Distributed power supply bearing control method of hybrid solid intelligent distribution transformer

Abstract

The invention relates to the technical field of power supply bearing control, and discloses a distributed power supply bearing control method of a hybrid solid-state intelligent distribution transformer, which is used for solving the problem that the power supply bearing control accuracy of the transformer is reduced due to unreasonable sampling window length during power supply bearing control. Acquiring real-time electrical data of the solid-state intelligent distribution transformer in the initial sampling window, evaluating to obtain an unreasonable index of the sampling window, judging whether the length of the current sampling window is reasonable or not, and if the length of the current sampling window is judged to be unreasonable, dynamically adjusting the length of the initial sampling window to obtain an actual sampling window length; the equivalent impedance change index is calculated according to the actual sampling window length, whether power bearing control intervention needs to be carried out or not is judged, if it is judged that power bearing control intervention needs to be carried out, the voltage feed-forward compensation amount is injected into the main control channel, and the accuracy of transformer power bearing control is effectively improved.

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 for a hybrid solid-state intelligent distribution transformer. Background Art

[0002] With the large-scale development of renewable energy, distributed power sources are gradually widely connected to the distribution network. To achieve flexible access and efficient energy scheduling of distributed power sources, solid-state distribution transformers, as a new generation of power electronic transformers, have been widely deployed in scenarios such as regional distribution systems, microgrids, and multi-energy complementary energy stations. Solid-state distribution transformers usually have functions such as high-frequency conversion, fine voltage regulation, and power flow direction control. On the basis of realizing traditional step-down power transmission, they can further support two-way power flow regulation, dynamic power response, harmonic suppression, and fault isolation, and are indispensable power equipment for building an environment with a high proportion of new energy access.

[0003] To improve the response ability of solid-state distribution transformers to disturbances of distributed power sources, existing technologies usually construct control criteria based on the estimated value of equivalent impedance, and determine whether there is a working condition that requires intervention in power load control by judging the dynamic change range of the equivalent impedance.

[0004] However, the above technologies have at least the following technical problems:

[0005] In the actual operating environment, the distribution systems served by solid-state distribution transformers have highly dynamic characteristics, manifested as intermittent access of distributed power sources, rapid load fluctuations, switching of energy storage system operation modes, cable sectioning, and temporary reconstruction of feeder topologies. These factors cause real-time operating parameters such as voltage and current to exhibit obvious non-stationary characteristics. Existing equivalent impedance calculation methods usually use a fixed-length time sampling window to process voltage and current data to obtain impedance estimation results. However, the static setting of the sampling window length is difficult to adapt to both fast-changing disturbances and steady-state operation scenarios at the same time, resulting in lagging disturbance identification, missing the opportunity for rapid adjustment response, or misjudging normal conditions as power disturbances, and then wrongly triggering the power load control process. It reduces the discrimination accuracy of solid-state distribution transformers for system dynamic states, may also cause frequent switching of control strategies, multiple ineffective issuance of power scheduling instructions, and even cause problems such as over-response of energy storage systems and deterioration of power quality, thereby weakening the operation stability and control robustness of the system. Summary of the Invention

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

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

[0008] Distributed power load control method for hybrid solid-state intelligent distribution transformer, comprising the following steps: Step 1: Set a voltage sensor and a current sensor at the output end of the solid-state intelligent distribution transformer, set an 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 unreasonableness index according to the collected real-time electrical data, and determine whether the current sampling window length is reasonable according to the sampling window unreasonableness index; Step 3: If it is determined that the current sampling window length is unreasonable, dynamically adjust the initial sampling window length according to the sampling window unreasonableness index to obtain the actual sampling window length; Step 4: According to the actual sampling window length, collect voltage and current data, and calculate the actual equivalent impedance estimation value within the actual sampling window according to the voltage and current data; Step 5: Calculate the equivalent impedance change index according to the actual equivalent impedance estimation value, and determine whether power load control intervention is required according to the equivalent impedance change index; Step 6: If it is determined that power load control intervention is required, obtain the output current value at the current moment, and perform a multiplication operation with the current equivalent impedance estimation value to obtain a voltage feed-forward compensation amount, and inject the voltage feed-forward compensation amount into the main control channel.

[0009] Preferably, the step of obtaining the sampling window unreasonableness index is: within the initial sampling window, collect the instantaneous voltage value and instantaneous current value of each sampling point to obtain a voltage sampling sequence and a current sampling sequence, and evaluate the electrical fluctuation influence coefficient according to 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 evaluate the impedance estimation stability influence coefficient according to the impedance estimation value sequence; evaluate the disturbance complexity influence coefficient through fast Fourier transform of the current sampling sequence; perform normalization processing on the electrical fluctuation influence coefficient, the impedance estimation stability influence coefficient and the disturbance complexity influence coefficient, and evaluate the sampling window unreasonableness 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 obtaining steps are: ; In the formula, represents the sampling window unreasonableness index, represents the normalized electrical fluctuation influence coefficient, represents the normalized impedance estimation stability influence coefficient, represents the normalized disturbance complexity influence coefficient, 、 、 The weight coefficients of the electrical fluctuation influence coefficient after normalization processing, the weight coefficient of the impedance estimation stability influence coefficient after normalization processing, and the weight coefficient of the disturbance complexity influence coefficient after normalization processing.

[0010] Preferably, the steps for obtaining the electrical fluctuation influence coefficient are as follows: perform 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; divide the voltage change sequence and the current change sequence into several sequence division segments of equal length, and each sequence division segment contains the difference values of L consecutive sampling points; perform square summation calculations on the voltage difference values and the current difference values within each sequence division segment respectively 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 the number of current jump points with difference values greater than the jump amplitude threshold within each sequence division segment; calculate the voltage disturbance factor based on the number of voltage jump points and the voltage change energy, and calculate the current disturbance factor based on the number of current jump points and the current change energy; perform an addition calculation on the voltage disturbance factor and the current disturbance factor to obtain the electrical fluctuation influence coefficient.

[0011] Preferably, the steps for obtaining the impedance estimation value sequence from the voltage sampling sequence and the current sampling sequence are as follows: within the initial sampling window, collect the instantaneous voltage value and the instantaneous current value of each sampling point respectively, and use the root mean square algorithm to calculate the voltage value within the initial sampling window to obtain the effective voltage value; use the root mean square algorithm to calculate the current value within the initial sampling window to obtain the effective current value; perform a ratio calculation on the effective voltage value and the effective current value to obtain the equivalent impedance estimation value, and obtain the impedance estimation value sequence based on the equivalent impedance estimation value of each sampling point.

[0012] Preferably, the steps for obtaining the impedance estimation stability influence coefficient are as follows: 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, and use the least squares linear fitting method to calculate the linear fitting line within each sliding window to obtain the corresponding fitting value sequence; for the center point of each sliding window interval, calculate the residual between the impedance estimation value and the fitting value; calculate the mean value of all residuals to obtain the average residual value, and count the number of center points exceeding twice the average residual value, denoted as the number of structural mutation points, and calculate 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 step of obtaining the disturbance complexity influence coefficient is as follows: perform a fast Fourier transform on the current sampling sequence to obtain frequency components and corresponding amplitudes; set frequency division boundaries, which include a fundamental wave region and a high-frequency region; square the amplitudes of all frequency components to obtain the energy values corresponding to the frequency components, sum up the energy values corresponding to each frequency component to obtain the total energy value, calculate the ratio of the energy value corresponding to each frequency component to the total energy value to obtain the energy proportion of each frequency component; count the sum of the energy proportions of all frequency components with frequencies higher than the high-frequency region as the high-frequency proportion; calculate the mean value of the energy values of each frequency component to obtain the spectral energy mean value, for each frequency component, calculate the deviation degree between its energy value and the spectral energy mean value, and perform square summation to obtain the spectral energy distribution dispersion; multiply the high-frequency proportion by the distribution dispersion to obtain the disturbance complexity influence coefficient.

[0014] Preferably, the step of determining whether the current sampling window length is reasonable according to the sampling window unreasonable index is as follows: compare the sampling window unreasonable index with the unreasonable threshold. If the sampling window unreasonable index is greater than or equal to the unreasonable threshold, it is determined that the current sampling window length is unreasonable; if the sampling window unreasonable index is less than the unreasonable threshold, it is determined that the current sampling window length is reasonable.

[0015] Preferably, the step of dynamically adjusting the initial sampling window length according to the sampling window unreasonable index to obtain the actual sampling window length is as follows: within the initial sampling window, collect the voltage and current data of each sampling point, record the time period between every two adjacent sampling points as the sampling sub-time period to obtain the sampling current value sequence, calculate the initial equivalent impedance estimation value of each sampling sub-time period according to the voltage and current data to obtain the initial impedance estimation value sequence; perform a first-order difference on the sampling 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, identify the maximum disturbance point, and record the corresponding sampling point as the maximum current disturbance time point. In the initial impedance estimation change sequence, identify the maximum disturbance point, and record the corresponding sampling point as the maximum impedance disturbance time point. Subtract the maximum impedance disturbance time point from the maximum current disturbance time point to obtain the disturbance response time difference; set the disturbance time difference threshold range, which includes a positive threshold and a negative threshold. If the disturbance response time difference is greater than or equal to the positive threshold, it is determined that the impedance estimation response lags, and shorten the initial sampling window length according to the sampling window unreasonable index to obtain the actual sampling window length; if the disturbance response time difference is less than or equal to the negative threshold, it is determined that the impedance estimation response is over-sensitive, and enlarge the initial sampling window length according to the sampling window unreasonable index to obtain the actual sampling window length; if the disturbance response time difference is less than the positive threshold and greater than the negative threshold, do not adjust the acquisition window length, and directly use the initial sampling window length as the actual sampling window length.

[0016] Preferably, the step of obtaining the equivalent impedance change index is as follows: obtain the actual impedance estimation value at the current time point and the actual impedance estimation value at the previous time point, calculate the difference between 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 amount; take the absolute value of the impedance change amount to obtain the equivalent impedance change index at the current time point.

[0017] Preferably, the step of determining whether power carrying control intervention is required according to the equivalent impedance change index is as follows: compare 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 a power access disturbance or load mutation currently, and power carrying 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 currently, and no power carrying control intervention is performed.

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

[0019] Obtain the real-time electrical data of the solid-state intelligent distribution transformer within the initial sampling window, and evaluate to obtain the sampling window unreasonable index, determine whether the length of the current sampling window is reasonable. If it is determined that the length of the current sampling window is unreasonable, dynamically adjust the length of the initial sampling window to obtain the actual sampling window length, calculate the equivalent impedance change index according to the actual sampling window length, and determine whether power carrying control intervention is required. If it is determined that power carrying control intervention is required, inject a voltage feedforward compensation amount in the main control channel, effectively improving the accuracy of the transformer power carrying control. Description of the Drawings

[0020] Figure 1 It is a flowchart of the distributed power carrying control method for the hybrid solid-state intelligent distribution transformer provided by the embodiment of the present application. Detailed Embodiments

[0021] The technical solutions in the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. In addition, the forms of each structure described in the following embodiments are only examples, and the distributed power carrying control method for the hybrid solid-state intelligent distribution transformer involved in the present invention is not limited to the structures described in the following embodiments. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0022] The present invention provides a distributed power carrying control method for a hybrid solid-state intelligent distribution transformer, as Figure 1 shown, including the following steps:

[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, within the initial sampling window, set uniform sampling points, and obtain the 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 values, current values, and corresponding sampling points;

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

[0025] In this embodiment, it should be specifically noted that the steps for obtaining the sampling window unreasonableness index are as follows:

[0026] Within the initial sampling window, collect the instantaneous voltage value and instantaneous current value of each sampling point to obtain a voltage sampling sequence and a current sampling sequence, and evaluate the electrical fluctuation influence coefficient based on the voltage sampling sequence and the current sampling sequence;

[0027] Obtain an impedance estimation value sequence based on the voltage sampling sequence and the current sampling sequence, and evaluate the impedance estimation stability influence coefficient based on the impedance estimation value sequence;

[0028] Evaluate the disturbance complexity influence coefficient for the current sampling sequence through fast Fourier transform;

[0029] Fast Fourier transform is an algorithm for efficiently implementing the discrete Fourier transform. Its principle is to convert the discrete signal in the time domain into the complex amplitude representation of each frequency component in the frequency domain through a series of decomposition and recombination operations, so as to reveal the main frequency components and their energy distributions contained in the signal.

[0030] Normalize the electrical fluctuation influence coefficient, the impedance estimation stability influence coefficient, and the disturbance complexity influence coefficient, and evaluate the sampling window unreasonableness index based on the normalized electrical fluctuation influence coefficient, the normalized impedance estimation stability influence coefficient, and the normalized disturbance complexity influence coefficient. The specific obtaining steps are as follows:

[0031] ;

[0032] In the formula, represents the sampling window unreasonableness index, Denoted as the electrical fluctuation influence coefficient after normalization, a larger electrical fluctuation influence coefficient indicates that the current is in a significant disturbance state. If the sampling window cannot respond quickly to these dynamic changes, it may cause impedance estimation lag or distortion, thereby reducing the effectiveness of the estimation. At this time, the adaptability between the sampling window setting and the actual system change deteriorates, indicating an increase in its unreasonableness degree. Therefore, the larger the electrical fluctuation influence coefficient, the more likely the current sampling window is unable to accurately capture the disturbance behavior, showing a positive proportional relationship with the sampling window unreasonableness index. Denoted as the impedance estimation stability influence coefficient after normalization, the impedance estimation stability influence coefficient represents the fluctuation amplitude of the equivalent impedance within consecutive sampling periods. When there are frequent jumps or violent fluctuations in the impedance estimation, it usually indicates that there are stability defects in the current time window during the estimation process and it cannot effectively balance data smoothness and dynamic response ability. This imbalance indicates an increase in the degree of mismatch between the sampling window and the system state. Therefore, the larger the impedance estimation stability influence coefficient, the more serious the unreasonableness of the sampling window, showing a positive proportional relationship between the two. Denoted 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 indicates that there are richer and finer-grained disturbance characteristics in the system. If the sampling window is set improperly, it may be difficult to distinguish key disturbance structures or cause frequency aliasing problems, thereby reducing the estimation or control accuracy. The stronger the complex disturbance, the more likely the existing window is unable to fully cover its characteristics, resulting in a decline in the estimation performance. Therefore, the higher the disturbance complexity, the higher the unreasonableness degree of the current sampling window, showing a positive proportional relationship with the unreasonableness index. 、 、 Denoted as the weight coefficient of the electrical fluctuation influence coefficient after normalization, the weight coefficient of the impedance estimation stability influence coefficient after normalization, and the weight coefficient of the disturbance complexity influence coefficient after normalization, and , 、 、 can be 0.3, 0.4, 0.3, 、 、 Obtained through the Analytic Hierarchy Process (AHP). The AHP is a multi-criteria decision-making method used to decompose complex decision-making problems into multiple hierarchical structures. By constructing a judgment matrix, calculating the eigenvector, and performing a consistency test, the relative importance among various evaluation indicators is determined. In this embodiment, the AHP is used to evaluate the relative weights of the electrical fluctuation influence coefficient, impedance estimation stability influence coefficient, and disturbance complexity influence coefficient in the determination of the unreasonableness of the sampling window. The specific steps include: First, construct a judgment matrix between every two of the three influence coefficients; second, normalize the matrix and solve the eigenvector corresponding to each index as the weight coefficient; finally, confirm the rationality of the judgment matrix through the calculation of the consistency ratio, so as to obtain a weighted coefficient with theoretical basis and achieve an objective evaluation of the sampling window unreasonableness index.

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

[0034] Perform a first-order difference operation on the voltage sampling sequence and the current sampling sequence respectively to obtain a voltage change sequence and a current change sequence;

[0035] Divide the voltage change sequence and the current change sequence into several sequence partition segments of equal length, and each sequence partition segment contains the difference values of L consecutive sampling points;

[0036] Perform a square sum calculation on the voltage difference values and the current difference values within each sequence partition segment respectively 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 the number of current jump points with difference values greater than the jump amplitude threshold within each sequence partition segment;

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

[0039] ;

[0040] In the formula, represents the voltage disturbance factor, M represents the number of sequence partition segments, L represents the number of sampling points included in the sequence partition segment, represents the number of voltage jump points, represents the voltage change energy, represents the voltage jump density, reflecting the frequency of voltage disturbance;

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

[0042] ;

[0043] Wherein, is expressed as the current disturbance factor, is expressed as the number of current jump points, is expressed as the current change energy, is expressed as the voltage jump density, reflecting the frequency of current disturbance;

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

[0045] By dual-characterizing the jump density and the disturbance amplitude, both the sudden disturbance caused by instantaneous large changes and the high-frequency disturbance trend formed by continuous small changes can be identified, so as to more comprehensively reflect the dynamic severity of the current operating state of the system. Compared with the traditional method, this calculation method has a clear structure and traceable parameters, and has good real-time performance and engineering adaptability, especially suitable for sensitive evaluation and intervention triggering of the rationality of the sampling window in the solid-state intelligent power distribution system.

[0046] In this embodiment, it should be specifically noted that the steps to obtain 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 the instantaneous current value of each sampling point are respectively collected, and the root mean square algorithm is used to calculate the voltage values in the initial sampling window to obtain the effective voltage value;

[0048] The root mean square algorithm is used to calculate the current values in the initial sampling window to obtain the effective current value;

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

[0050] In this embodiment, it should be specifically noted that the steps to obtain the impedance estimation stability influence coefficient are as follows:

[0051] Obtain the impedance estimation value sequence in the initial sampling window, obtain the number of samples in the impedance estimation value sequence, use a sliding time window, set the length of the sliding time window, and use the least squares linear fitting method to calculate the linear fitting line in each sliding window to obtain the corresponding fitting value sequence , where is expressed as the th to the The predicted value of the linear trend fitted by the impedance estimation values at the central position. The least squares linear fitting method is a mathematical method for fitting the trend of data points. Its principle is to find a straight line among a given set of data points such that the sum of the squares of the perpendicular distances (i.e., residuals) from all data points to this line is minimized. The specific approach is to consider the sub-segment position index as the independent variable and the impedance estimation value as the dependent variable according to the impedance estimation value sequence within each sliding window, and construct the local trend line by calculating the slope and intercept through the calculation formula. This method can effectively extract the linear change trend in the sequence, avoid the interference of individual point fluctuations on the overall trend, and is commonly used in the stability analysis of impedance estimation to determine whether the sequence maintains structural continuity;

[0052] For the central point of each sliding window interval, calculate the residual between the impedance estimation value and the fitted value, which reflects the degree of deviation of this 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 exceeding twice the average residual value, denoted as the number of structural mutation points. Calculate 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. The specific acquisition steps are as follows:

[0054] ;

[0055] In the formula, represents the impedance estimation stability influence coefficient, represents the number of structural mutation points, represents the number of samples in the impedance estimation value sequence, represents the sliding time window length. By statistically analyzing the proportion of such mutation points, the formula quantitatively reflects the frequency at which the trend in the estimation sequence is interrupted. The larger the value, the more unstable the estimation process. This principle can effectively distinguish normal perturbations from structural instability and provide a quantitative basis for the adaptive adjustment of system sampling parameters.

[0056] Using the algorithm based on the residual structure analysis of least squares linear fitting to calculate the impedance estimation stability influence coefficient can more deeply evaluate the trend continuity and local perturbation influence in the impedance estimation process. By sliding fitting to extract the linear trend of the estimation value 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 phenomena hidden in the overall change. Compared with traditional methods that only rely on variance or range, this algorithm has stronger structure recognition ability and noise resistance while maintaining parameter traceability, which helps to perceive the decrease in the coherence of the estimation result in advance and provides a more accurate criterion for dynamically adjusting the sampling window length.

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

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

[0059] According to the working characteristics of the power distribution equipment, select the frequency division boundary, which includes the fundamental wave region (such as 0 - 200 Hz) and the high-frequency region (such as 200 - 1000 Hz);

[0060] Square the amplitudes of all frequency components as the energy values corresponding to the frequency components, sum up the energy values corresponding to each frequency component to obtain the total energy value, and calculate the ratio of the energy value corresponding to each frequency component to the total energy value to obtain the energy proportion of each frequency component;

[0061] Statistically sum up the energy proportions of all frequency components with frequencies higher than the high-frequency region as the high-frequency proportion;

[0062] Calculate the mean value of the energy values of each frequency component to obtain the spectral energy mean value. For each frequency component, calculate the degree of deviation between its energy value and the spectral energy mean value, and perform square summation to obtain the spectral energy distribution dispersion;

[0063] Multiply the high-frequency proportion by the distribution dispersion to obtain the disturbance complexity influence coefficient. The high-frequency proportion reflects the proportion of non-steady-state and high-speed disturbance components in the current signal, representing the frequency level of the current interference received by the system; while 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 is a structural anomaly. By multiplying the two, it can avoid misjudgment caused by only high-frequency but scattered disturbances or only concentrated but low-frequency fluctuations, making this coefficient more sensitive to structural, high-amplitude, and high-frequency interferences, and improving the accuracy and engineering practicability of the system in dynamically determining the adaptability of the sampling window and regulating the disturbance response.

[0064] This algorithm integrates the structural characteristics of spectral energy and the frequency domain distribution form, and can comprehensively characterize the disturbance complexity in the current current signal from two dimensions. On the one hand, by statistically calculating the high-frequency proportion, it can identify whether there are a large number of non-fundamental wave disturbance components in the system, such as high-frequency interferences like spikes and harmonics; on the other hand, by measuring the spectral energy distribution dispersion, it can reveal whether the energy is concentrated in a few frequency bands and whether the disturbance has the characteristics of "sudden concentration". The product of the two not only reflects the frequency range of the disturbance but also reflects the degree of structural anomaly of the disturbance, effectively avoiding misjudgment or omission of a single index, and having higher sensitivity and discriminability.

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

[0066] Compare the unreasonable index of the sampling window with the unreasonable threshold. If the unreasonable index of the sampling window is greater than or equal to the unreasonable threshold, it is determined that the length of the current sampling window is unreasonable; if the unreasonable index of the sampling window is less than the unreasonable threshold, it is determined that the length of the current sampling window is reasonable. The unreasonable threshold is obtained through the adaptive threshold method, which is a method of dynamically determining the judgment threshold according to the historical operating state and real-time data of the system, avoiding the problem of the fixed threshold failing under different working conditions. This method continuously monitors key parameters such as electrical fluctuations, impedance changes, and disturbance characteristics within a preset time period, constructs the historical statistical distribution of the unreasonable index of the sampling window, and dynamically generates the reasonable threshold required for the current judgment in combination with statistical indicators such as moving average, standard deviation, or percentile. Specifically, when the system is in a stable state, the recorded unreasonable index of the sampling window has a central tendency, and the unreasonable threshold can be automatically set according to the weighted deviation range of its moving average, thus realizing a dynamic judgment mechanism with strong adaptability and low misjudgment rate.

[0067] Step 3: If it is determined that the length of the current sampling window is unreasonable, dynamically adjust the initial sampling window length according to the unreasonable index of the sampling window to obtain the actual sampling window length;

[0068] In this embodiment, it should be specifically noted that the step of dynamically adjusting the initial sampling window length according to the unreasonable index of the sampling window to obtain the actual sampling window length is as follows:

[0069] Within the initial sampling window, collect the voltage and current data of each sampling point, record the time period between every two adjacent sampling points as the sampling sub-time period, obtain the sampling current value sequence, calculate the initial equivalent impedance estimation value of each sampling sub-time period according to the voltage and current data, and then obtain the initial impedance estimation value sequence;

[0070] Perform the first-order difference on the sampling 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, identify the maximum disturbance point, and record the corresponding sampling point as the maximum current disturbance time point. In the initial impedance estimation change sequence, identify the maximum disturbance point, and record the corresponding sampling point as the maximum impedance disturbance time point. Subtract the maximum impedance disturbance time point from the maximum current disturbance time point to obtain the disturbance response time difference;

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

[0073] ;

[0074] In the formula, represents the actual sampling window length, represents the initial sampling window length, represents the unreasonable threshold, represents the sampling window unreasonableness index;

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

[0076] ;

[0077] In the formula, represents the actual sampling window length, represents the initial sampling window length, represents the unreasonable threshold, represents the sampling window unreasonableness index;

[0078] If the perturbation corresponding time difference is less than the positive threshold and greater than the negative threshold, it is determined that the current acquisition window length is appropriate, 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 perturbation response time difference is greater than or equal to the positive threshold, it indicates that the estimation response of the equivalent impedance lags significantly behind the actual occurrence time of the current perturbation, indicating that the current sampling window has insufficient tracking ability for system dynamic changes. A too long window will cause the estimated value to be averaged by historical data in multiple sampling periods, resulting in a delay in the response to sudden perturbations, thereby reducing the timeliness and control accuracy of the estimation. Therefore, when the response time difference exceeds the positive threshold, it means that the estimation lag is significant. To improve the real-time tracking ability of the estimation for perturbations, the current initial sampling window length should be shortened to make the window more sensitive and faster to reflect the actual changes of the system.

[0080] When the perturbation response time difference is less than or equal to the negative threshold, it indicates that the jump of the equivalent impedance estimation occurs earlier than the actual occurrence of the current perturbation, and the estimation result is too sensitive to small fluctuations or noises, resulting in a "pre-fluctuation" or misjudgment phenomenon. This hypersensitive response usually stems from a too short sampling window, making the estimation process rely on a very small amount of data, easily amplifying instantaneous noises or local perturbations, resulting in violent fluctuations and instability of the estimation result. 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 amplified to introduce more samples for smoothing processing, thereby suppressing noise interference and improving the stability and reliability of the estimation result.

[0081] Step 4: According to the actual sampling window length, collect voltage and current data, and calculate the actual equivalent impedance estimation value within the actual sampling window based on the voltage and current data;

[0082] Step 5: Calculate the equivalent impedance change index based on the actual equivalent impedance estimation value, and determine whether power carrying control intervention is required according to the equivalent impedance change index;

[0083] In this embodiment, it should be specifically noted that the steps for obtaining the equivalent impedance change index are as follows:

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

[0085] The equivalent impedance is an important parameter reflecting the load or system state, and its change is usually caused by power supply disturbances, load switching, or control responses. Through difference calculation, the change trend of impedance estimation between two adjacent moments can be captured, and taking the absolute value removes the influence of the change direction and only retains the change amplitude, making this index more suitable for determining whether there are significant disturbances. This method has a simple calculation process and sensitive response, can effectively reflect the dynamic fluctuation degree of the system state, and is an important basis for identifying whether the system is in an abnormal working condition or needs to trigger control intervention.

[0086] In this embodiment, it should be specifically noted that the steps for determining whether power carrying control intervention is required according to the equivalent impedance change index are as follows:

[0087] Compare 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 a power access disturbance or load mutation currently, and power carrying control intervention is carried out; 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 currently, and no power carrying control intervention is carried out. The intervention threshold can be obtained in various ways, including but not limited to: based on the historical statistical method, determining the threshold by calculating the mean and standard deviation of the equivalent impedance change index under steady-state operation conditions of the system; or using the sliding window real-time update method to construct a dynamic threshold according to the mean value of the impedance change index plus a set offset within a recent period of time; or extracting the optimal discrimination point from the historical data samples of the marked disturbance working conditions as a fixed threshold; or setting a preset threshold according to the 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, obtain the output current value at the current moment, perform a multiplication operation with the current equivalent impedance estimation value to obtain the voltage feedforward compensation amount, and inject the voltage feedforward compensation amount into the main control channel to achieve the early suppression of disturbances, thereby improving the steady-state holding ability of the system under multi-source shocks.

[0089] The main function of the main control channel is to receive various control signals (including reference voltage, feedback voltage, estimated parameters, and feedforward compensation amount), and generate adjustment signals for the main control loop of the transformer in real time according to the current operating state to achieve voltage output stability control. The feedforward compensation amount, as an early response signal, forms a complement with the feedback control mechanism to improve the ability to quickly suppress disturbances.

[0090] Finally: The above are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

[0091] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, and all should be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A distributed power supply load control method for a hybrid solid-state intelligent distribution transformer, characterized in that Including the following steps; 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 the 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 values, current values, and corresponding sampling points; Step 2: Evaluate the sampling window unreasonableness index based on the collected real-time electrical data, and determine whether the current sampling window length is reasonable according to the sampling window unreasonableness index; Step 3: If it is determined that the current sampling window length is unreasonable, dynamically adjust the initial sampling window length according to the sampling window unreasonableness index to obtain the actual sampling window length; Step 4: According to the actual sampling window length, collect voltage and current data, and calculate the actual equivalent impedance estimation value within the actual sampling window according to the voltage and current data; Step 5: Calculate the equivalent impedance change index according to the actual equivalent impedance estimation value, and determine whether power carrying control intervention is required according to the equivalent impedance change index; Step 6: If it is determined that power carrying control intervention is required, obtain the output current value at the current moment, perform a multiplication operation with the current equivalent impedance estimation value to obtain the voltage feedforward compensation amount, and inject the voltage feedforward compensation amount into the main control channel.

2. The distributed power supply carrying control method of the hybrid solid-state intelligent distribution transformer according to claim 1, wherein: The obtaining steps of the sampling window unreasonableness index are as follows: Within the initial sampling window, collect the instantaneous voltage value and instantaneous current value of each sampling point to obtain a voltage sampling sequence and a current sampling sequence, and evaluate the electrical fluctuation influence coefficient according to 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 evaluate the impedance estimation stability influence coefficient according to the impedance estimation value sequence; Evaluate the disturbance complexity influence coefficient through fast Fourier transform of the current sampling sequence; Normalize the electrical fluctuation influence coefficient, the impedance estimation stability influence coefficient, and the disturbance complexity influence coefficient, and evaluate the sampling window unreasonableness 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 obtaining steps are as follows: ； In the formula, represents the unreasonable index of the sampling window, represents the electrical fluctuation influence coefficient after normalization, represents the impedance estimation stability influence coefficient after normalization, represents the disturbance complexity influence coefficient after normalization, , , represent the weight coefficient of the electrical fluctuation influence coefficient after normalization, the weight coefficient of the impedance estimation stability influence coefficient after normalization, and the weight coefficient of the disturbance complexity influence coefficient after normalization.

3. The distributed power supply carrying control method of the hybrid solid-state intelligent distribution transformer according to claim 2, wherein The obtaining steps of the electrical fluctuation influence coefficient are as follows: Perform a first-order difference operation on the voltage sampling sequence and the current sampling sequence respectively to obtain a voltage change sequence and a current change sequence; Divide the voltage change sequence and the current change sequence into several sequence division segments of equal length, and each sequence division segment contains the difference values of L consecutive sampling points; Perform a square sum calculation on the voltage difference values and current difference values within each sequence division segment respectively 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 the number of current jump points with difference values greater than the jump amplitude threshold within each sequence division segment; Calculate the voltage disturbance factor according to the number of voltage jump points and the voltage change energy, and calculate the current disturbance factor according to the number of current jump points and the current change energy; Add the voltage disturbance factor and the current disturbance factor to calculate the electrical fluctuation influence coefficient.

4. The distributed power supply carrying control method of the hybrid solid-state intelligent distribution transformer according to claim 2, characterized in that, The steps of obtaining the impedance estimation value sequence according to the voltage sampling sequence and the current sampling sequence are as follows: Within the initial sampling window, collect the instantaneous voltage value and the instantaneous current value of each sampling point respectively, and use the root mean square algorithm to calculate the voltage values within the initial sampling window to obtain the effective voltage value; Use the root mean square algorithm to calculate the current values within the initial sampling window to obtain the effective current value; Perform a ratio calculation on the effective voltage value and the effective current value to obtain an equivalent impedance estimation value, and obtain an impedance estimation value sequence according to the equivalent impedance estimation value of each sampling point.

5. The distributed power supply load control method of the hybrid solid-state intelligent distribution transformer according to claim 2, characterized in that: The steps of obtaining the impedance estimation stability influence coefficient are as follows: 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 length of the sliding time window, and use the least squares linear fitting method to calculate the linear fitting line within each sliding window to obtain the corresponding fitting value sequence; For the center point of each sliding window interval, calculate the residual between the impedance estimation value and the fitting value; Calculate the mean value of all residuals to obtain the average residual value, and count the number of center points exceeding twice the average residual value, denoted as the number of structural mutation points. Calculate the impedance estimation stability influence coefficient according to the number of structural mutation points, the number of samples in the impedance estimation value sequence, and the length of the sliding time window.

6. The distributed power supply carrying control method of the hybrid solid-state intelligent distribution transformer according to claim 2, characterized in that: The steps of obtaining the disturbance complexity influence coefficient are as follows: Perform a fast Fourier transform on the current sampling sequence to obtain the frequency components and the corresponding amplitudes; Set the frequency division boundary, and the frequency division boundary includes the fundamental wave region and the high-frequency region; Square the amplitudes of all frequency components as the energy values corresponding to the frequency components, sum up the energy values corresponding to each frequency component to obtain the total energy value, and perform a 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; Statistically sum up the energy proportions of all frequency components with frequencies higher than the high-frequency region as the high-frequency proportion; Calculate the mean value of the energy values of each frequency component to obtain the spectral energy mean value. For each frequency component, calculate the deviation degree between its energy value and the spectral energy mean value, and perform square accumulation to obtain the spectral energy distribution dispersion; Perform a product calculation on the high-frequency proportion and the distribution dispersion to obtain the disturbance complexity influence coefficient.

7. The distributed power supply carrying control method of the hybrid solid-state intelligent distribution transformer according to claim 1, characterized in that: The steps of determining whether the current sampling window length is reasonable according to the sampling window unreasonableness index are as follows: Compare the sampling window unreasonableness index with the unreasonableness threshold. If the sampling window unreasonableness index is greater than or equal to the unreasonableness threshold, it is determined that the current sampling window length is unreasonable; if the sampling window unreasonableness index is less than the unreasonableness threshold, it is determined that the current sampling window length is reasonable.

8. The distributed power supply carrying 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 unreasonableness index to obtain the actual sampling window length are as follows: Within the initial sampling window, the voltage and current data of each sampling point are collected. The time period between every two adjacent sampling points is recorded as a sampling sub-time period, and a sampling current value sequence is obtained. Based on the voltage and current data, the initial equivalent impedance estimation value of each sampling sub-time period is calculated to obtain an initial impedance estimation value sequence. First-order differences are respectively performed on the sampling current value sequence and the initial impedance estimation value sequence to obtain a current disturbance change sequence and an 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 response time difference. A disturbance time difference threshold range is set, which includes a positive threshold and a negative threshold. If the disturbance response time difference is greater than or equal to the positive threshold, it is determined that the impedance estimation response lags, and the initial sampling window length is shortened according to the sampling window unreasonableness index to obtain the actual sampling window length. If the disturbance response time difference is less than or equal to the negative threshold, it is determined that the impedance estimation response is overly sensitive, and the initial sampling window length is enlarged according to the sampling window unreasonableness index to obtain the actual sampling window length. If the disturbance response time difference is less than the positive threshold and greater than the negative threshold, the collection window length is not adjusted, and the initial sampling window length is directly used as the actual sampling window length.

9. The distributed power supply carrying control method of the hybrid solid-state intelligent distribution transformer according to claim 1, wherein: The steps for obtaining the equivalent impedance change index are as follows: Obtain the actual impedance estimation value at the current time point and the actual impedance estimation value at the previous time point, calculate the difference between 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 amount; take the absolute value of the impedance change amount to obtain the equivalent impedance change index at the current time point.

10. The distributed power supply carrying control method of the hybrid solid-state intelligent distribution transformer according to claim 1, characterized in that: The steps for determining whether power carrying control intervention is required based on the equivalent impedance change index are as follows: Compare 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 a power access disturbance or load mutation currently, and power carrying 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 currently, and no power carrying control intervention is performed.

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