SVG controller modeling analysis method based on field experiment data

Abstract

The invention provides an SVG controller modeling analysis method based on field experiment data, and relates to the technical field of electric power grids. According to the method, the high-low penetration fault test is carried out on the SVG, the field experiment data is obtained, the calculation data of the transient model of the SVG under different model parameters are combined, the least square method is adopted to carry out parameter identification, and the optimal model parameters of the transient model of the SVG controller are obtained. The process avoids modeling of complex topology in the SVG and laboratory communication protocol debugging, is suitable for testing of SVGs of various models, and is high in field test result accuracy, simple in wiring and extremely low in risk. A field test result can truly reflect transient response characteristics of station-level current and reactive power, and a modeling path based on the field test is opened. The SVG control characteristic extraction efficiency is improved, the model precision and modeling efficiency of the complex topology SVG are improved, and therefore the analysis efficiency of the SVG analysis process is improved.

Description

Technical Field

[0001] The invention relates to the technical field of electric power grid, and in particular to a modeling and analysis method of an SVG controller based on field experimental data. Background Art

[0002] With the development of power systems and power electronics technology, static var generators (SVG) have become more and more important in the fields of transmission networks, distribution networks and renewable energy generation, and have become important equipment for active support of renewable energy / energy storage. Chain SVG has become one of the mainstream choices for reactive power compensation in power systems due to its low harmonics, low losses, and low withstand voltage of cascaded H-bridge power devices, and its application is becoming increasingly widespread.

[0003] As the basis for analyzing the operation characteristics of power grids and optimizing control, the correctness of SVG transient models affects the accuracy and reliability of simulation calculations. As a key device in SVG complete sets, the controller model and parameters determine the transient response characteristics of SVG when the power grid is disturbed. However, in reality, most manufacturers are unable to provide accurate models and parameters due to intellectual property protection or lack of testing technology. Only typical models and parameters can be used in transient simulation calculations of power grids, and their accuracy is difficult to accurately simulate the dynamic characteristics of actual power grids. Therefore, it is particularly important to study the parameter identification of new energy and energy storage reactive compensation devices, which directly affects the development of subsequent work.

[0004] At present, the data sources for modeling reactive power compensation devices are mostly based on laboratory autonomous hardware-in-the-loop testing (HIL). HIL real-time simulation technology represented by RTDS has played an important role in transient simulation of power systems. The application of RTDS simulation model and external controller to establish HIL simulation system has become a widely used system performance test solution. However, traditional new energy and energy storage reactive power compensation devices are mainly based on principle models and general models. However, the physical structure and control strategy of energy storage reactive power compensation devices are complex and diverse. The characteristics of SVG controllers from different manufacturers vary greatly. The general transient model cannot fully and truly reflect the operating characteristics of specific scenarios and specific SVG manufacturers' products and the impact on the new energy power generation system. There may be a large deviation between the simulation model characteristics and the actual operating characteristics, which will seriously affect the accuracy and credibility of the simulation analysis results. In addition, in the hardware-in-the-loop simulation test, the chain SVG topology is complex, the number of components in the device is large, and the interface and protocol of the SVG body drive and communication are different from those of conventional grid-connected converters. Its semi-physical modeling and grid-connected debugging cycle is long and occupies a lot of resources, which also seriously affects the modeling efficiency of SVG.

[0005] Therefore, the current SVG analysis process has the problem of low analysis efficiency. Summary of the invention

[0006] The present invention provides an SVG controller modeling and analysis method based on field experimental data, which can improve the analysis efficiency of the SVG analysis process.

[0007] In a first aspect, the present invention provides a method for modeling and analyzing an SVG controller based on field experimental data, the method comprising: obtaining field experimental data of a high-low breakdown fault test of an SVG controller to be modeled; the field experimental data comprising three-phase current data at the output end of the SVG controller and analog voltage data on the grid side; decomposing, transforming and calculating the field experimental data based on Simulink to obtain data to be identified, the data to be identified comprising active power, reactive power, positive sequence voltage, direct-axis current and quadrature-axis current; constructing a BPA electromechanical transient model of the SVG controller based on the working characteristics of the SVG controller, and determining parameters to be identified of the BPA electromechanical transient model; identifying the parameters to be identified based on the data to be identified and the BPA electromechanical transient model using the least squares method to obtain model parameters of the transient model of the SVG controller.

[0008] In one possible implementation, a method for acquiring field experimental data of a high-low breakdown fault test of the SVG controller to be modeled includes: disconnecting the SVG controller from the grid connection point, and retaining the connection between the SVG controller and the low-voltage side voltage acquisition device and the current acquisition device; applying an analog voltage signal to the secondary side of the high-voltage side voltage acquisition device using an analog voltage source, and the voltage phase of the analog voltage signal is consistent with the high-voltage side voltage phase; changing the voltage of the analog voltage source, performing a high-low breakdown fault test, and causing the SVG controller to perform a reactive response; and collecting the three-phase current data at the output end of the SVG controller and the analog voltage data on the grid side during the test.

[0009] In a possible implementation, the field experimental data is decomposed, transformed and calculated based on Simulink to obtain the data to be identified, including: using Simulink to perform positive and negative sequence decomposition on the simulated voltage data to obtain the positive sequence voltage; performing Park decoupling transformation based on the three-phase current data to obtain direct-axis current and quadrature-axis current; and calculating based on the positive sequence voltage, direct-axis current and quadrature-axis current to obtain the active power and reactive power.

[0010] In a possible implementation, based on the working characteristics of the SVG controller, a BPA electromechanical transient model of the SVG controller is constructed, and the parameters to be identified of the BPA electromechanical transient model are determined, including: determining the dynamic equation of the SVG controller based on the working characteristics of the SVG controller; analyzing the dynamic equation to determine the parameters to be identified involved in the identification; the parameters to be identified include a proportional coefficient, a slope of an SVG volt-ampere characteristic curve, a filter response delay, a proportional link time constant, an SVG response delay, an SVG output voltage upper limit, an SVG output voltage lower limit, a maximum capacitive current, and a maximum inductive current; based on the working characteristics of the SVG controller, constraints of the parameters to be identified are determined; based on the dynamic equation of the SVG controller and the constraints of the parameters to be identified, the BPA electromechanical transient model of the SVG controller is constructed.

[0011] In a possible implementation, the least square method is used to identify the parameters to be identified based on the data to be identified and the BPA electromechanical transient model to obtain the model parameters of the transient model of the SVG controller, including: determining multiple parameter combinations based on the value range and optimization step of the parameters to be identified, each parameter combination including a group of parameters to be identified; calculating the model output results under different parameter combinations based on the data to be identified, the BPA electromechanical transient model of the SVG controller, and the multiple parameter combinations; for any parameter combination, calculating the residual sum of squares between the model output result and the field experimental data under the parameter combination; comparing the residual sum of squares corresponding to each parameter combination, and determining the parameter combination with the smallest residual sum of squares as the model parameter of the transient model.

[0012] In a possible implementation, based on the data to be identified and the BPA electromechanical transient model, the least squares method is used to identify the parameters to be identified, and the model parameters of the transient model of the SVG controller are obtained, including: determining the proportional coefficient of the PI control link and the slope of the SVG volt-ampere characteristic curve of the feedback link based on the voltage output characteristics and current output characteristics of the SVG controller; constructing the objective function by the least squares method; setting the SVG output voltage upper limit, the SVG output voltage lower limit, the maximum capacitive current and the maximum inductive current as fixed values, and determining multiple first parameter combinations based on the constraints of the filter response delay, the proportional link time constant and the SVG response delay and the preset step size; traversing the multiple first parameter combinations to perform a first voltage drop simulation , calculate the objective function value of each first parameter combination; determine the first parameter combination with the smallest objective function value as the identification result of the first parameter; based on the identification result of the first parameter, set the filter response delay, the proportional link time constant and the SVG response delay as fixed values, and determine multiple second parameter combinations based on the constraints of the SVG output voltage upper limit, the SVG output voltage lower limit, the maximum capacitive current and the maximum inductive current and the preset step size; traverse the multiple second parameter combinations, perform a second voltage drop simulation, and calculate the objective function value of each second parameter combination; determine the second parameter combination with the smallest objective function value as the identification result of the second parameter; based on the identification result of the first parameter and the identification result of the second parameter, determine the model parameters of the transient model of the SVG controller.

[0013] In a possible implementation, the formula of the objective function is:

[0014]

[0015] Among them, J(α) is the objective function, Z Mi is the i-th group of parameters to be identified, Z(α) i is the calculated value of the i-th group of BPA electromechanical transient model, m is the number of parameter combinations to be identified, and W is the weight matrix.

[0016] In a possible implementation, traversing the multiple first parameter combinations, performing a first voltage drop simulation, and calculating the objective function value of each first parameter combination include: step 41, setting the first parameter combination of the current iteration process based on the constraints and preset step size of the filter response delay, the proportional link time constant, and the SVG response delay; step 42, based on the first parameter combination of the current iteration process, the data to be identified, and the BPA electromechanical transient model of the SVG controller, respectively perform a fault simulation test of a voltage drop of 90%, and determine the calculated value of the BPA electromechanical transient model; step 43, calculating the objective function value of the current iteration process based on the calculated value of the BPA electromechanical transient model and the field measured data; step 44, if the current iteration number is less than the maximum iteration number, repeating steps 41 to 44 until the current iteration number is greater than or equal to the maximum iteration number; if the current iteration number is greater than or equal to the maximum iteration number, exiting the iteration process.

[0017] In a possible implementation, traversing the multiple second parameter combinations, performing a second voltage drop simulation, and calculating an objective function value for each second parameter combination includes: step 71, setting a second parameter combination for the current iteration process based on constraints and a preset step size of an SVG output voltage upper limit, an SVG output voltage lower limit, a maximum capacitive current, and a maximum inductive current; step 72, performing fault simulation tests of voltage drops of 50% and 20% respectively based on the second parameter combination for the current iteration process, the data to be identified, and a BPA electromechanical transient model program of the SVG controller, and determining a calculated value of the BPA electromechanical transient model; step 73, calculating an objective function value for the current iteration process based on the calculated value of the BPA electromechanical transient model and the field measured data; step 74, if the current iteration number is less than the maximum iteration number, repeating steps 71 to 74 until the current iteration number is greater than or equal to the maximum iteration number; if the current iteration number is greater than or equal to the maximum iteration number, exiting the iteration process.

[0018] In a second aspect, an embodiment of the present invention provides an SVG controller modeling and analysis device based on field experimental data, the device comprising: a communication module, used to obtain field experimental data of a high and low breakdown fault test of an SVG controller to be modeled; the field experimental data comprises three-phase current data at the output end of the SVG controller and analog voltage data on the grid side; a processing module, used to decompose, transform and calculate the field experimental data based on Simulink to obtain data to be identified, the data to be identified comprises active power, reactive power, positive sequence voltage, direct-axis current and quadrature-axis current; based on the working characteristics of the SVG controller, a BPA electromechanical transient model of the SVG controller is constructed, and parameters to be identified of the BPA electromechanical transient model are determined; based on the data to be identified and the BPA electromechanical transient model, the parameters to be identified are identified using the least squares method to obtain model parameters of the transient model of the SVG controller.

[0019] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a memory and a processor, the memory storing a computer program, the processor being used to call and run the computer program stored in the memory to perform the steps of the method described in the first aspect and any possible implementation method of the first aspect.

[0020] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and wherein when the computer program is executed by a processor, the steps of the method described in the first aspect and any possible implementation method of the first aspect are implemented.

[0021] The present invention provides an SVG controller modeling and analysis method based on field experimental data. The present invention performs high and low wear fault tests on SVG to obtain field experimental data, combines the calculation data of the transient model of SVG under different model parameters, and uses the least square method to identify parameters to obtain the optimal model parameters of the transient model of the SVG controller. This process avoids the modeling of the complex topology inside the SVG and the laboratory communication protocol debugging, is suitable for testing various types of SVG, and the field test results are highly accurate, the wiring is simple, and the risk is extremely low. The field test results can truly reflect the transient response characteristics of the station-level current and reactive power, and open up a modeling path based on field tests. The present invention improves the efficiency of SVG control characteristic extraction, improves the model accuracy and modeling efficiency of complex topology SVG, and thus improves the analysis efficiency of the SVG analysis process. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0023] Figure 1 It is a flow chart of a method for modeling and analyzing an SVG controller based on field experimental data provided by an embodiment of the present invention;

[0024] Figure 2 This is a test wiring diagram of an SVG on-site fault ride-through test provided by an embodiment of the present invention;

[0025] Figure 3 It is a structural schematic diagram of a power calculation module based on Simulink provided by an embodiment of the present invention;

[0026] Figure 4 is a schematic diagram of an SVG controller model provided by an embodiment of the present invention;

[0027] Figure 5 It is a simplified block diagram of an SVG control model provided by an embodiment of the present invention;

[0028] Figure 6 It is a flowchart of a least squares parameter identification method provided by an embodiment of the present invention;

[0029] Figure 7 It is a schematic diagram of modeling and analysis results of an inductive high-power three-phase fault provided by an embodiment of the present invention;

[0030] Figure 8 It is a schematic diagram of modeling and analysis results of a capacitive low-power three-phase fault provided by an embodiment of the present invention;

[0031] Fig. 9 It is a structural schematic diagram of an SVG controller modeling and analysis device based on field experimental data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0032] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0033] In the description of the present invention, unless otherwise specified, " / " means "or", for example, A / B can mean A or B. "And / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, "at least one" and "plurality" refer to two or more. The words "first", "second", etc. do not limit the quantity and execution order, and the words "first", "second", etc. do not limit them to be different.

[0034] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way for easy understanding.

[0035] In addition, the terms "including" and "having" and any variations thereof mentioned in the description of this application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or modules is not limited to the listed steps or modules, but may optionally include other steps or modules that are not listed, or may optionally include other steps or modules that are inherent to these processes, methods, products or devices.

[0036] In order to make the purpose, technical solutions and advantages of the present invention more clear, the following will be described through specific embodiments in conjunction with the accompanying drawings of the present invention.

[0037] As described in the background art, the current SVG analysis process has the problem of low analysis efficiency.

[0038] In order to solve this technical problem, the present invention proposes a SVG controller modeling and analysis method based on field experimental data, explains the method principle of the field test, and performs parameter identification design based on the field test data. The advantage of the present invention is that it avoids the modeling of the complex topology inside the SVG and the laboratory communication protocol debugging problems, and is suitable for the extraction of control characteristics during the test of various types of SVG and fault crossing. Compared with the hardware-in-the-loop simulation test, the field test results are highly accurate, the wiring is simple, and the risk is extremely low. At the same time, the field test results can truly reflect the transient response characteristics of the station-level current and reactive power, and open up the modeling path based on field tests. The purpose of the present invention is to improve the efficiency of SVG control characteristic extraction and improve the model accuracy and modeling efficiency of complex topology SVG. The design method and ideas of the present invention are novel, universal, and highly popularizable. They have important industrial application value and social significance in improving the accuracy and modeling efficiency of large power grid simulation modeling.

[0039] like Figure 1 As shown, an embodiment of the present invention provides a SVG controller modeling and analysis method based on field experimental data. The method includes steps S101-S106.

[0040] S101. Acquire field experimental data of a high and low wear fault test of the SVG controller to be modeled.

[0041] In the embodiment of the present application, the field experimental data includes three-phase current data of the output end of the SVG controller and analog voltage data of the grid side.

[0042] As a possible implementation manner, the embodiment of the present invention can obtain field experimental data based on the following steps.

[0043] S1011. Disconnect the connection between the SVG controller and the grid connection point, and keep the connection between the SVG controller and the low-voltage side voltage collection device and current collection device.

[0044] S1012: Use an analog voltage source to apply an analog voltage signal to the secondary side of the high-voltage side voltage acquisition device.

[0045] In some embodiments, a voltage phase of the analog voltage signal is consistent with a voltage phase on the high-voltage side.

[0046] S1013. Change the voltage of the simulated voltage source to perform a high and low breakdown fault test, so that the SVG controller performs a reactive response.

[0047] S1014. Collect three-phase current data of the output end of the SVG controller and analog voltage data of the grid side during the test.

[0048] like Figure 2 As shown, an embodiment of the present invention provides a test wiring diagram for an SVG on-site fault ride-through test. The present invention proposes a general method for conducting on-site tests on SVG, which is applicable to high and low wear fault tests of SVG of various types and models. First, the SVG is disconnected from the grid connection side, the SVG is disconnected from the grid connection side, the voltage and current acquisition signals on the low-voltage side are retained, and an analog voltage source is used to apply an analog voltage signal to the secondary side that originally collects the high-voltage side voltage signal. The phase of the analog voltage source is consistent with the high-voltage side voltage, and the voltage is changed by the analog voltage source to perform high and low wear fault tests. The SVG makes a corresponding reactive response based on the sampled simulated fault voltage, and the data recorder analyzer measures the three-phase current signal of the SVG outlet and the applied grid-side analog voltage signal, thereby completing the extraction of the transient control characteristics of the SVG.

[0049] The present invention is applicable to various types and models of SVG, and can fully and truly reflect the operating characteristics of specific scenarios and products of specific SVG manufacturers. Traditional new energy and energy storage reactive compensation devices are mostly based on principle models and universal models. However, the physical structure and control strategy of reactive compensation devices are complex and diverse, and the characteristics of SVG controllers from different manufacturers vary greatly. The universal transient model cannot fully and truly reflect the operating characteristics of specific scenarios and products of specific SVG manufacturers and the impact on the new energy power generation system. There may be a large deviation between the simulation model characteristics and the actual operating characteristics, which will seriously affect the accuracy and credibility of the simulation analysis results.

[0050] S102, decomposing, transforming and calculating the field experimental data based on Simulink to obtain data to be identified.

[0051] In the embodiment of the present application, the data to be identified includes active power, reactive power, positive sequence voltage, direct axis current and quadrature axis current.

[0052] As a possible implementation manner, step S102 may be specifically implemented as steps S1021 - S1024 .

[0053] S1021. Use Simulink to perform positive-sequence and negative-sequence decomposition on the analog voltage data to obtain the positive-sequence voltage.

[0054] S1022. Perform Park decoupling transformation based on the three-phase current data to obtain a direct-axis current and a quadrature-axis current.

[0055] S1023. Perform calculation based on the positive-sequence voltage, the direct-axis current, and the quadrature-axis current to obtain the active power and the reactive power.

[0056] Exemplarily, the Park transform decoupling formula of the direct-axis current and the quadrature-axis current is as follows.

[0057]

[0058] Among them, i dq0 The direct axis current and the quadrature axis current form a vector, P idq0 is the vector formed by the quadrature axis power and the direct axis power, θ is the phase angle, i a is the phase A current, i b is the B phase current, i c is the C phase current.

[0059] For example, Figure 3As shown, an embodiment of the present invention provides a structural schematic diagram of a power calculation module based on Simulink. The power calculation module based on Simulink realizes the processing and conversion of field test data. First, the input of the power calculation module is the three-phase voltage of the simulated voltage source collected on site and the instantaneous value (sinusoidal quantity) of the three-phase current of the SVG outlet. After the positive sequence component decomposition and Park transformation decoupling of the Simulink simulation, the active power P, reactive power Q, positive sequence voltage Ut, direct axis current Id, and quadrature axis current Iq are calculated.

[0060] S103: construct a BPA electromechanical transient model of the SVG controller based on the working characteristics of the SVG controller, and determine parameters to be identified of the BPA electromechanical transient model.

[0061] As a possible implementation manner, step S103 may be specifically implemented as steps S1031 - S1034 .

[0062] S1031. Determine a dynamic equation of the SVG controller based on working characteristics of the SVG controller.

[0063] S1032. Analyze the dynamic equations to determine the parameters to be identified.

[0064] In some embodiments, the parameters to be identified include proportional coefficient, slope of SVG volt-ampere characteristic curve, filter response delay, proportional link time constant, SVG response delay, SVG output voltage upper limit, SVG output voltage lower limit, maximum capacitive current, and maximum inductive current.

[0065] S1033. Based on the working characteristics of the SVG controller, determine the constraint conditions of the parameters to be identified.

[0066] S1034. Based on the dynamic equations of the SVG controller and the constraints of the parameters to be identified, a BPA electromechanical transient model of the SVG controller is constructed.

[0067] For example, Figure 4 As shown, an embodiment of the present invention provides a schematic diagram of an SVG controller model. The present invention proposes a method for identifying SVG controller parameters using the least squares method based on field data. First, the controller parameters to be identified and their value ranges are selected, and a suitable step size is selected. For different parameter combinations of the controller, BPA software is used for transient simulation, a program is written to traverse all parameter groups, and the PSD-BPA transient stability program is called to modify the SVG controller data card parameters. Parameter identification is performed based on the least squares index of the transient simulation results and the measured data to complete the SVG modeling work.

[0068] like Figure 4As shown in the figure, the SVG controller model consists of a voltage measurement link, a damping controller, a PI regulator, a protection and limiting link, an SVG inverter, and a feedback link. It uses a voltage closed-loop control to control the target side bus voltage according to the voltage command to quickly adjust the reactive power output by the SVG. V is the SVG output voltage; VREF is the reference voltage; VSCS is the auxiliary signal; VT is the system side voltage; IS is the SVG output current; T1 is the time constant of the filter and the measurement loop; T2, T3, T4, and T5 are the lead-lag time constants respectively. When performing transient simulation, the damping controller in the SVG controller model is usually not used, so the lead-lag link parameters T2, T3, T4, and T5 are all 1s; K P is the proportional coefficient; TP is the time constant of the proportional link; K I is the integral coefficient; XT is the equivalent reactance between SVG and the system, using the data provided by the original equipment manufacturer; TS is the response delay of SVG; Kd is the slope of the volt-ampere characteristic curve of SVG; VMAX and VMIN are the upper and lower limits of the voltage limiting link respectively; ICMAX and ILMAX are the maximum capacitive current and the maximum inductive current respectively.

[0069] like Figure 5 As shown, the embodiment of the present invention provides a simple block diagram of an SVG control model. Based on the empirical value, the integral link in the model is considered to have a small magnification, so this link is ignored. The simplified SVG control block diagram is as follows Figure 5 shown.

[0070] The input quantity F is the effective value of the voltage V at the SVG control node and the voltage reference value V ref The difference between them is ΔV, and the output quantity Y is the reactive current component I output by SVG. q The relationship between the output Y and the input F is:

[0071]

[0072] Among them, K P is the proportionality coefficient, K d is the slope of the SVG's volt-ampere characteristic curve. Based on the input-output response characteristics of voltage and current, K can be determined. p , K d Parameter value.

[0073] according to Figure 4 The SVG controller model shown, the selected state vector X(α), measurement vector Z(α), input vector U and parameter vector α to be identified are as follows:

[0074] X (α) =[V P ,V I ,I S ];

[0075] Z(α)=[V,I S ] T ;

[0076] U=[V,V REF , V T ] T ;

[0077] α=[T1,T P ,T S ,V MAX ,V MIN ,I CMAX ,I LMAX ] T ;

[0078] Using the least squares estimation criterion and listing the objective function J(α), the SVG controller parameter identification problem can be expressed as a weighted least squares optimization problem under the constraints of the following dynamic equations:

[0079]

[0080] Among them, Z M is the field experimental data vector; W is the weight matrix, which is the inverse of the covariance matrix of the measurement vector; f is the differential equation describing the dynamic characteristics of SVG; h is the measurement equation.

[0081] Discretize the objective function J(α), and we get:

[0082]

[0083] Among them, J(α) is the objective function, Z Mi is the i-th group of parameters to be identified, Z(α) i is the calculated value of the i-th group of BPA electromechanical transient model, m is the number of parameter combinations of the parameters to be identified, and W is the weight matrix. ΔT is the sampling interval. By setting the value range and step size corresponding to each parameter in the parameter vector α to be identified, multiple groups of parameter values ​​can be obtained. Write a program to traverse all parameter groups, call the PSD-BPA transient stability program to modify the SVG controller data card parameters, and calculate the output value Z(α) of each group of parameters in turn. Compare the calculated value Z(α) of the model with the field experimental measurement value Z M Substitute the objective function into the formula and select a set of parameters with the smallest objective function as the final identification result.

[0084] S104 . Based on the data to be identified and the BPA electromechanical transient model, the least square method is used to identify the parameters to be identified, so as to obtain model parameters of the transient model of the SVG controller.

[0085] As a possible implementation manner, step S104 can be specifically implemented as steps A1-A4.

[0086] A1. Determine multiple parameter combinations based on the value range and optimization step size of the parameter to be identified.

[0087] In some embodiments, each parameter combination includes a set of parameters to be identified.

[0088] A2. Based on the data to be identified, the BPA electromechanical transient model of the SVG controller, and the multiple parameter combinations, the model output results under different parameter combinations are calculated.

[0089] A3. For any parameter combination, calculate the residual sum of squares between the model output result and the field experimental data under the parameter combination.

[0090] A4. Compare the residual sums of squares corresponding to the parameter combinations, and determine the parameter combination with the smallest residual sum of squares as the model parameters of the transient model.

[0091] As another possible implementation, Figure 6 As shown, step S104 may also be specifically implemented as steps B1-B4.

[0092] B1. Based on the voltage output characteristics and current output characteristics of the SVG controller, determine the proportional coefficient of the PI control link and the slope of the SVG volt-ampere characteristic curve of the feedback link.

[0093] B2. Use the least squares method to construct the objective function.

[0094] In some embodiments, the objective function is formulated as;

[0095]

[0096] Among them, J(α) is the objective function, Z Mi is the i-th group of parameters to be identified, Z(α) i is the calculated value of the i-th group of BPA electromechanical transient model, m is the number of parameter combinations to be identified, and W is the weight matrix.

[0097] B3. Assuming the SVG output voltage upper limit, the SVG output voltage lower limit, the maximum capacitive current and the maximum inductive current as fixed values, and based on the constraints of the filter response delay, the proportional link time constant and the SVG response delay and the preset step size, determine a plurality of first parameter combinations.

[0098] B4. Traverse multiple first parameter combinations, perform a first voltage drop simulation, and calculate the objective function value of each first parameter combination.

[0099] Exemplarily, step B4 may be specifically implemented as steps 41 to 44 .

[0100] Step 41: setting a first parameter combination of the current iteration process based on the constraints of the filter response delay, the proportional link time constant and the SVG response delay and the preset step size;

[0101] Step 42: Based on the first parameter combination of the current iteration process, the data to be identified, and the BPA electromechanical transient model of the SVG controller, a fault simulation test of a voltage drop of 90% is performed to determine a calculated value of the BPA electromechanical transient model;

[0102] Step 43, calculating the objective function value in the current iteration process based on the calculated value of the BPA electromechanical transient model and the field measured data;

[0103] Step 44: If the current number of iterations is less than the maximum number of iterations, repeat steps 41 to 44 until the current number of iterations is greater than or equal to the maximum number of iterations; if the current number of iterations is greater than or equal to the maximum number of iterations, exit the iteration process.

[0104] B5. The first parameter combination with the smallest objective function value is determined as the identification result of the first parameter.

[0105] B6. Based on the identification result of the first parameter, the filter response delay, the proportional link time constant and the SVG response delay are set as fixed values, and based on the constraints and preset step size of the SVG output voltage upper limit, the SVG output voltage lower limit, the maximum capacitive current and the maximum inductive current, multiple second parameter combinations are determined.

[0106] B7. Traverse multiple second parameter combinations, perform a second voltage drop simulation, and calculate the objective function value of each second parameter combination.

[0107] Exemplarily, step B7 can be specifically implemented as steps 71 to 74.

[0108] Step 71: Based on the constraints of the SVG output voltage upper limit, the SVG output voltage lower limit, the maximum capacitive current and the maximum inductive current and the preset step size, a second parameter combination of the current iteration process is set;

[0109] Step 72: Based on the second parameter combination of the current iteration process, the data to be identified, and the BPA electromechanical transient model program of the SVG controller, fault simulation tests of voltage drops of 50% and 20% are performed to determine the calculated value of the BPA electromechanical transient model;

[0110] Step 73, calculating the objective function value in the current iteration process based on the calculated value of the BPA electromechanical transient model and the field measured data;

[0111] Step 74: If the current number of iterations is less than the maximum number of iterations, repeat steps 71 to 74 until the current number of iterations is greater than or equal to the maximum number of iterations; if the current number of iterations is greater than or equal to the maximum number of iterations, exit the iteration process.

[0112] B8. Determine the second parameter combination with the smallest objective function value as the identification result of the second parameter.

[0113] B9. Determine model parameters of the transient model of the SVG controller based on the identification result of the first parameter and the identification result of the second parameter.

[0114] It should be noted that when the SVG bus voltage drops less deeply, the voltage and current limit links do not work, and the controller still works in the linear region. First, the parameters [T1, TP, TS] are identified. The identified parameter values ​​in the fixed model are in the next stage. When the SVG bus voltage drops significantly, the output of the SVG controller reaches the limit value and enters the nonlinear working area. Then the limit link parameters VMAX, VMIN, ICMAX, and ILMAX are identified. The parameter identification process is as follows: Figure 6 There are 7 parameters to be identified in the SVG controller model. Choosing a smaller step size to ensure the identification accuracy will result in too many parameter groups. The phased identification method can greatly reduce the number of parameter groups, save engineering calculation time, and improve parameter identification efficiency.

[0115] Compared with hardware-in-the-loop simulation tests, field test results are highly accurate, simple to wire and extremely low risk, avoiding the shortcomings of modeling the internal structure of the equipment and debugging the communication protocol. In addition, field test results can more realistically reflect the transient response characteristics of station-level current and reactive power, opening up a modeling path based on field tests and effectively improving the model accuracy and efficiency of complex topology SVGs.

[0116] like Figure 7 and Figure 8 As shown, an embodiment of the present invention provides a schematic diagram of modeling analysis results of a certain type of SVG controller. Figure 7 Table 1 is a schematic diagram of the modeling and analysis results of the inductive high-power three-phase fault. Figure 7 The deviation calculation results of the modeling results. Figure 7The working condition is that the SVG outputs inductive reactive power of 1.0pu, and the voltage drops to 20%Un in three phases. The low-break process is divided into three periods: A (before fault), B (during fault), and C (after fault) with reference to the fundamental positive sequence voltage. According to the response characteristics of active and reactive power, the B period is divided into B1 (transient) and B2 (steady state) intervals, and the C period is divided into C1 (transient) and C2 (steady state) intervals; the average deviation F1max of the transient interval, the average deviation F2max of the steady state interval, and the maximum deviation F3max of each period are calculated.

[0117] Table 1

[0118]

[0119] When the SVG outputs rated inductive reactive power, the simulated voltage drops to 20% in three phases, and the SVG output current changes from rated inductive to rated capacitive, supporting the grid voltage recovery. The deviation calculation results in Table 1 prove that the model parameters meet the accuracy requirements.

[0120] Figure 8 Table 2 is a schematic diagram of the modeling and analysis results of capacitive low-power three-phase faults. Figure 8 Deviation calculation results of modeling analysis results. Figure 8 The working condition is that the SVG outputs capacitive reactive power of 0.2pu and the voltage of three phases increases to 120%Un.

[0121] Table 2

[0122]

[0123] When the SVG outputs rated 20% capacitive reactive power, the simulated voltage rises to 120% in three phases, and the SVG output current changes from 20% capacitive to rated inductive, ensuring grid voltage recovery. The deviation calculation results in Table 2 prove that the model parameters meet the accuracy requirements.

[0124] The present invention provides an SVG controller modeling and analysis method based on field experimental data. The method performs high and low wear fault tests on the SVG to obtain field experimental data, combines the calculation data of the transient model of the SVG under different model parameters, and uses the least square method to identify parameters to obtain the optimal model parameters of the transient model of the SVG controller. The process avoids the modeling of the complex topology inside the SVG and the laboratory communication protocol debugging, is suitable for testing various types of SVG, and the field test results are highly accurate, the wiring is simple, and the risk is extremely low. The field test results can truly reflect the transient response characteristics of the station-level current and reactive power, and open up a modeling path based on field tests. The present invention improves the efficiency of SVG control characteristic extraction, improves the model accuracy and modeling efficiency of complex topology SVG, and thus improves the analysis efficiency of the SVG analysis process.

[0125] In addition, the SVG field test general method proposed by the present invention does not need to model the complex SVG topology, which reduces the probability of simulation debugging errors and shortens the debugging time. Each phase chain of the chain SVG is composed of a plurality of H-bridge converters in cascade, and the control structure is relatively complex, which makes modeling difficult and time-consuming.

[0126] The general method for SVG field test proposed in the present invention avoids the problems of difficulty and long cycle in debugging its communication protocol. Since the SVG applied to the new energy power station is a multi-level cascade topology, the main circuit and the controller interact a lot of physical quantities, and the signal transmission port of the simulation machine is far from enough. Some signals interact through the communication protocol, such as the DC side capacitor voltage and PWM pulse signal of each submodule. The platform converts all the DC side capacitor voltage and PWM pulse signal of the submodule transmitted through the communication protocol into electrical signals through the photoelectric transfer box, and performs physical quantity docking through the analog and digital ports. The laboratory hardware-in-the-loop test needs to debug its communication, and the debugging process is complicated and the cycle is long. The present invention avoids the problem of debugging its communication protocol.

[0127] The method of least squares method for SVG parameter identification proposed in this invention uses processed field test data, uses BPA software to perform transient simulation on different control parameter combinations, and performs parameter identification based on the least squares index of transient simulation results and field test results. The results show that the proposed parameter identification method can accurately identify SVG control parameters and meet the accuracy requirements of power system transient simulation.

[0128] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0129] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0130] Fig. 9 The schematic diagram of the structure of a SVG controller modeling and analysis device based on field experimental data provided by an embodiment of the present invention is shown. The modeling and analysis device 200 includes a communication module 201 and a processing module 202 .

[0131] The communication module 201 is used to obtain the field experimental data of the high and low penetration fault test of the SVG controller to be modeled; the field experimental data includes the three-phase current data of the output end of the SVG controller and the analog voltage data of the grid side.

[0132] The processing module 202 is used to decompose, transform and calculate the field experimental data based on Simulink to obtain the data to be identified, wherein the data to be identified includes active power, reactive power, positive sequence voltage, direct axis current and quadrature axis current; based on the working characteristics of the SVG controller, construct a BPA electromechanical transient model of the SVG controller, and determine the parameters to be identified of the BPA electromechanical transient model; based on the data to be identified and the BPA electromechanical transient model, use the least squares method to identify the parameters to be identified, and obtain the model parameters of the transient model of the SVG controller.

[0133] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A SVG controller modeling and analysis method based on field experimental data, characterized in that: The analysis method comprises: Acquire field experimental data of a high-low breakdown fault test of the SVG controller to be modeled; the field experimental data includes three-phase current data of the output end of the SVG controller and analog voltage data of the grid side; Decomposing, transforming and calculating the field experimental data based on Simulink to obtain the data to be identified, wherein the data to be identified includes active power, reactive power, positive sequence voltage, direct axis current and quadrature axis current; Based on the working characteristics of the SVG controller, a BPA electromechanical transient model of the SVG controller is constructed, and parameters to be identified of the BPA electromechanical transient model are determined; Based on the data to be identified and the BPA electromechanical transient model, the least square method is used to identify the parameters to be identified, so as to obtain model parameters of the transient model of the SVG controller.

2. The SVG controller modeling and analysis method based on field experimental data according to claim 1 is characterized in that: The method for acquiring field experimental data of the high and low wear fault test of the SVG controller to be modeled comprises: Disconnecting the connection between the SVG controller and the grid connection point, and retaining the connection between the SVG controller and the low-voltage side voltage collection device and the current collection device; An analog voltage source is used to apply an analog voltage signal to the secondary side of the high-voltage side voltage acquisition device, wherein the voltage phase of the analog voltage signal is consistent with the voltage phase of the high-voltage side; Changing the voltage of the simulated voltage source to perform a high and low breakdown fault test, so that the SVG controller performs a reactive response; During the test, the three-phase current data at the output end of the SVG controller and the analog voltage data on the grid side are collected.

3. The SVG controller modeling and analysis method based on field experimental data according to claim 1 is characterized in that: The decomposition, transformation and calculation of the field experimental data based on Simulink to obtain the data to be identified include: Using Simulink, performing positive-sequence and negative-sequence decomposition on the simulated voltage data to obtain the positive-sequence voltage; Perform Park decoupling transformation based on the three-phase current data to obtain direct-axis current and quadrature-axis current; The active power and reactive power are obtained by performing calculation based on the positive-sequence voltage, the direct-axis current and the quadrature-axis current.

4. The SVG controller modeling and analysis method based on field experimental data according to claim 1 is characterized in that: The step of constructing a BPA electromechanical transient model of the SVG controller based on the working characteristics of the SVG controller and determining parameters to be identified of the BPA electromechanical transient model includes: Determining a dynamic equation of the SVG controller based on the operating characteristics of the SVG controller; Analyze the dynamic equation to determine the parameters to be identified; the parameters to be identified include proportional coefficient, slope of SVG volt-ampere characteristic curve, filter response delay, proportional link time constant, SVG response delay, SVG output voltage upper limit, SVG output voltage lower limit, maximum capacitive current, and maximum inductive current; Determining the constraint conditions of the parameters to be identified based on the working characteristics of the SVG controller; Based on the dynamic equations of the SVG controller and the constraints of the parameters to be identified, a BPA electromechanical transient model of the SVG controller is constructed.

5. The SVG controller modeling and analysis method based on field experimental data according to claim 1 is characterized in that: The method of identifying the parameters to be identified based on the data to be identified and the BPA electromechanical transient model, and obtaining the model parameters of the transient model of the SVG controller, comprises: Based on the value range and optimization step of the parameters to be identified, multiple parameter combinations are determined, each parameter combination includes a group of parameters to be identified; based on the data to be identified, the BPA electromechanical transient model of the SVG controller, and the multiple parameter combinations, the model output results under different parameter combinations are calculated; For any parameter combination, calculating the residual sum of squares between the model output result and the field experimental data under the parameter combination; The residual sums of squares corresponding to the various parameter combinations are compared, and the parameter combination with the smallest residual sum of squares is determined as the model parameters of the transient model.

6. The SVG controller modeling and analysis method based on field experimental data according to claim 1 is characterized in that: The method of identifying the parameters to be identified based on the data to be identified and the BPA electromechanical transient model, and obtaining the model parameters of the transient model of the SVG controller, comprises: Based on the voltage output characteristics and current output characteristics of the SVG controller, determine the proportional coefficient of the PI control link and the slope of the SVG volt-ampere characteristic curve of the feedback link; The least square method is used to construct the objective function; Assuming that the SVG output voltage upper limit, the SVG output voltage lower limit, the maximum capacitive current and the maximum inductive current are fixed values, and based on the constraints of the filter response delay, the proportional link time constant and the SVG response delay and the preset step size, determining a plurality of first parameter combinations; Traversing the plurality of first parameter combinations, performing a first voltage drop simulation, and calculating an objective function value of each first parameter combination; The first parameter combination with the smallest objective function value is determined as the identification result of the first parameter; Based on the identification result of the first parameter, the filter response delay, the proportional link time constant and the SVG response delay are set as fixed values, and based on the constraints of the SVG output voltage upper limit, the SVG output voltage lower limit, the maximum capacitive current and the maximum inductive current and the preset step size, a plurality of second parameter combinations are determined; Traversing the plurality of second parameter combinations, performing a second voltage drop simulation, and calculating an objective function value of each second parameter combination; The second parameter combination with the smallest objective function value is determined as the identification result of the second parameter; Based on the identification result of the first parameter and the identification result of the second parameter, model parameters of the transient model of the SVG controller are determined.

7. The SVG controller modeling and analysis method based on field experimental data according to claim 6 is characterized in that: The formula of the objective function is: Among them, J(α) is the objective function, Z Mi is the i-th group of parameters to be identified, Z(α) i is the calculated value of the i-th group of BPA electromechanical transient model, m is the number of parameter combinations to be identified, and W is the weight matrix.

8. The SVG controller modeling and analysis method based on field experimental data according to claim 6 is characterized in that: The traversing the plurality of first parameter combinations, performing a first voltage drop simulation, and calculating an objective function value of each first parameter combination includes: Step 41: setting a first parameter combination of the current iteration process based on the constraints of the filter response delay, the proportional link time constant and the SVG response delay and the preset step size; Step 42: Based on the first parameter combination of the current iteration process, the data to be identified, and the BPA electromechanical transient model of the SVG controller, a fault simulation test of a voltage drop of 90% is performed to determine a calculated value of the BPA electromechanical transient model; Step 43, calculating the objective function value in the current iteration process based on the calculated value of the BPA electromechanical transient model and the field measured data; Step 44: If the current number of iterations is less than the maximum number of iterations, repeat steps 41 to 44 until the current number of iterations is greater than or equal to the maximum number of iterations; if the current number of iterations is greater than or equal to the maximum number of iterations, exit the iteration process.

9. The SVG controller modeling and analysis method based on field experimental data according to claim 6 is characterized in that: The traversing the plurality of second parameter combinations, performing a second voltage drop simulation, and calculating an objective function value of each second parameter combination includes: Step 71: Based on the constraints of the SVG output voltage upper limit, the SVG output voltage lower limit, the maximum capacitive current and the maximum inductive current and the preset step size, a second parameter combination of the current iteration process is set; Step 72: Based on the second parameter combination of the current iteration process, the data to be identified, and the BPA electromechanical transient model program of the SVG controller, fault simulation tests of voltage drops of 50% and 20% are performed to determine the calculated value of the BPA electromechanical transient model; Step 73, calculating the objective function value in the current iteration process based on the calculated value of the BPA electromechanical transient model and the field measured data; Step 74: If the current number of iterations is less than the maximum number of iterations, repeat steps 71 to 74 until the current number of iterations is greater than or equal to the maximum number of iterations; if the current number of iterations is greater than or equal to the maximum number of iterations, exit the iteration process.

10. A SVG controller modeling and analysis device based on field experimental data, characterized in that: include: The communication module is used to obtain the field experimental data of the high and low wear fault tests of the SVG controller to be modeled; The field experimental data includes three-phase current data of the output end of the SVG controller and analog voltage data of the grid side; A processing module is used to decompose, transform and calculate the field experimental data based on Simulink to obtain the data to be identified, wherein the data to be identified includes active power, reactive power, positive sequence voltage, direct axis current and quadrature axis current; based on the working characteristics of the SVG controller, a BPA electromechanical transient model of the SVG controller is constructed, and parameters to be identified of the BPA electromechanical transient model are determined; Based on the data to be identified and the BPA electromechanical transient model, the least square method is used to identify the parameters to be identified, so as to obtain model parameters of the transient model of the SVG controller.

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