Method and system for suppressing wide frequency oscillations in photovoltaic grid-connected systems

Abstract

This application discloses a method and system for suppressing broadband oscillations in photovoltaic grid-connected systems. First, the operating parameters of the photovoltaic grid-connected system within a first time period are acquired. Then, based on the operating parameters, a broadband oscillation detection result is generated. The broadband oscillation detection result indicates whether broadband oscillations exist in the photovoltaic grid-connected system within the first time period. Finally, in response to the presence of broadband oscillations in the photovoltaic grid-connected system within the first time period, a suppression strategy is executed. The suppression strategy includes at least one of the following strategies: an adaptive virtual inertia damping control strategy and a virtual impedance strategy based on wide-area measurement. This application can effectively suppress broadband oscillations in photovoltaic grid-connected systems, ensuring the safe, stable, and economical operation of the power grid.

Description

Technical Field

[0001] This application belongs to the field of new power system technology, and in particular relates to a broadband oscillation suppression method and system for photovoltaic grid connection. Background Technology

[0002] As the scale of grid-connected photovoltaic (PV) power continues to expand, the intermittency, randomness, and volatility of its output pose challenges to the stable operation of the power grid. PV power generation is significantly affected by environmental factors, exhibiting high uncertainty in disturbances and diverse oscillation types, severely impacting the grid-friendly nature of PV power plants. The wide-bandwidth oscillations of PV grid connection lead to grid instability. Summary of the Invention

[0003] This application provides a method and system for suppressing broadband oscillations in photovoltaic grid-connected systems, which can effectively suppress broadband oscillations in photovoltaic grid-connected systems and ensure the safe, stable and economical operation of the power grid.

[0004] In a first aspect, embodiments of this application provide a method for suppressing broadband oscillations in a photovoltaic grid-connected system. The method comprises: acquiring operating parameters of a photovoltaic grid-connected system within a first time period; generating a broadband oscillation detection result based on the operating parameters of the photovoltaic grid-connected system; the broadband oscillation detection result indicating whether broadband oscillations exist in the photovoltaic grid-connected system within the first time period; and executing a suppression strategy in response to the presence of broadband oscillations in the photovoltaic grid-connected system within the first time period; the suppression strategy includes at least one of the following strategies: an adaptive virtual inertia damping control strategy and a virtual impedance strategy based on wide-area measurement.

[0005] In conjunction with the first aspect, in one possible manner, the operating parameters include one or more of the following: voltage phasors, current phasors, system frequency, rate of change of frequency, active power, reactive power, and harmonic components.

[0006] In conjunction with the first aspect, in one possible manner, the operating parameters based on the photovoltaic grid-connected system include: Based on the operating parameters of the photovoltaic grid-connected system, the system output impedance of the photovoltaic grid-connected system is determined, and the system output impedance includes positive sequence impedance and negative sequence impedance; based on the system output impedance, a broadband oscillation detection result is generated.

[0007] In conjunction with the first aspect, in one possible manner, generating broadband oscillation detection results based on system output impedance includes: determining the system impedance ratio of the photovoltaic grid-connected system based on the ratio between the system output impedance and the grid input impedance; and generating broadband oscillation detection results based on the Nyquist curve of the system impedance ratio.

[0008] In conjunction with the first aspect, in one possible manner, generating a broadband oscillation detection result based on the Nyquist curve of the system impedance ratio includes: generating a broadband oscillation detection result in response to the Nyquist curve of the system impedance ratio not enclosing a reference point; the broadband oscillation detection result indicating that the photovoltaic grid-connected system does not have broadband oscillations during the first time period; or, determining the oscillation frequency of the photovoltaic grid-connected system in response to the Nyquist curve of the system impedance ratio enclosing a reference point; generating a broadband oscillation detection result in response to the oscillation frequency being within a predetermined broadband range; the broadband oscillation detection result indicating that the photovoltaic grid-connected system has broadband oscillations during the first time period.

[0009] In conjunction with the first aspect, in one possible approach, generating a broadband oscillation detection result based on the operating parameters of the photovoltaic grid-connected system includes: performing time-series alignment and normalization on the operating parameters; constructing a two-dimensional feature image from the normalized operating parameters according to a preset spatiotemporal mapping rule, wherein a pixel value in the two-dimensional feature image corresponds to a parameter value in the normalized operating parameters; inputting the two-dimensional feature image into a pre-trained convolutional neural network model; generating a broadband oscillation detection result based on the output of the convolutional neural network model; the broadband oscillation detection result indicating whether broadband oscillation exists in the photovoltaic grid-connected system within the first time period.

[0010] In conjunction with the first aspect, in one possible manner, the adaptive virtual inertia damping control strategy includes: simulating the rotor motion equations of a synchronous generator using a photovoltaic inverter; adaptively adjusting the virtual inertia J and damping factor D based on the deviation of the system's real-time angular frequency from the rated angular frequency; the virtual inertia... J The adaptive relationship between frequency and frequency is: ; in, For the system's real-time angular frequency, It is the absolute value of the difference between the rated angular frequencies. This refers to the virtual inertia given in the virtual synchronous machine control. J For actual virtual inertia, This is the inertia adjustment coefficient.

[0011] In conjunction with the first aspect, in one possible approach, the adaptive relationship between the damping factor and frequency is: ; in, To provide damping in the control of the virtual synchronous machine; D The actual damping during the operation of the virtual synchronizer; This is the ratio of given damping to given inertia.

[0012] In conjunction with the first aspect, in one possible approach, the virtual impedance strategy based on wide-area measurement includes: acquiring wide-area measurement data from the phasor measurement unit (PMU) of a photovoltaic power plant; analyzing the frequency, amplitude, and phase of the oscillation in real time based on the wide-area measurement data; and introducing a virtual impedance into the inverter control loop based on the analysis results to make the equivalent output impedance of the inverter exhibit inductive characteristics; wherein the virtual impedance is: .

[0013] Secondly, this application provides a broadband oscillation suppression system for photovoltaic grid connection, the system comprising: The acquisition unit is used to acquire the operating parameters of the photovoltaic grid-connected system within the first time period. A broadband oscillation detection unit is used to generate broadband oscillation detection results based on the operating parameters of the photovoltaic grid-connected system; the broadband oscillation detection results indicate whether broadband oscillation exists in the photovoltaic grid-connected system during the first time period; A broadband oscillation suppression unit is used to execute a suppression strategy in response to broadband oscillations occurring in the photovoltaic grid-connected system during the first time period; the suppression strategy includes at least one of the following strategies: an adaptive virtual inertia damping control strategy and a virtual impedance strategy based on wide-area measurement. Attached Figure Description

[0014] Figure 1 A schematic flowchart illustrating a broadband oscillation suppression method for photovoltaic grid connection provided in this application embodiment; Figure 2 A schematic flowchart of another broadband oscillation suppression method for photovoltaic grid connection provided in this application embodiment; Figure 3 This is a schematic diagram of a broadband oscillation suppression system for photovoltaic grid connection provided in an embodiment of this application. Detailed Implementation

[0015] The technical solutions of the embodiments 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, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0016] The terms "first," "second," "third," etc., used in the embodiments of this application are to distinguish different objects, rather than to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, it may include a series of steps or units, or optionally, steps or units not listed, or other steps or units inherent to these processes, methods, products, or devices. The terms "one embodiment" or "some embodiments," etc., mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of the embodiments of this application, do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized.

[0017] In the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0018] In the embodiments of this application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, and c can represent: a, or b, or c, or a and b, or a and c, or b and c, or a, b, and c, where a, b, and c can be single or multiple.

[0019] In the following embodiments of this application, the terms "component," "module," "system," etc., are used to refer to computer-related entities, hardware, firmware, combinations of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, and / or a computer. As illustrated, applications running on computing devices and computing devices can both be components. One or more components may reside in processes and / or execution threads, and components may be located on a single computer and / or distributed among two or more computers. Furthermore, these components can be executed from various computer-readable media on which various data structures are stored. Components can communicate, for example, via local and / or remote processes based on signals having one or more data packets (e.g., data from two components interacting with another component between a local system, a distributed system, and / or a network, such as the Internet interacting with other systems via signals).

[0020] The following explanations of some terms used in this application are provided to facilitate understanding by those skilled in the art.

[0021] 1. Photovoltaic grid-connected system A grid-connected photovoltaic (PV) system refers to a system that directly connects a solar photovoltaic power generation system to the public power grid (the State Grid). The electricity it generates is primarily used for its own purposes; any excess is sold back to the grid, and only when it needs more power is it purchased from the grid.

[0022] Key equipment in a photovoltaic grid-connected system includes a grid-connected inverter. The grid-connected inverter must be synchronized with the frequency, phase, and voltage of the power grid in order to convert the direct current generated by the photovoltaic system into alternating current compatible with the power grid and connect it to the grid.

[0023] The electricity generated by a grid-connected photovoltaic (PV) system is initially supplied directly to the user's own loads (such as home appliances and factory equipment). When PV power generation exceeds the user's immediate electricity demand, the excess electricity is fed back to the public grid through the meter, allowing the user to earn revenue (by selling electricity). When the PV system's power generation is insufficient (e.g., at night or on cloudy or rainy days) or when the user's electricity demand exceeds the PV system's capacity, the system automatically draws power from the public grid, seamlessly switching to ensure the continuity and stability of electricity supply.

[0024] 2. Wideband oscillation Wideband oscillation refers to the undesirable interaction between a photovoltaic power plant (connected to the grid via an inverter) and other components of the power grid (such as capacitive transmission lines, other power electronic devices, and traditional generators). It is a periodic fluctuation in voltage or current that is continuous or unstable and occurs within a wide frequency range (typically covering subsynchronous, synchronous, and supersynchronous frequency bands) from several hertz (Hz) to several kilohertz (kHz).

[0025] Unlike the low-frequency oscillations (typically 0.1-2.5 Hz) caused by traditional synchronous generators, broadband oscillations are caused by the interaction between the fast control links of power electronic devices (such as photovoltaic inverters) and grid impedance, and have a wider frequency range and more complex mechanisms.

[0026] Wideband oscillations in photovoltaic grid-connected systems can lead to the following hazards, resulting in grid instability: Continuous wideband current oscillations can cause overheating, insulation aging, and even direct damage to critical components (such as IGBT modules) in photovoltaic inverters, transformers, and capacitors; voltage and current waveform distortion can affect the normal operation of other sensitive loads at the same connection point; abnormal oscillation frequencies may trigger misjudgments in relay protection systems, leading to unnecessary grid disconnection or power outages; and to mitigate oscillation risks, grid dispatch may have to limit the output of photovoltaic power plants, resulting in a waste of renewable energy.

[0027] To solve the above problems, the following will combine... Figure 1 This application introduces a broadband oscillation suppression method for photovoltaic grid connection provided by embodiments. Please refer to [link to relevant documentation]. Figure 1 , Figure 1 This is a flowchart illustrating a broadband oscillation suppression method for photovoltaic grid connection provided in an embodiment of this application.

[0028] This broadband oscillation suppression method for grid-connected photovoltaic systems can be applied to such systems. It can be executed by the grid-connected broadband oscillation suppression system itself, by a processor within that system, or by a chip or chip system with processor functionality. For example... Figure 1 As shown, the broadband oscillation suppression method for photovoltaic grid connection may include, but is not limited to, the following steps: S101. Obtain the operating parameters of the photovoltaic grid-connected system during the first time period.

[0029] The operating parameters include one or more of the following: voltage phasor, current phasor, system frequency, rate of frequency change, active power, reactive power, and harmonic components. Voltage phasors include voltage amplitude and voltage phase angle. Current phasors include current amplitude and current phase angle. The rate of frequency change is the derivative of the system frequency with respect to time, i.e., df / dt. It describes the degree and direction of frequency change. Harmonic components are sinusoidal components in the voltage or current waveform whose frequencies are integer multiples of the fundamental frequency.

[0030] S102. Generate broadband oscillation detection results based on the operating parameters of the photovoltaic grid-connected system.

[0031] Among them, the broadband oscillation detection result indicates whether broadband oscillation exists in the photovoltaic grid-connected system during the first time period.

[0032] In this application, firstly, a mathematical model for photovoltaic grid connection can be established in advance. The method for establishing the mathematical model for photovoltaic grid connection is as follows.

[0033] An impedance model for a photovoltaic power station is constructed using a frequency domain linearization method. Based on actual parameters, frequency domain harmonic linearization is applied to the structure of the photovoltaic power station to establish a mathematical model of its impedance, providing a theoretical and model foundation for subsequent suppression strategies.

[0034] Considering the high voltage stability of the DC side of photovoltaic power plants, the stable voltage of the DC side is ignored in the modeling process. The focus is on the two core components of current inner loop control and phase-locked loop control. The broadband oscillation suppression strategy is derived by combining impedance analysis.

[0035] A sequence impedance modeling method in a static natural coordinate system is adopted. By transforming the AC variables from the time domain to the frequency domain and using the frequency domain convolution theorem and describing function method to handle nonlinear elements, a clear positive and negative sequence impedance model is established, laying a theoretical foundation for stability analysis and suppression strategy design. Taking phase a as an example, the electrical parameter equations of the photovoltaic power station in the natural static coordinate system can be expressed as: (1) Taking phase a as an example, the grid connection point voltage is calculated. The system frequency domain linearization considers both positive and negative sequence components. To linearize the phase a voltage, neglecting the zero-sequence component, the positive sequence phase a voltage consists of the fundamental frequency voltage, positive sequence harmonic voltage, and negative sequence harmonic voltage. The phase a voltage equation is expressed as: (2) in, This represents the amplitude of the system's fundamental positive-sequence voltage. The fundamental voltage angular frequency, This is the positive sequence voltage amplitude. The angular frequency of the positive-sequence voltage. The initial phase angle of the positive sequence voltage. The magnitude of the negative sequence voltage. The angular frequency of the negative sequence voltage. The initial phase angle is the negative sequence voltage.

[0036] The time-domain components in the stationary natural coordinate system Converted to the frequency domain, it is represented as: (3) in, Time-domain component voltage frequency, , This represents the frequency of the voltage impulse in the frequency domain. For the corresponding frequency domain impulse phase angle, the above formula is simplified to the form In this form, all other phase voltages are expressed in frequency domain form, which is as follows: (4) Similarly, the current corresponding to 'a' can be expressed in frequency domain form as: (5) in, ; ; ; ; ; .

[0037] Next, we can analyze the factors that cause broadband oscillations in photovoltaic grid connection, and the specific methods are as follows.

[0038] This paper analyzes the key influencing factors of control loops and electrical parameters in photovoltaic power plants on the system's broadband oscillation, and examines the impact of the inner current loop control parameters and LCL filter on the system's broadband oscillation. Based on the established impedance model, the impedance ratio stability of the system is evaluated through phase margin analysis and the Nyquist stability criterion, thereby determining the oscillation frequency and the system's stability, and ultimately identifying the system's broadband oscillation. The system stability is analyzed using impedance modeling analysis, simplifying the transfer function to impedance values, and then performing Nyquist stability analysis.

[0039] Impedance modeling analysis can simplify the complex transfer function of a system into an impedance model, and the Nyquist stability criterion can be used for system stability analysis. If the Nyquist curve of the system impedance ratio does not enclose the point (-1, j0), then the system is stable. From the perspective of phase margin, if the system output impedance... With power grid input impedance Phase margin at the intersection frequency A value greater than zero indicates a stable photovoltaic grid-connected system, thus accurately pinpointing the risk of system instability. Represented as: (6) in, The system output impedance, Power grid input impedance, This represents the phase margin.

[0040] The impedance model of the photovoltaic power station built in the previous text is expressed as follows: (7) Among them, by selecting parameters that significantly affect the higher-order coefficients of the impedance model, the mechanism of the parameters on the system's broadband oscillation was analyzed in depth. The results show that: the grid-side inductor in the LCL filter of the photovoltaic power station can enhance the system's stability to a certain extent within a certain range, and the possibility of causing broadband oscillation is small; the inverter-side inductor has a limited impact on broadband oscillation within a certain range; increasing the filter capacitor can improve stability to a certain extent, but if the capacitor value is too small, it is easy to cause broadband oscillation.

[0041] Broadband oscillations in photovoltaic grid-connected systems can be detected in the following two ways.

[0042] Method 1: Based on the mathematical model of photovoltaic grid connection and the analysis of the factors causing wideband oscillations in photovoltaic grid connection, the system output impedance of the photovoltaic grid connection system can be determined based on the operating parameters of the photovoltaic grid connection system. The system output impedance includes positive sequence impedance and negative sequence impedance; based on the system output impedance, wideband oscillation detection results are generated.

[0043] Based on the system output impedance, the broadband oscillation detection result can be generated, which may include: determining the system impedance ratio of the photovoltaic grid-connected system based on the ratio between the system output impedance and the grid input impedance; and then generating the broadband oscillation detection result based on the Nyquist curve of the system impedance ratio.

[0044] After obtaining the Nyquist curve of the system impedance ratio, it can be determined whether the Nyquist curve of the system impedance ratio includes a reference point. If the Nyquist curve of the system impedance ratio does not include the reference point, a broadband oscillation detection result is generated; this result indicates that there is no broadband oscillation in the photovoltaic grid-connected system within the first time period. If the Nyquist curve of the system impedance ratio includes the reference point, the oscillation frequency of the photovoltaic grid-connected system is determined; if the oscillation frequency is within a predetermined broadband range, a broadband oscillation detection result is generated; this result indicates that there is broadband oscillation in the photovoltaic grid-connected system within the first time period.

[0045] Method Two: Addressing the characteristics of strong uncertainty and complex oscillation features in photovoltaic power generation disturbances, and to solve the problems of high cost and low efficiency of traditional monitoring methods, this application provides a broadband oscillation monitoring method based on convolutional neural networks. Utilizing the advantages of convolutional neural networks in processing high-dimensional data, broadband oscillation suppression is performed on the photovoltaic grid-connected system, achieving real-time and accurate oscillation monitoring.

[0046] This paper utilizes convolutional neural networks (CNNs) to collect operational data and derived data from grid-connected photovoltaic (PV) systems. By fusing this data, the expressive power of features is improved. The same model can be trained with different features as input, resulting in faster classification speeds compared to previous methods. CNNs have important applications in image classification. The output of a convolutional layer can be represented as: (8) The non-linear characteristics of convolutional neural networks are achieved through activation functions. Commonly used activation functions include sigmoid and ReLU, etc. The mathematical expression of an activation function can be expressed as: (9) Data (including output voltage, current, active / reactive power, harmonics, etc.) of photovoltaic systems under broadband oscillation and normal conditions are extracted. Simulation yields RMS values ​​of voltage / current, harmonic values, phase angle, power, and frequency. This data is then constructed into an image and input into a convolutional neural network for monitoring. To avoid unclear color contrast in the image due to differences in parameter values, data normalization is performed. The normalized data is used to construct a feature image, where each pixel represents a parameter value. This transforms the oscillation monitoring problem into an image pattern recognition problem. The image is then input into the convolutional neural network model for training and recognition, enabling rapid and accurate classification and early warning of oscillation conditions. Broadband oscillation monitoring results for the photovoltaic grid-connected system are obtained.

[0047] S103. In response to the wideband oscillation of the photovoltaic grid-connected system during the first time period, a suppression strategy is executed.

[0048] If a wideband oscillation is detected in the photovoltaic grid-connected system during the first time period, a suppression strategy can be implemented. The suppression strategy may include, but is not limited to, Strategy 1 and Strategy 2 as follows.

[0049] Strategy 1: Adaptive Virtual Inertia Damping Control Strategy.

[0050] The rotor characteristics (rotor motion equation) of a synchronous generator can be simulated using a photovoltaic inverter. Based on the oscillation characteristics of the angular frequency during system operation, the virtual inertia can be calculated. J and damping factor D Adaptive adjustments are made by dynamically changing the virtual inertia. J and damping factor D It effectively reduces overshoot and suppresses oscillations when the system is subjected to disturbances.

[0051] Virtual Inertia J It can be dynamically adjusted according to the system frequency deviation; when the system frequency... Deviation from rated value At that time, virtual inertia J It will change accordingly, coefficient The sign is determined by the rate of frequency change, thus achieving intelligent adjustment.

[0052] System frequency refers to the rate of periodic change of alternating current in the power grid, measured in Hertz (Hz). In a grid-connected photovoltaic (PV) system, the system frequency is synchronized with the grid, and all grid-connected power generation units (including PV inverters) must track and maintain this frequency. The rated frequency (ω0) is the standard frequency value designed for the power grid, which can be 50Hz (corresponding to an angular frequency ω0 = 2π × 50 ≈ 314 rad / s). The system frequency deviation is the real-time angular frequency of the system. With the rated angular frequency The absolute value of the difference between them.

[0053] In this application, virtual inertia J The adaptive relationship between frequency and frequency can be expressed as: (10) in, This refers to the virtual inertia given in the virtual synchronous machine control. J For actual virtual inertia, It is the inertial adjustment coefficient, which is determined by the rate of change of angular frequency during the oscillation of the system during operation.

[0054] In some implementations, the adaptive relationship between the damping factor and frequency is expressed as: (11) in, To provide damping in the control of the virtual synchronous machine; D The actual damping during the operation of the virtual synchronizer; This is the ratio of given damping to given inertia.

[0055] It can be seen that the damping factor D With virtual inertia J The adaptive linkage not only ensures optimal matching between damping and inertia, avoiding new instability risks caused by parameter mismatch, but also effectively suppresses system overshoot and significantly improves transient stability. It ensures optimal damping while providing inertial support, effectively improving the system's dynamic response, reducing overshoot, and accelerating stabilization.

[0056] Strategy 2: Virtual impedance strategy based on wide-area measurement.

[0057] The virtual impedance strategy based on wide-area measurement can include acquiring wide-area measurement data from the phasor measurement unit (PMU) of a photovoltaic power plant; analyzing the frequency, amplitude, and phase of oscillations in real time based on the wide-area measurement data; and introducing virtual impedance into the inverter control loop based on the analysis results to make the inverter's equivalent output impedance exhibit inductive characteristics. The virtual impedance is: .

[0058] This strategy reshapes the output impedance of the power plant through a control algorithm. Using wide-area measurement data from the photovoltaic power plant's PMU as input for system oscillation identification, the frequency, amplitude, and phase of system oscillations are analyzed in real time. This makes the introduction of virtual impedance no longer arbitrary, allowing for precise analysis of the oscillation frequency and source, thus making the suppression strategy targeted. This application upgrades it to a closed-loop control system based on wide-area measurement. Introducing virtual impedance into the photovoltaic power plant control system enables the inverter's equivalent output impedance to exhibit inductive characteristics, which can more effectively absorb system oscillations. Combined with wide-area measurement data analysis from the PMU, it precisely suppresses broadband grid oscillations.

[0059] The Power Management Unit (PMU) is the fundamental data platform for the power grid, and the data for system oscillation identification comes from the PMU's wide-area measurement data. Introducing virtual impedance into the control loop of a photovoltaic power plant makes the inverter's equivalent transmission impedance exhibit inductive characteristics, enhancing its oscillation absorption capability and achieving precise suppression in conjunction with the PMU's wide-area measurement data. Adding virtual impedance to the system not only adjusts the equivalent impedance characteristics but also achieves power decoupling. This method does not introduce additional electrical components, incurs no additional hardware costs, and is easy to implement in engineering. Virtual impedance has negative resistive and positive inductive components: the negative resistance compensates for voltage drop, and the positive inductance achieves decoupling and suppresses circulating current. The virtual impedance voltage drop is calculated by an algorithm and subtracted from the reference voltage, thereby flexibly reshaping the equivalent output impedance, avoiding resonance with the grid impedance, and overcoming the energy consumption and heat generation problems of passive damping. The virtual inductive component achieves power decoupling and effectively suppresses system circulating current.

[0060] In the voltage-current dual closed-loop control structure, the virtual impedance is connected in series with the inverter's equivalent output impedance, forming a composite impedance network. This network suppresses wideband oscillations by reshaping the output impedance. Modeling and analyzing the characteristics of the virtual impedance, its mathematical model can be established in a three-phase stationary coordinate system as follows: (12) In the control strategy of photovoltaic grid-connected systems, the dual-closed-loop structure typically achieves coordinate transformation through Parker transformation to simplify the control system, reduce the complexity of control variables, and effectively avoid static errors. To further improve control performance, virtual impedance control also needs to be transformed to the dq rotating coordinate system to achieve decoupled control of the photovoltaic grid-connected inverter. After Parker transformation, the following mathematical model can be obtained: (13) The physical meaning of virtual impedance is equivalent to introducing a voltage drop into the original droop control, and its mathematical model in the complex frequency domain is expressed as: (14) Differential term , The voltage drop is very small, so for simplicity in modeling, we eliminate it, and represent it as: (15) The new strategy adds a virtual impedance voltage drop calculation step, and on the basis of dual closed-loop control, it incorporates the dq coordinate system. Calculated voltage and Calculated using the following formula Represented as: (16) Without introducing virtual impedance, the parameters of a three-phase grid-connected inverter are expressed as follows: (17) (18) With the introduction of virtual impedance, the voltage parameters of a three-phase grid-connected inverter are expressed as follows: (19) (20) The virtual impedance added in the above equation is expressed as: (twenty one) After adding virtual impedance, the system impedance model is expressed as: (twenty two) It is evident that introducing virtual impedance based on wide-area measurement in photovoltaic grid-connected systems can effectively suppress broadband oscillations. While adding impedance in a real circuit consumes electrical energy and reduces energy utilization, adding virtual impedance in a photovoltaic grid-connected system does not cause impedance heating, thus saving energy. Adding virtual impedance to a photovoltaic grid-connected system causes impedance changes while avoiding oscillation frequency ranges caused by insufficient margin, suppressing broadband oscillations in the system. Introducing virtual impedance into the control loop, by flexibly setting its resistive and inductive components, allows the equivalent output impedance to exhibit the desired characteristics. Negative resistance compensates for voltage drop, while positive inductance achieves decoupling, thus avoiding resonance with the grid impedance. Virtual impedance is calculated using algorithms and subtracted from the reference voltage, thereby flexibly reshaping the equivalent output impedance of the photovoltaic power station without adding physical hardware or components. This avoids resonance points with the grid impedance, while also avoiding energy consumption, active power loss, and heat generation problems caused by passive damping, reducing costs and improving efficiency. It absorbs oscillation energy, suppresses broadband oscillations, and makes the system more stable.

[0061] Please see Figure 2 , Figure 2 This is a flowchart illustrating another method for suppressing broadband oscillations in photovoltaic grid connection provided in this application embodiment. Figure 2As can be seen, the broadband oscillation suppression method for photovoltaic grid connection provided in this application embodiment may include the following: establishing a photovoltaic grid connection mathematical model; analyzing the mechanism of action of control loops and electrical parameters in the photovoltaic power station on broadband oscillation; extracting the main influencing factors of broadband oscillation; analyzing the effect of the photovoltaic power station's inner current loop control parameters and LCL filter on the system's broadband oscillation; analyzing the system's oscillation frequency and system stability based on the Nyquist stability criterion; using a convolutional neural network to monitor the system's broadband oscillation; detecting broadband oscillation; and suppressing broadband oscillation.

[0062] Furthermore, some embodiments of this application provide a broadband oscillation suppression system for photovoltaic grid connection; please refer to [link to relevant documentation]. Figure 3 , Figure 3 This is a schematic diagram of the structure of a broadband oscillation suppression system for photovoltaic grid connection provided in an embodiment of this application. Figure 3 It can be seen that the broadband oscillation suppression system 300 for photovoltaic grid connection includes: The acquisition unit 310 is used to acquire the operating parameters of the photovoltaic grid-connected system during the first time period; The broadband oscillation detection unit 320 is used to generate broadband oscillation detection results based on the operating parameters of the photovoltaic grid-connected system; the broadband oscillation detection results indicate whether broadband oscillation exists in the photovoltaic grid-connected system during the first time period; The wideband oscillation suppression unit 330 is used to execute a suppression strategy in response to the presence of wideband oscillation in the photovoltaic grid-connected system during the first time period; the suppression strategy includes at least one of the following strategies: an adaptive virtual inertia damping control strategy and a virtual impedance strategy based on wide-area measurement.

[0063] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0064] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0065] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0066] The units described above 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0067] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0068] If the aforementioned integrated units are implemented as software functional units and sold or used as independent products, they 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 all or 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 can be a personal computer, server, or network device, specifically a processor in the computer device) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium may include various media capable of storing program code, such as a USB flash drive, portable hard drive, magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM).

[0069] The above-described 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.

[0070] The steps in the method of this application embodiment can be adjusted, combined, or deleted according to actual needs.

Claims

1. A method for suppressing broadband oscillations in photovoltaic grid connection, characterized in that, The method includes: Obtain the operating parameters of the grid-connected photovoltaic system within the first time period; Based on the operating parameters of the photovoltaic grid-connected system, a broadband oscillation detection result is generated; the broadband oscillation detection result indicates whether broadband oscillation exists in the photovoltaic grid-connected system during the first time period. In response to the presence of wideband oscillations in the photovoltaic grid-connected system during the first time period, a suppression strategy is executed; the suppression strategy includes at least one of the following strategies: an adaptive virtual inertia damping control strategy and a virtual impedance strategy based on wide-area measurement.

2. The method according to claim 1, characterized in that, The operating parameters include one or more of the following: voltage phasor, current phasor, system frequency, rate of change of frequency, active power, reactive power, and harmonic components.

3. The method according to claim 1, characterized in that, The operating parameters based on the photovoltaic grid-connected system include: Based on the operating parameters of the photovoltaic grid-connected system, the system output impedance of the photovoltaic grid-connected system is determined, and the system output impedance includes positive sequence impedance and negative sequence impedance. Based on the system output impedance, a wideband oscillation detection result is generated.

4. The method according to claim 3, characterized in that, The generation of wideband oscillation detection results based on system output impedance includes: The system impedance ratio of the photovoltaic grid-connected system is determined based on the ratio between the system output impedance and the grid input impedance. Based on the Nyquist curve of the system impedance ratio, broadband oscillation detection results are generated.

5. The method according to claim 4, characterized in that, The Nyquist curve based on the system impedance ratio generates broadband oscillation detection results, including: The Nyquist curve for the system impedance ratio does not encircle the reference point, generating a broadband oscillation detection result; the broadband oscillation detection result indicates that the photovoltaic grid-connected system does not have broadband oscillations during the first time period; or, The oscillation frequency of the photovoltaic grid-connected system is determined in response to the Nyquist curve of the system impedance ratio enclosing a reference point; a broadband oscillation detection result is generated in response to the oscillation frequency being within a predetermined broadband range; the broadband oscillation detection result indicates that broadband oscillation exists in the photovoltaic grid-connected system during the first time period.

6. The method according to claim 1, characterized in that, The generation of broadband oscillation detection results based on the operating parameters of the photovoltaic grid-connected system includes: The operating parameters are then time-aligned and normalized. The normalized running parameters are constructed into a two-dimensional feature image according to the preset spatiotemporal mapping rules, where a pixel value in the two-dimensional feature image corresponds to a parameter value in the normalized running parameters. The two-dimensional feature image is input into a pre-trained convolutional neural network model; Based on the output of the convolutional neural network model, a broadband oscillation detection result is generated; the broadband oscillation detection result indicates whether broadband oscillation exists in the photovoltaic grid-connected system during the first time period.

7. The method according to any one of claims 1-6, characterized in that, The adaptive virtual inertia damping control strategy includes: The photovoltaic inverter simulates the rotor motion equation of a synchronous generator; Based on the deviation of the system's real-time angular frequency from the rated angular frequency, the virtual inertia J and damping factor D are adaptively adjusted. Virtual Inertia J The adaptive relationship between frequency and frequency is: ； in, For the system's real-time angular frequency, It is the absolute value of the difference between the rated angular frequencies. This refers to the virtual inertia given in the virtual synchronous machine control. J For actual virtual inertia, This is the inertia adjustment coefficient.

8. The method according to claim 7, characterized in that, The adaptive relationship between the damping factor and frequency is: ； in, To provide damping in the control of the virtual synchronous machine; D The actual damping during the operation of the virtual synchronizer; This is the ratio of a given damping to a given inertia.

9. The method according to any one of claims 1-6, characterized in that, The virtual impedance strategy based on wide-area measurement includes: Acquire wide-area measurement data from the phasor measurement unit (PMU) of a photovoltaic power plant; Based on the wide-area measurement data, the frequency, amplitude, and phase of the oscillation are analyzed in real time. Based on the analysis results, a virtual impedance is introduced into the inverter control loop to make the inverter's equivalent output impedance exhibit inductive characteristics. Wherein, the virtual impedance is: .

10. A broadband oscillation suppression system for photovoltaic grid connection, characterized in that, The system includes: The acquisition unit is used to acquire the operating parameters of the photovoltaic grid-connected system within the first time period. A broadband oscillation detection unit is used to generate broadband oscillation detection results based on the operating parameters of the photovoltaic grid-connected system; the broadband oscillation detection results indicate whether broadband oscillation exists in the photovoltaic grid-connected system during the first time period; A broadband oscillation suppression unit is used to execute a suppression strategy in response to broadband oscillations occurring in the photovoltaic grid-connected system during the first time period; the suppression strategy includes at least one of the following strategies: an adaptive virtual inertia damping control strategy and a virtual impedance strategy based on wide-area measurement.

Images