Source-load comprehensive equivalent modeling method and device considering new energy power generation

Abstract

The application provides a source-load comprehensive equivalent modeling method and device considering new energy power generation. The method comprises the following steps: acquiring current operation condition data of a power system; inputting the operation condition data into a parameter mapping model to obtain the numerical value of equivalent modeling parameters corresponding to the power system, the parameter mapping model being used to represent the numerical mapping relationship between the operation condition of the power system and the equivalent modeling parameters; configuring the corresponding parameters in a source-load comprehensive model corresponding to the power system according to the numerical value of the equivalent modeling parameters to obtain a source-load comprehensive equivalent model of the power system under the current operation condition, the source-load comprehensive model comprising a constant impedance load model, a motor load model and a new energy power generation model, and the power of constant current loads and constant power loads in the power system being equivalent to be converted into the power parameters of the motor load model. The application can solve the problem that the existing power system equivalent modeling error is large and the actual dynamic characteristics of the power system cannot be accurately represented.

Description

Technical Field

[0001] This application relates to the field of power system equivalent modeling, and in particular to a source-load integrated equivalent modeling method and apparatus that considers new energy power generation. Background Technology

[0002] With the advancement of dual-carbon goals, the penetration rate of new energy sources in power systems is increasing daily. New energy sources are being extensively integrated into both the sending and receiving ends of the power grid. This shift has significantly altered the power flow direction, voltage distribution, and short-circuit capacity of the grid, posing new challenges to the safe and stable operation of the power system. To assess the impact of new energy sources on the dynamic characteristics of the power system, accurate simulation analysis is required. However, modern power grids have numerous nodes and complex topologies. Detailed modeling of every line, transformer, and power source within the grid in system-level simulations would result in an extremely large system scale, a sharp increase in computational dimensionality, severely impacting simulation efficiency and even causing the simulation to fail to converge. Therefore, comprehensive source-load equivalent modeling of power systems incorporating new energy sources is of great significance for analyzing the interaction characteristics between the power system and external networks.

[0003] Current equivalent modeling methods are mainly divided into static equivalent modeling and electromechanical transient equivalent modeling. Static equivalent modeling primarily focuses on the aggregation of traditional loads or simply superimposing the capacity of power sources, failing to accurately characterize the actual dynamic characteristics of the power system. While electromechanical transient equivalent modeling considers the electromechanical dynamic characteristics of generators, it struggles to accurately reflect the rapid response characteristics of renewable energy sources during faults and the impact of control strategies on power system stability. Furthermore, although some studies have begun to extend these two methods towards dynamic equivalent modeling, most rely on offline identification to obtain equivalent modeling parameters. These parameters are typically tuned for specific operating conditions. When the power system's operating conditions change or renewable energy output fluctuates significantly, the fixed offline equivalent modeling parameters cannot accurately characterize the actual dynamic characteristics of the power system, leading to substantial errors in the simulation results. Summary of the Invention

[0004] This application provides a source-load integrated equivalent modeling method and apparatus that considers new energy power generation, in order to solve the defects of existing power system equivalent modeling, which has large errors and cannot accurately represent the actual dynamic characteristics of the power system.

[0005] The first aspect of this application provides a source-load integrated equivalent modeling method considering new energy power generation, including: Obtain current operating status data of the power system; The operating condition data is input into the parameter mapping model to obtain the numerical values ​​of the equivalent modeling parameters corresponding to the power system. The parameter mapping model is used to characterize the numerical mapping relationship between the operating conditions of the power system and the equivalent modeling parameters. Based on the values ​​of the equivalent modeling parameters, the corresponding parameters in the source-load integrated model of the power system are configured to obtain the source-load integrated equivalent model of the power system under the current operating conditions. The source-load integrated model includes a constant impedance load model, a motor load model, and a new energy power generation model. The power of the constant current load and constant power load in the power system is equivalently converted into the power parameters of the motor load model.

[0006] The second aspect of this application provides a source-load integrated equivalent modeling device considering new energy power generation, comprising: The acquisition module is used to acquire current operating condition data of the power system; The input module is used to input the operating condition data into the parameter mapping model to obtain the values ​​of the equivalent modeling parameters corresponding to the power system. The parameter mapping model is used to characterize the numerical mapping relationship between the operating conditions of the power system and the equivalent modeling parameters. The configuration module is used to configure the corresponding parameters in the source-load integrated model of the power system according to the values ​​of the equivalent modeling parameters, so as to obtain the source-load integrated equivalent model of the power system under the current operating conditions. The source-load integrated model includes a constant impedance load model, a motor load model and a new energy power generation model. The power of the constant current load and constant power load in the power system is equivalently converted into the power parameters of the motor load model.

[0007] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a source-load integrated equivalent modeling method considering new energy power generation as described in the first aspect above.

[0008] The fourth aspect of this application provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a source-load integrated equivalent modeling method considering new energy power generation as described in the first aspect above.

[0009] The fifth aspect of this application provides a computer program product, including a computer program that, when executed by a processor, implements a source-load integrated equivalent modeling method considering new energy power generation as described in the first aspect above.

[0010] The source-load integrated equivalent modeling method and apparatus considering new energy power generation provided in this application have at least the following effects: First, by constructing a source-load integrated model that includes a constant impedance load model, a motor load model, and a new energy power generation model, and by incorporating the power equivalents of constant current loads and constant power loads into the power parameters of the motor load model, comprehensive equivalent modeling of power sources and loads in the power system can be achieved, solving the problem that existing static equivalent modeling cannot accurately characterize the actual dynamic characteristics of the power system.

[0011] Second, by introducing a new energy power generation model into the source-load integrated model, explicit modeling of the characteristics of new energy power generation can be achieved, solving the problem that existing electromechanical transient equivalent modeling cannot accurately reflect the rapid response characteristics of new energy during faults and the impact of control strategies on the stability of the power system.

[0012] Third, by inputting operating condition data into the parameter mapping model to obtain the values ​​of equivalent modeling parameters under the current operating conditions, the equivalent modeling parameters can be adaptively updated as the operating conditions change, solving the problem that fixed offline equivalent modeling parameters cannot accurately represent the actual dynamic characteristics of the power system when obtaining equivalent modeling parameters through offline identification. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a flowchart illustrating a source-load integrated equivalent modeling method for considering new energy power generation, as shown in an embodiment of this application.

[0015] Figure 2 This is a schematic diagram of the fault response curve of the source-load integrated model under multiple operating conditions shown in the embodiments of this application.

[0016] Figure 3 This is a schematic diagram illustrating the selection of response functions in an embodiment of this application.

[0017] Figure 4 This is a schematic diagram of the fault response curves of a detailed simulation model of a power system under multiple operating conditions, as shown in an embodiment of this application.

[0018] Figure 5 This is a schematic diagram illustrating the mapping relationship between operating conditions and equivalent modeling parameters in an embodiment of this application.

[0019] Figure 6This is a schematic diagram illustrating the equivalent simulation results of the source-load integrated model based on a given working condition, as shown in the embodiments of this application.

[0020] Figure 7 This is a schematic diagram showing a comparison of simulation results between a detailed simulation model and a source-load integrated model under random operating conditions, as illustrated in an embodiment of this application.

[0021] Figure 8 This is a structural block diagram of a source-load integrated equivalent modeling device considering new energy power generation, as shown in an embodiment of this application.

[0022] Figure 9 This is a schematic diagram of the physical structure of an electronic device shown in an embodiment of this application, such as... Figure 9 As shown. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] Currently, a common approach to equivalent modeling is to perform offline equivalent aggregation using typical daily load curves or constant power output models. While these methods are computationally simple, they essentially use discrete, typical scenarios to approximate the actual continuously changing operating conditions, making it difficult to accurately describe the dynamic interaction characteristics of the power system when there are significant fluctuations in renewable energy output or abrupt changes in load characteristics. On the other hand, some studies have proposed clustering algorithms based on sensitivity analysis or trajectory similarity to group nodes within the power system for equivalent modeling. However, these clustering methods are mostly based on electromechanical transient models or steady-state power flow models, neglecting electromagnetic transient processes such as line impedance and transformer excitation branches within the power system. This results in insufficient accuracy when analyzing rapid dynamic processes such as voltage sags and high-frequency resonances.

[0025] To address the aforementioned issues, this application provides a source-load integrated equivalent modeling method based on a detailed electromagnetic transient model. This method enables accurate acquisition and adaptive updating of equivalent modeling parameters, and has significant engineering value for improving the simulation accuracy of power systems containing new energy sources.

[0026] Figure 1 This is a flowchart illustrating a source-load integrated equivalent modeling method for considering new energy power generation, as shown in an embodiment of this application. Figure 1 As shown, the source-load integrated equivalent modeling method for new energy power generation in this application may include: Step S101: Obtain the current operating condition data of the power system.

[0027] In this embodiment, the power system can be a distribution substation, distribution network, regional power grid, etc., and its specific type can be set according to actual needs.

[0028] Step S102: Input the operating condition data into the parameter mapping model to obtain the numerical values ​​of the equivalent modeling parameters corresponding to the power system. The parameter mapping model is used to characterize the numerical mapping relationship between the operating conditions of the power system and the equivalent modeling parameters.

[0029] In this embodiment, the parameter mapping model is pre-trained based on sample values ​​of multiple typical operating conditions of the power system and their corresponding equivalent modeling parameters. It contains the numerical mapping relationship between different operating conditions of the power system and different equivalent modeling parameters. Therefore, by inputting the operating condition data into the parameter mapping model, the numerical values ​​of the corresponding equivalent modeling parameters of the power system can be obtained.

[0030] Step S103: Based on the values ​​of the equivalent modeling parameters, configure the corresponding parameters in the source-load integrated model of the power system to obtain the source-load integrated equivalent model of the power system under the current operating conditions. The source-load integrated model includes a constant impedance load model, a motor load model, and a new energy power generation model. The power of constant current loads and constant power loads in the power system is equivalently converted into the power parameters of the motor load model.

[0031] In this embodiment, source-load integration refers to the unified aggregation of the power supply side (i.e., new energy power generation) and the load side (i.e., various types of electrical loads) in the power system.

[0032] In one embodiment, the new energy power generation model includes at least one of the following: a wind power new energy power generation model and a photovoltaic new energy power generation model. For ease of explanation, subsequent embodiments will be described using the scenario where the new energy power generation model includes both a wind power new energy power generation model and a photovoltaic new energy power generation model as examples, but this does not constitute a limitation on the scope of protection of this application.

[0033] In this embodiment, a constant impedance load refers to a load whose power is proportional to the square of the terminal voltage, a constant current load refers to a load whose power is proportional to the terminal voltage, and a constant power load refers to a load whose power does not change with the terminal voltage. The three are collectively referred to as the ZIP load model, which will be described in detail later.

[0034] In this embodiment, the source-load integrated model corresponding to the power system includes multiple parameters, including equivalent modeling parameters. Therefore, the equivalent modeling parameters in the source-load integrated model can be assigned values ​​based on the values ​​of the equivalent modeling parameters obtained in step S102, so that the parameters of the source-load integrated model match the current operating conditions, and the assigned source-load integrated model is used as the source-load integrated equivalent model of the power system under the current operating conditions.

[0035] In this embodiment, the current operating condition data of the power system is first obtained; then, the operating condition data is input into the parameter mapping model to obtain the values ​​of the equivalent modeling parameters corresponding to the power system; finally, the corresponding parameters in the source-load integrated model of the power system are configured according to the values ​​of the equivalent modeling parameters to obtain the source-load integrated equivalent model of the power system under the current operating condition. This method can achieve the following effects: First, by constructing a source-load integrated model that includes a constant impedance load model, a motor load model, and a new energy power generation model, and by incorporating the power equivalents of constant current loads and constant power loads into the power parameters of the motor load model, comprehensive equivalent modeling of power sources and loads in the power system can be achieved, solving the problem that existing static equivalent modeling cannot accurately characterize the actual dynamic characteristics of the power system.

[0036] Second, by introducing a new energy power generation model into the source-load integrated model, explicit modeling of the characteristics of new energy power generation can be achieved, solving the problem that existing electromechanical transient equivalent modeling cannot accurately reflect the rapid response characteristics of new energy during faults and the impact of control strategies on the stability of the power system.

[0037] Third, by inputting operating condition data into the parameter mapping model to obtain the values ​​of equivalent modeling parameters under the current operating conditions, the equivalent modeling parameters can be adaptively updated as the operating conditions change, solving the problem that fixed offline equivalent modeling parameters cannot accurately represent the actual dynamic characteristics of the power system when obtaining equivalent modeling parameters through offline identification.

[0038] The structure of each component model in the source-load integrated model is explained in detail below.

[0039] First, the constant impedance load model and its selection criteria in the source-load integrated model are explained.

[0040] In power systems, the relationship between load power, terminal voltage, and frequency under steady-state conditions is called the static load model. It is mainly used for power system analysis and calculations, such as power flow calculations, reactive power compensation device planning, voltage stability, frequency stability, and long-term dynamic process analysis. Its general form is as follows: (1); (2); Where P is the active power; Q is the reactive power; U is the voltage of the load bus; and f is the load frequency. Exponential load models and ZIP load models are widely used in static load models. The specific format of the ZIP load model is as follows: (3); (4); in, For frequency deviation, and Here, represents the coefficients of the frequency response. Under typical fault conditions, the system frequency changes very little. Therefore, when frequency changes are ignored, the response function of the ZIP load model can be written as: (5); (6); in, and It is a constant impedance load (Z-load) model; and It is a constant current load (I-load) model; and This is a constant power load (P-load) model. This model includes constant impedance, constant current, and constant power, hence it is called the ZIP load model. P and Q represent the active power and reactive power at rated voltage, respectively. , , , These represent the proportions of constant impedance, constant current, and constant power in the ZIP model, respectively.

[0041] In the source-load integrated model of this application, only a constant impedance load model is used for load equivalence, rather than a complete ZIP load model. This is because the source-load integrated model needs to address transient operating conditions and various fault conditions, while the traditional ZIP load model is for load equivalence under stable power system operating conditions. When facing fault conditions with significant voltage drops, constant current and constant power loads will no longer operate within their constant current and constant power ranges. Using constant current and constant power loads for equivalence at this time would result in significant errors. Therefore, the source-load integrated model of this application retains the constant impedance load model, which can still be accurately equivalenced during fault periods, and models constant current and constant power loads as motors of the same power, effectively incorporating them into the motor load model.

[0042] The following explains the motor load model in the source-load integrated model.

[0043] To enable the source-load integrated model to characterize the dynamic characteristics of the load, the dynamic part of the motor load model in this application adopts an electromagnetic transient induction motor model, the mathematical model of which is as follows: (7); In the formula: This is a transient potential; This refers to the d-axis stator current. This refers to the q-axis stator current. Slippage; This is the resistance torque; The drag torque coefficient is related to the rotational speed; For mechanical load torque; For rotor transient reactance; For stator reactance; For rotor reactance; A represents the mutual inductance between the stator and rotor; B and C are the mechanical torque coefficients.

[0044] In the source-load integrated model of this application, the power of constant current loads and constant power loads is equivalently converted into the power parameters of the motor load model. Through this processing method, the motor load model can uniformly represent the load components in the power system other than constant impedance loads in the form of a dynamic model, thereby enabling the source-load integrated model to maintain high equivalent accuracy under transient operating conditions and fault conditions.

[0045] The following describes the new energy power generation model in the source-load integrated model. The new energy power generation model in this application includes a wind power new energy power generation model and a photovoltaic new energy power generation model.

[0046] In this application, the wind power generation model adopts a direct-drive wind turbine power station model, and the photovoltaic power generation model adopts a photovoltaic power station model. Both the direct-drive wind turbine power station model and the photovoltaic power station model use the same inverter structure at the grid connection point. An inverter is a component that converts DC voltage to AC voltage. The inverter converts the DC voltage and current from the direct-drive wind turbine and photovoltaic cells, processed by uncontrolled rectification and a boost chopper circuit, into AC voltage and current at the grid connection point, outputting power to the AC grid. The grid-connected inverter control shared by both models will be explained below.

[0047] (1) Grid-connected inverter control: A grid-connected inverter consists of a controllable inverter bridge composed of insulated-gate bipolar transistors (IGBTs). The main function of the inverter is to maintain the stability of the DC-side capacitor voltage, transmit the active power output from the generator-side inverter to the grid, and output a given reactive power value based on a reactive power reference. Currently, grid-side voltage-oriented vector control technology is generally used for inverters, aligning the d-axis of the synchronous rotating coordinate system with the grid voltage vector. In this case, the projection of the grid voltage onto the q-axis is 0, i.e.: (8); in, This represents the d-axis component of the grid voltage; This represents the q-axis component of the grid voltage. This represents the combined voltage vector on the grid side. At this point, the active and reactive power exchanged between the inverter and the grid can be expressed as: (9); in, This refers to the active power exchanged between the grid-side frequency converter and the power grid. This refers to the reactive power exchanged between the grid-side frequency converter and the power grid. This refers to the d-axis current on the grid side. This represents the q-axis current on the grid side.

[0048] As can be seen from equation (9), the output of active power and reactive power can be independently controlled by controlling the projection of the grid-side current vector on the d-axis and q-axis. Therefore, the d-axis current can also be called active current, and the q-axis current can also be called reactive current.

[0049] During normal operation, the inverter determines the reference value of the d-axis current by controlling the DC-side capacitor voltage to maintain a stable DC-side capacitor voltage. In addition, to achieve unity power factor operation of wind turbines, the reactive power reference value is usually set to 0, at which point the reference value of the q-axis current is... It is also 0. According to the requirements of relevant grid connection standards, grid-connected inverters need to have a certain reactive power regulation capability. Therefore, the reactive power output of grid-connected inverters is generally around 5% of the active power output.

[0050] The mathematical model of the inverter in the dq rotating coordinate system is as follows: (10); in, The d-axis component of the inverter output; This refers to the q-axis component of the inverter output. For grid-side resistance; For grid-side reactance; The electrical angular velocity is the synchronous velocity of the power grid.

[0051] As can be seen from equation (10), after performing closed-loop PI control on the d-axis and q-axis currents, a cross-coupling term needs to be added. and Only then can the final voltage control value be obtained. and It can be represented as: (11); (2) Direct-drive fan model: The direct-drive wind turbine model uses permanent magnets to replace the excitation windings in a synchronous motor, and can be regarded as a synchronous motor with a constant excitation current. Its stator voltage equation in synchronous rotating coordinates is: (12); in: This refers to the stator d-axis voltage; This refers to the stator q-axis voltage; The current is the d-axis current of the stator. This refers to the stator q-axis current; Stator resistance; The electric angular velocity of the generator; For stator d-axis flux linkage; For the stator q-axis flux linkage.

[0052] Using vector control technology based on rotor flux orientation, the d-axis of the rotating coordinate system is positioned with the flux direction of the rotor permanent magnet. Therefore, the flux equations for the stator side d-axis and q-axis are: (13); in, The generator stator d-axis inductance; For the q-axis inductance of the generator stator; For permanent magnet flux linkage. The output electromagnetic torque equation of the direct-drive fan model is: (14); in, The number of pole pairs for a direct-drive fan.

[0053] Substituting equation (13) into equation (14), we get: (15); Assumption ,but: (16); As can be seen from equation (16), the electromagnetic torque of the generator can be controlled by controlling the q-axis current on the stator side, thereby further controlling the generator rotor speed.

[0054] (3) Photovoltaic cell model: The current-voltage (IV) characteristics of a photovoltaic cell can be described by the following mathematical model: (17); in, Photocurrent; This is the reverse saturation current; ν is the voltage across the battery; n is the ideality factor of the diode; k is the Boltzmann constant; T is the temperature; q is the elementary charge of the electron. It is a series resistor.

[0055] In summary, the source-load integrated model of this application consists of three parallel parts: a constant impedance load model, a motor load model, and a new energy power generation model (including a direct-drive wind turbine model, a photovoltaic cell model, and their shared grid-connected inverter control). It can comprehensively characterize the load characteristics and new energy power generation characteristics in the power system.

[0056] Since the transient response process of the source-load integrated model in this application is jointly affected by the response characteristics of the above four components, the transient response process of each component under fault conditions is analyzed below.

[0057] (1) Transient response process of constant impedance load model: The constant impedance load model exhibits impedance characteristics in the face of fault conditions, has no inertia, and completely follows voltage impedance response logic. The response logic is simple, and its current-voltage relationship is as follows: (18); (2) Transient response process of the motor load model: When the terminal voltage U of an asynchronous motor suddenly drops, due to the motor's mechanical inertia, the rotational speed n will not change abruptly; therefore, the slip s remains constant instantaneously. This is due to the electromagnetic torque. A voltage drop will cause a sudden and significant decrease in electromagnetic torque. At this time, the load torque remains constant, but the electromagnetic torque will be less than the load torque, and the motor will begin to decelerate. This is due to the reactive power of the asynchronous motor's excitation. Therefore, the excitation reactive power will decrease rapidly at the moment of voltage drop. Since the rotational speed remains unchanged, the slip s remains unchanged, the equivalent impedance remains unchanged, and the current decreases with the voltage drop, the leakage reactive power will also decrease instantaneously.

[0058] After a voltage drop, the motor enters a dynamic adjustment process, where the changes in active and reactive power become clearly differentiated. Due to the load demanding mechanical torque, the motor's slip (s) will significantly increase to maintain output power. After a voltage drop, the active power P will remain essentially unchanged or decrease slightly, and the absorbed active power will gradually recover from the original level, decreasing proportionally to the square of the voltage drop. In the initial stage of a voltage drop, the reduction in excitation reactive power dominates, resulting in a decrease in total reactive power. However, when the voltage drops to the critical voltage, the surge in leakage reactive power caused by the increased slip (S) will exceed the decrease in excitation reactive power, and the total reactive power Q will rebound sharply as the voltage further decreases.

[0059] Once the power grid fault is cleared and the voltage returns to normal, the huge slip causes the electromagnetic torque to momentarily exceed the load torque, driving the rotor to accelerate rapidly, and the slip gradually decreases. Finally, as the speed returns to the rated value, the electromagnetic torque and the load torque are balanced again, and the motor smoothly returns to its normal steady-state operating state.

[0060] (3) Transient response process of the new energy power generation model: The transient response of the new energy power generation model during a fault mainly involves two aspects: active power control and reactive power control.

[0061] During the fault, the active power control strategy remains at constant DC-side voltage control. A sudden drop in grid-side voltage prevents the grid-side inverter from delivering active power normally, causing it to accumulate on the DC-side capacitor and raise its voltage. When the voltage reaches the set threshold, the parallel unloading circuit activates, consuming the excess active power and preventing the DC-side voltage from rising further. However, because the capacitor voltage remains higher than the reference value after the fault occurs, the grid-side inverter will continuously increase the active current.

[0062] Regarding reactive power, when the grid voltage drops due to an external fault, the wind turbine needs to generate reactive power to support the grid voltage. According to relevant grid connection standards, the formula for calculating the reactive current output by the wind turbine during low-voltage ride-through is: (19); in, This is a reference value for the reactive current output of the wind turbine. This refers to the voltage at which the power grid fails. This is the rated current. The reference value for low-voltage ride-through reactive current in the source-load integrated detailed model of this application is: (20); During a fault, it is crucial to prioritize the reactive power supply from wind turbines to the grid. Therefore, a reactive power priority control strategy is adopted. This means that, within the constraints of inverter capacity, reactive current control is prioritized before determining the active current reference value. Thus, the upper limit of the active current is: (twenty one); in, This is the upper limit of the active current. This is the upper limit of the converter current.

[0063] Under the above constraints, the active current reference value can be expressed as: (twenty two); in, This is a reference value for active current; The active current reference value is obtained by controlling the DC side voltage. When the fault is cleared and the voltage returns to normal, the active current recovers to the level before the low voltage ride-through. The source-load integrated model in this application does not provide additional control for the inverter ride-through recovery process.

[0064] The transient response process analysis above shows that each component in the source-load integrated model has different response mechanisms and characteristics under fault conditions, and the parameter values ​​of each component directly affect the overall fault response curve of the source-load integrated model. Therefore, it is necessary to establish a functional relationship between the parameters of the source-load integrated model and the fault response curve so that equivalent modeling parameters can be obtained by back-calculating the response curve. This will be explained in detail later.

[0065] In conjunction with the above embodiments, in one implementation, the parameter mapping model of this application is obtained through the following steps: Step A1: Obtain sample values ​​of the equivalent modeling parameters corresponding to multiple preset working conditions.

[0066] For example, multiple preset operating conditions include, but are not limited to: peak load condition with large renewable energy generation, off-peak load condition with large renewable energy generation, peak load condition with small renewable energy generation, and off-peak load condition with small renewable energy generation. Each preset operating condition can be characterized by the ratio of load and active power of renewable energy generation to its rated power.

[0067] In this embodiment, the operating condition data includes: the ratio of active power of constant impedance loads to their rated power, the ratio of active power of motor loads to their rated power, and the ratio of active power of renewable energy generation to their rated power. The ratio of active power of renewable energy generation to its rated power includes the ratio of active power of wind power renewable energy generation to its rated power and the ratio of active power of photovoltaic power renewable energy generation to its rated power. Since each type of load and renewable energy generation uses its own fixed power factor, the change in operating condition is only related to active power, and there is no need to introduce reactive power as an additional operating condition variable.

[0068] In one embodiment, the equivalent modeling parameters include at least one of the following: active power of the motor load model, reactive power of the motor load model, active power of the wind power new energy power generation model, active power of the photovoltaic new energy power generation model, rated capacity of the motor load model, rotor winding resistance of the motor load model, fault ride active control coefficient of the new energy power generation model, and fault ride reactive control coefficient of the new energy power generation model.

[0069] The fault ride-through active power control coefficient of the renewable energy generation model refers to the control coefficient used by the inverter in the renewable energy generation model to limit the active current during low voltage ride-through. This coefficient affects the magnitude of the active power output of the renewable energy generation model during the fault period. The fault ride-through reactive power control coefficient of the renewable energy generation model refers to the control coefficient used by the renewable energy generation model to determine the reactive current reference value based on the grid voltage drop depth during low voltage ride-through. This coefficient affects the magnitude of the reactive current output of the renewable energy generation model during the fault period.

[0070] For ease of explanation, in the following embodiments, the case in which the equivalent modeling parameters include the above 8 parameters will be used as an example for description, but this does not constitute a limitation on the scope of protection of this application.

[0071] Step A2: Using the operating condition data samples of each preset working condition as independent variables and the sample values ​​of the corresponding equivalent modeling parameters as dependent variables, perform function fitting to obtain the parameter mapping model.

[0072] Specifically, the operating condition data of each preset working condition is used as the independent variable, and the sample values ​​of the equivalent modeling parameters corresponding to each preset working condition obtained in step A1 are used as the dependent variable. Function fitting is performed to obtain the mapping function from the operating condition data to the equivalent modeling parameters, i.e., the parameter mapping model.

[0073] After the parameter mapping model is established, for any given operating condition data, the corresponding equivalent modeling parameter values ​​can be quickly calculated using this mapping function without having to re-execute the complete equivalent modeling parameter acquisition process.

[0074] In this embodiment, by establishing a parameter mapping model through steps A1 and A2, it is possible to quickly extrapolate the equivalent modeling parameters from a limited number of typical operating condition samples to any operating condition. This allows the equivalent modeling parameters to be adaptively updated as the operating conditions change, avoiding the problem of having to re-identify offline for each operating condition.

[0075] In conjunction with the above embodiments, in one implementation, step A1 includes: Step A11: Simultaneously perturb multiple candidate parameters in the source-load integrated model, and introduce preset faults to perform batch simulations to obtain multiple sets of power response curves.

[0076] In this embodiment, candidate parameters refer to parameters in the source-load integrated model that may affect the fault response characteristics, including but not limited to the rated capacity, active power, reactive power, rotor winding resistance, rotor winding leakage reactance, stator winding resistance, stator winding leakage reactance, and excitation reactance of the motor load model; the active power and reactive power of the wind power new energy power generation model; the active power and reactive power of the photovoltaic new energy power generation model; and the fault ride active power control coefficient and fault ride reactive power control coefficient of the new energy power generation model.

[0077] Simultaneous perturbation refers to changing the values ​​of multiple candidate parameters at the same time in each simulation, rather than changing a single parameter one by one.

[0078] Preset faults can be set according to actual needs, such as typical fault types like low voltage faults.

[0079] In this embodiment, a power response curve can be used to represent the fault response curve. By simultaneously perturbing multiple candidate parameters and performing batch simulations, the power response curves of the source-load integrated model under different numerical combinations of each candidate parameter can be obtained, thus yielding multiple sets of power response curves. Each combination of candidate parameters corresponds to one power response curve.

[0080] The power response curve includes the active power response curve and the reactive power response curve.

[0081] Step A12: Perform function fitting with multiple candidate parameters as independent variables and multiple sets of power response curves as dependent variables. Based on the fitting results, select the dominant parameter from the multiple candidate parameters whose influence on the power response curve is greater than the preset level.

[0082] In this embodiment, by performing function fitting on the multiple sets of power response curves obtained in step A11, the functional relationship between each candidate parameter and the power response curve can be obtained. Based on the magnitude of the coefficients corresponding to each candidate parameter obtained from the fitting, the degree of influence of each candidate parameter on the power response curve can be determined, and the candidate parameter with a greater degree of influence can be taken as the dominant parameter.

[0083] In this embodiment, the selected dominant parameters include: the active power of the motor load model, the reactive power of the motor load model, the active power of the wind power new energy power generation model, the active power of the photovoltaic new energy power generation model, the rated capacity of the motor load model, the rotor winding resistance of the motor load model, the fault ride active control coefficient of the new energy power generation model, and the fault ride reactive control coefficient of the new energy power generation model, totaling eight dominant parameters, which are the aforementioned eight equivalent modeling parameters.

[0084] Step A13: Construct the functional relationship between the dominant parameters and multiple sets of power response curves.

[0085] In this embodiment, based on the dominant parameters obtained in step A12, batch simulations are performed again on the source-load integrated model. Using the dominant parameters as independent variables and the power response curve as the dependent variable, a function fit is performed to construct the functional relationship between the dominant parameters and the power response curve. This functional relationship can describe the influence of changes in the dominant parameters on the fault response characteristics of the source-load integrated model.

[0086] Step A14: Run the detailed simulation model of the driving power system under various preset operating conditions and introduce preset faults to obtain the actual power response curves of the power system under various preset operating conditions.

[0087] In this embodiment, the detailed simulation model refers to a simulation model that models each component in the power system (including various loads, wind turbines, photovoltaic power plants, etc.) in detail. By driving the detailed simulation model under various preset operating conditions and introducing the same preset faults as in step A11, the actual power response curves of the power system under each preset operating condition can be obtained, including the actual active power response curve and the actual reactive power response curve.

[0088] Step A15: Using the actual power response curve as the reference value, substitute it into the functional relationship to perform reverse calculation and obtain the values ​​of the dominant parameters under each preset operating condition.

[0089] In this embodiment, the actual power response curve obtained in step A14 is used as the benchmark value. It is then substituted into the functional relationship between the dominant parameters and the power response curve constructed in step A13 for back-calculation and solution. This yields the values ​​of the dominant parameters that match the power response curve of the source-load integrated model with the actual power response curve of the detailed simulation model.

[0090] Step A16: Based on the values ​​of the dominant parameters under each preset working condition, determine the sample values ​​of the equivalent modeling parameters corresponding to each preset working condition.

[0091] In this embodiment, the values ​​of the dominant parameters obtained in step A15 under each preset working condition can be used as sample values ​​of the equivalent modeling parameters for the corresponding preset working condition. Since the equivalent modeling parameters are the same as the dominant parameters in this embodiment, the values ​​of the dominant parameters under each preset working condition are the sample values ​​of the equivalent modeling parameters corresponding to that preset working condition.

[0092] In this embodiment, the response inversion of parameter perturbation and detailed simulation model based on the source-load integrated model can be realized, and the sample values ​​of equivalent modeling parameters under each preset working condition can be accurately obtained, providing reliable training data for the construction of parameter mapping model.

[0093] In conjunction with the above embodiments, in one implementation, step A12 may include: Using multiple candidate parameters as independent variables and the data of feature points extracted from multiple power response curves as dependent variables, a second-order polynomial fitting algorithm is used for regression fitting.

[0094] Specifically, from the multiple power response curves obtained in step A11, data from multiple feature points that characterize the response properties are extracted and used as the dependent variable for fitting. Using multiple candidate parameters as independent variables, a second-order polynomial fitting algorithm is used to extend the features of the independent variables, generating extended features containing all linear terms, quadratic interaction terms, and square terms. The regression coefficients are then solved using the least squares method to obtain the second-order polynomial function relationship between each candidate parameter and each feature point.

[0095] For example, suppose the candidate parameters include multiple parameters such as the rated capacity, active power, rotor winding resistance, and stator winding leakage reactance of the motor load model. After normalizing the perturbation range of each candidate parameter, the regression coefficients corresponding to each candidate parameter can be obtained through second-order polynomial fitting. If the regression coefficient corresponding to the active power of the motor load model is significantly greater than the regression coefficient corresponding to the stator winding leakage reactance, it indicates that the active power of the motor load model has a greater influence on the power response curve and can be selected as the dominant parameter, while the stator winding leakage reactance has a smaller influence on the power response curve and is not considered a dominant parameter. In this way, the dominant parameters that have a major influence on the power response curve can be selected from all candidate parameters based on the magnitude of the regression coefficients corresponding to each candidate parameter.

[0096] In this embodiment, by using a second-order polynomial fitting algorithm to perform regression fitting on the feature points of the candidate parameters and the power response curve, a quantitative assessment of the influence of each candidate parameter and the selection of the dominant parameter can be achieved.

[0097] In conjunction with the above embodiments, in one implementation, step A15 may include: Step A151: Sample data from multiple feature points from each real power response curve.

[0098] For example, for the actual power response curves under each preset operating condition obtained in step A14, the same feature point acquisition method as in step A12 is used to extract data for multiple feature points from the actual active power response curve and the actual reactive power response curve, respectively. For instance, data for feature points in the steady-state stage, fault transient stage, fault steady-state stage, and fault recovery transient stage are extracted from the actual active power response curve, and data for feature points in the corresponding stages are extracted from the actual reactive power response curve, thereby obtaining data for multiple feature points that correspond one-to-one with the dependent variable of the functional relationship in step A13.

[0099] Step A152: Using the data of multiple feature points as benchmark values, substitute them into the functional relationship to obtain a system of multivariate equations with the dominant parameter as the unknown.

[0100] For example, the data from each feature point extracted in step A151 are used as the dependent variable values ​​of each functional relationship constructed in step A13, while the dominant parameters are treated as unknowns, thus transforming each functional relationship into a multivariate equation with the dominant parameters as unknowns. Since there are multiple feature points, a system of multivariate equations consisting of multiple equations can be obtained. For instance, if data from 8 feature points are extracted, a system of multivariate equations containing 8 equations can be obtained, with the 8 dominant parameters as the unknowns.

[0101] Step A153: Solve the system of multivariate equations to obtain the values ​​of the dominant parameters under each preset working condition.

[0102] In this embodiment, solving the multivariate equations yields the values ​​of the dominant parameters that match the power response curve of the source-load integrated model with the actual power response curve of the detailed simulation model under each preset operating condition. For each preset operating condition, a set of dominant parameter values ​​corresponding to that operating condition can be obtained through steps A151 to A153 described above.

[0103] In this embodiment, by sampling the data of feature points from the real power response curve and substituting them into the functional relationship between the dominant parameters and the power response curve for back-calculation, it is possible to obtain equivalent modeling parameters based on the response inversion of the detailed simulation model, so that the power response curve of the source-load integrated model under each preset operating condition can accurately fit the real power response curve of the detailed simulation model.

[0104] In one embodiment, in conjunction with the above embodiments, the feature points include: steady-state feature points before the fault occurs, extreme drop points at the moment the fault occurs, transient maintenance feature points during the fault, and recovery extreme points at the moment the fault is cleared.

[0105] The steady-state characteristic points before the fault occur refer to the characteristic points extracted when the power response curve is in the steady-state stage before the preset fault occurs. The specific number can be set according to actual needs. In this embodiment, the steady-state characteristic points include two feature points: steady-state active power characteristic point and steady-state reactive power characteristic point, corresponding to feature point f1 and feature point f2, respectively.

[0106] The extreme point of the power drop at the moment of fault occurrence refers to the extreme point reached by the power response curve during the transient phase of the fault after the preset fault occurs. In this embodiment, the extreme point of the power drop includes one extreme point of active power drop, corresponding to feature point f3.

[0107] The transient sustaining feature points during a fault refer to the feature points extracted when the power response curve enters the steady-state phase after experiencing the transient phase of the fault during the preset fault duration. The specific number can be set according to actual needs. In this embodiment, the transient sustaining feature points include two feature points: the active power sustaining feature point and the reactive power sustaining feature point during the fault, corresponding to feature point f4 and feature point f7, respectively.

[0108] The recovery extreme point at the moment of fault clearing refers to the extreme point and recovery transition point reached by the power response curve during the fault recovery transient phase after the preset fault clearing. In this embodiment, the recovery extreme point includes three characteristic points: the active power recovery peak point, the active power recovery transition point, and the reactive power recovery peak point, corresponding to characteristic point f5, characteristic point f6, and characteristic point f8, respectively.

[0109] In this embodiment, there are a total of 8 feature points, from f1 to f8, which cover the steady-state stage before the fault occurs, the fault transient stage after the fault occurs, the fault steady-state stage, and the fault recovery transient stage, respectively, and can fully characterize the characteristics of the power response curve throughout the entire process.

[0110] In this embodiment, by dividing the power response curve into multiple stages according to the time sequence of the fault response and extracting feature points from each stage, a structured characterization of the entire process characteristics of the power response curve can be achieved, enabling the subsequent multivariate equation system constructed based on the feature points to simultaneously constrain the response accuracy of the equivalent modeling parameters at each fault stage.

[0111] In conjunction with the above embodiments, in one implementation, the steady-state characteristic point data is the average value of the power response curve during the steady-state phase before the fault occurs, and the transient maintenance characteristic point data is the average value of the power response curve during the fault steady-state phase during the fault duration.

[0112] Specifically, for steady-state characteristic points f1 and f2, the average values ​​of the active power response curve and the reactive power response curve during the steady-state phase before the fault occur are taken as their characteristic point data.

[0113] For transient sustaining characteristic points f4 and f7, the average values ​​of the active power response curve and reactive power response curve during the fault steady-state phase are taken as their characteristic point data.

[0114] The data for the extreme points f3, f5, f6, and f8 of the fall and recovery can be directly taken as the instantaneous values ​​of the power response curves at the corresponding times.

[0115] In this embodiment, by using the time-period average method to obtain the feature point data for steady-state feature points and transient maintenance feature points, a smooth representation of the steady-state and fault steady-state characteristics in the power response curve can be achieved, eliminating the interference of curve fluctuations on the feature point data and improving the numerical stability and solution accuracy of the subsequent multivariate equation system constructed based on the feature points.

[0116] In conjunction with the above embodiments, in one implementation, the multivariate equation system includes a steady-state equation system constructed based on steady-state feature points and a transient equation system constructed based on transient feature points, where transient feature points are feature points other than steady-state feature points. Accordingly, step A153 may include: Step A1531: Solve the steady-state equations to obtain the values ​​of active power and reactive power in the dominant parameters.

[0117] Specifically, the steady-state equations consist of two equations corresponding to steady-state characteristic points f1 and f2. Before solving the steady-state equations, based on the output data of each new energy power generation unit in the detailed simulation model of the power system, the active power of the wind power generation model and the active power of the photovoltaic power generation model are determined as the sum of the active power outputs of the corresponding wind turbines and photovoltaic power plants in the detailed simulation model, respectively, and are thus known quantities. At this point, only two unknown quantities remain in the steady-state equations: the active power and the reactive power of the motor load model. Matching the number of equations, the values ​​of the active and reactive power of the motor load model can be solved.

[0118] Step A1532: Substitute the values ​​of active power and reactive power as known quantities into the transient equation system to solve for the values ​​of the other parameters in the dominant parameters besides active power and reactive power.

[0119] Specifically, the transient equation set consists of six equations corresponding to the extreme point f3, the transient maintenance characteristic points f4 and f7, the recovery extreme points f5, f6, and f8. The active and reactive power values ​​of the motor load model obtained in step A1531, along with the previously determined active power values ​​of wind power and photovoltaic power, are substituted into the transient equation set as known quantities. At this point, the unknown quantities in the transient equation set are four parameters: the rated capacity of the motor load model, the rotor winding resistance of the motor load model, the fault ride active power control coefficient of the new energy power generation model, and the fault ride reactive power control coefficient of the new energy power generation model. These parameters can be obtained by solving for them.

[0120] In this embodiment, by dividing the multivariate equations into steady-state equations and transient equations, and solving them in stages according to the order of steady-state first and transient, the number of unknowns can be gradually reduced, thus reducing the difficulty of solving the equations.

[0121] The following section provides further explanation of the technical details involved in steps A11 to A16 above, including the steady-state output relationship of the source-load integrated model, the classification of fault transient response, the construction of parameter perturbation and function relationship, the response inversion of the detailed simulation model, and the full-condition mapping.

[0122] First, the steady-state output relationship of the source-load integrated model is analyzed.

[0123] The steady-state absorbed power of the source-load integrated model is obtained by subtracting the output of the wind power generation model and the photovoltaic power generation model from the power absorbed by the constant impedance load model, the motor load model, and the equivalent impedance. The specific relationship is as follows: (twenty three); in, , , , , , The active power of the source-load integrated model, equivalent impedance, constant impedance load model, motor load model, wind power new energy power generation model, and photovoltaic new energy power generation model are listed in order. , , , , , The reactive power of the source-load integrated model, equivalent impedance, constant impedance load model, motor load model, wind power new energy generation model, and photovoltaic new energy generation model are listed in order.

[0124] As can be seen from equation (23), the power response of the source-load integrated model in steady state depends on the active / reactive power output of the new energy power generation model and the active / reactive power absorbed by various loads. By determining the output of each component model in the source-load integrated model, the steady-state power output of the source-load integrated model to the power system can be determined. This is the theoretical basis of the steady-state equation set in step A1531 above, that is, by matching the active and reactive power of the source-load integrated model and the detailed simulation model in the steady state stage, the active and reactive power of the motor load model can be derived in reverse.

[0125] The transient output of the source-load integrated model under fault conditions is analyzed below.

[0126] In this application, based on the fault transient response characteristics of the source-load integrated model, the power response of the entire fault process is divided into four stages according to time sequence: the equivalent model steady-state stage before the fault occurs, the fault transient stage after the fault occurs, the fault steady-state stage during the fault duration, and the fault recovery transient stage after fault clearing. These four stages correspond to the aforementioned classification of feature points: steady-state feature points f1 and f2 correspond to the equivalent model steady-state stage, the drop extreme point f3 corresponds to the fault transient stage, the transient maintenance feature points f4 and f7 correspond to the fault steady-state stage, and the recovery extreme points f5, f6, and f8 correspond to the fault recovery transient stage.

[0127] To verify the fault response characteristics of the source-load integrated model, batch simulations were performed on the source-load integrated model. Figure 2 This is a schematic diagram of the fault response curve of the source-load integrated model under multiple operating conditions, as shown in the embodiments of this application. Figure 2As shown, it comprises two subplots. The upper subplot displays the active power response curves, with the horizontal axis representing the simulation time step and the vertical axis representing active power in units of pu. The figure contains 200 different curves, each corresponding to the active power response of the source-load integrated model under a set of randomly sampled parameter combinations. During the steady-state phase of the equivalent model, the active power of each curve remains at its respective steady-state level, and the differences between the curves reflect the differences in steady-state output of the source-load integrated model under different parameter combinations. During the fault transient phase, the active power of each curve shows a significant drop, with the magnitude of the drop varying depending on the parameter combination. During the fault steady-state phase, the active power of each curve gradually approaches its respective level maintained during the fault after the drop. During the fault recovery transient phase, the active power of each curve shows a significant recovery peak after the fault is cleared, and then gradually falls back to a new steady-state level.

[0128] The subplot below shows the reactive power response curves, with the horizontal axis representing the simulation time step and the vertical axis representing reactive power, in units of pu. It contains multiple different curves, corresponding to the same 200 sets of randomly sampled parameter combinations as the subplot above. During the steady-state phase of the equivalent model, the reactive power of each curve remains at its respective steady-state level. During the fault transient and fault steady-state phases, the reactive power of each curve changes significantly, with some curves showing a substantial increase in reactive power during the fault, reflecting the combined effect of the aforementioned surge in reactive power due to leakage flux from the asynchronous motor and the reactive power injection during low-voltage ride-through in the new energy power generation model. During the fault recovery transient phase, the reactive power of each curve shows a recovery peak after the fault is cleared, and then gradually returns to the steady-state level.

[0129] Depend on Figure 2 It can be seen that the fault response curves of the source-load integrated model exhibit obvious regular differences under different parameter combinations, and the response amplitude and dynamic process of each curve in the four stages are all affected by the parameter combination. This indicates that there is a definite functional relationship between the parameter values ​​of the source-load integrated model and the fault response curve.

[0130] The specific construction process of this functional relationship is explained below.

[0131] based on Figure 2 Simulation results show that, when facing low-voltage faults, the fault transient, fault steady-state, and fault recovery transient response characteristics of the source-load integrated model are affected by parameters such as the active / reactive power output of each component in the source-load integrated model during the fault, the rated capacity of the motor load model, the rotor winding resistance of the motor load model, and the active / reactive power control coefficients of the fault ride-through model in the new energy power generation model. To analyze the sensitivity of each parameter to the response curve and determine the dominant parameters affecting the transient response of the source-load integrated model, this application simultaneously perturbs multiple candidate parameters in the model based on the model's structure.

[0132] Specifically, the candidate parameters include the rated capacity of the motor load model. Active power / reactive power Rotor winding resistance Rotor winding leakage reactance Stator winding resistance Stator winding leakage reactance Magnetizing Reactor And the active power / reactive power of the wind power new energy generation model. Active power / reactive power of photovoltaic new energy power generation model Fault ride active power control coefficients of new energy power generation models Fault ride reactive power control coefficients of new energy power generation models .

[0133] All candidate parameters are simultaneously perturbed, and a preset fault is introduced for batch simulation to obtain multiple sets of power response curves. Then, a second-order polynomial fitting algorithm is used for regression fitting. Specifically, a second-order polynomial feature generator is used to modify the original input feature vector composed of candidate parameters. This is expanded to generate a new feature space containing all linear terms, quadratic interaction terms, and square terms. Subsequently, the coefficients of the regression model were solved using the least squares method. To minimize the predicted value Compared with the true value The mean squared error (MSE) between them. Its mathematical expression is as follows: (twenty four) in, The independent variables are composed of candidate parameters, and N is the total number of samples in the batch simulation. It is the true value of the feature point corresponding to the k-th sample. It is the feature vector of the k-th sample after being expanded by a second-order polynomial.

[0134] After normalizing the perturbation range of each candidate parameter, the coefficients of each candidate parameter in the fitted second-order function were compared, and the following eight parameters were finally determined as the dominant parameters that have the main influence on the fault response curve: active power / reactive power of the motor load model. Active power of wind power new energy generation model Active power of photovoltaic new energy power generation model Rated capacity of motor load model Rotor winding resistance of motor load model Fault ride active power control coefficients of new energy power generation models And the fault ride reactive power control coefficient of the new energy power generation model Considering that the reactive power output of new energy power generation models in actual engineering generally remains zero or stable at a specific value without change, the subsequent analysis will only focus on the active power of wind power and photovoltaic power generation models.

[0135] It should be noted that this application employs a method of simultaneously perturbing multiple parameters and selecting the dominant parameter through polynomial fitting, rather than methods such as Sobol sensitivity analysis that perturbs a single parameter one by one. This is because the influence of different parameters during a fault may exhibit cross-coupling effects, and the response results of perturbing multiple parameters are difficult to process directly using single-parameter sensitivity analysis methods. However, the interaction term in second-order polynomial fitting can capture the coupling relationship between parameters, making it more suitable for selecting the dominant parameter in this application.

[0136] Based on the eight dominant parameters obtained from the above screening, the source-load integrated model was simulated in batches again. Figure 3 This is a schematic diagram illustrating the selection of the response function in an embodiment of this application. For example... Figure 3 As shown, Figure 3 The graph consists of two sub-graphs. The upper sub-graph shows the active power response curves. The horizontal axis represents the simulation time step, and the vertical axis represents active power, measured in pu (units per unit area). The graph contains 200 different curves, each corresponding to the active power response of the source-load integrated model under a set of randomly sampled dominant parameter combinations. The graph marks the sampling locations of five feature points: f1 is located in the steady-state stage of the equivalent model, with its fitting objective being the mean of the active power response curves during this time period; f3 is located in the fault transient stage, corresponding to the extreme point during the active power drop process; f4 is located in the fault steady-state stage, with its fitting objective being the mean of the active power response curves during this time period; f5 is located in the fault recovery transient stage, corresponding to the peak point during the active power recovery process; and f6 is also located in the fault recovery transient stage, corresponding to the active power recovery transition point.

[0137] The subplot below shows the reactive power response curves, with the horizontal axis representing the simulation time step and the vertical axis representing reactive power, in units of pu. It also contains 200 different curves, corresponding to the same 200 sets of randomly sampled dominant parameter combinations as the subplot above. The plot marks the sampling locations of three feature points: f2 is located in the steady-state phase of the equivalent model, with its fitting objective being the mean of the reactive power response curves during this period; f7 is located in the fault steady-state phase, with its fitting objective being the mean of the reactive power response curves during this period; and f8 is located in the fault recovery transient phase, corresponding to the peak point during the reactive power recovery process.

[0138] This application, through analysis of the response curve characteristics, determined the target values ​​f1 to f8 for the aforementioned eight function curve fitting points. Using eight dominant parameters as independent variables, and... Figure 3The data from the characteristic points of the response curve are used as the dependent variable for optimization fitting, thus obtaining the functional relationship between the eight dominant parameters and the eight characteristic points of the power response curve. The fitting target for the four characteristic points f1, f2, f4, and f7 is the mean of the curve in their respective segments to ensure the numerical stability of the functional relationship. This functional relationship is the one constructed in step A13 above, relating the dominant parameters to the power response curve.

[0139] The following section describes the fault response characteristics of the detailed simulation model of the power system under multiple operating conditions, namely the technical details of step A14 mentioned above.

[0140] In actual operation, the detailed simulation model of the power system exhibits different operating conditions due to factors such as weekdays versus weekends, variations in meteorological conditions leading to differences in renewable energy output, and so on. It is important to clarify that for a given power system, the inherent parameters of its internal components (such as line impedance, transformer parameters, and motor mechanical parameters) remain constant; the only variable is the output level of each load and renewable energy generation unit, i.e., the operating condition. Therefore, for each specific operating condition, the steady-state power curve and fault response curve of the detailed simulation model under that condition can be obtained by driving the detailed simulation model and introducing a preset fault.

[0141] Figure 4 This is a schematic diagram of the fault response curves of a detailed simulation model of a power system under multiple operating conditions, as shown in an embodiment of this application. Figure 4 As shown, Figure 4 The graph consists of two sub-graphs. The upper sub-graph shows the active power response curves, with the horizontal axis representing the simulation time step and the vertical axis representing active power in PU. The graph contains 200 different curves, each corresponding to the active power response of a detailed simulation model under a randomly sampled operating condition. The graph uses [the graph's structure / indicators] as examples. Figure 3 The acquisition locations of the five feature points were marked in the same way. (And...) Figure 2 and Figure 3 Compared to the response curves of the integrated source-load model, the curves of the detailed simulation model exhibit similar four-stage response characteristics in their overall trend. However, the specific amplitude and dynamic process of each stage are determined by the actual operating conditions. The sub-figure below shows the reactive power response curves, with the horizontal axis representing the simulation time step and the vertical axis representing reactive power, in units of pu. It also contains 200 different curves, corresponding to the same 200 randomly sampled operating conditions as the sub-figure above. The sampling locations of three feature points are marked in the figure.

[0142] Depend on Figure 4It can be seen that the fault response curves of the detailed simulation model under different operating conditions also have clear four-stage characteristics, and the values ​​of each feature point are determined only by the operating conditions. Since the source-load integrated model and the detailed simulation model use the same component structure, their fault responses have the same response mechanism. Therefore, the same feature point acquisition method as the source-load integrated model can be used to extract the data of eight feature points, f1 to f8, from the actual power response curve of the detailed simulation model, as the benchmark values ​​for back-calculating the dominant parameters in step A15 above.

[0143] The following describes the specific process of obtaining the equivalent modeling parameters of the source-load integrated model based on the response inversion of the detailed simulation model, namely the technical details of the aforementioned steps A15 and A16.

[0144] Based on the foregoing analysis, the steady-state response and fault transient response of the detailed simulation model of the power system are determined solely by the system operating conditions. The operating condition data used in this application are the percentage of active power of various loads and renewable energy generation relative to their rated power; therefore, each operating condition data point is a variable between 0 and 1. Since different types of loads use their own fixed power factors, changes in operating conditions are only related to active power. Specific operating condition variables include: the proportion of active power of motor loads. Active power ratio of constant impedance load The proportion of active power generated by wind power and other new energy sources and the proportion of active power generated by photovoltaic new energy sources .

[0145] For each given operating condition, adopt the same as Figure 3 Using the same feature point acquisition method, data from eight feature points (f1 to f8) are extracted from the actual power response curve of the detailed simulation model. The extracted feature point data is used as the dependent variable value of the functional relationship between the dominant parameter constructed in step A13 above and the power response curve. The equivalent modeling parameters of the source-load integrated model under this operating condition can then be obtained by inverse solution.

[0146] To ensure accurate equivalence across processes, the inverse process first performs equivalence on the steady-state process. Based on the fitting results analyzed above, f1 and f2 are fitting functions for the steady-state operating condition of the source-load integrated model, with their dominant variables being only the active power / reactive power of the motor load model. Active power of wind power new energy generation model Active power of photovoltaic new energy power generation model There are a total of 4 variables. Since the steady-state condition is constrained by only two functions, the inverse optimization result may have multiple solutions. The fitting condition for the source-load integrated model in this application is that the output of each type of new energy source and load in the detailed simulation model to be fitted is known. Therefore, this application specifies the active power of the wind power new energy generation model here. Active power of photovoltaic new energy power generation model The values ​​of f1 and f2 represent the sum of the active power outputs of the wind turbine and the photovoltaic power station in the detailed simulation model, respectively. Therefore, in the actual fitting process, the two functions f1 and f2 are fitted first, and their variables are only the active power / reactive power of the motor load model. Two. Based on the four active power output scenarios obtained, the transient response process of the source-load integrated model is fitted to obtain the final parameters of the source-load integrated model.

[0147] Figure 5 This is a schematic diagram illustrating the mapping relationship between operating conditions and equivalent modeling parameters, as shown in the embodiments of this application. Figure 5 As shown, Figure 5 From right to left, it includes the operating condition module, typical response module, mapping function module, and dominant parameter module.

[0148] The operating condition module is derived from the detailed simulation model and includes operating condition variables such as P, P_PMSG, and P_PV, which represent the current actual operating conditions of the power system.

[0149] The typical response module contains eight typical response values, from r1 to r8, representing data from eight feature points extracted from the actual power response curve of the detailed simulation model under a given operating condition.

[0150] The mapping function module contains eight mapping functions, f1 to f8, which represent the functional relationships between the dominant parameters constructed in step A13 and the characteristic points of the power response curve. The typical response module and the mapping function module are connected by a bidirectional arrow, indicating that the typical response values ​​are substituted into the mapping functions for back-calculation.

[0151] The dominant parameter module is derived from the source-load integrated model and includes dominant parameters such as Smva, Rrpu, and KP_LV, which are the numerical values ​​of the equivalent modeling parameters obtained through reverse calculation.

[0152] Figure 5 The mapping relationship shown indicates that the operating conditions of the detailed simulation model determine the typical response, and the dominant parameters of the source-load integrated model can be obtained by inverse solving the mapping function. Therefore, each operating condition of the power system detailed simulation model can be mapped from the operating condition to the equivalent modeling parameters through the above calculation method, that is, a one-to-one correspondence is achieved from the detailed simulation model to the source-load integrated model.

[0153] To verify the accuracy of the above inversion method, Figure 6 This is a schematic diagram illustrating the equivalent simulation results of the source-load integrated model based on a given working condition, as shown in the embodiments of this application. Figure 6 The graph consists of two sub-graphs. The upper sub-graph shows the active power response curves, with the horizontal axis representing the simulation time step and the vertical axis representing active power in units of pu. The graph contains four curves, each corresponding to a different active power response curve from a detailed simulation model under a given operating condition and the active power response curve from a source-load integrated model obtained through an inversion method. The two curves highly overlap under each operating condition, maintaining good consistency in the steady-state, fault transient, fault steady-state, and fault recovery transient phases.

[0154] The subplot below shows the reactive power response curves, with the horizontal axis representing the simulation time step and the vertical axis representing reactive power in units of pu. It also contains four curves, corresponding to the same operating conditions as the subplot above. The reactive power response curves of the detailed simulation model and the integrated source-load model under each operating condition highly overlap, verifying that the equivalent modeling parameters obtained through the response inversion method enable the integrated source-load model to accurately fit the power response characteristics of the detailed simulation model under each given operating condition.

[0155] Depend on Figure 6 It can be seen that the response inversion method of this application can accurately obtain the corresponding equivalent modeling parameters for each given operating condition, so that the active power and reactive power response of the source-load integrated model in the whole process of the fault are consistent with the detailed simulation model, and the equivalent accuracy is good.

[0156] The above-described method achieves a one-to-one mapping from a detailed simulation model to a source-load integrated model based on a given operating condition. However, in practical engineering applications, the method for obtaining equivalent modeling parameters based on the response inversion of the detailed simulation model requires prior simulation of the detailed model, which significantly limits its usability. Therefore, this application further establishes a mapping relationship between typical operating conditions and equivalent modeling parameters to achieve equivalent modeling of the entire power system under all operating conditions based on sampling analysis of a limited number of typical operating conditions. This process is the technical detail of step A2 mentioned above.

[0157] As can be seen from the equivalence process in steps A11 to A16 above, the current process can achieve a one-to-one equivalence of the source-load integrated model based on a given operating condition. To construct the parameter mapping model, it is necessary to select typical operating conditions of the detailed power system simulation model in actual operation as preset operating conditions. For renewable energy generation, typical operating conditions include both high-generation and low-generation conditions; for load, typical operating conditions include load output under different seasons and time periods, such as peak load conditions and off-peak load conditions.

[0158] After selecting multiple typical operating conditions and completing the sample values ​​of the equivalent modeling parameters for each typical operating condition through steps A11 to A16, the operating condition data of each typical operating condition is used as the independent variable, and the sample values ​​of the corresponding equivalent modeling parameters (i.e., the values ​​of the dominant parameters) are used as the dependent variable. Function fitting is then performed again to obtain the parameter mapping model from the operating condition data to the equivalent modeling parameters. In this embodiment, eight typical operating conditions are selected for constructing the parameter mapping model.

[0159] After the parameter mapping model is established, for any given operating condition data, the corresponding equivalent modeling parameter values ​​can be quickly calculated through the parameter mapping model without having to re-drive the detailed simulation model for simulation and response inversion.

[0160] To verify the equivalence accuracy of the parameter mapping model under random conditions without training, Figure 7 This is a schematic diagram comparing the simulation results of a detailed simulation model and a source-load integrated model under random operating conditions, as shown in an embodiment of this application. Figure 7 As shown, Figure 7 It includes two sub-graphs, one above the other.

[0161] The upper subplot shows the active power response curves, with the horizontal axis representing the simulation time step and the vertical axis representing active power in units of pu. The plot contains two curves: the actual active power response curve of the detailed simulation model under this stochastic operating condition, and the active power response curve of the source-load integrated model after obtaining equivalent modeling parameters through a parameter mapping model. It can be seen that the two curves maintain good consistency in the steady-state, fault transient, fault steady-state, and fault recovery transient stages. A slight deviation exists at the peak value in the fault recovery transient stage, but the overall fitting accuracy is good.

[0162] The subplot below shows the reactive power response curves, with the horizontal axis representing the simulation time step and the vertical axis representing reactive power, in units of pu. It also contains two curves: the actual reactive power response curve of the detailed simulation model and the reactive power response curve of the source-load integrated model. The two curves maintain good consistency across all stages, verifying that the parameter mapping model has good generalization ability for stochastic operating conditions not involved in training.

[0163] Depend on Figure 7 As can be seen, by selecting eight typical operating conditions to construct a parameter mapping model, this application can achieve accurate equivalent modeling of the source-load integrated model to the detailed simulation model under random operating conditions without training, successfully realizing full-condition equivalent modeling of the detailed simulation model of the power system under multiple operating conditions. The above results demonstrate that the parameter mapping model of this application possesses generalization ability from a finite number of typical operating conditions to any operating condition, and the equivalent modeling parameters can adaptively update with changes in operating conditions, meeting the needs of practical engineering applications.

[0164] In summary, the solution presented in this application has at least the following technical effects: First, by constructing a source-load integrated model that includes a constant impedance load model, a motor load model, and a new energy power generation model, and by incorporating the power equivalents of constant current loads and constant power loads into the power parameters of the motor load model, it is possible to achieve comprehensive equivalent modeling of power sources and loads in the power system. This enables the source-load integrated model to accurately characterize the dynamic characteristics of the power system under transient operating conditions and fault conditions.

[0165] Second, by simultaneously perturbing multiple candidate parameters in the source-load integrated model and using a second-order polynomial fitting algorithm to screen the dominant parameters and construct the functional relationship between the parameters and the power response curve, it is possible to quantitatively evaluate and accurately identify the key parameters affecting the fault response characteristics, while also capturing the coupling effects between different parameters.

[0166] Third, by substituting the actual power response curve of the detailed simulation model into the functional relationship for back-calculation and solving in a two-stage approach of first steady state and then transient state, the equivalent modeling parameters based on the response inversion of the detailed simulation model can be accurately obtained.

[0167] Fourth, by constructing a parameter mapping model from operating condition data to equivalent modeling parameters based on multiple typical operating conditions, the equivalent modeling parameters can be adaptively updated as the operating conditions change. This eliminates the need to re-drive detailed simulation models for each operating condition to perform simulation and response inversion, thus meeting the application needs of power system operating conditions that vary greatly in actual engineering.

[0168] The following describes a source-load integrated equivalent modeling device for new energy power generation provided in this application. The source-load integrated equivalent modeling device for new energy power generation described below and the source-load integrated equivalent modeling method for new energy power generation described above can be referred to in correspondence.

[0169] Figure 8 This is a structural block diagram of a source-load integrated equivalent modeling device considering new energy power generation, as shown in an embodiment of this application. Figure 8 As shown, a source-load integrated equivalent modeling device 800 considering new energy power generation in this application may include: The acquisition module 801 is used to acquire the current operating condition data of the power system; The input module 802 is used to input the operating condition data into the parameter mapping model to obtain the values ​​of the equivalent modeling parameters corresponding to the power system. The parameter mapping model is used to characterize the numerical mapping relationship between the operating conditions of the power system and the equivalent modeling parameters. The configuration module 803 is used to configure the corresponding parameters in the source-load integrated model of the power system according to the values ​​of the equivalent modeling parameters, so as to obtain the source-load integrated equivalent model of the power system under the current operating conditions. The source-load integrated model includes a constant impedance load model, a motor load model and a new energy power generation model. The power of the constant current load and constant power load in the power system is equivalently converted into the power parameters of the motor load model.

[0170] According to the source-load integrated equivalent modeling device 800 provided in this application, which considers new energy power generation, the equivalent modeling parameters include at least one of the following: active power of the motor load model, reactive power of the motor load model, active power of the wind power new energy power generation model, active power of the photovoltaic new energy power generation model, rated capacity of the motor load model, rotor winding resistance of the motor load model, fault ride active power control coefficient of the new energy power generation model, and fault ride reactive power control coefficient of the new energy power generation model.

[0171] According to the source-load integrated equivalent modeling device 800 provided in this application, which considers new energy power generation, a training module is also included, which is used to: obtain sample values ​​of equivalent modeling parameters corresponding to multiple preset operating conditions; and perform function fitting with the operating condition data samples of each preset operating condition as independent variables and the sample values ​​of the corresponding equivalent modeling parameters as dependent variables to obtain the parameter mapping model.

[0172] According to the source-load integrated equivalent modeling device 800 considering new energy power generation provided in this application, the training module is used for: simultaneously perturbing multiple candidate parameters in the source-load integrated model and introducing preset faults for batch simulation to obtain multiple sets of power response curves; performing function fitting with the multiple candidate parameters as independent variables and the multiple sets of power response curves as dependent variables, and selecting the dominant parameters whose influence on the power response curves is greater than a preset degree from the multiple candidate parameters based on the fitting results; constructing the functional relationship between the dominant parameters and the multiple sets of power response curves; driving the detailed simulation model corresponding to the power system to run under each preset operating condition and introducing the preset faults to obtain the actual power response curves of the power system under each preset operating condition; using the actual power response curves as benchmark values, substituting them into the functional relationship for back-calculation to obtain the values ​​of the dominant parameters under each preset operating condition; and determining the sample values ​​of the equivalent modeling parameters corresponding to each preset operating condition based on the values ​​of the dominant parameters under each preset operating condition.

[0173] According to the source-load integrated equivalent modeling device 800 considering new energy power generation provided in this application, the training module is used to: use the multiple candidate parameters as independent variables and the data of feature points extracted from the multiple sets of power response curves as dependent variables, and use a second-order polynomial fitting algorithm to perform regression fitting.

[0174] According to the source-load integrated equivalent modeling device 800 for new energy power generation provided in this application, the training module is used to: sample data of multiple feature points from each of the real power response curves; use the data of the multiple feature points as reference values, substitute them into the functional relationship, and obtain a system of multivariate equations with the dominant parameter as the unknown; solve the system of multivariate equations to obtain the values ​​of the dominant parameter under each preset operating condition.

[0175] According to the source-load integrated equivalent modeling device 800 provided in this application, which considers new energy power generation, the feature points include: steady-state feature points before the fault occurs, drop extreme points at the moment the fault occurs, transient maintenance feature points during the fault duration, and recovery extreme points at the moment the fault is cleared.

[0176] According to the source-load integrated equivalent modeling device 800 provided in this application, which considers new energy power generation, the data of the steady-state characteristic points is the average value of the power response curve during the steady-state stage before the fault occurs, and the data of the transient maintenance characteristic points is the average value of the power response curve during the fault steady-state stage during the fault duration.

[0177] According to the source-load integrated equivalent modeling device 800 considering new energy power generation provided in this application, the multivariate equation system includes a steady-state equation system constructed based on steady-state characteristic points and a transient equation system constructed based on transient characteristic points. The transient characteristic points are characteristic points other than the steady-state characteristic points. The training module is used to: solve the steady-state equation system to obtain the values ​​of active power and reactive power in the dominant parameters; substitute the values ​​of active power and reactive power as known quantities into the transient equation system to solve for the values ​​of the remaining parameters in the dominant parameters other than active power and reactive power.

[0178] According to the source-load integrated equivalent modeling device 800 provided in this application, which considers new energy power generation, the operating condition data includes: the ratio of active power of constant impedance load to its rated power, the ratio of active power of motor load to its rated power, and the ratio of active power of new energy power generation to its rated power.

[0179] According to the source-load integrated equivalent modeling device 800 provided in this application, the new energy power generation model includes at least one of the following: wind power new energy power generation model and photovoltaic new energy power generation model.

[0180] Figure 9 This is a schematic diagram of the physical structure of an electronic device shown in an embodiment of this application, such as... Figure 9 As shown, the electronic device may include a processor 910, a communications interface 920, a memory 930, and a communication bus 940. The processor 910, communications interface 920, and memory 930 communicate with each other via the communication bus 940. The processor 910 can call logical instructions from the memory 930 to execute a source-load integrated equivalent modeling method considering new energy power generation.

[0181] Furthermore, the logical instructions in the aforementioned memory 930 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0182] On the other hand, this application also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute a source-load integrated equivalent modeling method considering new energy power generation provided by the above methods.

[0183] In another aspect, this application also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, is implemented to perform a source-load integrated equivalent modeling method considering new energy power generation provided by the methods described above.

[0184] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0185] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0186] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A source-load integrated equivalent modeling method considering new energy power generation, characterized in that, include: Obtain current operating status data of the power system; The operating condition data is input into the parameter mapping model to obtain the numerical values ​​of the equivalent modeling parameters corresponding to the power system. The parameter mapping model is used to characterize the numerical mapping relationship between the operating conditions of the power system and the equivalent modeling parameters. Based on the values ​​of the equivalent modeling parameters, the corresponding parameters in the source-load integrated model of the power system are configured to obtain the source-load integrated equivalent model of the power system under the current operating conditions. The source-load integrated model includes a constant impedance load model, a motor load model, and a new energy power generation model. The power of the constant current load and constant power load in the power system is equivalently converted into the power parameters of the motor load model.

2. The source-load integrated equivalent modeling method considering new energy power generation according to claim 1, characterized in that, The equivalent modeling parameters include at least one of the following: active power of the motor load model, reactive power of the motor load model, active power of the wind power new energy power generation model, active power of the photovoltaic new energy power generation model, rated capacity of the motor load model, rotor winding resistance of the motor load model, fault ride active control coefficient of the new energy power generation model, and fault ride reactive control coefficient of the new energy power generation model.

3. The source-load integrated equivalent modeling method considering new energy power generation according to claim 1, characterized in that, The parameter mapping model is obtained through the following steps: Obtain sample values ​​of equivalent modeling parameters corresponding to multiple preset working conditions; Using the operating condition data samples of each preset working condition as independent variables and the sample values ​​of the corresponding equivalent modeling parameters as dependent variables, a function is fitted to obtain the parameter mapping model.

4. The source-load integrated equivalent modeling method considering new energy power generation according to claim 3, characterized in that, The step of obtaining sample values ​​of equivalent modeling parameters corresponding to multiple preset working conditions includes: Multiple candidate parameters in the source-load integrated model are simultaneously perturbed, and a preset fault is introduced to perform batch simulations, resulting in multiple sets of power response curves; Using the multiple candidate parameters as independent variables and the multiple sets of power response curves as dependent variables, a function is fitted, and based on the fitting results, the dominant parameter whose influence on the power response curve is greater than a preset level is selected from the multiple candidate parameters. Construct the functional relationship between the dominant parameters and the multiple sets of power response curves; The detailed simulation model corresponding to the power system is driven to run under each of the preset operating conditions, and the preset fault is introduced to obtain the actual power response curve of the power system under each of the preset operating conditions; Using the actual power response curve as a reference value, the function relationship is substituted into the equation to perform reverse calculation and obtain the values ​​of the dominant parameters under each preset operating condition. Based on the values ​​of the dominant parameters under each preset working condition, determine the sample values ​​of the equivalent modeling parameters corresponding to each preset working condition.

5. The source-load integrated equivalent modeling method considering new energy power generation according to claim 4, characterized in that, The step of fitting a function using the multiple candidate parameters as independent variables and the multiple sets of power response curves as dependent variables includes: Using the multiple candidate parameters as independent variables and the data of feature points extracted from the multiple sets of power response curves as dependent variables, a second-order polynomial fitting algorithm is used for regression fitting.

6. The source-load integrated equivalent modeling method considering new energy power generation according to claim 4, characterized in that, The step of using the actual power response curve as a benchmark value and substituting it into the functional relationship for back-calculation to obtain the values ​​of the dominant parameters under each preset operating condition includes: Data for multiple feature points were sampled from each of the actual power response curves. Using the data of the multiple feature points as reference values ​​and substituting them into the functional relationship, a system of multivariate equations with the dominant parameter as the unknown quantity is obtained; Solve the system of multivariate equations to obtain the values ​​of the dominant parameters under each preset working condition.

7. The source-load integrated equivalent modeling method considering new energy power generation according to claim 6, characterized in that, The feature points include: steady-state feature points before the fault occurs, extreme drop points at the moment the fault occurs, transient maintenance feature points during the fault duration, and recovery extreme points at the moment the fault is cleared.

8. The source-load integrated equivalent modeling method considering new energy power generation according to claim 7, characterized in that, The data for the steady-state characteristic points are the average values ​​of the power response curves during the steady-state phase before the fault occurs, and the data for the transient maintenance characteristic points are the average values ​​of the power response curves during the steady-state phase of the fault during the fault duration.

9. The source-load integrated equivalent modeling method considering new energy power generation according to claim 7, characterized in that, The multivariate equation system includes a steady-state equation system constructed based on steady-state feature points and a transient equation system constructed based on transient feature points. The transient feature points are feature points other than the steady-state feature points. Solving the multivariate equation system to obtain the values ​​of the dominant parameters under each preset operating condition includes: Solve the steady-state equations to obtain the values ​​of active power and reactive power in the dominant parameters; The values ​​of the active power and the reactive power are substituted into the transient equations as known quantities to solve the problem, thereby obtaining the values ​​of the other parameters in the dominant parameters besides the active power and the reactive power.

10. The source-load integrated equivalent modeling method considering new energy power generation according to any one of claims 1-9, characterized in that, The operating condition data includes: the ratio of active power of constant impedance load to its rated power, the ratio of active power of motor load to its rated power, and the ratio of active power of new energy power generation to its rated power.

11. The source-load integrated equivalent modeling method considering new energy power generation according to any one of claims 1-9, characterized in that, The new energy power generation model includes at least one of the following: wind power new energy power generation model and photovoltaic new energy power generation model.

12. A source-load integrated equivalent modeling device considering new energy power generation, characterized in that, include: The acquisition module is used to acquire current operating condition data of the power system; The input module is used to input the operating condition data into the parameter mapping model to obtain the values ​​of the equivalent modeling parameters corresponding to the power system. The parameter mapping model is used to characterize the numerical mapping relationship between the operating conditions of the power system and the equivalent modeling parameters. The configuration module is used to configure the corresponding parameters in the source-load integrated model of the power system according to the values ​​of the equivalent modeling parameters, so as to obtain the source-load integrated equivalent model of the power system under the current operating conditions. The source-load integrated model includes a constant impedance load model, a motor load model and a new energy power generation model. The power of the constant current load and constant power load in the power system is equivalently converted into the power parameters of the motor load model.

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