Voltage Sag Assessment Method Based on Identification of Low-Voltage Ride-Through Control Parameters

Abstract

The present invention discloses a voltage sag assessment method based on identification of low voltage ride-through control parameters, which includes the following steps: S1. Establish a mathematical model of the output current of an inverter-type distributed power source and determine the key control parameters to be identified; S2. Convert the voltage and current in the monitoring data into positive and negative sequence components in the dq coordinate system; S3. Identify the key control parameters based on the regression tree algorithm and the least square method; S4. Establish a fault equivalent model of the inverter-type distributed power source during the low voltage ride-through process; S5. Calculate the voltage of each node and then obtain the output short-circuit current; S6. Conduct a fault voltage sag assessment based on the Monte Carlo method and the output short-circuit current of the inverter-type distributed power source. The present invention proposes a method for identifying control parameters of an inverter-type distributed power source during the low voltage ride-through process, improving the accuracy of voltage sag assessment.

Description

Technical Field

[0001] The present invention relates to the technical field of voltage sag assessment, and particularly to a voltage sag assessment method based on identification of low voltage ride-through control parameters. Background Art

[0002] With the continuous increase in the grid-connected capacity of inverter-based distributed power sources, large-scale disconnection of distributed power sources during a fault will further exacerbate the voltage sag problem. When a fault occurs in the power grid, the output current characteristics of distributed power sources are related to their low voltage ride-through strategies. However, due to factors such as inconsistent equipment characteristics and trade secrets, the control strategy parameters of distributed power sources are not directly disclosed, and there is currently no method to directly obtain low voltage ride-through control parameters. With the introduction of technical specifications such as technical regulations for photovoltaic grid connection and power quality detection standards for photovoltaic power plants, online monitoring data of the power quality at the connection point of distributed power sources can be obtained. Considering the differences in the voltage and current parameter trajectories of distributed power sources under different control parameters, how to identify the key control parameters of the low voltage ride-through strategy through online monitoring data is of great significance for voltage sag assessment.

[0003] When a distributed power source undergoes low voltage ride-through, it is necessary to fully consider the output characteristics of the distributed power source in the fault state. The fault output characteristics are related to the control strategy adopted. Different manufacturers adopt different control strategies and usually do not disclose low voltage ride-through control parameters due to trade secrets. Therefore, it is difficult to obtain low voltage ride-through control parameters, resulting in the inability to establish an accurate equivalent model of the distributed power source and making it difficult to accurately evaluate the voltage sag situation. The existing methods thus face the following challenges: there is currently no method to directly obtain low voltage ride-through control parameters through online monitoring data, and when an asymmetric fault occurs, the total short-circuit current output under the access of multiple inverter-based distributed power sources cannot be accurately calculated. Summary of the Invention

[0004] In order to solve the problem that it is difficult to obtain control parameters during low voltage ride-through of inverter-based distributed power sources, resulting in the inability to accurately evaluate the voltage sag situation, the present invention proposes a voltage sag assessment method based on identification of low voltage ride-through control parameters to solve the above problems.

[0005] The present application discloses a voltage sag assessment method based on identification of low voltage ride-through control parameters, including the following steps:

[0006] S1. Based on the negative sequence current under an asymmetric fault, establish a mathematical model of the output current of the inverter-based distributed power source and determine the key control parameters to be identified;

[0007] S2. Obtain online monitoring data and convert the voltage and current in the monitoring data into positive and negative sequence components in the dq coordinate system;

[0008] S3. Identify the key control parameters based on the regression tree algorithm and the least squares method;

[0009] S4. Establish a fault equivalent model of the inverter-type distributed power source during low-voltage ride-through according to the identification results of the key control parameters;

[0010] S5. Calculate the voltages of each node of the inverter-type distributed power source after the fault based on the fault equivalent model, and then obtain the output short-circuit current of the inverter-type distributed power source;

[0011] S6. Conduct a fault voltage sag assessment based on the Monte Carlo method and the output short-circuit current of the inverter-type distributed power source.

[0012] Preferably, the output current mathematical model of the inverter-type distributed power source includes a reactive power compensation current mathematical model, an active output current mathematical model, and a negative-sequence control current mathematical model.

[0013] Preferably, the expression of the reactive power compensation current mathematical model is as follows:

[0014]

[0015] Where, is the positive-sequence component of the reactive power compensation current, Upcc is the grid connection point voltage, I N is the rated current, K1 and K2 are the reactive power compensation current control parameters, α is the reactive power compensation current section point, and the magnitudes of K1 and K2 satisfy continuity at the section point;

[0016] The expression of the active output current mathematical model is as follows:

[0017]

[0018] Where, is the positive-sequence component of the active output current, K MAX is the inverter current limiting control parameter;

[0019] The expression of the negative-sequence control current mathematical model is as follows:

[0020]

[0021] Where, is the negative-sequence control current on the d-axis, is the negative-sequence control current on the q-axis, K N is the negative-sequence control parameter, is the d-axis negative-sequence component of the grid connection point voltage, is the q-axis negative-sequence component of the grid connection point voltage, is the d-axis positive-sequence component of the grid connection point voltage.

[0022] Preferably, the key control parameters to be identified include reactive power compensation current control parameters K1 and K2, reactive power compensation current segmentation point α, and inverter current limiting control parameter K MAX , and negative sequence control parameter K N .

[0023] Preferably, S2 includes the following steps:

[0024] Using the rotation factor to perform symmetrical component transformation on three-phase voltage and three-phase current, and separating the positive and negative sequences of three-phase voltage and current: [[ID=1S]]

[0025]

[0026] Among them, is the positive sequence component of the voltage or current of phase a, is the negative sequence component of the voltage or current of phase a, p a is the voltage or current of phase a, p b is the voltage or current of phase b, p c is the voltage or current of phase c;

[0027] Performing positive sequence component Park transformation and negative sequence component Park transformation on the positive and negative sequence components of three-phase voltage and current, and converting them into positive and negative sequence components of voltage and current in the dq coordinate system;

[0028] The positive sequence component Park transformation is as follows:

[0029]

[0030] The negative sequence component Park transformation is as follows:

[0031]

[0032] Among them, p + d is the positive sequence component of the voltage or current on the d axis, p + q is the positive sequence component of the voltage or current on the q axis, p + b is the positive sequence component of the voltage or current of phase b, p + c is the negative sequence component of the voltage or current of phase c, p - d is the negative sequence component of the voltage or current on the d axis, p - q is the negative sequence component of the voltage or current on the q axis, p - b is the negative sequence component of the voltage or current of phase b, p - cis the negative sequence component of the C-phase voltage or current, and θ is the rotation angle of the abc coordinate system relative to the dq coordinate system.

[0033] Preferably, the reactive power compensation current segmentation point α is identified by the regression tree algorithm, including the following steps:

[0034] Assume a set of input data (X, Y) = {(x1, y1), (x2, y2), (x3, y3), …, (x n , y n )}, for all initial data points, calculate the squared error where represents the mean value of Y

[0035] For each possible segmentation point s, divide the data into a left subset X L , Y L and a right subset X R , Y R , and calculate the mean value and squared error of each subset respectively. The overall weighted squared error of the segmentation point s is:

[0036]

[0037] where, MSE L is the squared error of the left subset, and MSE R is the squared error of the right subset;

[0038] Among all possible segmentation points, select the segmentation point s that minimizes the weighted squared error * = min MSE(s) as the optimal segmentation point, then the optimal segmentation point s * is the reactive power compensation current segmentation point α.

[0039] Preferably, the reactive power compensation current control parameters K1 and K2, the inverter current limiting control parameter K MAX , and the negative sequence control parameter K N are identified by the least squares fitting, including the following steps:

[0040] At the segmentation point s * , ensure that the function values of the fitting functions at the front and rear ends are the same at the segmentation point s * , that is:

[0041]

[0042] where, a1, b1 are the parameters of the linear fitting function before the segmentation point, and b2 is the parameter of the linear fitting function after the segmentation point;

[0043] Within each of the front and rear segments, let i be the starting point and j be the ending point, is the average value within each segment [i, j], and the least squares method is used for linear fitting. The fitting equation is: y = ax + b, where the slope a and the intercept b are obtained by minimizing the following sum of squared errors:

[0044]

[0045] where x k is the independent variable of the input data, and y k is the dependent variable of the input data.

[0046] The values of a and b that minimize E(a, b) are:

[0047]

[0048] where the fitting of the reactive power compensation current control parameters satisfies: K1 = -a1, K2 = b2, and the negative sequence control parameter K N = a;

[0049] The mathematical model of the active output current is transformed into:

[0050]

[0051] When it is determined that the inverter enters the current limiting state. Assuming there are n groups of data under the current limiting state then n values of K MAXi can be obtained. The mean value is taken for all K MAXi :

[0052]

[0053] The mean value of all K MAXi is the current limiting control parameter K MAX .

[0054] Preferably, S4 includes the following steps:

[0055] The identification result is obtained through the key control parameters, and the relationship between the short - circuit current I DG output by the inverter and the grid connection point U PCC is obtained as I DG = f(U PCC ), and thus a fault equivalent model of the inverter - type distributed power source during the low - voltage ride - through process is established.

[0056] Preferably, S5 includes the following steps:

[0057] S51. For the normal component network, the inverter - type distributed power source is regarded as a PQ node, and its current is taken as the output current during normal operation. At this time, the node voltage equation is:

[0058]

[0059] Among them, U 0 is a column vector composed of the normal components of the node voltages, and I 0 is a column vector composed of the normal components of the injected currents at each node, is the normal component of the voltage at node i, is the injected current at node i, i = 1, 2,..., f,..., n, Z is the node impedance matrix of the power grid, f is the fault node, and n is the total number of network nodes;

[0060] S52. For the fault component network, set the synchronous machine power supply and the inverter-type distributed power supply to zero, and add a reverse current source at the fault point. The node voltage equation is:

[0061]

[0062] Among them, ΔU is a column vector composed of the fault components of the node voltages, and If is a column vector composed of the fault components of the injected currents at each node, is the fault component of the voltage at node i, is the injected current at the fault point;

[0063] The injected current at the fault point is:

[0064]

[0065] Among them, Z ff is the self-impedance of node f, and z f is the fault resistance;

[0066] S53. Superimpose the normal voltage components and fault voltage components of each node to obtain the voltage of each node after the fault;

[0067] Based on the negative sequence current under the asymmetric fault, establish the calculation formula for the output short-circuit current of the i-th distributed power supply as:

[0068]

[0069] The output short-circuit current of N distributed power supplies is:

[0070]

[0071] S54. Repeat S51 to S53 until the grid-connected point voltages of the inverter-type distributed power supplies in the previous and subsequent calculations meet the following convergence conditions:

[0072] [[ID= sixth]]

[0073] Among them, is the voltage at the connection point of the distributed power source after the k-th iteration, and ε is the convergence accuracy;

[0074] S55. Calculate the fault current of each branch in the distribution network from the node voltages.

[0075] Preferably, the S6 includes the following steps:

[0076] S61. Obtain the parameters of each component in the power grid and set the number of Monte Carlo simulations;

[0077] S62. Generate multiple random faults according to the number of Monte Carlo simulations;

[0078] S63. Parallelly calculate the short-circuit current under each generated fault by using the output short-circuit current calculation method in S5;

[0079] S64. Obtain the voltage sag amplitude of the concerned bus under different faults according to the short-circuit current under each fault;

[0080] S65. Based on the voltage sag amplitudes of the concerned bus under multiple faults, calculate the expected voltage sag amplitude of the concerned bus through the following formula:

[0081]

[0082] where N is the number of Monte Carlo simulations, and V m is the voltage sag amplitude of the concerned bus calculated for the m-th fault.

[0083] Advantages of the present invention:

[0084] (1) The present invention combines the regression tree algorithm, the least squares method and mathematical model derivation, and proposes a method for identifying the control parameters of the inverter-type distributed power source during the low voltage ride-through process. The acquisition of the control parameters provides a more reliable basis for the calculation of the short-circuit current, the evaluation of the voltage sag, and the setting of the relay protection current.

[0085] (2) The present invention uses the identification results of the key control parameters of the distributed power source during the low voltage ride-through to establish a short-circuit current calculation model for multiple inverter-type distributed power sources connected, modifies the short-circuit current calculation formula of the inverter-type distributed power source, and improves the accuracy of the voltage sag evaluation. Description of the drawings

[0086] Figure 1 is the flowchart of the voltage sag evaluation method based on the identification of the low voltage ride-through control parameters according to the embodiment of the present invention;

[0087] Figure 2 is the schematic diagram of the relationship between the reactive power compensation current control parameters according to the embodiment of the present invention;

[0088] Figure 3 Schematic diagram of the relationship of active output current control parameters in the embodiments of the present invention;

[0089] Figure 4 Schematic diagram of the relationship of negative sequence control current control parameters in the embodiments of the present invention;

[0090] Figure 5 Schematic diagram of the fault equivalent model of the inverter-type distributed power source in the embodiments of the present invention;

[0091] Figure 6 Equivalent circuit diagram of the three-sequence network during asymmetric faults in the embodiments of the present invention;

[0092] Figure 7 Schematic diagram of the normal component network and the fault component network with a distributed power source connected in the embodiments of the present invention. Detailed implementation manners

[0093] To make the objectives, technical solutions and advantages of the present application clearer and more understandable, the following provides examples with reference to the accompanying drawings to further elaborate on the present application in detail.

[0094] The voltage sag assessment method based on the identification of low voltage ride-through control parameters has a process as Figure 1 shown.

[0095] First, identify the key low voltage ride-through control parameters based on the regression tree algorithm and the least square method.

[0096] S1. Based on the negative sequence current under asymmetric faults, establish a mathematical model of the output current of the inverter-type distributed power source, and determine the key control parameters to be identified.

[0097] The fault current characteristics of the inverter-type distributed power source are related to the low voltage ride-through strategy it adopts. Establish a corresponding short-circuit calculation equivalent model of the inverter-type distributed power source according to the control strategy it adopts. Generally, the short-circuit calculation model of the inverter-type distributed power source is equivalent to a controlled current source. Considering the low voltage ride-through control strategy for the negative sequence current under asymmetric faults, its output current calculation model is as follows:

[0098] The mathematical model expression of the reactive power compensation current is as follows:

[0099]

[0100] Among them, is the positive sequence component of the reactive power compensation current, U pcc is the grid connection point voltage, I Nis the rated current, K1 and K2 are the reactive compensation current control parameters, and α is the reactive compensation current segmentation point. K1 and K2 are continuous at the segmentation point. The parameters to be identified in the reactive compensation current mathematical model include the reactive compensation current segmentation point α and the reactive compensation current control parameters K1 and K2.

[0101] The mathematical model expression of active output current is as follows:

[0102]

[0103] in, is the positive sequence component of active output current, K MAX The parameter to be identified in the active output current mathematical model is the inverter current limiting control parameter K. MAX .

[0104] The mathematical model expression of negative sequence control current is as follows:

[0105]

[0106] in, is the negative sequence control current on the d-axis, is the negative sequence control current on the q axis, K N is the negative sequence control parameter, is the d-axis negative sequence component of the grid connection point voltage, is the q-axis negative sequence component of the grid-connected point voltage, is the d-axis positive sequence component of the grid connection point voltage. The parameter to be identified in the negative sequence control current mathematical model is the negative sequence control parameter K N In this implementation, the value range is [-1, 1]. When an asymmetric fault occurs in the power grid, a negative sequence component will be generated in the system. The negative sequence control current can suppress the double frequency fluctuation of the active power and reactive power in the power grid.

[0107] S2. Obtain online monitoring data and convert the voltage and current in the monitoring data into positive and negative sequence components in the dq coordinate system.

[0108] The three-phase voltage and current can be obtained through online monitoring data, and the rotation factor Perform symmetrical component transformation on the three-phase voltage and three-phase current to separate the positive and negative sequences of the three-phase voltage and current:

[0109]

[0110] in, is the positive sequence component of phase a voltage or current, is the negative sequence component of phase a voltage or current, p a is the voltage or current of phase a, p b is the voltage or current of phase b, pc is the C-phase voltage or current.

[0111] The positive and negative sequence components of the A-phase voltage and current are separated through Equation (4). The positive and negative sequence components of the B-phase and C-phase are three-phase symmetrical with those of the A-phase. After separating the positive and negative sequence components of the three-phase voltage and current, through the Park transformation in Equation (5), the voltage and current in the three-phase synchronous rotating abc coordinate system can be transformed into the voltage and current in the two-phase synchronous rotating dq coordinate system. The positive and negative sequence components of the monitored three-phase voltage and current can be transformed into the positive and negative sequence components of the voltage and current in the dq coordinate system through the Park transformation in Equations (5) and (6). The dq coordinate system can convert the three-phase AC input or output signal into the DC quantity on the dq axis, which is convenient for controlling the inverter.

[0112] The Park transformation of the positive sequence component is as follows:

[0113]

[0114] The Park transformation of the negative sequence component is as follows:

[0115]

[0116] where p + d is the positive sequence component of the voltage or current on the d axis, p + q is the positive sequence component of the voltage or current on the q axis, p + b is the positive sequence component of the B-phase voltage or current, p + c is the negative sequence component of the C-phase voltage or current, p - d is the negative sequence component of the voltage or current on the d axis, p - q is the negative sequence component of the voltage or current on the q axis, p - b is the negative sequence component of the B-phase voltage or current, p - c is the negative sequence component of the C-phase voltage or current, and θ is the rotation angle of the abc coordinate system relative to the dq coordinate system.

[0117] S3. Identify the key control parameters based on the regression tree algorithm and the least square method.

[0118] For identifying the key control parameters of low voltage ride through, the relationship between the grid connection point voltage and the reactive power compensation current is as Figure 2 shown. When the grid connection point voltage U pcc is less than the maximum reactive power compensation current that the inverter can provide at point α, this point is defined as the segmentation point α. As can be seen from the figure, the change of K1 affects the slope of the straight line. Fit α < Upcc The straight-line slope identification control parameter K1 less than 0.9, and the value range of the control parameter K1 is between 1.5 and 3. Since the two straight lines are continuous before and after at the segmentation point α, that is, the three satisfy K2 = K1(0.9 - α), then when the segmentation point α and the control parameter K1 are identified, the control parameter K2 can be obtained.

[0119] To ensure sufficient reactive current support during the low-voltage ride-through process, the active output current needs to satisfy Figure 3 The current-limiting control parameter K shown MAX is an arc curve radius. Through and relationship, K can be identified MAX .

[0120] To facilitate the identification of the negative-sequence control parameter, the independent variable N is defined d , N q to describe the relationship of Equation (3), where Thus, Equation (3) is simplified to:

[0121]

[0122] By fitting the slopes of the straight lines of (I d - , N d ), (I q - , N q ), the negative-sequence control parameter K can be identified N .

[0123] The reactive power compensation current segmentation point α is identified by the regression tree algorithm. The regression tree algorithm can find the best segmentation point according to the input data set. Through this method, the parameter α can be identified, and the tree structure is constructed by recursively dividing the data set into several subsets. The goal of the division is to make the output values of each subset (child node) as similar as possible, which is achieved by minimizing the variance within the subset.

[0124] Assume a set of input data is (X, Y) = {(x1, y1), (x2, y2), (x3, y3), …, (x n , y n ). For all the initial data points, calculate the squared error where represents the mean value of Y

[0125] For each possible segmentation point s, the data is divided into the left subset X L , Y L and the right subset X R , YR , calculate the mean and squared error of each subset respectively. The mean of the left subset The squared error of the left subset The mean of the right subset The squared error of the right subset The overall weighted squared error of the segmentation point s is:

[0126]

[0127] Among all possible segmentation points, select the segmentation point s that minimizes the weighted squared error * = min MSE(s) is used as the optimal segmentation point, then the optimal segmentation point s * is the segmentation point α of the reactive power compensation current.

[0128] The reactive power compensation current control parameters K1 and K2, the inverter current limiting control parameter K MAX , and the negative sequence control parameter K N are identified by least squares fitting.

[0129] In the piecewise linear fitting of the parameter model, the fitting curve should be continuous at the segmentation point. For this reason, at the segmentation point s * , ensure that the fitting functions at both ends are the same at the segmentation point s * , that is: [[ID=3�]]

[0130]

[0131] where a1 and b1 are the parameters of the linear fitting function before the segmentation point, and b2 is the parameter of the linear fitting function after the segmentation point. Through this continuity constraint, the continuity of the fitting curve is ensured.

[0132] In each of the front and back segments, let i be the starting point and j be the ending point, is the average value within each segment [i, j]. Linear fitting is performed using the least squares method, and the fitting equation is: y = ax + b, where the slope a and the intercept b are obtained by minimizing the following sum of squared errors:

[0133]

[0134] where x k is the independent variable of the input data, and y k is the dependent variable of the input data.

[0135] To find the values of a and b that minimize E(a, b), take the partial derivatives of the two parameters respectively and set their partial derivatives to 0:

[0136]

[0137] Solving the equations gives:

[0138]

[0139] Among them, the fitting of the reactive power compensation current control parameters satisfies: K1 = -a1, K2 = b2, and the negative sequence control parameter K N = a;

[0140] The mathematical model of the active output current is transformed into:

[0141]

[0142] When it is judged that the inverter enters the current limiting state, and a set of data under the current limiting state satisfies and a K MAX1 can be obtained. Assuming there are n sets of data under the current limiting state then n K MAXi values can be obtained. For all K MAXi values, take the mean value:

[0143]

[0144] The mean value of all K MAXi is the current limiting control parameter K MAX of the inverter.

[0145] After the identification of the above key control parameters for low voltage ride through is completed by the parameter identification module, the voltage sag is evaluated considering the access of multiple inverter-based distributed power sources.

[0146] S4. According to the identification results of the key control parameters, establish a fault equivalent model of the inverter-based distributed power source during low voltage ride through.

[0147] Through the key control parameters, the identification results are obtained, and the relationship between the short-circuit current I DG output by the inverter and the grid connection point U PCC is obtained as I DG = f(U PCC ), and thus the fault equivalent model of the inverter-based distributed power source during low voltage ride through as shown in Figure 5 is established.

[0148] In the traditional short-circuit current calculation of the power system containing inverter-based distributed power sources, the short-circuit current calculation formula of the distributed power source is In the formula, the calculation of the short-circuit current does not consider the negative sequence component of the short-circuit current output by the inverter during asymmetric faults as shown in Figure 6 . Figure 6 In (a), it represents the positive sequence network, Figure 6 in (b), it represents the negative sequence network, Figure 6In (c), it represents the zero-sequence network. The embodiment of this application adopts an iterative calculation and solution method considering negative-sequence current injection. The equivalent current source of the inverter-type distributed power source will appear in the negative-sequence component network, and the short-circuit current output by the inverter-type distributed power source is corrected to

[0149] S5. Calculate the voltages of each node of the inverter-type distributed power source after the fault according to the fault equivalent model, and then obtain the short-circuit current output by the inverter-type distributed power source. When considering the access of multiple distributed power sources as shown in Figure 7 , superimpose the short-circuit currents output by each distributed power source. The specific steps are as follows:

[0150] S51. For the normal component network, regard the inverter-type distributed power source as a PQ node, and its current is taken as the output current during normal operation. At this time, the node voltage equation is:

[0151]

[0152] Among them, U 0 is the column vector composed of the normal components of the voltages of each node, I 0 is the column vector composed of the normal components of the injected currents of each node, is the normal component of the voltage of node i, is the injected current of node i, i = 1, 2,..., f,..., n, Z is the grid node impedance matrix, f is the fault node, and n is the total number of network nodes.

[0153] S52. For the fault component network, set the synchronous machine power source and the inverter-type distributed power source to zero, and add a reverse current source at the fault point. The node voltage equation is:

[0154]

[0155] Among them, ΔU is the column vector composed of the fault components of the voltages of each node, If is the column vector composed of the fault components of the injected currents of each node, is the fault component of the voltage of node i, is the injected current at the fault point.

[0156] The fault component of the voltage at the fault point is:

[0157]

[0158] Among them, Z ff is the self-impedance of node f.

[0159] The voltage at the fault point is:

[0160]

[0161] Then the injected current at the fault point is obtained as:

[0162]

[0163] Substitute Equation (18) into Equation (15) to obtain the fault voltage components of each node.

[0164] S53. Superimpose the normal voltage components and fault voltage components of each node to obtain the voltage of each node after the fault, and correct the output short-circuit current of the inverter-type distributed power source according to Equations (1), (2), and (3). The magnitude of the short-circuit current output by the i-th distributed power source is The output short-circuit current I of N distributed power sources DGs is:

[0165]

[0166] S54. Repeat S51 - S53 until the grid connection point voltage of the inverter-type distributed power source satisfies the following convergence condition in two consecutive calculations:

[0167]

[0168] where is the voltage of the grid connection point of the distributed power source after the k-th iteration, and ε is the convergence accuracy.

[0169] S55. Calculate the fault current of each branch in the distribution network from the voltage of each node in Equation (16).

[0170] S6. Conduct a fault voltage sag assessment based on the Monte Carlo method and the output short-circuit current of the inverter-type distributed power source.

[0171] S61. Obtain the parameters of each component in the power grid and set the number of Monte Carlo simulations.

[0172] S62. Generate multiple random faults according to the number of Monte Carlo simulations.

[0173] S63. Calculate the short-circuit current under each generated fault in parallel using the short-circuit current calculation method in S5.

[0174] S64. Obtain the voltage sag amplitude of the concerned bus under different faults from Equation (17) according to the short-circuit current under each fault.

[0175] S65. Calculate the expected voltage sag amplitude of the concerned bus through the following formula based on the voltage sag amplitudes of the concerned bus under multiple faults:

[0176]

[0177] where N is the number of Monte Carlo simulations, V mThe voltage sag amplitude of the busbars of interest calculated for the m-th fault.

[0178] The basic principles, main features and advantages of the present invention have been shown and described above. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and what is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A voltage sag assessment method based on identification of low voltage ride-through control parameters, characterized in that It includes the following steps: S1. Based on the negative-sequence current under asymmetrical faults, establish a mathematical model for the output current of the inverter-type distributed power source and determine the key control parameters to be identified; The mathematical model for the output current of the inverter-type distributed power source includes a mathematical model for reactive power compensation current, a mathematical model for active output current, and a mathematical model for negative-sequence control current; The expression of the mathematical model for reactive power compensation current is as follows: Among them, is the positive-sequence component of the reactive power compensation current, U pcc is the grid connection point voltage, I N is the rated current, K1 and K2 are reactive power compensation current control parameters, α is the reactive power compensation current segmentation point, and the magnitudes of K1 and K2 satisfy continuity at the segmentation point; The expression of the mathematical model for active output current is as follows: Among them, is the positive sequence component of the active output current, and K MAX is the current limiting control parameter of the inverter; The expression of the mathematical model for negative-sequence control current is as follows: Among them, is the negative-sequence control current on the d-axis, is the negative-sequence control current on the q-axis, K N is the negative-sequence control parameter, is the d-axis negative-sequence component of the grid connection point voltage, is the q-axis negative-sequence component of the grid connection point voltage, is the d-axis positive-sequence component of the grid connection point voltage; S2. Obtain on-line monitoring data and convert the voltage and current in the monitoring data into positive and negative sequence components in the dq coordinate system; S3. Identify the key control parameters based on the regression tree algorithm and the least square method; S4. According to the identification results of the key control parameters, establish a fault equivalent model for the inverter-type distributed power source during the low voltage ride-through process; S5. Calculate the voltages of each node of the inverter-type distributed power source after the fault according to the fault equivalent model, and then obtain the output short-circuit current of the inverter-type distributed power source; S6. Conduct a fault voltage sag assessment based on the Monte Carlo method and the output short-circuit current of the inverter-type distributed power source.

2. The voltage sag assessment method based on identification of low voltage ride-through control parameters according to claim 1, wherein The key control parameters to be identified include the reactive power compensation current control parameters K1 and K2, the reactive power compensation current segmentation point α, the inverter current limiting control parameter K MAX , and the negative sequence control parameter K N .

3. The voltage sag assessment method based on identification of low voltage ride through control parameters according to claim 2, wherein The S2 includes the following steps: Using the rotation factor Perform symmetrical component transformation on three-phase voltage and three-phase current to separate the positive and negative sequences of three-phase voltage and current: Among them, is the positive-sequence component of the a-phase voltage or current, is the negative-sequence component of the a-phase voltage or current, p a is the a-phase voltage or current, p b is the b-phase voltage or current, p c is the c-phase voltage or current; Convert the positive and negative sequence components of the three-phase voltage and current into the positive and negative sequence components of the voltage and current in the dq coordinate system through the Park transformation of the positive sequence components and the Park transformation of the negative sequence components; The Park transformation of the positive sequence components is as follows: The Park transformation of the negative sequence components is as follows: where p + d is the positive-sequence component of the voltage or current on the d-axis, and p + q is the positive-sequence component of the voltage or current on the q-axis, and p + b is the positive-sequence component of the voltage or current on the b-phase, and p + c is the negative-sequence component of the voltage or current on the c-phase, and p - d is the negative-sequence component of the voltage or current on the d-axis, and p - q is the negative-sequence component of the voltage or current on the q-axis, and p - b is the negative-sequence component of the voltage or current on the b-phase, and p - c is the negative-sequence component of the voltage or current on the c-phase, and θ is the rotation angle of the abc coordinate system relative to the dq coordinate system.

4. The voltage sag assessment method based on low voltage ride-through control parameter identification according to claim 3, characterized in that The segmentation point α of the reactive power compensation current is identified by the regression tree algorithm, including the following steps: Suppose a set of input data is (X, Y) = {(x1, y1), (x2, y2), (x3, y3), …, (x n , y n )}, for all the initial data points, calculate the squared error where represents the mean of Y At each possible segmentation point s, the data is divided into a left subset X L , Y L and a right subset X R , Y R . The mean and squared error of each subset are calculated respectively, and the overall weighted squared error of the segmentation point s is: Among them, MSE L is the left subset squared error, and MSE R is the right subset squared error; Among all possible segmentation points, select the segmentation point s that minimizes the weighted squared error * Let =min MSE(s) be the optimal segmentation point, then the optimal segmentation point s * is the segmentation point α of the reactive power compensation current.

5. The voltage sag assessment method based on identification of low voltage ride-through control parameters according to claim 4, wherein The reactive power compensation current control parameters K1 and K2, the inverter current limiting control parameter K MAX , and the negative sequence control parameter K N are identified by least squares fitting, including the following steps: At the segmentation point s * , ensure that the fitting functions at both ends have the same function value at the segmentation point s * , that is: Where a1 and b1 are the parameters of the linear fitting function before the segmentation point, and b2 is the parameter of the linear fitting function after the segmentation point; Within each of the preceding and succeeding paragraphs, let i be the starting point and j be the ending point. is the average value within each segment [i, j]. Linear fitting is performed using the least squares method, and the fitting equation is: y = ax + b, where the slope a and the intercept b are obtained by minimizing the following sum of squared errors: Among them, x k is the independent variable of the input data, and y k is the dependent variable of the input data; The a value and b value that minimize E(a, b) are: Among them, the fitting of the reactive power compensation current control parameters satisfies: K1 = -a1, K2 = b2, and the negative sequence control parameter K N = a; Transform the mathematical model of the active output current into: When it is determined that the inverter enters the current limiting state, and assuming there are n sets of data in the current limiting state then n K values can be obtained MiXi values, and take the average of all K MAXi values: All K MAXi The mean value of which is the inverter current limiting control parameter K MAX .

6. The voltage sag assessment method based on identification of low voltage ride through control parameters according to claim 5, characterized in that The S4 includes the following steps: Obtain the identification result through key control parameters, and obtain the short-circuit current I of the inverter output DG and the grid connection point U PCC relationship I DG = f(U PCC ), and thus establish a fault equivalent model of the inverter-type distributed power source during the low-voltage ride-through process.

7. The voltage sag assessment method based on identification of low voltage ride-through control parameters according to claim 6, characterized in that The S5 includes the following steps: S51. For the normal component network, regard the inverter-type distributed power source as a PQ node, and its current is taken as the output current during normal operation. At this time, the node voltage equation is: Among them, U 0 is a column vector composed of the normal components of the voltages of each node, and I 0 is a column vector composed of the normal components of the injected currents of each node. is the normal component of the voltage of node i, is the injected current of node i, where i = 1, 2, …, f, …, n, Z is the node impedance matrix of the power grid, f is the fault node, and n is the total number of network nodes; S52. For the fault component network, set the synchronous machine power source and the inverter-type distributed power source to zero, add a reverse current source at the fault point, and the node voltage equation is: where ΔU is a column vector composed of fault components of node voltages, and I f is a column vector composed of fault components of injected currents at each node, is the fault component of the voltage at node i, is the injected current at the fault point; The injection current at the fault point is: Among them, Z ff is the self-impedance of node f, and z f is the fault resistance; S53. Superimpose the normal voltage components and fault voltage components of each node to obtain the voltage of each node after the fault; Based on the negative-sequence current under asymmetrical faults, establish a calculation formula for the output short-circuit current of the i-th distributed power source as: The output short-circuit current of N distributed power sources is: S54. Repeat S51 to S53 until the grid connection point voltage of the inverter-type distributed power source in the previous and subsequent calculations meets the following convergence condition: Among them, is the voltage at the connection point of the distributed power source after the k-th iteration, and ε is the convergence accuracy; S55. Calculate the fault current of each branch in the distribution network from the voltages of each node.

8. The voltage sag assessment method based on identification of low voltage ride-through control parameters according to claim 7, characterized in that The S6 includes the following steps: S61. Obtain the parameters of each component in the power grid and set the number of Monte Carlo simulation times; S62. Generate multiple random faults according to the number of Monte Carlo simulation times; S63. Parallelly calculate the short-circuit current under each generated fault by using the short-circuit current calculation method in S5; S64. Obtain the voltage sag amplitude of the bus under concern for different faults based on the short-circuit current in each fault; S65. Based on the voltage sag amplitudes of the bus under concern in multiple faults, calculate the expected voltage sag amplitude of the bus under concern through the following formula: where N is the number of Monte Carlo simulations, and V m is the voltage sag magnitude of the bus of interest calculated for the m-th fault.

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