A load model parameter identification method and system for electromagnetic transient simulation

Abstract

The application discloses a load model parameter identification method and system for electromagnetic transient simulation, and the method comprises the following steps: configuring element parameters, and determining a data set variance limit value; calculating the judgment data set variance of each data value of the element parameters by randomly scattering points, and taking the minimum calculated judgment data set variance as a variance reference value; based on the step length of the preset element parameters and the variance reference value, performing optimization calculation on the parameter value, and when the calculated judgment data set variance of the parameter value is smaller than the data set variance limit value, determining that the parameter value is the optimal solution of the element parameters.

Description

Technical Field

[0001] The present invention relates to the technical field of equivalent load modeling for electromagnetic transient simulation of a large power grid of a novel power system, and more specifically, to a load model parameter identification method and system for electromagnetic transient simulation. Background Art

[0002] Power system simulation plays a vital role in power system planning, dispatching, operation, and research. The accuracy of simulation results depends largely on the accuracy of the model. Load model parameters, as one of the four major model parameters of a power system, have a significant impact on power system simulation results. Therefore, establishing an accurate load model is crucial for ensuring the accuracy of power system simulation. Using an inappropriate load model can lead to significant discrepancies between simulation results and measured curves. Previous literature has shown that using different load models significantly impacts power system simulation results, and it has been found that different load model structures can even produce opposite results when conducting power system stability analysis. In new power systems with high penetration rates of renewable energy and power electronic equipment, the proportion of power electronic equipment in the load area is increasing, and their fault ride-through characteristics have a significant impact on the safety and stability of the AC power grid. Electromechanical transient simulation is no longer sufficient for research needs, and electromagnetic transient simulation methods are needed to study the operating characteristics and control and protection strategies of large power grids.

[0003] Active loads, such as distributed generation (DGs), energy storage, and electric vehicles, differ from traditional loads in terms of response timescales, control accuracy, and stability mechanisms. Therefore, electromagnetic transient methods are needed to study their impact on system operational characteristics and control and protection strategies after integration into the bulk grid. Current load models used for electromagnetic transient simulation of bulk grids employ the classic SLM structure, but their specific implementation differs from electromechanical transient simulation. Taking the ZIP model as an example, electromechanical transient simulation programs are phasor-based. For constant-power loads, the phase and amplitude of the current source can be directly calculated based on the phase and amplitude of the voltage at the load connection point, as well as the power. Electromagnetic transient simulation programs, on the other hand, are based on instantaneous values ​​and require a phase-locked loop (PLL) to phase-lock the three-phase voltage at the load connection point. Then, through negative feedback control, the amplitude and phase of the three-phase current source required to achieve the target power are calculated. High-proportion distributed generation and new power electronic loads exhibit distinct voltage, power, and frequency response characteristics from traditional loads, and their coupling with large-capacity DC power is even more complex. Existing load models and modeling methods struggle to effectively characterize their post-fault dynamic characteristics, making them incapable of supporting safe, stable operation and scientific planning of the new power system. There is little existing data on the research of models containing active loads such as distributed power sources, energy storage and electric vehicles suitable for electromagnetic transient simulation of large power grids.

[0004] Existing load modeling research primarily focuses on electromechanical transient simulation and has achieved significant results. In particular, the widespread application of the SLM load model structure and overall identification method has played a significant role in improving the accuracy of power grid simulation. my country's power system is rapidly transitioning to a new power system, with a large number of active loads such as distributed generation, energy storage, and electric vehicles connected to the grid. This has further increased the complexity of loads. Electromechanical transient models are no longer well adapted to the trend of power electronics in load equipment, making it difficult to address load complexity. Further research is needed on equivalent load models and parameter identification methods for electromagnetic transient simulation of large power grids.

[0005] Therefore, it is of great significance to study the load characteristics of new power systems and establish accurate electromagnetic transient load models in response to the needs of large power grid electromagnetic transient simulation. Summary of the Invention

[0006] The technical solution of the present invention provides a load model parameter identification method and system for electromagnetic transient simulation to solve the problem of how to accurately determine the electromagnetic transient load model parameters.

[0007] In order to solve the above problems, the present invention provides a load model parameter identification method for electromagnetic transient simulation, the method comprising:

[0008] Configure component parameters, determine the variance of the judgment data set when optimizing the parameter values ​​of the component parameters, and the data set variance limit;

[0009] The variance of the judgment data set of each data value of the component parameter is calculated by randomly scattering points, and the calculated minimum variance of the judgment data set is used as the variance reference value;

[0010] Based on the preset step size and variance reference value of the component parameter, the parameter value is optimized and calculated. When the variance of the judgment data set of the calculated parameter value is less than the data set variance limit, the parameter value is determined to be the optimal solution of the component parameter.

[0011] Preferably, the method further comprises:

[0012] The parameter values ​​of the configured component parameters are used as coordinate points in the two-dimensional space;

[0013] Randomly scatter points in the two-dimensional space, and calculate the variance of the judgment data set of each coordinate point in the two-dimensional space; and use the minimum value of the calculated variance of the judgment data set as the variance reference value;

[0014] Based on the variance reference value and the preset parameter step size of the configuration component parameters, the parameter value of the component parameter is adjusted, and the variance of the judgment data set of each coordinate point in the two-dimensional space is calculated until the preset data set variance limit is met. The parameter value that reaches the preset data set variance limit is taken as the optimal parameter value.

[0015] Preferably, the method further comprises:

[0016] When searching for the optimal solution of component parameters, set the maximum number of optimization calculations;

[0017] When the number of optimization calculations reaches the maximum number, the optimization calculation is stopped.

[0018] Preferably, the variance of the judgment data set includes: current I variance, active power P variance and reactive power Q variance;

[0019] The data set variance limits include: a current I variance upper limit value, an active power P variance upper limit value, and a reactive power Q variance upper limit value.

[0020] Preferably, the configuration of component parameters includes: component type, component name, parameter name, parameter type, parameter modification position, parameter initial value, parameter value upper limit and step size.

[0021] Preferably, the step of using the configured component parameters as coordinate points in a two-dimensional space further includes:

[0022] When identifying and optimizing a single parameter, the parameter value of the single parameter is used as a direct point;

[0023] When multiple parameters are identified and optimized, the parameter values ​​of the multiple parameters are combined in pairs, and the coordinate points of the combined pairs are used as points in the regular quadrilateral.

[0024] Preferably, the step of adjusting the parameter value of the component parameter based on the variance reference value and the parameter preset step of the configuration component parameter, and calculating the variance of the judgment data set of each coordinate point in the two-dimensional space includes:

[0025] When the component parameter is a single parameter, the parameter values ​​of the two points before and after the line are obtained based on the preset parameter step size, and the variance of the data set of the parameter values ​​of the two points before and after is calculated;

[0026] When the component parameter is two parameters, it is expanded to the four sides based on the preset parameter step size to calculate the data set variance of the four vertex parameter values.

[0027] Preferably, based on the variance reference value and the preset parameter step size of the configuration element parameter, the parameter value of the element parameter is adjusted, and the variance of the judgment data set of each coordinate point in the two-dimensional space is calculated. When the variance of the judgment data set is the active power P variance and the reactive power Q variance, the algorithm used is:

[0028]

[0029] Where N is the number of variances of the collected judgment data sets, REF(k) is the variance of the k-th judgment data set, RES(k) is the k-th corresponding data set, and A is the variance of active power (P) or reactive power (Q);

[0030] Expand the above formula to get the following formula:

[0031] A=((REF(0)-RES(0)) 2 +......+(REF(N)-RES(N)) 2 ) / (N-1)

[0032] When the variance of the data set is determined to be the variance of the current I, the above algorithm is improved to obtain the following formula:

[0033]

[0034] Right now:

[0035] Where,

[0036] V(k)=REF(k)-RES(k)

[0037]

[0038] Where V(k) is the difference between the variance of the K-th current I and the response data. is the average value of the difference between N current I variances and the response data, and B is the current I variance.

[0039] Preferably, when the optimal solution of the component parameters cannot be obtained, the step size of the parameters is reduced and the optimal calculation of the parameter values ​​is performed again.

[0040] According to another aspect of the present invention, a load model parameter identification system for electromagnetic transient simulation is provided, the system comprising:

[0041] The initialization unit is used to configure the component parameters, determine the variance of the judgment data set when optimizing the parameter values ​​of the component parameters, and the data set variance limit;

[0042] a calculation unit, configured to calculate the variance of the judgment data set of each data value of the component parameter by randomly scattering points, and use the calculated minimum variance of the judgment data set as a variance reference value;

[0043] The result unit is used to perform optimal calculation of the parameter value based on the preset step size and variance reference value of the component parameter. When the variance of the judgment data set of the calculated parameter value is less than the data set variance limit, the parameter value is determined to be the optimal solution of the component parameter.

[0044] Preferably, wherein:

[0045] The calculation unit is further configured to use the parameter values ​​of the configured component parameters as coordinate points in a two-dimensional space; randomly scatter points in the two-dimensional space to calculate the variance of the judgment data set for each coordinate point in the two-dimensional space; and use the minimum value of the calculated variances of the judgment data set as a variance reference value;

[0046] The result unit is further used to adjust the parameter value of the component parameter based on the variance reference value and the parameter preset step size of the configuration component parameter, and calculate the variance of the judgment data set of each coordinate point in the two-dimensional space until the preset data set variance limit is met, and the parameter value that reaches the preset data set variance limit is used as the optimal parameter value.

[0047] Preferably, the result unit is further configured to:

[0048] When searching for the optimal solution of component parameters, set the maximum number of optimization calculations;

[0049] When the number of optimization calculations reaches the maximum number, the optimization calculation is stopped.

[0050] Preferably, the initialization unit is used to configure the component parameters, including determining the variance of the judgment data set, wherein the variance of the judgment data set includes: current I variance, active power P variance and reactive power Q variance;

[0051] The data set variance limits include: a current I variance upper limit value, an active power P variance upper limit value, and a reactive power Q variance upper limit value.

[0052] Preferably, the initialization unit is used to configure component parameters, including: component type, component name, parameter name, parameter type, parameter modification position, parameter initial value, parameter value upper limit and step size.

[0053] Preferably, the calculation unit is used to use the configured component parameters as coordinate points in a two-dimensional space, and is further used to:

[0054] When identifying and optimizing a single parameter, the parameter value of the single parameter is used as a direct point;

[0055] When multiple parameters are identified and optimized, the parameter values ​​of the multiple parameters are combined in pairs, and the coordinate points of the combined pairs are used as points in the regular quadrilateral.

[0056] Preferably, the result unit is used to adjust the parameter value of the component parameter based on the variance reference value and the parameter preset step size of the configuration component parameter, and calculate the variance of the judgment data set of each coordinate point in the two-dimensional space, and is also used to:

[0057] When the component parameter is a single parameter, the parameter values ​​of the two points before and after the line are obtained based on the preset parameter step size, and the variance of the data set of the parameter values ​​of the two points before and after is calculated;

[0058] When the component parameter is two parameters, it is expanded to the four sides based on the preset parameter step size to calculate the data set variance of the four vertex parameter values.

[0059] Preferably, the result unit is used to adjust the parameter value of the component parameter based on the variance reference value and the parameter preset step size of the configuration component parameter, and calculate the variance of the judgment data set of each coordinate point in the two-dimensional space. When the variance of the judgment data set is the active power P variance and the reactive power Q variance, the algorithm used is:

[0060]

[0061] Where N is the number of variances of the collected judgment data sets, REF(k) is the variance of the k-th judgment data set, RES(k) is the k-th corresponding data set, and A is the variance of active power (P) or reactive power (Q);

[0062] Expand the above formula to get the following formula:

[0063] A=((REF(0)-RES(0)) 2 +......+(REF(N)-RES(N)) 2 ) / (N-1)

[0064] When the variance of the data set is determined to be the variance of the current I, the above algorithm is improved to obtain the following formula:

[0065]

[0066] Right now:

[0067] Where,

[0068] V(k)=REF(k)-RES(k)

[0069]

[0070] Where V(k) is the difference between the variance of the K-th current I and the response data. is the average value of the difference between N current I variances and the response data, and B is the current I variance.

[0071] Preferably, the result unit is further configured to reduce the parameter step size and recalculate the optimal parameter value when the optimal solution for the component parameter cannot be obtained. Based on another aspect of the present invention, the present invention provides a computer-readable storage medium storing a computer program for executing a load model parameter identification method for electromagnetic transient simulation.

[0072] According to another aspect of the present invention, the present invention provides an electronic device, the electronic device comprising: a processor and a memory; wherein,

[0073] Memory for storing processor-executable instructions;

[0074] The processor is used for reading executable instructions from a memory and executing the instructions to implement a load model parameter identification method for electromagnetic transient simulation.

[0075] The technical solution of the present invention provides a method and system for identifying load model parameters for electromagnetic transient simulation, wherein the method includes: configuring component parameters, determining the variance of the judgment data set when optimizing the parameter values ​​of the component parameters, and the data set variance limit; calculating the variance of the judgment data set of each data value of the component parameters by randomly scattering points, and using the calculated minimum judgment data set variance as the variance reference value; performing optimal calculation of the parameter value based on the preset step size of the component parameters and the variance reference value, and when the variance of the judgment data set of the calculated parameter value is less than the data set variance limit, determining the parameter value as the optimal solution for the component parameter. The technical solution of the present invention proposes a method and system for measuring and identifying key parameters of an equivalent load model suitable for power electronic equipment such as distributed power sources and energy storage, which can efficiently obtain accurate parameters of the active load equivalent model, improve the research efficiency of the load characteristics of the new power system, and provide strong support for the simulation analysis of the safety and stability of the power grid under the new power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] A more complete understanding of exemplary embodiments of the present invention may be obtained by referring to the following drawings:

[0077] Figure 1 This is a flow chart of a load model parameter identification method for electromagnetic transient simulation according to a preferred embodiment of the present invention;

[0078] Figure 2 A schematic diagram of an optimization process according to a preferred embodiment of the present invention;

[0079] Figure 3 This is a schematic diagram of the main interface for configuring the parameters of the present invention according to a preferred embodiment of the present invention;

[0080] Figure 4Schematic diagram of single-parameter and multi-parameter two-dimensional space according to a preferred embodiment of the present invention;

[0081] Figure 5 Schematic diagram of a model structure of a series circuit of a resistor R and an inductor L according to a preferred embodiment of the present invention;

[0082] Figure 6 Schematic diagram of the current variance scatter plot corresponding to the resistor R2 according to a preferred embodiment of the present invention;

[0083] Figure 7 Schematic diagram of the current variance scatter plot corresponding to the inductor L2 according to a preferred embodiment of the present invention;

[0084] Figure 8 A histogram of current variance corresponding to the resistor R2 and the inductor L2 according to a preferred embodiment of the present invention;

[0085] Figure 9 A schematic diagram of a current waveform viewed by ScopeView after finding the values ​​of R2 and L2 according to a preferred embodiment of the present invention; and

[0086] Figure 10 2 is a structural diagram of a load model parameter identification system for electromagnetic transient simulation according to a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0087] Exemplary embodiments of the present invention will now be described with reference to the accompanying drawings. However, the present invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to provide a thorough and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terminology used in the exemplary embodiments shown in the accompanying drawings is not intended to limit the present invention. In the accompanying drawings, identical elements are denoted by the same reference numerals.

[0088] Unless otherwise specified, the terms used herein (including technical terms) have the meanings commonly understood by those skilled in the art. In addition, it is understood that terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0089] Figure 1 The present invention is a flowchart of a method for identifying load model parameters for electromagnetic transient simulation according to a preferred embodiment of the present invention.

[0090] The present invention proposes a method for measuring and identifying the key parameters of an equivalent load model suitable for power electronic equipment including distributed power sources and energy storage devices. This method can efficiently obtain the accurate parameters of the active load equivalent model, improve the efficiency of research on the load characteristics of new power systems, and provide strong support for the simulation analysis of the safety and stability of power grids under new power systems.

[0091] like Figure 1 As shown, the present invention provides a load model parameter identification method for electromagnetic transient simulation, the method comprising:

[0092] Step 101: configuring component parameters, determining the variance of the judgment data set and the data set variance limit when optimizing the parameter values ​​of the component parameters;

[0093] Step 102: Calculate the variance of the judgment data set of each data value of the component parameter by randomly scattering points, and use the calculated minimum judgment data set variance as the variance reference value;

[0094] Step 103: Based on the preset step size and variance reference value of the component parameter, perform optimal calculation of the parameter value. When the variance of the judgment data set of the calculated parameter value is less than the data set variance limit, the parameter value is determined to be the optimal solution of the component parameter.

[0095] Preferably, the method further comprises:

[0096] The parameter values ​​of the configured component parameters are used as coordinate points in the two-dimensional space;

[0097] Randomly scatter points in the two-dimensional space and calculate the variance of the judgment data set of each coordinate point in the two-dimensional space; the minimum value of the calculated variance of the judgment data set is used as the variance reference value;

[0098] Based on the variance reference value and the preset parameter step size of the configuration component parameters, the parameter value of the component parameter is adjusted, and the variance of the judgment data set of each coordinate point in the two-dimensional space is calculated until the preset data set variance limit is met. The parameter value that reaches the preset data set variance limit is taken as the optimal parameter value.

[0099] Preferably, the method further comprises:

[0100] When searching for the optimal solution of component parameters, set the maximum number of optimization calculations;

[0101] When the number of optimization calculations reaches the maximum value, the optimization calculation is stopped.

[0102] Preferably, determining the variance of the data set includes: current I variance, active power P variance, and reactive power Q variance;

[0103] The data set variance limits include: the upper limit of current I variance, the upper limit of active power P variance, and the upper limit of reactive power Q variance.

[0104] Preferably, the configuration of component parameters includes: component type, component name, parameter name, parameter type, parameter modification position, parameter initial value, parameter value upper limit and step size.

[0105] Preferably, the configured component parameters are used as coordinate points in a two-dimensional space, further comprising:

[0106] When identifying and optimizing a single parameter, the parameter value of the single parameter is used as a direct point;

[0107] When multiple parameters are identified and optimized, the parameter values ​​of the multiple parameters are combined in pairs, and the coordinate points of the combined pairs are used as points in the regular quadrilateral.

[0108] Preferably, based on the variance reference value and the parameter preset step size of the configuration element parameter, adjusting the parameter value of the element parameter and calculating the variance of the judgment data set of each coordinate point in the two-dimensional space include:

[0109] When the component parameter is a single parameter, the parameter values ​​of the two points before and after the line are obtained based on the preset parameter step size, and the variance of the data set of the parameter values ​​of the two points before and after is calculated;

[0110] When the component parameter is two parameters, it is expanded to the four sides based on the preset parameter step size to calculate the data set variance of the four vertex parameter values.

[0111] The method adopted by the present invention is as follows:

[0112] (1) Identification process

[0113] The present invention uses the output data of the tool software acquisition module to display and analyze the data in real time. At the same time, it uses parameter identification and optimization algorithms to calculate new model parameters, and sets the new model parameters to the simulation model module in the simulation software through a Python script, and then starts a new round of simulation calculations; this process is repeated until the model parameter search is completed or the stopping criteria are reached.

[0114] The schematic diagram of the optimization process of the present invention is shown in the attached Figure 2 shown.

[0115] The various parts of the optimization process are described as follows:

[0116] 1) Parameter configuration

[0117] The main interface of parameter configuration is as shown in the attached Figure 3 shown.

[0118] As shown in the figure, parameter configuration consists of three parts:

[0119] ①Basic parameter configuration

[0120] This section includes the "Variance Upper Limit for Current I," "Variance Upper Limit for Active Power P," "Variance Upper Limit for Reactive Power Q," and "Number of Simulation Steps." When the optimization process reaches the number of simulation steps or the variance of the dataset reaches the set threshold, the optimization criteria are considered met and the optimization process stops.

[0121] The description of each parameter is shown in Table 1:

[0122] Table 1 Basic parameter configuration description

[0123]

[0124] ②Fault parameter configuration

[0125] This part of the present invention mainly sets the data collection duration under the fault model, including "fault start time", "fault end time", "fault start collection duration", "fault collection duration before fault ends", "fault collection duration after fault ends" and "total data collection duration".

[0126] The parameters of the present invention are shown in Table 2:

[0127] Table 2 Fault parameter configuration description

[0128]

[0129]

[0130] ③Component parameter configuration

[0131] This part of the present invention is used to configure the component parameters that need to be optimized, including "component type", "component name", "parameter name", "parameter type", "parameter modification position", "parameter initial value", "parameter value upper limit" and "minimum step size".

[0132] The meaning of each parameter is shown in Table 3:

[0133] Table 3 Component parameter configuration description

[0134]

[0135] 2) Randomly scatter

[0136] After the configuration of the component parameters of the present invention is completed, the component parameters are regarded as coordinate points in a two-dimensional space. When optimizing a single parameter, it is regarded as a point on a straight line; when optimizing multiple parameters, the parameters are combined in pairs and regarded as points in a regular quadrilateral. The single parameter and multi-parameter two-dimensional space diagrams are shown in the attached figure. Figure 4 shown.

[0137] In order to find the target value more quickly within the set range, the present invention performs a random point scattering before the optimization begins, calculates the variance of each point, and takes the minimum value as the reference value to find the first point close to the target value.

[0138] Preferably, based on the variance reference value and the preset parameter step size of the configuration element parameter, the parameter value of the element parameter is adjusted, and the variance of the judgment data set of each coordinate point in the two-dimensional space is calculated. When the variance of the judgment data set is the active power P variance and the reactive power Q variance, the algorithm used is:

[0139]

[0140] Where N is the number of variances of the collected judgment data sets, REF(k) is the variance of the k-th judgment data set, RES(k) is the k-th corresponding data set, and A is the variance of active power (P) or reactive power (Q);

[0141] Expand the above formula to get the following formula:

[0142] A=((REF(0)-RES(0)) 2 +......+(REF(N)-RES(N)) 2 ) / (N-1)

[0143] When the variance of the data set is determined to be the variance of the current I, the above algorithm is improved to obtain the following formula:

[0144]

[0145] Right now:

[0146] Where,

[0147] V(k)=REF(k)-RES(k)

[0148]

[0149] Where V(k) is the difference between the variance of the K-th current I and the response data. is the average value of the difference between N current I variances and the response data, and B is the current I variance.

[0150] Preferably, when the optimal solution of the component parameters cannot be obtained, the step size of the parameters is reduced and the optimal calculation of the parameter values ​​is performed again.

[0151] 3) Parameter optimization

[0152] At the beginning of the optimization stage, the present invention sets a step size according to the parameter range. For a single parameter, the parameters of the two points before and after the straight line are obtained according to the step size, and the variance of each point is calculated; for two-parameter optimization, the step size is expanded to the four sides, the parameters of the four vertices are obtained, and the variance of each point is calculated.

[0153] After obtaining the optimal solution with this step size, the new parameter point is used as the benchmark, and the above steps are repeated while continuing to expand with this step size.

[0154] If no better solution is obtained, reduce the step size and repeat the above steps with the new step size.

[0155] 4) End

[0156] The present invention stops optimizing when the calculated variance result satisfies the set value or the maximum number of optimizing steps has been reached.

[0157] (2) Algorithm implementation

[0158] This invention uses the least squares method as the core algorithm for parameter identification. During the parameter identification process, active power (P), reactive power (Q), and current (I) are used as the data sets. During steady-state simulation, the waveforms of active power (P) and reactive power (Q) are straight lines, while the waveform of current (I) is sinusoidal. Therefore, when using different data sets, corresponding algorithms must be designed for optimization.

[0159] When active power P and reactive power Q are used for optimization, the algorithm used is as follows:

[0160]

[0161] Where N is the number of collected data, REF(k) is the kth sample data set, and RES(k) is the kth response data set.

[0162] Expand the above formula to get the following formula:

[0163] A=((REF(0)-RES(0)) 2 +......+(REF(N)-RES(N)) 2 ) / (N-1)

[0164] When the current I is used for optimization, the above algorithm is improved to obtain the following formula:

[0165]

[0166] Right now:

[0167] Where,

[0168] V(k)=REF(k)-RES(k)

[0169]

[0170] In the above two results, the value of A determines that the deviation between the two straight lines is the smallest, and the value of B determines that the curve deviation is small, while at the same time satisfying the consistency of the curve change trend.

[0171] The present invention uses the least square method as the core algorithm for parameter identification. In the parameter identification process, active power P, reactive power Q and current I are used as data sets. By calculating the variance of each point, the optimal value is selected and the range is gradually narrowed until the optimal parameters are obtained. The effects of the present invention are shown in the attached figure. Figure 6-Figure 9 As shown, the load model parameter identification method and system for electromagnetic transient simulation proposed in the present invention can efficiently obtain the accurate parameters of the active load equivalent model, improve the automation level of model research, and enhance the research efficiency of the load characteristics of the new power system, and can provide strong support for the simulation analysis of the safety and stability of the power grid under the new power system.

[0172] The present invention has performed parameter identification on the resistor R, series circuit RL, series circuit RLC, π-type circuit and motor load, and verified the effectiveness of the proposed algorithm. Taking the parameter identification of the series circuit of resistor R and inductor L in the equivalent model as an example, the model structure of the series circuit of resistor R and inductor L is shown in the attached figure. Figure 5 shown.

[0173] In the original model of the present invention, the value of resistor R1 is 1010Ω and the value of L1 is 0.05H. The final identified value of R2 in the equivalent model is 1010.04Ω and the value of L2 is 0.0489844H. The various sets of parameters obtained during the optimization process are shown in Table 4.

[0174] The present invention first sets the variance limit to 0.001. Randomly scattering points, the current I variance corresponding to each point is calculated, and the first set of optimal solutions (parameters in the first group in the table) is obtained: R2 = 1001.25, L2 = 0.1525, variance = 0.0913252. Using 0.0913252 as the benchmark value, the next step of optimization is carried out.

[0175] Based on the step size (R2`=23.44, L2`=0.015625), the next set of parameters (the second set of parameters in the table) is obtained: R2=1024.69, L2=0.136875, and variance=0.0829104. The variance at this point is smaller than the benchmark value, so 0.0829104 is used as the new benchmark value for the next step of optimization.

[0176] Repeat step 2 and obtain new parameters at the step size (R2`=23.44, L2`=0.015625) until the parameters (the fifth group of parameters in the table) R2=1001.25, L2=0.09, and variance=0.0419404 are obtained. At this time, there is no better solution at this step size, so reduce the step size to 1 / 2 of the previous step size (R2`=11.72, L2`=0.0078125) and repeat step 2 with this step size;

[0177] Repeat step 3, and finally reduce the step size to R2`=23.44, L2`=0.015625, and obtain the parameters (the last set of parameters) R2=1010.04, L2=0.0489844, and variance=0.000873323. At this time, the variance 0.000873323 is less than the limit value 0.001, which meets the conditions and ends the optimization.

[0178] Table 4 Current variance corresponding to resistance and inductance

[0179]

[0180]

[0181] The current variance scatter points corresponding to resistor R2 are shown in the attached figure. Figure 5 shown.

[0182] The current variance scatter points corresponding to inductor L2 are shown in the attached figure. Figure 7 shown.

[0183] The current variance histogram corresponding to resistor R2 and inductor L2 is shown in the attached figure. Figure 8 shown.

[0184] Fill the values ​​of R2 and L2 found into the model and check the current waveform, as shown in the attached figure. Figure 9 shown.

[0185] From the above data, we can see that the closer the value of R2 is to R1, and the closer the value of L2 is to L1, the smaller the variance value is, and the final result is close to the target value.

[0186] It is not difficult to see from the above parameter identification calculation results that the load model parameter identification method and system for electromagnetic transient simulation of the present invention can efficiently obtain accurate parameters of the active load equivalent model.

[0187] Figure 10 2 is a structural diagram of a load model parameter identification system for electromagnetic transient simulation according to a preferred embodiment of the present invention.

[0188] from Figure 10 As shown, the present invention provides a load model parameter identification system for electromagnetic transient simulation, the system comprising:

[0189] The initialization unit 11 is used to configure the component parameters, determine the variance of the judgment data set when optimizing the parameter values ​​of the component parameters, and the data set variance limit;

[0190] A calculation unit 12 is used to calculate the variance of the judgment data set of each data value of the component parameter by randomly scattering points, and use the calculated minimum judgment data set variance as the variance reference value;

[0191] The result unit 13 is used to perform optimal calculation of the parameter value based on the preset step size and variance reference value of the component parameter. When the variance of the judgment data set of the calculated parameter value is less than the data set variance limit, the parameter value is determined to be the optimal solution of the component parameter.

[0192] Preferably, wherein:

[0193] The calculation unit is further configured to use the parameter values ​​of the configured component parameters as coordinate points in a two-dimensional space; randomly scatter points in the two-dimensional space to calculate the variance of the judgment data set for each coordinate point in the two-dimensional space; and use the minimum value of the calculated variances of the judgment data set as a variance reference value;

[0194] The result unit is further used to adjust the parameter value of the component parameter based on the variance reference value and the parameter preset step size of the configuration component parameter, and calculate the variance of the judgment data set of each coordinate point in the two-dimensional space until the preset data set variance limit is met, and the parameter value that reaches the preset data set variance limit is used as the optimal parameter value.

[0195] Preferably, the result unit is further configured to:

[0196] When searching for the optimal solution of component parameters, set the maximum number of optimization calculations;

[0197] When the number of optimization calculations reaches the maximum number, the optimization calculation is stopped.

[0198] Preferably, the initialization unit is used to configure the component parameters, including determining the variance of the judgment data set, wherein the variance of the judgment data set includes: current I variance, active power P variance and reactive power Q variance;

[0199] The data set variance limits include: a current I variance upper limit value, an active power P variance upper limit value, and a reactive power Q variance upper limit value.

[0200] Preferably, the initialization unit is used to configure component parameters, including: component type, component name, parameter name, parameter type, parameter modification position, parameter initial value, parameter value upper limit and step size.

[0201] Preferably, the calculation unit is used to use the configured component parameters as coordinate points in a two-dimensional space, and is further used to:

[0202] When identifying and optimizing a single parameter, the parameter value of the single parameter is used as a direct point;

[0203] When multiple parameters are identified and optimized, the parameter values ​​of the multiple parameters are combined in pairs, and the coordinate points of the combined pairs are used as points in the regular quadrilateral.

[0204] Preferably, the result unit is used to adjust the parameter value of the component parameter based on the variance reference value and the parameter preset step size of the configuration component parameter, and calculate the variance of the judgment data set of each coordinate point in the two-dimensional space, and is also used to:

[0205] When the component parameter is a single parameter, the parameter values ​​of the two points before and after the line are obtained based on the preset parameter step size, and the variance of the data set of the parameter values ​​of the two points before and after is calculated;

[0206] When the component parameter is two parameters, it is expanded to the four sides based on the preset parameter step size to calculate the data set variance of the four vertex parameter values.

[0207] Preferably, the result unit is used to adjust the parameter value of the component parameter based on the variance reference value and the parameter preset step size of the configuration component parameter, and calculate the variance of the judgment data set of each coordinate point in the two-dimensional space. When the variance of the judgment data set is the active power P variance and the reactive power Q variance, the algorithm used is:

[0208]

[0209] Where N is the number of variances of the collected judgment data sets, REF(k) is the variance of the k-th judgment data set, RES(k) is the k-th corresponding data set, and A is the variance of active power (P) or reactive power (Q);

[0210] Expand the above formula to get the following formula:

[0211] A=((REF(0)-RES(0)) 2 +......+(REF(N)-RES(N)) 2 ) / (N-1)

[0212] When the variance of the data set is determined to be the variance of the current I, the above algorithm is improved to obtain the following formula:

[0213]

[0214] Right now:

[0215] Where,

[0216] V(k)=REF(k)-RES(k)

[0217]

[0218] Where V(k) is the difference between the variance of the K-th current I and the response data. is the average value of the difference between N current I variances and the response data, and B is the current I variance.

[0219] Preferably, the result unit is further configured to reduce the parameter step size and recalculate the optimal parameter value when the optimal solution for the component parameter cannot be obtained. The present invention provides a computer-readable storage medium storing a computer program for executing a load model parameter identification method for electromagnetic transient simulation.

[0220] The present invention provides an electronic device, which includes: a processor and a memory; wherein,

[0221] Memory for storing processor-executable instructions;

[0222] The processor is used for reading executable instructions from a memory and executing the instructions to implement a load model parameter identification method for electromagnetic transient simulation.

[0223] It will be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention may be implemented in various computer languages, for example, the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0224] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0225] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0226] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0227] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0228] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

[0229] The invention has been described above with reference to a few embodiments. However, it is readily apparent to a person skilled in the art that other embodiments than the ones disclosed above are equally within the scope of the invention, as defined by the appended patent claims.

[0230] Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the technical field, unless explicitly defined otherwise herein. All references to "a / / the [means, component, etc.]" are to be interpreted openly as referring to at least one instance of the means, component, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not necessarily need to be performed in the exact order disclosed, unless explicitly stated otherwise.

Claims

1. A method for identifying load model parameters for electromagnetic transient simulation, the method comprising: Configure component parameters and determine the variance limit of the data set; The variance of the judgment data set of each data value of the component parameter is calculated by randomly scattering points, and the calculated minimum variance of the judgment data set is used as the variance reference value; Based on the preset step size and variance reference value of the component parameter, the parameter value is optimized and calculated. When the variance of the judgment data set of the calculated parameter value is less than the data set variance limit, the parameter value is determined to be the optimal solution of the component parameter; The method further comprises: The parameter values ​​of the configured component parameters are used as coordinate points in the two-dimensional space; Randomly scatter points in the two-dimensional space, and calculate the variance of the judgment data set of each coordinate point in the two-dimensional space; and use the minimum value of the calculated variance of the judgment data set as the variance reference value; Based on the variance reference value and the preset parameter step size of the configuration component parameter, the parameter value of the component parameter is adjusted, and the variance of the judgment data set of each coordinate point in the two-dimensional space is calculated until the preset data set variance limit is met, and the parameter value that meets the preset data set variance limit is taken as the optimal parameter value; The method of using the configured component parameters as coordinate points in the two-dimensional space further includes: When identifying and optimizing a single parameter, the parameter value of the single parameter is used as a direct point; When multiple parameters are identified and optimized, the parameter values ​​of the multiple parameters are combined in pairs, and the coordinate points of the combined pairs are used as points in the regular quadrilateral; Based on the variance reference value and the preset parameter step size of the configuration component parameters, the parameter values ​​of the component parameters are adjusted, and the variance of the judgment data set of each coordinate point in the two-dimensional space is calculated. When the variance of the judgment data set is the active power P variance and the reactive power Q variance, the algorithm used is: Where N is the number of variances of the collected judgment data sets, REF(k) is the kth judgment data set, RES(k) is the kth response data, and A is the variance of active power (P) or reactive power (Q); Expand the above formula to get the following formula: A=((REF(0)-RES(0)) 2 +......+(REF(N)-RES(N)) 2 ) / (N-1) When the variance of the data set is determined to be the variance of the current I, the above algorithm is improved to obtain the following formula: Right now: Where, V(k)=REF(k)-RES(k) Where V(k) is the difference between the Kth current I and the response data. is the average value of the difference between N currents I and the response data, and B is the variance of the current I.

2. The method according to claim 1, further comprising: When searching for the optimal solution of component parameters, set the maximum number of optimization calculations; When the number of optimization calculations reaches the maximum number, the optimization calculation is stopped.

3. The method according to claim 1, wherein configuring the component parameters comprises determining a variance of a judgment data set, wherein the variance of the judgment data set comprises: Current I variance, active power P variance, and reactive power Q variance; The data set variance limits include: a current I variance upper limit value, an active power P variance upper limit value, and a reactive power Q variance upper limit value.

4. The method according to claim 1, wherein configuring component parameters comprises: Component type, component name, parameter name, parameter type, parameter modification position, parameter initial value, parameter value upper limit and step size.

5. The method according to claim 1, wherein adjusting the parameter value of the component parameter based on the variance reference value and the parameter preset step size of the configuration component parameter, and calculating the variance of the judgment data set of each coordinate point in the two-dimensional space, comprises: When the component parameter is a single parameter, the parameter values ​​of the two points before and after the line are obtained based on the preset parameter step size, and the variance of the data set of the parameter values ​​of the two points before and after is calculated; When the component parameter is two parameters, it is expanded to the four sides based on the preset parameter step size to calculate the data set variance of the four vertex parameter values.

6. The method according to claim 1, when the optimal solution of the component parameters cannot be obtained, the step size of the parameters is reduced and the optimal calculation of the parameter values ​​is performed again.

7. A load model parameter identification system for electromagnetic transient simulation, the system comprising: Initialization unit, used to configure component parameters and determine the variance limit of the data set; a calculation unit, configured to calculate the variance of the judgment data set of each data value of the component parameter by randomly scattering points, and use the calculated minimum variance of the judgment data set as a variance reference value; The calculation unit is further configured to use the parameter values ​​of the configured component parameters as coordinate points in a two-dimensional space; randomly scatter points in the two-dimensional space to calculate the variance of the judgment data set for each coordinate point in the two-dimensional space; and use the minimum value of the calculated variances of the judgment data set as a variance reference value; The calculation unit is used to use the configured component parameters as coordinate points in the two-dimensional space, and is also used to: When identifying and optimizing a single parameter, the parameter value of the single parameter is used as a direct point; When multiple parameters are identified and optimized, the parameter values ​​of the multiple parameters are combined in pairs, and the coordinate points of the combined pairs are used as points in the regular quadrilateral; A result unit is used to perform an optimization calculation of the parameter value based on a preset step size and a variance reference value of the component parameter, and when the variance of the judgment data set of the calculated parameter value is less than the data set variance limit, determine that the parameter value is the optimal solution of the component parameter; The result unit is further configured to adjust the parameter value of the component parameter based on the variance reference value and the preset parameter step size of the configuration component parameter, and calculate the variance of the judgment data set of each coordinate point in the two-dimensional space until a preset data set variance limit is met, and the parameter value that meets the preset data set variance limit is used as the optimal parameter value; The result unit is used to adjust the parameter value of the component parameter based on the variance reference value and the parameter preset step size of the configuration component parameter, and calculate the variance of the judgment data set of each coordinate point in the two-dimensional space. When the variance of the judgment data set is the active power P variance and the reactive power Q variance, the algorithm used is: Where N is the number of variances of the collected judgment data sets, REF(k) is the kth judgment data set, RES(k) is the kth response data, and A is the variance of active power (P) or reactive power (Q); Expand the above formula to get the following formula: A=((REF(0)-RES(0)) 2 +......+(REF(N)-RES(N)) 2 ) / (N-1) When the variance of the data set is determined to be the variance of the current I, the above algorithm is improved to obtain the following formula: Right now: Where, V(k)=REF(k)-RES(k) Where V(k) is the difference between the Kth current I and the response data. is the average value of the difference between N currents I and the response data, and B is the variance of the current I.

8. The system according to claim 7, wherein the result unit is further configured to: When searching for the optimal solution of component parameters, set the maximum number of optimization calculations; When the number of optimization calculations reaches the maximum number, the optimization calculation is stopped.

9. The system according to claim 7, wherein the initialization unit is configured to configure the component parameters, including determining the variance of the judgment data set, wherein the variance of the judgment data set includes: Current I variance, active power P variance, and reactive power Q variance; The data set variance limits include: a current I variance upper limit value, an active power P variance upper limit value, and a reactive power Q variance upper limit value.

10. The system according to claim 7, wherein the initialization unit is configured to configure component parameters, comprising: Component type, component name, parameter name, parameter type, parameter modification position, parameter initial value, parameter value upper limit and step size.

11. The system according to claim 7, wherein the result unit is configured to adjust the parameter value of the component parameter based on the variance reference value and the parameter preset step size of the configuration component parameter, and calculate the variance of the judgment data set of each coordinate point in the two-dimensional space, and is further configured to: When the component parameter is a single parameter, the parameter values ​​of the two points before and after the line are obtained based on the preset parameter step size, and the variance of the data set of the parameter values ​​of the two points before and after is calculated; When the component parameter is two parameters, it is expanded to the four sides based on the preset parameter step size to calculate the data set variance of the four vertex parameter values.

12. The system according to claim 7, wherein the result unit is further configured to reduce the step size of the parameters and re-calculate the optimal parameter value when the optimal solution of the component parameters cannot be obtained.

13. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and the computer program is used to execute the method according to any one of claims 1 to 6.

14. An electronic device, characterized in that: The electronic device includes: a processor and a memory; wherein, The memory is a memory for storing instructions executable by the processor; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the method according to any one of claims 1 to 6.

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