A panoramic identification method, system, equipment, and medium for distribution network short-circuit parameters based on multi-frequency disturbance excitation.

Abstract

This invention discloses a method, system, equipment, and medium for panoramic identification of short-circuit parameters in distribution networks based on multi-frequency disturbance excitation. It belongs to the field of power system condition monitoring and fault analysis technology. The method includes: designing and generating a composite disturbance signal; injecting the composite disturbance signal into the bus under test of the distribution network through a controllable current source, and acquiring time-domain waveform data of the injected current signal and the bus voltage; performing frequency domain transformation to separate and extract voltage and current phasors in the frequency domain; obtaining short-circuit impedance measurements at each frequency point using a first algorithm; and identifying equivalent circuit parameters using the short-circuit impedance measurements at several characteristic frequency points through a parameter fitting method. Compared to traditional single-frequency measurements that can only acquire single-point data, this method provides more comprehensive panoramic parameter information, accurately reflecting the impedance characteristics of the distribution network at different frequencies, and is suitable for modern distribution networks containing a large number of nonlinear loads and power electronic equipment.

Description

Technical Field

[0001] This invention relates to the field of power system condition monitoring and fault analysis technology, specifically to a method, system, equipment, and medium for panoramic identification of distribution network short-circuit parameters based on multi-frequency disturbance excitation. Background Technology

[0002] With the continuous expansion of distribution network scale and the widespread integration of distributed power sources, accurately obtaining distribution network short-circuit parameters is of great significance for power grid planning, protection setting, and fault analysis. Short-circuit parameters mainly include short-circuit impedance, system equivalent reactance, and resistance, which directly affect the accuracy of fault current calculation. Existing short-circuit parameter identification methods mainly suffer from the following technical shortcomings: In recent years, online identification methods based on disturbance excitation have emerged. These methods inject small-amplitude test signals into the bus and deduce short-circuit parameters based on the voltage-current response relationship. While these methods avoid power outage testing, they generally use a single-frequency sinusoidal disturbance signal as the excitation source. A single-frequency disturbance signal can only obtain impedance information at that specific frequency and cannot reflect the impedance characteristics of the power grid at different frequencies. The impedance of a real distribution network has obvious frequency-dependent characteristics. Factors such as line distributed capacitance, transformer excitation characteristics, and load nonlinearity can all cause differences in impedance values ​​at different frequencies. Using parameters obtained from single-frequency measurements to represent the full-frequency characteristics will result in a large deviation, especially in modern distribution networks containing a large number of nonlinear loads and power electronic equipment, where this deviation is even more pronounced.

[0003] Existing methods also face the problem of background harmonic interference during measurement. Numerous harmonic sources exist in power distribution networks; background harmonics generated by equipment such as frequency converters, rectifiers, and electric arc furnaces can be superimposed on the measurement signal, affecting identification accuracy. Traditional single-frequency disturbance methods struggle to effectively distinguish between the injected test signal and background harmonics, leading to significant interference affecting the measurement results. When the background harmonic content is high, identification may even fail.

[0004] Existing methods typically require multiple repeated measurements to obtain impedance data at different frequencies, resulting in long measurement cycles and poor real-time performance. Each change in excitation frequency necessitates re-injecting the signal and waiting for the system response to reach steady state, a process that can last tens of minutes or even longer. During this time, the operating status of the distribution network may have changed, leading to inconsistent measurement conditions at different frequencies and affecting the reliability of the final identification results. Summary of the Invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by this invention is: how to overcome the shortcomings of existing single-frequency disturbance methods such as insufficient spectral information, weak anti-interference ability, and low efficiency of multi-frequency measurement, and to provide a method that can quickly, accurately, and with anti-interference capabilities achieve panoramic identification of short-circuit parameters across the entire frequency band of the distribution network.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a panoramic identification method for short-circuit parameters of distribution networks based on multi-frequency disturbance excitation, comprising, Design and generate a composite disturbance signal consisting of sinusoidal components with independent characteristic frequency points superimposed with a set amplitude; The composite disturbance signal is injected into the bus under test of the distribution network through a controllable current source, and the time-domain waveform data of the injected current signal and the bus voltage are collected by a synchronous sampling device. The acquired time-domain waveform data is transformed in the frequency domain, and the voltage phasor and current phasor corresponding to each characteristic frequency point are separated and extracted in the frequency domain. Based on the extracted voltage and current phasors at each characteristic frequency point, the short-circuit impedance measurement value at each frequency point is obtained through the first algorithm. By using short-circuit impedance measurements at several characteristic frequency points, and through parameter fitting methods, equivalent circuit parameters that characterize the short-circuit characteristics of the distribution network across the entire frequency band can be identified.

[0008] As a preferred embodiment of the panoramic identification method for distribution network short-circuit parameters based on multi-frequency disturbance excitation described in this invention, the step of designing and generating a composite disturbance signal consisting of sinusoidal components of mutually non-interfering characteristic frequency points superimposed with a set amplitude includes, Based on the background harmonic distribution of the power distribution network, select a set of non-interfering characteristic frequency points; For each selected characteristic frequency point, set the amplitude and initial phase of its sinusoidal component; The sinusoidal components at each characteristic frequency point are superimposed to generate a composite perturbation signal for injection.

[0009] As a preferred embodiment of the panoramic identification method for distribution network short-circuit parameters based on multi-frequency disturbance excitation described in this invention, the step of injecting a composite disturbance signal into the busbar under test of the distribution network through a controllable current source and acquiring time-domain waveform data of the injected current signal and the busbar voltage using a synchronous sampling device includes: The generated composite disturbance signal is controlled by a controllable current source and injected into a designated position of the bus under test for a preset duration. Simultaneously with the signal injection, a clock synchronized with the signal generation is used to synchronously acquire the three-phase voltage of the bus and the three-phase current of the injection point at a preset sampling rate, thereby obtaining voltage time-domain waveform data and current time-domain waveform data. The acquired voltage time-domain waveform data and current time-domain waveform data are subjected to quality checks to verify their completeness and amplitude rationality, and the data that passes the check are output as valid acquisition results.

[0010] As a preferred embodiment of the panoramic identification method for distribution network short-circuit parameters based on multi-frequency disturbance excitation described in this invention, the step of performing frequency domain transformation on the acquired time-domain waveform data and separating and extracting the voltage phasor and current phasor corresponding to each characteristic frequency point in the frequency domain includes: Fast Fourier transforms were performed on the time-domain waveform data of voltage and current respectively to obtain the corresponding frequency-domain complex number sequences; From the frequency domain complex sequence, locate and extract the voltage phasor and current phasor corresponding to each pre-defined characteristic frequency point; The voltage and current phasors extracted at each characteristic frequency point are subjected to windowed averaging.

[0011] As a preferred embodiment of the panoramic identification method for distribution network short-circuit parameters based on multi-frequency disturbance excitation described in this invention, the step of obtaining the short-circuit impedance measurement value at each frequency point through a first algorithm based on the extracted voltage phasors and current phasors at each characteristic frequency point includes: For each characteristic frequency point, the corresponding voltage phasor is divided by the corresponding current phasor to calculate the complex impedance at the current frequency; Based on the topology of the distribution network and the known transformer parameters, the complex impedance is compensated to obtain the compensated system short-circuit impedance. The system short-circuit impedance is decomposed into corresponding resistance and reactance components to form the short-circuit impedance measurement results at each frequency point.

[0012] As a preferred embodiment of the panoramic identification method for distribution network short-circuit parameters based on multi-frequency disturbance excitation described in this invention, the method of identifying equivalent circuit parameters characterizing the short-circuit characteristics of the distribution network across the entire frequency band by utilizing short-circuit impedance measurements at several characteristic frequency points and employing parameter fitting methods includes: To fit the measured short-circuit impedance values ​​at each characteristic frequency point into a continuous frequency response curve, an equivalent circuit mathematical model is established. Substitute the measured short-circuit impedance values ​​at each characteristic frequency point into the mathematical model of the equivalent circuit to construct an overdetermined set of equations with the model parameters as unknowns. The least squares method was used to solve the overdetermined system of equations, and the resistance and inductance parameters in the mathematical model of the equivalent circuit were identified.

[0013] As a preferred embodiment of the panoramic identification method for distribution network short-circuit parameters based on multi-frequency disturbance excitation described in this invention, the identification of resistance and inductance parameters in the equivalent circuit mathematical model includes, Using the identified equivalent circuit parameters, calculate the theoretical short-circuit impedance values ​​at each characteristic frequency point; The calculated theoretical values ​​of short-circuit impedance at each characteristic frequency point are compared with the measured results of short-circuit impedance at the corresponding characteristic frequency point, and the relative error of resistance and relative error of reactance at each frequency point are calculated. Determine whether the relative error of resistance and the relative error of reactance exceed the preset thresholds; If none of them exceed the limit, then output the final equivalent circuit parameters; If any error exceeds the limit, the weight allocation in the parameter fitting process is adjusted, and the parameter identification is performed again until all errors meet the requirements.

[0014] This invention provides a panoramic identification system for short-circuit parameters in distribution networks based on multi-frequency disturbance excitation.

[0015] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a panoramic identification system for short-circuit parameters of distribution networks based on multi-frequency disturbance excitation, comprising: a signal generation module, a signal acquisition module, a frequency domain transformation module, a calculation module, and an output module; The signal generation module is designed to generate a composite disturbance signal consisting of sinusoidal components with mutually independent characteristic frequency points superimposed with a set amplitude. The signal acquisition module injects a composite disturbance signal into the busbar under test of the distribution network through a controllable current source, and uses a synchronous sampling device to acquire the time-domain waveform data of the injected current signal and the busbar voltage. The frequency domain transformation module performs frequency domain transformation on the acquired time-domain waveform data, and extracts the voltage phasor and current phasor corresponding to each characteristic frequency point in the frequency domain. The calculation module obtains the short-circuit impedance measurement value at each frequency point based on the extracted voltage phasor and current phasor at each characteristic frequency point through a first algorithm. The output module uses short-circuit impedance measurements at several characteristic frequency points and a parameter fitting method to identify equivalent circuit parameters that characterize the short-circuit characteristics of the distribution network across the entire frequency band.

[0016] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, characterized in that the processor executes the computer program to implement the steps of the panoramic identification method for distribution network short-circuit parameters based on multi-frequency disturbance excitation.

[0017] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the panoramic identification method for distribution network short-circuit parameters based on multi-frequency disturbance excitation.

[0018] The beneficial effects of this invention are as follows: This invention uses a multi-frequency composite excitation signal to simultaneously acquire impedance data at multiple frequency points in a single measurement, constructing a complete frequency-impedance characteristic relationship. Compared to traditional single-frequency measurements that can only acquire single-point data, this method provides more comprehensive panoramic parameter information, accurately reflecting the impedance characteristics of the distribution network at different frequencies, and is particularly suitable for modern distribution networks containing a large number of nonlinear loads and power electronic equipment. The multi-frequency composite signal compresses the measurement process that requires multiple repetitions into a single operation, shortening the measurement time and improving efficiency. This invention effectively suppresses background noise interference by optimizing the selection of characteristic frequencies, avoiding the main harmonic frequency bands of the distribution network, and combining frequency domain filtering technology. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 The above is a flowchart of a panoramic identification method for distribution network short-circuit parameters based on multi-frequency disturbance excitation, provided as an embodiment of the present invention. Detailed Implementation

[0021] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0022] Example 1, referring to Figure 1 This is one embodiment of the present invention, which provides a panoramic identification method for short-circuit parameters of distribution networks based on multi-frequency disturbance excitation, including: Existing technologies suffer from drawbacks such as insufficient spectral information in single-frequency measurements, weak background harmonic interference suppression, and low efficiency in multi-frequency measurements. This invention proposes a panoramic identification method for distribution network short-circuit parameters based on multi-frequency disturbance excitation, primarily addressing the following technical problems: 1. Achieve accurate measurement of short-circuit impedance across the entire frequency band. By designing a composite disturbance signal containing multiple characteristic frequency components, the response characteristics of the distribution network at different frequencies are simultaneously excited during a single injection, obtaining impedance data covering a wide frequency band. Based on the multi-frequency domain measurement results, a complete frequency-impedance characteristic relationship is constructed, truly reflecting the frequency dependence characteristics of the distribution network's short-circuit parameters, and improving the comprehensiveness and accuracy of parameter identification.

[0023] 2. Enhance the anti-interference capability of measurement signals. To address the problem of background harmonic interference in the distribution network, the characteristic frequency of the disturbance signal is optimized to avoid the main harmonic frequency band. At the same time, frequency domain filtering and correlation detection technologies are used to effectively separate the injected signal from the background noise, thereby improving the identification reliability in strong interference environments.

[0024] 3. Shorten the parameter identification time cycle. By leveraging the advantages of multi-frequency composite excitation signals, data acquisition at multiple frequency points can be completed in a single measurement process. This reduces the measurement process that traditional methods require to be repeated multiple times to a single operation, significantly improving measurement efficiency and meeting the needs of rapid assessment of distribution network conditions.

[0025] The core idea of ​​this invention is to excite the distribution network using multi-frequency composite disturbance signals, analyze the voltage and current response relationships of different frequency components, simultaneously identify the short-circuit impedance at multiple frequency points, and construct a panoramic view of the frequency characteristics of the distribution network's short-circuit parameters. The technical solution mainly includes five core steps: multi-frequency disturbance signal generation, signal injection and response acquisition, frequency domain decomposition and feature extraction, multi-frequency impedance identification, and parameter fusion and verification.

[0026] 1. During the disturbance signal design phase, based on the frequency response characteristics and background harmonic distribution of the distribution network, several non-interfering characteristic frequency points are selected. The sinusoidal components of these frequencies are superimposed according to an optimized amplitude ratio to form a composite disturbance signal. This signal is injected into the bus under test through a controllable current source device, causing a small fluctuation in the bus voltage.

[0027] 2. During the signal acquisition phase, a high-precision synchronous sampling device is used to record the time-domain waveform data of the injected current and bus voltage. The acquired raw data includes the response caused by the injected signal and the original background signals of the distribution network.

[0028] 3. In the signal processing stage, the acquired time-domain data is subjected to a Fast Fourier Transform (FFT) to convert it to the frequency domain for analysis. In the frequency domain, the voltage and current phasors corresponding to each characteristic frequency point are extracted. By calculating the voltage-to-current ratio and phase difference at the characteristic frequency, the short-circuit impedance value at that frequency is obtained.

[0029] 4. In the parameter identification stage, a short-circuit impedance calculation model is established for each characteristic frequency, comprehensively considering the influence of line impedance, transformer impedance, and system equivalent impedance. Using measurement data from multiple frequency points, the resistance and inductance parameters of the distribution network's equivalent circuit are identified using the least squares fitting method. These parameters accurately describe the impedance characteristics of the distribution network at different frequencies.

[0030] 5. In the result verification stage, the identified parameters are substituted into the theoretical calculation formula to deduce the theoretical impedance value at each frequency point, and compared with the actual measured value for verification. Simultaneously, the rationality of the impedance frequency characteristics is analyzed to ensure that the identification results conform to physical laws. For frequency points with large errors, a secondary identification process is initiated for correction until the identification accuracy of all frequency points meets the requirements.

[0031] S1. Design and generate a composite disturbance signal consisting of sinusoidal components with mutually independent characteristic frequency points superimposed with a set amplitude; S2. Inject the composite disturbance signal into the busbar under test of the distribution network through a controllable current source, and use a synchronous sampling device to collect the time-domain waveform data of the injected current signal and the busbar voltage. S3. Perform frequency domain transformation on the acquired time-domain waveform data, and extract the voltage phasor and current phasor corresponding to each characteristic frequency point in the frequency domain. S4. Based on the extracted voltage phasors and current phasors at each characteristic frequency point, the short-circuit impedance measurement value at each frequency point is obtained through the first algorithm. S5. Using the short-circuit impedance measurements at several characteristic frequency points, the equivalent circuit parameters that characterize the short-circuit characteristics of the distribution network across the entire frequency band are identified through parameter fitting methods.

[0032] Example 2, an embodiment of the present invention, provides a panoramic identification method for distribution network short-circuit parameters based on multi-frequency disturbance excitation, based on the previous embodiment, including: S1. Designing and generating a composite disturbance signal consisting of sinusoidal components with mutually independent characteristic frequency points superimposed at a set amplitude includes the following steps: S11. Based on the background harmonic distribution of the distribution network, select a set of characteristic frequency points that do not interfere with each other.

[0033] The system first needs to determine the characteristic frequency points contained in the composite disturbance signal. Considering the high content of the 5th, 7th, and 11th harmonics in the distribution network, in order to avoid interference, the fundamental frequency of 50 Hz and other non-harmonic frequencies are selected as characteristic frequencies.

[0034] S12. For each selected characteristic frequency point, set the amplitude and initial phase of its sinusoidal component.

[0035] Let the angular frequency of the k-th characteristic frequency component be... The corresponding injection current amplitude is The initial phase is .

[0036] This method employs an equal amplitude allocation strategy, meaning that the current amplitude of all frequency components is equal. Each current component is set to 0.5% of the rated current, ensuring sufficient signal strength without significantly impacting the normal operation of the distribution network. The initial phase of each frequency component is generated using a pseudo-random sequence to ensure a small signal crest factor and avoid excessively large instantaneous values.

[0037] S13. The sinusoidal components at each characteristic frequency point are superimposed to generate a composite perturbation signal for injection.

[0038] Composite disturbance current signal Represented as the superposition of all frequency components: (1) In the formula, Let be the instantaneous value of the composite disturbance current (A), t be time (s), and k be the frequency component number. Let be the angular frequency (rad / s) of the k-th frequency component. Let be the current amplitude (A) of the k-th frequency component. The initial phase (rad) of the k-th frequency component.

[0039] Setting the current to 0.5% of the rated current ensures sufficient signal strength without significantly impacting the normal operation of the distribution network. The initial phase of each frequency component is generated using a pseudo-random sequence to ensure a small signal crest factor and avoid excessively large instantaneous values.

[0040] The generated composite disturbance signal is processed by a digital signal processor to obtain a discrete sampling point sequence. The sampling frequency is set to 2000 Hz, which is much higher than twice the highest characteristic frequency, thus meeting the requirements for signal reconstruction. This sampling sequence is output to a digital-to-analog converter (DAC) and converted into an analog control signal to drive a controllable current source. The controllable current source adopts a voltage-source inverter topology and uses pulse width modulation (PWM) technology to achieve precise control of the output current waveform, allowing the output current to follow the given composite disturbance signal.

[0041] S2. Injecting a composite disturbance signal into the busbar under test of the distribution network through a controllable current source, and acquiring time-domain waveform data of the injected current signal and busbar voltage using a synchronous sampling device, includes the following steps: S21. The generated composite disturbance signal is controlled by a controllable current source and injected into a designated position of the bus under test for a preset duration. During the disturbance signal design phase, based on the frequency response characteristics and background harmonic distribution of the distribution network, several non-interfering characteristic frequency points are selected. The sinusoidal components of these frequencies are superimposed according to an optimized amplitude ratio to form a composite disturbance signal. This signal is injected into the bus under test through a controllable current source device, causing a small fluctuation in the bus voltage.

[0042] The injection point is chosen on the incoming side of the busbar, where the voltage is relatively stable, facilitating accurate measurement of the voltage response. The duration of the injected current is set to 2 seconds, which includes multiple complete cycles of each characteristic frequency component, ensuring the accuracy of frequency domain analysis.

[0043] S22. While injecting the signal, use a clock synchronized with the signal generation to synchronously acquire the three-phase voltage of the bus and the three-phase current of the injection point at a preset sampling rate to obtain voltage time-domain waveform data and current time-domain waveform data.

[0044] Simultaneously with the injection of current, the data acquisition system records the instantaneous values ​​of the three-phase bus voltage and the injected three-phase current. Data acquisition employs the same sampling frequency of 2000 Hz as signal generation, and uses GPS timing signals to achieve strict synchronization of voltage and current sampling, with a time synchronization accuracy better than 1 microsecond. The acquired voltage and current data are processed by an anti-aliasing filter to remove frequency components higher than 1000 Hz, and then converted into digital signals by an analog-to-digital converter for storage.

[0045] S23. Perform quality checks on the acquired voltage time-domain waveform data and current time-domain waveform data, verify the integrity and amplitude rationality, and output the data that passes the check as valid acquisition results.

[0046] In this application embodiment, the quality check in S23 involves checking the quality of the collected raw data, primarily verifying the data's integrity and reasonableness. Data integrity is determined by checking whether the number of sampling points meets expectations; a 2-second sampling time at a 2000 Hz sampling rate should yield 4000 sampling points. Data integrity coefficient. Defined as the ratio of the actual number of sampling points to the theoretical number of sampling points: (2) In the formula, The data integrity coefficient. This represents the actual number of sampling points collected. This represents the theoretically required number of sampling points. When If the data is incomplete, it needs to be collected again.

[0047] Data validity checks include verifying that the voltage amplitude is within 80% to 120% of the rated voltage and that the current amplitude is within the expected injection current range. For data segments that do not meet quality requirements, the system will re-acquire data for that period. Data that passes the quality check is then transmitted to the frequency domain analysis module for further processing. The acquired voltage time-domain sequence is denoted as u(n), and the current time-domain sequence is denoted as i(n), where n represents the sampling point number, ranging from 1 to 4000.

[0048] In one alternative implementation, the quality check of S23 is based on a determination of signal stability.

[0049] For the acquired voltage time-domain waveform data and current time-domain waveform data, calculate their variance or rate of change using a sliding window of preset duration. If the variance of the voltage or current data in any window exceeds a preset percentage (e.g., 1%) of the square of its rated value, or the rate of change exceeds a preset percentage per second (e.g., 5%), then the data segment is considered unstable and needs to be reacquired; otherwise, the data is considered stable and output as a valid acquisition result.

[0050] In another alternative implementation, the quality check of S23 is based on the determination of signal purity (signal-to-noise ratio or harmonic distortion rate).

[0051] For the acquired voltage and current time-domain waveform data, perform Fast Fourier Transform (FFT) to calculate the ratio of the amplitude of the fundamental component to the sum of the amplitudes of the main background harmonics (such as the 5th and 7th harmonics), or calculate the total harmonic distortion (THD). If the signal-to-noise ratio of the voltage or current signal is lower than a preset threshold (e.g., 35 dB), or the THD exceeds a preset threshold (e.g., 5%), the data segment is deemed to be subject to excessive background interference and needs to be reacquired. Otherwise, the data purity is deemed to meet the requirements and is output as a valid acquisition result.

[0052] This invention systematically verifies the integrity of the acquired raw time-domain waveform data and judges the reasonableness of its amplitude range, effectively filtering out invalid data segments caused by sampling anomalies or transient interference, ensuring the authenticity and reliability of the data foundation for the input analysis stage. Furthermore, by integrating optional checking strategies such as signal stability assessment and purity analysis, this method can intelligently identify and exclude low-quality data caused by power grid transient fluctuations or strong background harmonic pollution, enhancing the invention's adaptability and robustness in complex operating environments. This early quality check not only avoids parameter identification deviations caused by erroneous data from the source, ensuring the accuracy of the final panoramic model, but also avoids unnecessary deep computational resource consumption and optimizes overall process efficiency through early detection and immediate resampling of invalid data.

[0053] S3. Perform frequency domain transformation on the acquired time-domain waveform data, and extract the voltage phasor and current phasor corresponding to each characteristic frequency point in the frequency domain, including the following steps: S31. Perform Fast Fourier Transform on the time-domain waveform data of voltage and current respectively to obtain the corresponding frequency-domain complex number sequences.

[0054] The acquired time-domain voltage and current signals contain injected multi-frequency disturbance components as well as the original background signals of the distribution network. Frequency domain analysis is needed to separate the responses at each characteristic frequency. The system performs Fast Fourier Transform on the voltage time-domain sequence u(n) and the current time-domain sequence i(n) respectively, converting the time-domain signals into frequency-domain signals.

[0055] The Fast Fourier Transform (FFT) transforms a discrete-time signal into a complex sequence in the frequency domain. Each element in the frequency domain sequence corresponds to a complex amplitude at a frequency point. The modulus of this complex number represents the amplitude of the frequency component, and the argument represents the phase of the frequency component. The transformation yields a voltage frequency domain sequence U(f) and a current frequency domain sequence I(f), where f represents the frequency variable, and the frequency resolution is the sampling frequency divided by the number of sampling points; that is, 2000 Hz divided by 4000 equals 0.5 Hz.

[0056] S32. Locate and extract the voltage phasor and current phasor corresponding to each pre-set characteristic frequency point from the frequency domain complex sequence.

[0057] Extract the complex values ​​of voltage and current corresponding to four characteristic frequency points from the frequency domain sequence. For the k-th characteristic frequency... Extract the frequency closest to the frequency in the frequency domain sequence. The frequency point is used to obtain the voltage phasor at that frequency. and current phasor .here and They are all complex numbers, containing both amplitude and phase information.

[0058] S33. Perform windowed averaging on the extracted voltage and current phasors at each characteristic frequency point.

[0059] In the embodiment of this application, the frequency domain transformation of S3 is to perform a fast Fourier transform on the acquired voltage time-domain waveform data and current time-domain waveform data respectively to obtain the corresponding frequency domain complex sequence; then, the voltage phasor and current phasor corresponding to each preset feature frequency point are located and extracted from the frequency domain sequence; and further, the phasors of each extracted feature frequency point are subjected to windowed averaging processing to suppress spectral leakage and improve the accuracy of phasor extraction.

[0060] In one alternative implementation, the frequency domain transformation of S3 is achieved by directly calculating the spectrum of the characteristic frequency points using the discrete Fourier transform.

[0061] Based on the principle of discrete Fourier transform, the complex spectrum values ​​of voltage and current time-domain waveform data at each preset characteristic frequency point are directly calculated, thereby obtaining the corresponding voltage phasor and current phasor without the need for full spectrum calculation.

[0062] In another alternative implementation, the frequency domain transformation of S3 is performed using a frequency separation method based on a digital filter bank.

[0063] For each characteristic frequency point, a corresponding digital bandpass filter is designed; these filters are used to filter the voltage and current time-domain waveform data respectively to obtain the time-domain signal corresponding to each frequency component; then, by performing amplitude and phase detection on the filtered time-domain signal, the voltage phasor and current phasor corresponding to each characteristic frequency point are extracted.

[0064] In this embodiment, the windowed averaging process in S33 employs frequency domain windowing technology. For each characteristic frequency point, not only is the value of that frequency point extracted, but also the values ​​of its two adjacent positive and negative frequency points, for a total of five frequency points. Then, a Hanning window weighted average is used to calculate a more accurate characteristic frequency phasor value. Windowing processing can reduce the influence of spectral leakage and improve the accuracy of frequency domain analysis. The voltage phasors of the four characteristic frequencies obtained after windowing processing are denoted as follows: The corresponding current phasor is denoted as These phasor values ​​will be used for subsequent impedance calculations.

[0065] In one alternative implementation, the windowed averaging process in S33 is a weighted average method using preset window function coefficients.

[0066] For each characteristic frequency point, the complex spectral values ​​of multiple consecutive adjacent frequency points centered on that point are extracted from the frequency domain sequence; the preset window function coefficients (e.g., the coefficient sequence corresponding to the Hamming window or Blackman window) are used as weights, and these complex spectral values ​​are multiplied and summed respectively. The summation result is used as the final voltage or current phasor value of that characteristic frequency point.

[0067] In another alternative implementation, the windowed averaging process of S33 is based on the interpolation estimation method of extended neighborhood frequency points.

[0068] For each characteristic frequency point, extract the complex spectral values ​​of five or more adjacent frequency points on both sides from the frequency domain sequence; based on the frequency and complex spectral values ​​of these frequency points, calculate the estimated complex spectral value at the characteristic frequency point through bilinear interpolation, and use the estimated value as the final voltage or current phasor value of the characteristic frequency point.

[0069] This invention utilizes Fast Fourier Transform (FFT) to convert the superimposed multi-frequency responses in the time domain to the frequency domain in a single step, thereby enabling the synchronous and independent location and extraction of voltage and current phasors at each preset characteristic frequency point. This fundamentally changes the traditional method that requires multiple frequency changes for serial measurements.

[0070] This invention optimizes the extraction of frequency domain phasors by applying a windowed average, thereby suppressing measurement errors caused by spectral leakage and the picket fence effect, and ensuring the accuracy of the phasor data at each frequency point.

[0071] S4. Based on the extracted voltage and current phasors at each characteristic frequency point, the short-circuit impedance measurement value at each frequency point is obtained through the first algorithm, including the following steps: S41. For each characteristic frequency point, divide the corresponding voltage phasor by the corresponding current phasor to calculate the complex impedance at the current frequency.

[0072] In the signal processing stage, the acquired time-domain data is subjected to a Fast Fourier Transform (FFT) to convert it to the frequency domain for analysis. In the frequency domain, the voltage and current phasors corresponding to each characteristic frequency point are extracted. By calculating the voltage-to-current ratio and phase difference at the characteristic frequency, the short-circuit impedance value at that frequency is obtained.

[0073] For each characteristic frequency, according to Ohm's law, the equivalent impedance at the measurement point at that frequency is equal to the voltage phasor divided by the current phasor at that frequency. The measurement impedance corresponding to the k-th characteristic frequency... Calculated as: (3) In the formula, Let be the measured impedance (Ω) at the k-th frequency. Let V be the voltage phasor (V) at the k-th frequency. Let be the current phasor (A) at the k-th frequency. It contains information on the magnitude and phase angle of the impedance. The magnitude represents the size of the impedance, and the phase angle reflects the ratio between the resistance and the reactance.

[0074] The complex impedance is decomposed into a real part and an imaginary part, with the real part corresponding to the resistance component. The imaginary part corresponds to the reactance component. However, the measured impedance It is not directly equal to the short-circuit impedance of the busbar, because there are also the effects of line impedance and transformer impedance between the measurement point and the short-circuit point.

[0075] S42. Based on the topology of the distribution network and the known transformer parameters, perform compensation calculations on the complex impedance to obtain the compensated system short-circuit impedance.

[0076] An impedance equivalent model is established based on the distribution network topology, and the short-circuit impedance is calculated from the measured impedance.

[0077] For a typical radial distribution network, the busbar is connected to the upstream power grid via a transformer, and the equivalent impedance of the transformer is... The equivalent impedance on the system side is When current is injected into the busbar, the measured impedance is the series combination of the transformer impedance and the system impedance. If the transformer parameters are known, the system short-circuit impedance can be obtained by subtracting the transformer impedance from the measured impedance. (4) In the formula, Let be the system short-circuit impedance (Ω) at the k-th frequency. Let be the equivalent impedance (Ω) of the transformer at the k-th frequency.

[0078] S43. Decompose the system short-circuit impedance into corresponding resistance and reactance components to form the short-circuit impedance measurement results at each frequency point.

[0079] After transformer impedance compensation, the system short-circuit impedance at four characteristic frequencies is obtained. These impedance values ​​reflect the equivalent impedance characteristics of the system at different frequencies and are direct measurement results of the short-circuit parameters of the distribution network. The short-circuit impedance at each frequency is decomposed into resistance components. and reactance component This forms a frequency characteristic dataset of short-circuit parameters.

[0080] In the embodiment of this application, the first algorithm of S4 is to divide the voltage phasor of each characteristic frequency point by its current phasor to obtain the complex impedance value of that point; subtract the known impedance value of the transformer at the corresponding frequency from the complex impedance value to obtain the short-circuit impedance on the system side; and finally separate the complex form of the short-circuit impedance into a resistive part and a reactant part.

[0081] In one alternative implementation, the first algorithm of S4 is as follows: for each characteristic frequency point, first calculate the amplitude ratio and phase difference of the voltage phasor and the current phasor respectively; use the amplitude ratio as the impedance amplitude and the phase difference as the impedance angle to directly form the impedance in complex form; then, according to the network topology, subtract the impedance of the transformer and the known line segment from the impedance in sequence to obtain the system short-circuit impedance.

[0082] In another alternative implementation, the first algorithm of S4 is: for each characteristic frequency point, according to the superposition theorem, the measured complex voltage is regarded as consisting of two parts: the background system voltage and the voltage drop generated by the injected current on the system impedance; by establishing a circuit equation that includes the unknown impedance of the system, the resistance and reactance components of the short-circuit impedance of the system are directly solved.

[0083] This invention does not simply use the impedance at the measurement point directly. Instead, it further compensates for the measured impedance based on the actual topology of the distribution network and known transformer parameters, eliminating the influence of fixed components such as transformers. This results in a system short-circuit impedance that better represents the system's characteristics. It overcomes the inherent error of equating the measured impedance with the system impedance in traditional methods, making the identification results closer to the true state of the power grid.

[0084] S5. Using short-circuit impedance measurements at several characteristic frequency points, and through parameter fitting methods, identify the equivalent circuit parameters that characterize the short-circuit characteristics of the distribution network across the entire frequency band. This includes the following steps: S51. To fit the short-circuit impedance measurements at each characteristic frequency point into a continuous frequency response curve, an equivalent circuit mathematical model is established.

[0085] To establish a continuous frequency-impedance relationship model, it is necessary to identify the equivalent circuit parameters of the distribution network.

[0086] The short-circuit impedance of a power distribution network mainly consists of the system's equivalent source impedance, transmission line impedance, and distribution line impedance. These impedance components can be represented by a series circuit of resistors and inductors. The system equivalent circuit includes equivalent resistance and equivalent inductance, and the short-circuit impedance at different frequencies can be expressed as the sum of resistive and inductive components. However, the equivalent resistance is not actually constant but increases with frequency, mainly due to the skin effect and proximity effect of the conductor.

[0087] To accurately describe the frequency characteristics of the resistor, this method employs a modified equivalent circuit model, dividing the equivalent resistance into a DC component and an additional AC component. The additional AC resistance is proportional to the square root of the frequency. The equivalent inductance can be approximated as a constant within the frequency range under consideration.

[0088] Based on the above equivalent circuit model, the real part of the theoretical short-circuit impedance at the kth characteristic frequency is... and the virtual part It can be represented as: (5) (6) In the formula, Let be the theoretical resistance value (Ω) at the k-th frequency. Equivalent DC resistance (Ω), For resistor frequency correction factor ( ), For the kth characteristic frequency (Hz), Let be the theoretical reactance (Ω) at k frequencies. The equivalent inductance is (H).

[0089] S52. Substitute the measured short-circuit impedance values ​​at each characteristic frequency point into the equivalent circuit mathematical model to construct an overdetermined set of equations with the model parameters as unknowns.

[0090] Using measurement data at four characteristic frequencies, an overdetermined system of equations is established to solve for the equivalent circuit parameters. The real and imaginary parts of the theoretical impedance are then compared with the real part of the measured impedance. and reactance Comparison, constructing the objective function : (7) In the formula, The objective function value The weighting coefficients for the k-th frequency point are given. Initially, all frequency points... (Equal weighting) If the error at a certain frequency point is greater than 5%, then that frequency point... (Downweighted), other frequency points remain unchanged. .

[0091] S53. Solve the overdetermined system of equations using the least squares method to identify the resistance and inductance parameters in the mathematical model of the equivalent circuit.

[0092] The least squares method is used to solve this optimization problem. The principle of the least squares method is to minimize the objective function by taking the partial derivatives with respect to the parameters and setting the partial derivatives to zero, thus obtaining a system of linear equations for the parameters.

[0093] Expanding the real part equations at the four frequency points, we obtain the equations for... and The system of linear equations has 4 equations and 2 unknowns, and is an overdetermined system. The optimal solution is obtained using the least squares method. and Similarly, the equivalent inductance can be directly solved from the imaginary part equation. The equation for the imaginary part is: Equal to measuring reactance The average of the results at the four frequency points is obtained. The optimal estimate.

[0094] After parameter identification, three key parameters of the equivalent circuit of the distribution network were obtained: DC resistance. Resistance frequency coefficient and equivalent inductance .

[0095] It should be further noted that the three key parameters of the distribution network equivalent circuit fully describe the frequency characteristics of the distribution network's short-circuit impedance, and can be used to calculate the short-circuit impedance value at any frequency, realizing the conversion from discrete frequency point measurements to a full-band parameter model. The identified parameter values ​​will be verified in the next step and saved to the parameter database for subsequent fault calculations.

[0096] To verify the accuracy of the identified equivalent circuit parameters, the parameters were substituted into the theoretical calculation formula to deduce the theoretical short-circuit impedance values ​​for each characteristic frequency, which were then compared with the measured values ​​from step four. For the k-th characteristic frequency, the real part of the theoretical impedance was calculated using formulas (5) and (6). and the virtual part .

[0097] Calculate the relative error between the theoretical and measured values ​​at each frequency point. Relative resistance error. and relative error of reactance Defined as: (8) (9) In the formula, Let be the relative error of the resistance at the k-th frequency. Let be the relative error of reactance at the k-th frequency. The error threshold for successful verification is set to 5%, meaning that the relative errors of resistance and reactance at all frequency points must be less than 5%.

[0098] Collect eight error values ​​(four resistance errors and four reactance errors) at four characteristic frequencies, and calculate the root mean square value of the error. : (10) In the formula is the root mean square error. When... When the identification result is accurate, the final parameters can be output.

[0099] If the error at a certain frequency point exceeds the threshold, the system will analyze the source of the error. Possible reasons include that the frequency point is greatly affected by background harmonic interference, insufficient measurement signal-to-noise ratio, or an inapplicable equivalent circuit model. For interference causes, the system will adjust the weight of the frequency point in parameter identification to reduce its impact on the final result, and then re-execute the parameter identification process in step five. Specifically, the system will change the weight coefficient introduced for each frequency point in the objective function formula (7), reducing the weight of the interfered frequency point to 0.5, while keeping the weight of other frequency points at 1, and then resolving the least squares problem. After verification and possible multiple rounds of iterative correction, the equivalent circuit parameters that meet the accuracy requirements are finally obtained.

[0100] Using the identified short-circuit parameters, the short-circuit current of the distribution network under different fault conditions can be calculated. Short-circuit current calculation is an important application step in verifying the accuracy of the parameters and is also one of the ultimate goals of this method.

[0101] When a three-phase short-circuit fault occurs on the busbar, the effective value of the short-circuit current is... The calculation formula is: (11) In the formula, This represents the effective value of the three-phase short-circuit current (A). The system's rated phase voltage (V), The short-circuit resistance (Ω) It represents the short-circuit reactance (Ω).

[0102] For a short-circuit fault at a power frequency of 50 Hz, the short-circuit resistance... Calculated according to formula (5) Short-circuit reactance Calculated according to formula (6) The calculated and Substituting into formula (11), the short-circuit current value can be obtained.

[0103] The calculated short-circuit current value is compared with the short-circuit current used in the distribution network protection setting calculation. Protection setting calculations are typically based on design parameters and empirical coefficients, which can lead to some deviation. This method, using parameters obtained from actual measurements, can more accurately reflect the actual situation of the distribution network, and the calculated short-circuit current is closer to the true value. If the deviation is large, it indicates inaccuracies in the design parameters or network model, requiring an update to the protection setting value.

[0104] Example 3 is an embodiment of the present invention, which provides a panoramic identification method for short-circuit parameters of distribution networks based on multi-frequency disturbance excitation. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0105] To verify the effectiveness of this patented method, a simulation model was established based on an actual 10 kV distribution network in a certain region. This distribution network is connected to the 110 kV system via a 10 MVA distribution transformer. The transformer's short-circuit voltage percentage is 6.5%, and its equivalent impedance at rated frequency is 4.23 ohms. The main distribution network line is 15 km long, using LGJ-240 conductors with a resistance of 0.132 ohms per kilometer and a reactance of 0.38 ohms per kilometer. The distribution network is connected to 2 MW of distributed photovoltaic power, 8 MW of conventional loads, and approximately 30% of the loads are nonlinear.

[0106] A detailed simulation model of the power distribution network was built on the MATLAB / Simulink platform to simulate the implementation process of the patented method. The disturbance signal generator, designed in the first step, generated a composite excitation current containing 50 Hz, 130 Hz, 210 Hz, and 370 Hz frequencies, with each frequency component having an amplitude of 5 amperes and an injection time of 2 seconds. The data acquisition system simultaneously recorded the bus voltage and injected current at a sampling rate of 2000 Hz, obtaining data from 4000 sampling points.

[0107] Fast Fourier Transform (FFT) was performed on the collected data to extract voltage and current phasors at four characteristic frequencies. At 50 Hz, the voltage phasor amplitude was 5.73 kV, the current phasor amplitude was 5.02 A, and the phase difference was 48.3 degrees. At 130 Hz, the voltage phasor amplitude was 7.21 kV, the current phasor amplitude was 4.98 A, and the phase difference was 62.7 degrees. At 210 Hz, the voltage phasor amplitude was 8.94 kV, the current phasor amplitude was 5.01 A, and the phase difference was 68.5 degrees. At 370 Hz, the voltage phasor amplitude was 12.8 kV, the current phasor amplitude was 4.96 A, and the phase difference was 74.2 degrees.

[0108] The measured impedance at each frequency is calculated based on the voltage and current phasors. After impedance compensation by the transformer, the system short-circuit impedance is obtained. At 50 Hz, the short-circuit resistance is 0.76 ohms and the short-circuit reactance is 1.35 ohms. At 130 Hz, the short-circuit resistance is 1.09 ohms and the short-circuit reactance is 1.39 ohms. At 210 Hz, the short-circuit resistance is 1.31 ohms and the short-circuit reactance is 1.41 ohms. At 370 Hz, the short-circuit resistance is 1.68 ohms and the short-circuit reactance is 1.44 ohms.

[0109] The equivalent circuit parameters were identified using the least squares method, yielding a DC resistance of 0.62 ohms, a resistance frequency coefficient of the square root of 0.055 ohms per Hz, and an equivalent inductance of 4.42 millihenries. These parameters were then substituted into the theoretical calculation formula to deduce the theoretical short-circuit impedance values ​​for each frequency, which were then compared with the measured values ​​for verification.

[0110] Table 1 shows the impedance measurements, theoretical calculations, and relative errors for four characteristic frequencies: Table 1 Comparison of impedance measurements and theoretical values ​​at characteristic frequencies

[0111] As shown in Table 1, the relative errors of resistance and reactance at all frequency points are less than 3%, meeting the verification accuracy requirements. The root mean square error is 2.0%, indicating that the identified parameters are highly consistent with the actual measurements.

[0112] To further verify the practicality of the method, the short-circuit current calculated by different methods was compared with actual fault recording data. Three short-circuit faults occurred in the distribution network during the test. Table 2 lists the comparison between the calculated results and measured values ​​for each method: Table 2 Comparison of short-circuit current calculation results and measured values ​​using different methods

[0113] As shown in Table 2, the error between the short-circuit current calculated by this method and the measured value is less than 2.5%, while the error of the traditional design parameter method is between 9% and 10%. This method significantly improves the accuracy of short-circuit current calculation.

[0114] To verify the superiority of multi-frequency measurement, the effects of single-frequency and multi-frequency measurements were compared. Measurement using a single-frequency 50 Hz excitation signal only yields the impedance value at that frequency, making it impossible to establish a frequency response model. When using this single-frequency impedance to calculate the short-circuit current at 130 Hz, the error reached 15.3%, while the error of our proposed method was only 2.1%. This demonstrates that considering the impedance frequency characteristics is crucial for improving the accuracy of harmonic short-circuit analysis.

[0115] The test also evaluated the impact of background harmonics on the measurement. During normal operation of the distribution network, the 5th and 7th harmonics accounted for 3.2% and 2.1% of the fundamental frequency, respectively. Since the characteristic frequency selected in this method avoids these harmonic frequencies, the impact of background harmonics on the measurement results is minimal. After frequency domain filtering, harmonic interference is effectively suppressed, and the measurement signal-to-noise ratio reaches 45 dB. In contrast, when the characteristic frequency is close to the harmonic frequency, the measurement signal-to-noise ratio drops to 28 dB, and the identification accuracy is significantly reduced.

[0116] Table 3 summarizes the comparison between this method and traditional methods in terms of key performance indicators: Table 3. Comparison of performance indicators of different short-circuit parameter identification methods

[0117] As shown in Table 3, this method is superior to traditional methods in terms of measurement efficiency, accuracy and anti-interference ability, and realizes rapid and accurate identification of short-circuit parameters in distribution networks.

[0118] Example 4 is an embodiment of the present invention. This embodiment provides a panoramic identification system for short-circuit parameters of distribution networks based on multi-frequency disturbance excitation, including a signal generation module, a signal acquisition module, a frequency domain transformation module, a calculation module, and an output module. The signal generation module is designed to generate a composite disturbance signal consisting of sinusoidal components with mutually independent characteristic frequency points superimposed with a set amplitude. The signal acquisition module injects a composite disturbance signal into the busbar under test of the distribution network through a controllable current source, and uses a synchronous sampling device to acquire the time-domain waveform data of the injected current signal and the busbar voltage. The frequency domain transformation module performs frequency domain transformation on the acquired time-domain waveform data, and extracts the voltage phasor and current phasor corresponding to each characteristic frequency point in the frequency domain. The calculation module obtains the short-circuit impedance measurement value at each frequency point based on the extracted voltage phasor and current phasor at each characteristic frequency point through a first algorithm. The output module uses short-circuit impedance measurements at several characteristic frequency points and a parameter fitting method to identify equivalent circuit parameters that characterize the short-circuit characteristics of the distribution network across the entire frequency band.

[0119] This embodiment also provides an electronic device applicable to the panoramic identification method of distribution network short-circuit parameters based on multi-frequency disturbance excitation, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the panoramic identification method of distribution network short-circuit parameters based on multi-frequency disturbance excitation as proposed in the above embodiment.

[0120] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the panoramic identification method for distribution network short-circuit parameters based on multi-frequency disturbance excitation as proposed in the above embodiments.

[0121] The storage medium proposed in this embodiment and the method for panoramic identification of distribution network short-circuit parameters based on multi-frequency disturbance excitation proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0122] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[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 it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A power distribution network short-circuit parameter panoramic identification method based on multi-frequency disturbance excitation, characterized in that: The method comprises the following steps: design and generate a composite disturbance signal composed of sine components with non-interfering characteristic frequency points superimposed at a set amplitude; inject the composite disturbance signal into the bus to be measured of the power distribution network through a controllable current source, and collect time-domain waveform data of the injected current signal and bus voltage using a synchronous sampling device; perform frequency domain transformation on the collected time-domain waveform data, and separate and extract voltage phasors and current phasors corresponding to each characteristic frequency point in the frequency domain; obtain short-circuit impedance measurement values of each frequency point through a first algorithm according to the extracted voltage phasors and current phasors of each characteristic frequency point; identify equivalent circuit parameters capable of representing the short-circuit characteristics of the power distribution network in the full frequency band through a parameter fitting method using the short-circuit impedance measurement values of several characteristic frequency points.

2. The multi-frequency disturbance-based power grid short circuit parameter panoramic identification method of claim 1, wherein: The method of designing and generating a composite disturbance signal composed of sine components with non-interfering characteristic frequency points superimposed at a set amplitude comprises the following steps: select a group of non-interfering characteristic frequency points according to the background harmonic distribution of the power distribution network; set the amplitude and initial phase of the sine component for each selected characteristic frequency point; superimpose the sine components of each characteristic frequency point to generate a composite disturbance signal for injection.

3. The multi-frequency disturbance-based power grid short parameter panoramic identification method according to claim 2, characterized in that: The method of injecting the composite disturbance signal into the bus to be measured of the power distribution network through a controllable current source, and collecting time-domain waveform data of the injected current signal and bus voltage using a synchronous sampling device comprises the following steps: control the controllable current source with the generated composite disturbance signal to inject the signal into a specified position of the bus to be measured and maintain for a predetermined time length; synchronously collect three-phase voltage of the bus and three-phase current of the injection point at a predetermined sampling rate using a clock synchronized with the signal generation to obtain voltage time-domain waveform data and current time-domain waveform data; perform quality inspection on the collected voltage time-domain waveform data and current time-domain waveform data to verify the integrity and amplitude reasonableness, and output the data passing the inspection as effective collection results.

4. The multi-frequency disturbance-based power grid short parameter panoramic identification method of claim 3, wherein: The method of performing frequency domain transformation on the collected time-domain waveform data, and separating and extracting voltage phasors and current phasors corresponding to each characteristic frequency point in the frequency domain comprises the following steps: perform fast Fourier transform on the time-domain waveform data of voltage and current respectively to obtain corresponding frequency-domain complex sequences; locate and extract voltage phasors and current phasors corresponding to each pre-set characteristic frequency point from the frequency-domain complex sequences; perform windowed average processing on the extracted voltage phasors and current phasors of each characteristic frequency point.

5. The multi-frequency disturbance-based power grid short parameter panoramic identification method of claim 4, wherein: The method of obtaining short-circuit impedance measurement values of each frequency point through a first algorithm according to the extracted voltage phasors and current phasors of each characteristic frequency point comprises the following steps: for each characteristic frequency point, divide the corresponding voltage phasor by the corresponding current phasor to calculate the complex impedance at the current frequency; perform compensation calculation on the complex impedance according to the topology structure of the power distribution network and the known transformer parameters to obtain the compensated system short-circuit impedance; decompose the system short-circuit impedance into corresponding resistance components and reactance components to form short-circuit impedance measurement results of each frequency point.

6. The multi-frequency disturbance-based power grid short parameter panoramic identification method of claim 5, wherein: The method of identifying equivalent circuit parameters capable of representing the short-circuit characteristics of the power distribution network in the full frequency band through a parameter fitting To fit the short-circuit impedance measurement values of each characteristic frequency point into a continuous frequency characteristic curve, an equivalent circuit mathematical model is established; The short-circuit impedance measurement values of each characteristic frequency point are substituted into the equivalent circuit mathematical model to construct an over-determined equation group with model parameters as unknowns; The over-determined equation group is solved by the least square method to identify the resistance parameters and inductance parameters in the equivalent circuit mathematical model.

7. The multi-frequency disturbance-based power distribution network short circuit parameter panoramic identification method of claim 6, wherein: The identification of the resistance parameters and inductance parameters in the equivalent circuit mathematical model includes, The short-circuit impedance theoretical values of each characteristic frequency point are calculated respectively by using the identified equivalent circuit parameters; The short-circuit impedance theoretical values of each characteristic frequency point calculated are compared with the short-circuit impedance measurement results of the corresponding characteristic frequency points to calculate the resistance relative error and reactance relative error of each frequency point; It is judged whether the resistance relative error and reactance relative error exceed a preset threshold value; If none of them exceeds, the final equivalent circuit parameters are outputted; If some of them exceeds, the weight distribution in the parameter fitting process is adjusted, and the parameter identification is performed again until all errors meet the requirements.

8. A power distribution network short-circuit parameter panoramic identification system based on multi-frequency disturbance excitation, applying the power distribution network short-circuit parameter panoramic identification method based on multi-frequency disturbance excitation as claimed in any one of claims 1-7, characterized in that, It includes: A signal generation module, a signal acquisition module, a frequency domain transformation module, a calculation module, and an output module; The signal generation module is designed and generates a composite disturbance signal composed of sine components of characteristic frequency points that do not interfere with each other and are superimposed according to a set amplitude; The signal acquisition module is to inject the composite disturbance signal into the bus to be measured of the power distribution network through a controllable current source, and to collect time-domain waveform data of the injected current signal and the bus voltage by using a synchronous sampling device; The frequency domain transformation module is to perform frequency domain transformation on the collected time-domain waveform data, and to separate and extract voltage phasors and current phasors corresponding to each characteristic frequency point in the frequency domain; The calculation module is to obtain short-circuit impedance measurement values of each frequency point by a first algorithm according to the extracted voltage phasors and current phasors of each characteristic frequency point; The output module is to identify equivalent circuit parameters that can represent the short-circuit characteristics of the power distribution network in the full frequency band by a parameter fitting method by using the short-circuit impedance measurement values of several characteristic frequency points. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The processor executes the computer program to realize the steps of the power distribution network short-circuit parameter panoramic identification method based on multi-frequency disturbance excitation in any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the power distribution network short-circuit parameter panoramic identification method based on multi-frequency disturbance excitation in any one of claims 1 to 7.

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