Virtual inertia dynamic coupling adaptive MPPT inversion control method and system

Abstract

The invention relates to the technical field of inverter control, in particular to a self-adaptive MPPT inversion control method and system based on virtual inertia dynamic coupling. According to the scheme, the theoretical maximum power (Pmpp) and the dynamic reduction margin (delta P) are calculated by monitoring the output of a photovoltaic array and the frequency change of a power grid in real time, and an adjustable power target Pref is generated. And according to a frequency change rate (RoCoF), dynamically calculating virtual inertia power Pinertia, coupling the virtual inertia power Pinertia with Pref to form an active instruction Pcmd, and controlling an inverter to dynamically adjust power generation power on the premise of ensuring rapid inertia response to form a closed-loop adaptive system. According to the scheme, the power margin is dynamically established, the frequency change rate is mapped into the virtual inertia demand, the virtual inertia demand is coupled to the active instruction after slope limitation, and the problems of inertia support and power sudden change suppression of the photovoltaic grid-connected system are solved.

Description

Technical Field

[0001] This invention relates to the field of inverter control technology, and more specifically, to an adaptive MPPT inverter control method and system with virtual inertia dynamic coupling. Background Technology

[0002] In the field of inverter control, research on adaptive MPPT technology is of paramount importance. Its core value lies in fundamentally changing the role of photovoltaic inverters, transforming them from "selfish" power generation units that simply pursue maximum energy capture into "grid-building" entities capable of intelligently sensing grid conditions and actively participating in stability regulation. Traditional MPPT algorithms exacerbate grid frequency disturbances under varying light conditions, while adaptive MPPT dynamically adjusts the power point and even actively reduces power generation to reserve reserve capacity. This seamlessly integrates virtual inertia and droop control functions, providing crucial fast frequency response (FFR) capabilities for high-proportion renewable energy grids. It fundamentally resolves the inherent contradiction between maximizing energy and maintaining system stability, making it a core enabling technology for building resilient, secure, and efficient new power systems.

[0003] Prior to the present invention, existing adaptive MPPT methods mainly adopted rule-based control based on local frequency or voltage threshold triggering. That is, after detecting that the system frequency drops below a certain value, the power is passively reduced and the reserve capacity is released by forcibly shifting the operating point of the photovoltaic array (such as constant power control CPP mode) or activating a preset derating curve. However, the core difficulties and key points in achieving seamless integration with virtual inertia are as follows: First, resolving the time scale conflict: the MPPT algorithm responds to changes in illumination at the millisecond level to pursue energy capture, while virtual inertia support needs to respond to frequency changes at the centisecond level to maintain stability. The inherent contradiction between the two in terms of control bandwidth can easily lead to coupled oscillations. Second, achieving real-time optimization of power reservation: it is necessary to avoid excessive reduction at the expense of power generation efficiency, while ensuring that the reserved capacity is sufficient to cope with the worst disturbances. This requires an online decision-making mechanism that can perceive the system's inertia level, the remaining frequency regulation capacity of energy storage, and the risk of disturbances in real time. Finally, and most importantly, breaking the isolation of the control loop: the dynamic adjustment of MPPT is deeply embedded into the power and frequency regulation equation of the virtual synchronous machine, so that its power change rate and frequency change rate (RoCoF) are dynamically matched. This allows the reduced power to be converted into a virtual inertia response in real time and without delay, rather than simply discarded. This involves complex nonlinear system decoupling and state observation problems. Summary of the Invention

[0004] In view of the above problems, this invention proposes an adaptive MPPT inverter control method and system with virtual inertia dynamic coupling. By dynamically establishing power margin, the frequency change rate is mapped to virtual inertia demand, and after slope limitation, it is coupled to active power command, thus solving the problems of inertia support and power mutation suppression in photovoltaic grid-connected systems.

[0005] According to a first aspect of the present invention, an adaptive MPPT inverter control method with dynamic coupling of virtual inertia is provided.

[0006] In one or more embodiments, preferably, the adaptive MPPT inverter control method with dynamic coupling of virtual inertia includes:

[0007] S1. Real-time acquisition of the output current and voltage of the local photovoltaic array, and measurement of the frequency signal at the grid common coupling point, and obtaining the real-time frequency change rate RoCoF through differential calculation;

[0008] S2. Based on the output current and voltage, the maximum power point tracking (MPPT) algorithm is used to calculate the theoretical maximum output power P_mpp under the current operating condition. According to the upper-level scheduling instructions or local judgment, a dynamic power reduction margin ΔP is set to determine an adjustable power target value P_ref = P_mpp - ΔP that is lower than P_mpp.

[0009] S3. Input the real-time frequency change rate RoCoF into a predefined mapping function to dynamically calculate the required raw value of virtual inertia response power P_inertia_raw, where P_inertia_raw is proportional to the absolute value of RoCoF. Based on the preset maximum virtual inertia power change rate dP_inertia_max and the system control period T_s, calculate the maximum allowable power change ΔP_lim within the current control period.

[0010] The setting value of dP_inertia_max is determined based on the rated power P_rated of the photovoltaic inverter and the requirements of the grid operation criteria. Its value ranges from 0.1 to 0.5 times P_rated per second, i.e., dP_inertia_max = k × P_rated, where k is a proportionality coefficient with a value range of 0.1 to 0.5 s. -1 T_s is the fixed operation period of the adaptive MPPT inverter control system;

[0011] Compare P_inertia_raw with the final virtual inertia response power P_inertia_prev of the previous control cycle and apply the slope constraint function: if the absolute value of the difference between P_inertia_raw and P_inertia_prev is less than or equal to ΔP_lim, then P_inertia for the current cycle = P_inertia_raw; otherwise, if P_inertia_raw is greater than P_inertia_prev, then P_inertia = P_inertia_prev + ΔP_lim; if P_inertia_raw is less than P_inertia_prev, then P_inertia = P_inertia_prev - ΔP_lim.

[0012] S4. Dynamically couple the adjustable power target value P_ref with the virtual inertia response power P_inertia to generate the inverter's final active power command P_cmd; when a sharp frequency change is detected, prioritize the rapid output of P_inertia, and dynamically adjust P_cmd to: P_cmd = P_ref - P_inertia.

[0013] S5. Input the active power command P_cmd into the inner loop controller of the inverter. By adjusting the modulation signal of the inverter, control its output power to track P_cmd, thereby realizing the dynamic and seamless conversion of part of the power originally used for maximum power generation into virtual inertia support power.

[0014] S6. Feed back the actual output power of the inverter and the grid frequency status to steps S2 and S3, and update the power target value P_ref and the virtual inertia requirement P_inertia in real time to form a closed-loop adaptive control system.

[0015] In one or more embodiments, preferably, the real-time acquisition of the output current and voltage of the local photovoltaic array, and the measurement of the frequency signal at the grid common coupling point, to obtain the real-time frequency change rate RoCoF through differential calculation, specifically includes:

[0016] The method to obtain the real-time rate of change (RoCoF) through differential calculation is to use the first-order backward difference method. The calculation formula is: RoCoF=(f_k-f_{k-1}) / T_s, where f_k is the frequency value at the current sampling time, f_{k-1} is the frequency value at the previous sampling time, and T_s is the sampling period.

[0017] Before calculating the frequency change rate RoCoF, the measured frequency signal is preprocessed through a low-pass filter to eliminate high-frequency noise interference.

[0018] In one or more embodiments, preferably, the step of calculating the theoretical maximum output power P_mpp under the current operating condition based on the output current and voltage using the Maximum Power Point Tracking (MPPT) algorithm, and setting a dynamic power reduction margin ΔP according to upper-level scheduling instructions or local judgment, thereby determining an adjustable power target value P_ref = P_mpp - ΔP lower than P_mpp, specifically includes:

[0019] The specific method for setting the dynamic power reduction margin ΔP based on local judgment is as follows: the value of ΔP is determined by a predefined lookup table based on the absolute value of the frequency deviation delta_f measured locally. The larger the absolute value of the frequency deviation, the larger the set value of ΔP.

[0020] The upper-layer scheduling instruction is a power reduction margin instruction sigma_ref from the upper-layer optimization controller. The dynamic power reduction margin ΔP is directly taken from this instruction, that is, ΔP = sigma_ref.

[0021] In one or more embodiments, preferably, the step of inputting the real-time frequency change rate RoCoF into a predefined mapping function to dynamically calculate the required raw value of virtual inertia response power P_inertia_raw, wherein P_inertia_raw is proportional to the absolute value of RoCoF, and calculating the maximum allowable power change ΔP_lim within the current control cycle based on the preset maximum virtual inertia power change rate dP_inertia_max and the system control cycle T_s, specifically includes:

[0022] The predefined mapping function is a linear function, expressed as: P_inertia = K_inertia × abs(RoCoF)

[0023] Where K_inertia is the virtual inertia gain coefficient;

[0024] The virtual inertia gain coefficient K_inertia is not a fixed value, but is dynamically adjusted according to the virtual inertia gain setpoint M_ref issued by the upper-level scheduling instruction, that is, K_inertia=M_ref;

[0025] The mapping function is a piecewise linear function, and P_inertia is zero when the absolute value of RoCoF is lower than the dead zone threshold.

[0026] When the absolute value of RoCoF exceeds the threshold, P_inertia is proportional to the absolute value of RoCoF minus the dead zone threshold.

[0027] In one or more embodiments, preferably, the step of dynamically coupling the adjustable power target value P_ref with the virtual inertia response power P_inertia to generate the final active power command P_cmd of the inverter; when a sharp frequency change is detected, priority is given to ensuring the rapid output of P_inertia, and P_cmd is dynamically adjusted to: P_cmd = P_ref - P_inertia, specifically including:

[0028] The judgment condition when a sudden change in frequency is detected is: whether the absolute value of the frequency change rate RoCoF exceeds a preset emergency action threshold R_lim;

[0029] After generating the final active power command P_cmd, a power change rate limit is also imposed on it to ensure that its change rate does not exceed the maximum allowable change rate dP_max that the photovoltaic inverter and the grid can withstand.

[0030] In one or more embodiments, preferably, the step of inputting the active power command P_cmd into the inner loop controller of the inverter, and controlling its output power to track P_cmd by adjusting the modulation signal of the inverter, thereby realizing the dynamic and seamless conversion of a portion of the power originally used for maximum power generation into virtual inertia-supported power, specifically includes:

[0031] The inner loop controller uses a proportional resonant controller or a PI controller to track the active power command P_cmd without steady-state error.

[0032] The modulation signal for adjusting the inverter refers to: generating a current reference signal by instantaneous power calculation based on the active power command P_cmd and the reactive power command Q_cmd, and generating a pulse signal to drive the switching transistor using space vector pulse width modulation (SVPWM) technology.

[0033] In one or more embodiments, preferably, the step of feeding back the actual output power of the inverter and the grid frequency status to steps S2 and S3, and updating the power target value P_ref and the virtual inertia requirement P_inertia in real time to form a closed-loop adaptive control system, specifically includes:

[0034] The specific method for updating the target power value P_ref in real time is as follows: the deviation between the actual photovoltaic output power P_actual and the target instruction P_cmd is used to generate a compensation amount delta_P through a proportional-integral controller, which is used to correct the estimated value of the theoretical maximum output power P_mpp in step S2 in real time.

[0035] According to a second aspect of the present invention, an adaptive MPPT inverter control system with virtual inertia dynamic coupling is provided.

[0036] In one or more embodiments, preferably, the adaptive MPPT inverter control system with dynamic virtual inertia coupling includes:

[0037] The real-time acquisition and processing module is used to acquire the output current and voltage of the local photovoltaic array in real time, and measure the frequency signal of the grid common coupling point, and obtain the real-time frequency change rate RoCoF through differential calculation;

[0038] The maximum power point and margin calculation module is used to calculate the theoretical maximum output power P_mpp under the current operating condition based on the output current and voltage using the maximum power point tracking (MPPT) algorithm. Based on the upper-level scheduling instructions or local judgment, a dynamic power reduction margin ΔP is set to determine an adjustable power target value P_ref = P_mpp - ΔP that is lower than P_mpp.

[0039] The virtual inertia demand mapping module is used to input the real-time frequency change rate RoCoF into a predefined mapping function to dynamically calculate the required raw value of virtual inertia response power P_inertia_raw, where P_inertia_raw is proportional to the absolute value of RoCoF. Based on the preset maximum virtual inertia power change rate dP_inertia_max and the system control period T_s, the maximum allowable power change ΔP_lim within the current control period is calculated.

[0040] The power dynamic coupling module is used to dynamically couple the adjustable power target value P_ref with the virtual inertia response power P_inertia to generate the inverter's final active power command P_cmd. When a sharp change in frequency is detected, the rapid output of P_inertia is prioritized, and P_cmd is dynamically adjusted to: P_cmd = P_ref - P_inertia.

[0041] The adaptive MPPT modulation and inverter control module is used to input the active power command P_cmd into the inner loop controller of the inverter. By adjusting the modulation signal of the inverter, it controls its output power to track P_cmd, thereby realizing the dynamic and seamless conversion of part of the power originally used for maximum power generation into virtual inertia support power.

[0042] The continuous feedback and adjustment module is used to feed back the actual output power of the inverter and the grid frequency status to steps S2 and S3, and update the power target value P_ref and the virtual inertia requirement P_inertia in real time, forming a closed-loop adaptive control system.

[0043] According to a third aspect of the present invention, a computer-readable storage medium is provided that stores computer program instructions thereon, which, when executed by a processor, implement the method as described in any one of the first aspects of the present invention.

[0044] According to a fourth aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method described in any one aspect of the present invention.

[0045] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0046] In this invention, by introducing a power change rate slope limiting function, the output process of virtual inertial power is smoothed, effectively avoiding the inverter output command step fluctuation problem caused by abrupt frequency changes.

[0047] In this invention, the adaptive coupling of dynamic power margin and virtual inertia response balances the requirements of maximum power point tracking and grid frequency support, thereby improving the operational stability of a high-proportion renewable energy grid.

[0048] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0049] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 This is a flowchart of an adaptive MPPT inverter control method with virtual inertia dynamic coupling according to an embodiment of the present invention.

[0052] Figure 2 This is a flowchart illustrating the real-time frequency change rate RoCoF obtained through differential calculation in the adaptive MPPT inverter control method with virtual inertia dynamic coupling according to an embodiment of the present invention.

[0053] Figure 3 This is a flowchart of an adaptive MPPT inverter control method with virtual inertia dynamic coupling according to an embodiment of the present invention.

[0054] Figure 4This is a flowchart illustrating the dynamic calculation of the original value of the virtual inertia response power in an adaptive MPPT inverter control method with dynamic coupling of virtual inertia according to an embodiment of the present invention.

[0055] Figure 5 This is a flowchart of an adaptive MPPT inverter control method with virtual inertia dynamic coupling according to an embodiment of the present invention.

[0056] Figure 6 This is a flowchart of an adaptive MPPT inverter control method with virtual inertia dynamic coupling according to an embodiment of the present invention.

[0057] Figure 7 This is a flowchart of forming a closed-loop adaptive control system in the virtual inertia dynamic coupling adaptive MPPT inverter control method of one embodiment of the present invention.

[0058] Figure 8 This is a structural diagram of an adaptive MPPT inverter control system with virtual inertia dynamic coupling according to an embodiment of the present invention.

[0059] Figure 9 This is a structural diagram of an electronic device according to one embodiment of the present invention. Detailed Implementation

[0060] In some of the processes described in the specification, claims, and accompanying drawings of this invention, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to different types.

[0061] 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, and 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.

[0062] In the field of inverter control, research on adaptive MPPT technology is of paramount importance. Its core value lies in fundamentally changing the role of photovoltaic inverters, transforming them from "selfish" power generation units that simply pursue maximum energy capture into "grid-building" entities capable of intelligently sensing grid conditions and actively participating in stability regulation. Traditional MPPT algorithms exacerbate grid frequency disturbances under varying light conditions, while adaptive MPPT dynamically adjusts the power point and even actively reduces power generation to reserve reserve capacity. This seamlessly integrates virtual inertia and droop control functions, providing crucial fast frequency response (FFR) capabilities for high-proportion renewable energy grids. It fundamentally resolves the inherent contradiction between maximizing energy and maintaining system stability, making it a core enabling technology for building resilient, secure, and efficient new power systems.

[0063] Prior to the present invention, existing adaptive MPPT methods mainly adopted rule-based control based on local frequency or voltage threshold triggering. That is, after detecting that the system frequency drops below a certain value, the power is passively reduced and the reserve capacity is released by forcibly shifting the operating point of the photovoltaic array (such as constant power control CPP mode) or activating a preset derating curve. However, the core difficulties and key points in achieving seamless integration with virtual inertia are as follows: First, resolving the time scale conflict: the MPPT algorithm responds to changes in illumination at the millisecond level to pursue energy capture, while virtual inertia support needs to respond to frequency changes at the centisecond level to maintain stability. The inherent contradiction between the two in terms of control bandwidth can easily lead to coupled oscillations. Second, achieving real-time optimization of power reservation: it is necessary to avoid excessive reduction at the expense of power generation efficiency, while ensuring that the reserved capacity is sufficient to cope with the worst disturbances. This requires an online decision-making mechanism that can perceive the system's inertia level, the remaining frequency regulation capacity of energy storage, and the risk of disturbances in real time. Finally, and most importantly, breaking the isolation of the control loop: the dynamic adjustment of MPPT is deeply embedded into the power and frequency regulation equation of the virtual synchronous machine, so that its power change rate and frequency change rate (RoCoF) are dynamically matched. This allows the reduced power to be converted into a virtual inertia response in real time and without delay, rather than simply discarded. This involves complex nonlinear system decoupling and state observation problems.

[0064] This invention provides an adaptive MPPT inverter control method and system with dynamic coupling of virtual inertia. This scheme dynamically establishes a power margin, maps the rate of frequency change to virtual inertia demand, and couples it to active power commands after slope limiting, thus solving the problems of inertia support and power surge suppression in photovoltaic grid-connected systems.

[0065] According to a first aspect of the present invention, an adaptive MPPT inverter control method with dynamic coupling of virtual inertia is provided.

[0066] Figure 1This is a flowchart of an adaptive MPPT inverter control method with virtual inertia dynamic coupling according to an embodiment of the present invention.

[0067] In one or more embodiments, preferably, the adaptive MPPT inverter control method with dynamic coupling of virtual inertia includes:

[0068] S1. Real-time acquisition of the output current and voltage of the local photovoltaic array, and measurement of the frequency signal at the grid common coupling point, and obtaining the real-time frequency change rate RoCoF through differential calculation;

[0069] S2. Based on the output current and voltage, the maximum power point tracking (MPPT) algorithm is used to calculate the theoretical maximum output power P_mpp under the current operating condition. According to the upper-level scheduling instructions or local judgment, a dynamic power reduction margin ΔP is set to determine an adjustable power target value P_ref = P_mpp - ΔP that is lower than P_mpp.

[0070] S3. Input the real-time frequency change rate RoCoF into a predefined mapping function to dynamically calculate the required raw value of virtual inertia response power P_inertia_raw, where P_inertia_raw is proportional to the absolute value of RoCoF. Based on the preset maximum virtual inertia power change rate dP_inertia_max and the system control period T_s, calculate the maximum allowable power change ΔP_lim within the current control period.

[0071] The setting value of dP_inertia_max is determined based on the rated power P_rated of the photovoltaic inverter and the requirements of the grid operation criteria. Its value ranges from 0.1 to 0.5 times P_rated per second, i.e., dP_inertia_max = k × P_rated, where k is a proportionality coefficient with a value range of 0.1 to 0.5 s. -1 T_s is the fixed operation period of the adaptive MPPT inverter control system;

[0072] Compare P_inertia_raw with the final virtual inertia response power P_inertia_prev of the previous control cycle and apply the slope constraint function: if the absolute value of the difference between P_inertia_raw and P_inertia_prev is less than or equal to ΔP_lim, then P_inertia for the current cycle = P_inertia_raw; otherwise, if P_inertia_raw is greater than P_inertia_prev, then P_inertia = P_inertia_prev + ΔP_lim; if P_inertia_raw is less than P_inertia_prev, then P_inertia = P_inertia_prev - ΔP_lim.

[0073] S4. Dynamically couple the adjustable power target value P_ref with the virtual inertia response power P_inertia to generate the inverter's final active power command P_cmd; when a sharp frequency change is detected, prioritize the rapid output of P_inertia, and dynamically adjust P_cmd to: P_cmd = P_ref - P_inertia.

[0074] S5. Input the active power command P_cmd into the inner loop controller of the inverter. By adjusting the modulation signal of the inverter, control its output power to track P_cmd, thereby realizing the dynamic and seamless conversion of part of the power originally used for maximum power generation into virtual inertia support power.

[0075] S6. Feed back the actual output power of the inverter and the grid frequency status to steps S2 and S3, and update the power target value P_ref and the virtual inertia requirement P_inertia in real time to form a closed-loop adaptive control system.

[0076] In this embodiment, the adaptive MPPT inverter control method with virtual inertia dynamic coupling is implemented as follows: First, the output current I_pv and output voltage U_pv of the photovoltaic array are collected in real time using current and voltage sensors installed at the output end of the local photovoltaic array. Simultaneously, the frequency signal f of the grid common coupling point (PCC) is measured in real time using a frequency measurement device installed at the PCC. Next, based on the collected real-time frequency signal f, the real-time frequency change rate RoCoF is obtained using differential calculation. Subsequently, based on the collected output current I_pv and output voltage U_pv, the theoretical maximum output power P_mpp under the current illumination and temperature conditions is calculated using the perturbation observation method (a commonly used maximum power point tracking MPPT algorithm). If an upper-level dispatch instruction from the grid dispatch center is received (e.g., a dispatch instruction requiring a 10% power reduction margin), a dynamic power reduction margin ΔP is set according to the instruction. If no upper-level dispatch instruction is received, the local controller determines the current grid frequency deviation (e.g., when the grid frequency is 50Hz higher than the rated frequency). The system determines the adjustable power target value P_ref = P_mpp - ΔP by setting ΔP to increase the power reduction margin. For example, when P_mpp = 100kW and ΔP = 10kW, P_ref = 90kW. Then, the real-time frequency change rate RoCoF is input into a predefined mapping function (such as a linear mapping function) to dynamically calculate the required raw value of the virtual inertia response power P_inertia_raw. P_inertia_raw is proportional to the absolute value of RoCoF. Simultaneously, based on the preset maximum virtual inertia power change rate dP_inertia_max and the system control cycle T_s, the maximum allowable power change ΔP_lim within the current control cycle is calculated. The setting value of dP_inertia_max is determined based on the rated power P_rated of the photovoltaic inverter (e.g., in this embodiment, the rated power P_rated = 100kW) and the grid operation criteria. Its value is 0.3 times P_rated per second, i.e., dP_inertia_max = 0.3s. -1×100kW=30kW / s, T_s is the fixed operation period of the adaptive MPPT inverter control system (e.g., T_s=0.02s), then ΔP_lim=dP_inertia_max×T_s=30kW / s×0.02s=0.6kW; then compare P_inertia_raw with the final virtual inertia response power P_inertia_prev of the previous control cycle, and apply the slope limiting function. If the absolute value of the difference between P_inertia_raw (e.g., 1.2kW) and P_inertia_prev (e.g., 1.0kW) (0.2kW) is less than ΔP_lim (0.6kW), then P_inertia = 1.2kW for the current cycle; if P_inertia_raw (e.g., 2.0kW) is greater than P_inertia_prev (1.0kW), and the difference (1.0kW) is greater than ΔP_lim If im(0.6kW), then P_inertia for the current cycle = 1.0kW + 0.6kW = 1.6kW. If P_inertia_raw (e.g., 0.2kW) is less than P_inertia_prev(1.0kW), and the absolute value of the difference (0.8kW) is greater than ΔP_lim(0.6kW), then P_inertia for the current cycle = 1.0kW - 0.6kW = 0.4kW. Then, the adjustable power target value P_ref is dynamically coupled with the virtual inertia response power P_inertia to generate the inverter's final active power command P_cmd. When a sudden change in frequency is detected by the frequency measurement device (e.g., the absolute value of RoCoF exceeds 0), the inverter will generate the final active power command P_cmd.At 5Hz / s, priority is given to ensuring the rapid output of P_inertia. At this time, P_cmd is dynamically adjusted to P_cmd = P_ref - P_inertia. For example, when P_ref = 90kW and P_inertia = 5kW, P_cmd = 85kW. Then, the active power command P_cmd is input to the inverter's inner loop controller (such as a PI controller). By adjusting the inverter's modulation signal (such as a pulse width modulation signal), the inverter's output power is controlled to track P_cmd. This dynamically and seamlessly converts the 5kW power originally used for maximum power generation (i.e., the power reduced when adjusting from P_ref = 90kW to P_cmd = 85kW) into virtual power. Inertia supports power; finally, the actual output power P_actual of the inverter is collected by a power sensor, and the grid frequency status is collected again. This data is fed back to the adjustable power target value calculation step and the virtual inertia demand calculation step, updating the power target value P_ref and the virtual inertia demand P_inertia in real time, forming a closed-loop adaptive control system. For example, when a 1kW deviation is detected between P_actual = 84kW and P_cmd = 85kW, and the grid frequency deviation decreases, ΔP can be adjusted to 8kW, updating P_ref to 92kW. Simultaneously, P_inertia is adjusted to 4kW according to the new RoCoF, thus updating P_cmd to 88kW.

[0077] Figure 2 This is a flowchart illustrating the real-time acquisition of the output current and voltage of the local photovoltaic array and the measurement of the frequency signal at the grid common coupling point in an adaptive MPPT inverter control method with virtual inertia dynamic coupling according to an embodiment of the present invention, and the obtaining of the real-time frequency change rate RoCoF through differential calculation.

[0078] like Figure 2 As shown, in one or more embodiments, preferably, the real-time acquisition of the output current and voltage of the local photovoltaic array, and the measurement of the frequency signal at the grid common coupling point, to obtain the real-time frequency change rate RoCoF through differential calculation, specifically includes:

[0079] S201. The method to obtain the real-time frequency change rate (RoCoF) through differential calculation is to use the first-order backward difference method. The calculation formula is: RoCoF=(f_k-f_{k-1}) / T_s, where f_k is the frequency value at the current sampling time, f_{k-1} is the frequency value at the previous sampling time, and T_s is the sampling period.

[0080] S202. Before calculating the frequency change rate RoCoF, the measured frequency signal is preprocessed through a low-pass filter to eliminate high-frequency noise interference.

[0081] In this embodiment, the real-time acquisition of the output current and voltage of the local photovoltaic array and the measurement of the frequency signal at the point of common coupling (PCC) are used to obtain the real-time frequency change rate RoCoF through differential calculation. The specific implementation is as follows: First, Hall effect current sensors and voltage sensors are installed on the positive and negative output lines of the local photovoltaic array, respectively. The current sensor has a measurement range of 0-200A and an accuracy of 0.5 class, while the voltage sensor has a measurement range of 0-1000V and an accuracy of 0.2 class. These two sensors are used to acquire the output current I_pv and output voltage U_pv of the photovoltaic array in real time, with a sampling frequency set to 50Hz. Simultaneously, a high-precision frequency measuring instrument is installed at the PCC. This instrument has a measurement range of 45-55Hz and an accuracy of 0.001Hz, and measures the frequency signal f at the PCC in real time, also with a sampling frequency of 50Hz. Then, before calculating the frequency change rate RoCoF, the measured frequency signal f is... A low-pass filter (such as an RC low-pass filter with a cutoff frequency set to 5Hz) is used for preprocessing to eliminate high-frequency noise interference caused by the start-up and shutdown of electrical equipment in the power grid. For example, if the original frequency signal contains 10Hz high-frequency noise, after passing through the low-pass filter, the 10Hz high-frequency noise signal is attenuated to less than 10% of its original amplitude. Then, the real-time frequency change rate RoCoF is calculated using the first-order backward difference method. The calculation formula is RoCoF = (f_k - f_k) / (f_k - f_k) {k-1}) / T_s, where f_k is the frequency value at the current sampling time (e.g., the kth second) (e.g., 50.02Hz), f_{k-1} is the frequency value at the previous sampling time (e.g., the k-1th second) (e.g., 50.00Hz), and T_s is the sampling period (since the sampling frequency is 50Hz, T_s = 1 / 50s = 0.02s). Substituting the above values ​​into the formula, we get RoCoF = (50.02Hz - 50.00Hz) / 0.02s = 1Hz / s.

[0082] Figure 3 This is a flowchart illustrating the adaptive MPPT inverter control method with virtual inertia dynamic coupling in one embodiment of the present invention. Based on the output current and voltage, the method uses the maximum power point tracking (MPPT) algorithm to calculate the theoretical maximum output power P_mpp under the current operating condition, and sets a dynamic power reduction margin ΔP according to the upper-level scheduling instructions or local judgment, thereby determining an adjustable power target value P_ref = P_mpp - ΔP that is lower than P_mpp.

[0083] like Figure 3As shown, in one or more embodiments, preferably, the step of calculating the theoretical maximum output power P_mpp under the current operating condition based on the output current and voltage using the Maximum Power Point Tracking (MPPT) algorithm, and setting a dynamic power reduction margin ΔP according to upper-level scheduling instructions or local judgment, thereby determining an adjustable power target value P_ref = P_mpp - ΔP lower than P_mpp, specifically includes:

[0084] S301. The specific method for setting the dynamic power reduction margin ΔP based on local judgment is as follows: the value of ΔP is determined by a predefined lookup table based on the absolute value of the frequency deviation delta_f measured locally. The larger the absolute value of the frequency deviation, the larger the set value of ΔP.

[0085] S302. The upper-layer scheduling instruction is a power reduction margin instruction sigma_ref from the upper-layer optimization controller. The dynamic power reduction margin ΔP is directly taken from this instruction, that is, ΔP = sigma_ref.

[0086] In this embodiment, based on the output current and voltage, the maximum power point tracking (MPPT) algorithm is used to calculate the theoretical maximum output power P_mpp under the current operating condition. A dynamic power reduction margin ΔP is set according to upper-level scheduling instructions or local judgment, thereby determining an adjustable power target value P_ref = P_mpp - ΔP lower than P_mpp. The specific implementation is as follows: First, based on the local photovoltaic array output current I_pv (e.g., 80A) and output voltage U_pv (e.g., 800V) collected by current and voltage sensors, the incremental conductance method (a maximum power point tracking (MPPT) algorithm) is used to calculate the current irradiance (e.g., 1000W / m²). 2) The theoretical maximum output power P_mpp under the temperature condition (such as 25°C) is calculated as follows: First, calculate the current power P = I_pv × U_pv = 80A × 800V = 64000W = 64kW according to the collected I_pv and U_pv. Then, adjust the voltage reference value through the incremental conductance method. When dI / dU = -I / U is detected, determine that the corresponding power at this time is P_mpp. In this embodiment, P_mpp = 65kW is calculated; if an upper-layer scheduling instruction from the upper-layer optimization controller is received, and this instruction contains a power reduction margin instruction sigma_ref (such as 5kW), then the dynamic power reduction margin ΔP is directly taken from this instruction, that is, ΔP = sigma_ref = 5kW; if no upper-layer scheduling instruction is received, the dynamic power reduction margin ΔP is set according to local judgment. The specific method is: obtain the actual grid frequency f (such as 50.05Hz) through a frequency measurement device installed at the grid common connection point, calculate the frequency deviation delta_f = |f - f_n|, where f_n is the grid rated frequency (such as 50Hz), then delta_f = |50.05Hz - 50Hz| = 0.05Hz. A query table is predefined, and the corresponding relationship between delta_f and ΔP is stored in this query table. For example, when delta_f ≤ 0.02Hz, ΔP = 2kW; when 0.02Hz < delta_f ≤ 0.05Hz, ΔP = 5kW; when delta_f > 0.05Hz, ΔP = 8kW. Since delta_f = 0.05Hz in this embodiment, ΔP = 5kW is determined from the query table; finally, calculate the adjustable power target value P_ref = P_mpp - ΔP = 65kW - 5kW = 60kW.

[0087] Figure 4 It is the flowchart of inputting the real-time rate of change of frequency RoCoF into a predefined mapping function in the virtual inertia dynamic coupling adaptive MPPT inverter control method of an embodiment of the present invention to dynamically calculate the original value P_inertia_raw of the virtual inertia response power required. Among them, P_inertia_raw is proportional to the absolute value of RoCoF, and based on the preset maximum rate of change of virtual inertia power dP_inertia_max and the system control period T_s, calculate the maximum allowable power change amount ΔP_lim within the current control period.

[0088] such as Figure 4As shown, in one or more embodiments, preferably, the step of inputting the real-time frequency change rate RoCoF into a predefined mapping function to dynamically calculate the required raw value of virtual inertia response power P_inertia_raw, wherein P_inertia_raw is proportional to the absolute value of RoCoF, and calculating the maximum allowable power change ΔP_lim within the current control cycle based on the preset maximum virtual inertia power change rate dP_inertia_max and the system control cycle T_s, specifically includes:

[0089] S401. The predefined mapping function is a linear function, expressed as: P_inertia = K_inertia × abs(RoCoF)

[0090] Where K_inertia is the virtual inertia gain coefficient;

[0091] S402. The virtual inertia gain coefficient K_inertia is not a fixed value, but is dynamically adjusted according to the virtual inertia gain setpoint M_ref issued by the upper-level scheduling instruction, that is, K_inertia=M_ref;

[0092] S403. The mapping function is a piecewise linear function. When the absolute value of RoCoF is lower than the dead zone threshold, P_inertia is zero.

[0093] S404. When the absolute value of RoCoF exceeds the threshold, P_inertia is proportional to the absolute value of RoCoF minus the dead zone threshold.

[0094] In this embodiment, the real-time frequency change rate RoCoF is input into a predefined mapping function to dynamically calculate the required raw value of virtual inertia response power P_inertia_raw, where P_inertia_raw is proportional to the absolute value of RoCoF. The specific implementation of calculating the maximum allowable power change ΔP_lim within the current control cycle based on the preset maximum virtual inertia power change rate dP_inertia_max and the system control cycle T_s is as follows: First, the preset maximum virtual inertia power change rate dP_inertia_max is determined according to the rated power P_rated of the photovoltaic inverter (e.g., 80kW) and the grid operation criteria. In this embodiment, the proportionality coefficient k = 0.2s is used. -1 (k ranges from 0.1 to 0.5s) -1 If ), then dP_inertia_max=k×P_rated=0.2s -1×80kW=16kW / s, the system control cycle T_s is the fixed operation cycle of the adaptive MPPT inverter control system (e.g., 0.01s), then the maximum allowable power change ΔP_lim=dP_inertia_max×T_s=16kW / s×0.01s=0.16kW; Next, obtain the real-time frequency change rate RoCoF (e.g., 0.8Hz / s) calculated by differentiation, and input it into a predefined mapping function. This mapping function is a piecewise linear function. First, a dead zone threshold is set (e.g., 0.2Hz / s). When the absolute value of RoCoF (e.g., 0.1Hz / s) is lower than the dead zone threshold (0.2Hz / s), P_inertia_raw is zero; when the absolute value of RoCoF (e.g., 0.8Hz / s) exceeds the dead zone threshold (0.2Hz / s), P_inertia_raw is proportional to the absolute value of RoCoF minus the dead zone threshold. The virtual inertia gain coefficient K_inertia is not a fixed value, but is dynamically adjusted according to the virtual inertia gain setpoint M_ref (e.g., 10kW·s / Hz) issued by the upper-level scheduling command. That is, K_inertia = M_ref = 10kW·s / Hz. At this time, P_inertia_raw = K_inertia × (abs(RoCoF) - dead zone threshold) = 10kW·s / Hz × (0.8Hz / s - 0.2Hz / s) = 10kW·s / Hz × 0.6Hz / s = 6kW. If the predefined mapping function is a linear function, the expression is P_inertia_raw = K_inertia × abs(RoCoF). Similarly, K_inertia = M_ref = 10kW·s / Hz, RoCoF = 0.8Hz / s. Substituting these values, we can get P_inertia_raw = 10kW·s / Hz × 0.8Hz / s = 8kW.

[0095] Figure 5 This is a flowchart illustrating the adaptive MPPT inverter control method with virtual inertia dynamic coupling in one embodiment of the present invention. The method dynamically couples the adjustable power target value P_ref with the virtual inertia response power P_inertia to generate the inverter's final active power command P_cmd. When a sharp frequency change is detected, priority is given to ensuring the rapid output of P_inertia, and P_cmd is dynamically adjusted to: P_cmd = P_ref - P_inertia.

[0096] like Figure 5As shown, in one or more embodiments, preferably, the step of dynamically coupling the adjustable power target value P_ref with the virtual inertia response power P_inertia to generate the final active power command P_cmd of the inverter; when a sharp frequency change is detected, priority is given to ensuring the rapid output of P_inertia, and P_cmd is dynamically adjusted to: P_cmd = P_ref - P_inertia, specifically including:

[0097] S501, the judgment condition when a sudden change in frequency is detected is: whether the absolute value of the frequency change rate RoCoF exceeds a preset emergency action threshold R_l im;

[0098] S502. After generating the final active power command P_cmd, a power change rate limit is also imposed on it to ensure that the change rate does not exceed the maximum allowable change rate dP_max that the photovoltaic inverter and the power grid can withstand.

[0099] In this embodiment, the adjustable power target value P_ref and the virtual inertial response power P_inertia are dynamically coupled to generate the inverter's final active power command P_cmd. When a sharp frequency change is detected, the rapid output of P_inertia is prioritized, and P_cmd is dynamically adjusted to P_cmd = P_ref - P_inertia. The specific implementation is as follows: First, obtain the adjustable power target value P_ref (e.g., 70kW) calculated in the previous steps and the virtual inertial response power P_inertia (e.g., 6kW) after slope limiting. Then, determine whether a sharp frequency change has been detected. The determination condition is whether the absolute value of the real-time frequency change rate RoCoF exceeds the preset emergency action threshold R_l. If im (e.g., 0.5Hz / s) is obtained, the current RoCoF is calculated to be 0.7Hz / s through frequency differentiation. Since its absolute value (0.7Hz / s) exceeds R_lim (0.5Hz / s), a sharp frequency change is detected. In this case, priority is given to ensuring the rapid output of P_inertia, and P_cmd is dynamically adjusted to P_cmd = P_ref - P_inertia = 70kW - 6kW = 64kW. If the absolute value of RoCoF does not exceed R_lim, P_cmd is generated according to the conventional coupling strategy (e.g., P_cmd = P_ref). After generating the final active power command P_cmd (64kW), a power change rate limit is imposed. The maximum allowable change rate dP_max that the photovoltaic inverter and grid can withstand is preset to 20kW / s. The change rate ΔP_rate = |P_cmd - P_cmd_prev| / T_s is calculated between the current P_cmd and the previous control cycle P_cmd_prev (e.g., 66kW), where T_ Since s = 0.02s, we can obtain ΔP_rate = |64kW - 66kW| / 0.02s = 2kW / 0.02s = 100kW / s. This rate of change exceeds dP_max (20kW / s), so P_cmd needs to be adjusted. The adjusted P_cmd = P_cmd_prev - dP_max × T_s = 66kW - 20kW / s × 0.02s = 66kW - 0.4kW = 65.6kW, ensuring that the rate of change of the adjusted P_cmd does not exceed dP_max.

[0100] Figure 6 This is a flowchart illustrating the adaptive MPPT inverter control method with virtual inertia dynamic coupling in one embodiment of the present invention. The active power command P_cmd is input into the inner loop controller of the inverter, and the output power is controlled to track P_cmd by adjusting the modulation signal of the inverter, thereby realizing the dynamic and seamless conversion of part of the power originally used for maximum power generation into virtual inertia support power.

[0101] like Figure 6As shown, in one or more embodiments, preferably, the step of inputting the active power command P_cmd into the inner loop controller of the inverter, and controlling its output power to track P_cmd by adjusting the modulation signal of the inverter, thereby realizing the dynamic and seamless conversion of a portion of the power originally used for maximum power generation into virtual inertia support power, specifically includes:

[0102] S601. The inner loop controller adopts a proportional resonant controller or a PI controller to track the active power command P_cmd without steady-state error.

[0103] S602, The modulation signal for adjusting the inverter refers to: generating a current reference signal by instantaneous power calculation based on the active power command P_cmd and the reactive power command Q_cmd, and generating a pulse signal to drive the switching transistor using space vector pulse width modulation (SVPWM) technology.

[0104] In this embodiment, the active power command P_cmd is input into the inner loop controller of the inverter. By adjusting the modulation signal of the inverter, its output power is controlled to track P_cmd, thereby dynamically and seamlessly converting part of the power originally used for maximum power generation into virtual inertia-supported power. The specific implementation is as follows: First, the active power command P_cmd (e.g., 65kW) after the power change rate limit is obtained, and the reactive power command Q_cmd (e.g., 10kvar) is set according to the reactive power demand of the grid. Next, P_cmd and Q_cmd are input into the inner loop controller of the inverter. This inner loop controller adopts a proportional resonant controller (PR controller). The PR controller can achieve zero steady-state error tracking at a specific frequency (e.g., the grid fundamental frequency of 50Hz), thereby ensuring that the inverter output power accurately tracks P_cmd. After that, the modulation signal of the inverter is adjusted. The specific process is as follows: Based on the active power command P_cmd and the reactive power command Q_cmd, the d-axis current reference signal i_d_re is generated by instantaneous power calculation method (e.g., instantaneous power calculation based on the dq coordinate system). The f-axis and q-axis current reference signals are i_q_ref, where i_d_ref is proportional to P_cmd and i_q_ref is proportional to Q_cmd. In this embodiment, i_d_ref = 150A and i_q_ref = 25A are calculated. Then, using space vector pulse width modulation (SVPWM) technology, a pulse signal to drive the IGBT switching transistors in the inverter is generated based on i_d_ref, i_q_ref, and the DC-side voltage of the inverter. The frequency of this pulse signal is set to 10kHz. The duty cycle of the signal controls the on and off times of the IGBT switch. When the IGBT switch operates according to the pulse signal, the inverter converts the DC power into AC power output. Its output power tracks P_cmd in real time. For example, when P_cmd is adjusted from 70kW to 65kW, the inverter output power is also dynamically adjusted from 70kW to 65kW. The 5kW power reduction, which was originally used for maximum power generation, is now dynamically and seamlessly converted into virtual inertia support power through this control method, providing inertia support for the grid.

[0105] Figure 7 This is a flowchart of the adaptive MPPT inverter control method with virtual inertia dynamic coupling in one embodiment of the present invention, in which the actual output power of the inverter and the grid frequency status are fed back to steps S2 and S3, and the power target value P_ref and virtual inertia requirement P_inertia are updated in real time to form a closed-loop adaptive control system.

[0106] like Figure 7As shown, in one or more embodiments, preferably, the step of feeding back the actual output power of the inverter and the grid frequency status to steps S2 and S3, and updating the power target value P_ref and the virtual inertia requirement P_inertia in real time to form a closed-loop adaptive control system, specifically includes:

[0107] The specific method for real-time updating of the power target value P_ref is as follows:

[0108] S701, the deviation between the actual photovoltaic output power P_actual and the target command P_cmd;

[0109] S702. A proportional-integral controller generates a compensation quantity delta_P, which is used to correct the estimated value of the theoretical maximum output power P_mpp in step S2 in real time.

[0110] In this embodiment, the actual output power of the inverter and the grid frequency status are fed back to steps S2 and S3 to update the power target value P_ref and the virtual inertia demand P_inertia in real time. The specific implementation of a closed-loop adaptive control system is as follows: First, the actual output power P_actual (such as 64.5 kW) of the inverter is collected in real time through a power sensor installed at the AC output terminal of the inverter. At the same time, the current grid frequency status (such as 50.03 Hz) is collected through a frequency measurement device at the point of common coupling of the grid. Then, the collected P_actual and the grid frequency status are fed back to step S2 (the maximum power point and margin calculation step) and step S3 (the virtual inertia demand mapping step). When updating the power target value P_ref in real time, the specific method is as follows: First, calculate the deviation ΔP_err = P_cmd - P_actual = 65 kW - 64.5 kW = 0.5 kW between the actual PV output power P_actual (64.5 kW) and the target command P_cmd (65 kW). Then, input ΔP_err into a proportional-integral (PI) controller. The proportional coefficient K_p of this PI controller is set to 0.2, and the integral coefficient K_i is set to 0.1. Through PI operation, a compensation amount delta_P = K_p × ΔP_err + K_i × ∫ΔP_err dt is generated. After calculation, in this embodiment, delta_P = 0.2 × 0.5 kW + 0.1 × (0.5 kW × 0.02 s) / 0.02 s = 0.1 kW + 0.05 kW = 0.15 kW. This compensation amount delta_P is used to correct the estimated value of the theoretical maximum output power P_mpp in step S2 (the maximum power point and margin calculation step), that is, the corrected P_mpp_new = the original P_mpp (such as 65 kW) + delta_P (0.15 kW) = 65.15 kW. At the same time, combined with the fed-back grid frequency status (50.03 Hz), recalculate the frequency deviation delta_f_new = |50.03 Hz - 50 Hz| = 0.03 Hz. According to the preset look-up table in step S3 (when delta_f ≤ 0.02 Hz, ΔP = 2 kW; when 0.02 Hz < delta_f ≤ 0.05 Hz, ΔP = 5 kW; when delta_f > 0.05 Hz, ΔP = 8 kW), determine the new dynamic power reduction margin ΔP_new = 5 kW, and then update the adjustable power target value P_ref_new = P_mpp_new - ΔP_new = 65.15 kW - 5 kW = 60.15 kW. When updating the virtual inertia demand P_inertia in real time, recalculate the real-time rate of change of frequency RoCoF_new based on the fed-back grid frequency signal. For example, at the current sampling moment, the frequency f_k = 50.03 Hz, the frequency at the previous sampling moment f_{k - 1} = 50.04 Hz, and the sampling period T_s = 0.If 0.02s, then RoCoF_new = (50.03Hz - 50.04Hz) / 0.02s = -0.5Hz / s, with an absolute value of 0.5Hz / s. Input RoCoF_new into the predefined mapping function (piecewise linear function, dead zone threshold 0.2Hz / s, K_inertia = 10kW·s / Hz) in step S3 to calculate the new raw value of virtual inertia response power P_inertia_raw_new = 10kW·s / Hz × (0.5Hz / s - 0.2Hz / s) = 3kW. Combined with the preset maximum rate of change of virtual inertia power dP_inertia_max = 16kW / s (corresponding to P_rated = 80kW, k = 0.2s). -1 Given a system control period T_s = 0.01s, calculate the maximum allowable power change ΔP_l for the current control period. im_new = 16kW / s × 0.01s = 0.16kW. Compared with P_inertia_prev (e.g., 2.8kW) in the previous control cycle, since the difference between P_inertia_raw_new (3kW) and P_inertia_prev (2.8kW) (0.2kW) is greater than ΔP_lim_new (0.16kW), the updated P_inertia_new in the current cycle is 2.8kW + 0.16kW = 2.96kW. Through the real-time updates of P_ref and P_inertia, the active power command P_cmd generated by the inverter can be dynamically adjusted according to the actual output and grid status. For example, the new P_cmd_new = P_ref_new - P_inertia_new = 60.15kW - 2.96kW = 57.19kW, ensuring that the inverter output power always adapts to the grid demand, forming a complete closed-loop adaptive control system, and realizing continuous optimization and adjustment of photovoltaic power output and virtual inertia support.

[0111] According to a second aspect of the present invention, an adaptive MPPT inverter control system with virtual inertia dynamic coupling is provided.

[0112] Figure 8 This is a structural diagram of an adaptive MPPT inverter control system with virtual inertia dynamic coupling according to an embodiment of the present invention.

[0113] In one or more embodiments, preferably, the adaptive MPPT inverter control system with dynamic virtual inertia coupling includes:

[0114] The real-time acquisition and processing module 801 is used to acquire the output current and voltage of the local photovoltaic array in real time, measure the frequency signal of the grid common connection point, and obtain the real-time frequency change rate RoCoF through differential calculation;

[0115] The maximum power point and margin calculation module 802 is used to calculate the theoretical maximum output power P_mpp under the current operating condition based on the output current and voltage using the maximum power point tracking (MPPT) algorithm, and to set a dynamic power reduction margin ΔP according to the upper-level scheduling instructions or local judgment, thereby determining an adjustable power target value P_ref = P_mpp - ΔP that is lower than P_mpp.

[0116] The virtual inertia demand mapping module 803 is used to input the real-time frequency change rate RoCoF into a predefined mapping function to dynamically calculate the required virtual inertia response power raw value P_inertia_raw, where P_inertia_raw is proportional to the absolute value of RoCoF. Based on the preset maximum virtual inertia power change rate dP_inertia_max and the system control period T_s, the maximum allowable power change ΔP_lim within the current control period is calculated.

[0117] The power dynamic coupling module 804 is used to dynamically couple the adjustable power target value P_ref with the virtual inertia response power P_inertia to generate the final active power command P_cmd of the inverter. When a sharp change in frequency is detected, the rapid output of P_inertia is prioritized, and P_cmd is dynamically adjusted to: P_cmd = P_ref - P_inertia.

[0118] The adaptive MPPT modulation and inverter control module 805 is used to input the active power command P_cmd into the inner loop controller of the inverter. By adjusting the modulation signal of the inverter, it controls its output power to track P_cmd, thereby realizing the dynamic and seamless conversion of part of the power originally used for maximum power generation into virtual inertia support power.

[0119] The continuous feedback and adjustment module 806 is used to feed back the actual output power of the inverter and the grid frequency status to steps S2 and S3, and update the power target value P_ref and the virtual inertia requirement P_inertia in real time to form a closed-loop adaptive control system.

[0120] According to a third aspect of the present invention, a computer-readable storage medium is provided that stores computer program instructions thereon, which, when executed by a processor, implement the method as described in any one of the first aspects of the present invention.

[0121] According to a fourth aspect of the present invention, an electronic device is provided. Figure 9 This is a structural diagram of an electronic device according to one embodiment of the present invention. Figure 9 The electronic device shown is a general-purpose virtual inertia dynamically coupled adaptive MPPT inverter control device. (Refer to...) Figure 9The electronic device can be a smartphone, tablet computer, or other similar device. The electronic device 900 includes a processor 901 and a memory 902. The processor 901 and the memory 902 are electrically connected.

[0122] The processor 901 is the control center of the electronic device 900. It connects various parts of the electronic device through various interfaces and lines. By running or calling computer programs stored in the memory 902, and calling data stored in the memory 902, it performs various functions of the electronic device and processes data, thereby monitoring the electronic device as a whole.

[0123] In this embodiment, the processor 901 in the electronic device 900 loads the instructions corresponding to the processes of one or more computer programs into the memory 902 according to the following steps, and the processor 901 runs the computer programs stored in the memory 902 to realize various functions, such as: real-time acquisition of the output current and voltage of the local photovoltaic array, and measurement of the frequency signal of the grid common coupling point, obtaining the real-time frequency change rate RoCoF through differential calculation; based on the output current and voltage, using the maximum power point tracking (MPPT) algorithm to calculate the theoretical maximum output power P_mpp under the current operating condition, and setting a dynamic power reduction margin ΔP according to the upper-level scheduling instructions or local judgment, thereby determining an adjustable power target value P_ref = P_mpp - ΔP lower than P_mpp; inputting the real-time frequency change rate RoCoF into a predefined mapping function to dynamically calculate the required virtual inertia response power raw value P_inertia_raw, where P_inertia_raw and RoCoF are absolutely The value is proportional to the value. Based on the preset maximum change rate of virtual inertia power dP_inertia_max and the system control period T_s, the maximum allowable power change ΔP_lim within the current control period is calculated. The adjustable power target value P_ref is dynamically coupled with the virtual inertia response power P_inertia to generate the inverter's final active power command P_cmd. When a sharp frequency change is detected, the rapid output of P_inertia is prioritized, and P_cmd is dynamically adjusted to: P_cmd = P_ref - P_inertia. The active power command P_cmd is input into the inverter's inner loop controller. By adjusting the inverter's modulation signal, its output power is controlled to track P_cmd, thereby realizing the dynamic and seamless conversion of part of the power originally used for maximum power generation into virtual inertia support power. The actual output power of the inverter and the grid frequency status are fed back to steps S2 and S3 to update the power target value P_ref and the virtual inertia requirement P_inertia in real time, forming a closed-loop adaptive control system.

[0124] In some embodiments, the electronic device 900 may further include: a display 903, a radio frequency circuit 904, an audio circuit 905, a wireless fidelity module 906, and a power supply 907. The display 903, radio frequency circuit 904, audio circuit 905, wireless fidelity module 906, and power supply 907 are electrically connected to the processor 901.

[0125] The display 903 can be used to display information input by the user or information provided to the user, as well as various graphical user interfaces, which can be composed of graphics, text, icons, video, and any combination thereof. The display 903 may include a display panel, which in some embodiments may be configured as a liquid crystal display (LCD) or an organic light-emitting diode (OLED).

[0126] The radio frequency circuit 904 can be used to transmit and receive radio frequency signals to establish wireless communication with network devices or other electronic devices, and to transmit and receive signals with network devices or other electronic devices.

[0127] The audio circuit 905 can be used to provide an audio interface between a user and an electronic device through a speaker or microphone.

[0128] The wireless fidelity module 906 can be used for short-range wireless transmission, helping users send and receive emails, browse websites, and access streaming media, providing users with wireless broadband internet access.

[0129] The power supply 907 can be used to power various components of the electronic device 900. In some embodiments, the power supply 907 can be logically connected to the processor 901 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system.

[0130] although Figure 9 As not shown in the diagram, the electronic device 900 may also include a camera, Bluetooth module, etc., which will not be described in detail here.

[0131] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0132] In this invention, by introducing a power change rate slope limiting function, the output process of virtual inertial power is smoothed, effectively avoiding the inverter output command step fluctuation problem caused by abrupt frequency changes.

[0133] In this invention, the adaptive coupling of dynamic power margin and virtual inertia response balances the requirements of maximum power point tracking and grid frequency support, thereby improving the operational stability of a high-proportion renewable energy grid.

[0134] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0135] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0136] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0137] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0138] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. An adaptive MPPT inverter control method with dynamic coupling of virtual inertia, characterized in that, The method includes: S1. Real-time acquisition of the output current and voltage of the local photovoltaic array, and measurement of the frequency signal at the grid common coupling point, and obtaining the real-time frequency change rate RoCoF through differential calculation; S2. Based on the output current and voltage, the maximum power point tracking (MPPT) algorithm is used to calculate the theoretical maximum output power P_mpp under the current operating condition. According to the upper-level scheduling instructions or local judgment, a dynamic power reduction margin ΔP is set to determine an adjustable power target value P_ref = P_mpp - ΔP that is lower than P_mpp. S3. Input the real-time frequency change rate RoCoF into a predefined mapping function to dynamically calculate the required raw value of virtual inertia response power P_inertia_raw, where P_inertia_raw is proportional to the absolute value of RoCoF. Based on the preset maximum virtual inertia power change rate dP_inertia_max and the system control period T_s, calculate the maximum allowable power change ΔP_lim within the current control period. Compare P_inertia_raw with the final virtual inertia response power P_inertia_prev of the previous control cycle and apply the slope constraint function: if the absolute value of the difference between P_inertia_raw and P_inertia_prev is less than or equal to ΔP_lim, then P_inertia for the current cycle = P_inertia_raw; otherwise, if P_inertia_raw is greater than P_inertia_prev, then P_inertia = P_inertia_prev + ΔP_lim; if P_inertia_raw is less than P_inertia_prev, then P_inertia = P_inertia_prev - ΔP_lim. S4. Dynamically couple the adjustable power target value P_ref with the virtual inertia response power P_inertia to generate the inverter's final active power command P_cmd; when a sharp frequency change is detected, prioritize the rapid output of P_inertia, and dynamically adjust P_cmd to: P_cmd = P_ref - P_inertia. S5. Input the active power command P_cmd into the inner loop controller of the inverter. By adjusting the modulation signal of the inverter, control its output power to track P_cmd, thereby realizing the dynamic and seamless conversion of part of the power originally used for maximum power generation into virtual inertia support power. S6. Feed back the actual output power of the inverter and the grid frequency status to steps S2 and S3, and update the power target value P_ref and the virtual inertia requirement P_inertia in real time to form a closed-loop adaptive control system.

2. The adaptive MPPT inverter control method with virtual inertia dynamic coupling as described in claim 1, characterized in that, The real-time acquisition of the output current and voltage of the local photovoltaic array, and the measurement of the frequency signal at the grid common coupling point, along with the calculation of the real-time frequency change rate RoCoF through differentiation, specifically includes: The method to obtain the real-time rate of change (RoCoF) through differential calculation is to use the first-order backward difference method. The calculation formula is: RoCoF=(f_k-f_{k-1}) / T_s, where f_k is the frequency value at the current sampling time, f_{k-1} is the frequency value at the previous sampling time, and T_s is the sampling period. Before calculating the frequency change rate RoCoF, the measured frequency signal is preprocessed through a low-pass filter to eliminate high-frequency noise interference.

3. The adaptive MPPT inverter control method with virtual inertia dynamic coupling as described in claim 1, characterized in that, The method involves calculating the theoretical maximum output power P_mpp under the current operating condition based on the output current and voltage using the Maximum Power Point Tracking (MPPT) algorithm, and setting a dynamic power reduction margin ΔP according to upper-level scheduling instructions or local judgment, thereby determining an adjustable power target value P_ref = P_mpp - ΔP that is lower than P_mpp. Specifically, this includes: The specific method for setting the dynamic power reduction margin ΔP based on local judgment is as follows: the value of ΔP is determined by a predefined lookup table based on the absolute value of the frequency deviation delta_f measured locally. The larger the absolute value of the frequency deviation, the larger the set value of ΔP. The upper-layer scheduling instruction is a power reduction margin instruction sigma_ref from the upper-layer optimization controller. The dynamic power reduction margin ΔP is directly taken from this instruction, that is, ΔP = sigma_ref.

4. The adaptive MPPT inverter control method with virtual inertia dynamic coupling as described in claim 1, characterized in that, The process involves inputting the real-time frequency change rate RoCoF into a predefined mapping function to dynamically calculate the required raw value of the virtual inertia response power, P_inertia_raw, where P_inertia_raw is proportional to the absolute value of RoCoF. Based on the preset maximum virtual inertia power change rate dP_inertia_max and the system control period T_s, the maximum allowable power change ΔP_lim within the current control period is calculated, specifically including: The predefined mapping function is a linear function, expressed as: P_inertia = K_inertia × abs(RoCoF) Where K_inertia is the virtual inertia gain coefficient; The virtual inertia gain coefficient K_inertia is not a fixed value, but is dynamically adjusted according to the virtual inertia gain setpoint M_ref issued by the upper-level scheduling instruction, that is, K_inertia=M_ref; The mapping function is a piecewise linear function, and P_inertia is zero when the absolute value of RoCoF is lower than the dead zone threshold. When the absolute value of RoCoF exceeds the threshold, P_inertia is proportional to the absolute value of RoCoF minus the dead zone threshold.

5. The adaptive MPPT inverter control method with dynamic coupling of virtual inertia as described in claim 1, characterized in that, The adjustable power target value P_ref is dynamically coupled with the virtual inertia response power P_inertia to generate the inverter's final active power command P_cmd. When a sharp frequency change is detected, priority is given to ensuring the rapid output of P_inertia, and P_cmd is dynamically adjusted to: P_cmd = P_ref - P_inertia, specifically including: The judgment condition when a sudden change in frequency is detected is: whether the absolute value of the frequency change rate RoCoF exceeds a preset emergency action threshold R_lim; After generating the final active power command P_cmd, a power change rate limit is also imposed on it to ensure that its change rate does not exceed the maximum allowable change rate dP_max that the photovoltaic inverter and the grid can withstand.

6. The adaptive MPPT inverter control method with dynamic coupling of virtual inertia as described in claim 1, characterized in that, The active power command P_cmd is input into the inner loop controller of the inverter. By adjusting the modulation signal of the inverter, its output power is controlled to track P_cmd, thereby dynamically and seamlessly converting a portion of the power originally used for maximum power generation into virtual inertia-supported power. Specifically, this includes: The inner loop controller uses a proportional resonant controller or a PI controller to track the active power command P_cmd without steady-state error. The modulation signal for adjusting the inverter refers to: generating a current reference signal by instantaneous power calculation based on the active power command P_cmd and the reactive power command Q_cmd, and generating a pulse signal to drive the switching transistor using space vector pulse width modulation (SVPWM) technology.

7. The adaptive MPPT inverter control method with virtual inertia dynamic coupling as described in claim 1, characterized in that, The step of feeding back the actual output power of the inverter and the grid frequency status to steps S2 and S3, and updating the power target value P_ref and the virtual inertia requirement P_inertia in real time, forms a closed-loop adaptive control system, specifically including: The specific method for updating the target power value P_ref in real time is as follows: the deviation between the actual photovoltaic output power P_actual and the target instruction P_cmd is used to generate a compensation amount delta_P through a proportional-integral controller, which is used to correct the estimated value of the theoretical maximum output power P_mpp in step S2 in real time.

8. An adaptive MPPT inverter control system with virtual inertia dynamic coupling, characterized in that, The system is used to implement the method as described in any one of claims 1-7, the system comprising: The real-time acquisition and processing module is used to acquire the output current and voltage of the local photovoltaic array in real time, and measure the frequency signal of the grid common coupling point, and obtain the real-time frequency change rate RoCoF through differential calculation; The maximum power point and margin calculation module is used to calculate the theoretical maximum output power P_mpp under the current operating condition based on the output current and voltage using the maximum power point tracking (MPPT) algorithm. Based on the upper-level scheduling instructions or local judgment, a dynamic power reduction margin ΔP is set to determine an adjustable power target value P_ref = P_mpp - ΔP that is lower than P_mpp. The virtual inertia demand mapping module is used to input the real-time frequency change rate RoCoF into a predefined mapping function to dynamically calculate the required raw value of virtual inertia response power P_inertia_raw, where P_inertia_raw is proportional to the absolute value of RoCoF. Based on the preset maximum virtual inertia power change rate dP_inertia_max and the system control period T_s, the maximum allowable power change ΔP_lim within the current control period is calculated. The power dynamic coupling module is used to dynamically couple the adjustable power target value P_ref with the virtual inertia response power P_inertia to generate the inverter's final active power command P_cmd. When a sharp change in frequency is detected, the rapid output of P_inertia is prioritized, and P_cmd is dynamically adjusted to: P_cmd = P_ref - P_inertia. The adaptive MPPT modulation and inverter control module is used to input the active power command P_cmd into the inner loop controller of the inverter. By adjusting the modulation signal of the inverter, it controls its output power to track P_cmd, thereby realizing the dynamic and seamless conversion of part of the power originally used for maximum power generation into virtual inertia support power. The continuous feedback and adjustment module is used to feed back the actual output power of the inverter and the grid frequency status to the maximum power point and margin calculation module and the virtual inertia demand mapping module, and update the power target value P_ref and the virtual inertia demand P_inertia in real time to form a closed-loop adaptive control system.

9. A computer-readable storage medium storing computer program instructions thereon, characterized in that, The computer program instructions, when executed by a processor, implement the method as described in any one of claims 1-7.

10. An electronic device comprising a memory and a processor, characterized in that, The memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method as described in any one of claims 1-7.

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