Reclosing coordination control method and system based on electrical parameter measurement

Abstract

The invention discloses a reclosing coordination control method and system based on electrical parameter measurement, particularly relates to the technical field of intelligent power grid fault processing and security defense, and is used for solving the problem that the reliability of fault judgment is reduced due to the fact that high-proportion distributed photovoltaic is connected to a power distribution network. A harmonic detection signal is injected during a reclosing waiting period, a response signal is measured, harmonic subspace energy distribution is extracted, an energy distribution matrix is constructed to calculate a nuclear norm change rate and form similarity, a system harmonic impedance stable state is judged through double verification, and finally a reclosing decision is executed according to a fault judgment parameter. The method can accurately identify the convergence characteristic of the dynamic process of the system, effectively distinguish the instantaneous fault from the permanent fault, and significantly improve the reclosing success rate and the power supply reliability of the distributed photovoltaic power distribution network.

Description

Technical Field

[0001] This invention relates to the field of smart grid fault handling and security defense technology, and more specifically, to a reclosing coordinated control method and system based on electrical parameter measurement. Background Technology

[0002] Automatic reclosing technology in distribution networks is a crucial means of ensuring power supply reliability, with its core function being the accurate identification of fault nature. With the high proportion of distributed photovoltaic (PV) systems integrated into distribution networks, the characteristics of system faults have changed significantly. The low-voltage ride-through capability of distributed PV systems requires them to maintain grid-connected operation during grid faults and continuously inject feedback current into the lines after the fault is cleared. To address the difficulty of fault identification brought about by PV integration, existing reclosing technologies have adopted active detection methods based on electrical parameter measurements. These methods differentiate between transient and permanent faults by injecting specific harmonic signals into the lines, measuring their response characteristics, and calculating parameters such as harmonic impedance.

[0003] However, existing methods for measuring electrical parameters based on active detection have shortcomings. During the reclosing process, the power distribution system undergoes dynamic processes such as circuit breaker opening and closing operations and distributed power supply control mode switching, which causes the system topology and equivalent harmonic impedance to exhibit rapid time-varying characteristics. This results in the response characteristics of the actively injected detection signal being strongly modulated, and the measured electrical parameters cannot accurately reflect the true state of the fault point, which seriously weakens the reliability of fault diagnosis and poses a hidden danger to the safe operation of the power distribution network. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a reclosing coordination control method and system based on electrical parameter measurement to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] The reclosing coordinated control method based on electrical parameter measurement includes:

[0007] S1. During the reclosing waiting period of the distribution network line, inject a preset harmonic detection signal of a single frequency or a combination of frequencies into the line.

[0008] S2. Measure the response signal of the harmonic detection signal in the line, perform time-frequency analysis on the response signal obtained from multiple consecutive cycles, and extract the energy distribution of the harmonic subspace within the preset frequency band.

[0009] S3. Construct an energy distribution matrix from the energy distribution of multiple consecutive periods, calculate the rate of change of the nuclear norm of the energy distribution matrix, and calculate the morphological similarity between the current energy distribution and the baseline energy distribution.

[0010] S4, judge whether the core norm change rate tends to zero, and take the shape similarity as the verification condition;

[0011] S5, if the core norm change rate tends to zero and the shape similarity is higher than the preset similarity threshold, it is determined that the system harmonic impedance is in a stable state; otherwise, auxiliary judgment is made in combination with the change trend of the correlation coefficient of the energy distribution of adjacent periods;

[0012] S6, when the system harmonic impedance is in a stable state, the fault discrimination parameter is calculated according to the current response signal and the reclosing decision is made.

[0013] Further, during the reclosing waiting period of the distribution network line, a preset single frequency or frequency combination harmonic detection signal is injected into the line, including:

[0014] During the reclosing waiting period of the distribution network line, based on the equivalent harmonic impedance characteristics of the distribution network line, a preset single frequency or frequency combination is selected from the frequency band avoiding resonance with the grid background harmonic;

[0015] During the reclosing waiting period of the distribution network line, the selected single frequency or frequency combination is set as a fixed value, or the single frequency or frequency combination is dynamically adjusted according to the harmonic spectrum characteristics of the grid-connected current of the distributed photovoltaic inverter.

[0016] Further, the response signal of the harmonic detection signal in the line is measured, the response signals measured in continuous multiple periods are subjected to time-frequency analysis, and the energy distribution of the harmonic subspace in the preset frequency band is extracted, including:

[0017] The response signal of the harmonic detection signal in the line is measured by using synchronous sampling method;

[0018] The response signals measured in continuous multiple periods are subjected to short-time Fourier transform to obtain a time-frequency distribution matrix;

[0019] The energy distribution of the harmonic subspace in the preset frequency band is extracted from the time-frequency distribution matrix, which is realized by calculating the sum of squares of the spectrum amplitudes in the preset frequency band.

[0020] Further, the energy distributions of continuous multiple periods are constructed into an energy distribution matrix, the core norm change rate of the energy distribution matrix is calculated, and the shape similarity between the current energy distribution and the reference energy distribution is calculated, including:

[0021] The extracted energy distribution of the harmonic subspace in the preset frequency band is arranged in time sequence to construct the energy distribution matrix;

[0022] The energy distribution matrix is subjected to singular value decomposition to calculate the core norm of the energy distribution matrix;

[0023] The change rate of the core norm of the energy distribution matrix in continuous multiple time windows is calculated;

[0024] The shape similarity between the current energy distribution and the reference energy distribution is calculated by a cosine similarity algorithm.

[0025] Further, singular value decomposition is performed on the energy distribution matrix to calculate the nuclear norm of the energy distribution matrix, including:

[0026] The singular value decomposition is performed on the constructed energy distribution matrix to obtain a plurality of singular values.

[0027] The sum of all singular values is calculated, and the sum is the nuclear norm of the energy distribution matrix.

[0028] The stability and low-rank characteristics of the energy distribution matrix are represented by calculating the sum of all singular values.

[0029] Further, the shape similarity between the current energy distribution and the reference energy distribution is calculated by a cosine similarity algorithm, including:

[0030] The current energy distribution and the reference energy distribution are represented as vectors respectively.

[0031] The dot product between the two vectors and the modulus of each vector are calculated.

[0032] The dot product is divided by the product of the modulus of the two vectors, and the result is the shape similarity between the current energy distribution and the reference energy distribution.

[0033] The reference energy distribution is an energy distribution obtained in advance when the system is in a stable operating state.

[0034] Further, it is determined whether the nuclear norm change rate tends to zero, and the shape similarity is used as a verification condition, including:

[0035] A preset nuclear norm change rate threshold is set, and it is determined whether the absolute value of the nuclear norm change rate is less than the preset nuclear norm change rate threshold.

[0036] At the same time, it is determined whether the shape similarity between the current energy distribution and the reference energy distribution is greater than a preset similarity threshold.

[0037] When the absolute value of the nuclear norm change rate is less than the preset nuclear norm change rate threshold and the shape similarity is greater than the preset similarity threshold, it is determined that the verification condition is met.

[0038] Further, if the nuclear norm change rate tends to zero and the shape similarity is higher than the preset similarity threshold, it is determined that the system harmonic impedance is in a stable state; otherwise, the change trend of the correlation coefficient of the adjacent period energy distribution is combined for auxiliary judgment, including:

[0039] When the absolute value of the rate of change of the nuclear norm is less than the preset threshold for the rate of change of the nuclear norm and the morphological similarity between the current energy distribution and the reference energy distribution is greater than the preset similarity threshold, the system harmonic impedance is determined to be in a stable state.

[0040] If the condition is not met, calculate the correlation coefficient sequence between the energy distributions of the most recent consecutive adjacent periods;

[0041] Analyze the changing trend of the correlation coefficient sequence and fit the slope of the correlation coefficient change over time using the least squares method;

[0042] When the fitting slope remains positive and the variance of the correlation coefficient sequence is less than the preset variance threshold, it is determined that the system harmonic impedance is transitioning to a stable state.

[0043] When the system harmonic impedance is transitioning to a stable state, the energy distribution matrix construction and stability judgment process is restarted. If the process is repeated more than the preset number of times and still fails to determine a stable state, the reclosing operation is blocked.

[0044] Furthermore, when the system harmonic impedance is in a steady state, fault discrimination parameters are calculated based on the current response signal, and reclosing decisions are executed, including:

[0045] The ratio of harmonic impedance amplitudes is calculated based on the current response signal when the system is in a steady state and used as a fault discrimination parameter.

[0046] The calculated harmonic impedance amplitude ratio is compared with a preset threshold.

[0047] When the ratio of harmonic impedance amplitudes is less than a preset threshold, it is determined to be a transient fault, and a reclosing operation is performed.

[0048] When the ratio of harmonic impedance amplitude is greater than or equal to a preset threshold, it is determined to be a permanent fault, and the reclosing operation is blocked.

[0049] On the other hand, the present invention provides a reclosing coordination control system based on electrical parameter measurement, comprising:

[0050] The harmonic injection module is used to inject a preset single frequency or frequency combination of harmonic detection signals into the distribution network line during the reclosing waiting period.

[0051] The signal measurement module is used to measure the response signal of the harmonic detection signal in the line, perform time-frequency analysis on the response signal obtained from multiple consecutive cycles, and extract the energy distribution of the harmonic subspace within the preset frequency band.

[0052] The energy analysis module is used to construct an energy distribution matrix from the energy distribution of multiple consecutive periods, calculate the rate of change of the nuclear norm of the energy distribution matrix, and calculate the morphological similarity between the current energy distribution and the baseline energy distribution.

[0053] The change judgment module is used to determine whether the rate of change of the nuclear norm approaches zero, and uses morphological similarity as the verification condition.

[0054] The state determination module is used to determine that the system harmonic impedance is in a stable state if the rate of change of the nuclear norm approaches zero and the morphological similarity is higher than the preset similarity threshold; otherwise, it makes an auxiliary judgment by combining the trend of the correlation coefficient of the energy distribution of adjacent periods.

[0055] The decision execution module is used to calculate fault discrimination parameters and execute reclosing decisions based on the current response signal when the system harmonic impedance is in a stable state.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] 1. By establishing a multi-dimensional criterion for the stability of system harmonic impedance, the reliability of fault identification in distribution networks with a high proportion of distributed photovoltaic power is effectively improved. By introducing a dual verification mechanism of the nuclear norm change rate analysis and morphological similarity of the energy distribution matrix of the harmonic subspace, the convergence characteristics of the system dynamic process after fault clearing can be accurately captured. The nuclear norm change rate characterizes the time-varying characteristics of harmonic impedance from the perspective of the overall energy distribution of the system, while morphological similarity verifies the consistency between the current state and the reference state from the perspective of energy distribution structure. The combination of the two forms a complete stability assessment system, which overcomes the limitation of single electrical parameter measurement being affected by the dynamic process of the system, significantly improves the adaptability of fault identification to changes in system operating conditions, and provides a more reliable judgment basis for reclosing decisions.

[0058] 2. By constructing an auxiliary judgment mechanism through correlation coefficient trend analysis, the robustness of the system's decision-making under complex operating conditions is enhanced. When the main criterion is not met, the system automatically switches to the correlation coefficient analysis of energy distribution in adjacent cycles. By fitting the trend of its change using the least squares method, it accurately identifies whether the system is in the process of transitioning to a stable state. This ensures both the speed of judgment under normal conditions and the accuracy of judgment under special operating conditions, forming a complete coordinated control strategy. It effectively solves the problem of fault characteristic changes caused by distributed photovoltaic access, provides technical support for the safe operation of the smart grid under the condition of large-scale new energy access, and meets the higher requirements of the smart grid development for the accuracy and reliability of fault handling. Attached Figure Description

[0059] Figure 1 The flowchart is a reclosing coordination control method based on electrical parameter measurement according to the present invention.

[0060] Figure 2 This is a schematic diagram of the reclosing coordinated control system based on electrical parameter measurement according to the present invention. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] Example 1: Figure 1 The present invention provides a reclosing coordinated control method based on electrical parameter measurement, comprising:

[0063] S1. During the reclosing waiting period of the distribution network line, inject a preset harmonic detection signal of a single frequency or a combination of frequencies into the line.

[0064] S2. Measure the response signal of the harmonic detection signal in the line, perform time-frequency analysis on the response signal obtained from multiple consecutive cycles, and extract the energy distribution of the harmonic subspace within the preset frequency band.

[0065] S3. Construct an energy distribution matrix from the energy distribution of multiple consecutive periods, calculate the rate of change of the nuclear norm of the energy distribution matrix, and calculate the morphological similarity between the current energy distribution and the baseline energy distribution.

[0066] S4. Determine whether the rate of change of the nuclear norm approaches zero, and use morphological similarity as a verification condition.

[0067] S5. If the rate of change of the nuclear norm approaches zero and the morphological similarity is higher than the preset similarity threshold, the harmonic impedance of the system is determined to be in a stable state; otherwise, the correlation coefficient of the energy distribution of adjacent periods is used as an auxiliary judgment.

[0068] S6. When the system harmonic impedance is in a stable state, calculate the fault discrimination parameters based on the current response signal and execute the reclosing decision.

[0069] During the reclosing waiting period of a distribution network line, a preset single frequency or frequency combination harmonic detection signal is injected into the line. The specific implementation process is as follows: First, during the reclosing waiting period of the distribution network line, based on the pre-analysis of the equivalent harmonic impedance characteristics of the distribution network line, a preset single frequency or frequency combination is selected from a specific frequency band. This equivalent harmonic impedance characteristic is calculated by applying test signals of different frequencies under normal line operation and measuring the response signals. Specifically, the impedance amplitude and phase information are obtained by the ratio of voltage to current. The selection of the specific frequency band is based on avoiding resonance with known background harmonics in the power grid. This is achieved in the following way: Before system commissioning or during normal operation, the voltage and current signals at the end of the line are subjected to spectrum analysis to obtain the distribution of background harmonics in the power grid, and the amplitude and frequency of each harmonic are recorded, for example, using power quality analysis. The instrument measured the harmonic voltage content at 250Hz to be 1.5% and the harmonic voltage content at 350Hz to be 2.1%. Simultaneously, the equivalent harmonic impedance characteristics of the distribution network lines in the frequency range of 50Hz to 1000Hz were measured using a frequency scanning method or an impedance analyzer to obtain impedance-frequency curves. Based on the aforementioned background harmonic spectrum and line impedance characteristics, frequency bands with background harmonic content below 3% and line impedance amplitude changes not exceeding 20% ​​were selected. For example, if the 250Hz to 350Hz frequency band was found to meet the conditions of background harmonic content being below 3% and impedance changes being stable, then a single frequency within this frequency band, such as 300Hz, or a frequency combination such as 280Hz and 320Hz, was selected as the preset injection frequency.

[0070] Secondly, during the reclosing waiting period of the distribution network line, the selected single frequency or frequency combination is set to a fixed value, or the single frequency or frequency combination is dynamically adjusted according to the harmonic spectrum characteristics of the grid-connected current of the distributed photovoltaic inverter. When set to a fixed value, the pre-selected frequency parameters are directly configured in the signal generation device, for example, a 300Hz±0.1Hz sine wave signal is generated using a DSP chip, and the harmonic detection signal of this fixed frequency is continuously injected throughout the reclosing waiting period. When dynamic adjustment is selected, the specific adjustment strategy includes: real-time monitoring of the current signal at the grid connection point of the distributed photovoltaic inverter; using synchronous sampling technology to collect the current waveform at a sampling rate of 6400 points per second; performing fast Fourier transform analysis on it to obtain its harmonic spectrum characteristics and calculating the harmonic current content of each order; if it is found that the harmonic current content exceeds the preset limit near the current preset injection frequency, for example, if the injection frequency is 300Hz and the harmonic content of the photovoltaic inverter output current at 300Hz exceeds 3%, then the frequency adjustment logic is activated; the goal of the adjustment is to avoid the frequency band with high harmonic current content and switch the injection frequency to the preset backup frequency. The backup frequency set is derived from the safe frequency band determined based on impedance and background harmonic analysis, for example, selecting 320Hz as the new injection frequency from the predetermined safe frequency band of 250Hz to 350Hz; the dynamic adjustment process is performed periodically at a frequency of 1 time per second during the entire reclosing waiting period. Before each adjustment, it is re-verified that the background harmonic content at the new frequency is less than 3% and the impedance characteristics are stable to ensure the effectiveness and safety of the injected signal. All frequency parameter adjustments are limited to a step size of 10Hz to avoid system disturbances, and the frequency accuracy during the adjustment process is controlled within ±0.2Hz.

[0071] The response signal of the harmonic detection signal in the line is measured, and time-frequency analysis is performed on the response signal obtained from multiple consecutive cycles to extract the energy distribution of the harmonic subspace within a preset frequency band. The specific implementation process is as follows: First, the response signal of the harmonic detection signal in the line is measured using a synchronous sampling method. Synchronous sampling refers to a sampling method in which the sampling clock is synchronized with the fundamental frequency of the power grid. Specifically, a phase-locked loop circuit is used to track the zero-crossing point of the fundamental voltage of the power grid in real time to generate synchronous sampling pulses. The sampling frequency is set to an integer multiple of the fundamental frequency. For example, for a 50Hz power grid system, the sampling frequency is set to 3200Hz, i.e., 64 sampling points per cycle. The measurement point of the response signal is at the line monitoring point downstream of the harmonic detection signal injection point. A current transformer with an accuracy class of 0.2 is used to collect the line current signal, and the analog signal is converted into a digital signal through a 16-bit analog-to-digital converter. The sampling time covers the entire reclosing waiting period, and data is continuously collected for no less than 10 power frequency cycles. During the acquisition process, the signal quality is monitored in real time, and automatic re-acquisition is performed when the signal-to-noise ratio is lower than 40dB.

[0072] Short-time Fourier transform (SFT) is performed on the response signals obtained from multiple consecutive period measurements to obtain the time-frequency distribution matrix. The specific processing steps include: dividing the acquired continuous time-domain signal into several time periods of equal length, each time period corresponding to a time window. The Hanning window function is used, the window length is set to 5 power frequency cycles, and the overlap rate of adjacent windows is set to 50%. The signal data within each window is windowed and then subjected to a fast Fourier transform to calculate the complex spectrum of each frequency component. The frequency resolution is determined by the window length; for example, 5 period windows correspond to a resolution of 10 Hz. The spectral analysis results of all time windows are arranged in chronological order to form a time-frequency distribution matrix. The row dimension of this matrix represents the frequency component, the column dimension represents the time window index, and the matrix elements represent the spectral amplitude of the corresponding frequency component within the corresponding time window. The spectral amplitude is the modulus of the complex spectrum, and the unit is volts.

[0073] The energy distribution of the harmonic subspace within a preset frequency band is extracted from the time-frequency distribution matrix. This is achieved by calculating the sum of squares of the spectral amplitudes within the preset frequency band. Specifically, the preset frequency band is determined based on the frequency of the injected harmonic detection signal. For example, when the injection frequency is 300Hz, the preset frequency band is set to 290Hz to 310Hz, with a bandwidth of 20Hz. The spectral amplitude data of all frequency components within this frequency band are extracted from the time-frequency distribution matrix for each time window. For each time window, the sum of squares of the spectral amplitudes of all frequency components within the preset frequency band is calculated to obtain the energy distribution value of the harmonic subspace within that time window, with the energy unit being volts squared. The energy distribution values ​​for all time windows are calculated continuously to form an energy distribution sequence, which characterizes the change of harmonic energy within the preset frequency band over time. In the calculation of the energy distribution value, the spectral amplitude is taken as a linear value rather than a decibel value, and no weighting processing is performed on the data before the sum of squares calculation to ensure the accuracy and consistency of the energy calculation. For abnormal time windows, such as when the signal amplitude exceeds 90% of the range within a certain time window, the energy distribution value of that window will be marked as invalid and replaced by the interpolation result of the adjacent window to ensure data continuity. Throughout the processing, frequency accuracy is controlled within ±1Hz and time resolution is controlled within 5ms to ensure the accuracy of subsequent analysis.

[0074] An energy distribution matrix is ​​constructed by analyzing the energy distributions over multiple consecutive periods. The rate of change of the nuclear norm of the energy distribution matrix is ​​calculated, and the morphological similarity between the current energy distribution and the baseline energy distribution is also calculated. The specific implementation process is as follows: First, the energy distributions of the harmonic subspace extracted within the preset frequency band are arranged in chronological order to construct an energy distribution matrix. The energy distribution data comes from the harmonic subspace energy distribution sequence obtained in the time-frequency analysis step. This sequence contains energy distribution values ​​for multiple consecutive time windows. Each energy distribution value is calculated by summing the squares of the spectral amplitudes within the preset frequency band. When constructing the matrix, the energy distribution value of each time window is used as a column of the matrix. The time windows are arranged from left to right according to the acquisition time. The number of rows in the matrix depends on the dimension of the energy distribution values. When the monitoring frequency band contains multiple frequency components, the energy distribution of each time window is a multi-dimensional vector. For example, when the monitoring frequency band contains 3 frequency components, the energy distribution of each time window is a vector containing 3 elements. The energy distribution matrix composed of 10 consecutive time windows is a 3-row, 10-column matrix. During the matrix construction process, it is ensured that the number of time windows is not less than 5 to guarantee the statistical significance of subsequent analysis.

[0075] The energy distribution matrix is ​​subjected to singular value decomposition (SVD) to calculate its nuclear norm. Specifically, this involves: performing SVD on the energy distribution matrix, which is a mathematical process of decomposing any matrix into the product of three specific matrices: a left singular vector matrix, a singular value matrix, and a right singular vector matrix. The column vectors of the left singular vector matrix form an orthonormal basis of the original matrix's row space, the column vectors of the right singular vector matrix form an orthonormal basis of the original matrix's column space, and the singular value matrix is ​​a diagonal matrix whose diagonal elements are the singular values. Several singular values ​​are obtained through matrix operations; these singular values ​​are the elements on the diagonal of the singular value matrix. The non-negative real elements are arranged in descending order. Each singular value represents the energy distribution intensity of the matrix along the direction of the corresponding singular vector. The sum of all the singular values ​​is the nuclear norm of the energy distribution matrix. The dimension of the nuclear norm is the same as that of the energy distribution value. For example, if the unit of the energy distribution value is volt squared, the unit of the nuclear norm is also volt squared. The stability and low-rank characteristics of the energy distribution matrix are characterized by calculating the sum of all singular values. When the system is in a stable state, the rank of the energy distribution matrix is ​​low, the nuclear norm value is small and changes smoothly. When the system is in a transient process, the rank of the matrix increases, the nuclear norm value increases and fluctuates more.

[0076] The rate of change of the nuclear norm of the energy distribution matrix over multiple consecutive time windows is calculated as follows: First, select a sequence of energy distribution matrices corresponding to multiple consecutive time windows. The number of time windows is determined based on the system response characteristics; for example, select the energy distribution matrix sequence of the most recent 10 time windows, with each time window interval being 5 milliseconds. Second, calculate the nuclear norm value of each energy distribution matrix to obtain a nuclear norm time series, which reflects the time-varying characteristics of the system's energy distribution. Third, calculate the relative rate of change between two adjacent nuclear norm values ​​in this series. The rate of change is calculated by dividing the difference between the subsequent nuclear norm value and the preceding nuclear norm value by the previous nuclear norm value. The norm value, expressed as a percentage, is calculated using the formula: Rate of Change = (Nuclear Norm [i] - Nuclear Norm [i-1]) / Nuclear Norm [i-1] × 100%, where i represents the time window number. For example, if the nuclear norm sequence for five consecutive time windows is [10.2, 10.1, 10.0, 9.9, 9.8] volts squared, then the calculated rate of change sequence is [-0.98%, -0.99%, -1.00%, -1.01%]. During the rate of change calculation, when the denominator, i.e., the previous nuclear norm value, is close to zero, the calculation is automatically skipped and the interpolation result of the adjacent rate of change is used.

[0077] The morphological similarity between the current energy distribution and the baseline energy distribution is calculated using a cosine similarity algorithm. Specifically, this involves: representing both the current and baseline energy distributions as vectors. The current energy distribution vector is composed of the energy distribution values ​​within the current time window, while the baseline energy distribution vector is composed of energy distribution values ​​pre-acquired when the system is in a stable operating state. The baseline energy distribution is obtained by collecting energy distribution data for at least 100 time windows during normal system operation, removing outliers, and taking the arithmetic mean as the baseline vector. The dot product between the two vectors is calculated; the dot product is the sum of the product of corresponding elements. The magnitude of each vector is calculated; the magnitude is the square root of the sum of the squares of the vector's elements. The dot product is divided by the product of the magnitudes of the two vectors. The result is the morphological similarity between the current and baseline energy distributions, with a similarity value ranging from 0 to 1, where a higher value indicates a higher similarity. Throughout the calculation process, data precision is retained to three decimal places. Automatic data validation is performed when the calculation result is abnormal. If the calculated magnitude value is less than 0.001, it is automatically set to 0.001 to avoid division by zero errors and ensure the robustness of the calculation process. The similarity calculation results are updated in real time, and are calculated once for each time window, providing data support for subsequent stability assessment.

[0078] Step S3 constructs an energy distribution matrix and analyzes its nuclear norm change rate, combined with cosine similarity evaluation, to achieve a multi-dimensional comprehensive judgment of the system's harmonic impedance stability. Compared to existing methods that rely solely on the change rate of a single electrical quantity, extracting deep system features through matrix singular value decomposition allows for more sensitive capture of the stable state after the transient process ends, effectively avoiding misjudgments caused by system oscillations or noise interference. The nuclear norm change rate characterizes the convergence characteristics of the overall energy distribution of the system, while cosine similarity ensures that the energy distribution pattern is consistent with the baseline state. The combination of these two significantly improves the accuracy and reliability of reclosing timing judgment.

[0079] The process for determining whether the nuclear norm number change rate approaches zero, using morphological similarity as a verification condition, is as follows: First, a preset nuclear norm number change rate threshold is set. This threshold is obtained through statistical analysis of historical data during normal system operation. Specifically, nuclear norm number change rate data are collected and statistically analyzed over multiple consecutive time windows when the system is in a stable state. The data collection period is no less than 24 hours, with a collection time window interval of 5 milliseconds, resulting in at least 10,000 sets of valid data. The absolute values ​​of these data are statistically analyzed, and their mean and standard deviation are calculated. The maximum absolute value of these data plus twice the standard deviation is taken as the preset nuclear norm number change rate threshold. For example, by analyzing the nuclear norm number change rate data under stable conditions, the calculated maximum absolute value is 0.5%, and the standard deviation is 0.1%, then the preset threshold is set to 0.7%. Determine whether the absolute value of the nuclear norm change rate is less than the preset nuclear norm change rate threshold. The nuclear norm change rate is derived from the continuous time window nuclear norm change rate sequence calculated in the previous step. Take the nuclear norm change rate value of the most recent time window for judgment, calculate its absolute value and compare it with the preset threshold. The comparison uses a numerical comparison algorithm and retains the precision to three decimal places.

[0080] Simultaneously, it is determined whether the morphological similarity between the current energy distribution and the baseline energy distribution is greater than a preset similarity threshold. The current energy distribution is derived from the energy distribution vector of the current time window, while the baseline energy distribution is a pre-acquired energy distribution vector obtained during stable system operation. The morphological similarity is calculated using a cosine similarity algorithm. The preset similarity threshold is determined based on system characteristics and reliability requirements, derived from extensive experimental data analysis, and is typically set within the range of 0.85 to 0.95. For example, experimental verification shows that when the similarity is greater than 0.9, the probability of the system being in a stable state exceeds 99%, therefore the preset similarity threshold is set to 0.9. During the judgment, the currently calculated morphological similarity value is compared with the preset threshold. The comparison operation uses a floating-point comparison algorithm, with a tolerance range set to 0.001.

[0081] The verification condition is satisfied when the absolute value of the nuclear norm change rate is less than a preset nuclear norm change rate threshold and the morphological similarity is greater than a preset similarity threshold. This dual judgment condition ensures the reliability of the system's stable state determination: the nuclear norm change rate condition ensures that the system's energy distribution tends to stabilize, and the morphological similarity condition ensures the consistency between the current energy distribution and the baseline stable state. Both conditions must be met simultaneously; if either condition is not met, the verification condition is deemed invalid. During the judgment process, the nuclear norm change rate is updated every 5 milliseconds, and the morphological similarity is updated every 10 milliseconds. Only when the conditions are met for three consecutive judgments is the verification condition finally satisfied to avoid misjudgments caused by accidental factors. All threshold parameters can be fine-tuned during system operation according to actual conditions, with the adjustment not exceeding 20% ​​of the initial value to ensure system adaptability while maintaining judgment stability. An exception handling mechanism is set up during the judgment process; when the input data is abnormal, the data verification process is automatically activated to ensure the reliability of the judgment results. The entire judgment process is executed in a loop until the verification conditions are met or the maximum number of judgments is reached. The maximum number of judgments is set according to the system response time requirements. For example, if the maximum number of judgments is set to 100, the corresponding total judgment time is 500 milliseconds.

[0082] If the nuclear norm rate of change approaches zero and the morphological similarity is higher than the preset similarity threshold, the system harmonic impedance is determined to be in a stable state; otherwise, the correlation coefficient of energy distribution in adjacent periods is used for auxiliary judgment. The specific implementation process is as follows: When the absolute value of the nuclear norm rate of change is less than the preset nuclear norm rate of change threshold and the morphological similarity between the current energy distribution and the reference energy distribution is greater than the preset similarity threshold, the system harmonic impedance is determined to be in a stable state. The nuclear norm rate of change is derived from the nuclear norm rate of change sequence of continuous time windows calculated in the previous step. The preset nuclear norm rate of change threshold is set based on the statistical analysis of historical data during stable system operation. The nuclear norm rate of change data of multiple consecutive time windows when the system is in a stable state is collected and statistically analyzed. The data collection time is no less than 24 hours, the time window interval is 5 milliseconds, and no less than 10,000 sets of valid data are collected. The absolute values ​​of these data are... Statistical analysis is performed to calculate the mean and standard deviation. The maximum absolute value of these data plus twice the standard deviation is taken as the preset nuclear norm change rate threshold. The current energy distribution is derived from the energy distribution vector of the current time window. The baseline energy distribution is the energy distribution vector pre-acquired when the system is running stably. The baseline energy distribution vector is obtained by collecting energy distribution data for at least 100 time windows during normal system operation, removing outliers, and taking the arithmetic mean. The morphological similarity is calculated using the cosine similarity algorithm. The preset similarity threshold is determined based on system characteristics and reliability requirements. It is derived through the analysis of a large amount of experimental data and is usually set in the range of 0.85 to 0.95.

[0083] When the above conditions are not met, the correlation coefficient sequence between the energy distributions of several consecutive adjacent periods is calculated. Specifically, this includes: selecting energy distribution vectors of several consecutive time windows, the number of time windows being determined based on the system response characteristics, for example, selecting the energy distribution vector sequence of the most recent 20 time windows, with each time window spaced 5 milliseconds apart; calculating the correlation coefficient between energy distribution vectors of adjacent time windows, using the Pearson correlation coefficient calculation method, the formula being the product of the covariance of the two vectors divided by their respective standard deviations, the formula being: Correlation coefficient = Cov(X,Y) / (σX×σY), where Cov(X,Y) represents the covariance of the two vectors, σX and σY represent the standard deviations of the two vectors respectively, the calculation result ranges from -1 to +1, positive values ​​indicate positive correlation, negative values ​​indicate negative correlation, and the larger the absolute value, the stronger the correlation; continuously calculating to obtain the correlation coefficient sequence, this sequence reflects the correlation of energy distribution changes over time, and the sequence length is 1 less than the number of time windows.

[0084] The analysis of the correlation coefficient sequence's changing trend involves fitting the slope of the correlation coefficient over time using the least squares method. Specifically, this includes: using the correlation coefficient sequence as the dependent variable and the time window number as the independent variable, with the time window numbers numbered consecutively starting from 1; performing linear fitting using the least squares method, a mathematical optimization method that finds the best-fit function by minimizing the sum of squared errors, resulting in the linear equation y = ax + b, where a is the slope parameter and b is the intercept parameter; during slope calculation, the time window interval is fixed, for example, 5 milliseconds per time window; and the goodness-of-fit is determined by calculating the coefficient R. 2 To evaluate this, the formula for calculating the coefficient of determination is R. 2 = 1 - (SSres / SStot), where SSres is the residual sum of squares and SStot is the total sum of squares. The fitting result is considered valid when the coefficient of determination is greater than 0.8.

[0085] When the fitted slope is consistently positive and the variance of the correlation coefficient sequence is less than the preset variance threshold, it is determined that the system harmonic impedance is transitioning to a stable state. A consistently positive fitted slope means that the fitted slope is greater than zero for multiple consecutive calculation periods, for example, the slope obtained in 5 consecutive calculations is greater than 0.001. The preset variance threshold is determined based on the correlation coefficient fluctuation characteristics during stable system operation. It is achieved by statistically analyzing the variance of the correlation coefficient sequence under stable system conditions, collecting at least 100 sets of correlation coefficient sequences during stable system operation, with each set containing 20 correlation coefficient values. The variance of each set is calculated, and the average of these variance values ​​plus one standard deviation is taken as the preset variance threshold. For example, by analyzing the correlation coefficient sequence under stable conditions, the calculated average variance is 0.002 and the standard deviation is 0.0005, then the preset variance threshold is set to 0.0025.

[0086] When the system harmonic impedance is transitioning to a stable state, the energy distribution matrix construction and stability assessment process restarts. This includes: re-acquiring energy distribution data from the latest time window to construct a new energy distribution matrix; recalculating the nuclear norm change rate and morphological similarity; and repeating the stability assessment process, updating all input parameters during each repetition. If the system fails to reach a stable state after exceeding a preset number of repetitions, the reclosing operation is blocked. The preset number of repetitions is determined based on the system's maximum allowable waiting time; for example, a maximum of 10 repetitions corresponds to a total waiting time of 50 milliseconds. During the reclosing operation, a blocking signal is generated and the reason for blocking is recorded, while a system anomaly alarm mechanism is activated. Throughout the assessment process, a timeout protection mechanism is implemented. When the total assessment time exceeds the maximum allowable time, the reclosing operation is automatically blocked. The maximum allowable time is set according to system characteristics, for example, 100 milliseconds, to ensure system safety. Each re-execution updates all calculation parameters and thresholds to ensure the assessment result matches the current system state. An anomaly handling mechanism includes data validity checks; when input data is abnormal, data verification and re-acquisition are automatically activated to ensure the reliability of the calculation results. The final judgment result is output as a binary signal. A high level indicates that the system is stable and reclosing is allowed, while a low level indicates that the system is unstable and reclosing is blocked, providing input signals for subsequent control logic.

[0087] When the system harmonic impedance is in a stable state, fault discrimination parameters are calculated based on the current response signal, and reclosing decisions are executed. The specific implementation process is as follows: The harmonic impedance amplitude ratio is calculated as a fault discrimination parameter based on the current response signal when the system harmonic impedance is in a stable state. The current response signal is the harmonic detection signal response measured when the system harmonic impedance is in a stable state. Line current signals are acquired through synchronous sampling, with the sampling clock synchronized with the grid fundamental frequency. The sampling frequency is set to an integer multiple of the fundamental frequency; for example, for a 50Hz grid system, the sampling frequency is set to 3200Hz, i.e., 64 sampling points per cycle. The calculation method for the harmonic impedance amplitude ratio is as follows: First, the voltage amplitude and current amplitude at the injected frequency component in the response signal are extracted. The current amplitude is obtained by performing a windowed Fourier transform on the sampled signal. The window function is a Hanning window with a window length of 5 power frequency cycles and a frequency resolution of 10Hz. Then, the voltage amplitude is divided by the current amplitude to obtain the harmonic impedance amplitude. Finally, the current harmonic impedance amplitude is divided by the reference harmonic impedance amplitude to obtain the harmonic impedance amplitude ratio. The reference harmonic impedance amplitude is the harmonic impedance amplitude measured in advance during normal system operation. It is calculated by injecting a harmonic detection signal of the same frequency before system commissioning or during normal operation and measuring the response signal. The acquisition time is no less than 10 power frequency cycles, and the average value is taken as the reference value.

[0088] The calculated harmonic impedance amplitude ratio is compared with a preset threshold, which is determined based on system line parameters and operating experience. This preset threshold is obtained through statistical analysis of historical fault data. Specifically, at least 1000 sets of harmonic impedance amplitude ratio data are collected during normal system operation, derived from measurement results under different operating conditions. The average and standard deviation of these data are calculated. Then, at least 100 sets of harmonic impedance amplitude ratio data are collected when a permanent fault occurs in the system. The average and standard deviation of these data are calculated again. The median value between the two averages is taken as the initial value of the preset threshold. Fine-tuning is then performed according to the required false alarm rate. The final preset threshold is typically set within the range of 1.2 to 1.8. For example, a preset threshold of 1.5 is determined through statistical analysis, and this value can be adjusted within ±0.2 based on actual operating conditions.

[0089] When the harmonic impedance amplitude ratio is less than a preset threshold, it is determined to be a transient fault, and a reclosing operation is executed. Specifically, this includes: generating a reclosing trigger signal, which is a high-level pulse signal with a pulse width of 100 milliseconds and a signal amplitude of 5V; sending the reclosing trigger signal to the line circuit breaker control circuit to initiate the circuit breaker closing operation, with a closing command transmission delay not exceeding 10 milliseconds; and simultaneously recording the fault discrimination parameters and judgment results, including the harmonic impedance amplitude ratio, preset threshold, judgment time, etc., with the recorded data retained for no less than 30 days. When the harmonic impedance amplitude ratio is greater than or equal to the preset threshold, it is determined to be a permanent fault, and the reclosing operation is blocked. Specifically, this includes: generating a blocking signal, which is a continuous low-level signal with a signal amplitude of 0V; sending the blocking signal to the line circuit breaker control circuit to prohibit the circuit breaker closing operation; simultaneously triggering the system alarm device to issue an audible and visual alarm signal, and recording detailed fault information for subsequent analysis, including the fault occurrence time, harmonic impedance amplitude ratio, preset threshold, etc.

[0090] Throughout the judgment process, a data verification mechanism is implemented. When the input signal quality is poor, data is automatically reacquired. Signal quality is judged by the signal-to-noise ratio (SNR). Reacquisition is triggered when the SNR falls below 40dB to ensure the reliability of the judgment results. A timeout protection mechanism is also included. If the judgment time exceeds the maximum allowable time, the reclosing operation is automatically blocked. The maximum allowable time is set according to system characteristics, for example, 200 milliseconds. A mechanism to prevent malfunctions is also implemented, requiring three consecutive consistent judgment results before the corresponding operation is executed. The judgment interval is 10 milliseconds to avoid misjudgments caused by accidental factors. All judgment parameters and thresholds can be adjusted online during system operation according to actual conditions, with the adjustment not exceeding 20% ​​of the initial value to ensure the system's adaptability to different operating conditions. The final output signal is electrically isolated through an opto-isolator to ensure the safety of the control loop. The output interface uses a standard industrial communication protocol, such as Modbus RTU, with a transmission rate of 9600bps and a data format of 8 data bits, 1 stop bit, and no parity bit.

[0091] Example 2: Figure 2 A schematic diagram of the reclosing coordination control system based on electrical parameter measurement of the present invention is provided. The reclosing coordination control system based on electrical parameter measurement includes:

[0092] The harmonic injection module is used to inject a preset single frequency or frequency combination of harmonic detection signals into the distribution network line during the reclosing waiting period.

[0093] The signal measurement module is used to measure the response signal of the harmonic detection signal in the line, perform time-frequency analysis on the response signal obtained from multiple consecutive cycles, and extract the energy distribution of the harmonic subspace within the preset frequency band.

[0094] The energy analysis module is used to construct an energy distribution matrix from the energy distribution of multiple consecutive periods, calculate the rate of change of the nuclear norm of the energy distribution matrix, and calculate the morphological similarity between the current energy distribution and the baseline energy distribution.

[0095] The change judgment module is used to determine whether the rate of change of the nuclear norm approaches zero, and uses morphological similarity as the verification condition.

[0096] The state determination module is used to determine that the system harmonic impedance is in a stable state if the rate of change of the nuclear norm approaches zero and the morphological similarity is higher than the preset similarity threshold; otherwise, it makes an auxiliary judgment by combining the trend of the correlation coefficient of the energy distribution of adjacent periods.

[0097] The decision execution module is used to calculate fault discrimination parameters and execute reclosing decisions based on the current response signal when the system harmonic impedance is in a stable state.

[0098] All calculations involved in the embodiments are dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0099] It should be noted that this invention can be deployed on the device itself to realize embedded applications, or it can run on a PC or other terminal with a user interface, thereby meeting various hardware environments and usage requirements.

[0100] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wireless or wired transmission; wired transmission methods include optical fiber, twisted pair, coaxial cable, etc.; wireless transmission includes infrared, microwave, etc. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center containing one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0101] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0102] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0103] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0104] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0105] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0106] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0107] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A reclosing coordinated control method based on electrical parameter measurement, characterized in that, include: S1. During the reclosing waiting period of the distribution network line, inject a preset harmonic detection signal of a single frequency or a combination of frequencies into the line. S2. Measure the response signal of the harmonic detection signal in the line, perform time-frequency analysis on the response signal obtained from multiple consecutive cycles, and extract the energy distribution of the harmonic subspace within the preset frequency band. S3. Construct an energy distribution matrix from the energy distribution of multiple consecutive periods, calculate the rate of change of the nuclear norm of the energy distribution matrix, and calculate the morphological similarity between the current energy distribution and the baseline energy distribution. S4. Determine whether the rate of change of the nuclear norm approaches zero, and use morphological similarity as a verification condition. S5. If the rate of change of the nuclear norm approaches zero and the morphological similarity is higher than the preset similarity threshold, the harmonic impedance of the system is determined to be in a stable state; otherwise, the correlation coefficient of the energy distribution of adjacent periods is used as an auxiliary factor for judgment. S6. When the system harmonic impedance is in a stable state, calculate the fault discrimination parameters based on the current response signal and execute the reclosing decision.

2. The reclosing coordinated control method based on electrical parameter measurement according to claim 1, characterized in that, During the reclosing waiting period of the distribution network line, a preset harmonic detection signal of a single frequency or a combination of frequencies is injected into the line, including: During the reclosing waiting period of the distribution network line, based on the equivalent harmonic impedance characteristics of the distribution network line, a preset single frequency or frequency combination is selected from the frequency band that avoids resonance with the background harmonics of the power grid. During the reclosing waiting period of the distribution network line, the selected single frequency or frequency combination is set to a fixed value, or the single frequency or frequency combination is dynamically adjusted according to the harmonic spectrum characteristics of the grid-connected current of the distributed photovoltaic inverter.

3. The reclosing coordinated control method based on electrical parameter measurement according to claim 1, characterized in that, The response signal of the harmonic detection signal in the line is measured, and time-frequency analysis is performed on the response signal obtained from multiple consecutive cycles to extract the energy distribution of the harmonic subspace within a preset frequency band, including: The response signal of the harmonic detection signal in the line is measured using a synchronous sampling method; The time-frequency distribution matrix is ​​obtained by performing a short-time Fourier transform on the response signal obtained from multiple consecutive period measurements. The energy distribution of the harmonic subspace within a preset frequency band is extracted from the time-frequency distribution matrix by calculating the sum of squares of the spectral amplitudes within the preset frequency band.

4. The reclosing coordinated control method based on electrical parameter measurement according to claim 1, characterized in that, The energy distributions of multiple consecutive periods are constructed into an energy distribution matrix. The rate of change of the nuclear norm of the energy distribution matrix is ​​calculated, and the morphological similarity between the current energy distribution and the baseline energy distribution is calculated, including: The energy distribution of the extracted harmonic subspace within the preset frequency band is arranged in chronological order to construct an energy distribution matrix; Perform singular value decomposition on the energy distribution matrix and calculate the nuclear norm of the energy distribution matrix; Calculate the rate of change of the nuclear norm of the energy distribution matrix over multiple consecutive time windows; The morphological similarity between the current energy distribution and the baseline energy distribution is calculated using the cosine similarity algorithm.

5. The reclosing coordinated control method based on electrical parameter measurement according to claim 4, characterized in that, Perform singular value decomposition on the energy distribution matrix and calculate the nuclear norm of the energy distribution matrix, including: Singular value decomposition is performed on the constructed energy distribution matrix to obtain several singular values; Summing all the singular values ​​obtained yields the nuclear norm of the energy distribution matrix. The stability and low-rank properties of the energy distribution matrix are characterized by calculating the sum of all singular values.

6. The reclosing coordinated control method based on electrical parameter measurement according to claim 4, characterized in that, The morphological similarity between the current energy distribution and the baseline energy distribution is calculated using the cosine similarity algorithm, including: The current energy distribution and the baseline energy distribution are represented as vectors; Calculate the dot product of two vectors and the magnitude of each vector; Dividing the dot product by the product of the magnitudes of the two vectors yields the morphological similarity between the current energy distribution and the baseline energy distribution. The baseline energy distribution is the energy distribution obtained in advance when the system is in a stable operating state.

7. The reclosing coordinated control method based on electrical parameter measurement according to claim 1, characterized in that, Determining whether the rate of change of the nuclear norm approaches zero, using morphological similarity as a verification condition, includes: Set a preset threshold for the rate of change of the nuclear norm, and determine whether the absolute value of the rate of change of the nuclear norm is less than the preset threshold for the rate of change of the nuclear norm; At the same time, it is determined whether the morphological similarity between the current energy distribution and the benchmark energy distribution is greater than a preset similarity threshold; When the absolute value of the rate of change of the nuclear norm is less than the preset threshold for the rate of change of the nuclear norm and the morphological similarity is greater than the preset threshold for similarity, the verification condition is deemed to be met.

8. The reclosing coordinated control method based on electrical parameter measurement according to claim 1, characterized in that, If the rate of change of the nuclear norm approaches zero and the morphological similarity is higher than the preset similarity threshold, then the system harmonic impedance is determined to be in a stable state. Otherwise, auxiliary judgment can be made by combining the trend of the correlation coefficient of energy distribution in adjacent periods, including: When the absolute value of the rate of change of the nuclear norm is less than the preset threshold for the rate of change of the nuclear norm and the morphological similarity between the current energy distribution and the reference energy distribution is greater than the preset similarity threshold, the system harmonic impedance is determined to be in a stable state. If the condition is not met, calculate the correlation coefficient sequence between the energy distributions of the most recent consecutive adjacent periods; Analyze the changing trend of the correlation coefficient sequence and fit the slope of the correlation coefficient change over time using the least squares method; When the fitting slope remains positive and the variance of the correlation coefficient sequence is less than the preset variance threshold, it is determined that the system harmonic impedance is transitioning to a stable state. When the system harmonic impedance is transitioning to a stable state, the energy distribution matrix construction and stability judgment process is restarted. If the process is repeated more than the preset number of times and still fails to determine a stable state, the reclosing operation is blocked.

9. The reclosing coordinated control method based on electrical parameter measurement according to claim 1, characterized in that, When the system harmonic impedance is in a stable state, fault discrimination parameters are calculated based on the current response signal, and reclosing decisions are executed, including: The ratio of harmonic impedance amplitudes is calculated based on the current response signal when the system is in a steady state and used as a fault discrimination parameter. The calculated harmonic impedance amplitude ratio is compared with a preset threshold. When the ratio of harmonic impedance amplitudes is less than a preset threshold, it is determined to be a transient fault, and a reclosing operation is performed. When the ratio of harmonic impedance amplitude is greater than or equal to a preset threshold, it is determined to be a permanent fault, and the reclosing operation is blocked.

10. A reclosing coordination control system based on electrical parameter measurement, used to implement the reclosing coordination control method based on electrical parameter measurement as described in any one of claims 1-9, characterized in that, include: The harmonic injection module is used to inject a preset single frequency or frequency combination of harmonic detection signals into the distribution network line during the reclosing waiting period. The signal measurement module is used to measure the response signal of the harmonic detection signal in the line, perform time-frequency analysis on the response signal obtained from multiple consecutive cycles, and extract the energy distribution of the harmonic subspace within the preset frequency band. The energy analysis module is used to construct an energy distribution matrix from the energy distribution of multiple consecutive periods, calculate the rate of change of the nuclear norm of the energy distribution matrix, and calculate the morphological similarity between the current energy distribution and the baseline energy distribution. The change judgment module is used to determine whether the rate of change of the nuclear norm approaches zero, and uses morphological similarity as the verification condition. The state determination module is used to determine that the system harmonic impedance is in a stable state if the rate of change of the nuclear norm approaches zero and the morphological similarity is higher than the preset similarity threshold; otherwise, it makes an auxiliary judgment by combining the trend of the correlation coefficient of the energy distribution of adjacent periods. The decision execution module is used to calculate fault discrimination parameters and execute reclosing decisions based on the current response signal when the system harmonic impedance is in a stable state.

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