Photovoltaic inverter voltage ride through parameter identification method, system, device and medium

Abstract

The invention discloses a photovoltaic inverter voltage ride-through parameter identification method, system and device and a medium, and the method comprises the steps: obtaining multivariate system data, and building a mathematical model of a photovoltaic inverter controller; the mathematical model is converted into a difference equation through transformation decoupling operation; based on the converted difference equation, acquiring input and output quantities of an inverter controller in real time, and judging whether voltage ride-through occurs or not and the voltage ride-through type to obtain a judgment result; based on a judgment result, optimizing the controller parameters by adopting an optimization algorithm to obtain an optimal controller parameter value; based on the optimal controller parameter value, when the to-be-identified controller parameter reaches the expected precision, the estimated value of the controller parameter is output, different voltage ride-through types can be identified online, the fault control parameter can be identified synchronously, and step-by-step identification of each parameter is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic inverter voltage ride-through, and in particular to a photovoltaic inverter voltage ride-through parameter identification method, system, device and medium. Background Art

[0002] In recent years, the renewable energy industry has experienced rapid growth. Accurate simulation models are crucial for the accuracy of power system simulation analysis. Currently, models from different renewable energy manufacturers differ, and all are packaged models. Even for the same model, control response characteristics vary under different operating conditions and fault conditions, making it difficult to understand their actual grid-related characteristics. Furthermore, simulation models for renewable energy units require numerous parameters, which are difficult to obtain. This reduces the accuracy of grid stability simulations, directly impacting grid operation and planning decisions. Furthermore, the control strategies and parameters of renewable energy units and their control systems impact the safety and stability of the grid. Currently, most controllers are designed under fixed operating conditions, and their control strategies and parameters primarily consider the grid-connected performance of the equipment, while neglecting the safety and stability requirements of the grid. To address the lack of clarity regarding the impact of renewable energy model parameters on grid stability, it is necessary to study the dominant parameters affecting renewable energy on grid stability, as well as methods for field testing and identification of these parameters, to improve grid stability.

[0003] Currently, relatively little research has been conducted on identifying the key parameters of photovoltaic inverter controllers operating under fault conditions during voltage ride-through. Furthermore, it is difficult to simultaneously identify both high-voltage ride-through and low-voltage ride-through, as well as the relevant key parameters associated with fault ride-through. Furthermore, intelligent algorithms, such as particle swarm optimization (PSO) and genetic optimization (GA), are employed to identify system parameters. PSO has limitations, such as a limited search range, low search accuracy, and a tendency to fall into local extremes. The GA optimization mechanism, based on evolutionary theory, is complex to program, resulting in a slow search speed and a degree of reliance on the selection of initial data. Furthermore, actual voltage ride-through conditions are complex and varied, and control methods vary across different voltage ride-through scenarios. Most identification methods target specific problems, modeling them within specific scenarios and simplifying or abstracting them. This facilitates analysis but also reduces model accuracy. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a photovoltaic inverter voltage ride-through parameter identification method, system, device and medium to solve the problem that it is difficult to simultaneously identify high voltage ride-through and low voltage ride-through and simultaneously identify relevant important parameters under fault ride-through.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for identifying parameters of a photovoltaic inverter voltage ride-through, comprising:

[0008] Obtain multivariate system data and establish a mathematical model for the photovoltaic inverter controller;

[0009] Convert the mathematical model into a differential equation through transformation and decoupling operations;

[0010] Based on the transformed differential equation, the input and output quantities of the inverter controller are obtained in real time, and the occurrence of voltage ride-through and the type of voltage ride-through are determined to obtain the judgment result;

[0011] Based on the judgment results, the optimization algorithm is used to optimize the controller parameters to obtain the optimal controller parameter values;

[0012] Based on the optimal controller parameter values, when the controller parameters to be identified reach the expected accuracy, the controller parameter estimation values ​​are output.

[0013] As a preferred solution of the photovoltaic inverter voltage ride-through parameter identification method described in the present invention, wherein:

[0014] The mathematical model of the photovoltaic inverter controller is established, comprising the following steps:

[0015] Adopt voltage source inverter based on sinusoidal pulse width modulation;

[0016] A dual closed-loop proportional-integral regulator with active and reactive power decoupling is used, including a voltage outer loop and a current inner loop;

[0017] Establish a mathematical model of the photovoltaic inverter controller;

[0018] Design control strategies for voltage ride-through conditions;

[0019] Determine the controller fault control parameters that need to be identified.

[0020] As a preferred solution of the photovoltaic inverter voltage ride-through parameter identification method described in the present invention, wherein:

[0021] The control strategy for designing voltage ride-through includes the following steps:

[0022] When voltage ride-through is detected, the inner current loop is directly controlled;

[0023] Adjust reactive current output by judging the voltage ride-through type;

[0024] The output of active current is controlled jointly according to the reactive current and the active current output by the dual closed-loop proportional-integral regulator;

[0025] Control the size of reactive power output through active and reactive current;

[0026] By controlling the flow of reactive power, the inverter voltage output is maintained stable.

[0027] The beneficial effect of this preferred technical solution is that by accurately controlling the current inner loop and active and reactive current output during voltage ride-through, the voltage stability of the inverter is effectively maintained, and the robustness and power quality of the system are improved.

[0028] As a preferred solution of the photovoltaic inverter voltage ride-through parameter identification method described in the present invention, wherein:

[0029] The step of determining the controller fault control parameters that need to be identified comprises the following steps:

[0030] Identify different important parameters according to different voltage ride-through types;

[0031] If the voltage ride-through type is determined to be high voltage ride-through, adopting a first control strategy and identifying first controller parameters;

[0032] If the voltage ride-through type is determined to be high voltage ride-through, a second control strategy is adopted and second controller parameters are identified.

[0033] As a preferred solution of the photovoltaic inverter voltage ride-through parameter identification method described in the present invention, wherein:

[0034] The optimization algorithm is used to optimize the selected controller parameters, comprising the following steps:

[0035] Set initial parameters, including initializing particle swarm size, particle position and velocity, and determining the maximum number of iterations;

[0036] Calculate the reference voltage command value of the inverter under voltage ride-through conditions;

[0037] The calculated reference voltage command value is used as input to calculate the fitness value of each particle;

[0038] Evaluate the current fitness value of each particle according to the fitness function;

[0039] Evaluate the optimal fitness value of the group according to the fitness function;

[0040] Update the particle's velocity and position;

[0041] If the particle position is not updated, return to calculate the fitness value until the termination condition is met;

[0042] When the termination condition is met, the controller parameters corresponding to the global optimal position are output.

[0043] The beneficial effect of this preferred technical solution is that, by iteratively updating the controller parameters through the optimization algorithm, accurate tracking of the inverter reference voltage command value in the case of voltage ride-through is achieved, effectively improving the dynamic response and stability of the system.

[0044] As a preferred solution of the photovoltaic inverter voltage ride-through parameter identification method described in the present invention, wherein:

[0045] The calculation of the fitness value of each particle includes the following steps:

[0046] Calculate the transfer function based on the controller parameters of the particle's current position;

[0047] Use the transfer function to calculate the current fitness value of each particle;

[0048] Minimizing a predetermined performance indicator is used as the fitness function.

[0049] As a preferred solution of the photovoltaic inverter voltage ride-through parameter identification method described in the present invention, wherein:

[0050] The reference voltage command value of the inverter under voltage ride-through is expressed as:

[0051]

[0052] in, and Represent the voltage reference command values ​​of the d-axis and q-axis, G1(s), G 11 (s), G 12 (s), G2(s), G 21 (s), G 22 (s) represents the system transfer function, and U dc (s) represent the reference value and actual value of DC side voltage respectively, Indicates the reference value of the q-axis current, i max Indicates the maximum current value allowed. and Q r (s) represent the reference value and actual value of reactive power, respectively, k qv Represents the reactive current support coefficient, i N It represents the effective value of the rated current at the grid connection point, and U represents the grid voltage.

[0053] In a second aspect, the present invention provides a photovoltaic inverter voltage ride-through parameter identification system, comprising:

[0054] An acquisition module is used to obtain multivariate system data and establish a mathematical model of the photovoltaic inverter controller;

[0055] The difference equation conversion module is used to convert the mathematical model into a difference equation through transformation and decoupling operations;

[0056] The detection and type judgment module is used to obtain the input and output quantities of the inverter controller in real time based on the converted differential equation, and to determine whether voltage ride-through occurs and the type of voltage ride-through, and obtain the judgment result;

[0057] The controller parameter optimization module is used to optimize the controller parameters based on the judgment results using the optimization algorithm to obtain the optimal controller parameter values;

[0058] The parameter identification module is used to output controller parameter estimation values ​​based on the optimal controller parameter values ​​when the controller parameters to be identified reach the expected accuracy.

[0059] In a third aspect, the present invention provides an electronic device, comprising:

[0060] Memory, used to store programs;

[0061] A processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the photovoltaic inverter voltage ride-through parameter identification method.

[0062] In a fourth aspect, the present invention provides a computer-readable storage medium, comprising: when the program is executed by a processor, the steps of implementing the photovoltaic inverter voltage ride-through parameter identification method are implemented.

[0063] The beneficial effects of the present invention are as follows: the present invention establishes a detailed mathematical model of the photovoltaic power generation system through a global parameter identification method under the voltage ride-through environment of the photovoltaic power generation system, taking into account the sequential control characteristics during low voltage ride-through (LVRT) and high voltage ride-through (HVRT), and proposes a multi-operating condition-step identification strategy to identify the global control parameters; at the same time, an improved algorithm is used to find the optimal solution by minimizing the residual modulus, which greatly reduces the randomness of the identification controller parameters, improves the accuracy of the identification results, realizes online identification and synchronous identification of all parameters, avoids step-by-step identification of each parameter, and further adds a forgetting factor on the basis of the particle swarm algorithm to reduce the weight of old data and enhance the role of new data, so that the identification method has correction capability, thereby improving the accuracy of the identification parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0065] Figure 1 A schematic diagram of the basic flow of a method for identifying voltage ride-through parameters of a photovoltaic inverter provided by one embodiment of the present invention;

[0066] Figure 2 A topology diagram of a photovoltaic inverter controller for a photovoltaic inverter voltage ride-through parameter identification method provided by one embodiment of the present invention;

[0067] Figure 3 A photovoltaic inverter according to an embodiment of the present invention provides a method for identifying parameters of a photovoltaic inverter voltage ride-through, and a photovoltaic grid-connected simulation diagram is constructed based on PSCAD5.0;

[0068] Figure 4 A control principle diagram of a photovoltaic inverter under high voltage ride-through conditions, according to a method for identifying photovoltaic inverter voltage ride-through parameters provided by one embodiment of the present invention;

[0069] Figure 5 A control principle diagram of a photovoltaic inverter under low voltage ride-through conditions, according to a photovoltaic inverter voltage ride-through parameter identification method provided by one embodiment of the present invention;

[0070] Figure 6 A schematic diagram of a complete flow chart of a photovoltaic inverter voltage ride-through parameter identification method provided by one embodiment of the present invention;

[0071] Figure 7 A PSCAD steady-state simulation result diagram of a photovoltaic inverter voltage ride-through parameter identification method provided by one embodiment of the present invention;

[0072] Figure 8 An inverter control strategy diagram of a photovoltaic inverter voltage ride-through parameter identification method provided by one embodiment of the present invention;

[0073] Figure 9 A PSCAD high voltage ride-through active and reactive power simulation result diagram of a photovoltaic inverter voltage ride-through parameter identification method provided by one embodiment of the present invention;

[0074] Figure 10 A PSCAD low voltage ride-through active and reactive power simulation result diagram of a photovoltaic inverter voltage ride-through parameter identification method provided by one embodiment of the present invention;

[0075] Figure 11 This is a diagram showing the identification results of kqv, iN, kid, and kiq under a low voltage ride-through environment for a photovoltaic inverter voltage ride-through parameter identification method provided by one embodiment of the present invention;

[0076] Figure 12 A test curve diagram of inverter control parameter identification values ​​under a low voltage ride-through environment of a photovoltaic inverter voltage ride-through parameter identification method provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0077] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0078] Example 1

[0079] Reference Figure 1 and Figure 6 , as one embodiment of the present invention, provides a method for identifying voltage ride-through parameters of a photovoltaic inverter, comprising:

[0080] S100: Acquire multivariate system data and establish a mathematical model of the photovoltaic inverter controller;

[0081] S200: converting the mathematical model into a differential equation through transformation decoupling operation;

[0082] S300: Based on the converted differential equation, the input and output quantities of the inverter controller are obtained in real time, and whether voltage ride-through occurs and the type of voltage ride-through are determined to obtain a determination result.

[0083] S400: Based on the judgment result, the controller parameters are optimized using an optimization algorithm to obtain optimal controller parameter values;

[0084] S500: Based on the optimal controller parameter value, when the controller parameter to be identified reaches the expected accuracy, output the controller parameter estimation value.

[0085] It should be noted that the new energy model parameters are difficult to obtain, the existing identification methods lack adaptability and accuracy under different voltage ride-through conditions, the controller design lacks comprehensive consideration of grid safety and stability, and the voltage ride-through conditions in actual operation are complex and changeable, resulting in a gap between model simplification and practical application; therefore, it is very important to consider the global parameter identification under the sequential control characteristics during low voltage ride-through and high voltage ride-through.

[0086] Therefore, in order to solve the above-mentioned problem that it is difficult to simultaneously identify high voltage ride through and low voltage ride through and simultaneously identify the relevant important parameters under fault ride through, through steps S100-S500, such as Figure 6 As shown in the figure, the entire process from acquiring system data to outputting controller parameter estimates is described in detail, taking into account the sequential control characteristics during low voltage ride-through and high voltage ride-through, establishing a detailed mathematical model of the photovoltaic power generation system, and proposing a multi-operating condition-step identification strategy to identify the global control parameters, realize online identification and synchronous identification of all parameters.

[0087] Example 2, reference Figure 2-Figure 5 , which is an embodiment of the present invention, provides a photovoltaic inverter voltage ride-through parameter identification method based on the previous embodiment, including:

[0088] In the embodiment of the present application, establishing a mathematical model of the photovoltaic inverter controller in step S100 includes the following steps:

[0089] Adopt voltage source inverter based on sinusoidal pulse width modulation;

[0090] A dual closed-loop proportional-integral regulator with active and reactive power decoupling is used, including a voltage outer loop and a current inner loop;

[0091] Establish a mathematical model of the photovoltaic inverter controller;

[0092] Design control strategies for voltage ride-through conditions;

[0093] Determine the controller fault control parameters that need to be identified.

[0094] In the embodiment of the present application, a control strategy is designed for voltage ride-through. When voltage ride-through occurs, the controller's control structure adopts a control method of directly controlling the inner loop by throwing out the outer loop. The specific control method is as follows:

[0095] The reactive current output of the inverter controller is directly controlled by judging the voltage ride-through level. The active current output is jointly controlled according to the reactive current and the active current output by the dual closed-loop PI regulator. The reactive power output is controlled by controlling the active and reactive currents, and the voltage stability of the inverter output is maintained by the flow of reactive power.

[0096] It should be noted that the detailed structure of the photovoltaic inverter, such as Figure 2 As shown in FIG, the main circuit of a photovoltaic inverter is generally a three-phase bridge structure, the control structure adopts a cascaded voltage outer loop and a current inner loop, and the controller adopts a PI regulator.

[0097] In an embodiment of the present application, determining the controller fault control parameters that need to be identified includes the following steps:

[0098] Identify different important parameters according to different voltage ride-through types;

[0099] If the voltage ride-through type is determined to be low voltage ride-through, a first control strategy is adopted and first controller parameters are identified;

[0100] If the voltage ride-through type is determined to be high voltage ride-through, a second control strategy is adopted and second controller parameters are identified.

[0101] In the embodiment of the present application, the first control strategy includes temporarily getting rid of the outer loop control and directly controlling the inner loop (current control loop), which helps to increase the grid voltage and maintain system stability by increasing reactive current output and limiting active power output; the first controller parameters include the converter reactive current support coefficient k qv , the effective value of the rated current at the grid connection point i N , Active power recovery speed k under low voltage ride-through control id , reactive power recovery speed k iq The second control strategy includes temporarily getting rid of the outer loop control and directly controlling the inner loop (current control loop) to suppress the grid voltage rise and maintain system stability by reducing or absorbing reactive current and limiting active power output. The second controller parameters include the maximum current value i ma , reactive current support coefficient K qv , active power recovery speed k under HVRT id , reactive power recovery speed k iq ;

[0102] In the embodiment of the present application, the mathematical model of the photovoltaic inverter controller is established in step S100, and further includes, when in use, deriving the transfer function G(s) of the controller based on the park transformation according to the established mathematical model of the controller, the PV grid-connected inverter of the photovoltaic grid-connected model adopts a vector control strategy based on grid voltage orientation, and adopts a voltage source inverter based on sinusoidal pulse width modulation (SPWM), whose control goal is to maintain the stability of the DC side voltage of the inverter, and selects a dual-loop control method with PQ decoupling, such as Figure 3 As shown in the figure, a simulation diagram of a photovoltaic grid-connected system is shown, which includes a photovoltaic array (PV Array), an inverter (Inverter) and related electrical connections and parameter settings. Figure 4 As shown in the figure, the control principle diagram of the photovoltaic inverter under high voltage ride-through conditions includes multiple mathematical operations and logic judgment modules, such as comparators, multipliers, square roots, etc. Figure 5As shown in the figure, the control principle diagram of the photovoltaic inverter under low voltage ride-through conditions. The outer loop control adopts constant DC voltage control, and generates the reference current value of the inner loop current control through the proportional-integral (PI) link. The inner loop uses the current as the reference, and generates the voltage command value of the controller, that is, the inverter output voltage, through the PI link, feedforward decoupling link, etc. In order to achieve the operation of the PV system under unity power factor, the q-axis reference current value is set to 0. The detailed mathematical model of the inverter in the dq coordinate system is described, including the voltage control equations of the d-axis and q-axis and the inner and outer loop control logic. This is a specific implementation of the above control strategy, which clarifies how to generate the voltage command value through the PI regulator. In the dq coordinate system, the mathematical model of the inverter is expressed as:

[0103]

[0104] In the embodiment of the present application, during the voltage ride-through process, since the fault duration is relatively short, the photovoltaic system can maintain connection with the grid during the fault. However, in order to maintain the stability of the grid in steady state, the inverter needs to directly control the output of the three-phase line within the range permitted by national standards. Therefore, when a voltage ride-through fault occurs in the grid, different voltage ride-through conditions are controlled. The inverter control mathematical model under low voltage ride-through is expressed as follows:

[0105]

[0106] In the embodiment of the present application, the inverter control mathematical model under high voltage ride-through is expressed as:

[0107]

[0108] In the embodiments of this application, Figure 6 The following figure shows a simulation diagram of a photovoltaic inverter. This diagram illustrates the behavior of the photovoltaic inverter under different operating conditions, especially during voltage ride-through. This is crucial for understanding the practical effects of controller parameter identification.

[0109] In the embodiment of the present application, converting the mathematical model into a differential equation through a transformation decoupling operation in step S200 includes converting the mathematical model into a differential equation through a Park transformation;

[0110] It should be noted that by using Park transformation to transform the decoupling operation of the mathematical model into a differential equation, the second-order array and iterative formula of the improved particle swarm algorithm with genetic factors are obtained as the identification system.

[0111] In the embodiment of the present application, adding a forgetting factor on the basis of the particle swarm algorithm can reduce the weight of old data, enhance the role of new data, and enable the identification method to have correction capabilities, thereby improving the accuracy of the identification parameters.

[0112] In the embodiment of the present application, the optimization algorithm in step S300 is a particle swarm optimization algorithm with genetic factors;

[0113] In an optional embodiment, the optimization algorithm in step S300 may also be a differential evolution algorithm;

[0114] In an optional embodiment, the optimization algorithm in step S300 may also be a simulated annealing algorithm;

[0115] In an optional embodiment, the differential evolution algorithm includes randomly generating a certain number of individuals, for each target vector, selecting three different individuals from the current population according to a specific strategy, generating a mutation vector based on the differences between them, combining the mutation vector with the target vector to generate a test vector, comparing the objective function value of the test vector with the original target vector, selecting the individuals with better performance as part of the next generation, repeating the mutation, crossover and selection operations until a preset termination condition is met, and finally outputting the controller parameters corresponding to the global optimal position;

[0116] In an optional embodiment, the simulated annealing algorithm includes setting an initial temperature, a cooling rate, and an initial solution, evaluating the quality of the initial solution based on a selected fitness function, generating a new candidate solution based on the current solution, and calculating the fitness value of the solution. If the new solution is worse than the current solution, the new solution is directly accepted. If the new solution is not as good as the current solution, the new solution is accepted with a certain probability according to the Metropolis criterion, and the temperature is lowered according to a predetermined cooling plan. As the temperature drops, the probability of accepting the worse solution gradually decreases. It is determined whether the termination condition is met (such as the temperature drops low enough or the maximum number of iterations is reached). If not, it returns to continue the iteration. When the termination condition is met, the current best solution is output as the controller parameter corresponding to the global optimal position.

[0117] It should be noted that while differential evolution may offer superior global search capabilities when dealing with complex, high-dimensional problems, the particle swarm optimization (PSO) algorithm can converge to a better local solution more quickly in certain situations. This makes it particularly suitable for applications where a satisfactory solution, rather than an absolute optimal solution, is sought. While the simulated annealing algorithm excels at avoiding local optima, it is inherently a single-point search process and therefore less efficient than the particle swarm optimization (PSO) algorithm in large-scale parallel computing environments. Furthermore, the PSO algorithm inherently supports parallelization, making it advantageous in processing large datasets or in applications requiring real-time response.

[0118] In the embodiment of the present application, the optimization algorithm is used to optimize the selected controller parameters in step S300, including the following steps:

[0119] Set initial parameters, including initializing particle swarm size, particle position and velocity, and determining the maximum number of iterations;

[0120] Calculate the reference voltage command value of the inverter under voltage ride-through conditions;

[0121] The calculated reference voltage command value is used as input to calculate the fitness value of each particle;

[0122] Evaluate the current fitness value of each particle according to the fitness function, and select the optimal fitness value and its position as the optimal position after the individual update through the historical best fitness value and the previous identification result;

[0123] Evaluate the current fitness function value of each particle and the optimal fitness value of the group according to the fitness function, and select the optimal fitness value and its position as the optimal position after the group is updated;

[0124] Update the particle's velocity and position;

[0125] If the particle position is not updated, return to calculate the fitness value until the termination condition is met;

[0126] When the termination condition is met, the controller parameters corresponding to the global optimal position are output.

[0127] In the embodiment of the present application, calculating the fitness value of each particle includes the following steps:

[0128] Calculate the transfer function based on the controller parameters of the particle's current position;

[0129] Use the transfer function to calculate the current fitness value of each particle;

[0130] Minimizing a predetermined performance indicator is used as the fitness function.

[0131] In the embodiment of the present application, the minimum residual modulus is used as the optimal fitness value estimation of the particle swarm algorithm with genetic factors;

[0132] In an optional embodiment, the predetermined performance indicator in step S300 may also be a mean square error, which quantifies the average square difference between the model prediction value and the actual observation value;

[0133] In an optional embodiment, the predetermined performance indicator in step S300 may also be a peak signal-to-noise ratio, which focuses more on signal quality, especially when the goal is to improve the clarity of the output signal and reduce the influence of noise;

[0134] It should be noted that while mean square error (MSE) can also effectively measure error, because it squares the error, larger errors are amplified. This can cause the algorithm to focus too much on reducing large errors while neglecting overall balance. Peak signal-to-noise ratio (PSNR) is primarily used in signal processing, particularly image quality assessment. For some control issues not related to image or signal quality, the concept of PSNR may not be intuitive or applicable. The advantage of using the residual modulus as a performance metric in this invention lies in its directness, low sensitivity to outliers, and high computational efficiency. It is particularly suitable for applications that require precise control of the error at each point rather than focusing solely on the average error.

[0135] In the embodiment of the present application, when a voltage ride-through fault occurs in the system, the type of voltage ride-through is first determined, and the specific steps of obtaining the improved particle swarm optimization algorithm with genetic factors and the iterative formula are as follows: Taking the fault parameter identification when voltage ride-through occurs as an example, the reference voltage command value of the inverter in the voltage ride-through condition is calculated as follows:

[0136]

[0137] in, and Represent the voltage reference command values ​​of the d-axis and q-axis, G1(s), G 11 (s), G 12 (s), G2(s), G 21 (s), G 22 (s) represents the system transfer function, and U dc (s) represent the reference value and actual value of DC side voltage respectively, Indicates the reference value of the q-axis current, i max Indicates the maximum current value allowed. and Q r (s) represent the reference value and actual value of reactive power, respectively, k qv Represents the reactive current support coefficient, i N It represents the effective value of the rated current at the grid connection point, and U represents the grid voltage;

[0138] In the embodiment of the present application, since the steady-state operation of the photovoltaic system is different from the inverter internal current control method during voltage ride-through, and the inverter output voltage is used as the fitness calculation value in the steady state, it is a second-order curve. Therefore, the inner loop control current is used as the particle fitness value to adjust the reference current of the d-axis and q-axis. and Expressed as:

[0139]

[0140] In the embodiment of the present application, since the system performs simulation identification in a time domain environment, an inverse pull transformation is used to obtain the pull transfer function H(s) of the inverter internal output voltage and the control current transfer function and the time domain transfer equation h(t):

[0141]

[0142] In the embodiment of the present application, the inverter internal control current reference value is expressed as:

[0143]

[0144] In the embodiment of the present application, based on the controller parameters of the current position of the particle, the transfer function is calculated as:

[0145]

[0146] In the embodiment of the present application, the transfer function is used to calculate the current fitness value of each particle as follows:

[0147]

[0148] Among them, y1 and y2 are and Estimated value through inner loop control;

[0149] In the embodiment of the present application, the arrays are respectively corresponding to the reactive current support coefficient k of the converter under low voltage ride-through. qv , the effective value of the rated current at the grid connection point i N , active power recovery speed k id , reactive power recovery speed k iq。 .

[0150] In the embodiment of the present application, the linear decreasing weight particle swarm optimization algorithm with genetic factors seeks the optimal solution with the goal of minimizing the residual modulus value, that is, the objective function is:

[0151]

[0152] In the embodiment of the present application, in order to further improve the identification accuracy, the particle is iterated and the velocity weight is changed during the iteration process. The previous identification solution is added to reduce the fluctuation of the identification result. The updated particle velocity and position are expressed as:

[0153]

[0154] Among them, kv represents the velocity matrix of all parameters to be identified, k(i) is the i-th particle, k best (i) The optimal iteration result for this particle, g best is the optimal result of the population, k preThe inverter identification result at the last moment is used as the forgetting factor, ω is the weight of its own speed, c1, c2, and c3 are the weights of different reference values, kvmax represents the reference limit of the speed, θ is the total number of iterations, and λ is the current number of iterations.

[0155] In the embodiment of the present application, the weight is changed during the identification process to reduce the fluctuation of the identification result and make the parameter identification result more accurate. The weight of the own speed is expressed as:

[0156]

[0157] Among them, kvmax is the reference limit of speed;

[0158] In the embodiment of the present application, c1, c2, and c3 are set to change during the iteration process, and the weights of different reference values ​​are expressed as follows:

[0159]

[0160] Among them, θ is the total number of iterations, λ is the current number of iterations. When the number exceeds a certain range, the influence of the previous iteration is reduced to ensure accuracy.

[0161] It should be noted that by finding the optimal solution by minimizing the residual modulus, different voltage ride-through fault conditions can be identified, and the internal working conditions of the photovoltaic grid-connected inverter with unknown voltage ride-through can be better displayed, which greatly reduces the randomness of the identification controller parameters and improves the accuracy of the identification results. The present invention can realize online identification and synchronous identification of all parameters, avoiding the step-by-step identification of each parameter. Further, by adding a forgetting factor on the basis of the particle swarm algorithm, the weight of old data can be reduced, the role of new data can be enhanced, and the identification method can have correction capabilities, thereby improving the accuracy of the identification parameters.

[0162] In the embodiment of the present application, in step S400, the iteration is stopped when the controller parameters to be identified reach a preset accuracy, and the output controller parameter estimation value includes:

[0163] By changing the weights during the identification process, the fluctuation of the identification results can be reduced.

[0164] It should be noted that when the parameters of the controller to be identified reach the preset accuracy and the iteration is stopped, the identification system outputs the estimated value of the controller parameter at this time, and the estimated value of the controller parameter is substituted into the expression of the differential equation to judge whether the controller parameters meet the conditions according to the output. At the same time, the identification result is substituted into the next operation process as a direction reference, and during the iteration process, the particle velocity weight is continuously changed based on the particle position, velocity and number of iterations to improve the particle identification speed and accuracy.

[0165] Example 3, reference Figure 7-12 , which is an embodiment of the present invention, provides a photovoltaic inverter voltage ride-through parameter identification method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0166] The simulation software used in this implementation is PSCAD5.0, and the model is solved using Fortran programming, and the solution accuracy gap is set to 0.02.

[0167] 1. Basic data of the example

[0168] At a temperature of T = 25°C and a light intensity of W = 1000W / m 2 A single-stage photovoltaic grid-connected system was simulated. The photovoltaic motor was connected to the grid via an inverter and then to the infinite system via a 35kV step-up transformer. The PV grid-connected inverter adopted a grid-voltage-oriented vector control strategy, whose control objective was to maintain the stability of the inverter's DC side voltage. A dual-loop control scheme with PQ decoupling was used. The outer loop employed constant DC voltage control, using a proportional-integral (PI) phase to generate a reference current for the inner loop current control. The inner loop uses current as a reference and, through the PI phase and feedforward decoupling phase, generates the controller's voltage command value, i.e., the inverter output voltage. To achieve unity power factor operation of the PV system, the q-axis reference current value was set to 0. The voltage ride-through level is set to ±30%. At the same time, in order to maintain the stable operation of the power grid under voltage ride-through conditions, the grid-connected inverter adopts the principle of directly using the inner loop control by discarding the outer loop. The low voltage ride-through is set to occur between 1.5 seconds and 2.5 seconds. The PSCAD simulation model is constructed to analyze the steady-state photovoltaic motor output waveform and photovoltaic grid-connected system simulation. Figure 8 As shown, the main system parameters such as capacitance and voltage are Figure 7 As shown, Figure 7 (a) is the constant DC voltage outer loop tracking, Figure 7 (b) is the fixed reactive power outer loop tracking, Figure 7 (c) is the active power output of the photovoltaic cell. When the system voltage goes through, the active and reactive power of the photovoltaic cell is as follows: Figure 9 、 Figure 10 shown.

[0169] 2. Case analysis

[0170] The number of particles is set to 50, the number of iterations is set to 300, and the low voltage ride-through k is set to qv The value is 1.5, i N The value is 4.125, and the algorithm used can meet the accuracy requirements of parameter identification with high accuracy. Therefore, the algorithm can be regarded as a parameter identification method of the inverter control system during photovoltaic power generation grid-connected operation through genetic particle swarm optimization. The simulation results are shown in Figure 2. Figure 11 shown.

[0171] Table 1 Comparison of controller parameter identification results and actual values

[0172] Parameter name Actual value Identification value error <![CDATA[k qv ]]> 1.500 1.49 0.67％ <![CDATA[i N ]]> 4.125 4.120 0.12％ d-axis current recovery speed 0.100 0.098 2.0％ q-axis current recovery speed 0.0875 0.0900 2.857％

[0173] According to Table 1 and Figure 11 The data shows that the inverter of the photovoltaic grid-connected system has certain fluctuations at different times during the voltage ride-through period, which has a certain impact on the identification results, but it always remains within the allowable error range. In order to further verify the effect of the identification algorithm, we also conducted a detailed test curve analysis, such as Figure 12 shown. Figure 12 (a) shows the test curve of the inverter control parameter identification results against the d-axis current under low wear environment, and Figure 12 (b) shows the test curves of the q-axis current. These test curves not only verify the effectiveness of the proposed algorithm, but also provide important insights into the performance of the inverter in actual operation.

[0174] Example 4 is an embodiment of the present invention. This embodiment is different from the first embodiment in that it provides a photovoltaic inverter voltage ride-through parameter identification system.

[0175] It should be noted that the technical solution of the photovoltaic inverter voltage ride-through parameter identification system and the technical solution of the photovoltaic inverter voltage ride-through parameter identification method mentioned above belong to the same concept. For details not described in detail in the technical solution of the photovoltaic inverter voltage ride-through parameter identification system in this embodiment, please refer to the description of the technical solution of the photovoltaic inverter voltage ride-through parameter identification method mentioned above.

[0176] In this embodiment, a photovoltaic inverter voltage ride-through parameter identification system includes:

[0177] An acquisition module is used to obtain multivariate system data and establish a mathematical model of the photovoltaic inverter controller;

[0178] The difference equation conversion module is used to convert the mathematical model into a difference equation through transformation and decoupling operations;

[0179] The detection and type judgment module is used to obtain the input and output quantities of the inverter controller in real time based on the converted differential equation, and to determine whether voltage ride-through occurs and the type of voltage ride-through, and obtain the judgment result;

[0180] The controller parameter optimization module is used to optimize the controller parameters based on the judgment results using the optimization algorithm to obtain the optimal controller parameter values;

[0181] The parameter identification module is used to output controller parameter estimation values ​​based on the optimal controller parameter values ​​when the controller parameters to be identified reach the expected accuracy.

[0182] This embodiment further provides an electronic device applicable to a method for identifying parameters of a photovoltaic inverter voltage ride-through, including:

[0183] Memory and processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement a photovoltaic inverter voltage ride-through parameter identification method as proposed in the above embodiment.

[0184] This embodiment further provides a storage medium having a computer program stored thereon. When the program is executed by a processor, a method for identifying voltage ride-through parameters of a photovoltaic inverter as proposed in the above embodiment is implemented.

[0185] The storage medium proposed in this embodiment and the method for implementing a photovoltaic inverter voltage ride-through parameter identification method proposed in the above embodiment belong to the same inventive concept. Technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0186] Through the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented with the help of software and necessary general-purpose hardware, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as a computer's floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various embodiments of the present invention.

[0187] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A photovoltaic inverter voltage ride-through parameter identification method, characterized in that: include: Obtain multivariate system data and establish a mathematical model for the photovoltaic inverter controller; Convert the mathematical model into a differential equation through transformation and decoupling operations; Based on the transformed differential equation, the input and output quantities of the inverter controller are obtained in real time, and the occurrence of voltage ride-through and the type of voltage ride-through are determined to obtain the judgment result; Based on the judgment results, the optimization algorithm is used to optimize the controller parameters to obtain the optimal controller parameter values; Based on the optimal controller parameter values, when the controller parameters to be identified reach the expected accuracy, the controller parameter estimation values ​​are output.

2. The photovoltaic inverter voltage ride-through parameter identification method according to claim 1, wherein: The mathematical model of the photovoltaic inverter controller is established, comprising the following steps: Adopt voltage source inverter based on sinusoidal pulse width modulation; A dual closed-loop proportional-integral regulator with active and reactive power decoupling is used, including a voltage outer loop and a current inner loop; Establish a mathematical model of the photovoltaic inverter controller; Design control strategies for voltage ride-through conditions; Determine the controller fault control parameters that need to be identified.

3. The photovoltaic inverter voltage ride-through parameter identification method according to claim 1 or 2, wherein: The control strategy for designing voltage ride-through includes the following steps: When voltage ride-through is detected, the inner current loop is directly controlled; Adjust reactive current output by judging the voltage ride-through type; The output of active current is controlled jointly according to the reactive current and the active current output by the dual closed-loop proportional-integral regulator; Control the size of reactive power output through active and reactive current; By controlling the flow of reactive power, the inverter voltage output is maintained stable.

4. The photovoltaic inverter voltage ride-through parameter identification method according to claim 3, wherein: The step of determining the controller fault control parameters that need to be identified comprises the following steps: Identify different important parameters according to different voltage ride-through types; If the voltage ride-through type is determined to be low voltage ride-through, a first control strategy is adopted and first controller parameters are identified; If the voltage ride-through type is determined to be high voltage ride-through, a second control strategy is adopted and second controller parameters are identified.

5. The photovoltaic inverter voltage ride-through parameter identification method according to claim 4, wherein: The optimization algorithm is used to optimize the selected controller parameters, comprising the following steps: Set initial parameters, including initializing particle swarm size, particle position and velocity, and determining the maximum number of iterations; Calculate the reference voltage command value of the inverter under voltage ride-through conditions; The calculated reference voltage command value is used as input to calculate the fitness value of each particle; Evaluate the current fitness value of each particle according to the fitness function; Evaluate the optimal fitness value of the group according to the fitness function; Update the particle's velocity and position; If the particle position is not updated, return to calculate the fitness value until the termination condition is met; When the termination condition is met, the controller parameters corresponding to the global optimal position are output.

6. The photovoltaic inverter voltage ride-through parameter identification method according to claim 5, wherein: The calculation of the fitness value of each particle includes the following steps: Calculate the transfer function based on the controller parameters of the particle's current position; Use the transfer function to calculate the current fitness value of each particle; Minimizing a predetermined performance indicator is used as the fitness function.

7. The photovoltaic inverter voltage ride-through parameter identification method according to claim 6, wherein: The reference voltage command value of the inverter under voltage ride-through is expressed as: in, and Represent the voltage reference command values ​​of the d-axis and q-axis, G1(s), G 11 (s), G 12 (s), G2(s), G 21 (s), G 22 (s) represents the system transfer function, and U dc (s) represent the reference value and actual value of DC side voltage respectively, Indicates the reference value of the q-axis current, i max Indicates the maximum current value allowed. and Q r (s) represent the reference value and actual value of reactive power, respectively, k qv Represents the reactive current support coefficient, i N It represents the effective value of the rated current at the grid connection point, and U represents the grid voltage.

8. A photovoltaic inverter voltage ride-through parameter identification system, applying the method according to any one of claims 1 to 7, characterized in that: include: An acquisition module is used to obtain multivariate system data and establish a mathematical model of the photovoltaic inverter controller; The difference equation conversion module is used to convert the mathematical model into a difference equation through transformation and decoupling operations; The detection and type judgment module is used to obtain the input and output quantities of the inverter controller in real time based on the converted differential equation, and to determine whether voltage ride-through occurs and the type of voltage ride-through, and obtain the judgment result; The controller parameter optimization module is used to optimize the controller parameters based on the judgment results using the optimization algorithm to obtain the optimal controller parameter values; The parameter identification module is used to output controller parameter estimation values ​​based on the optimal controller parameter values ​​when the controller parameters to be identified reach the expected accuracy.

9. An electronic device, characterized in that: include: Memory, used to store programs; A processor, configured to load the program to execute the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a program, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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