Online parameter identification method for new energy grid-connected inverter cluster and related device

Abstract

The application discloses a new energy grid-connected inverter cluster online parameter identification method and related device, including the following steps: dynamic mode decomposition is carried out on the three-phase voltage and total current signals of the point of common coupling, and a dominant spatiotemporal pattern set representing the overall dynamic behavior of the cluster is extracted; a parameterized virtual observer model is constructed for each inverter in the cluster, and a unified input excitation is generated based on the three-phase voltage signals of the point of common coupling and applied to all virtual observers; a collaborative loss function is set; an online adaptive optimization algorithm is used to minimize the collaborative loss function, and the multi-dimensional parameter vectors of all virtual observers are updated in parallel and in real time until convergence, and the identification result is output; the method and related device can be operated online and in real time, and can simultaneously identify the multi-dimensional key parameters of the new energy grid-connected inverter cluster.

Description

Technical Field

[0001] This invention belongs to the field of power system state estimation and power electronic equipment modeling, and relates to an online parameter identification method and related device for a new energy grid-connected inverter cluster. Background Technology

[0002] In the context of large-scale renewable energy grid integration, the fault characteristic analysis, protection setting, and stability control of the power grid highly rely on the accurate simulation of the transient behavior of grid-connected inverters. The transient behavior of the inverter is determined by its internal controller parameters (such as current loop PI parameters, phase-locked loop parameters, and limiting values). Since these parameters are core technical secrets of equipment manufacturers and may be adjusted during operation, they cannot be directly obtained. Therefore, the "parameter identification" technology, which inverts internal parameters through external electrical quantity measurements, becomes crucial.

[0003] Existing technologies still have the following inherent limitations, making it difficult to meet the needs of real-world complex power grid scenarios: 1. Poor scenario adaptability: Existing methods mainly target parameter identification of a single inverter under three-phase symmetrical faults. However, real-world renewable energy power plants often operate with multiple inverters in parallel, and the incidence of asymmetrical faults (such as single-phase grounding) in the power grid is higher. When multiple inverters are in parallel, the dynamic coupling between them is severe, rendering existing single-inverter identification models and decoupling methods ineffective.

[0004] 2. High model dependence: Most existing methods are based on the precise physical control block diagram of the inverter (such as the dq axis equation in the attached paper) to establish identification models. When facing a "black box" inverter with unknown control strategies, or when the control strategies are complex (such as having virtual inertia, resonant control, etc.), it is difficult to establish an accurate prior analytical model, which makes the identification methods unusable.

[0005] 3. Insufficient real-time performance: Existing methods generally employ offline global optimization algorithms such as particle swarm optimization and genetic algorithms. These algorithms require a complete transient data window and perform a large number of iterative calculations, taking hundreds of milliseconds to several seconds, which cannot meet the requirements of online monitoring, adaptive protection, and other applications for real-time parameter updates (millisecond level).

[0006] 4. Limited Identification Dimensions: Existing research focuses primarily on the current loop PI parameters. In reality, the phase-locked loop bandwidth, DC voltage control loop parameters, various limiting values ​​(current, power), and virtual impedance parameters collectively determine the inverter's transient characteristics. Identifying a single parameter cannot comprehensively characterize the equipment's features.

[0007] Therefore, there is an urgent need in this field to develop a new inverter cluster parameter identification technology that does not rely on an accurate prior physical model, can adapt to complex operating conditions of multi-machine parallel operation and asymmetric faults, can operate online in real time, and can simultaneously identify multi-dimensional key parameters. Summary of the Invention

[0008] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and related device for online parameter identification of new energy grid-connected inverter clusters. This method and related device can operate online in real time and can simultaneously identify multi-dimensional key parameters of new energy grid-connected inverter clusters.

[0009] To achieve the above objectives, this invention discloses an online parameter identification method for a new energy grid-connected inverter cluster, comprising the following steps: 1) Synchronously collect the three-phase voltage and total current signals at the point of common coupling during grid disturbances, as well as the three-phase current signals at the output of each inverter in the cluster; 2) Perform dynamic mode decomposition on the three-phase voltage and total current signals of the common connection point to extract the dominant spatiotemporal mode set that characterizes the overall dynamic behavior of the cluster; 3) Construct a parameterized virtual observer model for each inverter in the cluster, and generate a unified input excitation to all virtual observers based on the three-phase voltage signal of the common connection point; 4) Set a collaborative loss function, which simultaneously measures the individual fitting error between the output of each virtual observer and the measured current of the corresponding inverter, as well as the consistency error between the sum of the outputs of all virtual observers and the measured total current signal on the dominant spatiotemporal mode set. 5) An online adaptive optimization algorithm is adopted to minimize the collaborative loss function, and the multidimensional parameter vectors of all virtual observers are updated in parallel and in real time until convergence, and the identification results are output.

[0010] Furthermore, the dynamic mode decomposition process in step S) is as follows: construct the spatiotemporal snapshot matrix of voltage and total current, solve the eigenvalues ​​and eigenvectors of its approximate linear operator through singular value decomposition and low-rank approximation, and select the top r modes with the largest eigenvalue magnitudes as the dominant spatiotemporal mode set.

[0011] Furthermore, the parameterized virtual observer model in step 3) is a high-order linear state-space model or a nonlinear dynamic model containing a saturation limiting element. Its multidimensional parameter vector to be identified includes at least the current inner loop proportional coefficient, integral coefficient, current command limiting value, and phase-locked loop proportional coefficient and integral coefficient.

[0012] Furthermore, in step 4), the consistency error is measured by calculating the projection norm of the difference between the total predicted current and the total measured current onto the subspace spanned by the dominant spatiotemporal mode feature vector.

[0013] Furthermore, the online adaptive optimization algorithm in step 5) is an adaptive moment estimation algorithm or a recursive least squares method, and the parameter update is performed online within each sliding time window, the length of which is less than 50 milliseconds.

[0014] Furthermore, the collaborative loss function is:

[0015] in, These are adjustable weighting coefficients. For individual fitting loss, This is due to losses in cluster collaboration.

[0016] Furthermore, the higher-order linear state-space model is as follows:

[0017] in, For the observer state, The parameter vector to be identified; containing a matrix Key elements and amplitude limits , It is a saturation function.

[0018] This invention discloses an online parameter identification system for a new energy grid-connected inverter cluster, comprising: The acquisition module is used to synchronously acquire the three-phase voltage and total current signals at the point of common coupling during grid disturbances, as well as the three-phase current signals at the output of each inverter in the cluster. The decomposition module is used to perform dynamic pattern decomposition on the three-phase voltage and total current signals of the common connection point, and extract the dominant spatiotemporal pattern set that characterizes the overall dynamic behavior of the cluster. The generation module is used to construct a parameterized virtual observer model for each inverter in the cluster, and generate a unified input excitation to all virtual observers based on the three-phase voltage signal of the common connection point. The configuration module is used to configure the collaborative loss function, which simultaneously measures the individual fitting error between the output of each virtual observer and the measured current of the corresponding inverter, as well as the consistency error between the sum of the outputs of all virtual observers and the measured total current signal on the dominant spatiotemporal mode set. The optimization module is used to employ an online adaptive optimization algorithm to minimize the collaborative loss function, update the multidimensional parameter vectors of all virtual observers in parallel and in real time until convergence, and output the identification results.

[0019] This invention discloses a computer 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 the steps of the online parameter identification method for the new energy grid-connected inverter cluster.

[0020] This invention discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the online parameter identification method for the new energy grid-connected inverter cluster.

[0021] The present invention has the following beneficial effects: The online parameter identification method and related device for new energy grid-connected inverter clusters described in this invention simultaneously collects electrical quantities from the point of common connection and multiple inverters during operation; extracts the dominant dynamic features of the cluster from global data using dynamic mode decomposition; constructs a parameterized virtual observer for each inverter and builds a collaborative loss function using the extracted dynamic features as constraints; finally, adopts an online adaptive optimization algorithm to update the multidimensional parameter vectors of all observers in real time, achieving parallel decoupled identification. This invention eliminates the dependence on precise individual model, adapts to complex operating conditions, and can identify multidimensional parameters including current loop, phase-locked loop, and limiting value online. It has a fast identification speed and high accuracy, providing key technical support for state perception, protection setting, and stability analysis of new energy power plants. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0026] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0027] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0028] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0029] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0031] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0032] Example 1 refer to Figure 1 This invention proposes a three-layer identification framework of "data-driven feature extraction + virtual model collaborative observation + online gradient optimization". Its core idea is: dynamically extract feature patterns from the cluster's global measurement data as a "guide"; construct a flexible and adjustable virtual dynamic model for each inverter as an "observer"; coordinate all observers using the global feature guide, and quickly adjust the parameters of each observer through an online algorithm to match their output with the measured data, thereby retrieving the true parameters.

[0033] Specifically, the online parameter identification method for new energy grid-connected inverter clusters described in this invention includes the following steps: 1) Synchronous acquisition and preprocessing of multi-source heterogeneous data; Synchronous measurement units are deployed at the point of common coupling (PCC) of the new energy power plant and at the AC output of each inverter to be identified. When grid disturbances occur (voltage dips, surges, phase jumps, etc.), the following three sets of time series data are synchronously collected at a high-frequency sampling rate (e.g., above 10kHz): Global common quantity: Three-phase voltage at point PCC and total three-phase current .

[0034] Local individual quantity: number Three-phase current at the inverter output , .

[0035] The acquired raw data is preprocessed, including but not limited to: anti-aliasing filtering, DC bias removal, and data alignment.

[0036] 2) Cluster spatiotemporal dynamic feature extraction based on dynamic pattern decomposition (DMD); 21) Construct a high-dimensional observation matrix: Organize the preprocessed PCC point voltage disturbance and total current response data into a space-time snapshot matrix X and Y, where Y is the single-step delay of X in time.

[0037] 22) Execute the DMD algorithm: Solve for the eigenvalues ​​and eigenvectors of the best-fit linear operator A between matrices X and Y by using singular value decomposition (SVD) and low-rank approximation.

[0038] 23) Extracting the dominant dynamic pattern: Each DMD pattern contains a complex feature value. The real part determines the decay / growth rate of the mode, the imaginary part determines the oscillation frequency, and a spatial eigenvector. This describes the amplitude and phase distribution of the mode across each measurement channel. The channel with the largest energy (magnitude) is selected. Each pattern serves as the dominant dynamic pattern set. .

[0039] 24) Pattern Physical Correlation Analysis: Based on eigenvalues The frequency / attenuation characteristics can initially correlate different modes with underlying physical processes. For example, oscillation modes close to the power frequency may be related to phase-locked loop dynamics or current loop resonance; high-frequency attenuation modes may be related to the current loop's own dynamics; and low-frequency modes may be related to the power outer loop or grid strength. This analysis provides prior knowledge for subsequent parameter decoupling.

[0040] 3) Construct a virtual cooperative observer network; 31) Design a parametric virtual observer: Design a general-structured, parameter-adjustable virtual dynamic model (i.e., a virtual observer) for each inverter in the cluster. This model does not need to be an exact replica of the real controller, but rather has sufficient degrees of freedom to fit its input-output behavior. For example, a high-order state-space model with output limiting can be used.

[0041] in, For the observer state, The parameter vector to be identified; containing a matrix Key elements and amplitude limits , It is a saturation function.

[0042] 32) Unified excitation input: All virtual observers receive the same input excitation, i.e., the PCC voltage is obtained by processing the same phase-locked loop. shaft voltage This is to ensure that all observers are under the same grid conditions.

[0043] 33) Define the collaborative loss function: Construct a two-layer loss function ,in .

[0044] First layer: Individual fit loss : Measure the output of the virtual observer for each inverter Compared with the actual measured current The mean square error.

[0045] Second layer: Cluster coordination loss : Measure the sum of the outputs of all virtual observers Total current measured at PCC point The projection error on the dominant dynamic mode of DMD. That is, it forces the total predicted current and the total measured current to share the same dominant dynamic characteristics. This introduces a key global constraint, using the "collective behavior" information of the cluster to correct and guide the parameter updates of individual observers, achieving natural decoupling.

[0046] The total loss function is: ,in, These are adjustable weighting coefficients.

[0047] 4) Real-time parameter identification based on online adaptive optimization algorithm 41) Expanding the parameter vector to be identified: For the first... Taiwan inverter, define its multidimensional parameter vector:

[0048] This vector can encompass current loop PI, phase-locked loop PI, current limiting, virtual inertia time constant, etc.

[0049] 42) Online Optimization Engine: Employs optimization algorithms suitable for online, streaming data processing, such as an extension of the Adaptive Moment Estimation (Adam) algorithm or Recursive Least Squares (RLS). Within each short, sliding data time window (e.g., 5-10 milliseconds): Measurement data of the current time window , , Input system.

[0050] Forward propagation: Each virtual observer propagates based on the current parameters. Calculate the predicted current.

[0051] Calculate the collaborative loss function .

[0052] Backpropagation / gradient calculation: Calculating the loss function with respect to all parameters using automatic differentiation techniques. gradient .

[0053] Parameter update: Use optimizers such as Adam to update rules and fine-tune all parameters in real time. .

[0054]

[0055] in, For learning rate, This represents the number of online iteration steps.

[0056] 43) Convergence and Output: As online data continues to flow in, the parameter estimates are constantly updated and tend to stabilize. The system outputs the latest parameter identification results in real time. .

[0057] Example 2 Parameter identification of a photovoltaic power station's dual-unit parallel system under a single-phase ground fault.

[0058] 1) System configuration and experimental setup; Simulation platform: The system model was built in the MATLAB / Simulink and PLECS co-simulation environment.

[0059] Inverter cluster: It consists of two 500kW three-phase full-bridge photovoltaic inverters (Inv#1, Inv#2) connected in parallel to the 380V bus via an LCL filter, and connected to the 10kV grid via a step-up transformer.

[0060] Controller differences: Inv#1 uses conventional grid-following control with a current loop bandwidth of 200Hz; Inv#2 uses an initial version of grid-forming control with virtual inertia functionality, a current loop bandwidth of 150Hz, and a virtual inertia time constant of 0.5s. The phase-locked loop parameters and current limiting values ​​also differ between the two.

[0061] Fault setting: At simulation time t=2.0s, a metallic ground fault of phase A occurs on the 10kV side, causing the voltage of phase A at PCC point (380V side) to drop to 0.3pu, and the voltage of phases B and C to rise to 1.1pu.

[0062] 2) Perform this invention; Data acquisition: The PCC voltage, total current, and output current data of each of the two inverters are synchronously acquired at a frequency of 20kHz within a 200ms time window after the fault.

[0063] DMD Feature Extraction: DMD was performed on the PCC voltage and total current data, and four dominant patterns were extracted as a result: Mode 1: Frequency ≈ 50Hz, very slow attenuation, spatial vector shows strong correlation with phase A. Associated with the power grid asymmetry component.

[0064] Mode 2: Frequency ≈ 195Hz, fast decay (time constant about 20ms), associated with the current loop dynamics of Inv#1.

[0065] Mode 3: Frequency ≈ 155Hz, moderate attenuation (time constant approximately 30ms), related to the current loop dynamics and virtual inertia effects of Inv#2.

[0066] Mode 4: There is a slight frequency deviation (oscillation between 48-52Hz) around 50Hz, which is slow to decay and is related to the synchronous dynamic process of the phase-locked loops of the two inverters.

[0067] Constructing a cooperative observer: A third-order general virtual observer model is established for each of the two inverters, with the outputs including d-axis and q-axis current limiting.

[0068] The parameter vector to be identified is defined as: .

[0069] Set the weights of the collaborative loss function. .

[0070] Online identification: Using the Adam optimizer, initial learning rate .

[0071] A sliding time window with a window length of 10ms and a step size of 1ms is used for online updates.

[0072] Online identification started at t=2.005s, and after approximately 40ms (4 complete data windows) of iterative updates, the parameter estimates basically converged.

[0073] Results and verification; Identification accuracy: The identification error of all parameters is less than 5%, of which the error of the current loop PI parameter is less than 3% and the error of the limiting value is less than 2%.

[0074] Waveform fitting: The identified parameters are substituted into the actual controller models of the two inverters for simulation. The normalized root mean square error (NRMSE) between the output current waveforms of each inverter and the original "measured" waveforms is less than 2%.

[0075] Comparative experiment: The method described in the attached paper (requiring a known accurate single-machine model and assuming a symmetrical fault) was used to identify the two inverters individually. Under asymmetrical fault conditions, the dq-axis model mismatch led to a significant increase in identification error (some parameter errors exceeded 15%), and the phase-locked loop parameters could not be identified. This fully demonstrates the superiority of this invention under complex operating conditions.

[0076] Hardware-in-the-loop (HIL) verification; The aforementioned dual-machine system was reproduced on the HIL platform, which consists of an RTDS real-time simulator and an actual physical controller. The algorithm of this invention is deployed on an industrial control computer, exchanging real-time data with RTDS via a high-speed communication interface (such as Aurora). Experimental results show that, despite actual communication delays and measurement noise, the method of this invention can still converge stably within approximately 50ms, and the identification accuracy meets engineering requirements (error <8%), verifying its practicality.

[0077] This invention has the following characteristics: This invention is the first to systematically solve the problem of parameter decoupling and identification in multi-machine parallel scenarios, and extends the application scenario from ideal single-machine symmetrical faults to complex multi-machine asymmetrical fault conditions in actual power plants.

[0078] This invention extracts data features using DMD as global constraints and introduces a physical model framework through a virtual observer. This approach reduces the stringent dependence on precise individual models, enhances the method's universality, and avoids the poor physical interpretability of purely data-driven methods.

[0079] This invention employs an online optimization algorithm based on gradient descent to replace the offline swarm intelligence algorithm, reducing the parameter update time from seconds to milliseconds, enabling the identification results to be used for online state perception, adaptive protection setting adjustment, and real-time stability early warning.

[0080] This invention can simultaneously identify multi-dimensional parameters that affect transient characteristics, including not only the basic current loop PI, but also phase-locked loop parameters, limiting values, etc., providing complete data support for more refined simulation modeling and system analysis.

[0081] The collaborative observation mechanism of this invention uses global information to correct local errors. DMD itself has a certain filtering capability for noise, which enables the method to maintain good identification accuracy even when measurement noise and model mismatch exist.

[0082] Example 3 The online parameter identification system for new energy grid-connected inverter clusters of the present invention includes: The acquisition module is used to synchronously acquire the three-phase voltage and total current signals at the point of common coupling during grid disturbances, as well as the three-phase current signals at the output of each inverter in the cluster. The decomposition module is used to perform dynamic pattern decomposition on the three-phase voltage and total current signals of the common connection point, and extract the dominant spatiotemporal pattern set that characterizes the overall dynamic behavior of the cluster. The generation module is used to construct a parameterized virtual observer model for each inverter in the cluster, and generate a unified input excitation to all virtual observers based on the three-phase voltage signal of the common connection point. The configuration module is used to configure the collaborative loss function, which simultaneously measures the individual fitting error between the output of each virtual observer and the measured current of the corresponding inverter, as well as the consistency error between the sum of the outputs of all virtual observers and the measured total current signal on the dominant spatiotemporal mode set. The optimization module is used to employ an online adaptive optimization algorithm to minimize the collaborative loss function, update the multidimensional parameter vectors of all virtual observers in parallel and in real time until convergence, and output the identification results.

[0083] The module division in this embodiment is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in each embodiment of this application can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0084] Example 4 A computer device includes 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 the steps of an online parameter identification method for a new energy grid-connected inverter cluster. For example, the method includes: synchronously acquiring three-phase voltage and total current signals at the point of common coupling (PCC) during grid disturbances, as well as three-phase current signals from the outputs of each inverter within the cluster; performing dynamic mode decomposition on the PCC's three-phase voltage and total current signals to extract a dominant spatiotemporal mode set characterizing the overall dynamic behavior of the cluster; constructing a parameterized virtual observer model for each inverter within the cluster, and generating a unified input excitation to all virtual observers based on the PCC's three-phase voltage signals; setting a collaborative loss function, which simultaneously measures the individual fitting error between the output of each virtual observer and the measured current of the corresponding inverter, as well as the consistency error between the sum of all virtual observer outputs and the measured total current signal on the dominant spatiotemporal mode set; and employing an online adaptive optimization algorithm to minimize the collaborative loss function, updating the multidimensional parameter vectors of all virtual observers in parallel and in real-time until convergence, and outputting the identification result. The memory may include main memory, such as high-speed random access memory (RAM), or non-volatile memory, such as at least one disk storage device. The processor, network interface, and memory are interconnected via an internal bus, which may be an industry-standard architecture bus, a peripheral component interconnection standard bus, or an extended industry-standard architecture bus. The bus can be categorized as an address bus, data bus, or control bus. The memory stores programs; specifically, the program may include program code, which includes computer operation instructions. The memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0085] Example 5 A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the online parameter identification method for a new energy grid-connected inverter cluster. For example, the method includes: synchronously acquiring three-phase voltage and total current signals at the point of common coupling (PCC) during grid disturbances, as well as three-phase current signals at the outputs of each inverter within the cluster; performing dynamic pattern decomposition on the PCC's three-phase voltage and total current signals to extract a dominant spatiotemporal pattern set characterizing the overall dynamic behavior of the cluster; constructing a parameterized virtual observer model for each inverter within the cluster, and generating a unified input excitation to all virtual observers based on the PCC's three-phase voltage signals; setting a collaborative loss function that simultaneously measures the individual fitting error between the output of each virtual observer and the measured current of the corresponding inverter, as well as the consistency error between the sum of all virtual observer outputs and the measured total current signal on the dominant spatiotemporal pattern set; and employing an online adaptive optimization algorithm to minimize the collaborative loss function, updating the multidimensional parameter vectors of all virtual observers in parallel and in real-time until convergence, and outputting the identification result. Specifically, the computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include read-only memory (ROM), hard disk, flash memory, optical disk, magnetic disk, etc.

[0086] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0087] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1A device that provides the functions specified in one or more boxes.

[0088] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0089] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0090] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and disclosure of the invention. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.

[0091] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

[0092] The above description is merely a preferred embodiment of the present invention and does not constitute any limitation on the present invention. Any simple modifications, alterations, or equivalent structural changes made to the above embodiments based on the technical essence of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for online parameter identification of a new energy grid-connected inverter cluster, characterized in that, Includes the following steps: 1) Synchronously collect the three-phase voltage and total current signals at the point of common coupling during grid disturbances, as well as the three-phase current signals at the output of each inverter in the cluster; 2) Perform dynamic mode decomposition on the three-phase voltage and total current signals of the common connection point to extract the dominant spatiotemporal mode set that characterizes the overall dynamic behavior of the cluster; 3) Construct a parameterized virtual observer model for each inverter in the cluster, and generate a unified input excitation to all virtual observers based on the three-phase voltage signal of the common connection point; 4) Set a collaborative loss function, which simultaneously measures the individual fitting error between the output of each virtual observer and the measured current of the corresponding inverter, as well as the consistency error between the sum of the outputs of all virtual observers and the measured total current signal on the dominant spatiotemporal mode set. 5) An online adaptive optimization algorithm is adopted to minimize the collaborative loss function, and the multidimensional parameter vectors of all virtual observers are updated in parallel and in real time until convergence, and the identification results are output.

2. The online parameter identification method for new energy grid-connected inverter clusters according to claim 1, characterized in that, The dynamic mode decomposition process in step S) is as follows: construct the spatiotemporal snapshot matrix of voltage and total current, solve the eigenvalues ​​and eigenvectors of its approximate linear operator through singular value decomposition and low-rank approximation, and select the first r modes with the largest eigenvalue magnitude as the dominant spatiotemporal mode set.

3. The online parameter identification method for new energy grid-connected inverter clusters according to claim 1, characterized in that, The parameterized virtual observer model in step 3) is a high-order linear state-space model or a nonlinear dynamic model containing a saturation limiting element. Its multidimensional parameter vector to be identified includes at least the current inner loop proportional coefficient, integral coefficient, current command limiting value, and phase-locked loop proportional coefficient and integral coefficient.

4. The online parameter identification method for new energy grid-connected inverter clusters according to claim 1, characterized in that, In step 4), the consistency error is measured by calculating the projection norm of the difference between the total predicted current and the total measured current on the subspace spanned by the dominant spatiotemporal mode feature vector.

5. The online parameter identification method for new energy grid-connected inverter clusters according to claim 1, characterized in that, The online adaptive optimization algorithm in step 5) is an adaptive moment estimation algorithm or a recursive least squares method. The parameter update is performed online within each sliding time window, and the length of the sliding time window is less than 50 milliseconds.

6. The method for online parameter identification of new energy grid-connected inverter clusters according to claim 1, characterized in that, The collaborative loss function is: in, These are adjustable weighting coefficients. For individual fitting loss, This is due to losses in cluster collaboration.

7. The online parameter identification method for new energy grid-connected inverter clusters according to claim 1, characterized in that, The higher-order linear state-space model is: in, For the observer state, The parameter vector to be identified; containing a matrix Key elements and amplitude limits , It is a saturation function.

8. An online parameter identification system for a new energy grid-connected inverter cluster, characterized in that, include: The acquisition module is used to synchronously acquire the three-phase voltage and total current signals at the point of common coupling during grid disturbances, as well as the three-phase current signals at the output of each inverter in the cluster. The decomposition module is used to perform dynamic pattern decomposition on the three-phase voltage and total current signals of the common connection point, and extract the dominant spatiotemporal pattern set that characterizes the overall dynamic behavior of the cluster. The generation module is used to construct a parameterized virtual observer model for each inverter in the cluster, and generate a unified input excitation to all virtual observers based on the three-phase voltage signal of the common connection point. The configuration module is used to configure the collaborative loss function, which simultaneously measures the individual fitting error between the output of each virtual observer and the measured current of the corresponding inverter, as well as the consistency error between the sum of the outputs of all virtual observers and the measured total current signal on the dominant spatiotemporal mode set. The optimization module is used to employ an online adaptive optimization algorithm to minimize the collaborative loss function, update the multidimensional parameter vectors of all virtual observers in parallel and in real time until convergence, and output the identification results.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the online parameter identification method for new energy grid-connected inverter clusters as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the online parameter identification method for new energy grid-connected inverter clusters as described in any one of claims 1-7.

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