Inverter parameter identification method and device based on improved eagle algorithm, and medium

Abstract

The invention relates to the technical field of photovoltaic power generation system control parameter identification, in particular to an inverter parameter identification method and device based on an improved eagle algorithm and a medium, and the method comprises the steps: controlling the output of a photovoltaic cell array through a maximum power point tracking technology, and carrying out the optimization through a variable-step MPPT algorithm of an improved conductance increment method; key points required for identification are extracted according to the response characteristics of the low-voltage ride-through working condition, and an identification data set is established by using the key points of the actual measurement working condition; identifying a control parameter and a low-voltage ride-through parameter of the photovoltaic inverter through an improved eagle optimization algorithm; a photovoltaic power grid access simulation model is established according to actual station information, and hardware-in-loop simulation is performed on actual photovoltaic controller hardware so as to realize control parameter and control strategy integrated identification. According to the method, the electromechanical transient characteristics of the photovoltaic inverter can be combined, and the integrated identification of the control parameters and the strategy is realized by improving the optimization algorithm and the data processing strategy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of photovoltaic power generation system control parameter identification, in particular to an inverter parameter identification method, device and medium based on an improved fish eagle algorithm. BACKGROUND

[0002] Under the promotion of the "double carbon" strategic goal, photovoltaic power generation, as an important part of renewable energy, has a continuously rapid growth in installed capacity in the power system. With large-scale photovoltaic power integrated into the power grid, the grid-connection characteristics have increasingly highlighted the influence on the stability of the power grid.

[0003] The control strategy and parameters of the photovoltaic grid-connected inverter directly determine the transient characteristics of the grid-connection point during power grid faults, which is crucial for the safe and stable operation of the power grid. However, the core control parameters of the inverter are often difficult to obtain due to factors such as manufacturer secrecy and variable operating environments, which poses challenges to the establishment of an accurate simulation model of the photovoltaic power station, and further restricts the accurate analysis and research of the photovoltaic grid-connection characteristics. Moreover, existing parameter identification methods often face slow convergence speed and are prone to local optimum when dealing with such complex optimization problems, which makes it difficult to meet the high requirements for parameter identification accuracy and reliability of the electromechanical transient model of the photovoltaic inverter, especially in low-voltage ride-through dynamic conditions. SUMMARY

[0004] To solve the above-mentioned prior art problems, the present application provides an inverter parameter identification method, device and medium based on an improved fish eagle algorithm, which can combine the electromechanical transient characteristics of the photovoltaic inverter, improve the optimization algorithm and data processing strategy, and realize the integrated identification of control parameters and strategies, thereby improving the simulation accuracy and engineering applicability of the model.

[0005] In a first aspect, the embodiments of the present application provide an inverter parameter identification method based on an improved fish eagle algorithm, which comprises: building a photovoltaic power generation system control architecture model; controlling the photovoltaic cell array output through the maximum power point tracking technology, and optimizing the variable step MPPT algorithm of the improved incremental conductance method to ensure that the photovoltaic system can output electric energy with the highest efficiency; extracting the key points required for identification according to the response characteristics of the low-voltage ride-through condition, establishing an identification data set using the key points of the measured condition, and eliminating the limiting condition in the identification data set; identifying the control parameters and low-voltage ride-through parameters of the photovoltaic inverter through an improved fish eagle optimization algorithm; the improved fish eagle optimization algorithm includes initial population and fitness evaluation, exploration stage, exploitation stage, and grabbing and adjustment stage to obtain optimized equivalent parameters; According to actual station information, a photovoltaic grid access simulation model is built, and hardware-in-the-loop simulation is performed on actual photovoltaic controller hardware, so as to realize integrated identification of control parameters and control strategies.

[0006] According to some embodiments of the first aspect of the application, the photovoltaic power generation system control architecture model comprises: An equivalent circuit model of a photovoltaic cell is established, and a calculation formula of the equivalent circuit model of the photovoltaic cell is as follows: , wherein, represents an output current of the photovoltaic cell, represents an output voltage of the photovoltaic cell, represents a short-circuit current, represents an open-circuit voltage, and are intermediate coefficients, and calculation formulas are as follows: , , wherein, represents a current at a maximum power point, represents a voltage at the maximum power point.

[0007] According to some embodiments of the first aspect of the application, the variable step MPPT algorithm of the improved conductance increment method comprises: A voltage difference and a current difference between a current control period and a previous control period are calculated, and a conductance change rate is confirmed according to the voltage difference and the current difference; It is judged whether the absolute value of the voltage difference is 0, and in the case that the absolute value of the voltage difference is 0, it is judged whether the absolute value of the current difference is 0; in the case that the absolute value of the voltage difference is greater than 0, it is judged whether the conductance change rate is equal to a conductance balance condition at a maximum power point; In the case that the absolute value of the current difference is 0, a current control iteration step is equal to a previous control iteration step; in the case that the absolute value of the current difference is greater than 0, the current control iteration step is adjusted according to a positive or negative situation of the current difference; In the case that the conductance change rate is equal to the conductance balance condition at the maximum power point, the current control iteration step is equal to the previous control iteration step; in the case that the conductance change rate is greater than or less than the conductance balance condition at the maximum power point, the current control iteration step is adjusted according to a size relationship between the conductance change rate and the conductance balance condition at the maximum power point.

[0008] According to some embodiments of the first aspect of the application, a time period of a low-voltage ride-through working condition is divided into: Pre-disturbance stage; The disturbance period includes a first transient interval and a first steady-state interval; The post-disturbance stage includes a second transient interval and a second steady-state interval.

[0009] According to some embodiments of the first aspect of this application, the step of extracting and identifying the required key points based on the response characteristics of low-pressure ride-through conditions includes: Based on active current, reactive current, and inverter AC side voltage, key points are extracted from the pre-disturbance stage, the during-disturbance stage, and the post-disturbance stage.

[0010] According to some embodiments of the first aspect of this application, the improved Osprey optimization algorithm includes: Initial Population and Fitness Evaluation: An initial set of solutions is randomly generated within the solution space, and a fitness function is defined to evaluate the quality of the solutions; Exploration phase: For each optimized agent, select the position of the optimized agent with the better objective function value as the candidate solution set, calculate the new position in the exploration phase and update it; Mining phase: Simulate the behavior of an osprey carrying prey to a suitable location to calculate candidate locations for the mining phase; Grab and adjust phase: If the new fitness value is better, the new position is retained; otherwise, the position is regenerated with probability until the new fitness value is better. The mining stage also includes a Gaussian mutation operation, which introduces random perturbations through a Gaussian function to prevent premature convergence of the Osprey optimization algorithm.

[0011] According to some embodiments of the first aspect of this application, during the exploration phase, the expression for the candidate solution set corresponding to each optimized agent individual is as follows: , in, Indicates the first A set of candidate solutions for each osprey; Indicates the optimal solution; Indicates the first The objective function value corresponding to each osprey; Indicates the first The objective function value corresponding to each osprey; Indicates the first The group corresponding to each osprey; The formula for calculating the new location during the exploration phase is as follows: , in, This indicates the new location in the exploration phase. Indicates the first the value of the jth variable of the ith solution; the value of the jth variable of the ith solution; the value of the jth variable of the ith solution; the value of the jth variable of the ith solution; a random number in the interval [0, 1], a random number in the set .

[0012] According to some embodiments of the first aspect of the application, the calculation formula of the mining stage candidate position is as follows: , wherein, denotes the mining stage candidate position, the value of the jth variable of the ith solution; the value of the jth variable of the ith solution; the value of the jth variable of the ith solution; the upper bound of the jth variable, the lower bound of the jth variable; the lower bound of the jth variable; the current iteration number of the algorithm, a random number in the interval [0, 1]; the calculation formula of the mining stage candidate position after the Gaussian mutation operation is as follows: , wherein, denotes the mining stage candidate position after mutation, a random number in the interval [0, 1], 1.

[0013] In the second aspect, the embodiments of the application provide an electronic device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the improved fish eagle algorithm-based inverter parameter identification method of the first aspect.

[0014] In the third aspect, the embodiments of the application further provide a computer readable storage medium storing computer executable instructions for executing the improved fish eagle algorithm-based inverter parameter identification method of the first aspect.

[0015] ​The beneficial effects of the present application are embodied in that, by building a photovoltaic power generation system control architecture model, the output of the photovoltaic cell array is controlled through the maximum power point tracking technology, and the variable step MPPT algorithm of the improved incremental conductance method is used for optimization to ensure that the photovoltaic system can output power with the highest efficiency; the key points required for identification are extracted according to the response characteristics of the low-voltage ride-through working condition, the key points of the actual working condition are used to establish an identification data set, and the limiting working condition in the identification data set is eliminated; the control parameters and low-voltage ride-through parameters of the photovoltaic inverter are identified through the improved fish-eagle optimization algorithm; the improved fish-eagle optimization algorithm includes initial population and fitness evaluation, exploration stage, exploitation stage, and grabbing and adjustment stage to obtain optimized equivalent parameters; according to the actual station information, a photovoltaic grid connection simulation model is built, and the hardware-in-the-loop simulation is performed on the actual photovoltaic controller hardware to realize the integrated identification of the control parameters and the control strategy. Through this method, the electromechanical transient characteristics of the photovoltaic inverter can be combined, the optimization algorithm and the data processing strategy are improved, the integrated identification of the control parameters and the strategy is realized, and the simulation precision and the engineering applicability of the model are improved. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 The flowchart of the inverter parameter identification method based on the improved fish-eagle algorithm provided by the first aspect of the present application is shown in the figure. Figure 2 The flowchart of the photovoltaic equivalent circuit model provided by the first aspect of the present application is shown in the figure. Figure 3 The flowchart of the variable step MPPT algorithm of the improved incremental conductance method provided by the first aspect of the present application is shown in the figure. Figure 4 The flowchart of the improved fish-eagle optimization algorithm provided by the first aspect of the present application is shown in the figure. Figure 5 The flowchart of the improved fish-eagle optimization algorithm provided by the first aspect of the present application is shown in the figure. Figure 6 The flowchart of the variable step MPPT algorithm of the improved incremental conductance method provided by the first aspect of the present application is shown in the figure. Figure 7 The low-voltage ride-through period division diagram provided by the first aspect of the present application is shown in the figure. Figure 8 The algorithm convergence curve comparison diagram provided by the first aspect of the present application is shown in the figure. Figure 9 The algorithm convergence curve comparison diagram provided by another embodiment of the first aspect of the present application is shown in the figure. Figure 10 The identification result local diagram provided by the first aspect of the present application is shown in the figure. Figure 11 is a structural schematic diagram of an electronic device provided by an embodiment of a second aspect of the present application. DETAILED DESCRIPTION

[0017] Embodiments of the present application will be described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numbers represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and are only used to explain the present application, and cannot be understood as a limitation on the present application.

[0018] In the description of the present application, it should be understood that, in relation to the orientation description, for example, the orientation or position relationship indicated by the upper, lower, front, rear, left, right, etc. is based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application.

[0019] In the description of the present application, if the first, second, etc. are described, it is only for the purpose of distinguishing technical features, and cannot be understood as indicating or implying the relative importance of the technical features or implicitly indicating the number of technical features indicated or the order of the technical features indicated.

[0020] In the description of the present application, unless otherwise explicitly limited, the words such as arrangement, installation, connection, etc. should be broadly understood, and those skilled in the art can reasonably determine the specific meaning of the above words in the present application in combination with the specific content of the technical solution.

[0021] Under the promotion of the "double carbon" strategic goal, as an important part of renewable energy, the installed capacity of photovoltaic power generation in the power system continues to grow rapidly. With the large-scale integration of photovoltaic power into the power grid, the influence of its grid-connected characteristics on the stability of the power grid is increasingly prominent.

[0022] The control strategy and parameters of the photovoltaic grid-connected inverter directly determine the transient characteristics of the grid-connected point during power grid faults, which is crucial to the safe and stable operation of the power grid. However, the core control parameters of the inverter are often difficult to obtain due to factors such as manufacturer secrecy and variable operating environment, making it challenging to establish an accurate simulation model of the photovoltaic power station, which in turn restricts the precise analysis and research of photovoltaic grid-connected characteristics. Moreover, existing parameter identification methods often face slow convergence speed and are prone to local optimization when dealing with such complex optimization problems, making it difficult to meet the high requirements for parameter identification accuracy and reliability in the electromechanical transient model of the photovoltaic inverter, especially in low-voltage ride-through and other dynamic operating conditions.

[0023] In order to solve the above problems, the present application proposes an inverter parameter identification method based on an improved fish-eagle algorithm. The embodiments of the present application will be further described below with reference to the drawings.

[0024] Referring to Figure 1 , Figure 1 A method for inverter parameter identification based on an improved fish eagle algorithm is shown, which is also applied to an electronic device and executed by the electronic device. In other words, the method can be executed by software or hardware installed in the device, and the method includes the following steps: Step S100, a photovoltaic power generation system control architecture model is built.

[0025] It should be noted that by "building a photovoltaic power generation system control architecture model" and using a variable step MPPT algorithm based on improved incremental conductance method, the maximum power output control of the photovoltaic system is optimized from the source, providing a more realistic and more efficient benchmark model for subsequent parameter identification, and solving the problem of weak identification basis caused by inaccurate model.

[0026] Step S200, the photovoltaic cell array output is controlled by the maximum power point tracking technology, and the variable step MPPT algorithm based on improved incremental conductance method is used for optimization to ensure that the photovoltaic system can output electric energy with the highest efficiency.

[0027] In this step, the maximum power point tracking technology (MPPT) is used to control the photovoltaic cell array to maximize the conversion of light energy into electric energy, and to prioritize the output of active power. In order to ensure that the photovoltaic system always outputs electric energy with the highest efficiency, the variable step MPPT algorithm based on improved incremental conductance method is used for optimization.

[0028] It should be noted that the full name of MPPT is Maximum Power Point Tracking, which means maximum power point tracking.

[0029] Step S300, according to the response characteristics of low voltage ride through working condition, the key points required for identification are extracted, the key points of the measured working condition are used to establish the identification data set, and the limiting amplitude working condition in the identification data set is removed.

[0030] It should be noted that by "extracting the key points required for identification according to the response characteristics of low voltage ride through working condition" and "removing the limiting amplitude working condition", this method can accurately select the effective information that best reflects the characteristics of the control parameters from the measured data, eliminate the interference of abnormal working conditions, and improve the quality of the identification data set, laying a foundation for accurate identification.

[0031] Step S400, the control parameters and low voltage ride through parameters of the photovoltaic inverter are identified by the improved fish eagle optimization algorithm.

[0032] In this step, the improved fish eagle optimization algorithm includes initial population and fitness evaluation, exploration stage, exploitation stage, and grabbing and adjustment stage to obtain optimized equivalent parameters.

[0033] It should be noted that the improved fish eagle optimization algorithm balances the global exploration and local development ability through the cooperative search of the "exploration stage" and the "mining stage", and directly aims at the defect of the traditional optimization algorithm that is easy to "fall into local optimum", and significantly improves the success rate and efficiency of finding the global optimal solution or satisfactory solution in complex parameter space.

[0034] Step S500, according to the actual station information, build a photovoltaic grid access simulation model, and carry out hardware-in-the-loop simulation on the actual photovoltaic controller hardware, to realize integrated identification of control parameters and control strategy.

[0035] It should be noted that the actual station information is based on the operation data of the real photovoltaic power station, such as the geographic location of a certain station in Ningxia, the configuration of photovoltaic array, the grid connection condition, etc. These information is used to build a high-precision simulation model (such as on the RT-LAB semi-physical test platform). Specifically, it includes: station specifications, such as installed capacity, inverter model, environmental parameters; measured data: voltage, current waveform data obtained through hardware-in-the-loop simulation, used to verify the identification method.

[0036] In this step, the hardware-in-the-loop simulation is carried out on the actual photovoltaic controller hardware, and the control strategy and parameters of the inverter electromechanical transient model are effectively and accurately identified, realizing integrated identification of control parameters and control strategy.

[0037] The application improves the output efficiency of the photovoltaic system by improving the incremental conductance MPPT algorithm; enhances the representativeness of the data set by using the low-voltage ride-through key point extraction technology; improves the fish eagle optimization algorithm through the Gaussian mutation strategy to avoid premature convergence and improve the identification accuracy and convergence speed; realizes integrated identification of control parameters and strategy, and provides a reliable model for stable analysis of photovoltaic grid-connected system.

[0038] It should be noted that by "building a photovoltaic grid access simulation model" and carrying out "hardware-in-the-loop simulation", the method verifies the parameters and control strategy identified in the actual controller hardware environment in a closed loop, ensuring that the identification result is not only mathematically optimal, but also effective and reliable in actual engineering application, realizing integrated closed loop from parameter identification to strategy verification, and solving the problem of disconnection between theory and practice.

[0039] It should be noted that the application overcomes the key obstacles of difficult acquisition of photovoltaic inverter parameters, low model accuracy and poor effect of traditional algorithm in the background technology through the complete technical chain of building accurate model, optimizing data preprocessing, introducing advanced optimization algorithm and carrying out hardware-in-the-loop verification, and provides an effective way for realizing high-precision integrated identification of control parameters and strategy.

[0040] The beneficial technical effects of the present application are embodied in that, by building a photovoltaic power generation system control architecture model, the output of the photovoltaic cell array is controlled through the maximum power point tracking technology, and a variable step MPPT algorithm of the improved incremental conductance method is used for optimization to ensure that the photovoltaic system can output power with the highest efficiency; according to the response characteristics of the low-voltage ride-through working condition, the key points required for identification are extracted, the key points of the actual working condition are used to establish an identification data set, and the limiting working condition in the identification data set is eliminated; the control parameters and low-voltage ride-through parameters of the photovoltaic inverter are identified by improving the fish-eagle optimization algorithm; the improved fish-eagle optimization algorithm includes initial population and fitness evaluation, exploration stage, exploitation stage, and grabbing and adjustment stage to obtain optimized equivalent parameters; according to the actual station information, a photovoltaic grid connection simulation model is built, and hardware-in-the-loop simulation is performed on the actual photovoltaic controller hardware to realize integrated identification of control parameters and control strategies. Through this method, the electromechanical transient characteristics of the photovoltaic inverter can be combined, the optimization algorithm and data processing strategy are improved, the integrated identification of control parameters and strategies is realized, and the simulation precision and engineering applicability of the model are improved.

[0041] It can be understood that, with reference to Figure 2 , step S100 includes but is not limited to the following steps: Step S110, an equivalent circuit model of a photovoltaic cell is established.

[0042] It should be noted that the equivalent circuit model of the photovoltaic cell is used to describe the electrical characteristics of the photovoltaic cell itself, i.e., the output current and voltage relationship of the photovoltaic cell under given environmental conditions (such as light, temperature).

[0043] It should be noted that step S110 is a basic link of building a photovoltaic power generation system control architecture model (step S100), and its core role is to accurately describe the physical characteristics of the photovoltaic cell and provide reliable input-output relationship for the entire simulation model. By establishing an equivalent circuit model of the photovoltaic cell, the present application can simulate the electrical behavior of the photovoltaic cell under real environmental conditions (such as light intensity, temperature), i.e., the dynamic relationship between output current and voltage; the equivalent circuit model of the photovoltaic cell uses a simplified formula (based on easily obtained parameters such as short-circuit current and open-circuit voltage), which reduces the complexity of the model while maintaining the accuracy of the key electrical characteristics. This ensures that the simulation model from the source is consistent with the behavior of the real photovoltaic system, providing a high-precision benchmark platform for subsequent parameter identification. If the model is not accurate, parameter identification will be based on a weak foundation, resulting in a deviation in the results. Step S110 solves the problem of "weak identification foundation caused by inaccurate model" through physical modeling, and lays a solid foundation for MPPT optimization of step S200 and parameter identification of step S400.

[0044] In this step, the calculation formula of the equivalent circuit model of the photovoltaic cell is as follows: , wherein, represents the output current of the photovoltaic cell, represents the output voltage of the photovoltaic cell, represents the short-circuit current, represents the open-circuit voltage, and are intermediate coefficients, and the calculation formulas are as follows: , , wherein, represents the current at the maximum power point, represents the voltage at the maximum power point.

[0045] Specifically, the calculation formula of the photovoltaic cell equivalent circuit model can be expressed as: , wherein, represents the output current of the photovoltaic cell, represents the output voltage of the photovoltaic cell, represents the photo-generated current, represents the diode reverse saturation current, represents the electronic charge, represents the series resistance, represents the parallel resistance, represents the Boltzmann constant, represents the P-N junction ideal factor, represents the absolute temperature; the parameters in the expression , , , and I0are very sensitive to the environment and are difficult to set to an accurate value; considering the value is relatively large and the value is small, neglecting , represents the short-circuit current, let , the simplified formula is as follows: .

[0046] Referring to Figure 3 it can be understood that the variable step MPPT algorithm of the improved conductance increment method in step S200 includes but is not limited to the following steps: Step S210, the voltage difference and the current difference of the current control period and the last control period are calculated, and the conductance change rate is confirmed according to the voltage difference and the current difference.

[0047] It should be noted that step S210 monitors the output change of the photovoltaic cell in real time, and the conductance change rate is derived by calculating the voltage difference and the current difference of the adjacent control periods. This parameter is a key indicator for judging whether the system is running at the maximum power point, provides dynamic data input required for MPPT algorithm decision, ensures that the algorithm can respond to environmental changes (such as cloud cover), and provides a basis for step size adjustment.

[0048] Step S220, whether the absolute value of the voltage difference is 0 is judged, in the case of the absolute value of the voltage difference being 0, whether the absolute value of the current difference is 0 is judged; in the case of the absolute value of the voltage difference being greater than 0, whether the conductance change rate is equal to the conductance balance condition at the maximum power point is judged.

[0049] It should be noted that by judging whether the absolute value of the voltage difference is 0 and branching processing, the system running state is distinguished to decide the step size adjustment strategy: first, check whether the voltage change is zero: if the voltage change is zero, it indicates that the voltage is stable, and the system state needs to be further checked by checking the current change; if the voltage change is not zero, the conductance change rate is directly compared with the conductance balance condition at the maximum power point. Through step S220, unnecessary step size change under stable working conditions is avoided, and the maximum power point is quickly guided under dynamic working conditions, thereby improving the robustness of the algorithm.

[0050] Step S230, in the case of the absolute value of the current difference being 0, the current control iteration step size is equal to the last control iteration step size; in the case of the absolute value of the current difference being greater than 0, the current control iteration step size is adjusted according to the positive and negative of the current difference.

[0051] It should be noted that by processing the current difference and adjusting the step size, the step size is finely adjusted according to the current change when the voltage change is zero; if ΔI is zero, it indicates that the system is completely stable, and the step size is kept unchanged to avoid oscillation; if ΔI is not zero, the step size is adjusted according to the sign of ΔI (for example, ΔI> 0 increases the step size to speed up tracking, and ΔI< 0 reduces the step size to improve stability), which ensures that the algorithm can still efficiently track the maximum power point when the voltage is stable but the power changes, and reduces power fluctuations.

[0052] Step S240, in the case of the conductance change rate being equal to the conductance balance condition at the maximum power point, the current control iteration step size is equal to the last control iteration step size; in the case of the conductance change rate being greater than or less than the conductance balance condition at the maximum power point, the current control iteration step size is adjusted according to the size relationship between the conductance change rate and the conductance balance condition at the maximum power point.

[0053] It should be noted that the relationship between the conductance change rate and the equilibrium condition is processed and the step size is adjusted. When the voltage change is not zero, the step size is adjusted directly based on the deviation of the conductance change rate from the ideal value; if the conductance change rate is equal to the equilibrium condition, it means that the maximum power point has been approached, and the step size is maintained to stabilize the operation; if it deviates from the equilibrium condition, the step size is adjusted according to the deviation direction (for example, the conductance equilibrium condition at the maximum power point is increased when > -I / U, and vice versa), which realizes the variable step size control, makes the algorithm quickly approach when far away from the maximum power point, and finely adjusts when close to the maximum power point, balances the tracking speed and accuracy, and thus improves the overall efficiency of MPPT.

[0054] Specifically, Figure 6 To improve the variable step size MPPT algorithm flowchart of the conductance increment method, MPPT is one of the functions that photovoltaic must have, and the improved variable step size MPPT algorithm of the conductance increment method is used for optimization to ensure that the photovoltaic system always outputs power with the highest efficiency; the traditional variable step size method usually uses instead of the fixed step size parameter, and references a constant coefficient for system correction; the output characteristics of the photovoltaic cell can be represented as: , Among them, represents the conductance change rate, represents the voltage difference between the current control period and the last control period, represents the voltage difference between the current control period and the last control period, represents the voltage value of the current control period, represents the current value of the current control period, represents the current value of the last control period, represents the current value of the last control period, and on this basis, the value of is corrected to reduce the influence of current change on the output characteristics; let be the control period, the variable step size coefficient be , and the step size change be , then the step size is calculated as follows: , Among them, the variable step size coefficient , represents the current control iteration step size, represents the last control iteration step size; The specific calculation steps are as follows: (1) Calculate the difference between and , and The difference The incremental conductance method was used to calculate ;in, This refers to the output voltage of the photovoltaic cell in the previous control cycle. This refers to the output current of the photovoltaic cell in the previous control cycle. (2) Judgment Is it 0? If so, determine... Is it 0? If not, determine... Is it equal to ,in, This represents the conductance equilibrium condition at the maximum power point; (3) If If it equals 0, then ;like If it is not equal to 0, then check. Is it greater than 0? If it is greater than 0, then ;like If not greater than 0, then ; (4) Judgment Is it equal to If so, then If not, determine if dI / dU is greater than -I / U. Greater than If, then c; Not greater than ,but .

[0055] In one possible implementation, the low-pressure ride-through period is divided as follows: Pre-disturbance stage; The disturbance period includes a first transient interval and a first steady-state interval; The post-disturbance stage includes the second transient interval and the second steady-state interval.

[0056] Reference Figure 4 It is understood that the key points extracted and identified based on the response characteristics of the low-pressure ride-through condition in step S300 include, but are not limited to, the following steps: Step S310: Based on the active current, reactive current, and inverter AC side voltage, extract key points from the pre-disturbance stage, the period during the disturbance, and the post-disturbance stage.

[0057] It should be noted that step S310 selects the most representative dynamic response points from the measured data of the low voltage ride through (LVRT) working condition to construct a high-quality identification data set. The low voltage ride through working condition involves multiple stages before the disturbance, during the disturbance (including transient and steady state intervals), and after the disturbance (including transient and steady state intervals). Step S310 extracts key feature points such as voltage dip points, current peak values, and recovery points by analyzing the response characteristics of active current, reactive current, and inverter AC side voltage at these stages; these key points capture the influence of inverter control parameters in the dynamic process, avoiding noise and redundancy caused by full period data. In combination with the elimination of clipping conditions (abnormal data) in step S300, step S310 ensures that the identification data set focuses on effective dynamic response, improves the signal-to-noise ratio and representativeness of the data set, provides pure and efficient input data for parameter identification in step S400, and enhances the reliability of the identification results.

[0058] Specifically, to accurately identify the control parameters, key points required for identification need to be extracted according to the response characteristics of the low voltage ride through working condition. The low voltage ride through period is divided as shown in Figure 7 represents the active current, represents the reactive current, represents the inverter AC side voltage, A segment represents the pre-disturbance stage, B segment represents the disturbance period stage, C segment represents the post-disturbance stage, and B segment and C segment are not single intervals, but each has a subdivision: B segment is divided into transient interval and steady state interval , C segment is divided into transient interval and steady state interval .

[0059] Referring to Figure 5 , it can be understood that the improved fish eagle optimization algorithm in step S400 includes but is not limited to the following steps: Step S410, initial population and fitness evaluation: randomly generate an initial solution set in the solution space, and define a fitness function to evaluate the quality of the solution.

[0060] It should be noted that step S410 randomly generates a set of initial solutions (i.e. "population") in the parameter solution space, each solution representing a set of potential control parameter values; at the same time, a fitness function (such as an error function of simulation output and measured data) is defined to evaluate the quality of each solution; this step provides a starting point and direction for optimization, ensuring that the algorithm starts searching from a diversified initial solution, avoiding premature convergence, and providing a benchmark for comparison in subsequent stages.

[0061] Step S420, exploration stage: for each optimization agent individual, select the optimization agent individual position with better target function value as the candidate solution set, calculate the new position of the exploration stage and update. ​

[0062] It should be noted that step S420 involves a global exploration to discover potential optimal regions, simulating the exploration behavior of an osprey during predation. Each optimization agent (solution) moves towards the individual with a better objective function value ("candidate solution set"), calculates the new position, and through a random selection mechanism, the algorithm extensively searches within the solution space to avoid getting trapped in local minima. This step enhances the algorithm's global search capability, solves the bottleneck of traditional optimization algorithms being "slow in convergence and prone to getting trapped in local optima," and lays the foundation for finding the global optimal solution.

[0063] In one possible implementation, during the exploration phase, the expression for the candidate solution set corresponding to each optimization agent is as follows: , in, Indicates the first A set of candidate solutions for each osprey; Indicates the optimal solution; Indicates the first The objective function value corresponding to each osprey; Indicates the first The objective function value corresponding to each osprey; Indicates the first The group corresponding to each osprey; The formula for calculating new locations during the exploration phase is as follows: , in, Indicates a new location during the exploration phase. Indicates the first The solution of the first... The values ​​of the variables; Indicates the first The fish chosen by the osprey; Represents a random number in the interval [0,1]. Represents a set Random numbers in the array.

[0064] Step S430, Mining Stage: Simulate the behavior of an osprey carrying prey to a suitable location and calculate the candidate locations for the mining stage.

[0065] It should be noted that step S430 performs a fine-grained search in the local region and incorporates Gaussian mutation to avoid premature convergence. Simulating the behavior of an osprey carrying prey to a suitable location, fine-tuning is performed near the current optimal solution to calculate candidate locations for the mining stage. Crucially, this application introduces a Gaussian mutation operation, adding random perturbations to the original location using a Gaussian function to increase the randomness of the search. This allows the algorithm to escape local optima and continue exploring better solutions; this step balances local exploitation and global exploration, directly improving the accuracy of parameter identification and the robustness of the algorithm.

[0066] In this step, the exploitation stage also includes a Gaussian mutation operation, which introduces random perturbations through a Gaussian function to avoid premature convergence of the fish eagle optimization algorithm.

[0067] In one possible implementation, the calculation formula of the exploitation stage candidate position is as follows: wherein, denotes the exploitation stage candidate position, denotes the value of the jth variable of the ith solution, denotes the upper limit of the jth variable, denotes the lower limit of the jth variable; denotes the current iteration number of the algorithm, denotes a random number in the interval [0, 1]; The calculation formula of the exploitation stage candidate position update after the Gaussian mutation operation is as follows: wherein, denotes the exploitation stage candidate position after mutation, denotes a random number in the interval [0, 1], and takes the value 1. Step S440, the grasping and adjusting stage: if the new fitness value is better, the new position is retained, otherwise the position is regenerated with a probability until the new fitness value is better. It should be noted that step S440: dynamically evaluate and optimize the quality of the solution to ensure continuous progress of the algorithm; after each position update, compare the new fitness value with the old value: if the new value is better, accept the new position; otherwise, regenerate the position with a certain probability to avoid algorithm stagnation. This step forces the algorithm to continuously evolve towards a better direction by selectively retaining improved solutions until the termination condition (such as maximum iteration number or convergence threshold) is met. It ensures stable convergence of the identification process and finally outputs a high-precision global optimal parameter set.

[0068] Specifically, the improved fish eagle optimization algorithm is a mathematical modeling based on the behavior of fish eagles, which simulates the dynamic behavior of fish eagles to solve complex optimization problems, with the characteristics of easy parameter adjustment, fast convergence and strong adaptability.

[0069] ① Initial population and fitness evaluation:

[0070] ① Initial population and fitness evaluation:

[0071] ① Initial population and fitness evaluation: ​​​​Population initialization: Abstract the "optimization agent individual" as the "potential solution" of the optimization problem, and generate the initial solution set randomly in the solution space of the problem. The position corresponds to a set of parameters of the solution vector. Fitness function: According to the objective of the problem to be optimized, define the fitness function to evaluate the "good or bad degree" of the potential solution. The better the fitness value, the closer the individual is to the global optimal solution.

[0072] ②Exploration stage: For each fish eagle, the position of other fish eagles with better objective function values in the search space (i.e. individuals with better objective functions) is considered as underwater fish. The candidate solution set of each fish eagle is specified, which can be represented as: , The fish eagle will randomly identify and locate a fish from the fish school, and then launch an attack on it. To quantify this behavior, a mathematical model is constructed based on the dynamic characteristics of the fish eagle moving towards the target fish, and then the new position of the fish eagle is calculated. Define is a random number in the interval [0, 1]; represents a random number in the set has the following relationship: , The calculation relationship can be represented as: , If the objective function value of the new position is better than that of the previous position, replace the position of the fish eagle, which can be represented as: , where, is the new position of the i-th fish eagle in the first stage; is the objective function value of the i-th solution.

[0073] ③Exploitation stage: After the fish eagle kills a fish, it will carry it to a suitable location and eat it there. The second stage of population update is to simulate the natural behavior of the fish eagle to build a model. After modeling the behavior of "carrying fish to a suitable location", the position of the fish eagle in the search space will be slightly changed. On this basis, for each member in the population, a new random position needs to be calculated, and this position is defined as the "suitable eating position", which is the candidate position in the exploitation stage. It can be represented as: , If the objective function value is improved at this new position, replace the previous position of the corresponding fish eagle with this new position, which is represented as: , where, represents the i-th The new location for the osprey; Indicates the first The objective function value of each solution.

[0074] (4) Grabbing and Adjusting: After the osprey catches its prey, the algorithm gets stuck in a local optimum and readjusts its flight path to try again. After each update of the osprey's position, the fitness value of the new position is calculated. If the new fitness is better than the old fitness, the new position is retained. Otherwise, the position is regenerated according to a certain probability to avoid the algorithm stagnation. When the preset maximum number of iterations is reached, or the optimal fitness value no longer changes for several generations, the algorithm terminates and outputs the global optimum.

[0075] However, in the Osprey optimization algorithm, the mining phase may suffer from the problem of "over-focusing on searching near the current optimal solution," directly leading to premature convergence and getting trapped in local optima, unable to continue exploring better solutions at the global level. Gaussian mutation provides an effective solution to this deficiency. As a random search strategy, it significantly improves the randomness and diversity of the search process by introducing a random perturbation term following a Gaussian distribution onto the original individual values. With this characteristic, Gaussian mutation can help the algorithm escape the limitations of the current local optimum during the mining phase, preventing premature convergence and ultimately creating conditions for finding a better global solution. The Gaussian function used in this process is shown below: , in, A random number within the interval [0,1]. The value is 1, and the position update formula after mutation is shown below: , in, This represents the formula for updating the position after mutation.

[0076] For example, to verify the effectiveness of the proposed method, this application constructs a photovoltaic grid access simulation model based on information from an actual power station in Ningxia, and uses the RT-LAB hardware-in-the-loop testing platform to obtain the measured dataset of the photovoltaic controller; the convergence speed of the classic Osprey optimization algorithm and the improved Osprey optimization algorithm proposed in this application are compared, and the results are as follows: Figure 8 and Figure 9As shown, the improved Osprey optimization algorithm significantly outperforms the classic Osprey optimization algorithm in terms of both the search value and the search rate. The key to its performance improvement lies in the embedded weighting factor and Gaussian mutation mechanism. For scenarios with multiple solutions in a multidimensional test function, the weighting factor helps the algorithm overcome the limitations of local optima, while the integration of Gaussian mutation further significantly improves the algorithm's global search performance. This demonstrates that the improved Osprey optimization algorithm used in this application can more effectively improve the accuracy of model parameter identification. Applying the improved Osprey optimization algorithm to active current identification is as follows... Figure 10 As shown, the improved Osprey optimization algorithm matches the measured curve better and has higher accuracy.

[0077] It should be noted that the photovoltaic power generation system control architecture model refers to a simulation model used to simulate the complete behavior of a photovoltaic grid-connected system, with the aim of achieving integrated identification of control strategies and parameters. This model specifically includes the following components: Photovoltaic cell equivalent circuit model: calculating output current and voltage based on the physical parameters of the photovoltaic cells (such as short-circuit current and open-circuit voltage); MPPT control module: using an improved incremental conductance method with a variable step size MPPT algorithm to track the maximum power point of the photovoltaic array, ensuring efficient power output; Inverter module: converting DC power to AC power for grid connection, and involving low-voltage ride-through control strategies; Low-voltage ride-through condition processing module: extracting key points based on the response characteristics of low-voltage ride-through conditions, establishing an identification dataset, and eliminating limiting conditions; Parameter identification module: using an improved Osprey optimization algorithm to identify control parameters (such as MPPT parameters and low-voltage ride-through parameters); Hardware-in-the-loop simulation interface: building a simulation model based on actual site information and performing hardware-in-the-loop testing with the actual photovoltaic controller hardware. Overall, the photovoltaic power generation system control architecture model is an integrated simulation platform that covers all aspects from photovoltaic power generation to grid connection control, and is used to verify the accuracy of control strategies and parameter identification.

[0078] Optionally, such as Figure 11 As shown, the second aspect of this application also provides an electronic device 10, including a processor 11 and a memory 12. The memory 12 stores a program or instructions that can run on the processor 11. When the program or instructions are executed by the processor 11, they implement the various processes of the first aspect of the inverter parameter identification method based on the improved Osprey algorithm, and can achieve the same technical effect. To avoid repetition, they will not be described again here.

[0079] It should be noted that the devices in the embodiments of this application include: servers, terminals, or other devices besides terminals.

[0080] The above device structure does not constitute a limitation on the device, and the device can include more or fewer components than illustrated, or combine certain components, or different component arrangements, for example, the input unit can include a Graphics Processing Unit (GPU) and a microphone, and the display unit can be configured in the form of a liquid crystal display, an organic light-emitting diode, etc. The user input unit includes at least one of a touch panel and other input devices. The touch panel is also referred to as a touch screen. Other input devices can include, but are not limited to, a physical keyboard, function keys (such as volume control buttons, switch buttons, etc.), a trackball, a mouse, a joystick, and the like, which will not be described here.

[0081] The memory can be used to store software programs and various data. The memory can mainly include a first storage area storing programs or instructions and a second storage area storing data, wherein the first storage area can store an operating system, application programs or instructions required by at least one function (such as a sound playing function, an image playing function, etc.), and the like. In addition, the memory can include a volatile memory or a non-volatile memory, or the memory can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically EPROM (EEPROM), or a flash memory. The volatile memory can be a Random Access Memory (RAM), a Static RAM (SRAM), a Dynamic RAM (DRAM), a Synchronous DRAM (SDRAM), a Double Data Rate SDRAM (DDR SDRAM), an Enhanced SDRAM (ESDRAM), a Synchlink DRAM (SLDRAM), and a Direct Rambus RAM (DRRAM).

[0082] The processor can include one or more processing units; optionally, the processor integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to operating systems, user interfaces, and application programs, and the modem processor mainly processes wireless communication signals, such as a baseband processor. It can be understood that the above-mentioned modem processor can also not be integrated into the processor.

[0083] The embodiment of the present application also provides a readable storage medium, and the readable storage medium stores programs or instructions, the programs or instructions are executed by a processor to realize each process of the above-mentioned first aspect based on the improved fish eagle algorithm parameter identification method of the inverter embodiment, and the same technical effects can be achieved, to avoid repetition, which will not be described here. The processor is the processor in the device in the above-mentioned embodiment. The readable storage medium includes a computer readable storage medium, such as a ROM, a RAM, a magnetic disk or an optical disk. It should be noted that in this paper, the term "include", "contain" or any other variant is intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device. Without more limitations, the element defined by the statement "including a" does not exclude the presence of another identical element in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the method and device in the embodiment of the present application is not limited to the order of the functions shown or discussed, and can also include the functions performed in a substantially simultaneous manner or in the opposite order, for example, the described method can be performed in a different order than described. In addition, the features described with reference to some examples can be combined in other examples.

[0084] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment method can be realized by means of software and necessary general hardware platform, of course, it can also be realized by hardware, but in many cases, the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a plurality of instructions for making a terminal (which can be a mobile phone, computer, server, or network equipment, etc.) execute the method in each embodiment of the present application.

[0085] In the description of the embodiments of the present application, the terms "first", "second", "third", "fourth" are used only to describe purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second", "third", "fourth" can be explicitly or implicitly included one or more of the features. In the description of the present application, unless otherwise stated, the meaning of "a plurality of" is two or more.

[0086] In the description of the embodiments of the present application, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0087] Although the embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, replacements and variations can be made to the embodiments without departing from the principles and spirits of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for inverter parameter identification based on an improved Osprey algorithm, characterized in that, include: Establish a control architecture model for a photovoltaic power generation system; The output of the photovoltaic array is controlled by maximum power point tracking technology, and the variable step size MPPT algorithm with improved conductivity increment method is used for optimization to ensure that the photovoltaic system can output electrical energy with the highest efficiency. Based on the response characteristics of low-voltage ride-through conditions, the key points required for identification are extracted, and an identification dataset is established using the key points of the measured conditions. The amplitude-limiting conditions in the identification dataset are then removed. The control parameters and low-voltage ride-through parameters of the photovoltaic inverter are identified by an improved Osprey optimization algorithm. The improved Osprey optimization algorithm includes an initial population and fitness assessment, an exploration stage, a mining stage, and a grabbing and adjustment stage to obtain optimized equivalent parameters. A photovoltaic grid access simulation model was built based on actual site information, and hardware-in-the-loop simulation was performed on the actual photovoltaic controller hardware to achieve integrated identification of control parameters and control strategies.

2. The inverter parameter identification method based on the improved Osprey algorithm according to claim 1, characterized in that, The photovoltaic power generation system control architecture model includes: An equivalent circuit model of a photovoltaic cell is established, and the calculation formula for the equivalent circuit model is as follows: , in, Indicates the output current of the photovoltaic cell. Indicates the output voltage of the photovoltaic cell. Indicates short-circuit current. Indicates open-circuit voltage. and All are intermediate coefficients. and The calculation formulas are as follows: , , in, This represents the current at the point of maximum power. This represents the voltage at the point of maximum power.

3. The inverter parameter identification method based on the improved Osprey algorithm according to claim 1, characterized in that: The improved incremental conductance method variable step size MPPT algorithm includes: Calculate the voltage difference and current difference between the current control cycle and the previous control cycle, and determine the rate of change of conductivity based on the voltage difference and the current difference; Determine whether the absolute value of the voltage difference is 0. If the absolute value of the voltage difference is 0, determine whether the absolute value of the current difference is 0. If the absolute value of the voltage difference is greater than 0, determine whether the rate of change of conductivity is equal to the conductivity equilibrium condition at the maximum power point. When the absolute value of the current difference is 0, the current control iteration step size is equal to the previous control iteration step size; when the absolute value of the current difference is greater than 0, the current control iteration step size is adjusted according to the sign of the current difference. When the rate of change of conductivity is equal to the conductivity balance condition at the maximum power point, the current control iteration step size is equal to the previous control iteration step size; when the rate of change of conductivity is greater than or less than the conductivity balance condition at the maximum power point, the current control iteration step size is adjusted according to the relationship between the rate of change of conductivity and the conductivity balance condition at the maximum power point.

4. The inverter parameter identification method based on the improved Osprey algorithm according to claim 1, characterized in that: The time periods for the low-pressure ride-through condition are divided as follows: Pre-disturbance stage; The disturbance period includes a first transient interval and a first steady-state interval; The post-disturbance stage includes a second transient interval and a second steady-state interval.

5. The inverter parameter identification method based on the improved Osprey algorithm according to claim 4, characterized in that: The extraction and identification of key points based on the response characteristics of low-pressure ride-through conditions includes: Based on active current, reactive current, and inverter AC side voltage, key points are extracted from the pre-disturbance stage, the period during the disturbance, and the post-disturbance stage.

6. The inverter parameter identification method based on the improved Osprey algorithm according to claim 1, characterized in that: The improved Osprey optimization algorithm includes: Initial Population and Fitness Evaluation: An initial set of solutions is randomly generated within the solution space, and a fitness function is defined to evaluate the quality of the solutions; Exploration phase: For each optimized agent, select the position of the optimized agent with the better objective function value as the candidate solution set, calculate the new position in the exploration phase and update it; Mining phase: Simulate the behavior of an osprey carrying prey to a suitable location to calculate candidate locations for the mining phase; Grab and adjust phase: If the new fitness value is better, the new position is retained; otherwise, the position is regenerated with probability until the new fitness value is better. The mining stage also includes a Gaussian mutation operation, which introduces random perturbations through a Gaussian function to prevent premature convergence of the Osprey optimization algorithm.

7. The inverter parameter identification method based on the improved Osprey algorithm according to claim 6, characterized in that, During the exploration phase, the expression for the candidate solution set corresponding to each optimized agent individual is as follows: ， in, Indicates the first A set of candidate solutions for each osprey; Indicates the optimal solution; Indicates the first The objective function value corresponding to each osprey; Indicates the first The objective function value corresponding to each osprey; Indicates the first The group corresponding to each osprey; The formula for calculating the new location during the exploration phase is as follows: ， in, This indicates the new location in the exploration phase. Indicates the first The solution of the first... The values ​​of the variables; Indicates the first The fish chosen by the osprey; Represents a random number in the interval [0,1]. Represents a set Random numbers in the array.

8. The inverter parameter identification method based on the improved Osprey algorithm according to claim 6, characterized in that, The formula for calculating the candidate locations during the mining stage is as follows: , in, This indicates the candidate location for the mining stage. Indicates the first The solution of the first... The values ​​of the variables, Indicates the first The upper bound of each variable. Indicates the first The lower bound of each variable; This indicates the current iteration number of the algorithm. Represents a random number in the interval [0,1]. The calculation formula for updating candidate positions during the mining stage after the Gaussian mutation operation is as follows: ， in, This indicates the candidate position for the mining stage after mutation. Represents a random number within the interval [0,1]. The value is 1.

9. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements: the inverter parameter identification method based on the improved Osprey algorithm as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which are used for: the inverter parameter identification method based on the improved Osprey algorithm as described in any one of claims 1 to 8.

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