Electrical signal acquisition system based on intelligent dynamic sampling

Abstract

The invention belongs to the field of power equipment monitoring, and discloses an electrical signal acquisition system based on intelligent dynamic sampling, which comprises a signal acquisition module, a signal conditioning module, an abrupt change detection module, an impedance calculation module and an upper computer, and is characterized in that the signal acquisition module comprises a first ADC module, a second ADC module, a first MCU module and a second MCU module. Short-circuit impedance real-time calculation and waveform abrupt change detection are adopted for cooperative judgment, and when equipment operates stably, the sampling rate of the system is kept low; when impedance changes or waveform changes suddenly, the sampling rate is improved automatically, important signal changes are recorded accurately, and waveform abrupt change detection is carried out by adopting an edge detection algorithm improved based on an FIR (Finite Impulse Response) filter. The system ensures the time sequence consistency of data under different sampling frequencies through multi-channel data synchronization and timestamp management, and meanwhile, smooth transition is performed by applying an interpolation algorithm during sampling switching, so that the continuity of a data sequence is ensured. The data redundancy is reduced; and the integrity of key information is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment monitoring, and in particular to an electrical signal acquisition system based on intelligent dynamic sampling. Background Art

[0002] In recent years, with the development of electrical equipment monitoring technology, online monitoring systems have gradually been applied to power equipment. Typical online monitoring systems collect electrical signals such as current and voltage at a fixed frequency to analyze the operating status of the equipment. However, this type of sampling system faces two major challenges: First, due to the complex operating conditions of electrical equipment, the frequency of signal changes varies greatly under different conditions, and it is difficult for a fixed sampling rate to balance accuracy and efficiency; second, the amount of data is large at a high sampling rate, resulting in a large amount of data redundancy, increasing the storage and transmission burden. Therefore, the fixed sampling rate mode of the traditional sampling system is difficult to meet the needs of real-time monitoring of transformer winding deformation.

[0003] In order to solve the above problems, dynamic sampling technology has been proposed in recent years, which can automatically adjust the sampling frequency when the operating status of the equipment changes. In this way, the sampling rate can be reduced when the equipment is running smoothly to reduce redundant data; and the sampling rate can be increased when the equipment is abnormal to ensure that key state change information is captured. However, the existing dynamic sampling technology mostly relies on a single parameter judgment of the equipment working condition, cannot effectively integrate multi-dimensional information, and lacks intelligent recognition and judgment capabilities. This makes it difficult for the system to automatically and accurately adjust the sampling rate under complex working conditions, thus affecting the real-time and accuracy of monitoring. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is that the existing electrical signal acquisition system cannot effectively integrate multi-dimensional information and lacks intelligent recognition and judgment capabilities.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: an electrical signal acquisition system based on intelligent dynamic sampling, comprising: Signal acquisition module, signal conditioning module, mutation detection module, impedance calculation module, host computer; The signal acquisition module includes a first ADC module, a second ADC module, a first MCU module, and a second MCU module; the voltage and current are simultaneously acquired through the first ADC module and the second ADC module, and are converted into digital signals and transmitted to the signal conditioning module; The signal conditioning module performs preliminary processing on the collected signals, and the output voltage and current signals are input into the impedance calculation module and the mutation detection module; The impedance calculation module calculates the short-circuit impedance of the electrical equipment using the current and voltage signals, and analyzes the voltage and current signals using a least squares identification algorithm; The mutation detection module uses an improved edge detection algorithm to monitor rapid changes and mutations in the waveform; The host computer receives feedback information from the mutation detection module and the impedance calculation module, and controls the adjustment of the sampling rate according to the signal state monitored in real time.

[0007] As a preferred system of the electrical signal acquisition method based on intelligent dynamic sampling described in the present invention, wherein: the first MCU module and the second MCU module control the load conversion sampling rate, the first ADC module and the second ADC module are responsible for converting the analog quantity into a digital quantity, and two sets of acquisition systems are used. The voltage and current signals collected by the first set are used to calculate the short-circuit impedance, and the voltage and current signals collected by the second set are used for the mutation detection module; The signal processing module pre-processes and conditions the current and voltage signals obtained from the signal acquisition module to eliminate noise and enhance signal quality; The mutation detection module uses an edge detection algorithm to analyze the changes in current and voltage waveforms, detect whether there are mutations in the electrical signal, and trigger the dynamic sampling control of the system based on the detection results; The impedance calculation module calculates the short-circuit impedance of the transformer in real time, determines whether the device is abnormal by calculating the relationship between the current and voltage signals, and controls the dynamic sampling of the system according to the judgment result; The host computer serves as the central control unit of the system. The host computer communicates with each module and adjusts the working state of the system by real-time analysis of collected data and monitoring results.

[0008] On the other hand, an electrical signal acquisition method based on intelligent dynamic sampling using the system of the present invention comprises: The two ADC modules collect voltage and current signals according to the default sampling frequency; The impedance calculation module is used to analyze the voltage and current data collected by the first ADC module in real time to calculate the short-circuit impedance; adjusting a sampling mode according to the short-circuit impedance; Continuously monitoring the changes of the current and voltage waveforms acquired by the second ADC module through the mutation detection module; adjusting the sampling mode according to the change of the waveform; When adjusting the sampling mode, the data in the switching period is smoothly transitioned to obtain the collection result of the electrical signal.

[0009] As a preferred method of the electrical signal acquisition method based on intelligent dynamic sampling described in the present invention, the default sampling frequency includes: the system default monitoring mode sampling rate is 1kHz, and the ADC module starts to collect voltage and current signals according to the default sampling frequency.

[0010] As a preferred method of the electrical signal acquisition method based on intelligent dynamic sampling described in the present invention, wherein: the calculation of the short-circuit impedance includes calculating the short-circuit impedance based on a least squares identification algorithm; According to the loop equation of the transformer model, the relationship of the instantaneous value is expressed as: , , in, , is the primary and secondary winding voltage; , is the number of turns of the primary and secondary windings; is the main magnetic flux between the windings; , is the leakage inductance of the primary and secondary sides; , is the primary and secondary winding current; , is the primary and secondary side resistance; The standard form converted to least squares identification is: , Where k is the transformer ratio, , is the primary and secondary winding voltage; , is the leakage inductance of the primary and secondary sides; , is the primary and secondary winding current; , are the primary and secondary side resistances.

[0011] As a preferred method of the electrical signal acquisition method based on intelligent dynamic sampling described in the present invention, wherein: the short-circuit impedance also includes storing the calculated short-circuit impedance in the host computer, and the host computer records the short-circuit impedance value collected each time as time series data and stores it in a database; Each record should contain the timestamp, impedance value and acquisition frequency, and the reference value Z should be set according to the nameplate. base , the collected impedance value Z current Compare with the baseline value and calculate the rate of change: , The change threshold is set to 2.5%. When the change rate exceeds the threshold, an alarm signal is generated, and the sampling mode is adjusted through the host computer, and a sampling rate of 2.5 kHz is selected.

[0012] As a preferred method of the electrical signal acquisition method based on intelligent dynamic sampling described in the present invention, the change of the waveform includes eliminating the high-frequency noise generated in the sampling through the FIR filter, and the output signal is expressed as: , Where y[n] is the output of the filter at time point n, x[n] is the input of the filter at time point n, h[k] is the impulse response coefficient of the filter, M is the order of the filter plus 1, and n is the current time index; The edge detection algorithm is used to detect the points where the amplitude of the current or voltage signal changes dramatically to determine whether the signal has a sudden change; the original signal is differentiated to obtain the rate of change and ; In the discrete case, the differential is approximated by the difference, where is the sampling time interval; , , The threshold is passed through the mean of the differentiated signal , and standard deviation and To set: , , Continuous monitoring is performed through a sliding time window, where the sliding step is the inverse of the sampling rate; like Greater than or Greater than , it is judged as a mutation; When a mutation is detected, the sampling mode is adjusted to select a 2.5 kHz sampling rate.

[0013] As a preferred method of the electrical signal acquisition method based on intelligent dynamic sampling described in the present invention, wherein: the adjustment of the sampling mode also includes, when the short-circuit impedance change rate is less than 2.5% and no waveform mutation is detected, the host computer outputs an instruction to readjust the sampling mode and select a 1 kHz sampling rate; The smooth transition includes generating a synchronous clock signal for all acquisition channels through a synchronous clock source and generating a timestamp for the sampling point; At the moment of sampling switching, an interpolation algorithm is used to smooth the transition: , Among them, V interp is the interpolated data value, and is the adjacent real data point before and after the switching, t is the interpolation time, located at and middle.

[0014] A computer device comprises: a memory and a processor; the memory stores a computer program, wherein: the processor implements the steps of any one of the methods of the present invention when executing the computer program.

[0015] A computer-readable storage medium stores a computer program, wherein: when the computer program is executed by a processor, the steps of any one of the methods of the present invention are implemented.

[0016] Beneficial effects of the present invention: The electrical signal acquisition system based on intelligent dynamic sampling provided by the present invention ensures the timing consistency of data at different sampling frequencies through multi-channel data synchronization and timestamp management, and applies an interpolation algorithm for smooth transition during sampling switching to ensure the continuity of the data sequence, reduce data redundancy and ensure the integrity of key information. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use 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 ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0018] Figure 1 A schematic diagram of the structure of an acquisition system of an electrical signal acquisition system based on intelligent dynamic sampling provided by the first embodiment of the present invention; Figure 2 A voltage acquisition waveform at a high sampling rate in an electrical signal acquisition system based on intelligent dynamic sampling provided by a third embodiment of the present invention; Figure 3 A diagram showing the short-circuit impedance identification result of an electrical signal acquisition system based on intelligent dynamic sampling provided by the third embodiment of the present invention; Figure 4 A current detection result diagram of an edge detection algorithm of an electrical signal acquisition system based on intelligent dynamic sampling provided by the third embodiment of the present invention; Figure 5 A current signal spectrum diagram of an electrical signal acquisition system based on intelligent dynamic sampling before FIR filtering provided by the third embodiment of the present invention; Figure 6A current signal spectrum diagram of an electrical signal acquisition system based on intelligent dynamic sampling after FIR filtering provided by the third embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0020] Example 1, reference Figure 1 , as an embodiment of the present invention, provides an electrical signal acquisition system based on intelligent dynamic sampling, comprising: Signal acquisition module, signal conditioning module, mutation detection module, impedance calculation module, host computer; the signal acquisition module is responsible for acquiring voltage and current signals from the power equipment. When the system starts, the acquisition module starts sampling the signal, converts the analog signal into a digital signal, and simultaneously acquires the voltage and current through the first ADC module and the second ADC module. The collected digital signal is transmitted to the signal conditioning module through the hardware interface, and the signal conditioning module performs preliminary processing on the collected signal. The voltage and current signals output by the signal conditioning module will be input into the impedance calculation module. This module uses the current and voltage signals to calculate the short-circuit impedance of the electrical equipment, and mainly analyzes the voltage and current signals through the least squares identification algorithm. At the same time, it is sent to the mutation detection module. The mutation detection module uses an improved edge detection algorithm to monitor the rapid changes and mutations of the waveform. As the control center of the system, the host computer is responsible for managing the data of all acquisition modules, and automatically adjusts the working mode of the system according to the collected data, calculation results and detected abnormal information. After receiving feedback information from the mutation detection module and the impedance calculation module, the host computer will control the adjustment of the sampling rate according to the real-time monitored signal status.

[0021] Furthermore, the signal acquisition module is responsible for acquiring electrical signals such as current and voltage from the transformer and converting them into digital signals for subsequent processing. The MCU controller load converts the sampling rate, and the ADC is responsible for converting the analog quantity into a digital quantity. Two acquisition systems are used. The voltage and current signals collected by the first set are used to calculate the short-circuit impedance, and the voltage and current signals collected by the second set are used for the mutation detection module. Using two independent acquisition systems can enhance the redundancy and fault tolerance of the entire system. The signal processing module preprocesses and conditions the current and voltage signals obtained from the signal acquisition module to ensure that the signal is suitable for subsequent analysis and calculation. Including filtering, amplification, anti-interference and other processing to eliminate noise and enhance signal quality.

[0022] The mutation detection module detects whether there are mutations in the electrical signal (such as faults, abnormal waveform changes, etc.), and triggers the dynamic sampling control of the system based on the detection results. This module uses an edge detection algorithm to analyze the changes in the current and voltage waveforms and determine whether the signal has changed dramatically. The impedance calculation module calculates the short-circuit impedance of electrical equipment such as transformers in real time to reflect the health status of the equipment. By calculating the relationship between current and voltage signals, it is determined whether the equipment is abnormal (such as winding deformation). And the dynamic sampling control of the system is triggered based on the judgment results. The host computer, as the central control unit of the system, is responsible for integrating and analyzing the data of each module, and performing operations such as status monitoring, alarm, and sampling control. The host computer communicates with each module, and adjusts the working status of the system by real-time analysis of the collected data and monitoring results to ensure the efficiency and accuracy of the monitoring process.

[0023] System initialization and default sampling mode setting. After the system starts, it enters the default monitoring mode and the sampling rate is set to 1 kHz. The ADC module starts basic acquisition of voltage and current signals, and the data compression and transmission modules start working. At the same time, the system initializes short-circuit impedance calculation and waveform mutation detection, and enters the monitoring standby state.

[0024] The short-circuit impedance is monitored in real time and the threshold value is determined. The short-circuit impedance calculation module analyzes the voltage and current signals collected by the first ADC module in real time and calculates the current short-circuit impedance value. The impedance value calculated in real time is compared with the preset reference impedance value. If the impedance change exceeds the preset threshold of 2.5%, the sampling rate is automatically increased to 2.5kHz to ensure that the detailed data of the electrical signal is accurately collected.

[0025] The waveform mutation detection module continuously monitors the changes in the current and voltage waveforms obtained by the second ADC module. When a waveform mutation is detected, the system immediately triggers the high sampling mode and adjusts the sampling frequency to 2.5 kHz.

[0026] The system configures precise timestamps in multi-channel data acquisition and uses synchronized clocks to ensure the timing consistency of all data. When the sampling rate switches, the timing management module uses an interpolation algorithm to smoothly transition the data during the switching period to ensure the continuity and consistency of the data sequence.

[0027] When the device detects that the impedance and waveform output have returned to normal, the system restores the sampling rate to a low sampling frequency (1 kHz). The data compression and transmission module prioritizes processing new status data and integrates and transmits low-frequency data to the storage system.

[0028] Embodiment 2 is an embodiment of the present invention, which provides an electrical signal acquisition method based on intelligent dynamic sampling, including: S1: Two sets of ADC modules collect voltage and current signals according to the default sampling frequency.

[0029] The system default monitoring mode sampling rate is 1 kHz, and the ADC module starts to collect voltage and current signals according to the default sampling frequency.

[0030] S2: Utilize the impedance calculation module to analyze the voltage and current data collected by the first ADC module in real time to calculate the short-circuit impedance.

[0031] According to the transformer model, the loop equation is written, and the relationship between the instantaneous values ​​can be expressed as: , , In the formula, , is the primary and secondary winding voltage; , is the number of turns of the primary and secondary windings; is the main magnetic flux between the windings; , is the leakage inductance of the primary and secondary sides; , is the primary and secondary winding current; , are the primary and secondary side resistances.

[0032] The standard form converted to least squares identification is: , Where k is the transformer ratio, , is the primary and secondary winding voltage; , is the leakage inductance of the primary and secondary sides; , is the primary and secondary winding current; , are the primary and secondary side resistances.

[0033] It should be said that the least squares method is a classic parameter estimation method. It is based on the principle of minimizing the sum of squares of errors to achieve the best fitting effect of the model. The least squares method can be applied not only to parameter estimation of static systems, but also to parameter solution of dynamic systems. In addition, the least squares method is applicable to linear and nonlinear systems. In linear systems, parameters can be directly solved, while for nonlinear systems, they can be estimated by nonlinear least squares or linearization processing. The least squares method can also be used for offline and online system parameter identification. Offline identification is suitable for static systems or experimental data, while online identification is suitable for real-time monitoring and control systems. In the identification of transformer winding reactance parameters, the least squares method can ensure accuracy while quickly realizing the effective identification of reactance parameters, providing an efficient technical means for state monitoring and fault prediction of power equipment. The least squares algorithm is a mathematical process that estimates the unknown parameters in a mathematical model by minimizing the sum of squares of the objective function. Generally, the least squares identification algorithm is a one-time completion algorithm, which requires a large amount of calculation and occupies a large storage space, and it is difficult to achieve online identification. The recursive least squares method uses recursive estimation of parameters. The kth parameter estimate is the sum of the (k-1)th estimate and the correction amount. As the number of recursions increases, the correction amount gradually decreases until it approaches 0. The algorithm converges to the true value and can realize online identification of winding parameters.

[0034] S3: adjusting the sampling mode according to the short-circuit impedance.

[0035] The calculated short-circuit impedance is stored in the host computer, which records the short-circuit impedance value collected each time as time series data and stores it in the database. Each record should contain a timestamp, impedance value and collection frequency. The reference value Z is set according to the nameplate. base , the collected impedance value Z current Compare with the benchmark value and calculate the error percentage: , The change threshold is set to 2.5%. When the change rate exceeds the threshold, an alarm signal is generated, and the host computer outputs instructions to the STM32 (microcontroller) to control the ADS1115 (multi-speed programmable analog-to-digital converter) to select a 2.5kHZ sampling rate.

[0036] S4: Continuously monitor the changes of the current and voltage waveforms acquired by the second ADC module through the mutation detection module.

[0037] The FIR filter is used to eliminate high-frequency noise that is easily generated in sampling. The FIR (Finite Impulse Response) filter is a digital filter based on convolution. Its output signal depends only on the current and past input signals. The output signal is expressed as: , Where y[n] is the output of the filter at time point n, x[n] is the input of the filter at time point n, h[k] is the impulse response coefficient of the filter, M is the filter order plus 1, and n is the current time index; in this filter design, the lower passband frequency is set to 49Hz and the upper passband frequency is 51Hz. The order of the filter is set to 160, which is a relatively high order. The order of the FIR filter determines the accuracy and frequency response characteristics of the filter. A higher order can achieve a steeper transition band, thereby more accurately separating the passband and stopband.

[0038] S5: adjusting the sampling mode according to the change of the waveform.

[0039] The edge detection algorithm is used to detect the points where the amplitude of the current or voltage signal changes dramatically to determine whether the signal has a sudden change. Differentiate the original signal to obtain the change rate and. In the discrete case, the differential is used to approximate the differential, where is the sampling time interval: , , It should be noted that the traditional mutation detection method may be affected by noise, and the detection accuracy of mutation points is limited. The edge detection algorithm based on FIR improvement can keenly capture the points where the amplitude changes dramatically in the signal, and use them as mutation points, effectively avoiding noise interference. Especially when applied to current or voltage signals, through fine edge detection, the accuracy and robustness of mutation detection can be significantly improved, providing more reliable data support for system fault location and abnormality analysis.

[0040] Set a threshold for the rate of change to determine whether a mutation occurs. The threshold is calculated by dividing the mean of the differential signal by , and standard deviation and To set: , , Continuous monitoring is performed through a sliding time window, where the sliding step is the inverse of the sampling rate; like Greater than or Greater than , it is judged as a mutation.

[0041] When a mutation is detected, the sampling mode is adjusted to select a 2.5 kHz sampling rate.

[0042] Furthermore, the sampling rate is dynamically adjusted according to the change rate of the short-circuit impedance and the waveform mutation, reducing the continuous use of high sampling rates, saving system resources and reducing energy consumption. This innovation enables the system to adaptively switch to a lower sampling rate under low change rate conditions, improving efficiency while ensuring data integrity, and is more in line with the design concept of modern smart grid systems.

[0043] It is also important to know that when the short-circuit impedance change rate is less than 2.5% and no waveform mutation is detected, the host computer outputs instructions to the STM32 to control the two ADS1115s to select a 1kHz sampling rate.

[0044] S6: When adjusting the sampling mode, the data in the switching period is smoothly transitioned to obtain the collection result of the electrical signal.

[0045] All acquisition channels generate synchronous clock signals through the synchronous clock source, and generate timestamps for the sampling points. At the sampling switching moment, an interpolation algorithm is used to smoothly transition: , Among them, V interp is the interpolated data value, and is the adjacent real data point before and after the switching, t is the interpolation time, located at and middle.

[0046] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0047] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0048] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0049] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0050] Example 3, reference Figure 2-6 , which is an embodiment of the present invention, provides an electrical signal acquisition method based on intelligent dynamic sampling. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0051] Attached Figure 1 This is a schematic diagram of the acquisition system structure: Figure 1This is a block diagram of the intelligent dynamic sampling system, which includes two sets of signal acquisition modules for collecting voltage and current, each of which contains an MCU (microcontroller unit) and an ADC (analog-to-digital converter). The collected signals are input into the mutation detection module and the impedance calculation module after passing through the signal conditioning module, and are detected by the edge detection algorithm and the least squares method respectively. The detection results are transmitted to the host computer. After the host computer makes a judgment, it sends instructions to change the sampling rate to the two MCU controllers.

[0052] Attached Figure 2 The voltage waveform collected at a high sampling rate: The figure shows the voltage waveform collected at a sampling rate of 2.5 kHz. A higher sampling rate means more data points are collected per second, so that the details in the voltage waveform can be captured more accurately. It can provide more information in subsequent fault analysis, waveform analysis, etc.

[0053] Attached Figure 3 This is the short-circuit impedance identification result diagram: Figure 3 The figure is the changing trend of the short-circuit impedance value calculated during the experiment. The dotted line is the nameplate impedance parameter of the transformer, which forms a reference with the calculation result. When the transformer has no winding deformation, the short-circuit impedance fluctuates within the range of 0.6%. This error is less than the set short-circuit impedance detection threshold of 2%.

[0054] Attached Figure 4 This is the current detection result diagram of the edge detection algorithm: Figure 4 This is the edge algorithm detection result of a voltage sampling value when the transformer is operating normally. No abnormal mutation points are detected in the original voltage waveform and derivative waveform, which is consistent with the actual situation of normal operation.

[0055] Attached Figure 5 This is the spectrum of the current signal before FIR filtering: Through FFT spectrum analysis, there are multiple frequency components with small amplitudes after the frequency of 50Hz. These harmonics are integer multiples of the main frequency (such as 100Hz, 150Hz, 200Hz, etc.). These harmonics are common in power systems. The horizontal axis of the spectrum graph represents the frequency, and the unit is Hertz (Hz). In the Fourier transform, the signal is converted from the time domain to the frequency domain, and the horizontal axis shows the various frequency components in the signal. The vertical axis represents the amplitude, the strength of the signal at the corresponding frequency. In the frequency domain representation of the Fourier transform, the vertical axis shows the amplitude of each frequency component, indicating the energy or importance of the frequency in the signal.

[0056] Attached Figure 6 The current signal spectrum after FIR filtering: After FIR filtering, the main frequency components of the signal are clearly visible. The filter effectively eliminates high-frequency interference and retains the 50 Hz power frequency signal. It can be better used for impedance calculation and abnormal mutation point detection of power frequency power systems.

[0057] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An electrical signal acquisition system based on intelligent dynamic sampling, characterized in that: include: Signal acquisition module, signal conditioning module, mutation detection module, impedance calculation module, host computer; The signal acquisition module includes a first ADC module, a second ADC module, a first MCU module, and a second MCU module; the voltage and current are simultaneously acquired through the first ADC module and the second ADC module, and are converted into digital signals and transmitted to the signal conditioning module; The signal conditioning module performs preliminary processing on the collected signals, and the output voltage and current signals are input into the impedance calculation module and the mutation detection module; The impedance calculation module calculates the short-circuit impedance of the electrical equipment using the current and voltage signals, and analyzes the voltage and current signals using a least squares identification algorithm; The mutation detection module uses an improved edge detection algorithm to monitor rapid changes and mutations in the waveform; The host computer receives feedback information from the mutation detection module and the impedance calculation module, and controls the adjustment of the sampling rate according to the signal state monitored in real time.

2. The electrical signal acquisition system based on intelligent dynamic sampling according to claim 1, characterized in that: The first MCU module and the second MCU module control the load conversion sampling rate, the first ADC module and the second ADC module are responsible for converting analog quantities into digital quantities, and two acquisition systems are used. The voltage and current signals acquired by the first set are used to calculate the short-circuit impedance, and the voltage and current signals acquired by the second set are used for the mutation detection module; The signal processing module pre-processes and conditions the current and voltage signals obtained from the signal acquisition module to eliminate noise and enhance signal quality; The mutation detection module uses an edge detection algorithm to analyze the changes in current and voltage waveforms, detect whether there are mutations in the electrical signal, and trigger the dynamic sampling control of the system based on the detection results; The impedance calculation module calculates the short-circuit impedance of the transformer in real time, determines whether the device is abnormal by calculating the relationship between the current and voltage signals, and controls the dynamic sampling of the system according to the judgment result; The host computer serves as the central control unit of the system. The host computer communicates with each module and adjusts the working state of the system by real-time analysis of collected data and monitoring results.

3. An electrical signal acquisition method based on intelligent dynamic sampling using the system as claimed in any one of claims 1 or 2, characterized in that: The two ADC modules collect voltage and current signals according to the default sampling frequency; The impedance calculation module is used to analyze the voltage and current data collected by the first ADC module in real time to calculate the short-circuit impedance; adjusting a sampling mode according to the short-circuit impedance; Continuously monitoring the changes of the current and voltage waveforms acquired by the second ADC module through the mutation detection module; adjusting the sampling mode according to the change of the waveform; When adjusting the sampling mode, the data in the switching period is smoothly transitioned to obtain the collection result of the electrical signal.

4. The electrical signal acquisition method based on intelligent dynamic sampling according to claim 3, characterized in that: The default sampling frequency includes: the system default monitoring mode sampling rate is 1kHz, and the ADC module starts to collect voltage and current signals according to the default sampling frequency.

5. The electrical signal acquisition method based on intelligent dynamic sampling according to claim 4, characterized in that: The calculating of the short-circuit impedance includes calculating the short-circuit impedance based on a least squares identification algorithm; According to the loop equation of the transformer model, the relationship of the instantaneous value is expressed as: ， ， in, , is the primary and secondary winding voltage; , is the number of turns of the primary and secondary windings; is the main magnetic flux between the windings; , is the leakage inductance of the primary and secondary sides; , is the primary and secondary winding current; , is the primary and secondary side resistance; The standard form converted to least squares identification is: ， Where k is the transformer ratio, , is the primary and secondary winding voltage; , is the leakage inductance of the primary and secondary sides; , is the primary and secondary winding current; , are the primary and secondary side resistances.

6. The electrical signal acquisition method based on intelligent dynamic sampling according to claim 5, characterized in that: The short-circuit impedance also includes storing the calculated short-circuit impedance in a host computer, and the host computer records the short-circuit impedance value collected each time as time series data and stores it in a database; Each record should contain the timestamp, impedance value and acquisition frequency, and the reference value Z should be set according to the nameplate. base , the collected impedance value Z current Compare with the baseline value and calculate the rate of change: ， The change threshold is set to 2.5%. When the change rate exceeds the threshold, an alarm signal is generated, and the sampling mode is adjusted through the host computer, and a sampling rate of 2.5 kHz is selected.

7. The electrical signal acquisition method based on intelligent dynamic sampling according to claim 6, characterized in that: The waveform changes include eliminating the high-frequency noise generated during sampling through the FIR filter, and the output signal is expressed as: ， Where y[n] is the output of the filter at time point n, x[n] is the input of the filter at time point n, h[k] is the impulse response coefficient of the filter, M is the order of the filter plus 1, and n is the current time index; The edge detection algorithm is used to detect the points where the amplitude of the current or voltage signal changes dramatically to determine whether the signal has a sudden change; the original signal is differentiated to obtain the rate of change and ; In the discrete case, the differential is approximated by the difference, where is the sampling time interval; ， ， The threshold is passed through the mean of the differentiated signal , and standard deviation and To set: ， ， Continuous monitoring is performed through a sliding time window, where the sliding step is the inverse of the sampling rate; like Greater than or Greater than , it is judged as a mutation; When a mutation is detected, the sampling mode is adjusted to select a 2.5 kHz sampling rate.

8. The electrical signal acquisition method based on intelligent dynamic sampling according to claim 7, characterized in that: The adjustment of the sampling mode also includes that when the short-circuit impedance change rate is less than 2.5% and no waveform mutation is detected, the host computer outputs an instruction to readjust the sampling mode and select a 1 kHz sampling rate; The smooth transition includes generating a synchronous clock signal for all acquisition channels through a synchronous clock source and generating a timestamp for the sampling point; At the moment of sampling switching, an interpolation algorithm is used to smooth the transition: ， Among them, V interp is the interpolated data value, and is the adjacent real data point before and after the switching, t is the interpolation time, located at and middle.

9. A computer device comprising: Memory and processor; The memory stores a computer program, wherein the processor implements the steps of any one of the methods of claims 3-8 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 3 to 8 are implemented.

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