New energy photovoltaic inverter energy conversion control system

Abstract

The application discloses a new energy photovoltaic inverter energy conversion control system and relates to the technical field of new energy power generation grid connection. The system comprises the following modules: a signal acquisition module, which acquires grid-connected current instructions, grid voltage and current feedback signals; a virtual impedance generation module, which generates synchronous virtual impedance reference signals based on the grid voltage, wherein the virtual resistance component is negative to provide active damping; a grid working condition monitoring module, which monitors the voltage distortion rate and the frequency fluctuation rate to determine the grid working condition; a dynamic instruction generation module, which dynamically increases the inductance component weight in the virtual impedance signal according to the disturbance amplitude when the grid is in a non-ideal working condition, performs enhancement processing on the dominant disturbance type, and then superimposes the current negative feedback to the current instruction to generate a dynamic grid-connected current instruction; and a closed-loop control and driving module, which generates a switching driving signal based on the dynamic instruction and the feedback signal. The application can adapt to grid impedance changes and severe working conditions, and can improve grid stability and current control quality.

Description

Technical Field

[0001] This application relates to the field of new energy power generation grid connection technology, and in particular to the energy conversion control system of new energy photovoltaic inverters. Background Technology

[0002] With the increasing penetration rate of new energy power generation, photovoltaic inverters, as devices connecting photovoltaic arrays and the power grid, face severe challenges in ensuring the stability of their grid-connected operation. The power grid is not an ideal voltage source; its equivalent impedance varies over a wide range due to factors such as network structure, load switching, and faults. When the grid impedance is high (i.e., weak grid conditions), unfavorable resonance interactions can easily occur between the inverter output filter and the grid impedance, leading to grid current distortion, harmonic amplification, and even system instability. In addition, the voltage at the grid's point of common coupling often experiences background harmonic pollution and frequency fluctuations, which further exacerbate the instability risks of the inverter control loop.

[0003] Traditional grid-connected inverter control systems are typically designed based on ideal grid conditions (i.e., infinite grids or fixed low impedance). This fixed-parameter control strategy lacks adaptability to grid impedance variations. In weak grids or when grid conditions deteriorate, the original controller parameters may no longer be matched, leading to a decrease in system phase margin and a deterioration in dynamic performance. While existing technologies improve stability by online grid impedance identification or the introduction of active damping, these methods have limitations such as requiring complex calculations, potentially introducing response delays, or being effective only for specific frequency band disturbances. These limitations make it difficult to achieve grid-connected energy conversion control under conditions of wide-range grid impedance variations and multiple types of disturbances. Therefore, designing a photovoltaic inverter energy conversion control system that can adaptively adapt to grid impedance changes and intelligently respond to grid condition disturbances, thereby ensuring stability over a wide operating range, has become an urgent technical problem to be solved. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the embodiments of this application provide a new energy photovoltaic inverter energy conversion control system to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the energy conversion control system for the new energy photovoltaic inverter provided in this application specifically includes: The signal acquisition module is used to acquire the grid-connected current command of the photovoltaic inverter, the voltage signal of the grid common connection point, and the grid-connected current feedback signal. A virtual impedance generation module is used to generate a virtual impedance reference signal that is synchronized with the frequency and phase of the voltage signal based on the voltage signal. The power grid operating condition monitoring module is used to monitor the voltage distortion rate or frequency fluctuation rate of the power grid common connection point, and generate a power grid operating condition judgment result based on the voltage distortion rate and the frequency fluctuation rate. A dynamic command generation module is used to dynamically increase the weight of the virtual inductance component in the virtual impedance reference signal when the power grid condition determination result indicates that the power grid has entered a non-ideal operating condition, and to superimpose the weighted virtual impedance reference signal onto the grid-connected current command in the form of current negative feedback to obtain a dynamic grid-connected current command. Specifically, it includes: identifying the dominant disturbance type that causes the power grid to enter a non-ideal operating condition, the dominant disturbance type including voltage harmonic disturbance and frequency oscillation disturbance; if the dominant disturbance is voltage harmonic disturbance, then the frequency component corresponding to the harmonic order in the weighted virtual impedance reference signal is enhanced, and then current negative feedback superposition is performed; if the dominant disturbance is frequency oscillation disturbance, then the response speed of the virtual impedance reference signal to the frequency fluctuation rate is improved, and then current negative feedback superposition is performed. The closed-loop control and drive module is used to generate a switching drive signal through closed-loop adjustment based on the dynamic grid-connected current command and the grid-connected current feedback signal, so as to control the power switching devices of the photovoltaic inverter.

[0006] Optionally, the grid-connected current command of the photovoltaic inverter, the voltage signal of the grid's point of common coupling, and the grid-connected current feedback signal are acquired, specifically including: The grid-connected current command is obtained from the maximum power point tracking controller; The voltage signal at the power grid common coupling point is acquired using a voltage sensor; The current signal passing through the photovoltaic inverter is collected by a current sensor and used as the grid-connected current feedback signal.

[0007] Optionally, the current sensor is disposed between the output filter inductor of the photovoltaic inverter and the common connection point of the power grid, and the current sensor is disposed after the output filter inductor.

[0008] Optionally, based on the voltage signal, a virtual impedance reference signal synchronized with the frequency and phase of the voltage signal is generated, specifically including: The voltage signal is subjected to phase-locked loop processing to extract the phase angle of its fundamental positive sequence component; Construct a rotating coordinate system based on the phase angle; In the rotating coordinate system, the d-axis voltage component and the q-axis voltage component of the voltage signal are multiplied by independent virtual impedance coefficients, which include virtual resistance components and virtual inductance components. The virtual impedance reference signal is generated by performing an inverse coordinate transformation on the signal after the virtual impedance coefficient calculation.

[0009] Optionally, the virtual impedance coefficient includes a virtual resistance component and a virtual inductance component, and the sign of the virtual resistance component is negative.

[0010] Optionally, the virtual impedance coefficient is determined in advance offline based on the actual physical parameters of the output filter of the photovoltaic inverter and the desired system stability margin.

[0011] Optionally, the voltage distortion rate or frequency fluctuation rate of the power grid point of common coupling is monitored, and a power grid operating condition determination result is generated based on the voltage distortion rate and the frequency fluctuation rate, specifically including: The total harmonic distortion rate of the voltage signal is calculated as the voltage distortion rate; The fluctuation amount of the fundamental frequency of the voltage signal is calculated as the frequency fluctuation rate; The voltage distortion rate is compared with a first preset threshold, and the frequency fluctuation rate is compared with a second preset threshold; Based on the comparison results, a power grid condition determination result is generated, indicating whether the power grid is in an ideal or non-ideal condition.

[0012] Optionally, when the power grid condition determination result indicates that the power grid has entered a non-ideal operating condition, the weight of the virtual inductance component in the virtual impedance reference signal is dynamically increased, specifically including: Calculate the magnitude by which the voltage distortion rate exceeds the first preset threshold, or calculate the magnitude by which the frequency fluctuation rate exceeds the second preset threshold; Based on the calculated amplitude, determine the increase ratio of the virtual inductance component; The virtual inductance component in the virtual impedance coefficient is increased according to the stated increase ratio.

[0013] Optionally, based on the dynamic grid-connected current command and the grid-connected current feedback signal, a switching drive signal is generated through closed-loop regulation to control the power switching devices of the photovoltaic inverter, specifically including: The dynamic grid-connected current command is compared with the grid-connected current feedback signal to obtain the current error signal; The current error signal is input into a closed-loop regulator for processing to generate a modulated wave signal; The modulated wave signal is compared with the carrier signal to generate the switch drive signal.

[0014] Compared with the prior art, this application has the following beneficial effects: This application uses a virtual impedance generation module to create a stable impedance environment for the inverter control system that is synchronized with the power grid and has programmable parameters. This method eliminates the complexity and lag of passively measuring the real power grid impedance. It directly generates a reference signal containing virtual resistance and virtual inductance components through an algorithm, and the virtual resistance component is negative, which is equivalent to injecting active damping into the control loop. This ensures that no matter how the actual power grid impedance changes, the internal control loop of the inverter always "senses" a known and stable equivalent power grid impedance. This solves the risk of controller parameter mismatch caused by unknown and variable external impedance and improves the inherent stability margin of the system under a wide range of power grid impedances.

[0015] This application achieves intelligent perception and adaptive adjustment of control strategies to grid disturbances through the collaboration of a grid condition monitoring module and a dynamic command generation module. The system monitors the grid voltage distortion rate and frequency fluctuation rate in real time to accurately determine the grid condition. When a non-ideal condition is detected, it does not simply switch modes, but dynamically and proportionally increases the weight of the inductance component in the virtual impedance according to the magnitude of the disturbance exceeding the limit, achieving a match between the stability enhancement strength and the severity of the disturbance. Furthermore, the system can identify the dominant disturbance type (harmonics or frequency oscillations) and specifically enhance the suppression of the corresponding frequency components or improve the response speed, thereby suppressing specific disturbances with targeted strategies. Finally, the optimized virtual impedance signal is superimposed on the current command in the form of current negative feedback, and the closed-loop control module drives the power switch precisely accordingly, enabling the system to maintain high efficiency when the grid is good and prioritize robustness when the grid deteriorates, thus improving the adaptive and stable operation capability and power output quality of the photovoltaic inverter under complex grid conditions. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the energy conversion control system for a new energy photovoltaic inverter provided in one embodiment of this application. The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0018] 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 embodiments of the present invention, not all embodiments. 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.

[0019] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” used in the embodiments of this invention are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0020] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0021] Furthermore, the timing of the steps in the following system embodiments is merely an example and not a strict limitation.

[0022] In practice, the server-side equipment deployed in the energy conversion control system of a new energy photovoltaic inverter may consist of one or more devices. This energy conversion control system can be implemented as a business instance, a virtual machine, or hardware devices. For example, the energy conversion control system can be implemented as a business instance deployed on one or more devices in a cloud node. Simply put, the energy conversion control system can be understood as software deployed on a cloud node, used to provide the energy conversion control system to various user terminals. Alternatively, the energy conversion control system can also be implemented as a virtual machine deployed on one or more devices in a cloud node. This virtual machine contains application software for managing various user terminals. Or, the energy conversion control system can also be implemented as a server composed of numerous identical or different types of hardware devices, with one or more hardware devices configured to provide the energy conversion control system to various user terminals.

[0023] In terms of implementation, the energy conversion control system of the new energy photovoltaic inverter and the user terminal are mutually compatible. That is, if the energy conversion control system of the new energy photovoltaic inverter is an application installed on a cloud service platform, then the user terminal is a client that establishes a communication connection with the application; or if the energy conversion control system of the new energy photovoltaic inverter is implemented as a website, then the user terminal is implemented as a webpage; or if the energy conversion control system of the new energy photovoltaic inverter is implemented as a cloud service platform, then the user terminal is implemented as a mini-program in an instant messaging application.

[0024] like Figure 1 The diagram shown is a system architecture diagram of a new energy photovoltaic inverter energy conversion control system provided in an embodiment of the present invention.

[0025] The new energy photovoltaic inverter energy conversion control system 100 of this invention can be set up in a cloud server. In terms of implementation, it can be used as one or more service devices, or as an application installed in the cloud (e.g., a mobile service operator's server, server cluster, etc.), or it can be developed into a website. Depending on the functions implemented, the new energy photovoltaic inverter energy conversion control system 100 may include a signal acquisition module 101, a virtual impedance generation module 102, a power grid condition monitoring module 103, a dynamic instruction generation module 104, and a closed-loop control and drive module 105. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device's processor and can perform a fixed function, stored in the electronic device's memory.

[0026] In this embodiment of the invention, each of the above-mentioned modules in the energy conversion control system of the new energy photovoltaic inverter can be implemented independently and can be called by other modules. Here, "calling" can be understood as a module connecting to multiple modules of another type and providing corresponding services to those connected modules. For example, the sharing and evaluation module can call the same information acquisition module to obtain the information collected by that module. Based on the above characteristics, in the energy conversion control system of the new energy photovoltaic inverter provided in this embodiment of the invention, without modifying the program code, the applicable scope of the new energy photovoltaic inverter energy conversion control system architecture can be adjusted by adding modules and directly calling them, achieving cluster-based horizontal expansion, so as to achieve the purpose of quickly and flexibly expanding the new energy photovoltaic inverter energy conversion control system. In practical applications, the above-mentioned modules can be set in the same device or different devices, or they can be set in a virtual device, such as a service instance in a cloud server.

[0027] The following describes the components and specific workflow of the energy conversion control system for a new energy photovoltaic inverter, using specific embodiments as examples: The signal acquisition module 101 is used to acquire the grid-connected current command of the photovoltaic inverter, the voltage signal of the grid common connection point, and the grid-connected current feedback signal.

[0028] In some embodiments, acquiring the grid-connected current command of the photovoltaic inverter, the voltage signal of the grid point of common coupling, and the grid-connected current feedback signal specifically includes: The grid-connected current command is obtained from the maximum power point tracking controller; The voltage signal at the power grid common coupling point is acquired using a voltage sensor; The current signal passing through the photovoltaic inverter is collected by a current sensor and used as the grid-connected current feedback signal.

[0029] In some embodiments, the current sensor is disposed between the output filter inductor of the photovoltaic inverter and the grid common connection point, and the current sensor is disposed after the output filter inductor.

[0030] In this embodiment, the signal acquisition module is the starting part and data input source of the photovoltaic inverter energy conversion control system. Its function is to acquire the three key physical quantity signals necessary for all subsequent advanced control algorithms. The accurate and real-time acquisition of these three signals is the data prerequisite for the entire system to effectively solve the grid connection stability problem under a wide range of grid impedance changes.

[0031] In this embodiment, the steps of "acquiring the grid-connected current command of the photovoltaic inverter, the voltage signal of the grid common coupling point, and the grid-connected current feedback signal" define three specific data acquisition tasks that the module needs to complete. The grid-connected current command is a current target value signal from the front-end control unit, which represents the current waveform that the photovoltaic inverter should inject into the grid under ideal grid-connected conditions. The voltage signal of the grid common coupling point is a voltage measurement signal that directly reflects the real-time state of the grid. It is the three-phase AC voltage detected at the point where the output of the photovoltaic inverter is connected to the grid. The grid-connected current feedback signal is a current measurement signal used to realize closed-loop control. It is a true reflection of the current actually output by the photovoltaic inverter and flowing into the grid. The implementation of this step is the input condition for the system to perform a series of control actions such as virtual impedance generation, operating condition judgment, and dynamic command adjustment.

[0032] In this embodiment, the specific implementation of this step is as follows. First, the system obtains the grid-connected current command from the independently operating maximum power point tracking controller. This process is typically completed through reading the internal data bus or register of the digital controller, ensuring the alignment of power optimization goals and control goals. Second, the system directly connects one or more high-precision voltage sensors, such as Hall voltage sensors or resistive voltage divider isolation sampling circuits, to the three-phase conductors at the grid common coupling point to collect the instantaneous three-phase voltage values ​​at that point in real time and convert them into analog or digital signals that the controller can process. Finally, the system surrounds the output phase lines of the photovoltaic inverter with one or more high-precision current sensors, such as Hall current sensors, to collect the instantaneous current values ​​flowing through the conductors and uses them as the grid-connected current feedback signal. The analog signals collected by these sensors are synchronously sampled and digitized by an analog-to-digital converter to form a series of discrete time-series data for subsequent processing by the software algorithm module.

[0033] In this embodiment, this step provides accurate and reliable raw data for the entire control system. Acquiring the grid-connected current command clarifies the control objective; acquiring the voltage signal at the grid's point of common coupling enables the system to perceive real-time changes in the external grid environment, which is the basis for subsequently generating synchronous virtual impedance and judging grid operating conditions; acquiring the grid-connected current feedback signal enables the system to achieve precise current closed-loop tracking control. The completeness of this data ensures that the system does not operate blindly in an open-loop or incomplete information state, but can make decisions based on information. This is the first step in solving the stability problem caused by the unknown and volatile grid environment.

[0034] In this embodiment, the grid common coupling point voltage signal will be immediately sent to the virtual impedance generation module and the grid operating condition monitoring module; the grid connection current command and the grid connection current feedback signal will be sent to the closed-loop control and drive module; the grid connection current command will also be combined with the output of the dynamic command generation module.

[0035] In this embodiment, the step of "the current sensor is located between the output filter inductor of the photovoltaic inverter and the grid common connection point, and the current sensor is located after the output filter inductor" further defines the physical location of the grid-connected current feedback signal acquisition point. This is a key implementation detail. The output filter inductor is an important component in the main power circuit of the photovoltaic inverter, usually located on the output side of the inverter bridge arm, and is used to filter out switching frequency harmonics. The grid common connection point is the physical boundary between the inverter system and the external power grid. The limitation of "located after the output filter inductor" means that the installation position of the current sensor must ensure that the current it measures has already flowed through the entire inverter output filter inductor.

[0036] In this embodiment of the application, the implementation of this step means that the hardware circuit layout and installation must be arranged in the following spatial order: power switching devices of photovoltaic inverter, output filter inductor, current sensor, grid common connection point, the measuring probe of the current sensor is fixedly installed on the wire at this specific location, when the inverter is running, the current flowing through the wire is the current that is finally injected into the grid, and the current signal is sensed by the sensor and converted into an electrical signal output.

[0037] In this embodiment, this step improves the accuracy and directness of the grid-connected current feedback signal, thereby enhancing the closed-loop control performance. Since the output filter inductor itself stores and releases energy, the currents at its two ends are not exactly the same. The current collected before the filter inductor contains more inverter bridge arm switching ripple, while the current collected after the filter inductor is closer to the pure fundamental grid-connected current. Using this current, which more directly reflects the interaction with the grid, as the feedback signal allows the closed-loop controller to control the actual current injected into the grid, effectively avoiding control errors or oscillations caused by excessive internal switching noise in the feedback signal. This is crucial for maintaining current waveform quality and improving system stability margin in high-impedance or complex grid environments.

[0038] In this embodiment, this step is a concretization and optimization of the action of "collecting grid-connected current feedback signals," ensuring that the acquired feedback signals have control value. This optimized feedback signal will be directly used to compare with the dynamic grid-connected current command to generate a current error signal. Therefore, although this implementation detail seems simple, it amplifies the efficiency of all subsequent control algorithms by optimizing the data source, collectively serving to solve the technical problem of grid-connected stability.

[0039] The virtual impedance generation module 102 is used to generate a virtual impedance reference signal that is synchronized with the frequency and phase of the voltage signal based on the voltage signal.

[0040] In some embodiments, based on the voltage signal, a virtual impedance reference signal synchronized with the frequency and phase of the voltage signal is generated, specifically including: The voltage signal is subjected to phase-locked loop processing to extract the phase angle of its fundamental positive sequence component; Construct a rotating coordinate system based on the phase angle; In the rotating coordinate system, the d-axis voltage component and the q-axis voltage component of the voltage signal are multiplied by independent virtual impedance coefficients, which include virtual resistance components and virtual inductance components. The virtual impedance reference signal is generated by performing an inverse coordinate transformation on the signal after the virtual impedance coefficient calculation.

[0041] In this embodiment, the virtual impedance generation module generates a programmable virtual impedance reference signal that is completely synchronized with the power grid by processing the real-time voltage signal obtained from the power grid common coupling point through a series of algorithms. The function of this module abandons the path of passively measuring the real power grid impedance in traditional technology, and instead generates a desired impedance characteristic. It is a technical means to solve the grid connection stability problem under wide range of power grid impedance changes.

[0042] In the embodiments of this application, phase-locked loop (PLL) processing is an algorithmic process for extracting the fundamental positive sequence component synchronization information from a complex power grid voltage signal. The voltage signal at the power grid common connection point may contain harmonics, unbalanced components, and noise. The phase angle of the fundamental positive sequence component is an angular frequency signal that varies with time, representing the rotating phase of an ideal three-phase balanced voltage.

[0043] In this embodiment, the phase-locked loop (PLL) processing of the voltage signal to extract the phase angle of its fundamental positive-sequence component is specifically implemented through a software PLL algorithm. The software PLL algorithm first processes the acquired three-phase voltage signal... , , Perform a Clark transformation to convert it to a stationary two-phase coordinate system. and Then, a closed-loop control system, such as a phase-locked loop based on a second-order generalized integrator, is used to track... and The rotation angle of the positive sequence component of the fundamental wave ultimately outputs a continuously changing phase angle signal. For example, for an ideal grid voltage of 50Hz, the phase angle signal output by the phase-locked loop will be a ramp signal that increases linearly from 0 to 2π with a slope of 100π rad / s. This step establishes a precise time reference and synchronization basis for the generation of the entire virtual impedance, ensuring that the generated virtual impedance reference signal is completely locked with the grid voltage in frequency and phase. This is a prerequisite for subsequent decoupling control in the rotating coordinate system. The output phase angle signal of this step is the basis for constructing the rotating coordinate system and is directly used as the input for the next step.

[0044] In the embodiments of this application, constructing a rotating coordinate system is a mathematical transformation process aimed at establishing a two-dimensional reference system that rotates synchronously with the fundamental voltage of the power grid. This rotating coordinate system contains two mutually perpendicular axes, commonly referred to as the direct axis and the cross axis.

[0045] In this embodiment, the method for constructing a rotating coordinate system based on the phase angle is to apply the Park transformation matrix and use the phase angle signal obtained in the previous step to adjust the voltage components in the stationary two-phase coordinate system. and The mathematical expression for performing the Park transformation is as follows: as well as Through this transformation, the fundamental positive-sequence voltage component of the power grid, which originally alternates in a sinusoidal form in a stationary coordinate system, is converted into an approximately DC signal in a rotating coordinate system. and ,in, It is the component of voltage on the d-axis of the rotating coordinate system. It is the component of the voltage along the q-axis of the rotating coordinate system. For example, for an ideal three-phase balanced voltage, after transformation... It is a constant positive value, representing the amplitude of the voltage, while This value approaches zero. This step transforms the control problem of AC quantities into the control problem of DC quantities (or slowly changing variables), simplifying the design of subsequent control algorithms, especially facilitating accurate and independent programming settings of the two independent impedance components, virtual resistance and virtual inductance. The rotating coordinate system and its DC component generated in this step... and This provides a direct object and platform for the next computational step.

[0046] In this embodiment of the application, regarding "multiplying the d-axis voltage component and q-axis voltage component of the voltage signal by independent virtual impedance coefficients in the rotating coordinate system, wherein the virtual impedance coefficients include virtual resistance components and virtual inductance components," it realizes the "programming" of the physical impedance concept through an algorithm. The virtual impedance coefficient is a pre-designed complex number that performs calculations on the synchronized voltage components in the rotating coordinate system to simulate a voltage drop across a virtual impedance. The implementation of this step involves specific mathematical operations. First, the virtual impedance coefficient is defined as... ,in, It is a virtual resistance component. It is a virtual inductance component. It is the Laplace operator, which corresponds to differentiation or difference operations in discrete digital systems. In a rotating coordinate system, it respectively... shaft and Axis application of independent virtual impedance coefficient and .

[0047] The virtual resistance component is negative, a key non-obvious design feature. In control, it is equivalent to providing active damping to counteract the system's inherent resonance, and is an important means of enhancing stability. The virtual impedance coefficient... and The specific values ​​are pre-calculated based on the actual physical parameters of the photovoltaic inverter output filter and the desired system stability margin through offline frequency domain analysis or pole configuration, and then embedded in the control program. This step determines the equivalent grid impedance characteristics presented by the photovoltaic inverter output to the internal control loop, and by changing... Regardless of changes in the actual grid impedance, the internal control loop "senses" a known and stable virtual grid environment, thus resolving the controller parameter mismatch and stability degradation issues caused by unknown and variable external grid impedance. The output of this step... and These are DC (or slowly varying) signals that contain the programmed impedance characteristics, and they will be used as inputs for inverse coordinate transformation.

[0048] In this embodiment, the inverse coordinate transformation is the process of remapping the calculation results in the rotating coordinate system back to the actual three-phase stationary coordinate system. The result obtained after virtual impedance coefficient calculation is... Signal in coordinate system and It needs to be converted into a time-domain three-phase voltage reference signal that the controller can use for modulation and execution.

[0049] In this embodiment, the method for generating the virtual impedance reference signal by performing an inverse coordinate transformation on the signal after virtual impedance coefficient calculation is to apply an inverse Park transform, using the same phase angle signal to... and The mathematical expression for performing the inverse Park transform is: as well as Then, through the inverse Clark transformation, and After transforming back to the three-phase stationary coordinate system, a virtual impedance reference signal with a 120-degree electrical angle difference between the three phases is finally generated. , , Collectively referred to as This step concretizes the abstract, virtual impedance characteristic defined in a rotating coordinate system into a real physical quantity (voltage reference signal) that can be superimposed on the original current command. The result of this step... It is the final output of the entire module, and it will be directly passed to the dynamic instruction generation module as an element for reconstructing the grid-connected current instruction.

[0050] In some embodiments, the virtual impedance coefficient includes a virtual resistance component and a virtual inductance component, and the virtual resistance component is negative.

[0051] In some embodiments, the virtual impedance coefficient is pre-calculated offline based on the actual physical parameters of the output filter of the photovoltaic inverter and the desired system stability margin.

[0052] In this embodiment, the step of "the virtual impedance coefficient includes a virtual resistance component and a virtual inductance component, and the sign of the virtual resistance component is negative" explicitly defines the internal structure of the virtual impedance coefficient and the special physical properties of one of its components. The virtual resistance component is the real number part of the virtual impedance coefficient that is independent of frequency, and the virtual inductance component is the imaginary number part of the virtual impedance coefficient that is proportional to frequency. Specifying that the sign of the virtual resistance component is negative means that an impedance component with negative resistance characteristics is artificially introduced into the algorithm. The specific means of implementing this step is reflected in the digital controller's... and In assigning values ​​to parameters, during control system programming, the resistance parameter is used as the virtual impedance coefficient. Assign a value less than zero.

[0053] For example, in the d-axis channel, set ,at the same time Keep it as a positive number, such as 0.001H, and perform the operation in the rotating coordinate system. At that time, negative The item will produce a similar The voltage component with opposite sign. This step injects a "negative damping" or "active damping" into the control loop. During the dynamic process of the system, when a specific frequency oscillation trend is generated due to the interaction between the grid impedance and the inverter output filter, this negative virtual resistance term, through the above calculation, will generate a virtual voltage drop signal that is in phase with the oscillation voltage. After subsequent negative feedback superposition, this signal is equivalent to generating an "energy" that is in phase with the oscillation current to offset the energy dissipation in the original resonant circuit, thereby suppressing the amplification of the resonance peak and enhancing the stability margin of the system under a wide range of grid impedances. This feature is not achieved by adding physical loss components or complex feedforward compensation, but by a concise and profound parameter sign definition in the control algorithm. This feature is directly integrated with the aforementioned step of multiplying by the virtual impedance coefficient in the rotating coordinate system and is one of the elements constituting the "programmable virtual impedance".

[0054] In this embodiment, the step of "determining the virtual impedance coefficient in advance offline based on the actual physical parameters of the output filter of the photovoltaic inverter and the desired system stability margin" specifies the method and basis for obtaining the specific value of the virtual impedance coefficient, clarifying its non-real-time, non-adaptive, and fixed characteristics. The output filter of the photovoltaic inverter is known hardware in the main power circuit, typically an L-type or LCL-type structure. Its actual physical parameters include the filter inductance value, filter capacitance value, and line parasitic resistance value, which are fixed and measurable. The desired system stability margin is a design goal of the control system, usually quantized in the frequency domain as phase margin and amplitude margin.

[0055] This step is implemented through offline engineering calculations during the product design or controller programming phase. Specifically, firstly, a small-signal impedance model of the system is established, including the photovoltaic inverter itself, the output filter, and a representative grid impedance model with a wide range. Then, a virtual impedance coefficient is introduced into the model as an adjustable parameter. Based on the actual physical parameters of the output filter, the designer uses frequency domain analysis tools to scan and select a specific set of virtual resistance and virtual inductance values. Under these parameters, the Nyquist curve of the ratio of the equivalent impedance of the system seen from the inverter side to the equivalent impedance of the system seen from the grid side (i.e., loop gain) meets the pre-set stability margin requirements across all expected grid impedance variations.

[0056] For example, for a given LCL filter with a filter inductance of 2mH and a filter capacitor of 10μF, to ensure that the system phase margin is greater than 45 degrees when the grid short-circuit ratio varies from 1 to 50, a set of effective solutions can be determined by calculation: setting the d-axis virtual resistance to -0.4 ohms and the d-axis virtual inductance to 0.0015 henries. These calculated coefficient values ​​are then written as fixed constants into the controller's software program.

[0057] This step generates an optimal parameter set for the virtual impedance, which has undergone rigorous theoretical verification and optimization design. This ensures that the system has a robust foundation to cope with changes in grid impedance within the expected range before it is put into operation, avoiding the risks of instantaneous instability, computational burden, and parameter oscillation problems that may arise from online adaptive parameter tuning. This feature is closely integrated with all the aforementioned steps: it is a preparatory work completed before the "multiply by virtual impedance coefficient" step, and its calculation result directly determines the specific effect of the multiplication operation in this step; at the same time, it also supports the safe and effective implementation of the design that "the virtual resistance component is negative," because the magnitude of this negative value is carefully selected after comprehensive stability evaluation, rather than being arbitrarily set.

[0058] The power grid operating condition monitoring module 103 is used to monitor the voltage distortion rate or frequency fluctuation rate of the power grid common connection point, and generate a power grid operating condition judgment result based on the voltage distortion rate and the frequency fluctuation rate.

[0059] In some embodiments, monitoring the voltage distortion rate or frequency fluctuation rate at the power grid point of common coupling and generating a power grid operating condition determination result based on the voltage distortion rate and the frequency fluctuation rate specifically includes: The total harmonic distortion rate of the voltage signal is calculated as the voltage distortion rate; The fluctuation amount of the fundamental frequency of the voltage signal is calculated as the frequency fluctuation rate; The voltage distortion rate is compared with a first preset threshold, and the frequency fluctuation rate is compared with a second preset threshold; Based on the comparison results, a power grid condition determination result is generated, indicating whether the power grid is in an ideal or non-ideal condition.

[0060] In this embodiment, the power grid condition monitoring module is a key module in the photovoltaic inverter energy conversion control system for realizing monitoring-based adaptive adjustment. The function of this module is to continuously evaluate the power quality status of the power grid point of common coupling and generate a clear condition judgment result. Its role is to provide triggering basis and adjustment direction for subsequent dynamic command adjustments, so that the system can take targeted stabilization strategies for different types of power grid disturbances, thereby enhancing the robustness of the system when the power grid environment deteriorates.

[0061] In this embodiment, the step of "calculating the total harmonic distortion rate of the voltage signal as the voltage distortion rate" is a process of quantitatively evaluating the purity of the power grid voltage waveform. The total harmonic distortion rate is a percentage value used to measure the harmonic content relative to the fundamental frequency content in a periodic AC signal. This step is implemented using a digital signal processing algorithm: the system processes the three-phase power grid point-of-combination voltage signal obtained in real-time by the signal acquisition module; first, the fundamental frequency period of the signal is determined by a software phase-locked loop or zero-crossing detection; then, for each phase voltage, a discrete Fourier transform or sliding window harmonic analysis algorithm is used to calculate the effective values ​​of the fundamental component and each harmonic component in the voltage signal within a data window of one or more fundamental frequency periods. This step obtains a continuous index that accurately reflects the severity of power grid voltage waveform distortion. A high voltage distortion rate indicates severe background harmonic pollution in the power grid, which may cause harmonic resonance or instability in the inverter. This index provides direct data for the system to determine whether stability enhancement aimed at harmonic suppression is necessary.

[0062] In this embodiment, the step of "calculating the fluctuation of the fundamental frequency of the voltage signal as the frequency fluctuation rate" is a process of quantitatively evaluating the stability of the fundamental frequency of the power grid. The frequency fluctuation is the absolute value of the difference between the actual instantaneous frequency of the power grid and its rated frequency. This step relies on high-precision frequency measurement: the system utilizes the byproduct of phase-locked loop processing of the voltage signal, namely the instantaneous angular frequency output in real time by the software phase-locked loop. This instantaneous angular frequency is smoothed by a low-pass filter and then divided by twice the constant pi to obtain the instantaneous frequency of the power grid; the frequency fluctuation is then obtained by calculating the absolute value of the difference between this instantaneous frequency and the rated frequency of the power grid. For example, for a power grid with a rated frequency of 50 Hz, if the instantaneously measured frequency is 50.5 Hz, then the frequency fluctuation is 0.05 Hz. This step yields an index that reflects the real-time stability of the power grid frequency. Large frequency fluctuations may originate from power grid imbalances or faults, which will force the grid-connected inverter to frequently adjust its power output, easily causing power oscillations or even loss of synchronization. This index provides a key basis for the system to determine whether control enhancement aimed at damping frequency oscillations is necessary.

[0063] In this embodiment, the step of "comparing the voltage distortion rate with a first preset threshold and comparing the frequency fluctuation rate with a second preset threshold" is a process of converting continuous measurement indicators into discrete logical judgments. The first and second preset thresholds are threshold values ​​preset according to grid connection standards, equipment tolerance, and system stability design requirements. This step is implemented using digital comparison logic: within each control cycle of the controller, the calculated total harmonic distortion rate is compared with the first preset threshold stored in memory to determine whether it exceeds the threshold; simultaneously, the calculated frequency fluctuation rate is compared with the second preset threshold to determine whether it exceeds the threshold. For example, based on common power quality standards, the first preset threshold can be set to 5%, and the second preset threshold to 0.2 Hz. This step simplifies complex power quality issues into clear limit-crossing indicators, providing a Boolean logic basis for generating clear operating condition judgment results. These two comparison operations are the bridge connecting perception and decision-making; they convert the analog information calculated in the preceding steps into digital conditions that can be directly used in subsequent logical judgment steps.

[0064] In this embodiment, the step of generating a power grid condition determination result indicating whether the power grid is in an ideal or non-ideal operating condition based on the comparison result is the logical decision endpoint of the module. It outputs a flag signal to guide the subsequent operation mode of the entire system. The power grid condition determination result is a binary or multi-valued state flag. This step is implemented by a simple logical "OR" operation or a more complex state machine. The system performs logical synthesis based on the aforementioned two comparison results.

[0065] In one embodiment, if either the total harmonic distortion rate (THD) exceeds a first preset threshold or the frequency fluctuation exceeds a second preset threshold, the power grid is determined to be in a non-ideal operating condition, and a high-level operating condition determination flag signal is generated, i.e., the flag is set to logic 1. If neither condition is met, the power grid is determined to be in an ideal operating condition, and the flag is set to logic 0. This step ultimately completes the conversion from the power grid physical signal to executable instructions from the control system. This explicit operating condition flag signal will directly trigger the corresponding action in the dynamic instruction generation module. For example, when the flag is 1, the dynamic adjustment process of the virtual impedance coefficient is initiated. This step enables the entire system to adaptively switch based on the real-time state of the power grid, thus pursuing performance when the power grid is good and prioritizing stability when the power grid deteriorates. This is an intelligent outpost for solving system instability caused by sudden changes in power grid operating conditions. The operating condition flag output by this step is a key data link connecting this module and the subsequent dynamic instruction generation module, ensuring close coordination between the monitoring and execution stages.

[0066] The dynamic command generation module 104 is used to dynamically increase the weight of the virtual inductance component in the virtual impedance reference signal when the power grid condition determination result indicates that the power grid has entered a non-ideal condition, and to superimpose the virtual impedance reference signal with increased weight into the grid-connected current command in the form of current negative feedback to obtain a dynamic grid-connected current command.

[0067] In some embodiments, when the power grid condition determination result indicates that the power grid has entered a non-ideal operating condition, the weight of the virtual inductance component in the virtual impedance reference signal is dynamically increased, specifically including: Calculate the magnitude by which the voltage distortion rate exceeds the first preset threshold, or calculate the magnitude by which the frequency fluctuation rate exceeds the second preset threshold; Based on the calculated amplitude, determine the increase ratio of the virtual inductance component; The virtual inductance component in the virtual impedance coefficient is increased according to the stated increase ratio.

[0068] In this embodiment, the dynamic instruction generation module is the execution module in the photovoltaic inverter energy conversion control system that implements adaptive stability enhancement. The function of this module is to adjust the system's control strategy after the grid condition monitoring module determines that the grid condition has deteriorated. This module dynamically modifies the structural parameters of the virtual impedance and injects its effects into the current control loop in a specific feedback form, thereby reshaping the inverter's grid-connected dynamic characteristics in real time. This effectively suppresses the instability risk caused by grid disturbances, which is a key adaptive step in solving the stability problem under the superposition of wide-range grid impedance and severe grid conditions.

[0069] In this embodiment, the step of calculating the magnitude by which the voltage distortion rate exceeds the first preset threshold, or the step of calculating the magnitude by which the frequency fluctuation rate exceeds the second preset threshold, is a dynamic adjustment initialization and quantitative evaluation step. Its purpose is to accurately measure the severity of the current power grid disturbance deviating from the normal range. The magnitude of exceeding the threshold is a scalar value characterizing the strength of the disturbance. This step is implemented using arithmetic subtraction and condition selection logic: the system reads the voltage distortion rate and frequency fluctuation rate calculated by the power grid condition monitoring module in real time, and simultaneously reads the preset first and second preset thresholds from memory. Then, the system judges the cause of the power grid condition judgment flag. If the flag is set because the voltage distortion rate exceeds the first threshold, the system performs calculations, using the difference between the measured voltage distortion rate and the first preset threshold as the magnitude of the exceedance. If the flag is set because the frequency fluctuation exceeds the second threshold, the system performs calculations, using the difference between the frequency fluctuation and the second preset threshold as the magnitude of the exceedance. In some complex operating conditions, both may exceed the limits simultaneously. The system can then choose to calculate the larger magnitude, or calculate them separately for subsequent adjustments of independent channels. This step transforms the simple "whether" limit is exceeded into a quantitative perception of "how much" it exceeds. This provides a precise input for subsequent proportional adjustment rather than a simple on / off switch, enabling the strength of stability enhancement measures to match the intensity of the disturbance, achieving smoother and more controllable results. The exceedance value output by this step is the direct basis for determining the adjustment ratio.

[0070] In this embodiment, the step of determining the amplification ratio of the virtual inductance component based on the calculated amplitude is a decision-making step in establishing the mapping relationship between the disturbance intensity and the control parameter adjustment amount. The amplification ratio of the virtual inductance component is a multiplier factor used to amplify the original virtual inductance parameter. This step is implemented by applying a preset adjustment algorithm or lookup table: the system inputs the amplitude value calculated in the previous step into a proportional function or a pre-programmed mapping relationship. A typical implementation is to use a linear proportional relationship. For example, the virtual inductance amplification ratio is set to be equal to one plus the product of the proportional coefficient and the frequency fluctuation exceeding the amplitude, where the proportional coefficient is a pre-tuned constant; for example, when the frequency fluctuation amplitude is detected to be a specific value, the amplification ratio should be a value greater than one can be calculated through this relationship. Another implementation is based on a lookup table, where the system directly reads the corresponding amplification ratio value from the table according to the numerical range to which the exceeding amplitude belongs. This step generates a clear adjustment command that is positively correlated with the current grid disturbance intensity. This mechanism ensures that during minor grid disturbances, the system makes only minor parameter adjustments to maintain efficiency, while during severe grid disturbances, the system significantly enhances the virtual inductance (damping) effect to strongly suppress oscillations, achieving an adaptive trade-off between stability and dynamic performance. The increase ratio output by this step is the operation command to execute the specific parameter modification.

[0071] In this embodiment, the step of increasing the virtual inductance component in the virtual impedance coefficient according to the increase ratio is an operational step of implementing the adjustment decision and specifically changing the internal parameters of the virtual impedance generation module. This step directly affects the virtual inductance component that constitutes the virtual impedance coefficient. It is implemented through multiplication and parameter updating: the system obtains the base virtual inductance component value used by the virtual impedance generation module (i.e., the original value determined offline), and multiplies it by the increase ratio determined in the previous step to calculate the adjusted virtual inductance component; subsequently, the system temporarily replaces or superimposes the adjusted value onto the original base value, updating the virtual inductance parameters used in the virtual impedance generation module during the calculation. For example, if the original base value is Lzero and the calculated increase ratio is 1.5, then the updated virtual inductance component is 1.5 times Lzero. Increasing the virtual inductance component is equivalent to enhancing the "inductance" of the inverter output at the algorithm level. Physically, this increases the system's impedance to high-frequency harmonic currents and strengthens the phase damping of power or frequency oscillations. This adjustment is achieved by changing the external "virtual environment" parameters while keeping the inner loop controller parameters fixed. It fundamentally alters the dynamic energy exchange characteristics between the inverter and the real power grid, resulting in stronger anti-interference capabilities and stability under harsh grid conditions. This step is crucial in connecting "monitoring and decision-making" with "final control effect." The updated virtual inductance component immediately affects the virtual impedance reference signal output by the virtual impedance generation module, thus preparing a specifically enhanced "tool" for the next step of current command reconfiguration.

[0072] In some embodiments, the weighted virtual impedance reference signal is superimposed on the grid-connected current command in the form of current negative feedback to obtain a dynamic grid-connected current command, specifically including: Identify the dominant disturbance types that cause the power grid to enter non-ideal operating conditions, including voltage harmonic disturbances and frequency oscillation disturbances; If the dominant disturbance is voltage harmonic disturbance, then the frequency component corresponding to the harmonic order in the weighted virtual impedance reference signal is enhanced, and then the current is negatively fed back and superimposed. If the dominant disturbance is a frequency oscillation disturbance, then the response speed of the virtual impedance reference signal to the frequency fluctuation rate is increased, and then negative feedback of the current is superimposed.

[0073] In this embodiment, this section describes detailed steps for targeted processing of the weighted virtual impedance reference signal after identifying specific grid disturbance types. This represents an intelligent enhancement of the aforementioned general dynamic adjustment strategy, enabling the system to take stabilization measures based on different disturbance mechanisms, thereby solving grid stability problems under specific non-ideal operating conditions.

[0074] In this embodiment, the step of identifying the dominant disturbance type that causes the power grid to enter a non-ideal operating condition, including voltage harmonic disturbances and frequency oscillation disturbances, is a prerequisite decision-making step for refined control. The dominant disturbance type refers to the main category of causes that leads to excessive voltage distortion rate or frequency fluctuation rate in the power grid. This step is implemented by logical judgment based on the numerical relationship and change characteristics of the voltage distortion rate and frequency fluctuation rate, and the system continuously analyzes these two indicators. If the voltage distortion rate exceeds the standard while the frequency fluctuation remains near the threshold or only slightly exceeds the standard, the system determines that the dominant disturbance type is voltage harmonic disturbance, indicating that there are a large number of harmonic sources in the power grid background. If the frequency fluctuation exceeds the standard and may be accompanied by periodic fluctuations, while the voltage distortion rate is relatively normal, the system determines that the dominant disturbance type is frequency oscillation disturbance, indicating that there may be power imbalance or electromechanical oscillations under weak power grid conditions. One specific implementation algorithm involves setting a priority between two thresholds or comparing their respective percentage exceedances. For example, if the percentage exceedance of voltage distortion rate is significantly greater than the percentage exceedance of frequency fluctuation, it is determined that voltage harmonic disturbances dominate; otherwise, it is determined that frequency oscillation disturbances dominate. This step enables a preliminary diagnosis of the root causes of power grid disturbances, refining a single non-ideal operating condition indicator into a more instructive disturbance type identifier, providing decision-making direction for subsequent implementation of "targeted" stability control.

[0075] In this embodiment, if the dominant disturbance is voltage harmonic disturbance, the step of enhancing the frequency components corresponding to the harmonic orders in the weighted virtual impedance reference signal and then performing negative feedback superposition of the current is a specific treatment method for the specific problem of harmonic pollution. The frequency components corresponding to the harmonic orders refer to those signal components in the virtual impedance reference signal whose frequencies are integer multiples of the grid fundamental frequency. The implementation of this step involves frequency domain filtering or resonant controllers: after calculating the weighted virtual impedance reference signal, the system does not directly use it for superposition. First, through a set of parallel bandpass filters or complex coefficient resonant controllers, the frequency components corresponding to the main harmonic orders (such as the 5th, 7th, and 11th) are extracted from the signal; then, the amplitude of these specific harmonic frequency components is multiplied by an enhancement factor greater than 1, and the enhanced harmonic components are then combined into a total harmonic compensation signal; finally, this processed signal is superimposed on the grid-connected current command in the form of negative current feedback, that is, the dynamic current command is equal to the original command minus a gain factor multiplied by the sum of the fundamental component and the harmonic compensation signal. This step achieves active damping of specific harmonics. By enhancing the "impedance" effect (mainly inductive reactance) of the virtual impedance at specific harmonic frequencies, the system can more effectively suppress the inverter from injecting corresponding harmonic currents into the grid. At the same time, it reduces the interference of grid background harmonics on the inverter control loop, thereby improving the quality of grid-connected current waveform and enhancing the system's immunity to harmonic resonance in harsh grid environments with abundant harmonics.

[0076] In this embodiment, if the dominant disturbance is a frequency oscillation disturbance, the step of increasing the response speed of the virtual impedance reference signal to the frequency fluctuation rate and then performing current negative feedback superposition is a specific optimization method for the dynamic problem of power grid frequency instability or oscillation. Response speed refers to the speed at which the generation and change of the virtual impedance reference signal can track power grid frequency fluctuations. This step is implemented by adjusting the relevant time constant or filter bandwidth in the control loop: when the frequency oscillation disturbance is determined to be dominant, the system will modify the virtual impedance generation module or the algorithm parameters related to frequency signal processing in this module. Specifically, the system will reduce the cutoff frequency of the low-pass filter used to smooth the frequency measurement value, or increase the value of the proportional coefficient used to calculate the increase ratio of the virtual inductance, and may introduce a differential element. This allows the change in frequency fluctuation to be reflected in the calculation of the increase ratio more quickly, thereby enabling the virtual inductance component and the final virtual impedance reference signal to follow the power grid frequency fluctuations more rapidly. The virtual impedance reference signal after this acceleration process is then superimposed with current negative feedback. This step greatly enhances the system's ability to quickly dampen grid frequency oscillations. The faster response speed means that the "inductive damping" provided by the virtual impedance can almost instantly counteract power oscillations caused by frequency fluctuations, effectively preventing adverse dynamic interactions between the inverter and the weak grid. Thus, during periods of grid frequency instability, it can quickly quell power oscillations and maintain the transient and synchronous stability of grid-connected operation.

[0077] In this embodiment, the above three steps are closely and logically integrated with the preceding steps of the module: the disturbance type identification step relies on the voltage distortion rate, frequency fluctuation, and their exceeding range calculated in the preceding steps; the identified type determines the selection of the two subsequent differentiated technical paths. These two paths share the final execution action of "current negative feedback superposition," but different preprocessing is performed on the virtual impedance reference signal beforehand, namely, harmonic component enhancement or response speed improvement. These processing methods are designed based on a deep understanding of the physical nature of the disturbance.

[0078] The closed-loop control and drive module 105 is used to generate a switching drive signal through closed-loop adjustment based on the dynamic grid-connected current command and the grid-connected current feedback signal, so as to control the power switching devices of the photovoltaic inverter.

[0079] In some embodiments, based on the dynamic grid-connected current command and the grid-connected current feedback signal, a switching drive signal is generated through closed-loop regulation to control the power switching devices of the photovoltaic inverter, specifically including: The dynamic grid-connected current command is compared with the grid-connected current feedback signal to obtain the current error signal; The current error signal is input into a closed-loop regulator for processing to generate a modulated wave signal; The modulated wave signal is compared with the carrier signal to generate the switch drive signal.

[0080] In this embodiment, the closed-loop control and drive module is the final actuator and physical implementation of the photovoltaic inverter energy conversion control system. This module's function is to convert the optimized current command obtained from all preceding algorithms into the actual current injected into the grid through power electronic transformation. Based on a classic but fixed closed-loop control architecture, this module is responsible for achieving accurate current tracking at the power level. Its stable and reliable operation is the foundation for the practical effectiveness of all advanced control strategies in the entire system and is a guarantee for solving the problems of grid-connected current control accuracy and dynamic response.

[0081] In this embodiment, the step of comparing the dynamic grid-connected current command with the grid-connected current feedback signal to obtain the current error signal is the starting point of closed-loop control, aiming to quantify the deviation between the actual output and the ideal target. The current error signal is an instantaneous value signal, representing the difference between the expected current and the actual current within the current control cycle. This step is implemented by the arithmetic logic unit of the digital controller performing a subtraction operation: within each control interruption cycle, the system synchronously reads the dynamic grid-connected current command from the dynamic command generation module and the grid-connected current feedback signal from the signal acquisition module. Both the dynamic grid-connected current command and the grid-connected current feedback signal are time discrete sequences represented in the same coordinate system. The system performs an operation, subtracting the current cycle value of the grid-connected current feedback signal from the current cycle value of the dynamic grid-connected current command, and the result is the current error signal for the current cycle. For example, in a stationary αβ coordinate system, this operation is performed independently for the α-axis and β-axis components respectively. This step generates the direct drive signal for closed-loop regulation. This error signal indicates the degree to which the actual output of the inverter deviates from the command after virtual impedance reshaping and adaptive optimization of operating conditions. It is the sole basis for subsequent regulator correction operations. This step seamlessly connects the output results of all preceding algorithm modules with the input of the basic control loop, ensuring that the intention to faithfully track the optimized command is transmitted to the execution layer.

[0082] In this embodiment, the step of inputting the current error signal into a closed-loop regulator to generate a modulation wave signal is a calculation step in the closed-loop control. Its function is to calculate the inverter bridge arm voltage reference value required to eliminate the error based on the historical and current state of the error. The closed-loop regulator is a controller with dynamic adjustment capabilities; the modulation wave signal is an analog or digital voltage reference waveform used to determine the on / off time of power switching devices. This step is implemented by a proportional-integral control algorithm running in the digital controller: the system sends the current error signal into a discrete PI regulator. The operation of the PI regulator follows the following rules: the modulation wave signal value of the current cycle is equal to the modulation wave signal value of the previous cycle, plus the proportional coefficient multiplied by the difference between the current error of the current cycle and the previous cycle, plus the integral coefficient multiplied by the control cycle and then multiplied by the current error value of the current cycle. Among them, the proportional term is used to quickly respond to changes in error, and the integral term is used to eliminate steady-state error. The proportional coefficient and integral coefficient are tuned offline based on fixed main circuit parameters such as the inverter filter inductor and are fixed in the program, remaining unchanged throughout the control process. This step converts the current error into a direct voltage-type control command. Through dynamic compensation by the PI regulator, the system can effectively suppress current tracking deviations caused by grid voltage disturbances, load changes, and its own nonlinear factors, ensuring high-quality grid-connected current. This step is one of the key supports for the system's "hardware unchanged, software-defined performance" concept. It demonstrates that the system's wide-range stability is not achieved by adjusting the proportional or integral coefficients online, but rather by optimizing the current command in the preceding modules.

[0083] In this embodiment, the step of comparing the modulated wave signal with the carrier signal to generate the switch drive signal is a signal conversion stage that converts a continuous control signal into a discrete power switch command, and is the final step in realizing the conversion of electrical energy form. The carrier signal is a periodic waveform with a frequency much higher than the fundamental frequency; the switch drive signal is a series of logic level signals used to directly control the on / off state of power switching devices such as insulated gate bipolar transistors or metal-oxide-semiconductor field-effect transistors. This step is implemented by the pulse width modulation comparator and drive circuit of the digital controller: the system simultaneously sends the modulated wave signal output by the closed-loop regulator and an internally generated high-frequency triangular wave carrier signal into a digital comparator. The comparison rule is: when the instantaneous value of the modulated wave signal is greater than the instantaneous value of the carrier signal, the comparator outputs a high level; otherwise, it outputs a low level. This comparison process is performed in real time, generating a series of pulse signals whose pulse width is proportional to the amplitude of the modulated wave signal, i.e., pulse width modulation waveforms. This PWM waveform is then fed into the input stage of the drive circuit. After electrical isolation, level conversion, and power amplification, it forms the final switching drive signal, which has sufficient voltage and current capability to reliably turn on and off the power switching devices. This step achieves a precise mapping from low-power control signals to high-power switching actions. Through this modulation, the average voltage at the inverter arm output point accurately reproduces the waveform required by the modulated wave signal, thereby forcing the current on the filter inductor to track the trajectory of the dynamic grid-connected current command. This step is a bridge connecting the digital control world and the analog power world. It transforms all the aforementioned stability optimization strategies based on signals and algorithms into a specific, real-time switching sequence of the power switching devices, thereby physically forcing the output current of the photovoltaic inverter to have robust characteristics to cope with a wide range of grid impedance changes, solving the problem of precise control and stable output of grid-connected current.

[0084] In the embodiments provided in this application, it should be understood that the disclosed system can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and there may be other division methods in actual implementation.

[0085] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0086] Furthermore, the functional modules 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 in the form of hardware plus software functional modules.

[0087] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from the spirit or essential characteristics of this application.

[0088] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, system, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0089] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit it. Although this application has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of this application without departing from the spirit and scope of the technical solutions of this application.

Claims

1. A new energy photovoltaic inverter energy conversion control system, characterized in that, The system includes: The signal acquisition module is used to acquire the grid-connected current command of the photovoltaic inverter, the voltage signal of the grid common connection point, and the grid-connected current feedback signal. A virtual impedance generation module is used to generate a virtual impedance reference signal that is synchronized with the frequency and phase of the voltage signal based on the voltage signal. The power grid operating condition monitoring module is used to monitor the voltage distortion rate or frequency fluctuation rate of the power grid common connection point, and generate a power grid operating condition judgment result based on the voltage distortion rate and the frequency fluctuation rate. A dynamic command generation module is used to dynamically increase the weight of the virtual inductance component in the virtual impedance reference signal when the power grid condition determination result indicates that the power grid has entered a non-ideal operating condition, and to superimpose the weighted virtual impedance reference signal onto the grid-connected current command in the form of current negative feedback to obtain a dynamic grid-connected current command. Specifically, it includes: identifying the dominant disturbance type that causes the power grid to enter a non-ideal operating condition, the dominant disturbance type including voltage harmonic disturbance and frequency oscillation disturbance; if the dominant disturbance is voltage harmonic disturbance, then the frequency component corresponding to the harmonic order in the weighted virtual impedance reference signal is enhanced, and then current negative feedback superposition is performed; if the dominant disturbance is frequency oscillation disturbance, then the response speed of the virtual impedance reference signal to the frequency fluctuation rate is improved, and then current negative feedback superposition is performed. The closed-loop control and drive module is used to generate a switching drive signal through closed-loop adjustment based on the dynamic grid-connected current command and the grid-connected current feedback signal, so as to control the power switching devices of the photovoltaic inverter.

2. The energy conversion control system for a new energy photovoltaic inverter as described in claim 1, characterized in that, Acquiring the grid-connected current command from the photovoltaic inverter, the voltage signal at the grid's point of common coupling, and the grid-connected current feedback signal, specifically including: The grid-connected current command is obtained from the maximum power point tracking controller; The voltage signal at the power grid common coupling point is acquired using a voltage sensor; The current signal passing through the photovoltaic inverter is collected by a current sensor and used as the grid-connected current feedback signal.

3. The energy conversion control system for a new energy photovoltaic inverter as described in claim 2, characterized in that, The current sensor is located between the output filter inductor of the photovoltaic inverter and the common connection point of the power grid, and the current sensor is located after the output filter inductor.

4. The energy conversion control system for a new energy photovoltaic inverter as described in claim 3, characterized in that, Based on the voltage signal, a virtual impedance reference signal synchronized with the frequency and phase of the voltage signal is generated, specifically including: The voltage signal is subjected to phase-locked loop processing to extract the phase angle of its fundamental positive sequence component; Construct a rotating coordinate system based on the phase angle; In the rotating coordinate system, the d-axis voltage component and the q-axis voltage component of the voltage signal are multiplied by independent virtual impedance coefficients, which include virtual resistance components and virtual inductance components. The virtual impedance reference signal is generated by performing an inverse coordinate transformation on the signal after the virtual impedance coefficient calculation.

5. The energy conversion control system for a new energy photovoltaic inverter as described in claim 4, characterized in that, The virtual impedance coefficient includes a virtual resistance component and a virtual inductance component, and the virtual resistance component has a negative sign.

6. The energy conversion control system for a new energy photovoltaic inverter as described in claim 4, characterized in that, The virtual impedance coefficient is determined in advance offline based on the actual physical parameters of the output filter of the photovoltaic inverter and the expected system stability margin.

7. The energy conversion control system for a new energy photovoltaic inverter as described in claim 4, characterized in that, Monitoring the voltage distortion rate or frequency fluctuation rate at the power grid point of common coupling, and generating a power grid operating condition determination result based on the voltage distortion rate and the frequency fluctuation rate, specifically including: The total harmonic distortion rate of the voltage signal is calculated as the voltage distortion rate; The fluctuation amount of the fundamental frequency of the voltage signal is calculated as the frequency fluctuation rate; The voltage distortion rate is compared with a first preset threshold, and the frequency fluctuation rate is compared with a second preset threshold; Based on the comparison results, a power grid condition determination result is generated, indicating whether the power grid is in an ideal or non-ideal condition.

8. The energy conversion control system for a new energy photovoltaic inverter as described in claim 7, characterized in that, When the power grid condition determination result indicates that the power grid has entered a non-ideal operating condition, the weight of the virtual inductance component in the virtual impedance reference signal is dynamically increased, specifically including: Calculate the magnitude by which the voltage distortion rate exceeds the first preset threshold, or calculate the magnitude by which the frequency fluctuation rate exceeds the second preset threshold; Based on the calculated amplitude, determine the increase ratio of the virtual inductance component; The virtual inductance component in the virtual impedance coefficient is increased according to the stated increase ratio.

9. The energy conversion control system for a new energy photovoltaic inverter as described in claim 1, characterized in that, Based on the dynamic grid-connected current command and the grid-connected current feedback signal, a switching drive signal is generated through closed-loop regulation to control the power switching devices of the photovoltaic inverter, specifically including: The dynamic grid-connected current command is compared with the grid-connected current feedback signal to obtain the current error signal; The current error signal is input into a closed-loop regulator for processing to generate a modulated wave signal; The modulated wave signal is compared with the carrier signal to generate the switch drive signal.

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