A voltage and current sensor intelligent monitoring and adaptive compensation method, system, device and storage medium

Abstract

The present application relates to the technical field of sensors, and in particular to a voltage and current sensor intelligent monitoring and adaptive compensation method, system, device and storage medium. The signal characteristic spectrum is analyzed through fast Fourier transform, the multiple of the programmable gain amplifier and the filter parameter are dynamically adjusted, the problems of signal saturation and weak interference suppression under different working conditions are solved, the filtering effect and the anti-interference ability are improved; the error type is identified through multi-dimensional identification of basic deviation, statistical deviation and spectral deviation, the matching algorithm is adaptively selected, the error type is dynamically adjusted, the problem of insufficient accuracy of the full frequency band is solved, and the demand of new energy grid connection and other wide frequency band measurement scenes is met; through the double safety limit value, combined with the preset root cause library characteristic map to locate the root cause, the problems of frequent false alarm and missed alarm, difficult root cause positioning and low operation and maintenance efficiency are solved, the root cause positioning accuracy is improved, and it is helpful for operation and maintenance personnel to prioritize processing high-risk faults according to the warning level.

Description

Technical Field

[0001] This invention relates to the field of sensor technology, and in particular to a method, system, device, and storage medium for intelligent monitoring and adaptive compensation of voltage and current sensors. Background Technology

[0002] As the cornerstone of stable operation in modern society, the safety and reliability of power systems are receiving increasing attention. With the development of smart grids, ultra-high voltage transmission, and flexible DC transmission technologies, the need for accurate and real-time monitoring of the power grid's operating status is becoming increasingly urgent. Voltage and current, as the most fundamental and crucial operating parameters in the power system, directly determine the effectiveness of power grid monitoring, fault diagnosis, and protection control systems due to the sophistication of their measurement technologies. Especially when facing complex conditions such as transient faults, lightning strikes, and switching overvoltages, accurately capturing high-frequency traveling wave signals is of decisive significance for fault location and analysis. Therefore, broadband, high-precision, and highly integrated voltage and current sensors have become one of the core technological requirements in the current field of power system monitoring.

[0003] The currently available integrated sensor technology still has many shortcomings and is difficult to match the measurement and maintenance needs under complex working conditions. The specific shortcomings are as follows: (1) The control system of the existing integrated sensor technology mostly adopts the architecture of fixed gain amplifier + fixed parameter filter, which cannot adapt to different amplitude signals, and is prone to weak signal distortion or large signal saturation. Furthermore, when facing complex interference such as high frequency noise of switching power supply and external electromagnetic radiation, the filtering effect is attenuated and the anti-interference ability is weak.

[0004] (2) Existing integrated sensor technologies mostly compensate for amplitude deviation in a single dimension, without considering the impact of phase deviation and spectral deviation on measurement accuracy. Furthermore, the compensation algorithm is fixed and cannot be dynamically adjusted according to the error type, resulting in large fluctuations in accuracy across the entire frequency band, making it difficult to meet the wide-band measurement requirements.

[0005] (3) Existing integrated sensor technology relies only on static safety limits when determining faults, without adjusting the limits in conjunction with real-time operating conditions, which can easily lead to false alarms or missed alarms. Summary of the Invention

[0006] In view of the problems existing in the prior art, the present invention is proposed.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, embodiments of the present invention provide an intelligent monitoring and adaptive compensation method for voltage and current sensors, comprising an integrated voltage and current sensing assembly and a control system, wherein the control system is mounted on the integrated voltage and current sensing assembly, and the control system includes... The acquired real-time analog signal set is preprocessed with gain adjustment and filtering noise reduction. The preprocessed real-time analog signal set is then converted into a differential analog signal set and digitized to obtain the real-time signal set. The deviation between the real-time signal set and the simulation signal set after simulation with the built-in transmission model is compared and the abnormality of the deviation is determined. When an abnormality is found, a fault diagnosis and warning are executed; otherwise, compensation and correction are executed. The operational monitoring data of the acquired integrated voltage and current sensor is analyzed to determine the root cause of the fault and provide fault warning. The real-time signal set is compensated to correct the transmission error; Based on the characteristic spectrum of the real-time signal set, the gain control strategy and the filtering and noise reduction parameters are dynamically adjusted, and the parameters of the transmission model are updated accordingly.

[0008] As a preferred embodiment of the intelligent monitoring and adaptive compensation method for voltage and current sensors described in this invention, the obtained real-time signal set includes: The real-time analog signal set is obtained from the integrated sensing of target voltage and current, and the gain factor of the programmable gain amplifier is adjusted in real time according to its amplitude characteristics to realize the gain control of the real-time analog signal set. Based on the characteristic spectrum of the real-time analog signal set, the type of interference is determined, and the corresponding filter is activated to filter out the interference components in the signal while retaining the characteristic spectrum of the effective signal. The preprocessed real-time analog signal set is converted into two differential signals with equal amplitude and opposite phase to obtain a differential analog signal set. A high-precision analog-to-digital converter is used to quantize the discrete signals after sampling the differential analog signal set, and output a digital real-time signal set.

[0009] As a preferred embodiment of the intelligent monitoring and adaptive compensation method for voltage and current sensors described in this invention, the transmission model includes a voltage transmission model and a current transmission model. The specific method for obtaining the simulation signal set includes: the voltage transfer model extracts features from the voltage signals in the real-time signal set to obtain the basic parameters, spectral components and transient features of the voltage signals; similarly, the basic parameters, spectral components and transient features of the current signals can be obtained. The voltage transmission model is adapted to fine-tuning its own parameters based on real-time operating conditions. The basic parameters, spectral components, and transient characteristics of the voltage signal are used to simulate voltage signal transmission in a voltage transmission model with adaptive fine-tuning, resulting in a simulated voltage signal after the voltage transmission model is simulated. Similarly, the simulated current signal after simulating the voltage transfer model can be obtained; The simulated voltage signal and the simulated current signal are collectively referred to as the simulated signal set; The step of comparing the deviation between the real-time signal set and the simulation signal set after simulation by the built-in transmission model includes calculating the absolute deviation, relative deviation and phase deviation of the amplitude of each sampling point of the voltage signal in the real-time signal set and the corresponding sampling point of the voltage signal in the simulation signal set, and collectively referring to them as the basic deviation of the voltage signal. Within a preset time window, the root mean square error and peak deviation of the voltage signals in the real-time signal set and the corresponding voltage signals in the simulation signal set are calculated and collectively referred to as the statistical deviation of the voltage signals. Calculate the amplitude deviation between each harmonic frequency band of the voltage signal spectrum in the real-time signal set and the corresponding harmonic frequency band of the voltage signal spectrum in the simulation signal set, and record it as the spectral deviation of the voltage signal; Similarly, the fundamental deviation, statistical deviation, and spectral deviation of the current signal can be obtained.

[0010] As a preferred embodiment of the intelligent monitoring and adaptive compensation method for voltage and current sensors described in this invention, the determination of abnormal deviation includes: When the fundamental deviation, statistical deviation, and spectral deviation of the voltage and current signals meet the anomaly determination conditions, the deviation anomaly between the real-time signal set and the simulation signal set is recorded as an anomaly. The abnormality determination criteria include: if any of the basic deviations exceeds the upper limit of the deviation, it is determined to be abnormal; If the statistical deviation shows a continuous increasing trend within a continuous time window, it is determined to be a potential anomaly. If the statistical deviation shows a sudden jump in deviation within an adjacent time window, it is determined to be a sudden anomaly.

[0011] As a preferred embodiment of the intelligent monitoring and adaptive compensation method for voltage and current sensors described in this invention, the specific method for determining the root cause of the fault includes: calculating the absolute deviation between each parameter of the target integrated voltage and current sensor's operation monitoring data and its corresponding dynamic safety limit and static safety limit, obtaining the absolute deviation between the dynamic safety limit and static safety limit of each parameter of the operation monitoring data, and selecting each parameter of the operation monitoring data whose absolute deviation is greater than a preset deviation threshold, and recording them as each fault parameter of the operation monitoring data; By comparing each fault parameter in the operation monitoring data with the fault feature map in the root cause database, the root causes of each fault parameter in the operation monitoring data are matched, thereby determining the root causes of each fault of the target integrated voltage and current sensor. The specific methods for providing fault early warning include: The process of determining the fault warning level based on the root cause of the fault includes: if the fault parameters of the operation monitoring data corresponding to a certain fault root cause of the target integrated voltage and current sensor exceed the dynamic safety limit but do not exceed the static safety limit, then the warning level of the fault root cause of the target integrated voltage and current sensor is recorded as a level three warning. If a fault parameter in the operation monitoring data corresponding to a certain root cause of a fault in the target integrated voltage and current sensor exceeds the static safety limit, then the warning level of the root cause of the fault in the target integrated voltage and current sensor will be recorded as a level two warning. If a fault parameter in the operation monitoring data corresponding to a certain root cause of a fault in the target integrated voltage and current sensor exceeds the static safety limit setting ratio, then the warning level of that root cause of the fault in the target integrated voltage and current sensor will be recorded as a Level 1 warning. The fault warning level will be fed back.

[0012] As a preferred embodiment of the intelligent monitoring and adaptive compensation method for voltage and current sensors described in this invention, the step of compensating the real-time signal set to correct the transmission error includes: Based on the fundamental deviation, statistical deviation, and spectral deviation of the voltage and current signals, the transmission error type is identified, and a matching compensation algorithm is adaptively selected according to the error type and frequency band characteristics.

[0013] As a preferred embodiment of the intelligent monitoring and adaptive compensation method for voltage and current sensors described in this invention, the specific content of the parameter adaptive optimization module includes: The real-time signal set is subjected to a fast Fourier transform to obtain the characteristic spectrum of the real-time signal set. This spectrum is then matched with the characteristic spectrum corresponding to each operating condition mode to determine the current operating condition mode of the real-time signal set. Based on the current operating mode of the real-time signal set, specific parameter adjustment instructions for the gain control and the filtering and noise reduction are generated, and the strategy for the gain control and the parameters for the filtering and noise reduction are dynamically adjusted. After dynamically adjusting the gain control strategy and the filtering and noise reduction parameters, the corrected real-time dataset is compared with the original real-time dataset to identify the deviation. The real-time signal is then used as new training data to fine-tune the key parameters in the transfer model.

[0014] Secondly, embodiments of the present invention provide an intelligent monitoring and adaptive compensation system for voltage and current sensors, which includes a signal acquisition module that performs gain regulation and filtering noise reduction preprocessing on the acquired real-time analog signal set, converts the preprocessed real-time analog signal set into a differential analog signal set, and performs digital processing on it to obtain a real-time signal set. The deviation anomaly determination module compares the deviation between the real-time signal set and the simulation signal set after simulation by the built-in transmission model, and determines the deviation anomaly. If an anomaly is found, the fault diagnosis and early warning module is executed; otherwise, the compensation and correction module is executed. The fault diagnosis and early warning module analyzes the differences between the acquired operational monitoring data of the integrated voltage and current sensor and its corresponding safety limits to determine the root cause of the fault and issue a fault warning. The compensation and correction module compensates for the real-time signal set to correct the transmission error; The parameter adaptive optimization module dynamically adjusts the gain control strategy and the filtering and noise reduction parameters based on the characteristic spectrum of the real-time signal set, and updates the parameters of the transmission model accordingly.

[0015] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, they implement the steps of the intelligent monitoring and adaptive compensation method for voltage and current sensors as described in the first aspect of the present invention.

[0016] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, they implement the steps of the intelligent monitoring and adaptive compensation method for voltage and current sensors as described in the first aspect of the present invention.

[0017] The beneficial effects of this invention are as follows: This invention analyzes the characteristic spectrum of a signal through fast Fourier transform, dynamically adjusts the programmable gain amplifier factor and filter parameters, adapts to signals of different amplitudes, solves the problems of signal saturation and weak interference suppression under different operating conditions, and improves the filtering effect and anti-interference capability.

[0018] This invention identifies error types from multiple dimensions, including basic deviation, statistical deviation, and spectral deviation, and adaptively selects a matching algorithm to dynamically adjust according to the error type, thereby solving the problem of insufficient full-band accuracy and meeting the needs of wide-band measurement scenarios such as new energy grid connection.

[0019] This invention addresses the problems of frequent false alarms and missed alarms, difficulty in root cause location, and low operation and maintenance efficiency by using dynamic and static dual safety limits and combining them with a preset root cause database feature map to locate the root cause. It improves the accuracy of root cause location and helps operation and maintenance personnel prioritize handling high-risk faults according to the warning level. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0021] Figure 1 A flowchart of a method for intelligent monitoring and adaptive compensation of voltage and current sensors; Figure 2 A diagram of a computer device for intelligent monitoring and adaptive compensation methods for voltage and current sensors; Figure 3 A schematic diagram illustrating the voltage sampling principle of an intelligent monitoring and adaptive compensation method for voltage and current sensors; Figure 4 A schematic diagram of the current sampling principle of the intelligent monitoring and adaptive compensation method for voltage and current sensors. Figure 5 This is a schematic diagram of the system module integration for a voltage and current sensor intelligent monitoring and adaptive compensation method. Detailed Implementation

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0025] Example 1 Reference Figure 1 - Figure 5 This is the first embodiment of the present invention, which provides a method for intelligent monitoring and adaptive compensation of voltage and current sensors, including: A voltage and current integrated sensing assembly and a control system. The control system is mounted on the voltage and current integrated sensing assembly and includes... The acquired real-time analog signal set is preprocessed with gain adjustment and filtering noise reduction. The preprocessed real-time analog signal set is then converted into a differential analog signal set and digitized to obtain the real-time signal set. The deviation between the real-time signal set and the simulation signal set after simulation with the built-in transmission model is compared and the abnormality of the deviation is determined. When an abnormality is found, a fault diagnosis and warning are executed; otherwise, compensation and correction are executed. The operational monitoring data of the acquired integrated voltage and current sensor is analyzed to determine the root cause of the fault and provide fault warning. Compensation is provided for the real-time signal set to correct transmission errors; Based on the characteristic spectrum of the real-time signal set, the gain control strategy and filtering and noise reduction parameters are dynamically adjusted, and the parameters of the transmission model are updated accordingly.

[0026] Specifically, the connection between modules is as follows: the signal acquisition module is connected to the deviation anomaly determination module; the deviation anomaly determination module is connected to the fault diagnosis and early warning module and the compensation and correction module respectively; the fault diagnosis and early warning module and the compensation and correction module are connected to the parameter adaptive optimization module respectively; and the integrated management module is connected to the signal acquisition module, the deviation anomaly determination module, the compensation and correction module, and the parameter adaptive optimization module respectively.

[0027] It should be noted that the main body of the voltage and current integrated sensing assembly includes: an amorphous alloy composite magnetic core, which is made of an optimized iron-based amorphous alloy, forming the core platform for simultaneously sensing current magnetic fields and voltage electric fields.

[0028] A current sensing unit includes a fiber grating fixed to the surface of an amorphous alloy composite magnetic core by an adhesive layer.

[0029] A voltage sensing unit includes an electro-optic crystal integrated between an amorphous alloy composite magnetic core and a high-voltage electrode.

[0030] A built-in optical coupler is located inside the integrated sensor head.

[0031] The signal acquisition module performs gain adjustment and filtering noise reduction preprocessing on the acquired real-time analog signal set, converts the preprocessed real-time analog signal set into a differential analog signal set, and performs digital processing on it to obtain the real-time signal set.

[0032] It should be noted that the real-time signal set includes voltage signals and current signals.

[0033] Furthermore, this technical solution provides an integrated voltage and current sensor based on an amorphous alloy composite magnetic core, including an integrated voltage and current sensing assembly and a control system. The control system is installed on the integrated voltage and current sensing assembly and includes: a signal acquisition module, a deviation anomaly judgment module, a fault diagnosis and early warning module, a compensation and correction module, a parameter adaptive optimization module, and an integrated management module.

[0034] The signal acquisition module is connected to the deviation anomaly determination module, which in turn is connected to the fault diagnosis and early warning module and the compensation and correction module. The fault diagnosis and early warning module and the compensation and correction module are connected to the parameter adaptive optimization module. The integrated management module is connected to the signal acquisition module, the deviation anomaly determination module, the compensation and correction module, and the parameter adaptive optimization module.

[0035] The real-time signal set obtained includes, The real-time analog signal set is obtained from the integrated sensing of target voltage and current, and the gain factor of the programmable gain amplifier is adjusted in real time according to its amplitude characteristics to realize the gain control of the real-time analog signal set. Based on the characteristic spectrum of the real-time analog signal set, the type of interference is determined, the corresponding filter is activated to filter out the interference components in the signal, and the characteristic spectrum of the effective signal is retained. The preprocessed real-time analog signal set is converted into two differential signals with equal amplitude and opposite phase to obtain a differential analog signal set. A high-precision analog-to-digital converter is used to quantize the discrete signals after sampling the differential analog signal set, and output a digital real-time signal set.

[0036] Specifically, amplitude characteristics include, but are not limited to, peak value and RMS value.

[0037] Based on the characteristic spectrum of the real-time analog signal set, such as the noise frequency band analyzed by Fast Fourier Transform (FFT), the interference type is determined, such as 50Hz / 60Hz power frequency interference and high-frequency spike noise. The corresponding filter is then activated to filter out the interference components in the signal, such as high-frequency noise, power frequency harmonics, and external electromagnetic interference, while retaining the characteristic spectrum of the effective signal, such as the fundamental frequency and key subharmonics.

[0038] One specific example is that the filters include, but are not limited to, low-pass filters to filter out high-frequency noise, band-stop filters to suppress power frequency harmonics, and adaptive filters to dynamically cancel unknown interference.

[0039] Differential conversion circuits, such as instrumentation amplifiers and differential operational amplifiers, convert the signals into two differential signals with equal amplitude and opposite phase, thus obtaining a differential analog signal set.

[0040] It should be noted that the differential analog signal set includes differential voltage signals and differential current signals.

[0041] A high-precision analog-to-digital converter is used to sample the differential analog signal set at a preset sampling rate, such as 20kHz-1MHz. The discrete signal after the highest frequency of the signal needs to be quantized according to the quantization bit depth, such as 16 bits to 24 bits, to determine the signal resolution. The output digitized real-time signal set is transmitted to the subsequent modules in the form of a digital code stream or array.

[0042] The deviation anomaly determination module compares the deviation between the real-time signal set and the simulated signal set after simulation by the built-in transmission model, and determines the abnormality of the deviation. If an anomaly is found, the fault diagnosis and early warning module is executed; otherwise, the compensation and correction module is executed.

[0043] Transmission models include voltage transmission models and current transmission models; The specific methods for obtaining the simulation signal set include: the voltage transfer model extracts features from the voltage signals in the real-time signal set to obtain the basic parameters, spectral components, and transient features of the voltage signals; similarly, the basic parameters, spectral components, and transient features of the current signals can be obtained. The voltage transmission model is adapted to fine-tuning its own parameters based on real-time operating conditions. The basic parameters, spectral components, and transient characteristics of the voltage signal are used to simulate voltage signal transmission in a voltage transmission model with adaptive fine-tuning, resulting in a simulated voltage signal after the voltage transmission model is simulated. Similarly, the simulated current signal after simulating the voltage transfer model can be obtained; Simulated voltage signals and simulated current signals are collectively referred to as the simulated signal set; The deviation of the real-time signal set and the simulation signal set after simulation by the built-in transmission model is compared. This includes calculating the absolute deviation, relative deviation and phase deviation of the amplitude of each sampling point of the voltage signal in the real-time signal set and the corresponding sampling point of the voltage signal in the simulation signal set, which are collectively referred to as the basic deviation of the voltage signal. Within a preset time window, the root mean square error and peak deviation of the voltage signals in the real-time signal set and the corresponding voltage signals in the simulation signal set are calculated and collectively referred to as the statistical deviation of the voltage signals. Calculate the amplitude deviation between each harmonic frequency band of the voltage signal spectrum in the real-time signal set and the corresponding harmonic frequency band of the voltage signal spectrum in the simulation signal set, and record it as the spectral deviation of the voltage signal; Similarly, the fundamental deviation, statistical deviation, and spectral deviation of the current signal can be obtained.

[0044] Specifically, the voltage transfer model can be the voltage transfer model of CVT, and the current transfer model can be the current transfer model of LPCT.

[0045] A specific example, the voltage transfer model can be as follows: in, , These are the primary voltage and the intermediate voltage, respectively, in kV. The capacitance is expressed in °C, and 0.005 is the temperature coefficient, which is the rate of change of capacitance value per 10 °C.

[0046] The current transmission model can be specifically as follows: in, The initial magnetizing inductance at 25°C is expressed in mH. This is the real-time permeability, expressed in H / m. The value of free permeability can be () .

[0047] The specific methods for obtaining the simulation signal set include: the voltage transmission model extracts features from the voltage signals in the real-time signal set to obtain the basic parameters, spectral components and transient features of the voltage signals. Similarly, the basic parameters, spectral components and transient features of the current signals can be obtained.

[0048] It should be noted that the basic parameters include, but are not limited to, the instantaneous amplitude (peak and RMS values) of voltage and current signals, the frequency (fundamental frequency, such as 50Hz / 60Hz), and the phase (phase angle relative to the reference clock).

[0049] For example, if the real-time current signal has an effective value of 100A, a fundamental frequency of 50Hz, and a phase of 30°, then the current transmission model needs to simulate the ideal transmission result based on this.

[0050] The spectral components are decomposed into harmonic components of the signal using Fast Fourier Transform (FFT), which include, but are not limited to, the amplitude proportions and frequency distributions of the fundamental frequency (1st harmonic), lower harmonics (2nd to 10th harmonics), and higher harmonics (11th to 50th harmonics). The transmission characteristics of signals in different frequency bands differ significantly; for example, higher harmonics are prone to eddy current losses in magnetic cores, requiring separate simulations for each frequency band using the transmission model.

[0051] Transient characteristics specifically involve detecting whether the signal contains transient components, such as spike current during a short circuit or pulse voltage during a lightning strike, through the signal rate of change, such as... A transient intensity with a rate of change >1000 A / μs is considered a strong transient. The transmission of transient signals requires additional consideration of nonlinear factors such as core saturation and transient circuit response; therefore, the transmission model needs to be switched to transient simulation mode.

[0052] The voltage transmission model adjusts its parameters to adapt to real-time operating conditions.

[0053] It should be noted that the parameters include the capacitor voltage divider parameters, electromagnetic unit parameters, and damping parameters.

[0054] The capacitor voltage divider parameters include the capacitance values ​​of the high-voltage capacitor C1 and the low-voltage capacitor C2, which are affected by temperature. For example, the capacitance value changes by ±0.5% for every 10°C increase in temperature. The voltage transfer model needs to correct C1 and C2 based on real-time temperature sensor data.

[0055] Electromagnetic unit parameters include the turns ratio n of the intermediate transformer and the magnetizing inductance. The winding resistance R is affected by the magnetic permeability μ of the magnetic core. The μ of the amorphous alloy magnetic core changes with the magnetic field strength, and the current μ value needs to be inferred from the amplitude of the real-time signal.

[0056] Damping parameters include damping resistor Rd and damping inductor Ld, which are used to suppress ferromagnetic resonance. They need to be dynamically adjusted according to the harmonic content of the real-time signal. For example, when the harmonic ratio is >10%, Rd should be increased.

[0057] The basic parameters, spectral components, and transient characteristics of the voltage signal are used to simulate voltage signal transmission in a voltage transmission model with adaptive fine-tuning, resulting in a simulated voltage signal after the voltage transmission model is simulated.

[0058] It should be noted that the specific process of transmission simulation includes the following: (1) Capacitor voltage division stage: Calculate the primary side high voltage signal U1 after passing through the capacitor voltage division stage. , Intermediate voltage after voltage division: It takes into account the frequency characteristics of the capacitor. At high frequencies, the capacitive reactance decreases, so the voltage division ratio needs to be adjusted. (2) Electromagnetic induction stage: intermediate voltage The voltage is converted to the secondary side voltage by the intermediate transformer with a turns ratio n: It considers the turns ratio error caused by the excitation current and calculates the excitation current using the excitation inductance Lm: Correction: (3) Phase correction: Calculate the phase delay of the capacitive voltage divider (voltage leads current due to capacitive load) and the phase shift of the transformer winding, and sum them to obtain the total phase deviation. The phase of the simulated voltage signal is φ ,in This refers to the phase of the real-time signal.

[0059] Similarly, the simulated current signal after simulating the voltage transfer model can be obtained.

[0060] Simulated voltage signals and simulated current signals are collectively referred to as the simulated signal set.

[0061] Furthermore, its voltage sampling principle is as follows: A / B / C represents a three-phase power supply consisting of phases A, B, and C. Each phase is connected to its respective primary capacitor C01 / C02 / C03, and the ends are connected to the secondary capacitors C11 / C22 / C33. The ends of the secondary capacitors are grounded. The voltages Ua / Ub / Uc on the secondary capacitors are the line voltages, thus enabling the detection of the line voltage.

[0062] The principle of current sampling is as follows: A / B / C represents the three-phase power supply (phase A / phase B / phase C). a1ah / b1bh / c1ch are the primary coils of a current sensor with an amorphous alloy composite magnetic core. The coil ends are connected to R respectively. Ia / R Ib / R Ic Three resistors, the voltage U across the three resistors Ia / U Ib / U Ic It refers to the line current, enabling wideband response detection of the line current.

[0063] Specifically, the calculation process of the basic deviation includes: (1) calculating the absolute deviation of the amplitude of each sampling point of the real-time signal concentrated voltage signal from the corresponding sampling point of the simulated signal concentrated voltage signal, i.e.: And relative deviation, i.e.: in, , These represent the amplitudes of the voltage signal sampling points in the real-time signal set and the amplitudes of the corresponding sampling points in the voltage signal set in the simulation signal set, respectively.

[0064] (2) Calculate the phase difference between each sampling point of the real-time signal cascade voltage signal and the corresponding sampling point of the simulated signal cascade voltage signal: in. , These represent the phase of the voltage signal sampling points in the real-time signal set and the phase of the corresponding sampling points in the voltage signal set in the simulation signal set, respectively.

[0065] Within a preset time window, such as 10ms, calculate the root mean square error and peak deviation of the voltage signals in the real-time signal set and the corresponding voltage signals in the simulation signal set, and collectively refer to them as the statistical deviation of the voltage signals.

[0066] It should be noted that the root mean square error reflects the overall deviation level within the interval, while the peak deviation is the maximum amplitude deviation within the extraction window, reflecting the extreme deviation.

[0067] Calculate the amplitude deviation between each harmonic frequency band of the voltage signal spectrum in the real-time signal set and the corresponding harmonic frequency band of the voltage signal spectrum in the simulation signal set, and record it as the spectral deviation of the voltage signal.

[0068] The spectrum deviation calculation adopts a weighted deviation algorithm, in which the fundamental frequency band (50Hz / 60Hz) has a weight of 0.6, the 2nd-10th harmonic frequency band has a weight of 0.3, and the 11th-50th harmonic frequency band has a weight of 0.1; the total weighted deviation = fundamental deviation × 0.6 + lower harmonic deviation × 0.3 + higher harmonic deviation × 0.1, and the weight coefficients can be dynamically adjusted by the parameter adaptive optimization module.

[0069] Similarly, the fundamental deviation, statistical deviation, and spectral deviation of the current signal can be obtained.

[0070] Judgment deviation anomalies include, When the fundamental deviation, statistical deviation, and spectral deviation of voltage and current signals meet the anomaly determination criteria, the anomaly of the deviation between the real-time signal set and the simulated signal set is recorded as an anomaly. The abnormality judgment criteria include that if any of the basic deviations exceeds the upper limit of the deviation, it is judged as abnormal; If the statistical deviation shows a continuous increasing trend within a continuous time window, it is judged as a potential anomaly. If the statistical deviation shows a sudden jump in deviation within an adjacent time window, it is judged as a sudden anomaly.

[0071] Specifically, trend analysis is performed on the deviation values ​​within a continuous time window, such as 1 second. For example, the deviation slope is calculated through linear fitting. If the deviation continues to increase, such as a slope > 0.1% / s, it is still judged as a potential anomaly even if the current threshold is not exceeded.

[0072] The deviation difference between adjacent windows is detected. If the deviation suddenly jumps, such as ΔA% increasing from 0.5% to 5% instantly, and the jump amplitude is greater than 3 times the historical standard deviation, it is judged as a sudden anomaly.

[0073] The fault diagnosis and early warning module analyzes the differences between the acquired operational monitoring data of the integrated voltage and current sensor and its corresponding safety limits to determine the root cause of the fault and issue a fault warning.

[0074] The specific methods for determining the root cause of the fault include calculating the absolute deviation of each parameter of the target integrated voltage and current sensor's operation monitoring data from its corresponding dynamic safety limit and static safety limit, obtaining the absolute deviation of each parameter of the operation monitoring data from the dynamic safety limit and static safety limit, and selecting each parameter of the operation monitoring data whose absolute deviation is greater than a preset deviation threshold, and recording them as each fault parameter of the operation monitoring data.

[0075] Specifically, static safety limits refer to safety limits determined based on sensor design standards, such as a maximum operating temperature of 120℃ for the magnetic core and an insulation resistance ≥1000MΩ.

[0076] Dynamic safety limits specifically refer to safety limits generated and determined based on real-time operating conditions. For example, the core temperature limit is relaxed to 130℃ when overloaded, and the SNR limit is relaxed to 45dB when there is harmonic pollution.

[0077] By comparing each fault parameter in the operation monitoring data with the fault feature map in the root cause database, the root causes of each fault parameter in the operation monitoring data are matched, thereby determining the root causes of each fault in the target integrated voltage and current sensor.

[0078] A specific example, an example of a fault feature map, is shown in Table 1 below: Table 1 Examples of Fault Feature Maps

[0079] By comparing each fault parameter in the operation monitoring data with the fault feature map in the root cause database, the root causes of each fault parameter in the operation monitoring data are matched, thereby determining the root causes of each fault of the target integrated voltage and current sensor. Specific methods for fault early warning include: The process of determining the fault warning level based on the root cause of the fault includes: if the fault parameters of the operation monitoring data corresponding to a certain fault root cause of the target integrated voltage and current sensor exceed the dynamic safety limit but do not exceed the static safety limit, then the warning level of the fault root cause of the target integrated voltage and current sensor is recorded as a level three warning. If a fault parameter in the operation monitoring data corresponding to a certain root cause of a fault in the target integrated voltage and current sensor exceeds the static safety limit, then the warning level of the root cause of the fault in the target integrated voltage and current sensor will be recorded as a level two warning. If a fault parameter in the operation monitoring data corresponding to a certain root cause of a fault in the target integrated voltage and current sensor exceeds the static safety limit setting ratio, then the warning level of that root cause of the fault in the target integrated voltage and current sensor will be recorded as a Level 1 warning. The fault warning level will be fed back.

[0080] Compensation is performed on the real-time signal set to correct transmission errors, including, Based on the fundamental deviation, statistical deviation, and spectral deviation of the voltage and current signals, the transmission error type is identified, and a matching compensation algorithm is adaptively selected according to the error type and frequency band characteristics.

[0081] Specifically, an exemplary matching table for the compensation algorithm is shown in Table 2 below.

[0082] Table 2 Example of Compensation Algorithm Matching Table

[0083] By identifying error types from multiple dimensions, including basic deviation, statistical deviation, and spectral deviation, and adaptively selecting matching algorithms, the system dynamically adjusts the algorithm based on the error type to solve the problem of insufficient full-band accuracy and meet the needs of wide-band measurement scenarios such as new energy grid connection.

[0084] The parameter adaptive optimization module dynamically adjusts the gain control strategy and filtering and noise reduction parameters based on the characteristic spectrum of the real-time signal set, and updates the parameters of the transmission model accordingly to achieve iterative optimization, enabling the sensor to dynamically adapt to different operating conditions.

[0085] The specific contents of the parameter adaptive optimization module include: The real-time signal set is subjected to a fast Fourier transform to obtain the characteristic spectrum of the real-time signal set. This spectrum is then matched with the characteristic spectrum corresponding to each operating condition mode to determine the current operating condition mode of the real-time signal set. Based on the current operating mode of the real-time signal set, specific parameter adjustment instructions for gain control and filtering / denoising are generated, and the gain control strategy and filtering / denoising parameters are dynamically adjusted. After dynamically adjusting the gain control strategy and filtering and noise reduction parameters, the corrected real-time dataset is compared with the original real-time dataset to identify the deviation. The real-time signal is then used as new training data to fine-tune the key parameters in the transfer model.

[0086] Specifically, the operating modes include, but are not limited to, steady-state power frequency mode, transient traveling wave mode, and high-noise interference mode. Steady-state power frequency mode is characterized by a high concentration of spectral energy around 50Hz, resulting in a high signal-to-noise ratio. In this mode, extremely high small-signal accuracy is required. Transient traveling wave mode involves a large number of high-frequency components in the spectrum (kHz-MHz), necessitating wide bandwidth and high-speed response; phase accuracy is crucial. High-noise interference mode is characterized by strong interference noise in specific frequency bands, such as the switching power supply frequency, requiring focused suppression of noise at these specific frequencies.

[0087] The specific method for dynamically adjusting the gain control strategy is as follows: In steady-state power frequency mode, if the signal is weak, the gain is increased; in transient traveling wave mode, to prevent saturation of large current signals, the gain is automatically reduced or a low-gain channel is enabled.

[0088] The specific methods for dynamically adjusting the parameters of filtering and noise reduction are as follows: In steady-state power frequency mode, a narrowband notch filter is enabled to filter out specific harmonics; in transient traveling wave mode, a high-speed broadband filter is enabled to filter out only ultra-high frequency noise above the frequency band of interest; in high noise interference mode, an adaptive notch filter is dynamically generated and enabled to accurately filter out changing interference frequencies.

[0089] Fine-tuning key parameters in the transmission model, such as equivalent inductance, capacitance, and resistance, minimizes the error between the model's output and the actual measured values.

[0090] By analyzing the characteristic spectrum of the signal through Fast Fourier Transform, the gain of the programmable amplifier and the filter parameters are dynamically adjusted to adapt to signals of different amplitudes, solving the problems of signal saturation and weak interference suppression under different operating conditions, and improving the filtering effect and anti-interference capability.

[0091] Example 2 Reference Figure 1 - Figure 4 This is the second embodiment of the present invention.

[0092] The above is a schematic scheme of an intelligent monitoring and adaptive compensation method for voltage and current sensors. It should be noted that the technical solution of this intelligent monitoring and adaptive compensation system for voltage and current sensors belongs to the same concept as the technical solution of the aforementioned intelligent monitoring and adaptive compensation method for voltage and current sensors. Details not described in detail in this embodiment of the intelligent monitoring and adaptive compensation system for voltage and current sensors can be found in the description of the aforementioned intelligent monitoring and adaptive compensation method for voltage and current sensors.

[0093] This embodiment also provides an intelligent monitoring and adaptive compensation system for voltage and current sensors, including: The signal acquisition module performs gain adjustment and filtering noise reduction preprocessing on the acquired real-time analog signal set, converts the preprocessed real-time analog signal set into a differential analog signal set, and performs digital processing on it to obtain the real-time signal set. The deviation anomaly determination module compares the deviation between the real-time signal set and the simulation signal set after simulation by the built-in transmission model, and determines the deviation anomaly. If an anomaly is found, the fault diagnosis and early warning module is executed; otherwise, the compensation and correction module is executed. The fault diagnosis and early warning module analyzes the differences between the acquired operational monitoring data of the integrated voltage and current sensor and its corresponding safety limits to determine the root cause of the fault and issue a fault warning. The compensation and correction module compensates the real-time signal set to correct the transmission error; The parameter adaptive optimization module dynamically adjusts the gain control strategy and filtering and noise reduction parameters based on the characteristic spectrum of the real-time signal set, and updates the parameters of the transmission model accordingly.

[0094] This embodiment also provides an electronic device suitable for intelligent monitoring and adaptive compensation of voltage and current sensors, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the intelligent monitoring and adaptive compensation method for voltage and current sensors as proposed in the above embodiment.

[0095] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the intelligent monitoring and adaptive compensation method for voltage and current sensors as proposed in the above embodiments.

[0096] The storage medium proposed in this embodiment and the method for realizing intelligent monitoring and adaptive compensation of voltage and current sensors proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0097] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0098] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for intelligent monitoring and adaptive compensation of voltage and current sensors, characterized in that: It includes an integrated voltage and current sensing assembly and a control system. The control system is mounted on the integrated voltage and current sensing assembly and includes... The acquired real-time analog signal set is preprocessed with gain adjustment and filtering noise reduction. The preprocessed real-time analog signal set is then converted into a differential analog signal set and digitized to obtain the real-time signal set. The deviation between the real-time signal set and the simulation signal set after simulation with the built-in transmission model is compared and the abnormality of the deviation is determined. When an abnormality is found, a fault diagnosis and warning are executed; otherwise, compensation and correction are executed. The operational monitoring data of the acquired integrated voltage and current sensor is analyzed to determine the root cause of the fault and provide fault warning. The real-time signal set is compensated to correct the transmission error; Based on the characteristic spectrum of the real-time signal set, the gain control strategy and the filtering and noise reduction parameters are dynamically adjusted, and the parameters of the transmission model are updated accordingly.

2. The intelligent monitoring and adaptive compensation method for voltage and current sensors as described in claim 1, characterized in that: The obtained real-time signal set includes, The real-time analog signal set is obtained from the integrated sensing of target voltage and current, and the gain factor of the programmable gain amplifier is adjusted in real time according to its amplitude characteristics to realize the gain control of the real-time analog signal set. Based on the characteristic spectrum of the real-time analog signal set, the type of interference is determined, and the corresponding filter is activated to filter out the interference components in the signal while retaining the characteristic spectrum of the effective signal. The preprocessed real-time analog signal set is converted into two differential signals with equal amplitude and opposite phase to obtain a differential analog signal set. A high-precision analog-to-digital converter is used to quantize the discrete signals after sampling the differential analog signal set, and output a digital real-time signal set.

3. The intelligent monitoring and adaptive compensation method for voltage and current sensors as described in claim 2, characterized in that: The transmission model includes a voltage transmission model and a current transmission model; The specific method for obtaining the simulation signal set includes: the voltage transfer model extracts features from the voltage signals in the real-time signal set to obtain the basic parameters, spectral components and transient features of the voltage signals; similarly, the basic parameters, spectral components and transient features of the current signals can be obtained. The voltage transmission model is adapted to fine-tuning its own parameters based on real-time operating conditions. The basic parameters, spectral components, and transient characteristics of the voltage signal are used to simulate voltage signal transmission in a voltage transmission model with adaptive fine-tuning, resulting in a simulated voltage signal after the voltage transmission model is simulated. Similarly, the simulated current signal after simulating the voltage transfer model can be obtained; The simulated voltage signal and the simulated current signal are collectively referred to as the simulated signal set; The step of comparing the deviation between the real-time signal set and the simulation signal set after simulation by the built-in transmission model includes calculating the absolute deviation, relative deviation and phase deviation of the amplitude of each sampling point of the voltage signal in the real-time signal set and the corresponding sampling point of the voltage signal in the simulation signal set, and collectively referring to them as the basic deviation of the voltage signal. Within a preset time window, the root mean square error and peak deviation of the voltage signals in the real-time signal set and the corresponding voltage signals in the simulation signal set are calculated and collectively referred to as the statistical deviation of the voltage signals. Calculate the amplitude deviation between each harmonic frequency band of the voltage signal spectrum in the real-time signal set and the corresponding harmonic frequency band of the voltage signal spectrum in the simulation signal set, and record it as the spectral deviation of the voltage signal; Similarly, the fundamental deviation, statistical deviation, and spectral deviation of the current signal can be obtained.

4. The intelligent monitoring and adaptive compensation method for voltage and current sensors as described in claim 3, characterized in that: The abnormality in the determination includes, When the fundamental deviation, statistical deviation, and spectral deviation of the voltage and current signals meet the anomaly determination conditions, the deviation anomaly between the real-time signal set and the simulation signal set is recorded as an anomaly. The abnormality determination criteria include: if any of the basic deviations exceeds the upper limit of the deviation, it is determined to be abnormal; If the statistical deviation shows a continuous increasing trend within a continuous time window, it is determined to be a potential anomaly. If the statistical deviation shows a sudden jump in deviation within an adjacent time window, it is determined to be a sudden anomaly.

5. The intelligent monitoring and adaptive compensation method for voltage and current sensors as described in claim 4, characterized in that: The specific method for determining the root cause of the fault includes calculating the absolute deviation of each parameter of the target integrated voltage and current sensor's operation monitoring data from its corresponding dynamic safety limit and static safety limit, obtaining the absolute deviation of each parameter of the operation monitoring data from the dynamic safety limit and static safety limit, and selecting each parameter of the operation monitoring data whose absolute deviation is greater than a preset deviation threshold, and recording them as each fault parameter of the operation monitoring data. By comparing each fault parameter in the operation monitoring data with the fault feature map in the root cause database, the root causes of each fault parameter in the operation monitoring data are matched, thereby determining the root causes of each fault of the target integrated voltage and current sensor. The specific methods for providing fault early warning include: The process of determining the fault warning level based on the root cause of the fault includes: if the fault parameters of the operation monitoring data corresponding to a certain fault root cause of the target integrated voltage and current sensor exceed the dynamic safety limit but do not exceed the static safety limit, then the warning level of the fault root cause of the target integrated voltage and current sensor is recorded as a level three warning. If a fault parameter in the operation monitoring data corresponding to a certain root cause of a fault in the target integrated voltage and current sensor exceeds the static safety limit, then the warning level of the root cause of the fault in the target integrated voltage and current sensor will be recorded as a level two warning. If a fault parameter in the operation monitoring data corresponding to a certain root cause of a fault in the target integrated voltage and current sensor exceeds the static safety limit setting ratio, then the warning level of that root cause of the fault in the target integrated voltage and current sensor will be recorded as a Level 1 warning. The fault warning level will be fed back.

6. The intelligent monitoring and adaptive compensation method for voltage and current sensors as described in claim 5, characterized in that: The compensation of the real-time signal set to correct the transmission error includes, Based on the fundamental deviation, statistical deviation, and spectral deviation of the voltage and current signals, the transmission error type is identified, and a matching compensation algorithm is adaptively selected according to the error type and frequency band characteristics.

7. The intelligent monitoring and adaptive compensation method for voltage and current sensors as described in claim 6, characterized in that: The specific contents of the parameter adaptive optimization module include: The real-time signal set is subjected to a fast Fourier transform to obtain the characteristic spectrum of the real-time signal set. This spectrum is then matched with the characteristic spectrum corresponding to each operating condition mode to determine the current operating condition mode of the real-time signal set. Based on the current operating mode of the real-time signal set, specific parameter adjustment instructions for the gain control and the filtering and noise reduction are generated, and the strategy for the gain control and the parameters for the filtering and noise reduction are dynamically adjusted. After dynamically adjusting the gain control strategy and the filtering and noise reduction parameters, the corrected real-time dataset is compared with the original real-time dataset to identify the deviation. The real-time signal is then used as new training data to fine-tune the key parameters in the transfer model.

8. A voltage and current sensor intelligent monitoring and adaptive compensation system, based on the voltage and current sensor intelligent monitoring and adaptive compensation method according to any one of claims 1 to 7, characterized in that: It also includes a signal acquisition module, which performs gain control and filtering noise reduction preprocessing on the acquired real-time analog signal set, converts the preprocessed real-time analog signal set into a differential analog signal set, and performs digital processing on it to obtain a real-time signal set; The deviation anomaly determination module compares the deviation between the real-time signal set and the simulation signal set after simulation by the built-in transmission model, and determines the deviation anomaly. If an anomaly is found, the fault diagnosis and early warning module is executed; otherwise, the compensation and correction module is executed. The fault diagnosis and early warning module analyzes the differences between the acquired operational monitoring data of the integrated voltage and current sensor and its corresponding safety limits to determine the root cause of the fault and issue a fault warning. The compensation and correction module compensates for the real-time signal set to correct the transmission error; The parameter adaptive optimization module dynamically adjusts the gain control strategy and the filtering and noise reduction parameters based on the characteristic spectrum of the real-time signal set, and updates the parameters of the transmission model accordingly.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the intelligent monitoring and adaptive compensation method for voltage and current sensors according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the intelligent monitoring and adaptive compensation method for voltage and current sensors as described in any one of claims 1 to 7.

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