A parameter identification method and system for new energy units based on multiple test scenarios

Abstract

The present invention discloses a parameter identification method and system for a new energy unit based on multiple test scenarios, and belongs to the technical field of parameter identification of power generation equipment. The method of the present invention comprises: according to the actual engineering scenario of the new energy power generation equipment, a test system for parameter identification is built; a disturbance test is carried out on the test system to obtain the multi-scenario time-frequency domain response curve of the new energy power generation equipment; a simulation system corresponding to the test system is constructed, and the parameter group of the new energy power generation equipment to be identified is regarded as the parameter to be optimized of the particle swarm optimization algorithm, and the particle swarm is randomly initialized; the parameter group corresponding to each particle in the particle swarm is substituted into the simulation system for calculation, until the calculation result meets the termination condition of the calculation, and the optimal parameter group is determined, and the optimal parameter group is the parameter identification result. The present invention adopts the particle swarm optimization algorithm to carry out comprehensive identification of multiple test scenarios, which is conducive to achieving the unification of multiple identification targets, and at the same time improves the accuracy and efficiency of parameter identification.

Description

Technical Field

[0001] The present invention relates to the technical field of power generation equipment parameter identification, and more specifically, to a new energy unit parameter identification method and system based on multiple test scenarios. Background Art

[0002] Models and parameters of typical renewable energy power generation equipment Taking direct-drive wind turbines as an example, its basic topology and control principle are as follows: Figure 1 As shown, the main equipment includes a wind turbine, a permanent magnet synchronous motor, a machine-side converter, a grid-side converter and the like; the control system includes a main control, a machine-side converter control and a grid-side converter control.

[0003] The main control parameters that need to be identified are located in the following control links:

[0004] 1) The machine-side converter uses dq current decoupling control to achieve the delivery of wind turbine power; the machine-side converter mostly uses single-loop control, that is, the dq current reference value is directly given. The main control parameter is the proportional integral parameter K im and T im .

[0005] 2) The outer loop of the grid-side converter adopts constant DC voltage control, and the inner loop adopts dq current decoupling control to realize the grid-connected control of the wind turbine; the phase-locked loop (PLL) obtains the phase-locked angle from the grid-side voltage for vector control of the grid-side converter, that is, the grid-side converter adopts the grid voltage oriented control method. The main control parameters are the outer loop proportional integral parameter K pt and T pt , the inner loop proportional integral parameter K it and T it , and the phase-locked loop proportional integral parameter K pll and T pll .

[0006] 3) The voltage and current measurement values ​​required for converter control are generally input into the control system after passing through the digital filtering link. The first-order filtering link is considered here. The main control parameter is the filtering time constant T f .

[0007] 4) Wind turbine high and low voltage ride-through control will realize the inductive / capacitive reactive power output during high / low voltage ride-through and limit the active power. The main control parameter is the reactive voltage coefficient K QH and K QL , and the active power recovery speed V P wait.

[0008] In the study of simulation models, it is often necessary to compare multiple time domain and frequency domain responses to analyze and verify different models. Figure 2 shown.

[0009] Typical time domain responses of renewable energy power generation equipment include:

[0010] 1) Power step response of new energy power generation equipment. One of its uses is to assess the dynamic regulation performance of the control system, and its characteristic quantities include performance indicators such as overshoot and regulation time; the second use is to assess the stability of the system, and its characteristic quantities include the frequency and damping (or decay time constant) of the dominant oscillation mode.

[0011] 2) Transient response under grid grounding fault. The main concerns are: first, the response in the two short processes (1 to 2 power frequency cycles) of rapid voltage drop and instantaneous recovery; second, the active and reactive output characteristics during the voltage drop; and third, the recovery characteristics of active output after the fault is cleared.

[0012] 3) Reduce the short-circuit capacity on the grid side or connect series compensation capacitors on the grid side. Full-power converters such as direct-drive wind turbines and photovoltaics may experience subsynchronous frequency power oscillations when connected to weak AC systems. The short-circuit ratio (the ratio of short-circuit capacity to the capacity of new energy stations) can be used as an input condition for evaluating oscillation risks. For doubly fed wind turbines, similar power oscillations may also occur when connected to AC systems containing series compensation capacitors. The series compensation degree (here refers to the ratio of the capacitive reactance of the series compensation capacitor to the short-circuit reactance of the system) can be used as an input condition for evaluating oscillation risks. In the study of simulation models, the consistency of the two models can be confirmed by comparing the adaptability of the short-circuit ratio or the adaptability of the series compensation degree.

[0013] The frequency domain response of new energy power generation equipment refers to the Bode diagram of the transfer function (such as input impedance) defined under a set of input and output variables, which can be obtained by frequency scanning with the help of small-amplitude sinusoidal signal disturbance. This disturbance can be achieved in two ways: one is through the control system, that is, a small-amplitude sinusoidal signal is superimposed on the reference value of some controllers and used as a disturbance input. At the same time, the response of this frequency will also appear in other control quantities or electrical quantities, which can be used as output; the second is through the electrical system, that is, a small-amplitude harmonic current of a specific frequency is injected into the power grid through special equipment, and the signal of this frequency is extracted from the system electrical quantity, and the pair of electrical quantities of interest is used as input and output.

[0014] The particle swarm optimization algorithm uses an optimization algorithm to optimize the simulation model parameters to minimize the error between the simulation results and the actual test results. It is a feasible method for model parameter identification.

[0015] Particle swarm optimization (PSO) is an optimization method based on the evolution of swarm intelligence. d ), the calculation process is as follows:

[0016] 1) Set the particle swarm size N, inertia weight coefficient w, acceleration coefficients c1, c2, number of iterations t and other parameters.

[0017] 2) Randomly initialize the particle swarm, using the d-dimensional vector x i and v i Respectively represent the position and speed of the ith particle, and obtain their N-dimensional random initial values. According to the set objective function, the fitness value fi of each particle is obtained, and the current position of each particle is set as the particle's current optimal solution pBesti=fi, and gBest=min(fi) is taken as the group's current optimal solution.

[0018] 3) Update the particle speed and position through the following two formulas, and check whether the speed exceeds the maximum value V max or minimum value V min If the speed exceeds this range, the speed will be limited to this extreme value.

[0019]

[0020]

[0021] In the formula and are the position and velocity of particle i in the dth dimension at the kth iteration respectively; r1 and r2 are random numbers between [0,1]; is the position of the optimal point of particle i in the dth dimension after the k-1th iteration; is the position of the d-th dimension global optimal point of the entire group after the k-1th iteration.

[0022] 4) According to the set objective function, calculate the fitness value fi of the particle after the updated position. If the fitness value of particle i is better than the previous individual optimal solution pBesti, then its value is set to pBesti; if the best pBesti is better than the previous global optimal solution gBest, then its value is set to gBest.

[0023] 5) If the maximum number of iterations is reached, stop the calculation; otherwise, return to step 3).

[0024] From the above, it can be seen that compared with traditional synchronous generator sets, the coupling of various control links of renewable energy power generation equipment is tighter, and it is difficult to identify control parameters through external single disturbance or internal module division; and renewable energy power generation equipment has multi-time scale characteristics, and different disturbance characteristics are closely related to different control parameters, and their rules are more complicated. Summary of the invention

[0025] In view of the above problems, the present invention proposes a parameter identification method for a new energy unit based on multiple test scenarios, including:

[0026] According to the actual engineering scenarios of new energy power generation equipment, build a test system for parameter identification;

[0027] Conduct disturbance tests on the test system to obtain multi-scenario time-frequency domain response curves of new energy power generation equipment;

[0028] Construct a simulation system corresponding to the test system, regard the parameter group of the new energy power generation equipment to be identified as the parameters to be optimized by the particle swarm optimization algorithm, and randomly initialize the particle swarm

[0029] The parameter group corresponding to each particle in the particle swarm is substituted into the simulation system for calculation until the calculation result meets the termination condition of the calculation, and the optimal parameter group is determined, and the optimal parameter group is the parameter identification result.

[0030] Optional disturbance tests include: grid grounding fault disturbance, power step disturbance of renewable energy power generation equipment, sinusoidal small signal disturbance of renewable energy power generation equipment, reduction of grid-side short-circuit capacity or connection of series compensation capacitors on the grid side.

[0031] Optional, multi-scenario time-frequency domain response curves, including:

[0032] The power curve of the renewable energy power generation equipment obtained after applying the grid ground fault disturbance;

[0033] After applying a power step disturbance to the renewable energy power generation equipment, the power curve, as well as the frequency and damping data of the dominant oscillation mode are obtained;

[0034] After applying a small sinusoidal signal disturbance to a new energy power generation device, the frequency response curve of any electrical quantity to the disturbance is obtained;

[0035] The power curve, as well as the frequency and damping data of the dominant oscillation mode are obtained after applying the disturbance of reducing the short-circuit capacity on the grid side or connecting the series compensation capacitor on the grid side.

[0036] Optionally, the simulation system is specifically a digital simulation model, or a linearized mathematical model established based on the digital simulation model.

[0037] Optionally, the parameter group corresponding to each particle in the particle swarm is substituted into the simulation system for calculation, specifically:

[0038] Conduct a simulated disturbance test on the simulation system to obtain the curve data of the simulation test, and use the weighted error between the curve data and the multi-scenario time-frequency domain response curve as the fitness value of the particles in the particle swarm;

[0039] For the parameter group corresponding to the particles in the particle swarm, the individual optimal solution and the global optimal solution are calculated according to the fitness value. If the global optimal solution meets the convergence condition or the number of iterations reaches the limit, the global optimal solution is used as the optimal parameter group.

[0040] The present invention also proposes a parameter identification system for a new energy unit based on multiple test scenarios, comprising:

[0041] Data acquisition unit, to obtain multi-scenario time-frequency domain response curves of disturbance tests of new energy power generation equipment;

[0042] A simulation initialization unit is used to construct a parameter group to be identified for the new energy power generation equipment, the parameter group to be identified is regarded as a parameter to be optimized for the particle swarm optimization algorithm, and the particle swarm is randomly initialized;

[0043] The simulation identification unit substitutes the parameter group corresponding to each particle in the particle group into the simulation system for calculation until the calculation result meets the termination condition of the calculation, and determines the optimal parameter group, which is the parameter identification result.

[0044] Optional disturbance tests include: grid grounding fault disturbance, power step disturbance of renewable energy power generation equipment, sinusoidal small signal disturbance of renewable energy power generation equipment, reduction of grid-side short-circuit capacity or connection of series compensation capacitors on the grid side.

[0045] Optional, multi-scenario time-frequency domain response curves, including:

[0046] The power curve of the renewable energy power generation equipment obtained after applying the grid ground fault disturbance;

[0047] After applying a power step disturbance to the renewable energy power generation equipment, the power curve, as well as the frequency and damping data of the dominant oscillation mode are obtained;

[0048] After applying a sinusoidal small signal disturbance to the new energy power generation equipment, the frequency response curve of any electrical quantity to the disturbance is obtained; after applying the disturbance of reducing the short-circuit capacity on the grid side or connecting the series compensation capacitor on the grid side, the power curve, as well as the frequency and damping data of the dominant oscillation mode are obtained.

[0049] Optionally, the simulation system is specifically a digital simulation model, or a linearized mathematical model established based on the digital simulation model.

[0050] Optionally, the parameter group corresponding to each particle in the particle swarm is substituted into the simulation system for calculation, specifically:

[0051] Conduct a simulated disturbance test on the simulation system to obtain the curve data of the simulation test, and use the weighted error between the curve data and the multi-scenario time-frequency domain response curve as the fitness value of the particles in the particle swarm;

[0052] For the parameter group corresponding to the particles in the particle swarm, the individual optimal solution and the global optimal solution are calculated according to the fitness value. If the global optimal solution meets the convergence condition or the number of iterations reaches the limit, the global optimal solution is used as the optimal parameter group.

[0053] The present invention is based on multiple disturbance test scenarios to obtain the response characteristics of new energy power generation equipment at multiple time scales, overcoming the limitation that it is difficult to identify multiple control parameters in a traditional single test scenario, and can perform parameter identification more accurately;

[0054] The present invention adopts a particle swarm optimization algorithm to perform comprehensive identification of multiple test scenarios, which is conducive to achieving the unification of multiple identification targets and at the same time improves the accuracy and efficiency of parameter identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 The basic topology and control principle diagram of the direct-drive wind turbine in the background technology of the present invention;

[0056] Figure 2 A simplified schematic diagram of a direct-drive wind turbine / doubly-fed wind turbine connected to a power grid in the background technology of the present invention;

[0057] Figure 3 is a flow chart of the method of the present invention;

[0058] Figure 4 It is a system structure diagram of the present invention. DETAILED DESCRIPTION

[0059] Now, exemplary embodiments of the present invention are described with reference to the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided to disclose the present invention in detail and completely and to fully convey the scope of the present invention to those skilled in the art. The terms used in the exemplary embodiments shown in the accompanying drawings are not intended to limit the present invention. In the accompanying drawings, the same units / elements are marked with the same reference numerals.

[0060] Unless otherwise specified, the terms (including technical terms) used herein have the commonly understood meanings to those skilled in the art. In addition, it is understood that the terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0061] The present invention will be further described below in conjunction with specific embodiments:

[0062] The present invention proposes a parameter identification method for a new energy unit based on multiple test scenarios, such as Figure 3 As shown, including:

[0063] According to the actual engineering scenarios of new energy power generation equipment, build a test system for parameter identification;

[0064] Conduct disturbance tests on the test system to obtain multi-scenario time-frequency domain response curves of new energy power generation equipment;

[0065] Construct a simulation system corresponding to the test system, regard the parameter group of the new energy power generation equipment to be identified as the parameters to be optimized by the particle swarm optimization algorithm, and randomly initialize the particle swarm

[0066] The parameter group corresponding to each particle in the particle swarm is substituted into the simulation system for calculation until the calculation result meets the termination condition of the calculation, and the optimal parameter group is determined, and the optimal parameter group is the parameter identification result.

[0067] The above method is described in detail as follows:

[0068] Construction of test system and simulation system.

[0069] The test system is an actual engineering scenario, or a simulation scenario close to the actual engineering scenario; and the system used for parameter identification based on the test system is called a simulation system.

[0070] The test system can be implemented in two ways: one is the actual power system containing renewable energy power generation equipment; the other is to connect the physical controller of renewable energy power generation equipment to the digital-analog hybrid simulation platform to form a simulation test system close to the actual equipment characteristics.

[0071] The simulation system includes two types of models: one is the digital simulation model established in the electromagnetic transient simulation software, and the other is a linearized mathematical model established through deduction based on the digital simulation model.

[0072] In the test system, the following four disturbance tests were carried out:

[0073] 1) Apply a grounding fault to the power grid and obtain the power curve of the new energy power generation equipment;

[0074] 2) Apply power step disturbance to the renewable energy power generation equipment to obtain the frequency and damping of the dominant oscillation mode of the system in the dynamic process;

[0075] 3) Sinusoidal small signal disturbance of new energy power generation equipment, and obtaining the frequency response curve of any electrical quantity to the disturbance;

[0076] 4) Reduce the short-circuit capacity on the grid side or connect a series compensation capacitor on the grid side to obtain the system power curve after the disturbance, as well as the frequency and damping of the dominant oscillation mode.

[0077] The parameter optimization identification process includes the following:

[0078] Step 1: Construct a parameter group to be identified, which includes the control parameters of current control, DC voltage control, power control, phase-locked loop and other links of the new energy power generation equipment. This parameter group is regarded as the parameters to be optimized by the particle swarm optimization algorithm, and the particle swarm is randomly initialized.

[0079] Step 2: Substitute the parameter group corresponding to each particle into the simulation system, simulate and analyze the test process, and obtain the analysis results, that is, the curve data of the simulation test, and use the weighted error between the analysis results and the actual test results, that is, the multi-scenario time-frequency domain response curve, as the fitness value of the particle. Obtain the fitness values ​​of all particles, record the corresponding parameter groups, and calculate the individual optimal solution and the global optimal solution.

[0080] The objective function used to calculate the particle fitness value is as follows:

[0081]

[0082] Wherein, subscript T represents the test result, subscript S represents the simulation analysis result, and the number behind it represents the test number; er represents the error, k1, k2, k3 and k4 are weight coefficients; N is the total number of samples of time or frequency; P T1 (n) and Q T1 (n) respectively represent the active power and reactive power values ​​of the nth time step obtained in the experiment; λ T2 (n) represents the dominant characteristic value of the system obtained through mode analysis based on the test curve, λ S2 (n) represents the dominant eigenvalue of the system calculated directly by the linearized model. The real part of the dominant eigenvalue of the system represents the frequency of the oscillation mode, and the imaginary part represents the damping of the oscillation mode. T3 (n) represents the input admittance value of the new energy power generation equipment at the nth frequency point obtained by frequency scanning of the test system.

[0083] Step 3: Based on the iterative formula of the particle swarm optimization algorithm, loop through step 2 until the iteration termination condition is met. The obtained optimal parameter group is used as the parameter identification result.

[0084] The present invention also proposes a new energy unit parameter identification system 200 based on multiple test scenarios, such as Figure 4 As shown, including:

[0085] The data acquisition unit 201 obtains multi-scenario time-frequency domain response curves of the disturbance test of the new energy power generation equipment;

[0086] The simulation initialization unit 202 constructs a parameter group to be identified of the new energy power generation equipment, regards the parameter group to be identified as a parameter to be optimized of the particle swarm optimization algorithm, and randomly initializes the particle swarm;

[0087] The simulation identification unit 203 substitutes the parameter group corresponding to each particle in the particle group into the simulation system for calculation until the calculation result meets the termination condition of the calculation, and determines the optimal parameter group, which is the parameter identification result.

[0088] Among them, the disturbance test includes: grid grounding fault disturbance, power step disturbance of new energy power generation equipment, sinusoidal small signal disturbance of new energy power generation equipment, reducing the short-circuit capacity on the grid side or connecting the series compensation capacitor on the grid side.

[0089] Among them, the time-frequency domain response curves of multiple scenarios include:

[0090] The power curve of the renewable energy power generation equipment obtained after applying the grid ground fault disturbance;

[0091] After applying a power step disturbance to the renewable energy power generation equipment, the power curve, as well as the frequency and damping data of the dominant oscillation mode are obtained;

[0092] After applying a sinusoidal small signal disturbance to the new energy power generation equipment, the frequency response curve of any electrical quantity to the disturbance is obtained; after applying the disturbance of reducing the short-circuit capacity on the grid side or connecting the series compensation capacitor on the grid side, the power curve, as well as the frequency and damping data of the dominant oscillation mode are obtained.

[0093] The simulation system is specifically a digital simulation model, or a linearized mathematical model established based on the digital simulation model.

[0094] Among them, the parameter group corresponding to each particle in the particle swarm is substituted into the simulation system for calculation, specifically:

[0095] Conduct a simulated disturbance test on the simulation system to obtain the curve data of the simulation test, and use the weighted error between the curve data and the multi-scenario time-frequency domain response curve as the fitness value of the particles in the particle swarm;

[0096] For the parameter group corresponding to the particles in the particle swarm, the individual optimal solution and the global optimal solution are calculated according to the fitness value. If the global optimal solution meets the convergence condition or the number of iterations reaches the limit, the global optimal solution is used as the optimal parameter group.

[0097] The present invention is based on multiple disturbance test scenarios to obtain the response characteristics of new energy power generation equipment at multiple time scales, overcoming the limitation that it is difficult to identify multiple control parameters in a traditional single test scenario, and can perform parameter identification more accurately;

[0098] The present invention adopts a particle swarm optimization algorithm to perform comprehensive identification of multiple test scenarios, which is conducive to achieving the unification of multiple identification targets and at the same time improves the accuracy and efficiency of parameter identification.

[0099] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0100] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0101] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0102] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0103] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0104] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A parameter identification method for a new energy unit based on multiple test scenarios, the method comprising: According to the actual engineering scenarios of new energy power generation equipment, build a test system for parameter identification; Conduct disturbance tests on the test system to obtain multi-scenario time-frequency domain response curves of new energy power generation equipment; A simulation system corresponding to the experimental system is constructed, the parameter group of the new energy power generation equipment to be identified is regarded as the parameters to be optimized by the particle swarm optimization algorithm, and the particle swarm is randomly initialized; Substitute the parameter group corresponding to each particle in the particle swarm into the simulation system for calculation until the calculation result meets the termination condition of the calculation, and determine the optimal parameter group, which is the parameter identification result, specifically: Conduct a simulated disturbance test on the simulation system to obtain the curve data of the simulation test, and use the weighted error between the curve data and the multi-scenario time-frequency domain response curve as the fitness value of the particles in the particle swarm; The objective function used to calculate the particle fitness value is as follows: Wherein, subscript T represents the test result, subscript S represents the simulation analysis result, and the number behind it represents the test number; er represents the error, k1, k2, k3 and k4 are weight coefficients; N is the total number of samples of time or frequency; P T1 (n) and Q T1 (n) respectively represent the active power and reactive power values ​​of the nth time step obtained in the experiment; λ T2 (n) represents the dominant characteristic value of the system obtained through mode analysis based on the test curve, λ S2 (n) represents the dominant eigenvalue of the system calculated directly by the linearized model. The real part of the dominant eigenvalue of the system represents the frequency of the oscillation mode, and the imaginary part represents the damping of the oscillation mode; Y T3 (n) represents the input admittance value of the new energy power generation equipment at the nth frequency point obtained by frequency scanning of the test system; For the parameter group corresponding to the particles in the particle swarm, the individual optimal solution and the global optimal solution are calculated according to the fitness value. If the global optimal solution meets the convergence condition or the number of iterations reaches the limit, the global optimal solution is used as the optimal parameter group.

2. The method according to claim 1, wherein the disturbance test comprises: Grid grounding fault disturbance, power step disturbance of new energy power generation equipment, sinusoidal small signal disturbance of new energy power generation equipment, reduction of short-circuit capacity on the grid side or disturbance caused by connecting series compensation capacitor on the grid side.

3. According to the method of claim 1, the multi-scenario time-frequency domain response curve comprises: After applying the ground fault disturbance of the power grid, the power curve of the new energy power generation equipment is obtained; After applying a power step disturbance to the renewable energy power generation equipment, the power curve, as well as the frequency and damping data of the dominant oscillation mode are obtained; After applying a small sinusoidal signal disturbance to the renewable energy power generation equipment, the frequency response curve of any electrical quantity to the disturbance is obtained; After applying the disturbance of reducing the short-circuit capacity on the grid side or connecting the series compensation capacitor on the grid side, the power curve, as well as the frequency and damping data of the dominant oscillation mode are obtained.

4. According to the method of claim 1, the simulation system is specifically a digital simulation model, or a linearized mathematical model established based on the digital simulation model.

5. A parameter identification system for a new energy unit based on multiple test scenarios, the system comprising: Data acquisition unit, to obtain multi-scenario time-frequency domain response curves of disturbance tests of new energy power generation equipment; A simulation initialization unit is used to construct a parameter group to be identified for the new energy power generation equipment, the parameter group to be identified is regarded as a parameter to be optimized for the particle swarm optimization algorithm, and the particle swarm is randomly initialized; The simulation identification unit substitutes the parameter group corresponding to each particle in the particle swarm into the simulation system for calculation until the calculation result meets the termination condition of the calculation, and determines the optimal parameter group, which is the parameter identification result, specifically: Conduct a simulated disturbance test on the simulation system to obtain the curve data of the simulation test, and use the weighted error between the curve data and the multi-scenario time-frequency domain response curve as the fitness value of the particles in the particle swarm; The objective function used to calculate the particle fitness value is as follows: Wherein, subscript T represents the test result, subscript S represents the simulation analysis result, and the number behind it represents the test number; er represents the error, k1, k2, k3 and k4 are weight coefficients; N is the total number of samples of time or frequency; P T1 (n) and Q T1 (n) respectively represent the active power and reactive power values ​​of the nth time step obtained in the experiment; λ T2 (n) represents the dominant characteristic value of the system obtained through mode analysis based on the test curve, λ S2 (n) represents the dominant eigenvalue of the system calculated directly by the linearized model. The real part of the dominant eigenvalue of the system represents the frequency of the oscillation mode, and the imaginary part represents the damping of the oscillation mode; Y T3 (n) represents the input admittance value of the new energy power generation equipment at the nth frequency point obtained by frequency scanning of the test system; For the parameter group corresponding to the particles in the particle swarm, the individual optimal solution and the global optimal solution are calculated according to the fitness value. If the global optimal solution meets the convergence condition or the number of iterations reaches the limit, the global optimal solution is used as the optimal parameter group.

6. The system according to claim 5, wherein the disturbance test comprises: Grid grounding fault disturbance, power step disturbance of new energy power generation equipment, sinusoidal small signal disturbance of new energy power generation equipment, reduction of short-circuit capacity on the grid side or disturbance caused by connecting series compensation capacitor on the grid side.

7. The system according to claim 5, wherein the multi-scenario time-frequency domain response curve comprises: After applying the ground fault disturbance of the power grid, the power curve of the new energy power generation equipment is obtained; After applying a power step disturbance to the renewable energy power generation equipment, the power curve, as well as the frequency and damping data of the dominant oscillation mode are obtained; After applying a small sinusoidal signal disturbance to the renewable energy power generation equipment, the frequency response curve of any electrical quantity to the disturbance is obtained; After applying the disturbance of reducing the short-circuit capacity on the grid side or connecting the series compensation capacitor on the grid side, the power curve, as well as the frequency and damping data of the dominant oscillation mode are obtained.

8. According to the system of claim 5, the simulation system is specifically a digital simulation model, or a linearized mathematical model established based on the digital simulation model.

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