Where, is the voltage change, specifically the absolute value of the instantaneous amplitude change of the new energy band characteristic signal within the time interval of the sliding window; is the time length of the sliding window; is the rate of change of signal amplitude within the window width; for The voltage corresponding to the time window, for The voltage corresponding to the time window, is the starting point of the time window, is the end point of the time window; is the window sliding step; .

[0030] Furthermore, the calculated signal amplitude change rate is subjected to median filtering and Grubbs test to eliminate outliers.

[0031] S103: comparing the signal amplitude change rate with a preset range switching dynamic threshold, and determining whether to switch from the low-range mode to the high-range mode based on the comparison result; The setting formula for the preset range switching dynamic threshold is:

[0032] Where, Dynamic threshold for range switching, is the historical data basic threshold, is the time decay factor, is the real-time noise floor (three times the standard deviation of the signal in the sliding window).

[0033] Furthermore, after a period of processing, the time attenuation factor is adjusted by gradient descent method. Optimize.

[0034] The signal amplitude change rate is compared with the preset range switching dynamic threshold. When the signal amplitude change rate is greater than the range switching dynamic threshold, the low-range mode of the dual ADC parallel acquisition architecture is switched to the high-range mode, that is, the subsequent band characteristic signals are collected based on the high-range ADC.

[0035] S104: collecting subsequent band characteristic signals of the new energy device based on the dual ADC parallel acquisition architecture after the mode switching is completed.

[0036] Based on the dual ADC parallel acquisition architecture after the mode switching is completed, the subsequent band characteristic signals of the new energy equipment are collected based on the low-range ADC or the high-range ADC.

[0037] If the sample band characteristic signal's amplitude change rate exceeds the preset range switching dynamic threshold for several consecutive sampling cycles, the dual ADC parallel acquisition architecture is locked in high-range mode. If the sample band characteristic signal's amplitude change rate is less than half of the preset range switching dynamic threshold for several consecutive sampling cycles, the dual ADC parallel acquisition architecture is locked in low-range mode. This process involves range switching decisions, effectively preventing erroneous range switching caused by false triggering and reducing unnecessary range switching actions.

[0038] Furthermore, a hysteresis comparator can be set in the dual ADC parallel acquisition architecture, and an upper threshold can be set. and lower threshold , then the upper and lower threshold values ​​are: 、 .

[0039] S2: Performing time stamp synchronization correction processing on the collected band characteristic signals to generate global band characteristic signals; Generating a global band characteristic signal includes the following steps: S201: Configuring a programmable gate array to generate a pulse per second signal based on the programmable gate array; Based on the acquisition environment in this embodiment, a field programmable gate array (FPGA) is configured to generate a pulse per second signal (PPA). The pulse per second signal generated here can provide a reference for subsequent timestamp alignment.

[0040] S202: Analyze the sampling clock in the dual ADC parallel acquisition architecture and generate a global timestamp file; Analyze the sampling clocks of the high-range ADC and low-range ADC in a dual-ADC parallel acquisition architecture, align them based on pulse-per-second signals, and generate a global timestamp file.

[0041] S203: Divide the collected band characteristic signal into several band characteristic signal blocks, perform sampling alignment processing on the several band characteristic signal blocks based on the pulse per second signal, and generate a reference time stamp; The collected band characteristic signals are randomly divided into several band characteristic signal blocks, the sampling signals of several band characteristic signal blocks are aligned based on the second pulse signal, and the timestamp with the highest degree of aggregation is extracted as the reference timestamp.

[0042] S204: performing sub-sampling period compensation processing based on a cubic spline interpolation algorithm on the band characteristic signal blocks whose timestamps differ from the reference timestamp by more than a threshold value among the band characteristic signal blocks; The sub-sampling period compensation processing is performed based on the cubic spline interpolation algorithm, including the following steps: (1) Divide the timestamp difference interval of the band characteristic signal block to be processed into several subintervals, and select several interpolation nodes in each subinterval; For the band characteristic signal blocks whose timestamp difference with the reference timestamp is greater than the threshold, the timestamp difference interval is divided into subintervals, select interpolation nodes, where the positions of the interpolation nodes satisfy the following formula:

[0043] In the formula, is the position of the th interpolation node, is the initial position of the sub-interval, is the timestamp difference interval, is the number of interpolation nodes in the sub-interval.

[0044] (2) According to the real-time fluctuation of the timestamp difference interval, the interpolation step is adaptively set; According to the real-time fluctuation of the timestamp difference interval, the interpolation step is adaptively dynamically adjusted, and the interpolation compensation formula includes:

[0045] In the formula, is the interpolation step.

[0046] (3) For each sub-interval, a corresponding cubic spline function is constructed, and boundary adjustment optimization processing is performed; The expression of the cubic spline function includes:

[0047] In the formula, is the piecewise polynomial, is the absolute timestamp of the th interpolation node, , , , are the constant term, the first-order term coefficient, the second-order term coefficient and the third-order term coefficient, respectively, which determine the interval starting point offset, the interval slope change, the curvature change and the local fluctuation amplitude.

[0048] The four coefficients of the piecewise polynomial of the cubic spline function , , , need to meet: function value continuity , is the original sampling point value; first-order derivative continuity ; second-order derivative continuity .

[0049] Further, the cubic spline function is optimized with natural boundary conditions and fixed boundary conditions.

[0050] (4) Based on the constructed cubic spline function, interpolation compensation processing is performed on the interpolation nodes combined with the interpolation step; The constructed cubic spline function is solved, and interpolation compensation processing is performed on the interpolation nodes based on the solution combined with the interpolation step size.

[0051] (5) Perform interpolation error post-processing on the band characteristic signal block after interpolation compensation processing.

[0052] The band characteristic signal block after the interpolation compensation processing is subjected to interpolation error post-processing including interpolation error estimation, phase compensation and data smoothing.

[0053] S205: splicing all the band characteristic signal blocks in the original order based on the global timestamp mark file to generate a global band characteristic signal.

[0054] Based on the global timestamp mark file, all the split and processed band characteristic signal blocks are spliced ​​head to tail in the original order to generate the global band characteristic signal.

[0055] S3: performing weighted fusion processing on the global band characteristic signal to generate a weighted fusion processed global band characteristic signal; like Figure 5 As shown, generating a global band characteristic signal after weighted fusion processing includes the following steps: S301: Calculating the signal-to-noise ratios of the high-range signal component and the low-range signal component in the global band characteristic signal respectively; The formula for calculating the signal-to-noise ratio of the high-range signal component and the low-range signal component in the global band characteristic signal includes:

[0056]

[0057] Where, For high range signal-to-noise ratio, For low range signal-to-noise ratio, is the effective signal power collected by the high-range ADC, is the noise power collected by the high-range ADC, is the effective signal power collected by the low-range ADC, is the noise power collected by the low-range ADC.

[0058] S302: Determine a weighted coefficient based on an entropy weight method and a signal-to-noise ratio; The formula for calculating the weighted weight coefficient includes:

[0059]

[0060]

[0061]

[0062] Where, is the high range entropy value, is the low range entropy value, is the number of effective sampling points, is the high range relative weight, is the low range relative weight, is the high range weighting coefficient, It is the low range weighting coefficient.

[0063] S303: Based on the attention mechanism, weighted fusion processing is performed on the high-range signal component and the low-range signal component in the global band characteristic signal according to the weighted weight coefficient to generate a global band characteristic signal after weighted fusion processing.

[0064] Based on the attention mechanism, the high-range signal components and low-range signal components in the global band characteristic signal are weightedly fused according to the weighted weight coefficient to generate a global band characteristic signal after weighted fusion.

[0065] S4: Execute a fusion compensation mechanism on the global band characteristic signal after the weighted fusion processing to obtain a target band characteristic signal.

[0066] Execute the fusion compensation mechanism to obtain the target band characteristic signal, including the following steps: S401: extracting extremely weak band signals from the global band characteristic signal after weighted fusion processing based on a low-pass filter; A second-order Butterworth low-pass filter is used to suppress the high-frequency band signal in the global band characteristic signal after weighted fusion processing, and to extract the extremely weak band signal in the global band characteristic signal after weighted fusion processing.

[0067] S402: Perform dynamic gain amplification processing on extremely weak band signals; Three-level gain switching is used to perform dynamic gain amplification processing on extremely weak band signals.

[0068] S403: locating and separating transient noise in the extremely weak band signal after dynamic gain amplification processing; The extremely weak band signal after dynamic gain amplification is decomposed into 16 frequency band components using db8 wavelet. The Hilbert envelope energy of each frequency band component is calculated to identify and locate the frequency of transient noise, and a soft threshold is applied for truncation.

[0069] S404: Suppressing steady-state noise in the extremely weak band signal after dynamic gain amplification processing; The Kalman filter is used to suppress the steady-state noise in the extremely weak band signal after dynamic gain amplification.

[0070] S405: Reconstruct the processed extremely weak band signal and the original weighted fusion global band characteristic signal to obtain the target band characteristic signal. The target band characteristic signal includes multiple power parameters, such as voltage amplitude, frequency fluctuation, harmonic distortion rate, flicker, and other specific grid quality indicators.

[0071] First, the processed extremely weak band signal is mixed with the global band characteristic signal after the original weighted fusion processing, and blind source separation is performed. The weak target signal component is separated from the mixed signal based on the maximum non-Gaussian separation criterion. The weak target signal component is compared with the processed extremely weak band signal to obtain the denoising threshold. Then, the weak target signal component is subjected to wavelet threshold denoising based on the denoising threshold, and the amplitude attenuation is compensated by the Wiener filter. Its transfer function includes:

[0072] Where, is the transfer angular frequency, is the cut-off angular frequency.

[0073] At present, in the new energy industry, the measurement error of the band characteristics of new energy equipment leads to a high grid dispatching deviation rate. In view of the large error of traditional measurement methods, a new energy band characteristic measurement method based on dual ADC range switching is proposed, and the target band characteristic signal (that is, the accurate band characteristic signal of the new energy equipment) is finally obtained. After obtaining the accurate band characteristic signal, the accurate band characteristic signal can be used as the accurate basis to achieve accurate grid dispatching processing, that is, the target band characteristic signal finally obtained is used as the basis for grid dispatching, thereby achieving accurate grid dispatching processing.

[0074] In summary, the present invention provides a new energy band characteristic measurement method based on dual ADC range switching, sets a high / low dual-range ADC parallel acquisition architecture, realizes automatic range switching by calculating the signal amplitude change rate and dynamic threshold judgment, covers the full-range band characteristic signal acquisition of new energy under extreme working conditions, and takes into account the acquisition accuracy of high-amplitude fluctuation signals and extremely weak signals; based on timestamp synchronization correction and weighted fusion processing, eliminates the error caused by range switching; by executing the fusion compensation mechanism, combined with transient filtering and noise suppression technology, improves the capture ability of extremely weak signals, improves the metering efficiency of new energy band characteristics, and reduces metering errors. It is suitable for power quality monitoring and protection in new energy scenarios, and has the advantages of high reliability and low cost.

[0075] Example 2 This embodiment discloses a new energy band characteristic measurement system based on dual ADC range switching, including: A signal parallel acquisition module is configured to: construct a dual-ADC parallel acquisition architecture, and acquire band characteristic signals of new energy devices based on the dual-ADC parallel acquisition architecture; In this embodiment, building a dual ADC parallel acquisition architecture includes: Deploy high-range and low-range ADCs in parallel, configuring their sampling ranges, sampling rates, and resolutions. The high-range ADC's sampling range covers ±10V to ±1000V, while the low-range ADC's sampling range covers ±1mV to ±50V. Furthermore, configure the high-range ADC's sampling rate to 2MS / s and its resolution to 12 bits, while the low-range ADC's sampling rate to 10MS / s and its resolution to 16 bits. This means the low-range ADC has a higher sampling rate and resolution, making it easier to acquire signals in a range with higher signal density.

[0076] The band characteristic signals of new energy equipment collected based on the dual ADC parallel acquisition architecture include: Trigger the low-range mode of the dual-ADC parallel acquisition architecture to collect sample band characteristic signals of new energy equipment based on the low-range ADC; Calculate the signal amplitude change rate of the sample band characteristic signal based on the sliding window algorithm; Compare the signal amplitude change rate of the sample band characteristic signal with the preset range switching dynamic threshold, and determine whether to switch the low-range mode of the dual ADC parallel acquisition architecture to the high-range mode based on the comparison result; After the mode switching is completed, the dual ADC parallel acquisition architecture is used to collect the subsequent band characteristic signals of the new energy equipment.

[0077] When the signal amplitude change rate of the sample band characteristic signal is greater than the preset range switching dynamic threshold in several consecutive sampling cycles, the dual ADC parallel acquisition architecture is locked to the high range mode; when the signal amplitude change rate of the sample band characteristic signal is less than half of the preset range switching dynamic threshold in several consecutive sampling cycles, the dual ADC parallel acquisition architecture is locked to the low range mode.

[0078] A timestamp synchronization correction module is configured to: perform timestamp synchronization correction processing on the collected band characteristic signal to generate a global band characteristic signal; In this embodiment, performing timestamp synchronization correction processing on the collected band characteristic signal to generate a global band characteristic signal includes: Configure FPGA and generate pulse-per-second signals based on FPGA; Analyze the sampling clock in the dual-ADC parallel acquisition architecture to generate a global timestamp marking file; Divide the acquired waveband characteristic signals into a plurality of waveband characteristic signal blocks, sample and align the plurality of waveband characteristic signal blocks based on the second pulse signals, and generate reference timestamps; For waveband characteristic signal blocks with a timestamp difference greater than a threshold from the reference timestamps, perform sub-sampling period compensation processing based on a cubic spline interpolation algorithm; Based on the global timestamp marking file, splice all the waveband characteristic signal blocks in the original order to generate a global waveband characteristic signal.

[0079] Further, the sub-sampling period compensation processing based on the cubic spline interpolation algorithm includes: Divide the timestamp difference interval of the waveband characteristic signal block that needs to be processed into a plurality of sub-intervals, and select a plurality of interpolation nodes in each sub-interval; Adaptively set the interpolation step size according to the real-time fluctuation of the timestamp difference interval; For each sub-interval, construct a corresponding cubic spline function and perform boundary adjustment and optimization processing; Based on the constructed cubic spline function, perform interpolation compensation processing at the interpolation nodes in combination with the interpolation step size; Perform interpolation error post-processing on the waveband characteristic signal block after interpolation compensation processing.

[0080] The weighted fusion module is configured to perform weighted fusion processing on the global waveband characteristic signal to generate a global waveband characteristic signal after weighted fusion processing; In this embodiment, performing weighted fusion processing on the global waveband characteristic signal to generate a global waveband characteristic signal after weighted fusion processing includes: Respectively calculate the signal-to-noise ratios of high-range signal components and low-range signal components in the global waveband characteristic signal; Determine the weighted weight coefficients based on the entropy weight method in combination with the signal-to-noise ratios; Based on the attention mechanism, perform weighted fusion processing on the high-range signal components and the low-range signal components in the global waveband characteristic signal according to the weighted weight coefficients to generate a global waveband characteristic signal after weighted fusion processing.

[0081] The fusion compensation module is configured to execute a fusion compensation mechanism on the global waveband characteristic signal after weighted fusion processing to obtain a target waveband characteristic signal.

[0082] In this embodiment, executing the fusion compensation mechanism on the global waveband characteristic signal after weighted fusion processing to obtain a target waveband characteristic signal includes: The extremely weak band signal in the global band characteristic signal after weighted fusion processing is extracted based on the low-pass filter; Perform dynamic gain amplification on extremely weak band signals; Locate and separate transient noise in extremely weak band signals after dynamic gain amplification; Suppress the steady-state noise in extremely weak band signals after dynamic gain amplification; The processed extremely weak band signal and the original global band characteristic signal after weighted fusion processing are reconstructed to obtain the target band characteristic signal.

[0083] In summary, the system of the present invention sets up a parallel acquisition architecture of high / low dual-range ADC, realizes automatic range switching by calculating the signal amplitude change rate and dynamic threshold judgment, covers the full-range band characteristic signal acquisition of new energy under extreme working conditions, and takes into account the acquisition accuracy of high-amplitude fluctuation signals and extremely weak signals; based on timestamp synchronization correction and weighted fusion processing, it eliminates the errors caused by range switching; by executing the fusion compensation mechanism, combined with transient filtering and noise suppression technology, it improves the capture ability of extremely weak signals, improves the metering efficiency of new energy band characteristics, and reduces metering errors. It is suitable for power quality monitoring and protection in new energy scenarios, and has the advantages of high reliability and low cost.

[0084] Example 3 The purpose of this embodiment is to provide a computing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method of embodiment 1 when executing the program.

[0085] Example 4 The purpose of this embodiment is to provide a computer-readable storage medium, a computer-readable storage medium having a computer program stored thereon, which performs the steps of the method of embodiment 1 when executed by a processor.

[0086] The steps involved in the apparatuses of Examples 3 and 4 above correspond to those of Method Example 1. For detailed implementation, please refer to the relevant description of Example 1. The term "computer-readable storage medium" should be understood to mean a single medium or multiple media containing one or more instruction sets; it should also be understood to include any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and causing the processor to perform any of the methods of the present invention.

[0087] Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using a general-purpose computer device. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0088] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

[0089] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without any creative work are still within the scope of protection of the present invention.

Claims

1. A new energy band characteristic measurement method based on dual ADC range switching is characterized by: include: Building a dual-ADC parallel acquisition architecture, and collecting band characteristic signals of new energy equipment based on the dual-ADC parallel acquisition architecture; Performing time stamp synchronization correction processing on the collected band characteristic signals to generate global band characteristic signals; Performing weighted fusion processing on the global band characteristic signal to generate a weighted fusion processed global band characteristic signal; A fusion compensation mechanism is performed on the global band characteristic signal after the weighted fusion processing to obtain a target band characteristic signal.

2. The new energy band characteristic measurement method based on dual ADC range switching according to claim 1, characterized in that: The dual ADC parallel acquisition architecture is constructed by deploying a high-range ADC and a low-range ADC in parallel; configuring the sampling range, sampling rate and resolution of the high-range ADC and the low-range ADC respectively; Alternatively, a hysteresis comparator is provided in the dual ADC parallel acquisition architecture.

3. The new energy band characteristic measurement method based on dual ADC range switching according to claim 2 is characterized in that: The band characteristic signals of new energy devices are collected based on the dual ADC parallel acquisition architecture, specifically: triggering a low-range mode of the dual-ADC parallel acquisition architecture, and acquiring a sample band characteristic signal of a new energy device based on the low-range ADC; Calculating the signal amplitude change rate of the sample band characteristic signal based on a sliding window algorithm; Comparing the signal amplitude change rate with a preset range switching dynamic threshold, and determining whether to switch from the low-range mode to the high-range mode based on the comparison result; After the mode switching is completed, the subsequent band characteristic signals of the new energy equipment are collected based on the dual ADC parallel acquisition architecture; The calculated signal amplitude change rate is subjected to median filtering and Grubbs test to eliminate outliers.

4. The new energy band characteristic measurement method based on dual ADC range switching according to claim 3 is characterized in that: When the signal amplitude change rate of the sample band characteristic signal is greater than a preset range switching dynamic threshold in a plurality of consecutive sampling periods, the dual ADC parallel acquisition architecture is locked to a high range mode; Alternatively, when the signal amplitude change rate of the sample band characteristic signal is less than half of the preset range switching dynamic threshold in several consecutive sampling periods, the dual ADC parallel acquisition architecture is locked to the low range mode.

5. The new energy band characteristic measurement method based on dual ADC range switching according to claim 1 is characterized in that: The generating of the global band characteristic signal is specifically as follows: Configuring a programmable gate array to generate a pulse per second signal based on the programmable gate array; Analyze the sampling clock in the dual ADC parallel acquisition architecture and generate a global timestamp file; Dividing the collected band characteristic signal into a plurality of band characteristic signal blocks, performing sampling alignment processing on the plurality of band characteristic signal blocks based on the second pulse signal, and generating a reference timestamp; performing subsampling period compensation processing based on a cubic spline interpolation algorithm on the band characteristic signal blocks whose timestamps differ from the reference timestamp by more than a threshold value among the plurality of band characteristic signal blocks; All band characteristic signal blocks are spliced ​​in the original order based on the global time stamp mark file to generate a global band characteristic signal.

6. The new energy band characteristic measurement method based on dual ADC range switching according to claim 1, characterized in that: The global band characteristic signal after the weighted fusion process is generated as follows: Calculate the signal-to-noise ratios of the high-range signal component and the low-range signal component in the global band characteristic signal respectively; The weighted coefficient is determined based on the entropy weight method combined with the signal-to-noise ratio; Based on the attention mechanism, the high-range signal component and the low-range signal component in the global band characteristic signal are weightedly fused according to the weighted weight coefficient to generate a global band characteristic signal after weighted fusion processing.

7. The new energy band characteristic measurement method based on dual ADC range switching according to claim 1, characterized in that: The specific steps of obtaining the target band characteristic signal are as follows: Extracting extremely weak band signals from the global band characteristic signals after the weighted fusion processing based on a low-pass filter; Performing dynamic gain amplification processing on the extremely weak band signal; Locate and separate transient noise in extremely weak band signals after dynamic gain amplification; Suppress the steady-state noise in extremely weak band signals after dynamic gain amplification; The processed extremely weak band signal and the original global band characteristic signal after weighted fusion processing are reconstructed to obtain the target band characteristic signal.

8. The new energy band characteristic measurement system based on dual ADC range switching is characterized by: include: A signal parallel acquisition module is configured to: construct a dual-ADC parallel acquisition architecture, and acquire band characteristic signals of new energy devices based on the dual-ADC parallel acquisition architecture; A timestamp synchronization correction module is configured to: perform timestamp synchronization correction processing on the collected band characteristic signal to generate a global band characteristic signal; a weighted fusion module configured to: perform weighted fusion processing on the global band characteristic signal to generate a global band characteristic signal after weighted fusion processing; The fusion compensation module is configured to: perform a fusion compensation mechanism on the global band characteristic signal after the weighted fusion processing to obtain a target band characteristic signal.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the new energy band characteristic measurement method based on dual ADC range switching as described in any one of claims 1 to 7 are implemented.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps in the new energy band characteristic measurement method based on dual ADC range switching according to any one of claims 1 to 7 are implemented.

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