Method and system for identifying parameters of photovoltaic inverter controller

Abstract

The invention relates to the technical field of photovoltaic inverters, in particular to a photovoltaic inverter controller parameter identification method and system, and the method comprises the steps: building a mathematical model of a photovoltaic inverter controller, and determining controller parameters needing to be identified; the mathematical model is converted into a difference equation through transformation decoupling operation; the input and output quantity of an inverter controller is obtained in real time to serve as the input quantity of an identification system, and the preset performance index minimization serves as the optimal adaptive value estimation of an optimization algorithm; and stopping iteration when the parameters of the controller to be identified reach preset precision, and outputting estimated values of the parameters of the controller. The method has the advantages that the optimal solution is found through the idea of minimizing the residual module value, the randomness of identifying the parameters of the controller is greatly reduced, the accuracy of the identification result is improved, online identification and synchronous identification of all the parameters can be achieved, and step-by-step identification of all the parameters is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic inverters, and in particular to a method and system for identifying parameters of a photovoltaic inverter controller. Background Art

[0002] Yunnan's renewable energy is developing rapidly. It is estimated that the proportion of renewable energy installed capacity in Yunnan's power grid will exceed 33% in 2025. Accurate simulation models play a vital role in the accuracy of power system simulation analysis. At present, there are differences in the models of different renewable energy manufacturers, and they are all packaged models. Even if the same model, under different operating conditions and different fault forms, the control response characteristics are different, and it is not easy to grasp its actual grid-related characteristics. In addition, there are many simulation model parameters for renewable energy units, and these model parameters are not easy to obtain, resulting in a decrease in the accuracy of grid stability simulation, which directly affects the decision-making of grid operation and grid planning. At the same time, the control strategies and parameters of renewable energy units and their control systems affect the safety and stability level of the grid. At present, most controllers are designed under a certain fixed working condition, and the control strategies and parameters mainly consider the grid-connected performance of the equipment, but lack consideration of the safety and stability requirements of the grid. In view of the problem that the relationship between the parameters of renewable energy models and the stability of the grid is not clear enough, it is necessary to study the dominant parameters of renewable energy on grid stability and the field testing and identification methods of these parameters to improve the stability level of the grid. Summary of the invention

[0003] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

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

[0005] Therefore, the present invention provides a method and system for identifying parameters of a photovoltaic inverter controller, which can solve the problems mentioned in the background technology.

[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 controller, comprising establishing a mathematical model of the photovoltaic inverter controller and determining controller parameters that need to be identified;

[0008] Convert the mathematical model into a differential equation through transformation decoupling operation;

[0009] The input and output of the inverter controller are obtained in real time as the input of the identification system, and the minimization of the predetermined performance index is used as the optimal fitness value estimation of the optimization algorithm;

[0010] When the controller parameters to be identified reach the preset accuracy, the iteration is stopped and the estimated values ​​of the controller parameters are output.

[0011] As a preferred solution of the method for identifying the parameters of the photovoltaic inverter controller of the present invention, the mathematical model is converted into a differential equation by transformation decoupling operation, including:

[0012] The mathematical model is converted into a difference equation through Park transformation.

[0013] As a preferred solution of the method for identifying the parameters of the photovoltaic inverter controller of the present invention, the controller parameters to be identified are determined to include:

[0014] k rpd1 , k rid1 , k rpd2 , k rid2 , k rpq1 , k riq1 , k rpq2 , and k riq2 .

[0015] As a preferred solution of the method for identifying parameters of a photovoltaic inverter controller of the present invention, the optimization algorithm is a particle swarm algorithm with genetic factors.

[0016] As a preferred solution of the method for identifying the parameters of the photovoltaic inverter controller of the present invention, wherein: minimizing a predetermined performance index as the optimal fitness value estimation of the optimization algorithm includes:

[0017] The minimum residual modulus is used as the optimal fitness value estimation of the particle swarm algorithm with genetic factors.

[0018] As a preferred solution of the method for identifying the parameters of the photovoltaic inverter controller of the present invention, the iteration is stopped when the controller parameters to be identified reach a preset accuracy, and the output controller parameter estimation value includes:

[0019] By changing the weights during the identification process, the fluctuation of the identification results can be reduced.

[0020] As a preferred solution of the method for identifying the parameters of the photovoltaic inverter controller of the present invention, wherein: the inverter adopts a voltage source inverter based on sinusoidal pulse width modulation (SPWM);

[0021] The control structure of the controller adopts a double closed-loop PI regulator with PQ decoupling.

[0022] In a second aspect, the present invention provides a system for identifying parameters of a photovoltaic inverter controller, comprising: establishing a confirmation module for establishing a mathematical model of a photovoltaic inverter controller and determining controller parameters that need to be identified;

[0023] The conversion model is used to convert the mathematical model into a differential equation through transformation decoupling operations;

[0024] An acquisition optimization module is used to acquire the input and output quantities of the inverter controller in real time as the input quantities of the identification system, and to minimize the predetermined performance index as the optimal fitness value estimation of the optimization algorithm;

[0025] The output module is used to stop iteration when the controller parameters to be identified reach a preset accuracy and output the estimated values ​​of the controller parameters.

[0026] In a third aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0027] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above method when the computer program is executed by a processor.

[0028] Compared with the prior art, the beneficial effects of the present invention are as follows: by finding the optimal solution through the idea of ​​minimizing the residual modulus value, the randomness of the identification controller parameters is greatly reduced, and the accuracy of the identification results is improved. The present invention can realize online identification and synchronous identification of all parameters, avoiding step-by-step identification of each parameter, and further adding a forgetting factor on the basis of the particle swarm algorithm, which can reduce the weight of old data and enhance the role of new data, so that the identification method has correction ability, thereby improving the accuracy of the identification parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0030] Figure 1 A flow chart of a method for identifying parameters of a photovoltaic inverter controller.

[0031] Figure 2 For photovoltaic inverters.

[0032] Figure 3 Build a photovoltaic grid-connected simulation diagram for the photovoltaic inverter based on PSCAD5.0.

[0033] Figure 4 This is the control schematic diagram of the photovoltaic inverter.

[0034] Figure 5 This is the simulation diagram of the photovoltaic inverter.

[0035] Figure 6 The figure is a flow chart of one embodiment of a method for identifying parameters of a photovoltaic inverter controller.

[0036] Figure 7 Constant DC voltage outer loop tracking simulation results diagram.

[0037] Figure 8 Figure 2. Simulation results of fixed reactive power outer loop tracking.

[0038] Fig. 9 Photovoltaic cell output active power simulation results diagram.

[0039] Fig.10 This is a diagram of a photovoltaic grid-connected system.

[0040] Fig.11 This is the model diagram of the voltage power outer loop and the current inner loop.

[0041] Fig.12 This is the kp identification result diagram.

[0042] Fig.13 is the identification result of ki.

[0043] Fig.14 A schematic diagram of the internal structure of a computer device. DETAILED DESCRIPTION

[0044] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0045] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0046] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0047] Example 1

[0048] Reference Figure 1 to Figure 3 and Figure 6 , which is the first embodiment of the present invention, provides a method for identifying parameters of a photovoltaic inverter controller, which includes:

[0049] S1. Establish a mathematical model of the photovoltaic inverter controller and determine the controller parameters that need to be identified, where the second-order PI parameters are identified.

[0050] Furthermore, the controller parameters that need to be identified include:

[0051] k rpd1 , k rid1 , k rpd2 , k rid2 , k rpq1 , k riq1 , k rpq2 , and k riq2 .

[0052] It should be noted that k rpd1 , k rid1 , k rpd2 , k rid2 are the proportional and integral coefficients of the voltage outer loop controller respectively; k rpq1 , k riq1 and k rpq2 , k riq2 are the proportional and integral coefficients of the current inner loop controller respectively.

[0053] Furthermore, the inverter adopts a voltage source inverter based on sinusoidal pulse width modulation (SPWM);

[0054] The control structure of the controller adopts a double closed-loop PI regulator with PQ decoupling.

[0055] It should be noted that the detailed structure of the photovoltaic inverter is as follows Figure 2 As shown, the main circuit of the photovoltaic inverter is generally a three-phase bridge structure, the control structure adopts a cascade method of voltage outer loop and current inner loop, and the controller adopts a PI regulator.

[0056] S2. Convert the mathematical model into a differential equation through transformation decoupling operation.

[0057] Furthermore, the mathematical model is transformed into a differential equation through transformation decoupling operations including,

[0058] The mathematical model is converted into a difference equation through Park transformation.

[0059] It should be noted that by using Park transformation to transform the mathematical model decoupling operation 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.

[0060] S3. The input and output of the inverter controller are obtained in real time as the input of the identification system, and the predetermined performance index is minimized as the optimal fitness value estimation of the optimization algorithm.

[0061] Furthermore, the optimization algorithm is a particle swarm algorithm with genetic factors.

[0062] Further, the optimal fitness value estimation of the optimization algorithm using the minimization of a predetermined performance indicator includes:

[0063] The minimum residual modulus is used as the optimal fitness value estimation of the particle swarm algorithm with genetic factors.

[0064] Preferably, 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 make the identification method have correction ability, thereby improving the accuracy of the identification parameters.

[0065] S4. When the controller parameters to be identified reach a preset accuracy, the iteration is stopped and the estimated values ​​of the controller parameters are output.

[0066] Furthermore, the iteration is stopped when the controller parameters to be identified reach a preset accuracy, and the output controller parameter estimation values ​​include:

[0067] By changing the weights during the identification process, the fluctuation of the identification results can be reduced.

[0068] 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 values ​​of the controller parameters at this time, and the estimated values ​​of the controller parameters are 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 results are substituted into the next operation process as a direction reference, and the velocity weight of the particle is continuously changed based on the particle position, velocity and number of iterations during the iteration process to improve the particle identification speed and accuracy.

[0069] When in use, according to the established mathematical model of the controller, the controller transfer function G(s) is derived based on Park transformation. When the main circuit parameters are known, the controller parameters that need to be identified are k rpd1 , k rid1 , k rpd2 , k rid2 , k rpq1 , k riq1 , k rpq2 , k riq2。The PV grid-connected inverter of the Yunnan power grid photovoltaic grid-connected model adopts a vector control strategy based on grid voltage orientation and a voltage source inverter based on sinusoidal pulse width modulation (SPWM). Its control goal is to maintain the stability of the DC side voltage of the inverter, and a dual-loop control method with PQ decoupling is selected. 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 realize the operation of the PV system at unity power factor, the q-axis reference current value is set to 0. Thus, its mathematical model in dq coordinates is established:

[0070]

[0071] In the formula, k rpd1 , k rid1 , k rpd2 , k rid2 are the proportional and integral coefficients of the voltage outer loop controller respectively; k rpq1 , k riq1 and k rpq2 , k riq2 are the proportional and integral coefficients of the current inner loop controller respectively.

[0072] Furthermore, the specific steps of obtaining the improved particle swarm algorithm with genetic factors and the iteration formula in the above text are as follows: The particle swarm iteration format can be deduced as follows:

[0073]

[0074] First, the transfer function of the particle fitness value is introduced:

[0075]

[0076] The relationship between population particles and transfer function is:

[0077]

[0078] Let the array kp(n, 4) be the parameters to be identified of kp, and ki(n, 4) be the parameters to be identified of ki. n refers to the number of particles in the population, and one to four refer to the kp and ki parameters from the outer ring to the inner ring and from the d axis to the q axis respectively.

[0079] The linear decreasing weight particle swarm optimization algorithm with genetic factors seeks the optimal solution by minimizing the residual modulus value, that is, the objective function is:

[0080]

[0081] Where y 1 ,y 2 for and The estimated value of , according to equations (4) and (5), the solution of the improved particle swarm algorithm is: kp, ki. In order to further improve the identification accuracy, the particles are iterated and the speed weight is changed during the iteration process, and the previous identification solution is added to reduce the fluctuation of the identification result:

[0082]

[0083] In formula (6), kv is the speed matrix of all parameters to be identified, k(i) is the i-th particle, kbest(i) is the optimal iteration result of this particle, gbest is the optimal result of the population, kpre is the inverter identification result at the last moment as the forgetting factor, ω is the weight of its own speed, and c1, c2, and c3 are the weights of different reference values ​​respectively.

[0084] At the same time, the weights are changed during the identification process to reduce the fluctuation of the identification results and make the parameter identification results more accurate.

[0085]

[0086] kvmax as a reference limit for speed

[0087] At the same time, set c1, c2, and c3 to change during the iteration process:

[0088]

[0089] Where 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.

[0090] According to equations (4) to (8), accurate results of parameter identification can be obtained.

[0091] On the other hand, the steps of Particle Swarm Optimization are as follows:

[0092] Step 1: Set initial parameters, generate initial solution, and initialize particle position and velocity.

[0093] Step 2: Calculate the fitness value of each particle using formulas (2)-(5).

[0094] Step 3: 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 formulas (6)-(8) and the historical best fitness value and the previous identification result.

[0095] Step 4: 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 best position after the group is updated.

[0096] Step 5: As the number of iterations increases, update the particle's speed and position information according to the formula.

[0097] Step 6: If the particle position is not updated, return to step 2 until the termination condition is met.

[0098] In summary, the beneficial effect of a method for identifying parameters of a photovoltaic inverter controller is to find the optimal solution by minimizing the residual modulus value, greatly reduce the randomness of the identified controller parameters, and improve the accuracy of the identification results. The present invention can realize online identification and synchronous identification of all parameters, avoid step-by-step identification of each parameter, and further add a forgetting factor on the basis of the particle swarm algorithm, which can reduce the weight of old data and enhance the role of new data, so that the identification method has correction ability, thereby improving the accuracy of the identification parameters.

[0099] Example 2

[0100] Reference Figure 4 , Figure 5 and Figure 7 to Figure 13 , which is an embodiment of the present invention, provides a method for identifying parameters of a photovoltaic inverter controller. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0101] Example simulation and analysis

[0102] The simulation software used in this paper is PSCAD5.0, Fortran programming is used to solve the model, and the solution accuracy gap is set to 0.02.

[0103] 1. Basic data of the example

[0104] At temperature T = 25°C and light intensity W = 1000W / m 2The single-stage photovoltaic grid-connected system is connected to the grid for simulation. The photovoltaic motor is connected to the grid through the inverter, and then connected to the infinite system through a 35kv step-up transformer for simulation. The PV grid-connected inverter adopts a vector control strategy based on grid voltage orientation. Its control goal is to maintain the stability of the DC side voltage of the inverter, and a dual-loop control method with PQ decoupling is selected. 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, the feedforward decoupling link, etc. In order to realize the operation of the PV system at unity power factor, the q-axis reference current value is set to 0. The constructed PSCAD simulation model is analyzed. The output waveform of the photovoltaic motor in steady state and the simulation of the photovoltaic grid-connected system are as follows Figures 10-11 As shown, the main system parameters such as capacitor voltage are Figure 7 to Figure 9 shown.

[0105] 2. Example Analysis

[0106] The number of particles is 50, the number of iterations is 300, the kp value is set to 2.0, and the ki value is set to 0.005. The results are as follows Figure 4 , 5 The algorithm used in the figure 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 when photovoltaic power generation is connected to the grid through genetic particle swarm algorithm. The simulation results are shown in Figures 12-13 shown.

[0107] Table 1 Comparison of the identification results and actual values ​​of kp and ki parameters

[0108] Identify type Actual value Identification value error Kp 2.0 2.02 1％ Ki 0.005 0.00504 1％

[0109] According to Table 1 and Figures 12-13 The data displayed shows that the inverter of the photovoltaic grid-connected system produces certain fluctuations in different periods of time, which has a certain impact on the identification results, but it is always kept within the error range of 1%.

[0110] Example 3

[0111] This embodiment provides a system for identifying parameters of a photovoltaic inverter controller, which includes an establishment confirmation module for establishing a mathematical model of a photovoltaic inverter controller and determining controller parameters that need to be identified;

[0112] The conversion model is used to convert the mathematical model into a differential equation through transformation decoupling operations;

[0113] An acquisition optimization module is used to acquire the input and output quantities of the inverter controller in real time as the input quantities of the identification system, and to minimize the predetermined performance index as the optimal fitness value estimation of the optimization algorithm;

[0114] The output module is used to stop iteration when the controller parameters to be identified reach a preset accuracy and output the estimated values ​​of the controller parameters.

[0115] The above-mentioned unit modules may be embedded in or independent of the processor in the computer device in the form of hardware, or may be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned modules.

[0116] Example 4

[0117] This embodiment provides a computer device, which may be a terminal, and its internal structure diagram may be as follows: Fig.14 As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for identifying parameters of a photovoltaic inverter controller is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0118] The present embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements: establishing a mathematical model of a photovoltaic inverter controller and determining controller parameters that need to be identified; converting the mathematical model into a differential equation through a transformation decoupling operation; acquiring the input and output quantities of the inverter controller in real time as the input quantities of the identification system, and minimizing a predetermined performance indicator as the optimal fitness value estimation of the optimization algorithm; stopping iteration when the controller parameters to be identified reach a preset accuracy, and outputting the estimated values ​​of the controller parameters.

[0119] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for identifying parameters of a photovoltaic inverter controller, characterized in that: include, Establish the mathematical model of the photovoltaic inverter controller and determine the controller parameters that need to be identified; Converting the mathematical model into a differential equation through a transformation decoupling operation; The input and output of the inverter controller are obtained in real time as the input of the identification system, and the minimization of the predetermined performance index is used as the optimal fitness value estimation of the optimization algorithm; When the controller parameters to be identified reach the preset accuracy, the iteration is stopped and the estimated values ​​of the controller parameters are output.

2. The method for identifying parameters of a photovoltaic inverter controller according to claim 1, characterized in that: The converting of the mathematical model into a differential equation through a transformation decoupling operation includes: The mathematical model is converted into a difference equation through Park transformation.

3. The method for identifying parameters of a photovoltaic inverter controller according to claim 2, characterized in that: The controller parameters to be identified include: k rpd1 , k rid1 , k rpd2 , k rid2 , k rpq1 , k riq1 , k rpq2 , and k riq2 .

4. The method for identifying parameters of a photovoltaic inverter controller according to claim 3, characterized in that: The optimization algorithm is a particle swarm algorithm with genetic factors.

5. The method for identifying parameters of a photovoltaic inverter controller according to claim 4, characterized in that: The method of minimizing a predetermined performance index as the optimal fitness value estimation of the optimization algorithm includes: The minimum residual modulus is used as the optimal fitness value estimation of the particle swarm algorithm with genetic factors.

6. The method for identifying parameters of a photovoltaic inverter controller according to claim 5, characterized in that: The iteration is stopped when the controller parameters to be identified reach a preset accuracy, and the controller parameter estimation values ​​output include: By changing the weights during the identification process, the fluctuation of the identification results can be reduced.

7. The method for identifying parameters of a photovoltaic inverter controller according to claim 6, characterized in that: The inverter adopts a voltage source inverter based on sinusoidal pulse width modulation (SPWM); The control structure of the controller adopts a PQ decoupled double closed-loop PI regulator.

8. A system for identifying parameters of a photovoltaic inverter controller, characterized in that: include: Establish a confirmation module for establishing a mathematical model of a photovoltaic inverter controller and determining controller parameters that need to be identified; A conversion model, used for converting the mathematical model into a differential equation through a transformation decoupling operation; An acquisition optimization module is used to acquire the input and output quantities of the inverter controller in real time as the input quantities of the identification system, and to minimize the predetermined performance index as the optimal fitness value estimation of the optimization algorithm; The output module is used to stop iteration when the controller parameters to be identified reach a preset accuracy and output the estimated values ​​of the controller parameters.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer 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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