Power grid multi-range intelligent sensing method and system based on MEMS integration

Abstract

The invention discloses a power grid multi-range intelligent sensing method and system based on MEMS integration, and relates to the technical field of intelligent sensing, and the method comprises the steps: connecting an MEMS sensing unit to three parallel measurement channels, and connecting the MEMS sensing unit to a controllable signal routing module in parallel; after gating, the signals are sent to a successive approximation analog-to-digital converter for digitalization through nonlinear gain amplification and anti-aliasing filtering; determining a measuring range based on a sampling sequence and a sliding window ratio, and automatically switching channels through a threshold value and a hysteresis mechanism; performing baseline correction on the switching data and performing weighted fusion according to sensitivity and bandwidth; and after the laboratory performs segmented calibration and consistency verification on the three channels, calibration parameters are written and stored, and are transmitted to an upper system through an isolation power supply according to a preset protocol. Automatic range switching is achieved through a sliding window and a hysteresis mechanism, baseline drift correction and weighting fusion according to the proportion of sensitivity and bandwidth are adopted after channel switching, and measurement continuity and precision consistency are ensured.

Description

Technical Field

[0001] This invention relates to the field of intelligent sensing technology, specifically to a multi-range intelligent sensing method and system for power grids based on MEMS integration. Background Technology

[0002] In the operation and monitoring of power systems, it is necessary to measure the current and voltage in transmission and distribution lines in real time and continuously to detect faults promptly, assess load conditions, and ensure the safe operation of the power grid. Traditional current transformers and voltage transformers are large in size and have high installation costs, and their measurement range is usually limited, making it difficult to simultaneously meet the requirements of high accuracy for small ranges and wide dynamic range for large ranges. When the grid load changes significantly, single-range sensing devices often suffer from saturation distortion or insufficient resolution. In addition, to accommodate the measurement of different current or voltage amplitudes, existing technologies usually require multiple sets of hardware devices and external switches for switching, which not only increases equipment costs but also has disadvantages such as slow switching response, data gaps, and complex maintenance.

[0003] In recent years, MEMS technology has been widely used in the field of micro sensors. However, most existing MEMS-based power grid sensing solutions can only measure within a fixed range and cannot directly meet the wide dynamic requirements of the power grid, which range from a few amperes to thousands of amperes and voltages from tens of volts to thousands of volts. Existing multi-range measurement solutions, if implemented using multiple discrete MEMS devices, still face problems such as large size, low system integration, long switching delay, and complex back-end signal conditioning. Especially in high-voltage, high-current scenarios, the isolation design between the sensing end and the communication end is not perfect, posing potential safety hazards. Summary of the Invention

[0004] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide a MEMS-integrated multi-range intelligent sensing method and system for power grids to solve the above-mentioned technical problems.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a multi-range intelligent sensing method for power grids based on MEMS integration, comprising:

[0006] The MEMS sensing unit is connected to three parallel measurement channels, including the first measurement channel, the second measurement channel and the third measurement channel, and the outputs of the three channels are connected in parallel to the controllable signal routing module.

[0007] The MEMS sensor output signal after being selected by the signal routing module is input to the front-end signal conditioning module. The conditioned analog signal is then sent to the successive approximation analog-to-digital converter for digitization. The front-end signal conditioning module includes an adaptive nonlinear gain amplifier and a dynamic anti-aliasing low-pass filter.

[0008] Based on the digital sampling sequence output by the analog-to-digital converter, the range level is determined by the ratio of the sampled value in the sliding window to the upper limit of the corresponding measurement channel. The determination result is fed back to the signal routing module. The signal routing module is controlled to switch between the first measurement channel, the second measurement channel and the third measurement channel through preset thresholds and hysteresis mechanisms to ensure that the best measurement signal is acquired under different current or voltage amplitudes.

[0009] Baseline drift correction is performed on the digital sampling data generated after switching, and weighted fusion is performed based on channel sensitivity and bandwidth ratio at the transient point of channel switching to maintain the continuity of the measurement curve;

[0010] Under laboratory conditions, the first, second, and third measurement channels were calibrated in segments. The first measurement channel was fitted with a polynomial, the second measurement channel with a piecewise spline, and the third measurement channel with a power function. After calibration, the consistency of the overlapping segments was checked to correct the measurement differences between the channels.

[0011] The calibration parameters are written into a non-volatile memory, and the digital sampling data is transmitted to the host system through the controller according to a preset protocol. An isolated power supply is used in the field power supply circuit to achieve enhanced isolation between the sensing end and the communication end.

[0012] The present invention is further configured such that the adaptive nonlinear gain amplifier samples the amplitude of the output signal in real time, calculates the ratio of the amplitude of the output signal to the upper limit of the range of the corresponding measurement channel, switches to the first gain mode when the amplitude is less than a first preset threshold, switches to the second gain mode when the amplitude is greater than or equal to the first preset threshold and less than the second preset threshold, and switches to the third gain mode when the amplitude is greater than or equal to the second preset threshold.

[0013] The present invention is further configured such that the dynamic anti-aliasing low-pass filter is a second-order active Bessel filter, with the cutoff frequency set as a reference value. When the ratio of the output signal amplitude to the upper limit of the range is greater than or equal to a second preset threshold, the cutoff frequency is increased by a preset amplitude gain factor; otherwise, the cutoff frequency is maintained at the reference value. The cutoff frequency is adjusted by a programmable capacitor or resistor network.

[0014] The present invention is further configured such that the length of the sliding window is fixed at N sampling points. When the determination results in M ​​consecutive sliding windows all meet the requirement to switch to the next range channel, the switching is triggered by a hysteresis mechanism. The hysteresis mechanism is used to prevent frequent switching near the range switching threshold due to short-term signal jitter.

[0015] The present invention is further configured such that baseline drift correction includes collecting idle baseline data, using exponential decay weighting to remove the baseline from subsequent sampled data, and when the absolute error of the difference between the current sample and the baseline value after exponential decay processing is less than the set baseline drift threshold, the baseline is considered stable; otherwise, the baseline value is dynamically updated to correct the drift.

[0016] The present invention is further configured such that, during the segmented calibration process, the first measurement channel calibration uses a quadratic or cubic polynomial fitting model to regress the data across the entire measurement range, with the fitting residual being less than or equal to a first preset error threshold; the second measurement channel calibration uses a piecewise cubic spline interpolation model to fit the data within a predetermined sub-segment, ensuring that the fitting residual within each sub-segment is less than or equal to a second preset error threshold; and the third measurement channel calibration uses a power function fitting model to fit the data across the entire measurement range, with the fitting residual being less than or equal to a third preset error threshold.

[0017] The present invention is further configured such that, for the consistency test, the measured values ​​are collected for the overlapping sections of adjacent channels, the relative error is calculated, and when the relative error is greater than the upper limit of the consistency error, the calibration curve of the corresponding channel is iteratively adjusted and refitted until the relative error is not greater than the upper limit of the consistency error.

[0018] The present invention is further configured such that the digital sampling data is encapsulated into communication frames by the controller according to the preset Modbus RTU or IEC61850 protocol and sent to the host system. The communication frames adopt 16-bit CRC check and have a maximum length of 256 bytes. In the field power supply circuit, the sensing end and the communication end are reinforced by an isolation power supply of not less than 3kV.

[0019] The invention is further configured to include: the controller periodically reads calibration parameters and threshold parameters from non-volatile memory, triggers a benchmark verification task through a software timer, and enters an automatic verification state when the difference between the online measurement result and the calibration benchmark exceeds the drift threshold, and updates the calibration parameters according to the verification result.

[0020] This invention also provides a MEMS-integrated multi-range intelligent sensing system for power grids, used to implement the aforementioned MEMS-integrated multi-range intelligent sensing method for power grids, comprising:

[0021] Access module: Connects the MEMS sensing unit to three parallel measurement channels, including the first measurement channel, the second measurement channel and the third measurement channel, and connects the output of the three channels in parallel to the controllable signal routing module;

[0022] Input module: The MEMS sensor output signal selected by the signal routing module is input to the front-end signal conditioning module, and the conditioned analog signal is sent to the successive approximation analog-to-digital converter for digitization. The front-end signal conditioning module includes an adaptive nonlinear gain amplifier and a dynamic anti-aliasing low-pass filter.

[0023] Judgment Module: Based on the digital sampling sequence output by the analog-to-digital converter, the range level is determined by the ratio of the sampled value in the sliding window to the upper limit of the corresponding measurement channel. The judgment result is fed back to the signal routing module. The signal routing module switches between the first measurement channel, the second measurement channel and the third measurement channel through preset thresholds and hysteresis mechanisms to ensure that the best measurement signal is collected under different current or voltage amplitudes.

[0024] The calibration module performs baseline drift correction on the digital sampling data generated after switching, and performs weighted fusion based on channel sensitivity and bandwidth ratio at the transient point of channel switching to maintain the continuity of the measurement curve;

[0025] Calibration module: The first, second and third measurement channels are calibrated in segments under laboratory conditions. The first measurement channel uses polynomial fitting, the second measurement channel uses piecewise spline fitting, and the third measurement channel uses power function fitting. After calibration, the consistency of overlapping segments is checked to correct measurement differences between channels.

[0026] The writing module writes the calibration parameters into the non-volatile memory and transmits the digital sampling data to the host system through the controller according to the preset protocol. An isolated power supply is used in the field power supply circuit to achieve enhanced isolation between the sensing end and the communication end.

[0027] This invention provides a multi-range intelligent sensing method and system for power grids based on MEMS integration. The method connects a MEMS sensing unit to three parallel measurement channels, including a first measurement channel, a second measurement channel, and a third measurement channel. The outputs of the three channels are connected in parallel to a controllable signal routing module. The MEMS sensing output signal, selected by the signal routing module, is input to a front-end signal conditioning module. The conditioned analog signal is then sent to a successive approximation analog-to-digital converter for digitization. The front-end signal conditioning module includes an adaptive nonlinear gain amplifier and a dynamic anti-aliasing low-pass filter. Based on the digitized sampling sequence output by the analog-to-digital converter, the range level is determined according to the ratio of the sampled value within a sliding window to the upper limit of the corresponding measurement channel. The determination result is fed back to the signal routing module. A preset threshold and hysteresis mechanism control the signal routing module in the first and second measurement channels. Switching between the first, second, and third measurement channels ensures optimal signal acquisition under varying current or voltage amplitudes. Baseline drift correction is performed on the digitally sampled data generated after switching. Weighted fusion is performed at the channel switching transient based on channel sensitivity and bandwidth ratio to maintain measurement curve continuity. The first, second, and third measurement channels are calibrated segmentally under laboratory conditions: polynomial fitting for the first channel, piecewise spline fitting for the second channel, and power function fitting for the third channel. After calibration, consistency checks are performed on overlapping sections to correct measurement differences between channels. Calibration parameters are written to non-volatile memory, and digitally sampled data is transmitted to the host system via the controller according to a preset protocol. An isolated power supply is used in the field power supply circuit to enhance isolation between the sensing end and the communication end, resulting in the following benefits:

[0028] 1. Automatic switching and real-time response: The range level is determined by the ratio of the sampled value in the sliding window to the upper limit of the channel range. Combined with preset thresholds and hysteresis mechanisms, the signal routing module is driven to switch between channels. It can automatically switch when the current or voltage amplitude crosses different ranges without human intervention. It can ensure measurement continuity and real-time performance when the load changes dynamically, and avoid measurement blind spots or data loss.

[0029] 2. Adaptive signal conditioning and improved signal-to-noise ratio: The front-end signal conditioning module employs an adaptive nonlinear gain amplifier and a dynamic anti-aliasing low-pass filter. Both the amplification factor and the filter cutoff frequency seamlessly switch in real time according to the input amplitude. Under low-signal conditions, it automatically activates the highest gain mode to improve measurement resolution; under high-signal conditions, it automatically reduces the gain and expands the filter bandwidth to avoid saturation distortion and retain necessary high-frequency information, thereby improving the signal-to-noise ratio and measurement stability.

[0030] 3. Baseline drift correction and weighted fusion to ensure data continuity: After channel switching, baseline drift correction is performed on the digital sampling data, and weighted fusion is performed at the switching point according to the sensitivity and bandwidth ratio of each channel to eliminate the sudden changes and deviations introduced by the channel switching transient, realize the smooth transition of the measurement curve, and ensure the continuity and accuracy consistency of the results when measuring across ranges.

[0031] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0033] Figure 1 A flowchart illustrating a MEMS-integrated multi-range intelligent sensing method for power grids is shown as an exemplary embodiment of the present invention.

[0034] Figure 2 This is a schematic diagram illustrating the structure of a MEMS-integrated multi-range intelligent sensing system for power grids, as an exemplary embodiment of the present invention. Detailed Implementation

[0035] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0036] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0037] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0038] Example 1:

[0039] A MEMS-integrated multi-range intelligent sensing method for power grids, such as Figure 1 As shown, it includes:

[0040] The MEMS sensing unit is connected to three parallel measurement channels, including the first measurement channel, the second measurement channel and the third measurement channel, and the outputs of the three channels are connected in parallel to the controllable signal routing module.

[0041] The MEMS sensor output signal after being selected by the signal routing module is input to the front-end signal conditioning module. The conditioned analog signal is then sent to the successive approximation analog-to-digital converter for digitization. The front-end signal conditioning module includes an adaptive nonlinear gain amplifier and a dynamic anti-aliasing low-pass filter.

[0042] Based on the digital sampling sequence output by the analog-to-digital converter, the range level is determined by the ratio of the sampled value in the sliding window to the upper limit of the corresponding measurement channel. The determination result is fed back to the signal routing module. The signal routing module is controlled to switch between the first measurement channel, the second measurement channel and the third measurement channel through preset thresholds and hysteresis mechanisms to ensure that the best measurement signal is acquired under different current or voltage amplitudes.

[0043] Baseline drift correction is performed on the digital sampling data generated after switching, and weighted fusion is performed based on channel sensitivity and bandwidth ratio at the transient point of channel switching to maintain the continuity of the measurement curve;

[0044] Under laboratory conditions, the first, second, and third measurement channels were calibrated in segments. The first measurement channel was fitted with a polynomial, the second measurement channel with a piecewise spline, and the third measurement channel with a power function. After calibration, the consistency of the overlapping segments was checked to correct the measurement differences between the channels.

[0045] The calibration parameters are written into a non-volatile memory, and the digital sampling data is transmitted to the host system through the controller according to a preset protocol. An isolated power supply is used in the field power supply circuit to achieve enhanced isolation between the sensing end and the communication end.

[0046] Specifically, a micro current sensing structure and a micro voltage sensing structure are integrated on a single silicon substrate. The current sensing structure includes a Hall plate and a micro spiral coil magnetic flux coupling unit, and the voltage sensing structure includes a micro capacitor unit composed of a fixed electrode and a movable electrode. It is fabricated through microfabrication processes such as deep reactive ion etching and thin film deposition to obtain a MEMS sensing unit.

[0047] The present invention is further configured such that the adaptive non - linear gain amplifier samples the amplitude of the output signal in real - time, calculates the ratio of the output signal amplitude to the upper limit of the measurement channel range. When it is less than the first preset threshold, it switches to the first gain mode; when it is greater than or equal to the first preset threshold and less than the second preset threshold, it switches to the second gain mode; when it is greater than or equal to the second preset threshold, it switches to the third gain mode. Specifically, the structure of the adaptive non - linear gain amplifier includes an input buffer stage, a gain control stage, and an output stage. Among them, the gain control stage dynamically adjusts the internal feedback network according to the ratio of the input signal amplitude to the upper limit of the selected measurement channel range to achieve non - linear switching of the gain. The analog signal from the front - end buffer op - amp is sampled in real - time, and the ratio of this amplitude to the upper limit of the current channel range is calculated; then this ratio is compared with the preset first threshold and second threshold to determine the switching of the gain mode. The input buffer stage provides the signal from the MEMS sensor to the gain control stage in the form of a high input impedance. The sampling unit captures the instantaneous peak value of this signal at regular intervals and compares it with the upper limit of the current active channel range to obtain a ratio R; the first threshold T1 and the second threshold T2 are preset in the control register in advance. If R < T1, the amplifier enters the first gain mode and outputs the maximum gain; if T1 ≤ R < T2, it switches to the second gain mode and uses a medium magnification; if R ≥ T2, it switches to the third gain mode and outputs the lowest gain. After the threshold comparison, the microcontroller updates the programmable feedback element in the gain control stage through the SPI interface to complete the gain mode switching. In the first gain mode, the feedback network is configured with the minimum feedback resistance value to obtain the highest magnification, which is suitable for weak signals to improve the resolution; in the second gain mode, the feedback resistance value is moderate, taking into account certain noise suppression while ensuring that the signal does not saturate; in the third gain mode, the feedback resistance value is the largest to ensure that the amplifier output does not saturate and distort when facing a large - amplitude input, and the dynamic range is extended.

[0048] The present invention is further configured such that the dynamic anti-aliasing low-pass filter is a second-order active Bessel filter, with the cutoff frequency set as a reference value. When the ratio of the output signal amplitude to the upper limit of the measurement range is greater than or equal to a second preset threshold, the cutoff frequency is increased by a preset amplitude gain factor; otherwise, the cutoff frequency is maintained at the reference value. The cutoff frequency is adjusted through a programmable capacitor or resistor network. Specifically, the dynamic anti-aliasing low-pass filter adopts a second-order active Bessel structure to ensure optimal group delay linearity at the reference cutoff frequency, avoiding transient signal distortion. By real-time monitoring of the ratio of the amplifier output signal amplitude to the upper limit of the selected measurement channel range, when this ratio reaches the preset second threshold, the controller increases the filter cutoff frequency according to a preset amplitude gain factor to retain high-frequency transient information; when the amplitude ratio is lower than the second threshold, the filter cutoff frequency remains at the reference value. The dynamic adjustment of the cutoff frequency is achieved through a programmable capacitor or resistor network, and the controller updates its registers as needed to complete the switching of the filter response bandwidth within tens of microseconds. The second-order active Bessel filter consists of two operational amplifier stages and a feedback network. Each stage includes fixed resistors and capacitors, generating a second-order low-pass response with optimal group delay linearity and minimal impulse distortion. Compared to other filter types, the Bessel filter preserves signal waveform integrity near the cutoff frequency, making it suitable for smoothing transient or impulsive power grid interference. During the design phase, a reference cutoff frequency is determined for each measurement channel. This frequency is sufficient to filter out noise components exceeding the required bandwidth without attenuating the signal's normal spectral range. When the amplitude ratio is below a second threshold, the filter maintains this reference cutoff frequency, ensuring a smooth measurement output without significant group delay distortion. Working in conjunction with the adaptive gain amplification section, the filter input receives the amplified signal, and a high-speed sampling circuit calculates the ratio of the current signal amplitude to the upper limit of the measurement channel. If this ratio is greater than or equal to the second threshold (indicating the signal is approaching the channel's upper range and contains important high-frequency information), a bandwidth extension mechanism is triggered; otherwise, it remains in the reference state. When the amplitude ratio meets the bandwidth expansion condition, the controller writes the corresponding configuration values ​​to the programmable capacitor or resistor network inside the filter via a digital interface. This reduces the equivalent impedance (or equivalent capacitance) of the feedback network, thereby increasing the cutoff frequency according to the preset amplitude gain factor. This adjustment is completed in microseconds to ensure a rapid response when high-amplitude signals appear, preserving the corresponding high-frequency signal details. Several parallel capacitor or resistor units are pre-installed inside the filter, each connected to the circuit via a switch array. When the controller updates the register, the corresponding switches open or close, changing the combination of capacitors or resistors, thus altering the time constant of the original feedback network and adjusting the cutoff frequency. Because the Bessel structure is sensitive to changes in component values, the design of the programmable network must ensure a stable filter response after switching, without ringing or distortion.When the signal amplitude ratio falls below the second threshold, the controller configures all programmable elements to a basic combination, allowing the filter to maintain the reference cutoff frequency of the initial design. At this point, the filter eliminates noise with optimal impulse response and minimum bandwidth, remaining stable and power-efficient when the measurement channel does not require high-frequency content.

[0049] The invention is further configured such that the sliding window length is fixed at N sampling points. When the judgment results in M ​​consecutive sliding windows all meet the requirement to switch to the next range channel, a hysteresis mechanism triggers the switching. The hysteresis mechanism is used to prevent frequent switching near the range switching threshold due to short-term signal jitter. Specifically, a sliding window with a fixed length of N sampling points is defined, and the ratio of the current channel signal amplitude to the upper limit of the range is calculated in real time. The continuity detection of the sliding window judgment results is combined with the hysteresis mechanism. Only when the switching conditions are met in M ​​consecutive sliding window judgments is the range channel switching actually performed. This design can avoid frequent switching caused by short-term signal jitter and improve system stability. The number of sampling points included in the sliding window is determined during the system initialization phase. A typical value is set according to the ADC sampling rate and the expected response delay. In a feasible embodiment of the invention, N=16 is selected at 100kSPS, corresponding to a time width of approximately 160μs. This window stores N consecutive sampled values ​​for ratio calculation and statistics. After each ADC completes a set of samples, the controller inserts the latest read sampled value into the sliding window buffer and discards the oldest sampled value. The ratio of all sampled values ​​in the window to the current channel's upper limit is calculated sequentially, and the maximum ratio R_max is used as the judgment criterion for this window. If R_max is less than the preset low threshold T1, it is determined that "switching to a smaller range is possible"; if R_max is between the low threshold T1 and the high threshold T2, it is determined that "maintaining the medium range" is possible; if R_max is greater than or equal to the high threshold T2, it is determined that "switching to a larger range should be performed". This judgment result is output by the state machine and used for the continuity judgment of subsequent consecutive windows. M consecutive windows are defined, where M consecutive sliding windows represent M overlapping sliding time periods, each containing N latest sampled points. The judgment results for each time period are placed in a judgment queue of length M in chronological order. The signal routing module is triggered to switch only when all M judgment results in the judgment queue are consistent and indicate a need to switch to the same target channel; otherwise, the current channel remains unchanged. This mechanism uses M as the window depth to ensure that even if a sliding window triggers an incorrect judgment due to short-term jitter, it will not immediately lead to channel switching, thus avoiding repeated switching. When M consecutive judgment results are the same and meet the switching conditions, the controller sends a switching command to the signal routing module to execute the range channel switching. If it is necessary to return to the previous channel or switch to another channel after switching, the corresponding judgment conditions must be met in the new M consecutive sliding windows before the switching is truly completed.

[0050] The invention is further configured such that baseline drift correction includes, after acquiring idle baseline data, applying exponential decay weights to subsequent sampled data for baseline removal. When the absolute error of the difference between the current sample and the baseline value, after exponential decay processing, is less than a set baseline drift threshold, the baseline is considered stable; otherwise, the baseline value is dynamically updated to correct the drift. Specifically, the baseline drift correction module aims to eliminate zero-point drift caused by changes in the sensor output due to environmental or device characteristics, ensuring long-term stability of measurement results. This module first acquires a segment of basic data under idle conditions to form an initial baseline value, and then processes subsequent sampled data using exponential decay weights: each time a new sample arrives, it is compared with the current baseline and the baseline is updated with weights. When the weighted difference between the new sample and the baseline is less than a preset drift threshold, it indicates that the baseline has not changed significantly; if the difference exceeds the threshold, the baseline is updated to a weighted combination containing the latest sampled information, thereby dynamically correcting the drift.

[0051] The invention is further configured such that, during the segmented calibration process, the first measurement channel calibration employs a quadratic or cubic polynomial fitting model to regress the data across the entire measurement range, with the fitting residual being less than or equal to a first preset error threshold; the second measurement channel calibration employs a piecewise cubic spline interpolation model to fit the data within predetermined sub-segments, ensuring that the fitting residual within each sub-segment is less than or equal to a second preset error threshold; and the third measurement channel calibration employs a power function fitting model to fit the data across the entire measurement range, with the fitting residual being less than or equal to a third preset error threshold. Specifically, in a multi-range intelligent sensing system, different measurement channels correspond to different measurement ranges. Therefore, to ensure measurement accuracy across the entire range, it is necessary to calibrate the most suitable fitting model for each channel's response characteristics: the first measurement channel covers a small range, and a simple yet highly accurate polynomial model can be used for overall regression; the second measurement channel covers a medium range, and to account for the nonlinear changes in response of each sub-segment, a piecewise cubic spline interpolation model is used to fit each predetermined sub-segment separately; the third measurement channel covers a large range, and its sensing output characteristics approximate a power function relationship, so a power function model is used for overall fitting. After each channel completes independent fitting, a consistency error check is performed on the fitting results in the overlapping sections to correct for differences between channels.

[0052] The invention is further configured such that, for the consistency check, the measured values ​​are collected separately for the overlapping sections of adjacent channels, and the relative error is calculated. When the relative error exceeds the upper limit of the consistency error, the calibration curve of the corresponding channel is iteratively adjusted and refitted until the relative error does not exceed the upper limit of the consistency error. Specifically, to ensure the consistency of measurement results of adjacent range channels in the overlapping section, after the initial calibration of each channel is completed, a consistency check needs to be performed on the adjacent channels within their overlapping working range. This check process includes: collecting a set of measured values ​​in the overlapping section of two adjacent channels respectively; comparing the calibration output of channel A with the calibration output of channel B point by point for this set of values ​​and calculating the relative error; when the relative error at any point exceeds the pre-set upper limit of the consistency error, the iterative adjustment process is initiated, the calibration curve of the corresponding channel is incrementally corrected and refitted until the relative error of all overlapping sections does not exceed the upper limit of the consistency error. This logic ensures that there are no obvious abrupt changes in the transition section when measuring from a small range to a large range.

[0053] The present invention is further configured such that the digital sampling data is encapsulated into communication frames by the controller according to the preset Modbus RTU or IEC61850 protocol and sent to the host system. The communication frames adopt 16-bit CRC check and have a maximum length of 256 bytes. In the field power supply circuit, the sensing end and the communication end are reinforced by an isolation power supply of not less than 3kV.

[0054] The invention is further configured to include: the controller periodically reads calibration parameters and threshold parameters from non-volatile memory, triggers a benchmark verification task through a software timer, and enters an automatic verification state when the difference between the online measurement result and the calibration benchmark exceeds the drift threshold, and updates the calibration parameters according to the verification result.

[0055] Example 2:

[0056] Please see Figure 2 This exemplary MEMS-integrated multi-range intelligent sensing system for power grids is used to implement the aforementioned MEMS-integrated multi-range intelligent sensing method for power grids, comprising:

[0057] Access module: Connects the MEMS sensing unit to three parallel measurement channels, including the first measurement channel, the second measurement channel and the third measurement channel, and connects the output of the three channels in parallel to the controllable signal routing module;

[0058] Input module: The MEMS sensor output signal selected by the signal routing module is input to the front-end signal conditioning module, and the conditioned analog signal is sent to the successive approximation analog-to-digital converter for digitization. The front-end signal conditioning module includes an adaptive nonlinear gain amplifier and a dynamic anti-aliasing low-pass filter.

[0059] Judgment Module: Based on the digital sampling sequence output by the analog-to-digital converter, the range level is determined by the ratio of the sampled value in the sliding window to the upper limit of the corresponding measurement channel. The judgment result is fed back to the signal routing module. The signal routing module switches between the first measurement channel, the second measurement channel and the third measurement channel through preset thresholds and hysteresis mechanisms to ensure that the best measurement signal is collected under different current or voltage amplitudes.

[0060] The calibration module performs baseline drift correction on the digital sampling data generated after switching, and performs weighted fusion based on channel sensitivity and bandwidth ratio at the transient point of channel switching to maintain the continuity of the measurement curve;

[0061] Calibration module: The first, second and third measurement channels are calibrated in segments under laboratory conditions. The first measurement channel uses polynomial fitting, the second measurement channel uses piecewise spline fitting, and the third measurement channel uses power function fitting. After calibration, the consistency of overlapping segments is checked to correct measurement differences between channels.

[0062] The writing module writes the calibration parameters into the non-volatile memory and transmits the digital sampling data to the host system through the controller according to the preset protocol. An isolated power supply is used in the field power supply circuit to achieve enhanced isolation between the sensing end and the communication end.

[0063] It should be noted that the MEMS-integrated multi-range intelligent sensing system for power grids provided in the above embodiments and the MEMS-integrated multi-range intelligent sensing method for power grids provided in the above embodiments belong to the same concept. The specific ways in which each module and unit performs operations have been described in detail in the method embodiments and will not be repeated here. In practical applications, the MEMS-integrated multi-range intelligent sensing system for power grids provided in the above embodiments can be assigned to different functional modules as needed, that is, the internal structure of the system can be divided into different functional modules to complete all or part of the functions described above. This is not a limitation here.

[0064] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0065] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0066] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0067] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0068] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0069] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0070] In the several embodiments provided in this application, it should be understood that the disclosed system can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0071] The units described as separate components may or may not be physically separate. 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 the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0072] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0073] If the aforementioned functions are implemented as 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 this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0074] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A multi-range intelligent sensing method for power grids based on MEMS integration, characterized in that, include: The MEMS sensing unit is connected to three parallel measurement channels, including the first measurement channel, the second measurement channel and the third measurement channel, and the outputs of the three channels are connected in parallel to the controllable signal routing module. The MEMS sensor output signal after being selected by the signal routing module is input to the front-end signal conditioning module. The conditioned analog signal is then sent to the successive approximation analog-to-digital converter for digitization. The front-end signal conditioning module includes an adaptive nonlinear gain amplifier and a dynamic anti-aliasing low-pass filter. Based on the digital sampling sequence output by the analog-to-digital converter, the range level is determined by the ratio of the sampled value in the sliding window to the upper limit of the corresponding measurement channel. The determination result is fed back to the signal routing module. The signal routing module is controlled to switch between the first measurement channel, the second measurement channel and the third measurement channel through preset thresholds and hysteresis mechanisms to ensure that the best measurement signal is acquired under different current or voltage amplitudes. Baseline drift correction is performed on the digital sampling data generated after switching, and weighted fusion is performed based on channel sensitivity and bandwidth ratio at the transient point of channel switching to maintain the continuity of the measurement curve; Under laboratory conditions, the first, second, and third measurement channels were calibrated in segments. The first measurement channel was fitted with a polynomial, the second measurement channel with a piecewise spline, and the third measurement channel with a power function. After calibration, the consistency of the overlapping segments was checked to correct the measurement differences between the channels. The calibration parameters are written into a non-volatile memory, and the digital sampling data is transmitted to the host system through the controller according to a preset protocol. An isolated power supply is used in the field power supply circuit to achieve enhanced isolation between the sensing end and the communication end.

2. The method for multi-range intelligent sensing of power grids based on MEMS integration according to claim 1, characterized in that, The adaptive nonlinear gain amplifier samples the output signal amplitude in real time, calculates the ratio of the output signal amplitude to the upper limit of the corresponding measurement channel range, and switches to the first gain mode when the amplitude is less than the first preset threshold, switches to the second gain mode when the amplitude is greater than or equal to the first preset threshold and less than the second preset threshold, and switches to the third gain mode when the amplitude is greater than or equal to the second preset threshold.

3. The method for multi-range intelligent sensing of power grids based on MEMS integration according to claim 1, characterized in that, The dynamic anti-aliasing low-pass filter is a second-order active Bessel filter. The cutoff frequency is set as a reference value. When the ratio of the output signal amplitude to the upper limit of the range is greater than or equal to the second preset threshold, the cutoff frequency is increased by a preset amplitude gain factor. Otherwise, the cutoff frequency is maintained at the reference value. The cutoff frequency is adjusted by a programmable capacitor or resistor network.

4. The method for multi-range intelligent sensing of power grids based on MEMS integration according to claim 1, characterized in that, The sliding window length is fixed at N sampling points. When the judgment results in M ​​consecutive sliding windows meet the requirement to switch to the next range channel, the switching is triggered by the hysteresis mechanism. The hysteresis mechanism is used to prevent frequent switching near the range switching threshold due to short-term signal jitter.

5. The method for multi-range intelligent sensing of power grids based on MEMS integration according to claim 1, characterized in that, Baseline drift correction involves collecting idle baseline data and then using exponential decay weights to remove the baseline from subsequent sampled data. When the absolute error of the difference between the current sample and the baseline value after exponential decay is less than the set baseline drift threshold, the baseline is considered stable; otherwise, the baseline value is dynamically updated to correct the drift.

6. The method for multi-range intelligent sensing of power grids based on MEMS integration according to claim 1, characterized in that, During the segmented calibration process, the calibration of the first measurement channel adopts a quadratic or cubic polynomial fitting model to regress the data of the entire range segment, and the fitting residual is less than or equal to the first preset error threshold. The calibration of the second measurement channel uses a piecewise cubic spline interpolation model to fit the data in the predetermined sub-segments, ensuring that the fitting residual in each sub-segment is less than or equal to the second preset error threshold; the calibration of the third measurement channel uses a power function fitting model to fit the full range of data, and the fitting residual is less than or equal to the third preset error threshold.

7. The method for multi-range intelligent sensing of power grids based on MEMS integration according to claim 1, characterized in that, The consistency test collects the measured values ​​for the overlapping sections of adjacent channels and calculates the relative error. When the relative error is greater than the upper limit of the consistency error, the calibration curve of the corresponding channel is iteratively adjusted and refitted until the relative error is no greater than the upper limit of the consistency error.

8. The method for multi-range intelligent sensing of power grids based on MEMS integration according to claim 1, characterized in that, Digital sampling data is encapsulated into communication frames by the controller according to the preset Modbus RTU or IEC 61850 protocol and sent to the host system. The communication frames use 16-bit CRC check and have a maximum length of 256 bytes. In the field power supply circuit, the sensing end and the communication end are reinforced by an isolation power supply of no less than 3kV.

9. The method for multi-range intelligent sensing of power grids based on MEMS integration according to claim 1, characterized in that, Also includes: The controller periodically reads calibration parameters and threshold parameters from non-volatile memory and triggers a benchmark verification task through a software timer. When the difference between the online measurement result and the calibration benchmark exceeds the drift threshold, it enters the automatic verification state and updates the calibration parameters according to the verification result.

10. A MEMS-integrated multi-range intelligent sensing system for power grids, used to implement the MEMS-integrated multi-range intelligent sensing method for power grids as described in any one of claims 1-9, characterized in that, include: Access module: Connects the MEMS sensing unit to three parallel measurement channels, including the first measurement channel, the second measurement channel and the third measurement channel, and connects the output of the three channels in parallel to the controllable signal routing module; Input module: The MEMS sensor output signal selected by the signal routing module is input to the front-end signal conditioning module, and the conditioned analog signal is sent to the successive approximation analog-to-digital converter for digitization. The front-end signal conditioning module includes an adaptive nonlinear gain amplifier and a dynamic anti-aliasing low-pass filter. Judgment Module: Based on the digital sampling sequence output by the analog-to-digital converter, the range level is determined by the ratio of the sampled value in the sliding window to the upper limit of the corresponding measurement channel. The judgment result is fed back to the signal routing module. The signal routing module switches between the first measurement channel, the second measurement channel and the third measurement channel through preset thresholds and hysteresis mechanisms to ensure that the best measurement signal is collected under different current or voltage amplitudes. The calibration module performs baseline drift correction on the digital sampling data generated after switching, and performs weighted fusion based on channel sensitivity and bandwidth ratio at the transient point of channel switching to maintain the continuity of the measurement curve; Calibration module: The first, second and third measurement channels are calibrated in segments under laboratory conditions. The first measurement channel uses polynomial fitting, the second measurement channel uses piecewise spline fitting, and the third measurement channel uses power function fitting. After calibration, the consistency of overlapping segments is checked to correct measurement differences between channels. The writing module writes the calibration parameters into the non-volatile memory and transmits the digital sampling data to the host system through the controller according to the preset protocol. An isolated power supply is used in the field power supply circuit to achieve enhanced isolation between the sensing end and the communication end.

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