Distributed power load control method of hybrid solid-state intelligent distribution transformer

Abstract

The present invention relates to the technical field of power load control and discloses a distributed power load control method for a hybrid solid-state intelligent distribution transformer, which is used to solve the problem that an unreasonable sampling window length may occur during power load control, resulting in reduced accuracy of transformer power load control. The method comprises the following steps: obtaining real-time electrical data of the solid-state intelligent distribution transformer within an initial sampling window, evaluating and obtaining an unreasonable sampling window index, determining whether the current sampling window length is reasonable, and if the current sampling window length is determined to be unreasonable, dynamically adjusting the initial sampling window length to obtain an actual sampling window length, calculating an equivalent impedance change index based on the actual sampling window length, and determining whether power load control intervention is required. If it is determined that power load control intervention is required, injecting a voltage feedforward compensation amount into a main control channel, thereby effectively improving the accuracy of transformer power load control.

Description

Technical Field

[0001] The present invention relates to the technical field of power load control, and more particularly to a distributed power load control method of a hybrid solid-state intelligent distribution transformer. Background Art

[0002] With the large-scale development of renewable energy, distributed power sources are increasingly being integrated into distribution networks. To enable flexible integration and efficient energy dispatch of distributed power sources, solid-state distribution transformers, a new generation of power electronic transformers, have been widely deployed in regional distribution systems, microgrids, and multi-energy complementary energy stations. Solid-state distribution transformers typically offer high-frequency conversion, fine voltage regulation, and energy flow control. Beyond traditional step-down transmission, they support bidirectional power flow regulation, dynamic power response, harmonic suppression, and fault isolation, making them indispensable power equipment for environments with a high proportion of renewable energy access.

[0003] In order to improve the responsiveness of solid-state distribution transformers to distributed power disturbances, existing technologies usually construct control criteria based on equivalent impedance estimation. By judging the dynamic change amplitude of the equivalent impedance, it is determined whether there is a working condition that requires intervention in power load control.

[0004] However, the above technology has at least the following technical problems:

[0005] In real-world operating environments, the distribution systems served by solid-state distribution transformers are highly dynamic, characterized by intermittent access to distributed generation (DGs), rapid load fluctuations, energy storage system operating mode switching, cable segmentation, and temporary reconfiguration of feeder topology. These factors lead to significant non-stationary characteristics in real-time operating parameters such as voltage and current. Existing equivalent impedance calculation methods typically use a fixed-length time sampling window to process voltage and current data to derive impedance estimates. However, static sampling window lengths make it difficult to adapt to both fast-changing disturbances and steady-state operation. This can lead to delayed disturbance identification, missed opportunities for rapid regulation responses, or misidentification of normal conditions as power supply disturbances, erroneously triggering the power load control process. This reduces the accuracy of solid-state distribution transformers in identifying system dynamics and can also cause frequent switching of control strategies, multiple invalid power dispatch commands, and even over-response of the energy storage system and deterioration of power quality, thereby weakening the system's operational stability and control robustness. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, the present invention provides a distributed power supply load control method of a hybrid solid-state intelligent distribution transformer to solve the problems existing in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] The distributed power load control method of the hybrid solid-state intelligent distribution transformer includes the following steps: Step 1: Setting a voltage sensor and a current sensor at the output end of the solid-state intelligent distribution transformer, setting an initial sampling window length, setting uniform sampling points within the initial sampling window, and obtaining real-time electrical data of the solid-state intelligent distribution transformer through the voltage sensor and the current sensor within the initial sampling window, wherein the electrical data includes voltage value, current value and corresponding sampling points; Step 2: Evaluating the sampling window unreasonable index based on the collected real-time electrical data, and determining whether the current sampling window length is reasonable based on the sampling window unreasonable index; Step 3: If it is determined that the current sampling window length is unreasonable, The initial sampling window length is dynamically adjusted according to the sampling window unreasonable index to obtain the actual sampling window length; Step 4: According to the actual sampling window length, the voltage and current data are collected, and the actual equivalent impedance estimation value within the actual sampling window is calculated based on the voltage and current data; Step 5: The equivalent impedance change index is calculated based on the actual equivalent impedance estimation value, and it is determined whether power load control intervention is required based on the equivalent impedance change index; Step 6: If it is determined that power load control intervention is required, the output current value at the current moment is obtained, and the product operation is performed with the current equivalent impedance estimation value to obtain the voltage feedforward compensation amount, and the voltage feedforward compensation amount is injected into the main control channel.

[0009] Preferably, the sampling window unreasonable index acquisition step is: within the initial sampling window, collecting the instantaneous voltage value and instantaneous current value of each sampling point to obtain a voltage sampling sequence and a current sampling sequence, and evaluating the electrical fluctuation influence coefficient according to the voltage sampling sequence and the current sampling sequence; obtaining an impedance estimation value sequence according to the voltage sampling sequence and the current sampling sequence, and evaluating the impedance estimation stability influence coefficient according to the impedance estimation value sequence; evaluating the current sampling sequence by fast Fourier transform to obtain a disturbance complexity influence coefficient; normalizing the electrical fluctuation influence coefficient, the impedance estimation stability influence coefficient, and the disturbance complexity influence coefficient, and evaluating the sampling window unreasonable index according to the normalized electrical fluctuation influence coefficient, the normalized impedance estimation stability influence coefficient, and the normalized disturbance complexity influence coefficient. The specific acquisition steps are: Where, Expressed as the sampling window unreasonable index, Expressed as the normalized electrical fluctuation influence coefficient, Expressed as the impedance estimation stability influence coefficient after normalization, Expressed as the normalized perturbation complexity influence coefficient, 、 、 It is expressed as the weight coefficient of the normalized electrical fluctuation influence coefficient, the weight coefficient of the normalized impedance estimation stability influence coefficient, and the weight coefficient of the normalized disturbance complexity influence coefficient.

[0010] Preferably, the steps for obtaining the electrical fluctuation influence coefficient are: performing first-order difference operations on the voltage sampling sequence and the current sampling sequence respectively to obtain a voltage change sequence and a current change sequence; dividing the voltage change sequence and the current change sequence into a number of sequence segments of equal length, each sequence segment containing the differential values of L consecutive sampling points; performing square sum calculations on the voltage differential values and the current differential values in each sequence segment respectively to obtain voltage change energy and current change energy; setting a jump amplitude threshold, and obtaining the number of voltage jump points and the number of current jump points whose differential values are greater than the jump amplitude threshold in each sequence segment; calculating the voltage disturbance factor based on the number of voltage jump points and the voltage change energy, and calculating the current disturbance factor based on the number of current jump points and the current change energy; adding the voltage disturbance factor and the current disturbance factor to obtain the electrical fluctuation influence coefficient.

[0011] Preferably, the steps of obtaining an impedance estimation value sequence based on a voltage sampling sequence and a current sampling sequence are as follows: within an initial sampling window, respectively collecting the instantaneous voltage value and instantaneous current value of each sampling point, and calculating the voltage value within the initial sampling window using a root mean square algorithm to obtain a voltage effective value; calculating the current value within the initial sampling window using a root mean square algorithm to obtain a current effective value; performing a ratio calculation on the voltage effective value and the current effective value to obtain an equivalent impedance estimation value, and obtaining an impedance estimation value sequence based on the equivalent impedance estimation value of each sampling point.

[0012] Preferably, the step of obtaining the impedance estimation stability influence coefficient is as follows: obtaining the impedance estimation value sequence within the initial sampling window, obtaining the number of samples in the impedance estimation value sequence, using a sliding time window, setting the sliding time window length, and using the least squares straight line fitting method to calculate the linear fitting line within each sliding window to obtain the corresponding fitting value sequence; for each sliding window interval center point, calculating the residual between the impedance estimation value and the fitting value; calculating the mean of all residuals to obtain the average residual value, and counting the number of center points exceeding twice the average residual value, recorded as the number of structural mutation points, and calculating the impedance estimation stability influence coefficient based on the number of structural mutation points, the number of samples in the impedance estimation value sequence, and the sliding time window length.

[0013] Preferably, the steps for obtaining the disturbance complexity influence coefficient are: performing fast Fourier transform on the current sampling sequence to obtain frequency components and corresponding amplitudes; setting a frequency division boundary, which includes a fundamental wave region and a high frequency region; squaring the amplitudes of all frequency components as the energy values corresponding to the frequency components, summing the energy values corresponding to each frequency component to obtain a total energy value, and performing ratio calculation on the energy value corresponding to each frequency component and the total energy value to obtain the energy proportion of each frequency component; summing the energy proportions of all frequency components with a statistical frequency higher than the high frequency region as the high frequency proportion; performing mean calculation on the energy values of each frequency component to obtain a spectrum energy mean, calculating the degree of deviation of its energy value from the spectrum energy mean for each frequency component, and performing square accumulation to obtain the spectrum energy distribution discreteness; multiplying the high frequency proportion by the distribution discreteness to obtain the disturbance complexity influence coefficient.

[0014] Preferably, the step of determining whether the current sampling window length is reasonable based on the sampling window unreasonable index is: comparing the sampling window unreasonable index with the unreasonable threshold; if the sampling window unreasonable index is greater than or equal to the unreasonable threshold, determining that the current sampling window length is unreasonable; if the sampling window unreasonable index is less than the unreasonable threshold, determining that the current sampling window length is reasonable.

[0015] Preferably, the steps of dynamically adjusting the length of the initial sampling window according to the sampling window unreasonable index to obtain the actual sampling window length are as follows: within the initial sampling window, collecting the voltage and current data of each sampling point, recording the time period between each two adjacent sampling points as a sampling sub-time period, obtaining a sampling current value sequence, calculating the initial equivalent impedance estimation value of each sampling sub-time period based on the voltage and current data, and obtaining an initial impedance estimation value sequence; performing first-order difference on the sampling current value sequence and the initial impedance estimation value sequence respectively to obtain a current disturbance change sequence and an initial impedance estimation change sequence; in the current disturbance change sequence, identifying the maximum disturbance point, recording the corresponding sampling point as the maximum current disturbance time point, and in the initial impedance estimation change sequence, identifying the maximum disturbance point, recording the corresponding sampling point as the maximum current disturbance time point. The point is recorded as the maximum impedance disturbance time point, and the maximum current disturbance time point is subtracted from the maximum impedance disturbance time point to obtain the disturbance corresponding time difference; the disturbance time difference threshold range is set, and the disturbance time difference threshold range includes a positive threshold and a negative threshold. If the disturbance corresponding time difference is greater than or equal to the positive threshold, it is determined that the impedance estimation response is delayed, and the initial sampling window length is shortened according to the sampling window unreasonable index to obtain the actual sampling window length; if the disturbance corresponding time difference is less than or equal to the negative threshold, it is determined that the impedance estimation response is sensitive, and the initial sampling window length is enlarged according to the sampling window unreasonable index to obtain the actual sampling window length; if the disturbance corresponding time difference is less than the positive threshold and greater than the negative threshold, the acquisition window length is not adjusted, and the initial sampling window length is directly used as the actual sampling window length.

[0016] Preferably, the step of obtaining the equivalent impedance change index is: obtaining the actual impedance estimation value at the current time point and the actual impedance estimation value at the previous time point, performing difference calculation on the actual impedance estimation value at the current time point and the actual impedance estimation value at the previous time point to obtain the impedance change; taking the absolute value of the impedance change to obtain the equivalent impedance change index at the current time point.

[0017] Preferably, the step of determining whether power load control intervention is needed based on the equivalent impedance change index is: comparing the equivalent impedance change index with the intervention threshold; if the equivalent impedance change index is greater than or equal to the intervention threshold, it is determined that there is currently a power access disturbance or a load mutation, and power load control intervention is performed; if the equivalent impedance change index is less than the intervention threshold, it is determined that there is currently no power access disturbance or load mutation, and power load control intervention is not performed.

[0018] The technical effects and advantages of the present invention are as follows:

[0019] Real-time electrical data of the solid-state intelligent distribution transformer is obtained within the initial sampling window, and the sampling window unreasonable index is evaluated to determine whether the current sampling window length is reasonable. If the current sampling window length is determined to be unreasonable, the initial sampling window length is dynamically adjusted to obtain the actual sampling window length. The equivalent impedance change index is calculated based on the actual sampling window length, and it is determined whether power load control intervention is needed. If it is determined that power load control intervention is needed, a voltage feedforward compensation amount is injected into the main control channel to effectively improve the accuracy of transformer power load control. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a flow chart of a distributed power supply load control method for a hybrid solid-state intelligent distribution transformer provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] The technical solutions of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. In addition, the forms of the various structures described in the following embodiments are merely examples. The distributed power supply load control method of the hybrid solid-state intelligent distribution transformer involved in the present invention is not limited to the various structures described in the following embodiments. All other implementations obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0022] The present invention provides a distributed power supply load control method for a hybrid solid-state intelligent distribution transformer, such as Figure 1 As shown, the following steps are included:

[0023] Step 1: Set a voltage sensor and a current sensor at the output end of the solid-state intelligent distribution transformer, set the initial sampling window length, set uniform sampling points within the initial sampling window, and obtain real-time electrical data of the solid-state intelligent distribution transformer through the voltage sensor and the current sensor within the initial sampling window. The electrical data includes voltage value, current value and corresponding sampling points;

[0024] Step 2: Evaluate the sampling window unreasonable index based on the collected real-time electrical data, and determine whether the current sampling window length is reasonable based on the sampling window unreasonable index;

[0025] In this embodiment, it should be specifically explained that the steps for obtaining the sampling window unreasonable index are:

[0026] In the initial sampling window, the instantaneous voltage value and instantaneous current value of each sampling point are collected to obtain the voltage sampling sequence and the current sampling sequence. The electrical fluctuation influence coefficient is obtained based on the voltage sampling sequence and the current sampling sequence.

[0027] Obtain an impedance estimation value sequence according to the voltage sampling sequence and the current sampling sequence, and obtain an impedance estimation stability influence coefficient according to the impedance estimation value sequence evaluation;

[0028] The current sampling sequence is evaluated by fast Fourier transform to obtain the disturbance complexity influence coefficient;

[0029] Fast Fourier transform is an algorithm that efficiently implements discrete Fourier transform. Its principle is to convert discrete signals in the time domain into complex amplitude representations of each frequency component in the frequency domain through a series of decomposition and recombination operations, thereby revealing the main frequency components contained in the signal and their energy distribution.

[0030] The electrical fluctuation influence coefficient, the impedance estimation stability influence coefficient, and the disturbance complexity influence coefficient are normalized. The sampling window unreasonable index is obtained based on the normalized electrical fluctuation influence coefficient, the normalized impedance estimation stability influence coefficient, and the normalized disturbance complexity influence coefficient. The specific acquisition steps are as follows:

[0031] ;

[0032] Where, Expressed as the sampling window unreasonable index, It is expressed as the normalized electrical fluctuation influence coefficient. A larger electrical fluctuation influence coefficient indicates that the system is currently in a state of significant disturbance. If the sampling window cannot quickly respond to these dynamic changes, it may cause impedance estimation to lag or be distorted, thereby reducing the effectiveness of the estimation. At this point, the sampling window setting becomes less adaptable to the actual system changes, indicating an increase in its irrationality. Therefore, the larger the electrical fluctuation influence coefficient, the less likely the current sampling window is to accurately capture the disturbance behavior, and it is directly proportional to the sampling window irrationality index. It is expressed as the normalized impedance estimation stability coefficient, which represents the fluctuation amplitude of the equivalent impedance within a continuous sampling period. When the impedance estimation frequently jumps or fluctuates violently, it usually indicates that the current time window has stability defects in the estimation process and cannot effectively balance data smoothness and dynamic response capabilities. This imbalance indicates that the mismatch between the sampling window and the system state is increasing. Therefore, the larger the estimation stability coefficient, the more serious the irrationality of the sampling window, and the two are in direct proportion. It is expressed as the disturbance complexity influence coefficient after normalization. The disturbance complexity influence coefficient reflects the complexity of the high-frequency components or spectral structure in the current signal. When this coefficient increases, it means that there are richer and finer-grained disturbance features in the system. If the sampling window is not set properly, it may be difficult to distinguish the key disturbance structure or cause frequency aliasing problems, thereby reducing the estimation or control accuracy. The stronger the complex disturbance, the more likely the existing window will not be able to fully cover its characteristics, resulting in a decrease in estimation performance. Therefore, the higher the disturbance complexity, the more unreasonable the current sampling window is, and it is directly proportional to the unreasonable index. 、 、 is expressed as the weight coefficient of the normalized electrical fluctuation influence coefficient, the weight coefficient of the normalized impedance estimation stability influence coefficient, and the weight coefficient of the normalized disturbance complexity influence coefficient, and , 、 、 It can be 0.3, 0.4, 0.3, 、 、 Obtained through the Analytic Hierarchy Process (AHP), a multi-criteria decision-making method used to decompose complex decision-making problems into multiple hierarchical structures. By constructing a judgment matrix, calculating eigenvectors, and performing consistency checks, the relative importance of each evaluation indicator is determined. In this embodiment, the AHP is used to evaluate the relative weights of the electrical fluctuation influence coefficient, the impedance estimation stability influence coefficient, and the disturbance complexity influence coefficient in the determination of sampling window irrationality. The specific steps include: first, constructing a judgment matrix between the three influence coefficients; second, normalizing the matrix and solving the eigenvector corresponding to each indicator as a weight coefficient; finally, confirming the rationality of the judgment matrix through consistency ratio calculation, thereby obtaining a weight coefficient with a theoretical basis and achieving an objective evaluation of the sampling window irrationality index.

[0033] In this embodiment, it should be specifically explained that the steps for obtaining the electrical fluctuation influence coefficient are:

[0034] Performing first-order difference operations on the voltage sampling sequence and the current sampling sequence respectively to obtain the voltage change sequence and the current change sequence;

[0035] The voltage change sequence and the current change sequence are divided into a number of sequence segments of equal length, each of which contains the differential values of L consecutive sampling points;

[0036] The voltage difference value and the current difference value in each sequence segment are squared and summed to obtain the voltage change energy and the current change energy;

[0037] Set a jump amplitude threshold, and obtain the number of voltage jump points and current jump points whose differential values are greater than the jump amplitude threshold in each sequence segment;

[0038] The voltage disturbance factor is calculated based on the number of voltage jump points and the voltage change energy. The specific acquisition steps are as follows:

[0039] ;

[0040] Where, It is expressed as the voltage disturbance factor, M is the number of sequence segments, and L is the number of sampling points contained in the sequence segments. Expressed as the number of voltage trip points, Expressed as voltage change energy, Expressed as voltage jump density, it reflects the frequency of voltage disturbances;

[0041] The current disturbance factor is calculated based on the number of current jump points and the current change energy. The specific acquisition steps are as follows:

[0042] ;

[0043] Where, Expressed as the current disturbance factor, Expressed as the number of current trip points, Expressed as the current change energy, It is expressed as voltage jump density, which reflects the frequency of current disturbance;

[0044] The voltage disturbance factor and the current disturbance factor are added together to obtain the electrical fluctuation influence coefficient.

[0045] By characterizing both jump density and disturbance amplitude, this approach can identify both sudden disturbances caused by large, instantaneous changes and high-frequency disturbance trends caused by continuous, small changes, thereby more comprehensively reflecting the dynamic severity of the system's current operating state. Compared to traditional methods, this calculation method offers a clearer structure, traceable parameters, and excellent real-time performance and engineering adaptability. It is particularly suitable for sensitively evaluating the rationality of sampling windows and triggering interventions in solid-state intelligent power distribution systems.

[0046] In this embodiment, it should be specifically explained that the steps for obtaining the impedance estimation value sequence according to the voltage sampling sequence and the current sampling sequence are as follows:

[0047] In the initial sampling window, the instantaneous voltage value and instantaneous current value of each sampling point are collected respectively, and the voltage value in the initial sampling window is calculated using the root mean square algorithm to obtain the effective value of the voltage;

[0048] The current value in the initial sampling window is calculated using the root mean square algorithm to obtain the effective value of the current;

[0049] The ratio of the voltage effective value to the current effective value is calculated to obtain an equivalent impedance estimation value, and an impedance estimation value sequence is obtained based on the equivalent impedance estimation value of each sampling point.

[0050] In this embodiment, it should be specifically explained that the steps for obtaining the impedance estimation stability influence coefficient are:

[0051] Obtain the impedance estimation value sequence within the initial sampling window, obtain the number of samples in the impedance estimation value sequence, use a sliding time window, set the sliding time window length, use the least squares straight line fitting method to calculate the linear fitting line within each sliding window, and obtain the corresponding fitting value sequence ,in Expressed as To The least squares straight line fitting method is a mathematical method for fitting the trend of data points. Its principle is to find a straight line in a given set of data points so that the sum of the squares of the vertical distances (i.e., residuals) from all data points to the straight line is minimized. The specific method is to use the impedance estimation value sequence in each sliding window, regard the sub-segment position index as the independent variable, and the impedance estimation value as the dependent variable, and calculate the slope and intercept through the calculation formula to construct a local trend line. This method can effectively extract the linear change trend in the sequence and avoid the interference of individual point fluctuations on the overall trend. It is often used in impedance estimation stability analysis to determine whether the sequence maintains structural continuity;

[0052] For each center point of the sliding window interval, the residual between the impedance estimate and the fitted value is calculated to reflect the degree of deviation of the point from the overall trend;

[0053] Calculate the mean of all residuals to obtain the average residual value, and count the number of central points that are more than twice the average residual value, which is recorded as the number of structural mutation points. The impedance estimation stability influence coefficient is calculated based on the number of structural mutation points, the number of samples in the impedance estimation value sequence, and the length of the sliding time window. The specific steps for obtaining it are:

[0054] ;

[0055] Where, Expressed as the impedance estimation stability influence coefficient, Expressed as the number of structural mutation points, is expressed as the number of samples in the sequence of impedance estimates, Expressed as the length of the sliding time window, the formula quantitatively reflects the frequency of trend interruptions in the estimated sequence by counting the proportion of such mutation points. A larger value indicates a more unstable estimation process. This principle can effectively distinguish between normal disturbances and structural instability, providing a quantitative basis for stability in the adaptive adjustment of system sampling parameters.

[0056] The stability influencing coefficient of impedance estimation is calculated using an algorithm based on least squares linear fitting residual structural analysis, which enables a more in-depth assessment of trend continuity and the impact of local disturbances during the impedance estimation process. By extracting the linear trend of the estimated value through sliding fitting and calculating the residual offset of each position relative to the fitted trend, this method can not only identify significant jump points but also reveal microstructural mutations or short-term instability hidden in the overall changes. Compared with traditional methods that rely solely on variance or range, this algorithm has stronger structural recognition capabilities and noise resistance while maintaining parameter traceability. It helps to perceive the decline in the consistency of the estimation results in advance and provides a more accurate criterion for dynamically adjusting the sampling window length.

[0057] In this embodiment, it should be specifically explained that the steps for obtaining the disturbance complexity influence coefficient are:

[0058] Perform fast Fourier transform on the current sampling sequence to obtain the frequency components and corresponding amplitudes;

[0059] Based on the working characteristics of the power distribution equipment, the frequency division boundary is selected. The frequency division boundary includes the fundamental wave area (such as 0-200Hz) and the high frequency area (such as 200-1000Hz);

[0060] The amplitudes of all frequency components are squared to obtain the energy values corresponding to the frequency components. The energy values corresponding to the frequency components are summed to obtain the total energy value. The energy values corresponding to the frequency components are then divided by the total energy value to obtain the energy proportion of each frequency component.

[0061] The sum of the energy proportions of all frequency components with frequencies higher than the high frequency zone is statistically calculated as the high frequency proportion;

[0062] The energy values of each frequency component are averaged to obtain the spectrum energy mean. For each frequency component, the degree of deviation between its energy value and the spectrum energy mean is calculated and squared and accumulated to obtain the spectrum energy distribution dispersion.

[0063] The disturbance complexity impact coefficient is calculated by multiplying the high-frequency ratio with the distribution dispersion. The high-frequency ratio reflects the proportion of non-steady-state, high-speed disturbance components in the current signal, representing the frequency level of the disturbance currently affecting the system. The spectral energy distribution dispersion measures the unevenness of the frequency energy distribution, reflecting whether the disturbance is concentrated in a specific frequency band and whether there are structural anomalies. By multiplying the two, it is possible to avoid misjudgments caused by only high-frequency but dispersed disturbances or only concentrated but low-frequency fluctuations. This makes the coefficient more sensitive to structural, high-amplitude, and high-frequency disturbances, improving the accuracy and engineering practicality of the system in dynamically determining the adaptability of the sampling window and regulating disturbance responses.

[0064] This algorithm combines the structural characteristics of spectral energy with its frequency domain distribution, comprehensively characterizing the complexity of disturbances in the current signal from two dimensions. Firstly, by counting the proportion of high frequencies, it identifies whether a large number of non-fundamental disturbance components, such as spikes, harmonics, and other high-frequency interference, are present in the system. Secondly, the dispersion of the spectral energy distribution measures whether the energy is concentrated in a small number of frequency bands, revealing whether the disturbance exhibits "burst concentration" characteristics. The product of these two factors reflects both the frequency range and the degree of structural anomaly of the disturbance, effectively avoiding misjudgments or omissions based on a single indicator and demonstrating greater sensitivity and discriminative power.

[0065] In this embodiment, it should be specifically explained that the steps for determining whether the current sampling window length is reasonable according to the sampling window unreasonableness index are as follows:

[0066] The sampling window unreasonable index is compared with the unreasonable threshold. If the sampling window unreasonable index is greater than or equal to the unreasonable threshold, the current sampling window length is judged to be unreasonable; if the sampling window unreasonable index is less than the unreasonable threshold, the current sampling window length is judged to be reasonable. The unreasonable threshold is obtained by the adaptive threshold method. The adaptive threshold method is a method that dynamically determines the judgment threshold based on the system's historical operating status and real-time data, avoiding the problem of fixed thresholds failing under different operating conditions. This method continuously monitors key parameters such as electrical fluctuations, impedance changes, and disturbance characteristics within a preset time period to construct a historical statistical distribution of the sampling window unreasonable index, and combines statistical indicators such as sliding mean, standard deviation, or percentile to dynamically generate the reasonable threshold required for the current judgment. Specifically, when the system is in a stable state, the recorded sampling window unreasonable index has a centralized trend. The unreasonable threshold can be automatically set based on the weighted deviation range of its sliding mean, thereby realizing a dynamic judgment mechanism with strong adaptability and low misjudgment rate.

[0067] Step 3: If the current sampling window length is determined to be unreasonable, the initial sampling window length is dynamically adjusted according to the sampling window unreasonableness index to obtain the actual sampling window length;

[0068] In this embodiment, it should be specifically explained that the steps for dynamically adjusting the initial sampling window length according to the sampling window unreasonableness index to obtain the actual sampling window length are as follows:

[0069] Within the initial sampling window, the voltage and current data of each sampling point are collected, and the time period between each two adjacent sampling points is recorded as a sampling sub-time period to obtain a sequence of sampled current values. The initial equivalent impedance estimation value of each sampling sub-time period is calculated based on the voltage and current data, thereby obtaining a sequence of initial impedance estimation values;

[0070] Performing first-order differences on the sampled current value sequence and the initial impedance estimation value sequence respectively to obtain the current disturbance change sequence and the initial impedance estimation change sequence;

[0071] In the current disturbance change sequence, the maximum disturbance point is identified and the corresponding sampling point is recorded as the maximum current disturbance time point. In the initial impedance estimation change sequence, the maximum disturbance point is identified and the corresponding sampling point is recorded as the maximum impedance disturbance time point. The maximum impedance disturbance time point is subtracted from the maximum current disturbance time point to obtain the disturbance corresponding time difference.

[0072] Set the disturbance time difference threshold range, which includes positive and negative thresholds. If the disturbance time difference is greater than or equal to the positive threshold, the impedance estimation response is determined to be delayed. The initial sampling window length is shortened according to the sampling window unreasonable index to obtain the actual sampling window length. The specific acquisition steps are as follows:

[0073] ;

[0074] Where, It represents the actual sampling window length, It is represented as the initial sampling window length, Indicated as unreasonable threshold, Expressed as sampling window unreasonable index;

[0075] If the disturbance corresponding time difference is less than or equal to the negative threshold, the impedance estimation response is determined to be hypersensitive. The initial sampling window length is amplified according to the sampling window unreasonable index to obtain the actual sampling window length. The specific acquisition steps are as follows:

[0076] ;

[0077] Where, It represents the actual sampling window length, It is represented as the initial sampling window length, Indicated as unreasonable threshold, Expressed as sampling window unreasonable index;

[0078] If the disturbance corresponding time difference is less than the positive threshold and greater than the negative threshold, the current acquisition window length is determined to be moderate, and the acquisition window length is not adjusted. The initial sampling window length is directly used as the actual sampling window length.

[0079] When the disturbance response time difference is greater than or equal to the positive threshold, the estimated equivalent impedance response significantly lags behind the actual occurrence of the current disturbance, indicating that the current sampling window is insufficiently capable of tracking system dynamics. An excessively long window causes the estimated value to be averaged over multiple sampling periods using historical data, resulting in a delayed response to sudden disturbances and reduced estimation timeliness and control accuracy. Therefore, a response time difference exceeding the positive threshold indicates significant estimation lag. To improve the estimation's ability to track disturbances in real time, the initial sampling window should be shortened to make it more sensitive and responsive to actual system changes.

[0080] When the disturbance response time difference is less than or equal to the negative threshold, it means that the jump of the equivalent impedance estimation occurs earlier than the actual current disturbance. The estimation result is too sensitive to small fluctuations or noise, resulting in "pre-fluctuation" or misjudgment. This hypersensitivity to response usually stems from a sampling window that is too short, making the estimation process dependent on a very small amount of data, which easily amplifies transient noise or local disturbances, causing the estimation result to fluctuate violently and lack stability. Therefore, when the response time difference reaches below the negative threshold, it indicates that the current window is too sensitive. The initial sampling window length should be increased and more samples should be introduced for smoothing to suppress noise interference and improve the stability and reliability of the estimation result.

[0081] Step 4: Collect voltage and current data according to the actual sampling window length, and calculate the actual equivalent impedance estimate within the actual sampling window based on the voltage and current data;

[0082] Step 5: Calculate the equivalent impedance variation index based on the actual equivalent impedance estimate, and determine whether power load control intervention is required based on the equivalent impedance variation index;

[0083] In this embodiment, it should be specifically explained that the steps for obtaining the equivalent impedance variation index are:

[0084] Obtain the actual impedance estimate value at the current time point and the actual impedance estimate value at the previous time point, calculate the difference between the actual impedance estimate value at the current time point and the actual impedance estimate value at the previous time point to obtain the impedance change; take the absolute value of the impedance change to obtain the equivalent impedance change index at the current time point.

[0085] Equivalent impedance is a key parameter reflecting the load or system state. Its changes are typically caused by power disturbances, load switching, or control responses. Difference calculation captures the changing trend of impedance estimates between two adjacent moments, while taking the absolute value removes the influence of the direction of change, retaining only the magnitude of the change, making this index more suitable for determining the presence of significant disturbances. This method is simple to calculate and highly responsive, effectively reflecting the dynamic fluctuations of system states. It is an important basis for identifying abnormal system conditions or the need for control intervention.

[0086] In this embodiment, it should be specifically explained that the steps of determining whether power load control intervention is required according to the equivalent impedance change index are:

[0087] The equivalent impedance change index is compared with the intervention threshold. If the equivalent impedance change index is greater than or equal to the intervention threshold, it is determined that there is a power access disturbance or a sudden load change, and power load control intervention is performed; if the equivalent impedance change index is less than the intervention threshold, it is determined that there is no power access disturbance or a sudden load change, and power load control intervention is not performed. The intervention threshold can be obtained in a variety of ways, including but not limited to: based on historical statistics, the threshold is determined by calculating the mean and standard deviation of the equivalent impedance change index of the system under steady-state operating conditions; or using a sliding window real-time update method to construct a dynamic threshold based on the mean value of the impedance change index in the most recent period plus a set offset; or extracting the optimal judgment point as a fixed threshold from historical data samples with marked disturbance conditions; or using engineering rules to set a preset threshold based on system scale, load type, or power supply characteristics. The above methods can be used alone or in combination to meet the control accuracy requirements in different scenarios.

[0088] Step 6: If it is determined that power load control intervention is required, the output current value at the current moment is obtained and multiplied by the current equivalent impedance estimate to obtain the voltage feedforward compensation amount. The voltage feedforward compensation amount is injected into the main control channel to achieve early suppression of disturbances, thereby improving the system's ability to maintain steady state under multi-source impacts.

[0089] The main control channel receives multiple control signals (including reference voltage, feedback voltage, estimated parameters, and feedforward compensation) and generates real-time regulation signals for the transformer's main control loop based on the current operating status, achieving voltage output stability control. The feedforward compensation, acting as an early response signal, complements the feedback control mechanism, improving the ability to quickly suppress disturbances.

[0090] Finally: 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 in the scope of protection of the present invention.

[0091] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A distributed power supply load control method for a hybrid solid-state intelligent distribution transformer, characterized in that: The following steps are included: Step 1: Set a voltage sensor and a current sensor at the output end of the solid-state intelligent distribution transformer, set the initial sampling window length, set uniform sampling points within the initial sampling window, and obtain real-time electrical data of the solid-state intelligent distribution transformer through the voltage sensor and the current sensor within the initial sampling window. The electrical data includes voltage value, current value and corresponding sampling points; Step 2: Evaluate the sampling window unreasonable index based on the collected real-time electrical data, and determine whether the current sampling window length is reasonable based on the sampling window unreasonable index; Step 3: If the current sampling window length is determined to be unreasonable, the initial sampling window length is dynamically adjusted according to the sampling window unreasonableness index to obtain the actual sampling window length; Step 4: Collect voltage and current data according to the actual sampling window length, and calculate the actual equivalent impedance estimate within the actual sampling window based on the voltage and current data; Step 5: Calculate the equivalent impedance variation index based on the actual equivalent impedance estimate, and determine whether power load control intervention is required based on the equivalent impedance variation index; Step 6: If it is determined that power load control intervention is required, the current output current value is obtained and multiplied by the current equivalent impedance estimate to obtain the voltage feedforward compensation value, which is then injected into the main control channel. The steps for obtaining the sampling window unreasonable index are as follows: In the initial sampling window, the instantaneous voltage value and instantaneous current value of each sampling point are collected to obtain the voltage sampling sequence and the current sampling sequence. The electrical fluctuation influence coefficient is obtained based on the voltage sampling sequence and the current sampling sequence. Obtain an impedance estimation value sequence according to the voltage sampling sequence and the current sampling sequence, and obtain an impedance estimation stability influence coefficient according to the impedance estimation value sequence evaluation; The current sampling sequence is evaluated by fast Fourier transform to obtain the disturbance complexity influence coefficient; The electrical fluctuation influence coefficient, the impedance estimation stability influence coefficient, and the disturbance complexity influence coefficient are normalized. The sampling window unreasonable index is obtained based on the normalized electrical fluctuation influence coefficient, the normalized impedance estimation stability influence coefficient, and the normalized disturbance complexity influence coefficient. The specific acquisition steps are as follows: ； Where, Expressed as the sampling window unreasonable index, Expressed as the normalized electrical fluctuation influence coefficient, Expressed as the impedance estimation stability influence coefficient after normalization, Expressed as the normalized perturbation complexity influence coefficient, 、 、 It is expressed as the weight coefficient of the normalized electrical fluctuation influence coefficient, the weight coefficient of the normalized impedance estimation stability influence coefficient, and the weight coefficient of the normalized disturbance complexity influence coefficient.

2. The distributed power load control method of the hybrid solid-state intelligent distribution transformer according to claim 1 is characterized in that: The steps for obtaining the electrical fluctuation influence coefficient are as follows: Performing first-order difference operations on the voltage sampling sequence and the current sampling sequence respectively to obtain the voltage change sequence and the current change sequence; The voltage change sequence and the current change sequence are divided into a number of sequence segments of equal length, each of which contains the differential values of L consecutive sampling points; The voltage difference value and the current difference value in each sequence segment are squared and summed to obtain the voltage change energy and the current change energy; Set a jump amplitude threshold, and obtain the number of voltage jump points and current jump points whose differential values are greater than the jump amplitude threshold in each sequence segment; The voltage disturbance factor is calculated based on the number of voltage jump points and the voltage change energy, and the current disturbance factor is calculated based on the number of current jump points and the current change energy; The voltage disturbance factor and the current disturbance factor are added together to obtain the electrical fluctuation influence coefficient.

3. The distributed power load control method of the hybrid solid-state intelligent distribution transformer according to claim 1, characterized in that: The step of obtaining the impedance estimation value sequence according to the voltage sampling sequence and the current sampling sequence is as follows: In the initial sampling window, the instantaneous voltage value and instantaneous current value of each sampling point are collected respectively, and the voltage value in the initial sampling window is calculated using the root mean square algorithm to obtain the effective value of the voltage; The current value in the initial sampling window is calculated using the root mean square algorithm to obtain the effective value of the current; The ratio of the voltage effective value to the current effective value is calculated to obtain an equivalent impedance estimation value, and an impedance estimation value sequence is obtained based on the equivalent impedance estimation value of each sampling point.

4. The distributed power supply load control method of the hybrid solid-state intelligent distribution transformer according to claim 1, characterized in that: The steps for obtaining the impedance estimation stability influence coefficient are as follows: Obtaining an impedance estimation value sequence within an initial sampling window, obtaining the number of samples in the impedance estimation value sequence, using a sliding time window, setting the sliding time window length, and using a least squares straight line fitting method to calculate a linear fitting line within each sliding window to obtain a corresponding fitting value sequence; For each sliding window interval center point, the residual between the impedance estimate and the fitted value is calculated; The mean of all residuals is calculated to obtain the average residual value, and the number of central points that are more than twice the average residual value is counted and recorded as the number of structural mutation points. The impedance estimation stability influence coefficient is calculated based on the number of structural mutation points, the number of samples in the impedance estimation value sequence, and the length of the sliding time window.

5. The distributed power supply load control method of the hybrid solid-state intelligent distribution transformer according to claim 1, characterized in that: The steps for obtaining the disturbance complexity influence coefficient are: Perform fast Fourier transform on the current sampling sequence to obtain the frequency components and corresponding amplitudes; Set the frequency division boundary, which includes the fundamental wave area and the high frequency area; The amplitudes of all frequency components are squared to obtain the energy values corresponding to the frequency components. The energy values corresponding to the frequency components are summed to obtain the total energy value. The energy values corresponding to the frequency components are then divided by the total energy value to obtain the energy proportion of each frequency component. The sum of the energy proportions of all frequency components with frequencies higher than the high frequency zone is statistically calculated as the high frequency proportion; The energy values of each frequency component are averaged to obtain the spectrum energy mean. For each frequency component, the degree of deviation between its energy value and the spectrum energy mean is calculated and squared and accumulated to obtain the spectrum energy distribution dispersion. The disturbance complexity influence coefficient is obtained by multiplying the high frequency proportion and the distribution dispersion.

6. The distributed power load control method of the hybrid solid-state intelligent distribution transformer according to claim 1, characterized in that: The step of determining whether the current sampling window length is reasonable according to the sampling window unreasonable index is as follows: The sampling window unreasonable index is compared with the unreasonable threshold. If the sampling window unreasonable index is greater than or equal to the unreasonable threshold, the current sampling window length is determined to be unreasonable; if the sampling window unreasonable index is less than the unreasonable threshold, the current sampling window length is determined to be reasonable.

7. The distributed power supply load control method of the hybrid solid-state intelligent distribution transformer according to claim 1, characterized in that: The steps of dynamically adjusting the initial sampling window length according to the sampling window unreasonable index to obtain the actual sampling window length are as follows: In the initial sampling window, the voltage and current data of each sampling point are collected, and the time period between each two adjacent sampling points is recorded as a sampling sub-time period to obtain a sequence of sampled current values. The initial equivalent impedance estimation value of each sampling sub-time period is calculated based on the voltage and current data to obtain a sequence of initial impedance estimation values; Performing first-order differences on the sampled current value sequence and the initial impedance estimation value sequence respectively to obtain the current disturbance change sequence and the initial impedance estimation change sequence; In the current disturbance change sequence, the maximum disturbance point is identified and the corresponding sampling point is recorded as the maximum current disturbance time point. In the initial impedance estimation change sequence, the maximum disturbance point is identified and the corresponding sampling point is recorded as the maximum impedance disturbance time point. The maximum impedance disturbance time point is subtracted from the maximum current disturbance time point to obtain the disturbance corresponding time difference. Set the disturbance time difference threshold range, which includes positive and negative thresholds. If the disturbance corresponding time difference is greater than or equal to the positive threshold, it is determined that the impedance estimation response is delayed. The initial sampling window length is shortened according to the sampling window unreasonable index to obtain the actual sampling window length. If the disturbance corresponding time difference is less than or equal to the negative threshold, the impedance estimation response is determined to be hypersensitive, and the initial sampling window length is amplified according to the sampling window unreasonable index to obtain the actual sampling window length; If the disturbance corresponding time difference is less than the positive threshold and greater than the negative threshold, the acquisition window length is not adjusted and the initial sampling window length is directly used as the actual sampling window length.

8. The distributed power load control method of a hybrid solid-state intelligent distribution transformer according to claim 1, characterized in that: The steps for obtaining the equivalent impedance change index are: Obtain the actual impedance estimate value at the current time point and the actual impedance estimate value at the previous time point, calculate the difference between the actual impedance estimate value at the current time point and the actual impedance estimate value at the previous time point to obtain the impedance change; take the absolute value of the impedance change to obtain the equivalent impedance change index at the current time point.

9. The distributed power supply load control method of the hybrid solid-state intelligent distribution transformer according to claim 1, characterized in that: The step of determining whether power load control intervention is required according to the equivalent impedance change index is as follows: The equivalent impedance change index is compared with the intervention threshold. If the equivalent impedance change index is greater than or equal to the intervention threshold, it is determined that there is a power access disturbance or a load mutation, and power load control intervention is performed; if the equivalent impedance change index is less than the intervention threshold, it is determined that there is no power access disturbance or load mutation, and power load control intervention is not performed.

Images