Voltage sag evaluation method based on low voltage ride through control parameter identification

Abstract

The invention discloses a voltage sag evaluation method based on low voltage ride through control parameter identification, comprising the following steps: S1, establishing an inverter distributed power supply output current mathematical model, and determining key control parameters to be identified; s2, voltage and current in the monitoring data are converted into positive and negative sequence components under a dq coordinate system; s3, identifying the key control parameters based on a regression tree algorithm and a least square method; s4, establishing a fault equivalent model of the inverter distributed power supply in the low voltage ride through process; s5, voltage of each node is calculated, and then output short-circuit current is obtained; and S6, performing fault voltage sag evaluation based on the Monte Carlo method and the output short-circuit current of the inverter type distributed power supply. According to the method for identifying the control parameters of the inverter distributed power supply in the low voltage ride through process, the accuracy of voltage sag evaluation is improved.

Description

Technical Field

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

[0002] With the continuous increase in the grid-connected capacity of inverter-type distributed power sources, the large-scale disconnection of distributed power sources when a fault occurs will further aggravate the voltage sag problem. When a grid fault occurs, the output current characteristics of the distributed power source are related to its low voltage ride-through strategy, but the distributed power source control strategy parameters will not be directly disclosed due to factors such as inconsistent equipment characteristics and commercial confidentiality, and there is currently no direct method to obtain low voltage ride-through control parameters. With the introduction of technical specifications such as photovoltaic grid access technical regulations and photovoltaic power station power quality detection standards, online monitoring data of power quality at the grid-connected points of distributed power sources can be obtained. Considering the differences in the trajectory changes of voltage and current parameters 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 the distributed power source is in low voltage ride-through, it is necessary to fully consider the output characteristics of the distributed power source under the fault state. The fault output characteristics are related to the control strategy adopted. Different manufacturers adopt different control strategies and manufacturers usually do not publish low voltage ride-through control parameters due to commercial confidentiality. Therefore, the low voltage ride-through control parameters are difficult to obtain, resulting in the inability to establish an accurate distributed power source equivalent model and making it difficult to accurately evaluate the voltage sag. The existing methods face the following challenges: There is currently no method to directly obtain the low voltage ride-through control parameters through online monitoring data, and when an asymmetric fault occurs, the total short-circuit current output when multiple inverter-type distributed power sources are connected cannot be accurately calculated. Summary of the invention

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

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

[0006] S1. Based on the negative sequence current under asymmetric fault, a mathematical model of the output current of the inverter-type distributed power supply is established, and the key control parameters to be identified are determined;

[0007] S2, obtaining online monitoring data, and converting the voltage and current in the monitoring data into positive and negative sequence components in the dq coordinate system;

[0008] S3, identify key control parameters based on regression tree algorithm and least squares method;

[0009] S4. Based on the identification results of key control parameters, a fault equivalent model of the inverter-type distributed power source during the low voltage ride-through process is established;

[0010] S5. Calculate the voltage 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;

[0011] S6. Fault voltage sag assessment is performed based on the Monte Carlo method and the output short-circuit current of the inverter-type distributed power supply.

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

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

[0014]

[0015] in, is the positive sequence component of reactive compensation current, Upcc is the grid connection point voltage, I N is the rated current, K1 and K2 are the reactive compensation current control parameters, α is the reactive compensation current segmentation point, and the sizes of K1 and K2 meet the requirement of continuity at the segmentation point;

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

[0017]

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

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

[0020]

[0021] 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-connected 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-connected point voltage.

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

[0023] Preferably, S2 comprises the following steps:

[0024] Using Twiddle Factors 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:

[0025]

[0026] 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, p c is the c-phase voltage or current;

[0027] The positive and negative sequence components of the three-phase voltage and current are transformed into the positive and negative sequence components of the voltage and current in the dq coordinate system through the positive sequence component Park transformation and the negative sequence component Park transformation;

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

[0029]

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

[0031]

[0032] Among them, p + d is the voltage or current positive sequence component of the d-axis, p + q is the voltage or current positive sequence component of 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 phase c voltage or current, p - d is the voltage or current negative sequence component of the d-axis, p - q is the voltage or current negative sequence component of 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 compensation current segmentation point α is identified by a regression tree algorithm, comprising the following steps:

[0034] Assume that a set of input data is (X, Y) = {(x1, y1), (x2, y2), (x3, y3), ..., (x n ,y n )}, for all initial data points, calculate the square error in Represents the mean of Y

[0035] For each possible segmentation point s, divide the data into the left subset X L , Y L With the right subset X R , Y R , calculate the mean and square error of each subset respectively, and the overall weighted square error of segment point s is:

[0036]

[0037] Among them, MSE L is the left subset square error, MSE R is the right subset square error;

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

[0039] Preferably, the reactive compensation current control parameters K1 and K2, the inverter current limiting control parameter K MAX , Negative sequence control parameter K N The identification is performed by least squares fitting, including the following steps:

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

[0041]

[0042] Among them, 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;

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

[0044]

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

[0046] Find the values ​​of a and b that minimize E(a, b):

[0047]

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

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

[0050]

[0051] when Determine whether the inverter enters the current limiting state. Assume that there are n sets of data in the current limiting state. Then we can get n K MAXi value, for all K MAXi Take the mean:

[0052]

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

[0054] Preferably, S4 comprises the following steps:

[0055] The identification results are obtained through key control parameters, and the inverter output short-circuit current I is obtained. DG And the grid point U PCC Relationship I DG =f(U PCC ), and thus establish the fault equivalent model of inverter-type distributed power supply during low voltage ride-through.

[0056] Preferably, S5 comprises the following steps:

[0057] S51. For the normal component network, the inverter-type distributed power source is regarded as the 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 the column vector composed of the normal components of the node voltage, I 0 is the column vector consisting of the normal component of the current injected into each node, is the normal component of the voltage at 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;

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

[0061]

[0062] Among them, ΔU is the column vector composed of the fault components of each node voltage, If is the column vector composed of the fault components of each node injection current, is the voltage fault component at node i, Inject current into the fault point;

[0063] The fault point injection current is:

[0064]

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

[0066] S53, superimposing the normal voltage component and the fault voltage component of each node to obtain the voltage of each node after the fault;

[0067] Based on the negative sequence current under asymmetric fault, the output short-circuit current calculation formula of the i-th distributed generation is established as follows:

[0068]

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

[0070]

[0071] S54, repeat S51 to S53 until the grid connection point voltage of the inverter-type distributed power source in the two previous calculations meets the following convergence conditions:

[0072]

[0073] in, is the voltage of the distributed generation grid connection point after the kth iteration, ε is the convergence accuracy;

[0074] S55. Calculate the fault current of each branch in the distribution network based on the voltage of each node.

[0075] Preferably, S6 comprises the following steps:

[0076] S61, obtaining parameters of each component in the power grid and setting the number of Monte Carlo simulations;

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

[0078] S63, in parallel, using the output short-circuit current calculation method in S5 to calculate the short-circuit current generated under each fault;

[0079] S64, according to the short-circuit current under each fault, obtaining the voltage sag amplitude of the busbar concerned under different faults;

[0080] S65. Based on the voltage sag amplitude of the bus concerned under multiple faults, the expected voltage sag amplitude of the bus concerned is calculated by the following formula:

[0081]

[0082] Where N is the number of Monte Carlo simulations, V m The voltage sag amplitude of the busbar concerned calculated for the mth fault.

[0083] Beneficial effects of the present invention:

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

[0085] (2) The present invention uses the identification results of key control parameters of low voltage crossing of distributed power sources to establish a short-circuit current calculation model for the connection of multiple inverter-type distributed power sources, corrects the calculation formula of the short-circuit current of the inverter-type distributed power sources, and improves the accuracy of voltage sag assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 It is a flow chart of a voltage sag assessment method based on low voltage ride through control parameter identification according to an embodiment of the present invention;

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

[0088] Figure 3 Schematic diagram of active output current control parameter relationship of an embodiment of the present invention;

[0089] Figure 4 A schematic diagram of the relationship between control parameters of negative sequence control current according to an embodiment of the present invention;

[0090] Figure 5 This is a schematic diagram of an inverter-type distributed power supply fault equivalent model according to an embodiment of the present invention;

[0091] Figure 6 It is an equivalent circuit diagram of a three-sequence network during an asymmetric fault according to an embodiment of the present invention;

[0092] Figure 7 Schematic diagram of a normal component network and a faulty component network to which a distributed power source is connected according to an embodiment of the present invention. DETAILED DESCRIPTION

[0093] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and examples.

[0094] Voltage sag assessment method based on low voltage ride-through control parameter identification, the process is as follows Figure 1 shown.

[0095] Firstly, the key control parameters of low voltage ride through are identified based on regression tree algorithm and least squares method.

[0096] S1. Based on the negative sequence current under asymmetric fault, a mathematical model of the output current of the inverter-type distributed power supply is established, and the key control parameters to be identified are determined.

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

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

[0099]

[0100] in, is the positive sequence component of reactive compensation current, U pcc is the grid connection point voltage, I Nis the rated current, K1 and K2 are reactive compensation current control parameters, α is the reactive compensation current segmentation point, and 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 is the inverter current limiting control parameter. The parameter to be identified in the active output current mathematical model is the inverter current limiting control parameter K MAX .

[0104] The negative sequence control current mathematical model expression 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-connected 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-connected 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 of the voltage and current of phase a are separated by equation (4). The positive and negative sequence components of phases b and c satisfy the three-phase symmetry with the positive and negative sequence components of phase a. After the positive and negative sequence of the three-phase voltage and current are separated, the voltage and current in the three-phase synchronous rotating abc coordinate system can be converted into the voltage and current in the two-phase synchronous rotating dq coordinate system by Park transformation of equation (5). The monitored positive and negative sequence components of the three-phase voltage and current can be converted into the positive and negative sequence components of the voltage and current in the dq coordinate system by Park transformation of 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 positive sequence component Park transform is as follows:

[0113]

[0114] The negative sequence component Park transform is as follows:

[0115]

[0116] Among them, p + d is the voltage or current positive sequence component of the d-axis, p + q is the voltage or current positive sequence component of 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 phase c voltage or current, p - d is the voltage or current negative sequence component of the d-axis, p - q is the voltage or current negative sequence component of the q axis, p - b is the negative sequence component of the voltage or current of phase b, 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 key control parameters based on regression tree algorithm and least squares method.

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

[0119] To ensure that the low voltage ride-through process has sufficient reactive current support, the active output current must meet Figure 3 The current limiting control parameter K is shown MAX It is a circular curve Radius, through and The relationship can be identified as K MAX .

[0120] To facilitate the identification of negative sequence control parameters, define the independent variable N d , N q To describe the relationship of formula (3), Therefore, formula (3) is simplified to:

[0121]

[0122] Through (I d - , N d ), (I q - , N q ) The slope of the straight line can be fitted to identify the negative sequence control parameter K N .

[0123] The reactive 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. The parameter α can be identified by this method, 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 value of each subset (subnode) as similar as possible, which is achieved by minimizing the variance within the subset.

[0124] Assume that a set of input data is (X, Y) = {(x1, y1), (x2, y2), (x3, y3), ..., (x n ,y n )}, for all initial data points, calculate the square error in Represents the mean of Y

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

[0126]

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

[0128] Reactive compensation current control parameters K1 and K2, inverter current limiting control parameter K MAX , Negative sequence control parameter K N Identification was performed by least squares fitting.

[0129] In the piecewise linear fitting of the parameter model, the fitting curve should remain continuous at the segmentation points. * At the segmentation point s, ensure that the fitting functions at the front and rear ends are * The function value is the same, that is:

[0130]

[0131] Among them, 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 guaranteed.

[0132] In each of the previous and next segments, let i be the starting point and j be the ending point. is the average value in each segment [i, j], and the least square method is used for linear fitting. The fitting equation is: y = ax + b, where the slope a and intercept b are obtained by minimizing the following sum of squared errors:

[0133]

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

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

[0136]

[0137] Solving the equation yields:

[0138]

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

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

[0141]

[0142] when Determine whether the inverter has entered the current limiting state, a set of data in the current limiting state satisfy We can find a K MAX1 , assuming there are n sets of data in the current limiting state Then we can get n K MAXi value, for all K MAXi Take the mean:

[0143]

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

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

[0146] S4. Based on the identification results of key control parameters, a fault equivalent model of the inverter-type distributed power supply during the low voltage ride-through process is established.

[0147] The identification results are obtained through key control parameters, and the inverter output short-circuit current I is obtained. DG And the grid point U PCC Relationship I DG =f(U PCC ), thus establishing Figure 5 The fault equivalent model of the inverter-type distributed power supply during low voltage ride-through is shown.

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

[0149] S5. Calculate the voltage 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. Figure 7 When multiple distributed power sources are connected, the short-circuit current output by each distributed power source is superimposed. The specific steps are as follows:

[0150] S51. For the normal component network, the inverter-type distributed power source is regarded as the 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 node voltage, I 0 is the column vector consisting of the normal component of the current injected into each node, is the normal component of the voltage at 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, the synchronous machine power supply and the inverter-type distributed power supply are set to zero, and a reverse current source is added to the fault point. The node voltage equation is:

[0154]

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

[0156] The voltage fault component 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 fault point injection current is obtained as:

[0162]

[0163] Substituting equation (18) into equation (15) can obtain the fault component of the voltage at each node.

[0164] S53, superimpose the normal voltage component and the fault voltage component 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 supply according to equations (1), (2), and (3). The short-circuit current output by the i-th distributed power supply is N distributed power supplies output short-circuit current I DGs for:

[0165]

[0166] S54, repeat S51 to S53 until the grid connection point voltage of the inverter-type distributed power source in the two previous calculations meets the following convergence conditions:

[0167]

[0168] in, is the voltage of the distributed generation grid connection point after the kth iteration, and ε is the convergence accuracy.

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

[0170] S6. Fault voltage sag assessment is performed based on the Monte Carlo method and the output short-circuit current of the inverter-type distributed power supply.

[0171] S61. Obtain 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, in parallel, using the output short-circuit current calculation method in S5 to calculate the short-circuit current generated under each fault.

[0174] S64. According to the short-circuit current under each fault, the voltage sag amplitude of the bus concerned under different faults is obtained by formula (17).

[0175] S65. Based on the voltage sag amplitude of the bus concerned under multiple faults, the expected voltage sag amplitude of the bus concerned is calculated by the following formula:

[0176]

[0177] Where N is the number of Monte Carlo simulations, V mThe voltage sag amplitude of the busbar concerned calculated for the mth fault.

[0178] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A voltage sag assessment method based on low voltage ride-through control parameter identification, characterized in that: The following steps are involved: S1. Based on the negative sequence current under asymmetric fault, a mathematical model of the output current of the inverter-type distributed power supply is established, and the key control parameters to be identified are determined; S2, obtaining online monitoring data, and converting the voltage and current in the monitoring data into positive and negative sequence components in the dq coordinate system; S3, identify key control parameters based on regression tree algorithm and least squares method; S4. Based on the identification results of key control parameters, a fault equivalent model of the inverter-type distributed power source during the low voltage ride-through process is established; S5. Calculate the voltage 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. Fault voltage sag assessment is performed based on the Monte Carlo method and the output short-circuit current of the inverter-type distributed power supply.

2. The voltage sag assessment method based on low voltage ride through control parameter identification according to claim 1, characterized in that: The inverter-type distributed power supply output current mathematical model includes a reactive compensation current mathematical model, an active output current mathematical model and a negative sequence control current mathematical model.

3. The voltage sag assessment method based on low voltage ride through control parameter identification according to claim 2 is characterized in that: The reactive compensation current mathematical model expression is as follows: in, is the positive sequence component of reactive compensation current, U pcc is the grid connection point voltage, I N is the rated current, K1 and K2 are the reactive compensation current control parameters, α is the reactive compensation current segmentation point, and the sizes of K1 and K2 meet the requirement of continuity at the segmentation point; The mathematical model expression of the active output current is as follows: in, is the positive sequence component of active output current, K MAX is the inverter current limiting control parameter; The negative sequence control current mathematical model expression is as follows: 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-connected 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-connected point voltage.

4. The voltage sag assessment method based on low voltage ride through control parameter identification according to claim 3 is characterized in that: The key control parameters to be identified include reactive compensation current control parameters K1 and K2, reactive compensation current segmentation point α, inverter current limiting control parameter K MAX , Negative sequence control parameter K N .

5. The voltage sag assessment method based on low voltage ride through control parameter identification according to claim 4, characterized in that: The S2 comprises the following steps: Using Twiddle Factors 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: 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, p c is the c-phase voltage or current; The positive and negative sequence components of the three-phase voltage and current are transformed into the positive and negative sequence components of the voltage and current in the dq coordinate system through the positive sequence component Park transformation and the negative sequence component Park transformation; The positive sequence component Park transform is as follows: The negative sequence component Park transform is as follows: Among them, p + d is the voltage or current positive sequence component of the d-axis, p + q is the voltage or current positive sequence component of 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 phase c voltage or current, p - d is the voltage or current negative sequence component of the d-axis, p - q is the voltage or current negative sequence component of the q axis, p - b is the negative sequence component of the voltage or current of phase b, 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.

6. The voltage sag assessment method based on low voltage ride through control parameter identification according to claim 5, characterized in that: The reactive compensation current segmentation point α is identified by a regression tree algorithm, comprising the following steps: Assume that a set of input data is (X,Y) = {(x1,y1),(x2,y2),(x3,y3),…,(x n ,y n )}, for all initial data points, calculate the square error in Represents the mean of Y For each possible segmentation point s, divide the data into the left subset X L , Y L With the right subset X R , Y R , calculate the mean and square error of each subset respectively, and the overall weighted square error of segment point s is: Among them, MSE L is the left subset square error, MSE R is the square error of the right subset; Among all possible segmentation points, select the segmentation point s that minimizes the weighted square error * =min MSE(s) is the optimal segmentation point, then the optimal segmentation point s * That is the reactive compensation current segmentation point α.

7. The voltage sag assessment method based on low voltage ride through control parameter identification according to claim 6, characterized in that: The reactive compensation current control parameters K1 and K2, the inverter current limiting control parameter K MAX , Negative sequence control parameter K N The identification is performed by least squares fitting, including the following steps: At segmentation point s * At the segmentation point s, ensure that the fitting functions at the front and rear ends are * The function value is the same, that is: Among them, 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; In each of the previous and next segments, let i be the starting point and j be the ending point. is the average value in each segment [i, j], and the least square method is used for linear fitting. The fitting equation is: y = ax + b, where the slope a and intercept b are obtained by minimizing the following sum of squared errors: Among them, x k is the independent variable of the input data, y k is the dependent variable of the input data; Find the values ​​of a and b that minimize E(a, b): Among them, the reactive compensation current control parameter fitting satisfies: K1 = -a1, K2 = b2, and the negative sequence control parameter K N =a; The mathematical model of active output current is transformed into: when Determine whether the inverter enters the current limiting state. Assume that there are n sets of data in the current limiting state. Then we can get n K MAXi value, for all K MAXi Take the mean: All K MAXi The mean value of is the inverter current limiting control parameter K MAX .

8. The voltage sag assessment method based on low voltage ride through control parameter identification according to claim 7, characterized in that: The S4 comprises the following steps: The identification results are obtained through key control parameters, and the inverter output short-circuit current I is obtained. DG And the grid point U PCC Relationship I DG =f(U PCC ), and thus establish the fault equivalent model of inverter-type distributed power supply during low voltage ride-through.

9. The voltage sag assessment method based on low voltage ride through control parameter identification according to claim 8, characterized in that: The S5 comprises the following steps: S51. For the normal component network, the inverter-type distributed power source is regarded as the 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 the column vector composed of the normal components of the node voltage, I 0 is the column vector consisting of the normal component of the current injected into each node, is the normal component of the voltage at 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; S52. For the fault component network, the synchronous machine power supply and the inverter-type distributed power supply are set to zero, and a reverse current source is added to the fault point. The node voltage equation is: Where ΔU is the column vector composed of the fault components of each node voltage, I f is the column vector of the fault components of the current injected into each node, is the voltage fault component at node i, Inject current into the fault point; The fault point injection current is: Among them, Z ff is the self-impedance of node f, z f is the fault resistance; S53, superimposing the normal voltage component and the fault voltage component of each node to obtain the voltage of each node after the fault; Based on the negative sequence current under asymmetric fault, the output short-circuit current calculation formula of the i-th distributed generation is established as follows: The output short-circuit current of N distributed power supplies is: S54, repeat S51 to S53 until the grid connection point voltage of the inverter-type distributed power source in the two previous calculations meets the following convergence conditions: in, is the voltage of the distributed generation grid connection point after the kth iteration, ε is the convergence accuracy; S55. Calculate the fault current of each branch in the distribution network based on the voltage of each node.

10. The voltage sag assessment method based on low voltage ride through control parameter identification according to claim 9, characterized in that: The S6 comprises the following steps: S61, obtaining parameters of each component in the power grid and setting the number of Monte Carlo simulations; S62, generating multiple random faults according to the number of Monte Carlo simulations; S63, in parallel, using the output short-circuit current calculation method in S5 to calculate the short-circuit current generated under each fault; S64, according to the short-circuit current under each fault, obtaining the voltage sag amplitude of the busbar concerned under different faults; S65. Based on the voltage sag amplitude of the bus concerned under multiple faults, the expected voltage sag amplitude of the bus concerned is calculated by the following formula: Where N is the number of Monte Carlo simulations, V m The voltage sag amplitude of the busbar concerned calculated for the mth fault.

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