Photovoltaic area voltage overrun control method and device based on regional collaborative U-shaped confluence strategy

Abstract

The invention relates to the field of photovoltaic voltage control, and discloses a photovoltaic area voltage overrun control method and device based on an area cooperation U-shaped confluence strategy, which are used for solving the problem that when the photovoltaic area voltage control is carried out, the acquired data is influenced by large equipment, so that the misjudgment of power abnormity is caused, and the method comprises the following steps of: acquiring power data in a time window; if the current power is judged to be abnormal for the first time, acquiring industrial load data in a time window, evaluating to obtain an industrial load sudden change index, performing industrial load sudden change judgment, and if the industrial load sudden change is judged to occur, correcting the power data; and performing second power abnormality judgment according to the corrected power data, and if the current power abnormality is judged for the second time, executing a U-shaped confluence strategy, continuing to collect the power data in real time, and performing real-time dynamic adjustment, thereby effectively improving the accuracy of the collected power data, reducing power abnormality misjudgment, and improving the reasonability of voltage control.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic voltage control, and more particularly to a method and device for controlling over-limit voltage of a photovoltaic substation area based on a regional collaborative U-shaped busbar connection strategy. Background Art

[0002] With the rapid development and wide application of distributed photovoltaic power generation technology, the operation of rural power grids faces new challenges, especially in the power grid lines for photovoltaic grid connection. During the peak period of photovoltaic power generation, due to the reverse feeding of electric energy into the power grid, the voltage of relevant lines increases, even exceeding the specified voltage upper limit; while during the period without photovoltaic power generation, the line voltage drops to normal or low levels. Such long-term voltage fluctuations not only have a negative impact on the user's power consumption experience but also increase the operation burden of power grid equipment.

[0003] Some old power grid lines are prone to large line voltage drops and power losses during reverse feeding of photovoltaic power due to line aging, small wire diameters, and long power supply radii. The existing power operation optimization method is to add a dedicated grid connection line for photovoltaic users. This method can effectively reduce the load on the main power supply line during the peak period of photovoltaic power generation in the daytime, improve the photovoltaic power consumption capacity, and avoid over-limit voltage in the substation area caused by reverse feeding of photovoltaic power.

[0004] However, in the process of implementing the inventive technical solution in the embodiments of the present application, it is found that the above technologies have at least the following technical problems:

[0005] In practical applications, the load conditions in the substation area are dynamically changing, and the load characteristics are different in different time periods and seasons. Simply adding a new line cannot flexibly adjust the operation state of the power grid, and the collected data may be affected by large equipment, resulting in fluctuations and being misjudged as power anomalies, leading to power adjustment, wasting resources and causing circuit instability. Summary of the Invention

[0006] In order to overcome the above defects of the prior art, the present invention provides a method and device for controlling over-limit voltage of a photovoltaic substation area based on a regional collaborative U-shaped busbar connection strategy 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] A photovoltaic substation voltage overlimit control method based on a regional collaborative U-shaped confluence strategy includes the following steps: Step 1: Adopt the sliding time window method, and use the monitoring device to collect power data in real time. The power data includes voltage data, current data, power factor, and photovoltaic output data, and obtain the power data within the time window; Step 2: Evaluate the power anomaly index based on the power data within the time window, and perform the first power anomaly judgment according to the power anomaly index; Step 3: If it is determined that the current power is abnormal for the first time, obtain the industrial load data at each sampling time point within the time window. The industrial load data includes voltage unbalance degree, current unbalance degree, harmonic content change rate, and load curve mutation rate. Evaluate the industrial load mutation index based on the industrial load data, and perform the industrial load mutation judgment according to the industrial load mutation index; Step 4: If it is determined that an industrial load mutation has occurred, correct the power data to obtain the corrected power data, re-evaluate the power anomaly index based on the corrected power data, and perform the second power anomaly judgment; Step 5: If it is determined that the current power is normal for the second time, continue to collect power data. If it is determined that the current power is abnormal for the second time, execute the U-shaped confluence strategy, adjust the reactive power of the photovoltaic inverter through reactive power compensation of the photovoltaic inverter, and obtain the adjusted power data; Step 6: Obtain the voltage safety range and the adjusted voltage data, and perform a voltage anomaly judgment based on the adjusted voltage and the voltage safety range; Step 7: If it is determined that the current voltage is abnormal, perform an intelligent route switch, and continue to collect power data in real time and dynamically adjust in real time.

[0009] Preferably, the step of obtaining the power anomaly index is as follows: Obtain the voltage data and the rated voltage value within the time window, and calculate the voltage influence degree according to the voltage data and the rated voltage; Obtain the current data within the time window, and calculate the current influence degree according to the current data; Obtain the power factor within the time window, and calculate the power factor influence degree according to the power factor; Obtain the photovoltaic output data within the time window, and calculate the photovoltaic processing influence degree according to the photovoltaic output data; Normalize the voltage influence degree, current influence degree, power factor influence degree, and photovoltaic processing influence degree, and evaluate the power anomaly index based on the normalized voltage influence degree, current influence degree, power factor influence degree, and photovoltaic processing influence degree. The specific obtaining steps are as follows: In the formula, E total represents the power anomaly index, E U represents the voltage influence degree, E I represents the current influence degree, E PF represents the power factor influence degree, E PV represents the photovoltaic processing influence degree. The square sum method is adopted to ensure that all abnormal factors can affect the final index.

[0010] Preferably, the first power anomaly determination step based on the power anomaly index is as follows: compare the power anomaly index with the anomaly threshold. If the power anomaly index is greater than or equal to the anomaly threshold, it is determined that there is a current power anomaly; if the power anomaly index is less than the anomaly threshold, it is determined that the current power is normal and no adjustment is made.

[0011] Preferably, the industrial load mutation index acquisition step is as follows: obtain three-phase voltage data within a time window, and evaluate the voltage unbalance coefficient through the norm method according to the three-phase voltage data; obtain three-phase current data within a time window, and evaluate the current unbalance coefficient through the norm method according to the three-phase current data; obtain harmonic content data within a time window, perform clustering processing on the harmonic content data using the K-means clustering method, and calculate the harmonic content change coefficient according to the clustering result; obtain the load signal within a time window, decompose the load signal into low-frequency and high-frequency components using discrete wavelet transform, extract the high-frequency component, and use the energy magnitude of the high-frequency component as the load mutation coefficient; normalize the voltage unbalance coefficient, current unbalance coefficient, harmonic content change coefficient, and load mutation coefficient, and evaluate the industrial load mutation index according to the normalized voltage unbalance coefficient, current unbalance coefficient, harmonic content change coefficient, and load mutation coefficient. The specific acquisition steps are as follows: LM = a1×VU + a2×CU + a3×HV + a4×LM; where LM represents the industrial load mutation index, VU represents the voltage unbalance coefficient, CU represents the current unbalance coefficient, HV represents the harmonic content change coefficient, LM represents the load mutation coefficient, and a1, a2, a3, and a4 represent the weight coefficients of the voltage unbalance coefficient, current unbalance coefficient, harmonic content change coefficient, and load mutation coefficient, respectively.

[0012] Preferably, the voltage unbalance coefficient acquisition step is as follows: construct a voltage data matrix according to the three-phase voltage data within the time window; calculate the average value of the three-phase voltage at each sampling time point to obtain the voltage average value vector; calculate the difference between the three-phase voltage at each sampling time point and the three-phase voltage average value, and construct a voltage deviation matrix; use the norm method to calculate the norm of the voltage deviation matrix according to the voltage deviation matrix, which is used to measure the overall voltage deviation within the window. The specific acquisition steps are as follows: where ||ΔU|| F represents the norm of the voltage deviation matrix, T represents the number of sampling time points, 3 represents the number of phases, and ΔU ij represents the deviation data at the i-th row and j-th column in the voltage deviation matrix; obtain the rated voltage value, and calculate the ratio of the norm of the voltage deviation matrix to the rated voltage value to obtain the voltage unbalance coefficient.

[0013] Preferably, the step of obtaining the harmonic content change coefficient is as follows: Step 3.1: Take the harmonic content data as the clustering feature, and take all the harmonic content data within the time window as the data set, and each harmonic content data in the data set is a data point; Step 3.2: Use the silhouette coefficient method to determine the number of clusters K of the data set; Step 3.3: Randomly select K data points in the data set as the initial cluster centers. For each data point, calculate its Euclidean distance to each initial cluster center. For each data point, traverse the K initial cluster centers and assign it to the cluster corresponding to the nearest initial cluster center; Step 3.4: After traversing all the data points, obtain the initial clusters. For each initial cluster, calculate the mean value of the data points inside it to obtain a new cluster center; Step 3.5: Repeat Step 3.3 and Step 3.4 until the cluster centers no longer change, and obtain the final clusters and the final cluster centers; Step 3.6: Calculate the mean value of all the final cluster centers to obtain the mean value of the cluster centers, and calculate the harmonic content change coefficient according to the final cluster centers and the mean value of the cluster centers. The specific obtaining steps are as follows: In the formula, HV represents the harmonic content change coefficient, K is the number of clusters, C k represents the k-th final cluster center, C represents the mean value of the cluster centers, and max(THD) represents the maximum harmonic content data within the time window.

[0014] Preferably, the step of judging the industrial load mutation according to the industrial load mutation index is as follows: Compare the industrial load mutation index with the mutation threshold. If the industrial load mutation index is greater than or equal to the mutation threshold, it is judged that an industrial load mutation has occurred; if the industrial load mutation index is less than the mutation threshold, it is judged that no industrial load mutation has occurred.

[0015] Preferably, the step of obtaining the corrected power data is as follows: Obtain the power data within the time window, calculate the ratio of the industrial load mutation index to the mutation threshold and then subtract 1 to obtain the correction factor; Obtain the rated value of the power data, multiply the difference between the power data within the time window and the rated value of the power data by the correction factor to obtain the power data adjustment amount; Calculate the difference between the voltage data and the power data adjustment amount to obtain the corrected power data.

[0016] Preferably, the step of judging the voltage abnormality according to the adjusted voltage and the voltage safety range is as follows: Compare the adjusted voltage with the voltage safety range. If the adjusted voltage is lower than the low voltage threshold, it is judged that the current voltage is abnormal; if the adjusted voltage is higher than the high voltage threshold, it is judged that the current voltage is abnormal; if the adjusted voltage is lower than the high voltage threshold and higher than the low voltage threshold, it is judged that the current voltage is normal and no intelligent route switching is performed.

[0017] Preferably, a voltage over-limit control device for a photovoltaic substation area based on a regional collaborative U-shaped current collection strategy, the device comprising: a power data acquisition module for acquiring power data within a time window through a monitoring device, the power data including voltage data, current data, power factor, and photovoltaic output data, and transmitting the power data within the time window to a first power anomaly determination module; a first power anomaly determination module for evaluating a power anomaly index based on the power data within the time window and performing a first power anomaly determination according to the power anomaly index; an industrial load mutation determination module, if it is determined that there is a current power anomaly, acquiring industrial load data at each sampling time point within the time window, the industrial load data including voltage unbalance degree, current unbalance degree, harmonic content change rate, and load curve mutation rate, evaluating an industrial load mutation index based on the industrial load data, and performing an industrial load mutation determination according to the industrial load mutation index; a second power anomaly determination module, if it is determined that an industrial load mutation has occurred, correcting the power data to obtain corrected power data, re-evaluating a power anomaly index based on the corrected power data, and performing a second power anomaly determination; a U-shaped current collection strategy execution module, if it is determined that the power is normal, continuing to acquire power data, if it is determined that there is a power anomaly, executing the U-shaped current collection strategy, compensating for reactive power through a photovoltaic inverter, adjusting the reactive power of the photovoltaic inverter to obtain adjusted power data, and transmitting the adjusted power data to an intelligent route switching module; an intelligent route switching module for obtaining a voltage safety range, obtaining adjusted voltage data based on the adjusted power data, comparing the adjusted voltage with the voltage safety range, and if the adjusted voltage is not within the voltage safety range, performing an intelligent route switching; a real-time dynamic adjustment module for continuing to acquire power data in real time and performing real-time dynamic adjustment.

[0018] Technical effects and advantages of the present invention:

[0019] Acquire power data within a time window, perform a first power anomaly determination. If it is determined that there is a current power anomaly in the first determination, acquire industrial load data within the time window, evaluate an industrial load mutation index, and perform an industrial load mutation determination. If it is determined that an industrial load mutation has occurred, correct the power data, perform a second power anomaly determination based on the corrected power data. If it is determined that there is a current power anomaly in the second determination, execute the U-shaped current collection strategy, and continue to acquire power data in real time and perform real-time dynamic adjustment, effectively improving the accuracy of the acquired power data, reducing misjudgment of power anomalies, and improving the rationality of voltage control. Description of the Drawings

[0020] Figure 1 It is a flowchart of a voltage over-limit control method for a photovoltaic substation area based on a regional collaborative U-shaped current collection strategy provided by an embodiment of the present application

[0021] Figure 2 Structural diagram of a photovoltaic substation voltage over-limit control device based on a regional collaborative U-shaped confluence strategy provided by an embodiment of the present application. Specific implementation manners

[0022] Next, the technical solutions in the present invention will be clearly and completely described in conjunction with the accompanying drawings in the present invention. In addition, the forms of each structure described in the following embodiments are merely examples, and a photovoltaic substation voltage over-limit control method and device based on a regional collaborative U-shaped confluence strategy involved in the present invention are 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.

[0023] The present invention provides a photovoltaic substation voltage over-limit control method based on a regional collaborative U-shaped confluence strategy, as Figure 1 shown, including the following steps:

[0024] Step 1: Adopt a sliding time window, and collect power data in real time through a monitoring device. The power data includes voltage data, current data, power factor, and photovoltaic output data, and obtain the power data within the time window;

[0025] The sliding time window is a dynamic data processing method. It continuously updates data within a fixed time range (such as 5 minutes, 10 minutes), and as time goes by, the window moves every certain time (such as 1 second or 30 seconds), and the latest power data is used for analysis. This method ensures that the system always makes judgments based on the latest power state, rather than relying on data sampling at fixed time intervals, thereby improving the continuity and accuracy of power grid state monitoring.

[0026] Using a sliding time window can improve the real-time performance of the system because it can continuously update data and avoid the lag problem that may be brought by a fixed detection time period. In addition, it can smooth short-term abnormal fluctuations, reduce misjudgments, and make the triggering of the U-shaped confluence strategy and intelligent line switching more accurate. The sliding window can also dynamically adapt to load changes in different time periods, ensuring more stable abnormal detection and not causing unnecessary adjustments due to a sudden change in data at a certain time.

[0027] Step 2: Evaluate the power anomaly index based on the power data within the time window, and perform the first power anomaly judgment according to the power anomaly index;

[0028] In this embodiment, it should be specifically noted that the steps for obtaining the power anomaly index are:

[0029] Obtain the voltage data and the rated voltage value within the time window, and calculate the voltage influence degree according to the voltage data and the rated voltage. The specific obtaining steps are:

[0030]

[0031] Wherein, E U represents the degree of voltage influence, T represents the number of sampling time points, U(t) represents the voltage data at the t-th sampling time point, and U ref represents the rated voltage value;

[0032] Obtain the current data within the time window, and calculate the current influence degree based on the current data. The specific obtaining steps are as follows:

[0033]

[0034] Wherein, E I represents the current influence degree, T represents the number of sampling time points, I(t) represents the current data at the t-th sampling time point, I(t + 1) represents the current data at the next sampling time point adjacent to the t-th sampling time point, and I max represents the maximum current data within the time window;

[0035] Obtain the power factor within the time window, and calculate the power factor influence degree based on the power factor. The specific obtaining steps are as follows:

[0036]

[0037] Wherein, E PF represents the power factor influence degree, T represents the number of sampling time points, PF(t) represents the power factor at the t-th sampling time point, and its value range is between 0 and 1. 0.95 is the set reference power factor. Generally, it is considered that when the power factor is lower than 0.95, the reactive power influence is greater. Take the cube to amplify the influence of the low power factor on the anomaly index, but it will not overly affect the power factor close to 1;

[0038] Obtain the photovoltaic output data within the time window, and calculate the photovoltaic processing influence degree based on the photovoltaic output data. The specific obtaining steps are as follows:

[0039]

[0040] Wherein, E PV represents the photovoltaic processing influence degree, T represents the number of sampling time points, P PV (t) represents the photovoltaic output data at the t-th sampling time point, P PV (t + 1) represents the photovoltaic output data at the next sampling time point adjacent to the t-th sampling time point, and P max represents the maximum photovoltaic output data within the time window;

[0041] Normalize the voltage influence degree, current influence degree, power factor influence degree, and photovoltaic processing influence degree. Normalization is a data preprocessing method used to convert variables with different dimensions to the same numerical range for unified comparison and calculation. Evaluate the power anomaly index based on the normalized voltage influence degree, current influence degree, power factor influence degree, and photovoltaic processing influence degree. The specific acquisition steps are as follows:

[0042]

[0043] In the formula, E total represents the power anomaly index, E U represents the voltage influence degree, E I represents the current influence degree, E PF represents the power factor influence degree, E PV represents the photovoltaic processing influence degree. The square sum method is used to ensure that all abnormal factors can affect the final index.

[0044] In this embodiment, it should be specifically noted that the steps for the first power anomaly judgment based on the power anomaly index are as follows:

[0045] Compare the power anomaly index with the anomaly threshold. If the power anomaly index is greater than or equal to the anomaly threshold, it is determined that the current power is abnormal; if the power anomaly index is less than the anomaly threshold, it is determined that the current power is normal and no adjustment is made. The anomaly threshold is obtained through the adaptive threshold method. The adaptive threshold method is a method for dynamically adjusting the anomaly detection threshold. It automatically calculates and adjusts the threshold according to factors such as historical power data, real-time grid status, and load fluctuation characteristics, so that it can adapt to different working conditions, rather than using a fixed static threshold. Compared with a fixed threshold, the adaptive threshold can be adjusted according to changes in time, season, electricity load, photovoltaic output, etc., improving the accuracy of power anomaly detection.

[0046] Step 3: If it is determined that the current power is abnormal in the first judgment, obtain the industrial load data at each sampling time point within the time window. The industrial load data includes voltage unbalance degree, current unbalance degree, harmonic content change rate, and load curve mutation rate. Evaluate the industrial load mutation index based on the industrial load data and perform industrial load mutation judgment according to the industrial load mutation index;

[0047] In this embodiment, it should be specifically noted that the steps for obtaining the industrial load mutation index are as follows:

[0048] Obtain three-phase voltage data within a time window. Based on the three-phase voltage data, evaluate the voltage unbalance factor through the norm method. The three-phase voltage data refers to the voltage values of phase A, phase B, and phase C measured separately in a three-phase AC power grid, usually in volts, representing the changes in the three-phase voltage at different time points. Phase A, phase B, and phase C are the three power supply lines in a three-phase AC power system, corresponding to the three independent components of the three-phase voltage respectively. In a standard three-phase power supply system, the voltage amplitudes of phases A, B, and C are equal, and the phases differ by 120°, jointly constituting a balanced power system;

[0049] The norm method is a mathematical method used to measure the deviation degree of vectors and matrices. In power system analysis, the norm method is often used to evaluate the unbalance degree of three-phase voltage and current, and quantify abnormal conditions by calculating the overall deviation of the data. For example, the norm can be used to measure the deviation of three-phase voltage or current within a time window to help identify problems such as sudden changes in industrial loads and uneven power supply. The core advantage of the norm method is that it simultaneously considers the overall trend of multiple data points, avoiding the influence of single-point errors on the calculation results, making it widely used in fields such as power grid anomaly detection, data analysis, and machine learning.

[0050] Obtain three-phase current data within a time window. Based on the three-phase current data, evaluate the current unbalance factor through the norm method;

[0051] Obtain harmonic content data within a time window, perform clustering processing on the harmonic content data using the K-means clustering method, and calculate the harmonic content change coefficient based on the clustering processing results;

[0052] Obtain a load signal within a time window, select a wavelet basis function suitable for mutation detection, use discrete wavelet transform to decompose the load signal into low-frequency and high-frequency components, extract the high-frequency components for analyzing mutation situations. Discrete wavelet transform is a signal time-frequency analysis method that decomposes a signal into low-frequency and high-frequency components to extract the local features of the signal. Calculate the energy magnitude of the high-frequency components based on the high-frequency components to quantify the degree of mutation, and use the energy magnitude of the high-frequency components as the load mutation coefficient. The specific acquisition steps are as follows:

[0053]

[0054] In the formula, LM represents the energy magnitude of the high-frequency components, that is, the load mutation coefficient, W H (t) represents the high-frequency component at the t-th sampling time point, and T represents the number of sampling time points;

[0055] Normalize the voltage unbalance coefficient, current unbalance coefficient, harmonic content change coefficient, and load mutation coefficient, and evaluate the industrial load mutation index based on the normalized voltage unbalance coefficient, current unbalance coefficient, harmonic content change coefficient, and load mutation coefficient. The specific acquisition steps are as follows:

[0056] LM = a1×VU + a2×CU + a3×HV + a4×LM;

[0057] In the formula, LM represents the industrial load mutation index, VU represents the voltage unbalance coefficient. When some industrial equipment suddenly starts or stops, it will cause a three-phase voltage offset and increase the voltage unbalance degree. Therefore, an increase in the voltage unbalance coefficient indicates that the system has received a stronger industrial load impact, resulting in a larger industrial load mutation index. This relationship enables the voltage unbalance coefficient to be used as an important characteristic parameter for identifying industrial load mutations. CU represents the current unbalance coefficient. When industrial equipment suddenly starts and stops or the load fluctuates violently, it will cause a sudden increase or decrease in the current of a certain phase, destroying the balance of the three-phase current and then increasing the current unbalance coefficient. Therefore, an increase in the current unbalance coefficient indicates that the industrial load mutation has a greater impact on the power grid, causing the industrial load mutation index to increase accordingly. HV represents the harmonic content change coefficient. When nonlinear industrial loads suddenly start and stop or their operating states change rapidly, it will cause a sharp change in the current harmonic components, resulting in an increase in the harmonic content fluctuation. Therefore, an increase in the harmonic content change coefficient indicates that the industrial load mutation has a greater impact on the power grid, causing the industrial load mutation index to increase accordingly. LM represents the load mutation coefficient. When high-power equipment suddenly starts, stops, or the load changes rapidly, it will cause a sharp change in the power demand in the power grid within a short period of time, resulting in an increase in the load mutation coefficient. a1, a2, a3, and a4 represent the weight coefficients of the voltage unbalance coefficient, current unbalance coefficient, harmonic content change coefficient, and load mutation coefficient respectively, and a1 + a2 + a3 + a4 = 1. a1, a2, a3, and a4 are obtained through the analytic hierarchy process. For example, a1, a2, a3, and a4 can be 0.2, 0.2, 0.3, and 0.3. The analytic hierarchy process is a multi-criteria decision-making method used to determine the relative importance of various factors in complex problems. It decomposes the decision-making problem into an objective layer, a criterion layer, and an index layer by constructing a hierarchical structure model, and then uses the pairwise comparison method to establish a judgment matrix and calculate the weight coefficients of each factor.

[0058] In this embodiment, it should be specifically noted that the steps for obtaining the voltage unbalance coefficient are as follows:

[0059] Construct a voltage data matrix based on the three-phase voltage data within the time window. The specific matrix is:

[0060]

[0061] Wherein, U represents the voltage data matrix, the number of rows represents the number of sampling time points within the time window, data is collected once at each time point t, and U A (t) represents the phase A voltage at the t-th sampling time point, and U B (t) represents the phase B voltage at the t-th sampling time point, and U C (t) represents the phase C voltage at the t-th sampling time point;

[0062] Calculate the average value of the three-phase voltages at each sampling time point to obtain the voltage average value vector, which is specifically expressed as:

[0063]

[0064] Wherein, represents the voltage average value vector, represents the average value of the three-phase voltages at the t-th sampling time point, and is used as a reference value to measure the degree of imbalance;

[0065] Calculate the difference between the three-phase voltages at each sampling time point and the average value of the three-phase voltages, and construct a voltage deviation matrix. The specific matrix is:

[0066]

[0067] Wherein, ΔU represents the voltage deviation matrix, indicating the deviation of the three-phase voltages at each sampling time point relative to the average value. If the three-phase of the power grid is balanced, all values should be close to zero;

[0068] Using the norm method, calculate the norm of the voltage deviation matrix based on the voltage deviation matrix, which is used to measure the overall voltage deviation within the window. The specific acquisition steps are as follows:

[0069]

[0070] Wherein, ||ΔU|| F represents the norm of the voltage deviation matrix, T represents the number of sampling time points, and ΔU ij represents the deviation data at the i-th row and j-th column in the voltage deviation matrix. After squaring and summing and then taking the square root, it is to ensure that the deviations of all sampling time points and three-phase data are considered;

[0071] Obtain the rated voltage value, and calculate the ratio of the norm of the voltage deviation matrix to the rated voltage value to obtain the voltage unbalance factor.

[0072] The voltage unbalance factor is calculated using a matrix calculation method, which can comprehensively analyze the changes in three-phase voltages within a sliding time window. Compared with the traditional point-by-point calculation method, this approach can consider the voltage fluctuation trend of the entire time window and improve the detection accuracy of industrial load mutations. Using the norm to calculate the overall voltage deviation can effectively avoid misjudgments caused by short-term fluctuations at individual time points and make the anomaly detection more stable. At the same time, this method can process multiple data points simultaneously through matrix operations, more accurately reflect the voltage unbalance characteristics caused by industrial loads (such as large motors, welding machines), and improve the ability to identify mutant loads.

[0073] In this embodiment, it should be specifically noted that the steps for obtaining the current unbalance factor are as follows:

[0074] Based on the three-phase current data within the time window, a current data matrix is constructed. The specific matrix is:

[0075]

[0076] In the formula, I represents the current data matrix, the number of rows represents the number of sampling time points within the time window, and data is collected once at each time point t. I A (t) represents the current of phase A at the t-th sampling time point, and I B (t) represents the current of phase B at the t-th sampling time point, and I C (t) represents the current of phase C at the t-th sampling time point;

[0077] Calculate the average value of the three-phase currents at each sampling time point to obtain the current average value vector, which is specifically expressed as:

[0078]

[0079] In the formula, represents the current average value vector, represents the average value of the three-phase currents at the t-th sampling time point, which is used as a reference value to measure the degree of unbalance;

[0080] Calculate the difference between the three-phase currents at each sampling time point and the average value of the three-phase currents, and construct a current deviation matrix. The specific matrix is:

[0081]

[0082] In the formula, ΔI represents the current deviation matrix, indicating the deviation of the three-phase currents at each sampling time point relative to the average value;

[0083] Using the norm method, calculate the norm of the current deviation matrix based on the current deviation matrix to measure the overall current deviation within the window. The specific acquisition steps are as follows:

[0084]

[0085] In the formula, ||ΔI|| F represents the norm of the current deviation matrix, T represents the number of sampling time points, and ΔI ij represents the deviation data at the i-th row and j-th column in the current deviation matrix. After squaring and summing and then taking the square root, it is to ensure that the deviations of all sampling time points and three-phase data are considered;

[0086] Obtain the rated current value, and calculate the ratio of the norm of the current deviation matrix to the rated current value to obtain the current unbalance coefficient.

[0087] In this embodiment, it should be specifically noted that the steps for obtaining the harmonic content change coefficient are as follows:

[0088] Step 3.1: Use the harmonic content data as the clustering feature, and use all the harmonic content data within the time window as the data set, and each harmonic content data in the data set is a data point;

[0089] Step 3.2: Use the silhouette coefficient method to determine the number of clusters K of the data set;

[0090] The silhouette coefficient method is an unsupervised learning evaluation method used to measure the quality of data clustering to determine the number of clusters K. It evaluates the compactness of each data point in the current cluster and the separation degree from the nearest neighbor cluster by calculating the silhouette coefficient of each data point. Generally, select the K that maximizes the average silhouette coefficient as the optimal number of clusters to ensure that the data points are closely aggregated in each cluster and far from other clusters, thereby improving the accuracy and stability of clustering.

[0091] Step 3.3: Randomly select K data points in the data set as the initial cluster centers. For each data point, calculate its Euclidean distance to each initial cluster center. The specific acquisition method is as follows:

[0092]

[0093] In the formula, d(P,β) represents the Euclidean distance from the data point to the cluster center, where P represents the data point and β represents the initial cluster center. For each data point, traverse the K initial cluster centers and assign it to the cluster corresponding to the nearest initial cluster center;

[0094] The Euclidean distance is a geometric distance calculation method used to measure the straight-line distance between two points, and its definition is the square root of the sum of the squares of the differences of the corresponding dimensions of the coordinates of two points in a multi-dimensional space.

[0095] Step 3.4: After traversing all data points to obtain the initial clustering clusters, for each initial clustering cluster, calculate the mean value of the data points within it to obtain a new clustering center;

[0096] Step 3.5: Repeat Step 3.3 and Step 3.4 until the clustering center no longer changes, to obtain the final clustering clusters and the final clustering center;

[0097] Step 3.6: Calculate the mean value of all the final clustering centers to obtain the mean value of the clustering centers, and calculate the harmonic content change coefficient based on the final clustering centers and the mean value of the clustering centers. The specific obtaining steps are as follows:

[0098]

[0099] In the formula, HV represents the harmonic content change coefficient, K is the number of clusters, C k represents the k-th final clustering center, represents the mean value of the clustering centers, and max(THD) represents the maximum harmonic content data within the time window.

[0100] In this embodiment, it should be specifically noted that the steps for judging industrial load mutation according to the industrial load mutation index are as follows:

[0101] Compare the industrial load mutation index with the mutation threshold. If the industrial load mutation index is greater than or equal to the mutation threshold, it is judged that an industrial load mutation has occurred; if the industrial load mutation index is less than the mutation threshold, it is judged that no industrial load mutation has occurred. The mutation threshold is obtained through the adaptive threshold method.

[0102] Step 4: If it is judged that an industrial load mutation has occurred, correct the power data to shield the influence of the industrial load within the time window on the power data, obtain the corrected power data, re-evaluate based on the corrected power data to obtain the power anomaly index, and perform the second power anomaly judgment;

[0103] In this embodiment, it should be specifically noted that the steps for obtaining the corrected power data are as follows:

[0104] Obtain the power data within the time window, calculate the ratio of the industrial load mutation index to the mutation threshold and then subtract 1 to obtain the correction factor;

[0105] Obtain the rated value of the power data. The rated value of the power data includes the rated voltage value, rated current value, rated power factor, and rated PV output. Subtract the rated value of the power data within the time window from the power data and then perform a product calculation with the correction factor to obtain the power data adjustment amount. The power data adjustment amount includes the voltage data adjustment amount, current data adjustment amount, power factor adjustment amount, and PV output data adjustment amount. For example, to calculate the voltage data adjustment amount, the specific steps are as follows:

[0106] TU(t) = (U(t) - U ref ) × λ;

[0107] Wherein, TU(t) represents the voltage data adjustment amount at the t-th sampling time point, U(t) represents the voltage data at the t-th sampling time point, U ref represents the rated voltage value, and λ represents the correction factor;

[0108] Perform a difference calculation on the voltage data and the power data adjustment amount to obtain the corrected power data. The corrected power data includes corrected voltage data, corrected current data, corrected power factor, and corrected photovoltaic output data. For example, to calculate the corrected voltage data, the specific steps are as follows:

[0109] JU(t) = U(t) - TU(t);

[0110] JU(t) represents the corrected voltage data at the t-th sampling time point, U(t) represents the voltage data at the t-th sampling time point, and TU(t) represents the voltage data adjustment amount at the t-th sampling time point.

[0111] Step 5: If it is determined for the second time that the current power is normal, return to Step 1 to continue collecting power data. If it is determined for the second time that the current power is abnormal, execute the U-shaped busbar connection strategy, adjust the reactive power of the photovoltaic inverter through reactive power compensation of the photovoltaic inverter, and obtain the adjusted power data;

[0112] The U-shaped busbar connection strategy is a grid voltage regulation method based on regional collaborative optimization, which is used to handle the problem of over-limit voltage in the photovoltaic substation area. The core of the U-shaped busbar connection strategy lies in dynamically adjusting the reactive power of multiple photovoltaic inverters so that they can coordinate when the grid voltage is abnormal, thereby improving the stability of the grid in the photovoltaic substation area.

[0113] Reactive power compensation of the photovoltaic inverter is a method of using the photovoltaic inverter to adjust reactive power to optimize grid voltage, power factor, and power quality. When the voltage is too high, the inverter absorbs reactive power to reduce the voltage; when the voltage is too low, the inverter provides reactive power to increase the voltage. This method dynamically adjusts the reactive power by real-time monitoring of the grid state, enabling the photovoltaic grid-connected system to reduce grid fluctuations and losses while maintaining normal power quality.

[0114] Step 6: Obtain the voltage safety range, obtain the adjusted voltage data based on the adjusted power data, and perform a voltage abnormality determination based on the adjusted voltage and the voltage safety range;

[0115] In this embodiment, it should be specifically noted that the step of performing a voltage abnormality determination based on the adjusted voltage and the voltage safety range is as follows:

[0116] Compare the adjusted voltage with the voltage safety range. If the adjusted voltage is lower than the low-voltage threshold, it is determined that the current voltage is abnormal; if the adjusted voltage is higher than the high-voltage threshold, it is determined that the current voltage is abnormal; if the adjusted voltage is lower than the high-voltage threshold and higher than the low-voltage threshold, it is determined that the current voltage is normal and no intelligent route switching is performed.

[0117] Step 7: If it is determined that the current voltage is abnormal, perform intelligent route switching, continue to collect power data in real time, and adjust dynamically in real time.

[0118] In this embodiment, it should be specifically noted that, as Figure 2 shown, a photovoltaic substation voltage overlimit control device based on a regional collaborative U-shaped busbar connection strategy, the device includes:

[0119] A power data acquisition module, which is used to collect power data within a time window through a monitoring device. The power data includes voltage data, current data, power factor, and photovoltaic output data, and transmits the power data within the time window to the first power anomaly judgment module;

[0120] The first power anomaly judgment module is used to evaluate a power anomaly index based on the power data within the time window and perform the first power anomaly judgment according to the power anomaly index;

[0121] An industrial load mutation judgment module. If it is determined that the current power is abnormal, it obtains the industrial load data at each sampling time point within the time window. The industrial load data includes voltage unbalance degree, current unbalance degree, harmonic content change rate, and load curve mutation rate, evaluates an industrial load mutation index based on the industrial load data, and performs industrial load mutation judgment according to the industrial load mutation index;

[0122] The second power anomaly judgment module. If it is determined that an industrial load mutation occurs, it corrects the power data to obtain corrected power data, re-evaluates a power anomaly index based on the corrected power data, and performs the second power anomaly judgment;

[0123] The U-shaped busbar connection strategy execution module. If it is determined that the power is normal, it continues to collect power data. If it is determined that the power is abnormal, it executes the U-shaped busbar connection strategy, compensates reactive power through a photovoltaic inverter, adjusts the reactive power of the photovoltaic inverter to obtain adjusted power data, and transmits the adjusted power data to the intelligent route switching module;

[0124] The intelligent route switching module is used to obtain the voltage safety range, obtain the adjusted voltage data according to the adjusted power data, compare the adjusted voltage with the voltage safety range, and perform intelligent route switching if the adjusted voltage is not within the voltage safety range;

[0125] The real-time dynamic adjustment module is used to continuously collect power data in real time and perform real-time dynamic adjustment.

[0126] Finally, the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

[0127] 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 of them should be covered by 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 method for controlling over-limit voltage in photovoltaic areas based on regional coordinated U-shaped confluence strategy, characterized in that: The following steps are involved: Step 1: Use the sliding time window method to collect power data in real time through the monitoring device. The power data includes voltage data, current data, power factor and photovoltaic output data, and obtain the power data within the time window; Step 2: Evaluate the power data within the time window to obtain the power anomaly index, and make the first power anomaly judgment based on the power anomaly index; Step 3: If the current power abnormality is determined for the first time, the industrial load data of each sampling time point in the time window is obtained. The industrial load data includes voltage imbalance, current imbalance, harmonic content change rate and load curve mutation rate. The industrial load mutation index is obtained according to the industrial load data evaluation, and the industrial load mutation is determined according to the industrial load mutation index; Step 4: If it is determined that a sudden change in industrial load occurs, the power data is corrected to obtain corrected power data, and the power anomaly index is evaluated again based on the corrected power data, and a second power anomaly judgment is performed; Step 5: If the current power is normal for the second time, continue to collect power data; if the current power is abnormal for the second time, execute the U-type confluence strategy, adjust the reactive power of the photovoltaic inverter through the reactive power compensation of the photovoltaic inverter, and obtain the adjusted power data; Step 6: Obtain the voltage safety range and the adjusted voltage data, and make a voltage abnormality judgment based on the adjusted voltage and the voltage safety range; Step 7: If the current voltage is judged to be abnormal, intelligent route switching is performed, and real-time power data collection continues, and real-time dynamic adjustments are made.

2. The method for controlling over-limit voltage in photovoltaic areas based on regional coordinated U-shaped confluence strategy according to claim 1, characterized in that: The steps for obtaining the power anomaly index are as follows: The voltage data and the rated voltage value within the time window are obtained, and the voltage impact degree is calculated based on the voltage data and the rated voltage; Obtain current data within the time window, and calculate the current impact degree based on the current data; Obtain the power factor within the time window, and calculate the power factor influence degree according to the power factor; Obtain the photovoltaic output data within the time window, and calculate the impact of photovoltaic treatment based on the photovoltaic output data; The voltage influence degree, current influence degree, power factor influence degree and photovoltaic treatment influence degree are normalized, and the power anomaly index is obtained according to the normalized voltage influence degree, current influence degree, power factor influence degree and photovoltaic treatment influence degree. The specific acquisition steps are as follows: In the formula, E total Expressed as power anomaly index, E U Expressed as the voltage influence degree, E I Expressed as the degree of current influence, E PF Expressed as the degree of influence of power factor, E PV It is expressed as the degree of influence of photovoltaic treatment, using the sum of squares method to ensure that all abnormal factors can affect the final index.

3. The method for controlling over-limit voltage in photovoltaic areas based on regional coordinated U-shaped confluence strategy according to claim 1 is characterized in that: The steps of performing the first power anomaly judgment according to the power anomaly index are as follows: Compare the power anomaly index with the anomaly threshold, and if the power anomaly index is greater than or equal to the anomaly threshold, determine that the current power is abnormal; If the power anomaly index is less than the anomaly threshold, it is determined that the current power is normal and no adjustment is performed.

4. The method for controlling over-limit voltage in photovoltaic areas based on regional coordinated U-shaped confluence strategy according to claim 1 is characterized in that: The steps for obtaining the industrial load mutation index are as follows: The three-phase voltage data is obtained within the time window, and the voltage unbalance coefficient is obtained by evaluating the three-phase voltage data through the norm method; Acquire three-phase current data within the time window, and obtain the current unbalance coefficient by evaluating the three-phase current data through the norm method; The harmonic content data is obtained within the time window, the K-means clustering method is used to cluster the harmonic content data, and the harmonic content variation coefficient is calculated based on the clustering results; Obtain the load signal within the time window, use discrete wavelet transform to decompose the load signal into low-frequency and high-frequency components, extract the high-frequency component, and use the energy of the high-frequency component as the load mutation coefficient; The voltage unbalance coefficient, current unbalance coefficient, harmonic content variation coefficient and load mutation coefficient are normalized, and the industrial load mutation index is obtained by evaluating the normalized voltage unbalance coefficient, current unbalance coefficient, harmonic content variation coefficient and load mutation coefficient. The specific acquisition steps are as follows: LM=a1×VU+a2×CU+a3×HV+a4×LM; In the formula, LM represents the industrial load mutation index, VU represents the voltage unbalance coefficient, CU represents the current unbalance coefficient, HV represents the harmonic content variation coefficient, LM represents the load mutation coefficient, a1, a2, a3, and a4 represent the weight coefficient of the voltage unbalance coefficient, the weight coefficient of the current unbalance coefficient, the weight coefficient of the harmonic content variation coefficient, and the weight coefficient of the load mutation coefficient.

5. The method for controlling over-limit voltage in photovoltaic areas based on regional coordinated U-shaped confluence strategy according to claim 4 is characterized in that: The steps for obtaining the voltage unbalance coefficient are: Construct a voltage data matrix according to the three-phase voltage data in the time window; Calculate the mean value of the three-phase voltage at each sampling time point to obtain the voltage mean vector; The difference between the three-phase voltage at each sampling time point and the three-phase voltage mean is calculated, and a voltage deviation matrix is ​​constructed; The norm method is used to calculate the norm of the voltage deviation matrix based on the voltage deviation matrix, which is used to measure the overall voltage deviation in the window. The specific acquisition steps are as follows: In the formula, ||ΔU|| F It is represented as the norm of the voltage deviation matrix, T represents the number of sampling time points, 3 represents the number of phases, ΔU ij Represented as the deviation data of the i-th row and j-th column in the voltage deviation matrix; The rated voltage value is obtained, and the voltage unbalance coefficient is calculated by calculating the ratio of the norm of the voltage deviation matrix to the rated voltage value.

6. The method for controlling over-limit voltage in photovoltaic areas based on regional coordinated U-shaped confluence strategy according to claim 4, characterized in that: The steps for obtaining the harmonic content variation coefficient are as follows: Step 3.1: Use the harmonic content data as clustering features, all the harmonic content data in the time window as a data set, and each harmonic content data in the data set as a data point; Step 3.2: Use the silhouette coefficient method to determine the number of clusters K of the data set; Step 3.3: Randomly select K data points in the data set as the initial cluster centers. For each data point, calculate its Euclidean distance to each initial cluster center. For each data point, traverse the K initial cluster centers and assign it to the cluster corresponding to the nearest initial cluster center. Step 3.4: After traversing all data points, the initial clusters are obtained. For each initial cluster, the mean of the data points in it is calculated to obtain a new cluster center. Step 3.5: Repeat steps 3.3 and 3.4 until the cluster center no longer changes, and obtain the final cluster and the final cluster center; Step 3.6: Calculate the mean of all final cluster centers to obtain the cluster center mean, and calculate the harmonic content variation coefficient based on the final cluster center and the cluster center mean. The specific acquisition steps are: In the formula, HV is the harmonic content variation coefficient, K is the cluster number, C k Represented as the kth final cluster center, It is represented as the mean of the cluster center, and max(THD) is represented as the maximum harmonic content data in the time window.

7. The method for controlling over-limit voltage in photovoltaic areas based on regional coordinated U-shaped confluence strategy according to claim 1, characterized in that: The steps of judging industrial load mutation according to the industrial load mutation index are as follows: The industrial load mutation index is compared with the mutation threshold. If the industrial load mutation index is greater than or equal to the mutation threshold, it is determined that an industrial load mutation has occurred; if the industrial load mutation index is less than the mutation threshold, it is determined that no industrial load mutation has occurred.

8. The method for controlling over-limit voltage in photovoltaic areas based on regional coordinated U-shaped confluence strategy according to claim 1, characterized in that: The steps of obtaining the corrected power data are as follows: Obtain the power data within the time window, calculate the ratio of the industrial load mutation index to the mutation threshold, and then subtract 1 to obtain the correction factor; Obtaining a rated value of power data, subtracting the rated value of power data from the power data in the time window and multiplying the result by a correction factor to obtain a power data adjustment amount; The difference between the voltage data and the power data adjustment amount is calculated to obtain the corrected power data.

9. The method for controlling over-limit voltage in photovoltaic areas based on regional coordinated U-shaped confluence strategy according to claim 1, characterized in that: The steps of determining voltage abnormality based on the adjusted voltage and the voltage safety range are as follows: The adjusted voltage is compared with the voltage safety range. If the adjusted voltage is lower than the low voltage threshold, the current voltage is judged to be abnormal; if the adjusted voltage is higher than the high voltage threshold, the current voltage is judged to be abnormal. If the adjusted voltage is lower than the high voltage threshold and higher than the low voltage threshold, the current voltage is judged to be normal and no intelligent route switching is performed.

10. A photovoltaic area voltage over-limit control device based on a regional collaborative U-shaped confluence strategy, used to implement a photovoltaic area voltage over-limit control method based on a regional collaborative U-shaped confluence strategy as described in any one of claims 1 to 9, characterized in that: The device comprises: The power data acquisition module is used to collect power data within the time window through the monitoring device, the power data includes voltage data, current data, power factor and photovoltaic output data, and transmit the power data within the time window to the first power anomaly judgment module; A first power anomaly judgment module is used to obtain a power anomaly index based on power data evaluation within a time window, and perform a first power anomaly judgment based on the power anomaly index; The industrial load mutation judgment module obtains the industrial load data of each sampling time point in the time window if the current power is judged to be abnormal. The industrial load data includes voltage imbalance, current imbalance, harmonic content change rate and load curve mutation rate. The industrial load mutation index is obtained according to the industrial load data evaluation, and the industrial load mutation judgment is performed according to the industrial load mutation index. The second power anomaly judgment module, if it is judged that a sudden change in industrial load occurs, corrects the power data to obtain corrected power data, re-evaluates the power anomaly index based on the corrected power data, and performs a second power anomaly judgment; The U-shaped confluence strategy execution module continues to collect power data if it is judged that the power is normal. If it is judged that the power is abnormal, the U-shaped confluence strategy is executed to adjust the reactive power of the photovoltaic inverter through the reactive power compensation of the photovoltaic inverter to obtain the adjusted power data, and transmit the adjusted power data to the intelligent route switching module; An intelligent route switching module is used to obtain a voltage safety range, obtain adjusted voltage data according to the adjusted power data, compare the adjusted voltage with the voltage safety range, and perform intelligent route switching if the adjusted voltage is not within the voltage safety range; The real-time dynamic adjustment module is used to continue to collect power data in real time and perform real-time dynamic adjustments.

Images