An electrical signal acquisition system based on intelligent dynamic sampling

Abstract

The present invention belongs to the field of power equipment monitoring, and discloses an electrical signal acquisition system based on intelligent dynamic sampling, including: a signal acquisition module, a signal conditioning module, a mutation detection module, an impedance calculation module, and a 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 system adopts real-time calculation of short-circuit impedance and waveform mutation detection for collaborative judgment. When the equipment is running smoothly, the system sampling rate remains low frequency; when the impedance changes or the waveform mutates, the sampling rate is automatically increased to accurately record important signal changes, and an improved edge detection algorithm based on FIR filter is used for waveform mutation detection. The system 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.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment monitoring, and particularly 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. A typical online monitoring system collects electrical signals such as current and voltage at a fixed frequency to analyze the operating state of the equipment. However, such a sampling system faces two major challenges: First, due to the complex operating conditions of electrical equipment, the change frequency of signals varies greatly under different conditions, and it is difficult for a fixed sampling rate to balance accuracy and efficiency; second, at a high sampling rate, the amount of data is large, resulting in a large amount of data redundancy and increasing the storage and transmission burden. Therefore, the fixed sampling rate mode of traditional sampling systems is difficult to meet the requirements of real-time monitoring of transformer winding deformation.

[0003] To solve the above problems, dynamic sampling technology has been proposed in recent years, which can automatically adjust the sampling frequency when the operating state of the equipment changes. In this way, the sampling rate can be reduced during the stable operation of the equipment to reduce redundant data; while when the equipment has an abnormality, the sampling rate can be increased to ensure the capture of key state change information. However, existing dynamic sampling technologies mostly rely on the judgment of a single parameter of the equipment working condition, unable to effectively integrate multi-dimensional information, and lacking 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 existing problems, the present invention is proposed.

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

[0006] To solve the above technical problems, the present invention provides the following technical solution: An electrical signal acquisition system based on intelligent dynamic sampling, comprising:

[0007] A signal acquisition module, a signal conditioning module, a mutation detection module, an impedance calculation module, and a host computer;

[0008] 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 after being converted into digital signals, they are transmitted to the signal conditioning module;

[0009] The signal conditioning module performs preliminary processing on the acquired signals, and the output voltage and current signals are input into the impedance calculation module and the mutation detection module;

[0010] 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 through the least squares identification algorithm;

[0011] The mutation detection module uses an improved edge detection algorithm to monitor the rapid changes and mutations of the waveform;

[0012] The host computer receives the feedback information from the mutation detection module and the impedance calculation module, and controls the adjustment of the sampling rate according to the real-time monitored signal state.

[0013] As a preferred system of the electrical signal acquisition method based on intelligent dynamic sampling according to the present invention, wherein: the first MCU module and the second MCU module control the load to convert the sampling rate, the first ADC module and the second ADC module are responsible for converting analog quantities into digital quantities, and two sets of 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;

[0014] The signal processing module preprocesses and conditions the current and voltage signals obtained from the signal acquisition module, eliminates noise and enhances the signal quality;

[0015] The mutation detection module adopts an edge detection algorithm to analyze the changes in the current and voltage waveforms, detect whether mutations occur in the electrical signals, and trigger the dynamic sampling control of the system according to the detection results;

[0016] The impedance calculation module calculates the short-circuit impedance of the transformer in real time, judges whether the equipment is abnormal by calculating the relationship between the current and voltage signals, and controls the dynamic sampling of the system according to the judgment results;

[0017] 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 analyzing the acquired data and monitoring results in real time.

[0018] On the other hand, an electrical signal acquisition method based on intelligent dynamic sampling using the system according to the present invention includes:

[0019] Two sets of ADC modules respectively acquire voltage and current signals according to the default sampling frequency;

[0020] The impedance calculation module is used to analyze the voltage and current data acquired by the first ADC module in real time and calculate the short-circuit impedance;

[0021] Adjust the sampling mode according to the short - circuit impedance;

[0022] Through the mutation detection module, continuously monitor the changes in the current and voltage waveforms obtained by the second ADC module;

[0023] Adjust the sampling mode according to the changes in the waveforms;

[0024] When adjusting the sampling mode, perform smooth transition on the data during the switching period to obtain the acquisition result of the electrical signal.

[0025] As a preferred method of the electrical signal acquisition method based on intelligent dynamic sampling according to the present invention, wherein: the default sampling frequency includes that the system default monitoring mode sampling rate is 1 kHz, and the ADC module starts to acquire voltage and current signals according to the default sampling frequency.

[0026] As a preferred method of the electrical signal acquisition method based on intelligent dynamic sampling according to the present invention, wherein: calculating the short - circuit impedance includes calculating the short - circuit impedance based on the least - squares identification algorithm;

[0027] According to the loop equation of the transformer model, the relationship of instantaneous values is expressed as:

[0028] ,

[0029] ,

[0030] wherein, 、 are the primary and secondary winding voltages; 、 are the primary and secondary winding turns; is the main magnetic flux between windings; 、 are the primary and secondary leakage inductances; 、 are the primary and secondary winding currents; 、 are the primary and secondary resistances;

[0031] Converted into the standard form of least - squares method identification as:

[0032] ,

[0033] wherein, k is the transformer turns ratio, 、 are the primary and secondary winding voltages; 、 are the primary and secondary leakage inductances; 、 are the primary and secondary winding currents; , are the primary and secondary resistances.

[0034] As a preferred method of the electrical signal acquisition method based on intelligent dynamic sampling according to the present invention, wherein: the short-circuit impedance further includes storing the calculated short-circuit impedance in the upper computer, and the upper computer records each collected short-circuit impedance value as time series data and stores it in the database;

[0035] Each record should include a timestamp, an impedance value, and a sampling frequency, and set a reference value Z according to the nameplate base , and compare the collected impedance value Z current with the reference value to calculate the change rate:

[0036] ,

[0037] Set the change threshold to 2.5%. When the change rate exceeds the threshold, generate an alarm signal and adjust the sampling mode through the upper computer to select a sampling rate of 2.5 kHz.

[0038] As a preferred method of the electrical signal acquisition method based on intelligent dynamic sampling according to the present invention, wherein: the change of the waveform includes eliminating the high-frequency noise generated in sampling through a FIR filter, and the output signal is expressed as:

[0039] ,

[0040] 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;

[0041] Detect the points with sharp amplitude changes in the current or voltage signal through an edge detection algorithm to determine whether the signal has a mutation; differentiate the original signal to obtain the change rate and ; in the discrete case, approximate the differential with a difference, where is the sampling time interval;

[0042] ,

[0043] ,

[0044] The threshold is set through the mean value , and the standard deviation and as follows:

[0045] ,

[0046] ,

[0047] Continuous monitoring is performed by sliding a time window, and the sliding step size is the reciprocal of the sampling rate;

[0048] If is greater than or is greater than , it is determined as a mutation;

[0049] When a mutation is detected, the sampling mode is adjusted and selected to be a sampling rate of 2.5 kHz.

[0050] As a preferred method of the electrical signal acquisition method based on intelligent dynamic sampling according to the present invention, wherein: the adjustment of the sampling mode further 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 re-adjust the sampling mode and select a sampling rate of 1 kHz;

[0051] The smooth transition includes generating a synchronous clock signal for all acquisition channels through a synchronous clock source to generate a timestamp for the sampling points;

[0052] At the sampling switching moment, the interpolation algorithm is used for smooth transition:

[0053] ,

[0054] wherein, V interp is the interpolated data value, and are adjacent real data points before and after the switch, t is the interpolation moment, and is located and in the middle.

[0055] A computer device, comprising: a memory and a processor; the memory stores a computer program, wherein: when the processor executes the computer program, the steps of the method described in any one of the present invention are implemented.

[0056] A computer-readable storage medium, on which a computer program is stored, wherein: when the computer program is executed by a processor, the steps of the method described in any one of the present invention are implemented.

[0057] Advantages 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 at the same time applies the 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. Description of the Drawings

[0058] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0059] Figure 1 Schematic diagram of the acquisition system structure of an electrical signal acquisition system based on intelligent dynamic sampling provided for the first embodiment of the present invention;

[0060] Figure 2 Voltage acquisition waveform at high sampling rate in an electrical signal acquisition system based on intelligent dynamic sampling provided for the third embodiment of the present invention;

[0061] Figure 3 Short-circuit impedance identification result diagram of an electrical signal acquisition system based on intelligent dynamic sampling provided for the third embodiment of the present invention;

[0062] Figure 4 Edge detection algorithm current detection result diagram of an electrical signal acquisition system based on intelligent dynamic sampling provided for the third embodiment of the present invention;

[0063] Figure 5 Current signal spectrogram of an electrical signal acquisition system based on intelligent dynamic sampling before FIR filtering provided for the third embodiment of the present invention;

[0064] Figure 6 Current signal spectrogram of an electrical signal acquisition system based on intelligent dynamic sampling after FIR filtering provided for the third embodiment of the present invention. Specific embodiments

[0065] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific embodiments of the present invention in conjunction with the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

[0066] Embodiment 1, referring to Figure 1 , which is an embodiment of the present invention, provides an electrical signal acquisition system based on intelligent dynamic sampling, including:

[0067] Signal acquisition module, signal conditioning module, mutation detection module, impedance calculation module, host computer; The signal acquisition module is responsible for obtaining voltage and current signals from power equipment. When the system starts, the acquisition module begins signal sampling, converts analog signals into digital signals, and simultaneously obtains voltage and current through the first ADC module and the second ADC module. The acquired digital signals are transmitted to the signal conditioning module through a hardware interface, and the signal conditioning module performs preliminary processing on the acquired signals. The voltage and current signals output by the signal conditioning module are input into the impedance calculation module. This module calculates the short-circuit impedance of electrical equipment using current and voltage signals, mainly by analyzing 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 rapid changes and mutations in waveforms. The host computer, as the control center of the system, is responsible for managing the data of all acquisition modules and automatically adjusting the working mode of the system according to the acquired data, calculation results, and detected abnormal information. After receiving feedback information from the mutation detection module and the impedance calculation module, the host computer controls the adjustment of the sampling rate according to the real-time monitored signal status.

[0068] Furthermore, the signal acquisition module is responsible for obtaining electrical signals such as current and voltage from the transformer and converting them into digital signals for subsequent processing. The MCU controller is responsible for converting the sampling rate, and the ADC is responsible for converting analog quantities into digital quantities. Two sets of 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. 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 signals are suitable for subsequent analysis and calculation. It includes processing such as filtering, amplification, and anti-interference to eliminate noise and enhance signal quality.

[0069] The mutation detection module detects whether mutations occur in electrical signals (such as faults, abnormal waveform changes, etc.) and triggers the dynamic sampling control of the system according to the detection results. This module uses an edge detection algorithm to analyze the changes in current and voltage waveforms and determine whether the signal has changed violently. 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 determines whether the equipment is abnormal (such as winding deformation). And it triggers the dynamic sampling control of the system according to the judgment result. 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 state of the system through real-time analysis of the acquired data and monitoring results to ensure the efficiency and accuracy of the monitoring process.

[0070] System initialization and default sampling mode setting. After the system starts, it enters the default monitoring mode with the sampling rate set to 1 kHz. The ADC module starts the basic acquisition of voltage and current signals, and the data compression and transmission module starts working. At the same time, the system initializes the short-circuit impedance calculation and waveform mutation detection and enters the monitoring standby state.

[0071] Perform real-time monitoring of short-circuit impedance and threshold determination. 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. Compare the impedance value calculated in real time with the preset reference impedance value. If the impedance change amplitude exceeds the preset threshold of 2.5%, automatically increase the sampling rate to 2.5 kHz to ensure that detailed data of electrical signals are accurately collected.

[0072] Waveform mutation detection and high-frequency sampling switching. 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.

[0073] The system configures accurate timestamps in multi-channel data acquisition and uses a synchronous clock 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.

[0074] When the device detects that the states of impedance and waveform output return to normal, the system restores the sampling rate to the low sampling frequency (1 kHz). The data compression and transmission module preferentially processes the new status data and integrates and transmits the low-frequency data to the storage system.

[0075] Embodiment 2, an embodiment of the present invention, provides an electrical signal acquisition method based on intelligent dynamic sampling, including:

[0076] S1: Two sets of ADC modules respectively collect voltage and current signals according to the default sampling frequency.

[0077] The default sampling rate of the system's default monitoring mode is 1 kHz, and the ADC module starts to collect voltage and current signals according to the default sampling frequency.

[0078] S2: Use the impedance calculation module to analyze the voltage and current data collected by the first ADC module in real time and calculate the short-circuit impedance.

[0079] According to the transformer model, write the loop equation, and the relationship of instantaneous values can be expressed as:

[0080] ,

[0081] ,

[0082] In the formula, and are the voltages of the primary and secondary windings; and are the number of turns of the primary and secondary windings; is the main magnetic flux between windings; and are the leakage inductances of the primary and secondary sides; and are the currents of the primary and secondary windings; and are the resistances of the primary and secondary sides.

[0083] It is transformed into the standard form of least squares identification as:

[0084] ,

[0085] where k is the transformer turns ratio, and are the voltages of the primary and secondary windings; and are the leakage inductances of the primary and secondary sides; and are the currents of the primary and secondary windings; and are the resistances of the primary and secondary sides.

[0086] It should be noted that the least squares method is a classical parameter estimation method. It is based on the principle of minimizing the sum of squared errors to achieve the best fitting effect of the model. The least squares method can be applied not only to the parameter estimation of static systems but also extended to the parameter solution of dynamic systems. In addition, the least squares method is applicable to both linear and nonlinear systems. It directly solves the parameters in linear systems, while for nonlinear systems, it can be estimated through methods such as nonlinear least squares or linearization. The least squares method can also be used for off-line and on-line system parameter identification. Off-line identification is suitable for static systems or experimental data, while on-line identification is suitable for real-time monitoring and control systems. In the identification of transformer winding reactance parameters, the least squares method can effectively identify the reactance parameters quickly while ensuring accuracy, providing an efficient technical means for the condition monitoring and fault prediction of power equipment. The least squares algorithm is a mathematical process of estimating unknown parameters in a certain mathematical model by minimizing the sum of squares of the optimization objective function. Generally, the least squares identification algorithm is a one-time completion algorithm, which requires a large amount of computation and occupies a large storage space, making it difficult to achieve on-line identification. The recursive least squares method uses the recursive estimation of parameters. The k-th parameter estimation value is the sum of the (k - 1)-th estimation value and the correction amount, and as the number of recursions increases, the correction amount gradually decreases until it approaches 0, and the algorithm converges near the true value, enabling on-line identification of winding parameters.

[0087] S3: Adjust the sampling mode according to the short - circuit impedance.

[0088] Store the calculated short - circuit impedance in the host computer. The host computer records each collected short - circuit impedance value as time - series data and stores it in the database. Each record should include a timestamp, impedance value, and sampling frequency. Set the reference value Z according to the nameplate. base , and compare the collected impedance value Z current with the reference value to calculate the percentage error:

[0089] ,

[0090] Set the change threshold to 2.5%. When the change rate exceeds the threshold, generate an alarm signal and output an instruction to the STM32 (microcontroller) through the host computer to control the ADS1115 (multi - range programmable analog - to - digital converter) to select a sampling rate of 2.5 kHz.

[0091] S4: Continuously monitor the changes in the current and voltage waveforms obtained by the second ADC module through the mutation detection module.

[0092] Eliminate the high - frequency noise that is easily generated during sampling through the FIR filter. 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:

[0093] ,

[0094] 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; in the design of this filter, the lower cut - off frequency of the passband is set to 49 Hz and the upper cut - off frequency is 51 Hz. 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, thus more precisely separating the passband and stopband.

[0095] S5: Adjust the sampling mode according to the changes in the waveform.

[0096] Detect the points with drastic amplitude changes in the current or voltage signal through the edge - detection algorithm to determine whether the signal has mutated. Differentiate the original signal to obtain the change rate and. In the discrete case, approximate the differential with the difference, where is the sampling time interval:

[0097] ,

[0098] ,

[0099] It should be noted that traditional mutation detection methods may be affected by noise and have limited detection accuracy for mutation points. The edge detection algorithm improved based on FIR can sensitively capture the points with drastic amplitude changes in the signal and regard 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 anomaly analysis.

[0100] Set a threshold for the rate of change to determine whether a mutation occurs. The threshold is set by the mean value , and the standard deviation and as follows:

[0101] ,

[0102] ,

[0103] Continuous monitoring is carried out through a sliding time window, and the sliding step size is the reciprocal of the sampling rate;

[0104] If is greater than or is greater than , it is judged as a mutation.

[0105] When a mutation is detected, the adjustment of the sampling mode is carried out and the selected sampling rate is 2.5 kHz.

[0106] Furthermore, the sampling rate is dynamically adjusted according to the rate of change of the short-circuit impedance and the waveform mutation situation, reducing the continuous use of high sampling rate, saving system resources and reducing energy consumption. This innovation enables the system to adaptively switch to a lower sampling rate under low rate-of-change conditions, improving efficiency while ensuring data integrity, and better conforming to the design concept of modern smart grid systems.

[0107] It should also be noted that when the rate of change of the short-circuit impedance is less than 2.5% and no waveform mutation is detected, the host computer outputs an instruction to the STM32 to control the two ADS1115s to select a sampling rate of 1 kHz.

[0108] S6: When making the adjustment of the sampling mode, smooth transition is performed on the data during the switching period to obtain the acquisition result of the electrical signal.

[0109] Generate a synchronous clock signal for all acquisition channels through a synchronous clock source to generate timestamps for sampling points. At the sampling switching moment, use an interpolation algorithm for smooth transition:

[0110] ,

[0111] where V interp is the data value after interpolation, and are adjacent real data points before and after the switch, t is the interpolation moment, and it is located between and .

[0112] 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, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs, etc., which can store program codes.

[0113] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device.

[0114] More specific examples (nonexhaustive list) of computer-readable media include the following: electrical connections (electronic devices) having one or more wirings, portable computer disk cartridges (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber devices, and portable compact disc read-only memory (CDROM). Additionally, the computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then stored in a computer memory.

[0115] 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 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, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGA), field programmable gate arrays (FPGA), etc.

[0116] Example 3, referring to Figures 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.

[0117] Appendix Figure 1 is a schematic diagram of the acquisition system structure: Figure 1 is a block diagram of the intelligent dynamic sampling system, which includes two sets of signal acquisition modules for collecting voltage and current. Each set includes an MCU (microcontroller unit) and an ADC (analog-to-digital converter). The collected signals are input to 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 upper computer. After discrimination by the upper computer, instructions to change the sampling rate are sent to the two MCU controllers.

[0118] Appendix Figure 2 is the voltage acquisition waveform 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 collecting more data points per second, thus enabling more precise capture of the details in the voltage waveform. It can provide more information in subsequent fault analysis, waveform analysis, etc.

[0119] Appendix Figure 3It is a graph of short-circuit impedance identification results: Figure 3 It is the changing trend of the short-circuit impedance value calculated during the experiment. The dotted line is the impedance parameter on the transformer nameplate, which forms a reference with the calculation result. When there is no winding deformation in the transformer, the short-circuit impedance fluctuates within the range of 0.6%, and this error is less than the set short-circuit impedance detection threshold of 2%.

[0120] Appendix Figure 4 It is a graph of the current detection result of the edge detection algorithm: Figure 4 It is the detection result of the edge algorithm for the sampled voltage value of a section during the normal operation of the transformer. No abnormal mutation points are detected in both the original voltage waveform and the derivative waveform, which conforms to the actual situation of normal operation.

[0121] Appendix Figure 5 It is the spectrum diagram of the current signal before FIR filtering: Through FFT spectrum analysis, there are multiple frequency components with small amplitudes after the frequency of 50 Hz. These harmonics are integer multiples of the main frequency (such as 100 Hz, 150 Hz, 200 Hz, etc.). These harmonics are common in power systems. The abscissa of the spectrum diagram represents frequency, with the unit of hertz (Hz). In the Fourier transform, the signal is converted from the time domain to the frequency domain, and the abscissa shows the various frequency components in the signal. The ordinate represents amplitude, which is the intensity of the signal at the corresponding frequency. In the frequency-domain representation of the Fourier transform, the ordinate shows the amplitude of each frequency component, indicating the energy or importance of that frequency in the signal.

[0122] Appendix Figure 6 It is the spectrum diagram of the current signal 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 power frequency signal of 50 Hz. It can be better used for impedance calculation and abnormal mutation point detection in the power frequency power system.

[0123] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within 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 device using the current and voltage signals obtained by the first ADC module, 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 the rapid changes and mutations of the current and voltage waveforms obtained by the second ADC module; The host computer receives feedback information from the mutation detection module and the impedance calculation module, controls the adjustment of the sampling rate according to the signal state monitored in real time, and uses an interpolation algorithm to smoothly transition the data in the adjustment period when the sampling rate is adjusted; Calculating 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: Among them, U1 and U2 are the primary and secondary winding voltages; N1 and N2 are the primary and secondary winding turns; Φ M is the main magnetic flux between the windings; L1, L2 are the primary and secondary leakage inductances; i1, i2 are the primary and secondary winding currents; R1, R2 are the primary and secondary resistances; The standard form converted to least squares identification is: Among them, k is the transformer ratio, U1 and U2 are the primary and secondary winding voltages; L1 and L2 are the primary and secondary side leakage inductances; i1 and i2 are the primary and secondary winding currents; R1 and R2 are the primary and secondary side resistances; 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 rate is adjusted through the host computer, and a sampling rate of 2.5 kHz is selected; 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[p] 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 change rates U' and I'; in the discrete case, the differential is used to approximate the differential, where Δt is the sampling time interval; The threshold is the mean μ of the differentiated signal. U , μ I and standard deviation σ U and σ I To set: T U =μ U +3s U T I =μ I +3s I Continuous monitoring is performed through a sliding time window, where the sliding step is the inverse of the sampling rate; If U'[i] is greater than T U or I'[i] is greater than T I , it is judged as a mutation; When a mutation is detected, the sampling rate is adjusted to 2.5 kHz.

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 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 conditioning 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 and 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 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, V i and V i+1 is the adjacent real data point before and after the switch, q is the interpolation time, located at T i+1 and T i middle.

6. 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 3-5 when executing the computer program.

7. 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 5 are implemented.

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