Inverter parallel control system and method based on virtual complex impedance

Abstract

The invention discloses an inverter parallel control system and method based on virtual complex impedance. The system comprises a distributed virtual complex impedance regulation and control unit, an intelligent impedance coordination control unit, a dynamic power grid adaptation unit, a fault-tolerant management unit, a self-adaptive learning unit and a distributed communication and synchronization unit. The distributed virtual complex impedance regulation and control unit is used for regulating and controlling distributed virtual complex impedance among the plurality of inverters; the intelligent impedance coordination control unit is responsible for impedance coordination control between the inverters; the dynamic power grid adaptation unit adapts to power grid fluctuation by adjusting virtual complex impedance; the fault-tolerant management unit is used for providing fault-tolerant capability of the system, and the adaptive learning unit is used for continuously optimizing virtual complex impedance parameters; and the distributed communication and synchronization unit is used for ensuring communication and operation synchronization among the inverters. Stable operation can be kept under the conditions of power grid fluctuation, load change and the like, fluctuation of current and voltage is reduced, and the phenomenon of system instability is effectively prevented.

Description

Technical Field

[0001] The present invention relates to the technical field of power electronics, and in particular to an inverter parallel control system based on virtual complex impedance and a method thereof. Background Art

[0002] With the rapid development of power electronics technology, inverters are increasingly used in various power systems, especially in the fields of distributed power generation, microgrids and new energy grid connection. The parallel control system of inverters has become the key to achieving efficient energy conversion and distribution. However, when multiple inverters are operated in parallel, due to the mutual influence between the inverters and the complex and changeable power grid environment, the system often faces problems such as voltage fluctuations, frequency deviations and uneven load distribution, making it difficult to ensure system stability.

[0003] In order to improve the stability and reliability of multi-inverter parallel systems, virtual complex impedance control technology has gradually become a research hotspot. Virtual complex impedance simulates impedance characteristics such as resistance, inductance or capacitance at the output end of the inverter to improve the dynamic response performance of the inverter. However, the existing virtual complex impedance control technology is often difficult to effectively cope with various grid fluctuations and load changes in complex power grid environments, resulting in a large risk of instability in the system under high load or frequent fluctuations. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: an inverter parallel control system based on virtual complex impedance, mainly including a distributed virtual complex impedance control unit, an intelligent impedance coordination control unit, a dynamic grid adaptation unit, a fault-tolerant management unit, an adaptive learning unit and a distributed communication and synchronization unit;

[0006] The distributed virtual complex impedance control unit is responsible for controlling the distributed virtual complex impedance between multiple inverters;

[0007] The intelligent impedance coordination control unit is responsible for impedance coordination control between inverters;

[0008] The dynamic grid adaptation unit is responsible for detecting and responding to changes in the grid, and adapting to grid fluctuations by adjusting the virtual complex impedance;

[0009] The fault-tolerant management unit is used to provide the system with fault-tolerant capability, ensuring that the system can still operate stably when a single inverter fails or is abnormal;

[0010] The adaptive learning unit is used to continuously optimize the virtual complex impedance parameters;

[0011] The distributed communication and synchronization unit is used to ensure communication and operation synchronization between inverters, and realize seamless information sharing and coordinated control.

[0012] As a preferred solution of the inverter parallel control system based on virtual complex impedance described in the present invention, wherein: the distributed virtual complex impedance control unit includes a real-time state monitoring module, a distributed communication module and an adaptive impedance adjustment module;

[0013] The real-time status monitoring module is used to monitor the output voltage, current and load conditions of each inverter;

[0014] The distributed communication module is used to realize information sharing between inverters to ensure synchronization of control of inverters;

[0015] The adaptive impedance adjustment module dynamically adjusts the virtual complex impedance parameters of each inverter according to the received overall system state.

[0016] As a preferred solution of the inverter parallel control system based on virtual complex impedance described in the present invention, wherein: the intelligent impedance coordination control unit includes an information fusion module, an intelligent algorithm module and a feedback adjustment module;

[0017] The information fusion module is used to integrate the operation data uploaded by each inverter to generate a global operation status;

[0018] The intelligent algorithm module is used to calculate the best virtual complex impedance adjustment solution;

[0019] The feedback regulation module can dynamically adjust the impedance coordination strategy according to the real-time power grid fluctuations and changes in operation data.

[0020] As a preferred solution of the inverter parallel control system based on virtual complex impedance and the method thereof according to the present invention, wherein: the calculation of the optimal virtual complex impedance adjustment solution includes the following steps:

[0021] S1: System initialization and parameter definition

[0022] S11: Set initial values ​​for various variables in the system, including the number of inverters N, the number of grid conditions M, the initial voltage V0(t), current I0(t), power P0(t) of each inverter, and the weight coefficient α i , β i , γi , δ j ,∈ j , η;

[0023] S12: Define the integration time range [t0,t f ], the range should cover the complete operation cycle of the inverter system;

[0024] S2: Real-time data collection

[0025] S21: Collect real-time voltage V from each inverter i (t) and current I i (t) data;

[0026] S22: Monitor the fluctuation of the power grid and collect the power P of the power grid j (t) data;

[0027] S3: Based on the real-time voltage V of each inverter i (t), calculate the global average voltage Used for subsequent deviation calculation;

[0028] S4: Virtual complex impedance adjustment calculation

[0029] S41: Calculate the deviation of each inverter voltage from the global average voltage and apply an exponential function Get the deviation value through the parameter β i Adjust the degree to which bias affects the results;

[0030] S42: Calculate the current I of each inverter i (t) The effect on virtual complex impedance, applying the adjustment factor Reduce the impact of large current fluctuations on system stability;

[0031] S43: Collected grid power P j (t) is corrected and the function is calculated To ensure that grid fluctuations do not have excessive impact on the virtual complex impedance adjustment scheme;

[0032] S5: Formula Integration and Integral

[0033] S51: Integrate the above parts into a formula for calculating the virtual complex impedance, denoted as Z opt ;

[0034] S52: Formula Z opt In the time range [t0,t f ], and calculate the time average value to finally obtain a fixed optimal virtual complex impedance value, denoted as Z opt-final .

[0035] S6: Based on the calculated optimal virtual complex impedance Z opt , adjust the virtual complex impedance parameters of the inverter in real time to ensure the stability of the system under the current grid conditions and load conditions;

[0036] S7: Continuously monitor the voltage, current and power data of the inverter system and evaluate the system stability. If the system is detected to be unstable, repeat the above steps and dynamically adjust the virtual complex impedance value to maintain the continuous stability of the system.

[0037] As a preferred solution of the inverter parallel control system based on virtual complex impedance of the present invention, wherein: opt The formula is:

[0038]

[0039] in:

[0040] Integration represents the evaluation of the entire system operating status within a time frame;

[0041] represents the sum of all N inverters;

[0042] α i : is the adjustment weight coefficient of the inverter, based on the load distribution of the inverter;

[0043] Exponential function, used to measure the inverter output voltage V i (t) and the global average voltage The deviation, β i Control bias sensitivity of results;

[0044] 1+γ i ·|I i (t)|: represents the inverter output current I i (t) Related regulatory factors, γ i is the influence coefficient of current;

[0045] represents the sum of all M grid conditions;

[0046] To process the grid power P j The influence of (t),∈ j is the power coefficient, η is the nonlinear adjustment index, δ i Control the impact of this item on system stability;

[0047] Value range meaning:

[0048] Z optThe value range of represents the possible range of values ​​of the system's virtual complex impedance, which determines the regulation strategy and stability of the system under different operating conditions.

[0049] As a preferred solution of the inverter parallel control system based on virtual complex impedance of the present invention, wherein: opt The formula contains three interactive behaviors, as follows:

[0050] Ⅰ. Current I i (t) vs. voltage deviation Interaction between:

[0051] when When the current I i (t) For the overall optimization goal Z opt The contribution of will decrease rapidly, therefore, the system will tend to reduce the voltage deviation. This interactive mechanism effectively limits the current fluctuation under the condition of large voltage deviation and ensures the stability of the inverter. Increases above a certain threshold, then the item has an effect on Z opt The contribution of the system will be greatly reduced, causing the system to be more inclined to reduce the current I i (t) and improve the overall stability of the system;

[0052] Ⅱ. Current I i (t) and parameter α i Interaction behavior:

[0053] When α i When the current I i (t) Fluctuation on Z opt The influence of α is relatively small, and the system will be more relaxed in regulating the current; on the contrary, when α i When the current increases, the fluctuation of Z opt The impact of will be significantly enhanced, and the system will control the current more strictly to reduce its negative impact on the overall performance. This interaction ensures that under different operating conditions, the system's sensitivity to current can be dynamically adjusted to maintain stability; if α i Less than a certain value α thresh , the impact of the current will be amplified, prompting the system to respond more actively to current fluctuations; if α i Greater than α thresh , then the system will be more tolerant to current fluctuations;

[0054] III. Power P j (t) and parameter ∈ j Interaction behavior:

[0055] When the power P j When (t) is small, the overall expression tends to 1, and when Pj As (t) increases, the impact of the power term will be exponentially amplified, thus significantly improving the effect on Z opt This interactive mechanism ensures that the system can maintain the stability of low-power operation when the power is small, but when the power increases, the system will pay more attention to its impact on the overall stability and make appropriate adjustments; when the power P j (t) is greater than a certain critical value P thresh , the exponential term will amplify rapidly, allowing the system to more strictly control power fluctuations and prevent the system from becoming unstable under high power.

[0056] As a preferred solution of the inverter parallel control system based on virtual complex impedance described in the present invention, wherein: the dynamic grid adaptation unit includes a grid monitoring module, a fast response module and a grid adaptation algorithm module;

[0057] The power grid monitoring module is used to monitor the changes of power grid parameters in real time;

[0058] The fast response module is used to quickly adjust the virtual complex impedance value according to the power grid monitoring results;

[0059] The grid adaptation algorithm module can use an algorithm to calculate the optimal impedance parameters to cope with different grid disturbances.

[0060] As a preferred solution of the inverter parallel control system based on virtual complex impedance described in the present invention, wherein: the fault-tolerant management unit includes a fault detection module, a fault isolation module and a load redistribution module;

[0061] The fault detection module is used to monitor the operating status of each inverter and quickly identify faults;

[0062] The fault isolation module is used to isolate the faulty inverter when a fault occurs to prevent it from affecting the entire system;

[0063] The load redistribution module is used to redistribute the load to other normally operating inverters after a fault occurs.

[0064] As a preferred solution of the inverter parallel control system based on virtual complex impedance described in the present invention, wherein: the adaptive learning unit includes a historical data analysis module, an online learning module and a prediction module;

[0065] The historical data analysis module is used to analyze and store historical operation data to optimize future operation strategies;

[0066] The online learning module can learn in real time during operation and adjust impedance parameters to adapt to current operating conditions;

[0067] The prediction module predicts possible operating states based on historical data and real-time states, and adjusts control strategies in advance.

[0068] The control method of the inverter parallel control system based on virtual complex impedance as above comprises the following steps:

[0069] Step 1: System Initialization

[0070] Initialize key parameters in the system, including the number of inverters, grid conditions, initialization voltage, current, virtual complex impedance parameters, and weight coefficients of various inverters;

[0071] Define the cycle range of real-time data collection of the system to ensure that the complete operation cycle of the inverter system is covered;

[0072] Step 2: Real-time data collection

[0073] Collect output voltage and current data from each inverter in real time;

[0074] Monitor power grid fluctuations and collect key grid parameters including power grid;

[0075] Step 3: Global state calculation

[0076] Based on the real-time voltage data of each inverter, the global average voltage is calculated for subsequent virtual complex impedance adjustment calculation;

[0077] Summarize the real-time current data of each inverter, calculate the global current status, and provide an assessment of the overall load condition for the system;

[0078] Step 4: Virtual Complex Impedance Adjustment Calculation

[0079] Calculate the deviation of each inverter output voltage from the global average voltage, and apply an exponential function to adjust the impact of the deviation value to ensure the system's sensitivity to voltage deviation;

[0080] Based on the current data of each inverter, calculate its impact on the virtual complex impedance, and control the impact of current fluctuations on system stability through adjustment factors;

[0081] Correct the collected power grid power data and adjust the parameters in the virtual complex impedance calculation formula to ensure that the impact of power grid fluctuations on the system is within a controllable range;

[0082] Step 5: Calculation of optimal virtual complex impedance value

[0083] The calculation results of the above parts are integrated into an overall formula to construct an optimization model for virtual complex impedance adjustment;

[0084] Integrate the formula to cover the defined time range and calculate the globally optimal virtual complex impedance value;

[0085] Step 6: Virtual Complex Impedance Adjustment

[0086] According to the calculated optimal virtual complex impedance value, the virtual complex impedance parameters of each inverter are adjusted in real time to ensure the stability of the system under the current grid conditions and load conditions;

[0087] Evaluate the stability of the system based on the real-time feedback of voltage, current and power data, and make adjustments if necessary to ensure continuous and stable operation of the system;

[0088] Step 7: Adaptive Learning and Optimization

[0089] Analyze historical operation data, summarize rules, and provide a basis for optimizing future operation strategies;

[0090] Using real-time data, the virtual complex impedance parameters are dynamically optimized through an online learning module to adapt to changing operating conditions;

[0091] Through the prediction module, the possible operating status is predicted based on historical data and real-time status, and the control strategy is adjusted in advance to ensure that the system remains stable in future operations;

[0092] Step 8: Fault management and response

[0093] Monitor the operating status of each inverter in real time to quickly identify and locate faults;

[0094] When a fault occurs, the faulty inverter is quickly isolated, and the load is redistributed to other normally operating inverters through the load redistribution module to maintain the stable operation of the system;

[0095] Step 9: System Communication and Synchronization

[0096] Ensure the synchronization of communication and operation between inverters, realize seamless information sharing and coordinated control, and ensure the overall collaborative working efficiency of the system;

[0097] Step 10: Continuous monitoring and optimization

[0098] Continuously monitor the system's operating data, including voltage, current, power and grid fluctuations, to ensure the system's real-time stability;

[0099] Based on the monitoring results, the virtual complex impedance parameters are continuously and dynamically adjusted to optimize the system and ensure that the system can cope with various complex operating conditions.

[0100] Beneficial effects of the present invention:

[0101] 1. The present invention monitors the output voltage, current and grid status of the inverter in real time, and applies intelligent algorithms to dynamically adjust the virtual complex impedance parameters. The system can maintain stable operation under conditions such as grid fluctuations and load changes, reduce current and voltage fluctuations, and effectively prevent the occurrence of system instability.

[0102] 2. The present invention monitors the changes in grid parameters in real time through a dynamic grid adaptation unit and quickly adjusts the virtual complex impedance value of the inverter. The system can quickly adapt to complex working conditions such as grid fluctuations and frequency changes, ensuring that the inverter system operates stably in a changing grid environment.

[0103] 3. The present invention uses a fault-tolerant management unit to quickly detect and isolate faulty inverters and redistribute the load to normally operating inverters, ensuring that when a single inverter fails or is abnormal, the system can still operate stably and avoid the risk of overall system failure.

[0104] 4. The present invention monitors the grid fluctuation in real time through the dynamic grid adaptation unit, and combined with the regulation of the fast response module, the system can quickly adjust the virtual complex impedance parameters when the grid conditions change, ensuring the stability of the system under various working conditions. This adaptive adjustment capability effectively prevents the inverter instability caused by grid fluctuations or load changes, thereby improving the reliability of the overall system.

[0105] 5. The intelligent algorithm module in the present invention uses machine learning or optimization algorithms, combined with real-time collected inverter operation data, to calculate the optimal virtual complex impedance adjustment scheme. This calculation process fully considers the complex interactive effects of voltage deviation, current fluctuation, and grid power, so that the final calculated virtual complex impedance scheme can reach the global optimum under different inverters and complex grid conditions, ensuring the overall performance and stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0106] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0107] Figure 1 It is a structural block diagram of the inverter parallel control system based on virtual complex impedance of the present invention. DETAILED DESCRIPTION

[0108] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0109] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0110] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0111] Secondly, the present invention is described in detail with reference to the schematic diagram. When describing the embodiments of the present invention in detail, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0112] Reference Figure 1 , which is an embodiment of the present invention, provides an inverter parallel control system based on virtual complex impedance, which mainly includes a distributed virtual complex impedance control unit, an intelligent impedance coordination control unit, a dynamic grid adaptation unit, a fault-tolerant management unit, an adaptive learning unit and a distributed communication and synchronization unit.

[0113] The distributed virtual complex impedance control unit is responsible for controlling the distributed virtual complex impedance between multiple inverters, and dynamically adjusts the virtual complex impedance parameters of each inverter according to the needs of the overall system by monitoring the operating status of each inverter in real time.

[0114] The distributed virtual complex impedance control unit includes:

[0115] Real-time status monitoring module: monitors the output voltage, current and load conditions of each inverter;

[0116] Distributed communication module: realizes information sharing among inverters and ensures synchronization of control of each inverter;

[0117] Adaptive impedance adjustment module: dynamically adjusts the virtual complex impedance parameters of each inverter according to the received overall system status.

[0118] The intelligent impedance coordination control unit is responsible for impedance coordination control between inverters. The intelligent algorithm analyzes the operation information shared by each inverter and coordinately adjusts the virtual complex impedance value to optimize the stability of the system.

[0119] The intelligent impedance coordination control unit includes:

[0120] Information fusion module: used to integrate the operation data uploaded by each inverter to generate the global operation status;

[0121] Intelligent algorithm module: used to calculate the best virtual complex impedance adjustment solution;

[0122] Feedback regulation module: Dynamically adjust the impedance coordination strategy according to real-time grid fluctuations and changes in operating data.

[0123] The dynamic grid adaptation unit is responsible for detecting and responding to changes in the grid, and adapting to grid fluctuations by adjusting the virtual complex impedance to ensure stable operation of the system.

[0124] The dynamic grid adaptation unit includes:

[0125] Power grid monitoring module: real-time monitoring of changes in power grid parameters;

[0126] Fast response module: quickly adjusts the virtual complex impedance value according to the grid monitoring results;

[0127] Grid adaptation algorithm module: uses the algorithm to calculate the optimal impedance parameters to deal with different grid disturbances.

[0128] The fault-tolerant management unit is used to provide the system with fault-tolerant capabilities, ensuring that the system can still operate stably when a single inverter fails or is abnormal.

[0129] The fault-tolerance management unit includes:

[0130] Fault detection module: monitors the operating status of each inverter and quickly identifies faults;

[0131] Fault isolation module: When a fault occurs, isolate the faulty inverter to prevent it from affecting the entire system;

[0132] Load redistribution module: after a fault occurs, redistribute the load to other normally operating inverters.

[0133] The adaptive learning unit is used to continuously optimize the virtual complex impedance parameters so that the system gradually learns and adapts to various complex operating conditions.

[0134] Adaptive learning units include:

[0135] Historical data analysis module: analyzes and stores historical operation data to optimize future operation strategies;

[0136] Online learning module: real-time learning during operation, adjusting impedance parameters to suit current operating conditions;

[0137] Prediction module: Based on historical data and real-time status, predict possible operating status and adjust control strategies in advance.

[0138] The distributed communication and synchronization unit is used to ensure communication and operation synchronization between inverters, and realize seamless information sharing and coordinated control.

[0139] Specifically, the step of calculating the best virtual complex impedance adjustment solution includes the following steps:

[0140] S1: System initialization and parameter definition

[0141] S11: Set initial values ​​for various variables in the system, including the number of inverters N, the number of grid conditions M, the initial voltage V0(t), current I0(t), power P0(t) of each inverter, and the weight coefficient α i , β i , γ i , δ j ,∈ j , η;

[0142] S12: Define the integration time range [t0,t f ], the range should cover the complete operation cycle of the inverter system;

[0143] S2: Real-time data collection

[0144] S21: Collect real-time voltage V from each inverter i (t) and current I i (t) data;

[0145] S22: Monitor the fluctuation of the power grid and collect the power P of the power grid j (t) data;

[0146] S3: Based on the real-time voltage V of each inverter i (t), calculate the global average voltage Used for subsequent deviation calculation;

[0147] S4: Virtual complex impedance adjustment calculation

[0148] S41: Calculate the deviation of each inverter voltage from the global average voltage and apply an exponential function Get the deviation value through the parameter β i Adjust the degree to which bias affects the results;

[0149] S42: Calculate the current I of each inverter i (t) The effect on virtual complex impedance, applying the adjustment factor Reduce the impact of large current fluctuations on system stability;

[0150] S43: Collected grid power P j (t) is corrected and the function is calculated To ensure that grid fluctuations do not have excessive impact on the virtual complex impedance adjustment scheme;

[0151] S5: Formula Integration and Integral

[0152] S51: Integrate the above parts into a formula for calculating the virtual complex impedance, denoted as Z opt ;

[0153] S52: Formula Z opt In the time range [t0,t f ], and calculate the time average value to finally obtain a fixed optimal virtual complex impedance value, denoted as Z opt-final .

[0154] S6: Based on the calculated optimal virtual complex impedance Z opt , adjust the virtual complex impedance parameters of the inverter in real time to ensure the stability of the system under the current grid conditions and load conditions;

[0155] S7: Continuously monitor the voltage, current and power data of the inverter system and evaluate the system stability. If the system is detected to be unstable, repeat the above steps and dynamically adjust the virtual complex impedance value to maintain the continuous stability of the system.

[0156] Through the above steps of real-time data collection, deviation analysis, grid fluctuation correction, formula integration and integral calculation, the optimal virtual complex impedance adjustment scheme is finally generated to ensure the stability of the system under different load and grid conditions. This process enables the system to adapt to changing operating conditions through continuous dynamic feedback and adjustment.

[0157] Furthermore, the Z opt The formula is:

[0158]

[0159] in:

[0160] Integration represents the evaluation of the entire system operating status within a time frame;

[0161] represents the sum of all N inverters;

[0162] α i : is the adjustment weight coefficient of the inverter, based on the load distribution of the inverter;

[0163] Exponential function, used to measure the inverter output voltage V i (t) and the global average voltage The deviation, β iControl bias sensitivity of results;

[0164] 1+γ i ·|I i (t)|: represents the inverter output current I i (t) Related regulatory factors, γ i is the influence coefficient of current;

[0165] represents the sum of all M grid conditions;

[0166] To process the grid power P j The influence of (t),∈ j is the power coefficient, η is the nonlinear adjustment index, δ i Control the impact of this item on system stability;

[0167] Value range meaning:

[0168] Z opt The value range of represents the possible range of values ​​of the system's virtual complex impedance, which determines the regulation strategy and stability of the system under different operating conditions.

[0169] Furthermore, the Z opt The formula contains three interactive behaviors, as follows:

[0170] Ⅰ. Current I i (t) vs. voltage deviation Interaction between:

[0171] when When the current I i (t) For the overall optimization goal Z opt The contribution of will decrease rapidly (because the effect of negative exponent will suppress the influence of current term), therefore, the system will tend to reduce the voltage deviation. This interaction mechanism effectively limits the current fluctuation under the condition of large voltage deviation and ensures the stability of inverter.

[0172] If the voltage deviation Increases above a certain threshold, then the item has an effect on Z opt The contribution of the system will be greatly reduced, causing the system to be more inclined to reduce the current I i (t) and improve the overall stability of the system.

[0173] Ⅱ. Current I i (t) and parameter α i Interaction behavior:

[0174] When α i When the current I i (t) Fluctuation on Zopt The influence of α is relatively small, and the system will be more relaxed in regulating the current; on the contrary, when α i When the current increases, the fluctuation of Z opt The impact of current is significantly enhanced, and the system controls current more tightly to reduce its negative impact on overall performance. This interaction ensures that under different operating conditions, the system's sensitivity to current can be dynamically adjusted to maintain stability.

[0175] If α i Less than a certain value α thresh , the impact of the current will be amplified, prompting the system to respond more actively to current fluctuations; if α i Greater than α thresh , the system will be more tolerant to current fluctuations.

[0176] III. Power P j (t) and parameter ∈ j Interaction behavior:

[0177] When the power P j When (t) is small, the overall expression tends to 1, and when P j As (t) increases, the impact of the power term will be exponentially amplified, thus significantly improving the effect on Z opt This interactive mechanism ensures that the system can maintain the stability of low-power operation when the power is small, but when the power increases, the system will pay more attention to its impact on the overall stability and make appropriate adjustments.

[0178] When the power P j (t) is greater than a certain critical value P thresh , the exponential term will amplify rapidly, allowing the system to more strictly control power fluctuations and prevent the system from becoming unstable under high power.

[0179] In summary:

[0180] The present invention monitors the output voltage, current and grid status of the inverter in real time, and applies intelligent algorithms to dynamically adjust the virtual complex impedance parameters. The system can maintain stable operation under conditions such as grid fluctuations and load changes, reduce current and voltage fluctuations, and effectively prevent the occurrence of system instability.

[0181] The present invention monitors the changes of grid parameters in real time through a dynamic grid adaptation unit and quickly adjusts the virtual complex impedance value of the inverter. The system can quickly adapt to complex working conditions such as grid fluctuations and frequency changes, ensuring stable operation of the inverter system in a changeable grid environment.

[0182] The present invention uses a fault-tolerant management unit to quickly detect and isolate faulty inverters and redistribute the load to normally operating inverters, ensuring that when a single inverter fails or is abnormal, the system can still operate stably and avoid the risk of overall system failure.

[0183] The present invention monitors the grid fluctuation in real time through the dynamic grid adaptation unit, and combined with the regulation of the fast response module, the system can quickly adjust the virtual complex impedance parameters when the grid conditions change, ensuring the stability of the system under various working conditions. This adaptive adjustment capability effectively prevents the inverter instability caused by grid fluctuations or load changes, thereby improving the reliability of the overall system.

[0184] The intelligent algorithm module in the present invention uses machine learning or optimization algorithms, combined with real-time collected inverter operation data, to calculate the optimal virtual complex impedance adjustment scheme. This calculation process fully considers the complex interactive effects of voltage deviation, current fluctuation, and grid power, so that the final calculated virtual complex impedance scheme can reach the global optimum under different inverters and complex grid conditions, ensuring the overall performance and stability of the system.

[0185] The present invention ensures real-time communication and operation synchronization between inverters through distributed communication and synchronization units, so that each inverter can perform control operations in the same state, avoiding control errors caused by information lag or asynchrony. This seamless information sharing mechanism further improves the coordination and overall stability of the system.

[0186] The system of the present invention utilizes a distributed virtual complex impedance control unit and an intelligent impedance coordination control unit to achieve collaborative optimization between inverters. Through the application of global data fusion and intelligent algorithms, the system can coordinate the virtual complex impedance between multiple inverters, avoiding the phenomenon of overload or underload of a single inverter, and enabling the overall system to achieve optimal operating status.

[0187] In the present invention, by monitoring and quickly responding to grid fluctuations, the system can adjust the virtual complex impedance in time, avoiding system instability caused by grid disturbances. Especially in the case of high load or drastic changes in grid conditions, the system can still maintain overall stable operation through precise algorithm adjustment.

[0188] The control method of the inverter parallel control system based on virtual complex impedance as above comprises the following steps:

[0189] Step 1: System Initialization

[0190] Initialize key parameters in the system, including the number of inverters, grid conditions, initialization voltage, current, virtual complex impedance parameters, and weight coefficients of various inverters;

[0191] Define the cycle range of real-time data collection of the system to ensure that the complete operation cycle of the inverter system is covered;

[0192] Step 2: Real-time data collection

[0193] Collect output voltage and current data from each inverter in real time to ensure data accuracy and timeliness;

[0194] Monitor grid fluctuations and collect key grid parameters including grid power and frequency, so that the system can respond to grid changes in real time;

[0195] Step 3: Global state calculation

[0196] Based on the real-time voltage data of each inverter, the global average voltage is calculated for subsequent virtual complex impedance adjustment calculation;

[0197] Summarize the real-time current data of each inverter, calculate the global current status, and provide an assessment of the overall load condition for the system;

[0198] Step 4: Virtual Complex Impedance Adjustment Calculation

[0199] Calculate the deviation of each inverter output voltage from the global average voltage, and apply an exponential function to adjust the impact of the deviation value to ensure the system's sensitivity to voltage deviation;

[0200] Based on the current data of each inverter, calculate its impact on the virtual complex impedance, and control the impact of current fluctuations on system stability through adjustment factors;

[0201] Correct the collected power grid power data and adjust the parameters in the virtual complex impedance calculation formula to ensure that the impact of power grid fluctuations on the system is within a controllable range;

[0202] Step 5: Calculation of optimal virtual complex impedance value

[0203] The calculation results of the above parts are integrated into an overall formula to construct an optimization model for virtual complex impedance adjustment;

[0204] Integrate the formula to cover the defined time range and calculate the globally optimal virtual complex impedance value;

[0205] Step 6: Virtual Complex Impedance Adjustment

[0206] According to the calculated optimal virtual complex impedance value, the virtual complex impedance parameters of each inverter are adjusted in real time to ensure the stability of the system under the current grid conditions and load conditions;

[0207] Evaluate the stability of the system based on the real-time feedback of voltage, current and power data, and make adjustments if necessary to ensure continuous and stable operation of the system;

[0208] Step 7: Adaptive Learning and Optimization

[0209] Analyze historical operation data, summarize rules, and provide a basis for optimizing future operation strategies;

[0210] Using real-time data, the virtual complex impedance parameters are dynamically optimized through an online learning module to adapt to changing operating conditions;

[0211] Through the prediction module, the possible operating status is predicted based on historical data and real-time status, and the control strategy is adjusted in advance to ensure that the system remains stable in future operations;

[0212] Step 8: Fault management and response

[0213] Monitor the operating status of each inverter in real time to quickly identify and locate faults;

[0214] When a fault occurs, the faulty inverter is quickly isolated, and the load is redistributed to other normally operating inverters through the load redistribution module to maintain the stable operation of the system;

[0215] Step 9: System Communication and Synchronization

[0216] Ensure the synchronization of communication and operation between inverters, realize seamless information sharing and coordinated control, and ensure the overall collaborative working efficiency of the system;

[0217] Step 10: Continuous monitoring and optimization

[0218] Continuously monitor the system's operating data, including voltage, current, power and grid fluctuations, to ensure the system's real-time stability;

[0219] Based on the monitoring results, the virtual complex impedance parameters are continuously and dynamically adjusted to optimize the system and ensure that the system can cope with various complex operating conditions.

[0220] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An inverter parallel control system based on virtual complex impedance, characterized in that: It includes a distributed virtual complex impedance control unit, an intelligent impedance coordination control unit, a dynamic power grid adaptation unit, a fault-tolerant management unit, an adaptive learning unit, and a distributed communication and synchronization unit; The distributed virtual complex impedance control unit is responsible for controlling the distributed virtual complex impedance between multiple inverters; The intelligent impedance coordination control unit is responsible for impedance coordination control between inverters; The dynamic grid adaptation unit is responsible for detecting and responding to changes in the grid, and adapting to grid fluctuations by adjusting the virtual complex impedance; The fault-tolerant management unit is used to provide the system with fault-tolerant capability, ensuring that the system can still operate stably when a single inverter fails or is abnormal; The adaptive learning unit is used to continuously optimize the virtual complex impedance parameters; The distributed communication and synchronization unit is used to ensure communication and operation synchronization between inverters, and realize seamless information sharing and coordinated control.

2. The inverter parallel control system based on virtual complex impedance according to claim 1, characterized in that: The distributed virtual complex impedance control unit includes a real-time status monitoring module, a distributed communication module and an adaptive impedance adjustment module; The real-time status monitoring module is used to monitor the output voltage, current and load conditions of each inverter; The distributed communication module is used to realize information sharing between inverters to ensure synchronization of control of inverters; The adaptive impedance adjustment module dynamically adjusts the virtual complex impedance parameters of each inverter according to the received overall system state.

3. The inverter parallel control system based on virtual complex impedance according to claim 1, characterized in that: The intelligent impedance coordination control unit includes an information fusion module, an intelligent algorithm module and a feedback adjustment module; The information fusion module is used to integrate the operation data uploaded by each inverter to generate a global operation status; The intelligent algorithm module is used to calculate the best virtual complex impedance adjustment solution; The feedback regulation module can dynamically adjust the impedance coordination strategy according to the real-time power grid fluctuations and changes in operation data.

4. The inverter parallel control system based on virtual complex impedance according to claim 3, characterized in that: The method of calculating the best virtual complex impedance adjustment scheme comprises the following steps: S1: System initialization and parameter definition S11: Set initial values ​​for various variables in the system, including the number of inverters N, the number of grid conditions M, the initial voltage V0(t), current I0(t), power P0(t) of each inverter, and the weight coefficient α i , β i , γ i , δ j ,∈ j , η; S12: Define the integration time range [t0,t f ], the range should cover the complete operation cycle of the inverter system; S2: Real-time data collection S21: Collect real-time voltage V from each inverter i (t) and current I i (t) data; S22: Monitor the fluctuation of the power grid and collect the power P of the power grid j (t) data; S3: Based on the real-time voltage V of each inverter i (t), calculate the global average voltage Used for subsequent deviation calculation; S4: Virtual complex impedance adjustment calculation S41: Calculate the deviation of each inverter voltage from the global average voltage and apply the exponential function Get the deviation value through the parameter β i Adjust the degree to which bias affects the results; S42: Calculate the current I of each inverter i (t) The effect on virtual complex impedance, applying the adjustment factor Reduce the impact of large current fluctuations on system stability; S43: Collected grid power P j (t) is corrected and the function is calculated To ensure that grid fluctuations do not have excessive impact on the virtual complex impedance adjustment scheme; S5: Formula Integration and Integral S51: Integrate the above parts into a formula for calculating the virtual complex impedance, denoted as Z opt ; S52: Formula Z opt In the time range [t0,t f ], and calculate the time average value to finally obtain a fixed optimal virtual complex impedance value, denoted as Z opt-final . S6: Based on the calculated optimal virtual complex impedance Z opt , adjust the virtual complex impedance parameters of the inverter in real time to ensure the stability of the system under the current grid conditions and load conditions; S7: Continuously monitor the voltage, current and power data of the inverter system and evaluate the system stability. If the system is detected to be unstable, repeat the above steps and dynamically adjust the virtual complex impedance value to maintain the continuous stability of the system.

5. The inverter parallel control system based on virtual complex impedance according to claim 4, characterized in that: The Z opt The formula is: in: Integration represents the evaluation of the entire system operating status within a time frame; represents the sum of all N inverters; α i : is the adjustment weight coefficient of the inverter, based on the load distribution of the inverter; Exponential function, used to measure the inverter output voltage V i (t) and the global average voltage The deviation, β i Control bias sensitivity of results; 1+γ i ·|I i (t)|: represents the inverter output current I i (t) Related regulatory factors, γ i is the influence coefficient of current; represents the sum of all M grid conditions; To process the grid power P j The influence of (t),∈ j is the power coefficient, η is the nonlinear adjustment index, δ i Control the impact of this item on system stability; Value range meaning: Z opt The value range of represents the possible range of values ​​of the system's virtual complex impedance, which determines the regulation strategy and stability of the system under different operating conditions.

6. The inverter parallel control system based on virtual complex impedance according to claim 5, characterized in that: The Z opt The formula contains three interactive behaviors, as follows: Ⅰ. Current I i (t) vs. voltage deviation Interaction between: when When the current I i (t) For the overall optimization goal Z opt The contribution of will decrease rapidly, therefore, the system will tend to reduce the voltage deviation. This interactive mechanism effectively limits the current fluctuation under the condition of large voltage deviation and ensures the stability of the inverter. Increases above a certain threshold, then the item has an effect on Z opt The contribution of the system will be greatly reduced, causing the system to be more inclined to reduce the current I i (t) and improve the overall stability of the system; Ⅱ. Current I i (t) and parameter α i Interaction behavior: When α i When the current I i (t) Fluctuation on Z opt The influence of α is relatively small, and the system will be more relaxed in regulating the current; on the contrary, when α i When the current increases, the fluctuation of Z opt The impact of will be significantly enhanced, and the system will control the current more strictly to reduce its negative impact on the overall performance. This interaction ensures that under different operating conditions, the system's sensitivity to current can be dynamically adjusted to maintain stability; if α i Less than a certain value α thresh , the impact of the current will be amplified, prompting the system to respond more actively to current fluctuations; if α i Greater than α thresh , then the system will be more tolerant to current fluctuations; III. Power P j (t) and parameter ∈ j Interaction behavior: When the power P j When (t) is small, the overall expression tends to 1, and when P j As (t) increases, the impact of the power term will be exponentially amplified, thus significantly improving the effect on Z opt This interactive mechanism ensures that the system can maintain the stability of low-power operation when the power is small, but when the power increases, the system will pay more attention to its impact on the overall stability and make appropriate adjustments; when the power P j (t) is greater than a certain critical value P thresh , the exponential term will amplify rapidly, allowing the system to more strictly control power fluctuations and prevent the system from becoming unstable under high power.

7. The inverter parallel control system based on virtual complex impedance according to claim 1, characterized in that: The dynamic power grid adaptation unit includes a power grid monitoring module, a fast response module and a power grid adaptation algorithm module; The power grid monitoring module is used to monitor the changes of power grid parameters in real time; The fast response module is used to quickly adjust the virtual complex impedance value according to the power grid monitoring results; The grid adaptation algorithm module can use an algorithm to calculate the optimal impedance parameters to cope with different grid disturbances.

8. The inverter parallel control system based on virtual complex impedance according to claim 1, characterized in that: The fault-tolerant management unit includes a fault detection module, a fault isolation module and a load redistribution module; The fault detection module is used to monitor the operating status of each inverter and quickly identify faults; The fault isolation module is used to isolate the faulty inverter when a fault occurs to prevent it from affecting the entire system; The load redistribution module is used to redistribute the load to other normally operating inverters after a fault occurs.

9. The inverter parallel control system based on virtual complex impedance according to claim 1, characterized in that: The adaptive learning unit includes a historical data analysis module, an online learning module and a prediction module; The historical data analysis module is used to analyze and store historical operation data to optimize future operation strategies; The online learning module can learn in real time during operation and adjust impedance parameters to adapt to current operating conditions; The prediction module predicts possible operating states based on historical data and real-time states, and adjusts control strategies in advance.

10. The control method of the inverter parallel control system based on virtual complex impedance according to claim 1, characterized in that: The following steps are involved: Step 1: System Initialization Initialize key parameters in the system, including the number of inverters, grid conditions, initialization voltage, current, virtual complex impedance parameters, and weight coefficients of various inverters; Define the cycle range of real-time data collection of the system to ensure that the complete operation cycle of the inverter system is covered; Step 2: Real-time data collection Collect output voltage and current data from each inverter in real time; Monitor power grid fluctuations and collect key grid parameters including power grid; Step 3: Global state calculation Based on the real-time voltage data of each inverter, the global average voltage is calculated for subsequent virtual complex impedance adjustment calculation; Summarize the real-time current data of each inverter, calculate the global current status, and provide an assessment of the overall load condition for the system; Step 4: Virtual Complex Impedance Adjustment Calculation Calculate the deviation of each inverter output voltage from the global average voltage, and apply an exponential function to adjust the impact of the deviation value to ensure the system's sensitivity to voltage deviation; Based on the current data of each inverter, calculate its impact on the virtual complex impedance, and control the impact of current fluctuations on system stability through adjustment factors; Correct the collected power grid power data and adjust the parameters in the virtual complex impedance calculation formula to ensure that the impact of power grid fluctuations on the system is within a controllable range; Step 5: Calculation of optimal virtual complex impedance value The calculation results of the above parts are integrated into an overall formula to construct an optimization model for virtual complex impedance adjustment; Integrate the formula to cover the defined time range and calculate the globally optimal virtual complex impedance value; Step 6: Virtual Complex Impedance Adjustment According to the calculated optimal virtual complex impedance value, the virtual complex impedance parameters of each inverter are adjusted in real time to ensure the stability of the system under the current grid conditions and load conditions; Evaluate the stability of the system based on the real-time feedback of voltage, current and power data, and make adjustments if necessary to ensure continuous and stable operation of the system; Step 7: Adaptive Learning and Optimization Analyze historical operation data, summarize rules, and provide a basis for optimizing future operation strategies; Using real-time data, the virtual complex impedance parameters are dynamically optimized through an online learning module to adapt to changing operating conditions; Through the prediction module, the possible operating status is predicted based on historical data and real-time status, and the control strategy is adjusted in advance to ensure that the system remains stable in future operations; Step 8: Fault management and response Monitor the operating status of each inverter in real time to quickly identify and locate faults; When a fault occurs, the faulty inverter is quickly isolated, and the load is redistributed to other normally operating inverters through the load redistribution module to maintain the stable operation of the system; Step 9: System Communication and Synchronization Ensure the synchronization of communication and operation between inverters, realize seamless information sharing and coordinated control, and ensure the overall collaborative working efficiency of the system; Step 10: Continuous monitoring and optimization Continuously monitor the system's operating data, including voltage, current, power and grid fluctuations, to ensure the system's real-time stability; Based on the monitoring results, the virtual complex impedance parameters are continuously and dynamically adjusted to optimize the system and ensure that the system can cope with various complex operating conditions.

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