A multi-objective optimization grid-connected inverter off-grid control method and system

Abstract

The application discloses a multi-target optimization network-constructing inverter off-grid control method and system, relates to the technical field of inverters, and comprises the following steps: constructing an inverter off-grid control platform comprising a data monitoring layer, a parameter optimization layer and a parameter control layer; collecting electrical monitoring data sets output by the inverter through the data monitoring layer, and calculating off-grid state parameter sets based on the electrical monitoring data sets; performing VOC key control parameter optimization based on the off-grid state parameter sets and a preset target function, and obtaining optimal control parameters; and lowering the optimal control parameters to the parameter control layer to perform off-grid control on the inverter. The application solves the problem that the existing inverter off-grid control method lacks sufficient adaptive capacity, thereby leading to a decline in control performance.

Description

Technical Field

[0001] This application relates to the field of inverter technology, specifically to a multi-objective optimized off-grid control method and system for grid-connected inverters. Background Technology

[0002] With the rapid development of new energy power generation, grid-connected inverters are being used more and more widely in power systems. However, traditional off-grid control methods struggle to balance multiple control objectives. While ensuring output voltage stability, they are unable to achieve precise control of the output frequency or effective suppression of grid-side current. Moreover, when faced with complex and variable load conditions, traditional control methods lack sufficient adaptive capability, leading to a decline in control performance and affecting the quality and reliability of power supply. Summary of the Invention

[0003] This application provides a multi-objective optimized grid-connected inverter off-grid control method and system, which solves the technical problem that existing inverter off-grid control methods lack sufficient adaptive capability, leading to a decline in control performance.

[0004] The technical solution to the above-mentioned technical problems in this application is as follows:

[0005] In a first aspect, this application provides a multi-objective optimized off-grid control method for grid-connected inverters, the method comprising:

[0006] Construct an inverter off-grid control platform that includes a data monitoring layer, a parameter optimization layer, and a parameter control layer;

[0007] In the inverter off-grid control platform, the electrical monitoring dataset output by the inverter is collected through the data monitoring layer, and the off-grid status parameter set is calculated based on the electrical monitoring dataset;

[0008] In the parameter optimization layer, the key control parameters of VOC are optimized based on the off-grid state parameter set and the preset objective function to obtain the optimal control parameters.

[0009] The optimal control parameters are delegated to the parameter control layer to enable off-grid control of the inverter.

[0010] Secondly, this application provides a multi-objective optimized grid-connected inverter off-grid control system, comprising:

[0011] The platform building module is used to build an inverter off-grid control platform that includes a data monitoring layer, a parameter optimization layer, and a parameter control layer;

[0012] The parameter acquisition module is used to acquire the electrical monitoring dataset output by the inverter through the data monitoring layer on the inverter off-grid control platform, and calculate the off-grid status parameter set based on the electrical monitoring dataset;

[0013] The parameter optimization module is used in the parameter optimization layer to optimize key VOC control parameters based on the off-grid state parameter set and a preset objective function, and obtain the optimal control parameters.

[0014] The off-grid control module is used to delegate the optimal control parameters to the parameter control layer to perform off-grid control of the inverter.

[0015] This application provides one or more technical solutions, which have at least the following technical effects or advantages:

[0016] This application provides a multi-objective optimized off-grid control method and system for grid-connected inverters. First, an off-grid control platform is constructed, comprising a data monitoring layer, a parameter optimization layer, and a parameter control layer. This enables hierarchical management of off-grid control, clearly defining the functions of each layer and improving the systematic nature and effectiveness of the control. Second, the data monitoring layer collects electrical monitoring data from the inverter output and calculates the off-grid state parameter set, providing a comprehensive understanding of the inverter's operating status and a reliable data foundation for subsequent parameter optimization. Then, the parameter optimization layer optimizes key VOC control parameters based on the off-grid state parameter set and a preset objective function, obtaining the optimal control parameters. This approach balances multiple control objectives, including output voltage stability, precise output frequency control, and effective suppression of grid-side current, overcoming the limitations of traditional control methods in addressing multiple objectives simultaneously. Finally, the optimal control parameters are delegated to the parameter control layer for off-grid control of the inverter. The control parameters are dynamically adjusted according to the actual operating status, enhancing the control's adaptive capability.

[0017] Through the above technical solution, this application can quickly collect load change information based on different off-grid status parameters, calculate a new set of off-grid status parameters, and then optimize VOC key control parameters at the parameter optimization layer, adjust control parameters in real time, and ensure that the inverter can operate stably under various operating conditions. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application, 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating a multi-objective optimization method for off-grid control of a grid-connected inverter provided in an embodiment of this application.

[0020] Figure 2This is a schematic diagram of the structure of a multi-objective optimized grid-connected inverter off-grid control system provided in an embodiment of this application.

[0021] The components represented by each number in the attached diagram are explained below:

[0022] Platform construction module 11, parameter acquisition module 12, parameter optimization module 13, and off-grid control module 14. Detailed Implementation

[0023] This application provides a multi-objective optimized grid-connected inverter off-grid control method and system to address the technical problem that existing inverter off-grid control methods lack sufficient adaptive capability, leading to a decline in control performance.

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

[0025] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0026] In the description of this application, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use this application. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that this application can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid unnecessarily obscuring the description of this application. Therefore, this application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0027] Example 1, as Figure 1 As shown in the figure, this application provides a multi-objective optimized off-grid control method for grid-connected inverters, including:

[0028] S10: Construct an inverter off-grid control platform that includes a data monitoring layer, a parameter optimization layer, and a parameter control layer;

[0029] In this embodiment, an off-grid control platform for the inverter is constructed, and different functions are managed in layers, including a data monitoring layer, a parameter optimization layer, and a parameter control layer. The data monitoring layer is responsible for collecting electrical monitoring data output by the inverter in real time. The parameter optimization layer performs calculations and analysis based on the collected data to find the optimal control parameters to achieve multi-objective optimized control. The parameter control layer performs off-grid control of the inverter according to the optimized parameters to ensure that the inverter can operate stably and efficiently in the off-grid state.

[0030] Furthermore, when constructing the inverter off-grid control platform, communication and data interaction between different layers must be considered. Electrical monitoring datasets collected by the data monitoring layer must be promptly transmitted to the parameter optimization layer, and the optimal control parameters calculated by the parameter optimization layer must be smoothly implemented in the parameter control layer. Simultaneously, the hardware and software configurations of each layer need to be designed and selected according to actual requirements to ensure the platform's stability and reliability.

[0031] The data monitoring layer includes the inverter main circuit, output filter, local load and grid connection interface; the parameter optimization layer embeds a multi-objective optimization algorithm; and the parameter control layer includes a virtual oscillator control unit, an adaptive virtual impedance unit and a current feedforward control unit.

[0032] In this embodiment, the inverter main circuit converts DC power to AC power, and its operating state directly affects the inverter's output performance; the output filter is used to filter the AC power output by the inverter, reduce harmonic content, and improve power quality; the local load is the power supply object of the inverter, and its characteristics and requirements will affect the inverter's control strategy; the grid connection interface provides a channel for the connection between the inverter and the grid, and plays a key role in the switching between off-grid and grid-connected states.

[0033] The parameter optimization layer incorporates a multi-objective optimization algorithm that considers multiple control objectives, such as output voltage stability, precise output frequency control, and effective suppression of grid-side current. Through analysis and calculation of the off-grid state parameter set and the preset objective function, the optimal control parameters are found within the VOC key control parameter adjustment space, enabling the inverter to meet multiple performance requirements during off-grid operation.

[0034] The virtual oscillator control unit in the parameter control layer generates an initial reference voltage signal based on the acquired inverter output current, given active and reactive power reference values, and optimal control parameters. The adaptive virtual impedance unit dynamically calculates the virtual impedance voltage drop based on real-time reactive power distribution errors and corrects the initial reference voltage signal to improve the inverter's reactive power distribution capability and output voltage stability. The current feedforward control unit generates a reverse compensation signal based on the acquired grid-side current, compensates for the corrected voltage signal to obtain a compensated voltage signal, converts it into a modulation wave, and drives the inverter to achieve off-grid control.

[0035] Specifically, this multi-objective optimization-based off-grid control method for grid-connected inverters can quickly collect load change information based on different off-grid state parameters and calculate a new set of off-grid state parameters. Then, at the parameter optimization layer, it optimizes key VOC control parameters and adjusts control parameters in real time to ensure stable operation of the inverter under various operating conditions.

[0036] S20: On the inverter off-grid control platform, the electrical monitoring dataset output by the inverter is collected through the data monitoring layer, and the off-grid status parameter set is calculated based on the electrical monitoring dataset;

[0037] In this embodiment, the data monitoring layer uses its included inverter main circuit, output filter, local load and grid connection interface and other devices to comprehensively and in real time collect electrical parameters such as current, voltage and power output by the inverter, reflecting the current operating status of the inverter.

[0038] Secondly, based on the collected electrical monitoring dataset, an off-grid status parameter set is calculated. This set includes, but is not limited to, parameters such as the inverter's output frequency, phase, active power, and reactive power. These parameters accurately describe the inverter's operating characteristics in off-grid conditions, providing data support for subsequent parameter optimization.

[0039] Furthermore, during the calculation of the off-grid status parameter set, the collected electrical monitoring data is preprocessed to remove noise and interference, thereby improving the accuracy and reliability of the data. Simultaneously, appropriate calculation methods and algorithms are selected based on different application scenarios and requirements to ensure that the calculated off-grid status parameter set accurately reflects the actual operating status of the inverter.

[0040] By calculating the off-grid status parameter set, it is possible to promptly identify potential problems and anomalies that may occur during off-grid operation of the inverter.

[0041] The electrical monitoring dataset includes three-phase voltage and current, local load current, and grid-side current.

[0042] In this embodiment of the application, the collected electrical monitoring dataset includes three-phase voltage and current, local load current, and grid-side current.

[0043] Three-phase voltage and current reflect the basic characteristics of the inverter's output power, demonstrating the quality and stability of the power. Local load current reflects the actual power consumption of the load connected to the inverter; monitoring it allows us to understand the load's size and trends. Grid-side current in off-grid mode reflects the potential interaction between the inverter and the grid, as well as information such as current surges at the moment of disconnection.

[0044] Secondly, after collecting electrical monitoring datasets such as three-phase voltage and current, local load current, and grid-side current, existing calculation models and algorithms are used to process them. For example, by analyzing the three-phase voltage and current, the active and reactive power output of the inverter can be calculated; combined with the local load current, parameters such as the load's power factor can be further analyzed. By monitoring and analyzing the grid-side current, it is possible to determine whether there are abnormal current surges or leakage, and to take appropriate protective measures in a timely manner.

[0045] Furthermore, based on the electrical monitoring dataset, an off-grid status parameter set is calculated, including:

[0046] Based on the three-phase voltage and current, calculate the inverter output voltage amplitude, voltage frequency, active power and reactive power;

[0047] The total reactive power consumed by the local load is calculated based on the local load current.

[0048] The voltage amplitude, voltage frequency, active power, reactive power, total reactive power, and grid-side current are used as the off-grid state parameter set.

[0049] In this embodiment, firstly, the inverter output voltage amplitude, voltage frequency, active power, and reactive power are calculated based on the values ​​of the three-phase voltage and current.

[0050] Specifically, the voltage amplitude is determined by performing specific mathematical operations on the three-phase voltages, such as taking the effective value; the voltage frequency can be obtained by analyzing the periodic changes of the three-phase voltages using frequency measurement algorithms; and the calculation of active and reactive power is completed based on the power calculation formula, combined with factors such as the phase relationship between the three-phase voltage and current.

[0051] For example, the three-phase instantaneous voltage and current signals are typically obtained through ADC sampling, and the three-phase instantaneous voltages are u a u b u c The three-phase instantaneous currents are i a i bi c 。 Using a phase-locked loop algorithm, the three-phase stationary coordinate system abc is transformed into a two-phase stationary coordinate system αβ. Then, in the αβ coordinate system, the magnitude of the voltage space vector is the peak value of the phase voltage.

[0052] Secondly, the total reactive power consumed by the local load is obtained by using a calculation method related to the local load current and load characteristics. Factors such as load type and impedance characteristics need to be considered during this process to ensure the accuracy of the calculation results.

[0053] For example, the total reactive power consumed by the local load is calculated based on power conservation, Q load =Q inv -Q grid Q inv This represents the reactive power output of the inverter; a positive value indicates that the inverter generates reactive power, and a negative value indicates that it absorbs reactive power. Q grid表示 Reactive power on the grid side refers to the reactive power absorbed from or transmitted to the grid; Q load This represents the total reactive power consumed by the local load. Since the load typically consumes reactive power, this value is always positive.

[0054] Then, the calculated voltage amplitude, voltage frequency, active power, reactive power, total reactive power, and grid-side current are integrated to form an off-grid state parameter set. This off-grid state parameter set reflects the inverter's operating status in off-grid conditions, providing accurate data for subsequent optimization of key VOC control parameters at the parameter optimization layer.

[0055] By comprehensively analyzing and processing the above parameters, we can better understand the performance of the inverter, promptly identify potential problems, and lay a solid foundation for achieving multi-objective optimized control.

[0056] S30: In the parameter optimization layer, the VOC key control parameters are optimized based on the off-grid state parameter set and the preset objective function to obtain the optimal control parameters;

[0057] In this embodiment, optimization is performed based on the off-grid state parameter set and a preset objective function, which considers multiple control objectives such as output voltage stability, precise output frequency control, and effective suppression of grid-side current. The search is conducted within the VOC key control parameter adjustment space, and different parameter combinations are continuously tried in conjunction with the inverter operating information provided by the off-grid state parameter set.

[0058] During the search process, each set of parameters is evaluated to determine whether it enables the inverter to better meet the preset objective function during off-grid operation. For example, for output voltage stability, it is checked whether the fluctuation range of the inverter output voltage is within the allowable error range under this parameter combination; for precise output frequency control, it is evaluated whether the frequency deviation meets the requirements; for effective suppression of grid-side current, it is checked whether it can effectively reduce current surges and leakage.

[0059] After multiple iterations and comparisons, an optimal set of control parameters was found. These parameters enable the inverter to achieve optimal operating performance in off-grid conditions while balancing multiple control objectives. The optimal control parameters include key parameters required by the virtual oscillator control unit, adaptive virtual impedance unit, and current feedforward control unit, providing guidance for subsequent off-grid control.

[0060] The objective function is constructed with minimizing the output voltage deviation, output frequency deviation, and grid-side current as the optimization objectives.

[0061] In this embodiment, an objective function is constructed with the goal of minimizing the output voltage deviation, output frequency deviation, and grid-side current.

[0062] For example, a specific expression can be: J = w 1( V out - V ref ) 2 + w 2( f out - f ref ) 2 + w 3 I 2 grid .in, w 1. w 2. w 3 is a weighting coefficient used to adjust the relative importance of each optimization objective, and is usually determined according to the actual system requirements. V out This is the actual output voltage. V ref For reference voltage, ( V out - V ref ) 2 This represents the square of the output voltage deviation; f out This is the actual output frequency. f ref For reference frequency, (f ou t− f ref ) 2 This represents the square of the output frequency deviation. I grid For grid-side current, I 2 grid It represents the square of the grid-side current.

[0063] Furthermore, the values ​​of the weighting coefficients need to be adjusted reasonably based on the actual application scenario and the degree of importance attached to different control objectives. When optimizing at the parameter optimization layer, multi-objective optimization algorithms are employed, such as genetic algorithms and particle swarm optimization. Taking the genetic algorithm as an example, a set of initial combinations of key VOC control parameters is first randomly generated as a population, with each parameter combination corresponding to an individual in the population.

[0064] Then, the fitness of each individual in the population is evaluated according to the objective function. The higher the fitness value, the closer the individual is to the optimal solution.

[0065] Next, new populations are generated through genetic operations such as selection, crossover, and mutation. This process is repeated until a termination condition is met, such as reaching the maximum number of iterations or the objective function value converging to a small range.

[0066] Through the optimization process, the optimal VOC key control parameters are found under multiple control objectives by utilizing the information provided by the off-grid state parameter set. This makes the inverter's output voltage more stable and the output frequency more accurate when operating off-grid, while effectively suppressing grid-side current and improving the overall performance and reliability of the inverter.

[0067] Furthermore, based on the off-grid state parameter set and the preset objective function, VOC key control parameters are optimized to obtain the optimal control parameters, including:

[0068] The adjustment space of key VOC control parameters is obtained, wherein the key VOC control parameters include active power-frequency droop coefficient, reactive power-voltage droop coefficient, amplitude stability term gain and current feedback gain;

[0069] Several initial parameter sets are randomly generated within the VOC key control parameter adjustment space;

[0070] The off-grid status parameter set and the several initial parameter sets are randomly combined to generate several off-grid control schemes;

[0071] Based on the aforementioned off-grid control schemes and preset objective functions, the key control parameters for VOC are optimized to obtain the optimal control parameters.

[0072] In this embodiment, firstly, the adjustment space for key VOC control parameters is obtained. This space defines the value ranges of parameters such as the active power-frequency droop coefficient, the reactive power-voltage droop coefficient, the amplitude stability term gain, and the current feedback gain. The determination of the value range is based on the physical characteristics of the inverter, design requirements, and limitations of the actual application scenario, ensuring that the inverter can operate safely and stably when the parameters vary within a reasonable range.

[0073] Secondly, several initial parameter sets are randomly generated within the VOC key control parameter adjustment space. This random generation method can cover a wide range of parameter combinations, avoiding the optimization process from getting stuck in local optima. Each initial parameter set represents a possible control strategy, including the specific values ​​of four control parameters: active power-frequency droop coefficient, reactive power-voltage droop coefficient, amplitude stability term gain, and current feedback gain.

[0074] Then, the off-grid status parameter set and the initial parameter set are randomly combined to generate several off-grid control schemes. The off-grid status parameter set reflects the current operating status of the inverter, and the initial parameter set represents different control strategies. These are combined to form a specific control scheme for the current operating state. Each off-grid control scheme corresponds to a possible inverter operating mode. By evaluating and comparing the generated schemes, the control strategy most suitable for the current operating condition is found.

[0075] Subsequently, key VOC control parameters are optimized based on the off-grid control scheme and a preset objective function. The preset objective function aims to minimize output voltage deviation, output frequency deviation, and grid-side current, considering multiple performance indicators of the inverter. During the optimization process, multi-objective optimization algorithms, such as genetic algorithms or particle swarm optimization, are used to evaluate each off-grid control scheme. For each scheme, its fitness value is calculated based on the objective function; a higher fitness value indicates that the scheme is closer to the optimal solution.

[0076] Ultimately, the optimal control parameters were selected from numerous off-grid control schemes. These optimal control parameters enable the inverter to achieve the best balance in multiple aspects, including output voltage stability, precise output frequency control, and grid-side current suppression, during off-grid operation. This improves the overall performance and reliability of the inverter, ensuring stable and efficient power supply to local loads under various off-grid conditions.

[0077] Furthermore, based on the aforementioned off-grid control schemes and preset objective functions, VOC key control parameters are optimized to obtain the optimal control parameters, including:

[0078] Based on digital twins, simulation modeling of off-grid operation systems is performed to generate an off-grid operation simulation space;

[0079] Within the off-grid operation simulation space, control simulations are performed according to the several off-grid control schemes, and several control simulation results are output. The control simulation results include simulated voltage deviation, simulated output frequency deviation, and simulated grid-side current.

[0080] The fitness of the several control simulation results is evaluated according to the preset objective function, and the fitness of several parameters is output.

[0081] Using a multi-objective optimization algorithm, the key control parameters of VOC are optimized based on the aforementioned initial parameter sets and parameter fitness to obtain the optimal control parameters.

[0082] In this embodiment, firstly, a simulation model of the off-grid operating system is performed based on digital twin technology. Digital twins can construct a virtual model highly similar to the actual off-grid operating system. This model encompasses the inverter main circuit, output filter, local load, and grid connection interface, thereby generating an off-grid operation simulation space. Within this simulation space, various possible off-grid operating conditions and load changes can be simulated.

[0083] Secondly, within the off-grid operation simulation space, control simulations are performed based on several previously generated off-grid control schemes. For each off-grid control scheme, the simulation model simulates the inverter's operation under that scheme and outputs corresponding control simulation results, specifically including simulated voltage deviation, simulated output frequency deviation, and simulated grid-side current. The output simulation results can intuitively reflect the performance of each off-grid control scheme at different performance indicators.

[0084] Subsequently, the fitness of several control simulation results is evaluated based on a preset objective function. The preset objective function aims to minimize the output voltage deviation, output frequency deviation, and grid-side current. By substituting each control simulation result into the objective function, the fitness of several parameters is obtained.

[0085] Furthermore, the higher the parameter fitness, the closer the off-grid control scheme is to the optimal solution, and the better it can meet the multiple performance requirements of the inverter when operating off-grid.

[0086] Finally, a multi-objective optimization algorithm is used to optimize the key control parameters of VOC based on several initial parameter sets and several parameter fitnesss. Taking a genetic algorithm as an example, the initial parameter sets are subjected to selection, crossover, and mutation operations based on parameter fitness to generate new parameter combinations and off-grid control schemes.

[0087] The above process is repeated continuously, with control simulations and fitness evaluations performed on the newly generated schemes, until termination conditions are met, such as reaching the maximum number of iterations or the objective function value converging to a small range. Finally, the optimal control parameters are selected from numerous schemes. These parameters enable the inverter to achieve the best results in output voltage stability, precise output frequency control, and grid-side current suppression during off-grid operation, improving the overall performance and reliability of the inverter and ensuring stable and efficient power supply to local loads under various complex off-grid conditions.

[0088] Specifically, based on the aforementioned initial parameter sets and parameter fitness, VOC key control parameters are optimized to obtain the optimal control parameters, including:

[0089] The initial parameter set is set as the initial solution, and several initial solutions are arranged in descending order of parameter fitness to generate an initial solution sequence;

[0090] From the initial solution sequence, a predetermined proportion of solutions are selected as optimal solutions, resulting in multiple optimal solutions, wherein the predetermined proportion is less than or equal to 20%.

[0091] Based on the multiple optimal solutions, adaptive crossover and mutation operations are performed to generate offspring parameter sets;

[0092] The sub-parameter set is merged with the multiple optimal solutions to generate an extended solution set. The extended solution set is then evaluated and sorted based on its parameter fitness. According to the sorting results, the solution with the highest fitness among the preset ratios is selected to form the next generation initial solution sequence.

[0093] Repeat the iterative optimization until the iteration termination condition is met, and select the solution with the highest fitness from the last solution sequence as the optimal control parameter output.

[0094] In this embodiment of the application, the initial parameter set is first set as the initial solution, and the initial solutions are arranged in descending order of parameter fitness to form an initial solution sequence.

[0095] Secondly, a predetermined proportion of solutions are selected as optimal solutions from the initial solution sequence, with the predetermined proportion being less than or equal to 20%. Choosing a smaller proportion ensures the quality of the optimal solutions, resulting in solutions with higher performance in satisfying the predetermined objective function.

[0096] Next, adaptive crossover and mutation operations are performed on the selected multiple optimal solutions to generate offspring parameter sets. Adaptive crossover and mutation operations can generate new parameter combinations while preserving the characteristics of optimal solutions, increasing the diversity of solutions and preventing the algorithm from getting trapped in local optima. In the crossover operation, new parameter combinations are generated by exchanging some parameters of optimal solutions; the mutation operation randomly perturbs certain parameters to further explore the solution space.

[0097] Then, the offspring parameter set is merged with multiple optimal solutions to form an expanded solution set. The expanded solution set is then evaluated and ranked based on parameter fitness. The solutions with the highest fitness, representing a pre-defined proportion, are selected to form the next generation's initial solution sequence. This process continuously filters out better solutions, causing the solution sequence to gradually approach the optimal solution during iteration.

[0098] Repeat the iterative optimization process described above until the iteration termination condition is met. The iteration termination condition can be reaching the maximum number of iterations or the objective function value converging to a small range. For example, the termination condition is reaching the maximum number of iterations, 1000, which indicates that enough attempts and calculations have been performed.

[0099] Finally, the solution with the highest fitness is selected from the last solution sequence as the optimal control parameter output. The optimal control parameter enables the inverter to achieve the best results in terms of output voltage stability, precise output frequency control and grid-side current suppression when operating off-grid, thereby improving the overall performance and reliability of the inverter.

[0100] S40: Delegate the optimal control parameters to the parameter control layer to perform off-grid control of the inverter.

[0101] In this embodiment, after obtaining the optimal control parameters, the optimal control parameters are passed down from the parameter optimization layer to the parameter control layer. As the layer that directly controls the inverter, the parameter control layer immediately applies the parameters to the inverter's off-grid control process upon receiving the optimal control parameters.

[0102] Throughout the off-grid control process, the parameter control layer continuously monitors the inverter's operating status, including parameters such as output voltage, output frequency, and grid-side current. If any abnormalities are detected, such as the output voltage exceeding the allowable range or the output frequency deviating excessively, the parameter control layer promptly feeds this information back to the parameter optimization layer. Based on the feedback, the parameter optimization layer re-optimizes the key VOC control parameters using the off-grid state parameter set and a preset objective function to obtain new optimal control parameters. These new parameters are then sent back to the parameter control layer for adjustment, forming a closed-loop control process that ensures the inverter maintains optimal operating performance in off-grid conditions.

[0103] Specifically, step S40 in the method includes:

[0104] The virtual oscillator control unit generates an initial reference voltage signal based on the collected inverter output current, the given active and reactive power reference values, and the optimal control parameters.

[0105] The adaptive virtual impedance unit dynamically calculates the virtual impedance voltage drop based on the real-time reactive power distribution error, and corrects the initial reference voltage signal to obtain the corrected voltage signal.

[0106] The current feedforward control unit generates a reverse compensation signal based on the collected grid-side current, compensates the correction voltage signal to obtain a compensation voltage signal, and converts the compensation voltage signal into a modulation wave to drive the inverter to work, thereby realizing off-grid control.

[0107] In this embodiment, after receiving the collected inverter output current, given active and reactive power reference values, and optimal control parameters, the virtual oscillator control unit generates an initial reference voltage signal based on its internal control algorithm and logic. The accuracy of the initial reference voltage signal directly affects the inverter's output performance.

[0108] Furthermore, the adaptive virtual impedance unit monitors reactive power distribution errors in real time. Due to changes in load characteristics and the influence of the power grid environment, reactive power distribution may deviate. The adaptive virtual impedance unit dynamically calculates the virtual impedance voltage drop based on the errors that occur. By adjusting the magnitude of the virtual impedance, the inverter's output characteristics are improved, the accuracy of reactive power distribution is enhanced, and a corrected voltage signal that better meets actual operating requirements is obtained.

[0109] The current feedforward control unit collects the grid-side current and generates a reverse compensation signal based on the collected current information. Since fluctuations in the grid-side current can affect the inverter's output voltage, generating a reverse compensation signal can effectively compensate for these effects. The compensation voltage signal is converted into a modulated wave, which drives the inverter, thereby achieving off-grid control of the inverter.

[0110] Throughout the off-grid control process, the virtual oscillator control unit provides the basic reference signal, the adaptive virtual impedance unit dynamically adjusts according to actual conditions, and the current feedforward control unit compensates for the impact of grid-side current. This collaborative approach enables the inverter to better adapt to different load variations and grid environments during off-grid operation.

[0111] In summary, compared with existing technologies, this application first obtains a reasonable parameter adjustment space and randomly generates an initial parameter set during the optimization process of key VOC control parameters. Then, it combines the off-grid state parameter set to form an off-grid control scheme and uses a multi-objective optimization algorithm for optimization. At the same time, it uses digital twin technology for simulation modeling and control simulation to more accurately evaluate the performance of the scheme. Furthermore, it adopts adaptive crossover and mutation operations to increase the diversity of solutions, avoid the algorithm getting trapped in local optima, and enable the inverter to achieve the best balance of output voltage, frequency, and current control during off-grid operation. This improves the overall performance and reliability of the inverter and ensures that it can stably and efficiently supply power to local loads under various complex off-grid conditions.

[0112] In summary, the embodiments of this application have at least the following technical effects:

[0113] This application provides a multi-objective optimization method for off-grid control of grid-connected inverters. First, an off-grid control platform is constructed, comprising a data monitoring layer, a parameter optimization layer, and a parameter control layer. This enables hierarchical management of off-grid control, clearly defining the functions of each layer and improving the systematic nature and effectiveness of the control. Second, the data monitoring layer collects electrical monitoring data from the inverter output and calculates the off-grid state parameter set, providing a comprehensive understanding of the inverter's operating status and a reliable data foundation for subsequent parameter optimization. Then, the parameter optimization layer optimizes key VOC control parameters based on the off-grid state parameter set and a preset objective function, obtaining the optimal control parameters. This approach balances multiple control objectives, including output voltage stability, precise output frequency control, and effective suppression of grid-side current, overcoming the limitations of traditional control methods that struggle to address multiple objectives simultaneously. Finally, the optimal control parameters are delegated to the parameter control layer for off-grid control of the inverter. The control parameters are dynamically adjusted based on the actual operating status, enhancing the control's adaptive capability. Through the above technical solution, this application can quickly collect load change information based on different off-grid status parameters, calculate a new set of off-grid status parameters, and then optimize VOC key control parameters at the parameter optimization layer, adjust control parameters in real time, and ensure that the inverter can operate stably under various operating conditions.

[0114] Example 2, as Figure 2 As shown, based on the same inventive concept as the multi-objective optimized grid-connected inverter off-grid control method provided in Embodiment 1, this application also provides a multi-objective optimized grid-connected inverter off-grid control system, including:

[0115] Platform building module 11 is used to build an inverter off-grid control platform that includes a data monitoring layer, a parameter optimization layer, and a parameter control layer;

[0116] The parameter acquisition module 12 is used to acquire the electrical monitoring dataset output by the inverter through the data monitoring layer on the inverter off-grid control platform, and calculate the off-grid status parameter set based on the electrical monitoring dataset.

[0117] The parameter optimization module 13 is used to optimize the key control parameters of VOC based on the off-grid state parameter set and the preset objective function in the parameter optimization layer, so as to obtain the optimal control parameters.

[0118] Off-grid control module 14 is used to decentralize the optimal control parameters to the parameter control layer and perform off-grid control on the inverter.

[0119] Furthermore, in one embodiment, the data monitoring layer includes an inverter main circuit, an output filter, a local load, and a grid connection interface; the parameter optimization layer embeds a multi-objective optimization algorithm; and the parameter control layer includes a virtual oscillator control unit, an adaptive virtual impedance unit, and a current feedforward control unit.

[0120] Furthermore, in one embodiment of the application, the electrical monitoring dataset includes three-phase voltage and current, local load current, and grid-side current.

[0121] Furthermore, in one embodiment of the application, the off-grid status parameter set is calculated based on the electrical monitoring dataset, including:

[0122] Based on the three-phase voltage and current, calculate the inverter output voltage amplitude, voltage frequency, active power and reactive power;

[0123] The total reactive power consumed by the local load is calculated based on the local load current.

[0124] The voltage amplitude, voltage frequency, active power, reactive power, total reactive power, and grid-side current are used as the off-grid state parameter set.

[0125] The objective function is constructed with minimizing the output voltage deviation, output frequency deviation, and grid-side current as the optimization objectives.

[0126] Furthermore, in one embodiment, optimizing key VOC control parameters based on the off-grid state parameter set and a preset objective function to obtain optimal control parameters includes:

[0127] The adjustment space of key VOC control parameters is obtained, wherein the key VOC control parameters include active power-frequency droop coefficient, reactive power-voltage droop coefficient, amplitude stability term gain and current feedback gain;

[0128] Several initial parameter sets are randomly generated within the VOC key control parameter adjustment space;

[0129] The off-grid status parameter set and the several initial parameter sets are randomly combined to generate several off-grid control schemes;

[0130] Based on the aforementioned off-grid control schemes and preset objective functions, the key control parameters for VOC are optimized to obtain the optimal control parameters.

[0131] Furthermore, in one embodiment, the optimal control parameters for VOC are obtained by optimizing the key control parameters based on the plurality of off-grid control schemes and a preset objective function, including:

[0132] Based on digital twins, simulation modeling of off-grid operation systems is performed to generate an off-grid operation simulation space;

[0133] Within the off-grid operation simulation space, control simulations are performed according to the several off-grid control schemes, and several control simulation results are output. The control simulation results include simulated voltage deviation, simulated output frequency deviation, and simulated grid-side current.

[0134] The fitness of the several control simulation results is evaluated according to the preset objective function, and the fitness of several parameters is output.

[0135] Using a multi-objective optimization algorithm, the key control parameters of VOC are optimized based on the aforementioned initial parameter sets and parameter fitness to obtain the optimal control parameters.

[0136] Specifically, in one embodiment, the optimization of key VOC control parameters is performed based on the aforementioned initial parameter sets and parameter fitness to obtain optimal control parameters, including:

[0137] The initial parameter set is set as the initial solution, and several initial solutions are arranged in descending order of parameter fitness to generate an initial solution sequence;

[0138] From the initial solution sequence, a predetermined proportion of solutions are selected as optimal solutions, resulting in multiple optimal solutions, wherein the predetermined proportion is less than or equal to 20%.

[0139] Based on the multiple optimal solutions, adaptive crossover and mutation operations are performed to generate offspring parameter sets;

[0140] The sub-parameter set is merged with the multiple optimal solutions to generate an extended solution set. The extended solution set is then evaluated and sorted based on its parameter fitness. According to the sorting results, the solution with the highest fitness among the preset ratios is selected to form the next generation initial solution sequence.

[0141] Repeat the iterative optimization until the iteration termination condition is met, and select the solution with the highest fitness from the last solution sequence as the optimal control parameter output.

[0142] In one embodiment, the off-grid control module 14 is specifically used for:

[0143] The virtual oscillator control unit generates an initial reference voltage signal based on the collected inverter output current, the given active and reactive power reference values, and the optimal control parameters.

[0144] The adaptive virtual impedance unit dynamically calculates the virtual impedance voltage drop based on the real-time reactive power distribution error, and corrects the initial reference voltage signal to obtain the corrected voltage signal.

[0145] The current feedforward control unit generates a reverse compensation signal based on the collected grid-side current, compensates the correction voltage signal to obtain a compensation voltage signal, and converts the compensation voltage signal into a modulation wave to drive the inverter to work, thereby realizing off-grid control.

[0146] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0147] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0148] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A multi-objective optimization method for off-grid control of grid-connected inverters, characterized in that, The methods include: Construct an inverter off-grid control platform that includes a data monitoring layer, a parameter optimization layer, and a parameter control layer; In the inverter off-grid control platform, the electrical monitoring dataset output by the inverter is collected through the data monitoring layer, and an off-grid status parameter set is calculated based on the electrical monitoring dataset, including: Calculate the inverter output voltage amplitude, voltage frequency, active power, and reactive power based on the three-phase voltage and current. The total reactive power consumed by the local load is calculated based on the local load current. Voltage amplitude, voltage frequency, active power, reactive power, total reactive power, and grid-side current are used as the off-grid status parameter set. In the parameter optimization layer, based on the off-grid state parameter set and a preset objective function, VOC key control parameters are optimized to obtain the optimal control parameters, including: The adjustment space of key VOC control parameters is obtained, wherein the key VOC control parameters include active power-frequency droop coefficient, reactive power-voltage droop coefficient, amplitude stability term gain and current feedback gain; Several initial parameter sets are randomly generated within the VOC key control parameter adjustment space; The off-grid status parameter set and the several initial parameter sets are randomly combined to generate several off-grid control schemes; Based on the aforementioned off-grid control schemes and preset objective functions, the key control parameters for VOC are optimized to obtain the optimal control parameters; The optimal control parameters are delegated to the parameter control layer to perform off-grid control of the inverter, including: The virtual oscillator control unit generates an initial reference voltage signal based on the collected inverter output current, the given active and reactive power reference values, and the optimal control parameters. The adaptive virtual impedance unit dynamically calculates the virtual impedance voltage drop based on the real-time reactive power distribution error, and corrects the initial reference voltage signal to obtain the corrected voltage signal. The current feedforward control unit generates a reverse compensation signal based on the collected grid-side current, compensates the correction voltage signal to obtain a compensation voltage signal, and converts the compensation voltage signal into a modulation wave to drive the inverter to work, thereby realizing off-grid control.

2. The multi-objective optimization method for off-grid control of a grid-connected inverter according to claim 1, characterized in that, An inverter off-grid control platform is constructed, comprising a data monitoring layer, a parameter optimization layer, and a parameter control layer. The data monitoring layer includes the inverter main circuit, output filter, local load, and grid connection interface. The parameter optimization layer embeds a multi-objective optimization algorithm. The parameter control layer includes a virtual oscillator control unit, an adaptive virtual impedance unit, and a current feedforward control unit.

3. The multi-objective optimization method for off-grid control of a grid-connected inverter according to claim 1, characterized in that, The data monitoring layer collects electrical monitoring datasets output by the inverter, which include three-phase voltage and current, local load current, and grid-side current.

4. The multi-objective optimization method for off-grid control of a grid-connected inverter according to claim 1, characterized in that, An objective function is constructed with the goal of minimizing the output voltage deviation, output frequency deviation, and grid-side current.

5. The multi-objective optimization method for off-grid control of a grid-connected inverter according to claim 1, characterized in that, Based on the aforementioned off-grid control schemes and preset objective functions, VOC key control parameters are optimized to obtain the optimal control parameters, including: Based on digital twins, simulation modeling of off-grid operation systems is performed to generate an off-grid operation simulation space; Within the off-grid operation simulation space, control simulations are performed according to the several off-grid control schemes, and several control simulation results are output. The control simulation results include simulated voltage deviation, simulated output frequency deviation, and simulated grid-side current. The fitness of the several control simulation results is evaluated according to the preset objective function, and the fitness of several parameters is output. Using a multi-objective optimization algorithm, the key control parameters of VOC are optimized based on the aforementioned initial parameter sets and parameter fitness to obtain the optimal control parameters.

6. The multi-objective optimization method for off-grid control of a grid-connected inverter according to claim 5, characterized in that, Based on the aforementioned initial parameter sets and parameter fitness, VOC key control parameters are optimized to obtain the optimal control parameters, including: The initial parameter set is set as the initial solution, and several initial solutions are arranged in descending order of parameter fitness to generate an initial solution sequence; From the initial solution sequence, a predetermined proportion of solutions are selected as optimal solutions, resulting in multiple optimal solutions, wherein the predetermined proportion is less than or equal to 20%. Based on the multiple optimal solutions, adaptive crossover and mutation operations are performed to generate offspring parameter sets; The sub-parameter set is merged with the multiple optimal solutions to generate an extended solution set. The extended solution set is then evaluated and sorted based on its parameter fitness. According to the sorting results, the solution with the highest fitness among the preset ratios is selected to form the next generation initial solution sequence. Repeat the iterative optimization until the iteration termination condition is met, and select the solution with the highest fitness from the last solution sequence as the optimal control parameter output.

7. A multi-objective optimized grid-connected inverter off-grid control system, characterized in that, A method for implementing a multi-objective optimization grid-connected inverter off-grid control according to any one of claims 1-6, comprising: The platform building module is used to build an inverter off-grid control platform that includes a data monitoring layer, a parameter optimization layer, and a parameter control layer; The parameter acquisition module is used to acquire the electrical monitoring dataset output by the inverter through the data monitoring layer on the inverter off-grid control platform, and calculate the off-grid status parameter set based on the electrical monitoring dataset; The parameter optimization module is used in the parameter optimization layer to optimize key VOC control parameters based on the off-grid state parameter set and a preset objective function, and obtain the optimal control parameters. The off-grid control module is used to delegate the optimal control parameters to the parameter control layer to perform off-grid control of the inverter.

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