Method, device and equipment for identifying unknown control parameters of photovoltaic power generation system and storage medium

Abstract

The embodiment of the invention discloses a photovoltaic power generation system unknown control parameter identification method, a photovoltaic power generation system unknown control parameter identification device, computer equipment and a computer readable storage medium. The method comprises the following steps that current control model information of the photovoltaic power generation system is obtained, a theoretical transfer function is determined according to the current control model information, and the theoretical transfer function comprises unknown control parameters to be recognized; constructing a sensitivity solving formula of the unknown control parameters; when the photovoltaic power generation system reaches a stable state, applying a preset disturbance signal to the input current of the photovoltaic power generation system, obtaining the input and output signals of the photovoltaic power generation system after the preset disturbance signal is applied, and calculating the system frequency response according to the input and output signals; and fitting the system frequency response into an actual transfer function, comparing the actual transfer function with a theoretical transfer function, and solving unknown control parameters by using a sensitivity solving formula. Therefore, the unknown control parameters of the photovoltaic system can be calculated.

Description

Technical Field

[0001] The present application belongs to the field of photovoltaic power generation technology, and in particular, relates to a method for identifying unknown control parameters of a photovoltaic power generation system, an apparatus for identifying unknown control parameters of a photovoltaic power generation system, a computer device, and a computer-readable storage medium. Background Art

[0002] In recent years, photovoltaic grid-connected power generation systems have been rapidly popularized around the world. While providing us with green and clean energy, they also bring challenges to the stable operation of the power grid. The establishment of a photovoltaic grid-connected power generation system model is the key to studying its interaction with the power grid and other power electronic interface devices in the power grid. In fact, when planning and operating the power system, it is usually necessary to model the photovoltaic power generation system connected to the grid, and use simulation methods to study its output characteristics and its potential impact on the power grid.

[0003] However, in the current power system analysis, due to the lack of information, the power grid company cannot directly use the model of the commercial inverter developed, so in most cases it depends on the manufacturer. For confidentiality reasons, the model provided by the manufacturer is usually limited in detail, and its corresponding control parameters often need to be targeted according to the actual application scenario. The above factors directly or indirectly lead to the lack of effective models of commercial inverters in actual engineering applications. Traditional electromechanical transient simulation methods cannot accurately simulate the response and control characteristics of equipment. This is because there are generally many power electronic devices in the new energy power generation system, and their action characteristics are complex. The existing model is difficult to fully meet the requirements of electromagnetic transient efficient simulation, and cannot adapt to the simulation scenario of large-scale new energy power generation connected to the power system and consumes a lot of simulation resources. Therefore, it is necessary to improve the existing modeling and simulation methods. Based on this, how to make up for the simulation accuracy problem in the traditional electromechanical transient and electromagnetic transient modeling methods is a technical problem that technicians in this field need to solve urgently.

[0004] The preceding description is intended to provide general background information and does not necessarily constitute prior art. Summary of the invention

[0005] Based on this, it is necessary to propose a method for identifying unknown control parameters of a photovoltaic power generation system, an unknown control parameter identification device for a photovoltaic power generation system, a computer device and a computer-readable storage medium to address the above problems, which can accurately identify the unknown control parameters in the photovoltaic power generation system.

[0006] The present application solves the technical problem by adopting the following technical solutions:

[0007] The present application provides a method for identifying unknown control parameters of a photovoltaic power generation system, comprising the following steps: obtaining current control model information of the photovoltaic power generation system, determining a theoretical transfer function according to the current control model information, wherein the theoretical transfer function includes unknown control parameters to be identified; constructing a sensitivity solution for the unknown control parameters; when the photovoltaic power generation system reaches a stable state, applying a preset disturbance signal to an input current of the photovoltaic power generation system, obtaining input and output signals of the photovoltaic power generation system after applying the preset disturbance signal, and calculating a system frequency response according to the input and output signals; fitting the system frequency response to an actual transfer function, comparing the actual transfer function with the theoretical transfer function, and obtaining the unknown control parameters using a sensitivity solution.

[0008] In an optional embodiment of the present application, determining the theoretical transfer function according to the current control model information includes: determining a voltage calculation formula for the output voltage of the control system according to the current control model information, where the voltage calculation formula is expressed as:

[0009]

[0010] In the formula, K p and K i are the proportional and integral coefficients of the d-axis current controller, which are unknown control parameters; L2 is the filter inductor on the bridge arm side, i 2dref is the d-axis current reference value, u cd and i 2d They are the d-axis voltage and current after passing through the inductor L2. The filter inductance on the bridge arm side, the d-axis current reference value, the d-axis voltage and the d-axis current can be directly acquired. The d-axis current control expression is determined according to the voltage calculation formula, which is expressed as:

[0011]

[0012] The d-axis current reference value is used as the input of the single-input single-output linear time-invariant model, the grid-side current is used as the output of the single-output linear time-invariant model, and the theoretical transfer function is determined according to the d-axis current control expression, which is expressed as:

[0013]

[0014] In an optional embodiment of the present application, constructing a sensitivity solution of an unknown control parameter includes: changing the complex frequency s in the theoretical transfer function to the jω form, and constructing an amplitude sensitivity solution and a phase sensitivity solution respectively; the amplitude sensitivity solution is expressed as:

[0015]

[0016] The phase sensitivity solution is expressed as:

[0017]

[0018] θ is the unknown control parameter, including the proportional coefficient K p and the integral coefficient K i ; S M is the amplitude sensitivity of the unknown control parameter, S θ is the phase sensitivity of the unknown control parameter.

[0019] In an optional embodiment of the present application, the system frequency response is calculated based on the input and output signals, including: obtaining the steady-state value when the photovoltaic power generation system is in a stable state, removing the steady-state value in the input and output signals to obtain the disturbance input and output signals; filtering the input and output signals using a moving average filtering algorithm to obtain filtered input and output signals; converting the filtered input and output signals in the form of time domain signals into recorded data of frequency domain signals using a fast Fourier transform; and calculating the system frequency response based on the recorded data.

[0020] In an optional embodiment of the present application, the system frequency response is calculated according to the recorded data, including: calculating the auto-power spectrum of the input signal of the photovoltaic power generation system and the cross-power spectrum of the input and output signals according to the recorded data; calculating the system frequency response according to the auto-power spectrum and the cross-power spectrum, and the calculation method is expressed as:

[0021]

[0022] Where H(ω) is the system frequency response; G XX (ω) is the auto-power spectrum; G XY (ω) is the cross power spectrum.

[0023] In an optional embodiment of the present application, the unknown control parameters include a proportional coefficient and an integral coefficient; the unknown control parameters are obtained using a sensitivity solution formula, including: calculating the sensitivities of the proportional coefficient and the integral coefficient respectively according to the sensitivity calculation formula; selecting an unknown control parameter with a greater sensitivity as a first parameter, and an unknown control parameter with a smaller sensitivity as a second parameter; first solving for the value of the first parameter, and then solving for the value of the second parameter after solving for the value of the first parameter.

[0024] In an optional embodiment of the present application, the method also includes: obtaining a known mechanism model, the mechanism model is determined by the main circuit topology and control method of the photovoltaic power generation system; substituting the solved unknown control parameters into the mechanism model to construct a simulation model of the photovoltaic power generation system.

[0025] The present application also provides an unknown control parameter identification device for a photovoltaic power generation system, comprising: a construction module, used to obtain current control model information of the photovoltaic power generation system, determine a theoretical transfer function based on the current control model information, wherein the theoretical transfer function includes unknown control parameters to be identified; construct a sensitivity solution for the unknown control parameters; a disturbance module, used to apply a preset disturbance signal to the input current of the photovoltaic power generation system after the photovoltaic power generation system reaches a stable state, obtain the input and output signals of the photovoltaic power generation system after the preset disturbance signal is applied, and calculate the system frequency response based on the input and output signals; and an identification module, used to fit the system frequency response to an actual transfer function, compare the actual transfer function with the theoretical transfer function, and use the sensitivity solution to obtain the unknown control parameters.

[0026] The present application also provides a computer device, including a processor and a memory: the processor is used to execute a computer program stored in the memory to implement the aforementioned method.

[0027] The present application also provides a computer-readable storage medium storing a computer program, which implements the aforementioned method when the computer program is executed by a processor.

[0028] The embodiments of the present application have the following beneficial effects:

[0029] This application can find easily identifiable parameters for the unknown parameters of the photovoltaic power generation system by applying appropriate disturbance signals and sampling periods, reasonably selecting data length, performing data preprocessing and parameter sensitivity analysis. By applying appropriate identification tools and algorithms, unknown parameters can be effectively obtained, improving the accuracy and applicability of the simulation model.

[0030] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following preferred embodiments are specifically cited and described in detail with the accompanying drawings. It should be understood that the above general description and the detailed description below are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0032] in:

[0033] Figure 1 A schematic diagram of a flow chart of a method for identifying unknown control parameters of a photovoltaic power generation system provided by an embodiment;

[0034] Figure 2 A main circuit topology and single current loop control system block diagram of a photovoltaic grid-connected inverter provided by an embodiment;

[0035] Figure 3 A schematic diagram of a SISO LTI model provided by an embodiment;

[0036] Figure 4 A schematic diagram of a d-axis current control model provided by an embodiment;

[0037] Figure 5 A diagram showing the relationship between internal functional modules of a photovoltaic power generation system unknown control parameter identification device provided by an embodiment;

[0038] Figure 6 A schematic block diagram of the structure of a computer device provided by an embodiment. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0040] The establishment of a photovoltaic grid-connected power generation system model is the key to studying its interaction with the power grid and other power electronic interface devices in the power grid. However, in the current power system analysis, due to the lack of information, power grid companies cannot directly use the models of commercial inverters developed, so in most cases they rely on manufacturers. For confidentiality reasons, the models provided by manufacturers usually have limited detailed information, and their corresponding control parameters often need to be specifically calibrated according to the actual application scenarios. The above factors have directly or indirectly led to the lack of effective models of commercial inverters in actual engineering applications.

[0041] Building a new power system with new energy as the main body, after a high proportion of new energy is connected, it faces sub- / super-synchronous oscillation, transient overvoltage, and increasingly prominent frequency stability problems. At present, the models of new energy generators include electromechanical transient models and electromagnetic transient models, but both have defects. The inherent limitations of the frequency bandwidth in the electromechanical model limit its application scope and cannot capture the dynamic behavior of electromagnetic research. However, the complete electromagnetic transient model has high requirements for simulation resources, and the existing models have many models, different characteristics, and confidential parameters. Based on this, there are the following problems.

[0042] First, manufacturers have relatively complete electromagnetic transient models that can describe the dynamic response of new energy units under fault conditions. However, for the protection of intellectual property rights, the models obtained by users are all "black box" or "gray box" models. It is difficult to obtain all model parameters from the equipment nameplate or manual provided by the manufacturer. In view of this, identifying unknown parameters based on the black box or gray box model of the new energy unit has important theoretical value and practical significance.

[0043] Second, a lot of research has been done on the modeling of new energy units at home and abroad. On the one hand, detailed models are proposed, mainly used to analyze switching frequency dynamics, test new topologies, and design new control strategies to improve transient performance. On the other hand, various simplified models are proposed. Parameters for new energy units. Compared with physical parameters, control parameters have a greater impact on the operating status and dynamic characteristics of the unit and are more meaningful for identification.

[0044] In addition, current power grid analysis mainly relies on digital simulation or hybrid simulation, and accurate models are a necessary condition for obtaining correct simulation results. For photovoltaic power generation, the equations of the mechanism model are clear, and the focus is on the identification of model parameters. There have been many research results on the identification of equivalent circuit parameters of photovoltaic panels or arrays, and the identification of parameters of MPPT controllers or models, but there are relatively few research results on the identification of inverter control parameters.

[0045] And with the large-scale access of renewable energy power generation, the proportion of renewable energy units has increased year by year, posing new challenges to the modeling of new power systems. Traditional electromechanical transient simulation methods cannot accurately simulate the response and control characteristics of equipment. This is because renewable energy power generation systems generally contain a large number of power electronic devices with complex operating characteristics. Existing models are difficult to fully meet the requirements of efficient electromagnetic transient simulation, and cannot adapt to the simulation scenarios of large-scale renewable energy power generation access to power systems and consume a lot of simulation resources. Therefore, it is necessary to improve the existing modeling and simulation methods. Based on the above problems and current situations, this application proposes a method for identifying unknown control parameters of a photovoltaic power generation system. In order to clearly describe a method for identifying unknown control parameters of a photovoltaic power generation system provided in this embodiment, please refer to Figure 1 to Figure 4 , including steps S110 to S130.

[0046] In summary, the method provided in this application determines the fusion model framework under the premise of considering the grid-connected control characteristics, implements some structures through mechanism modeling, and obtains some unknown parameters through parameter identification. This modeling method is a method for systematically analyzing and modeling complex systems under the premise of considering the grid-connected control characteristics, so as to solve at least one technical problem existing in the above-mentioned background technology.

[0047] Step S110: obtaining current control model information of the photovoltaic power generation system, determining a theoretical transfer function according to the current control model information, wherein the theoretical transfer function includes unknown control parameters to be identified; and constructing a sensitivity solution formula for the unknown control parameters.

[0048] The fusion modeling method is a method for systematically analyzing and modeling complex systems under the premise of considering the grid-connected control characteristics. The goal of this method is to establish a system model that can fully reflect the control characteristics and simplify the modeling process.

[0049] For the known parts of the system, a model based on the control mechanism is established through mechanism modeling to ensure the accuracy and reliability of the model. For the parts with unknown parameters, parameter identification is performed through experimental data. The parameter identification process includes selecting appropriate disturbance signals and sampling periods, reasonably selecting data length, performing data preprocessing and parameter sensitivity analysis to find parameters that are easy to identify. By applying appropriate identification tools and algorithms, unknown parameters can be effectively obtained, improving the accuracy and applicability of the simulation model.

[0050] The method provided in this application is applied to a photovoltaic power generation system, wherein the photovoltaic power generation unit in the system generally consists of a photovoltaic array, an inverter (DC / AC) grid connection and a control system. The main circuit topology of the system can be specifically referred to as Figure 2 As shown: the DC side is a photovoltaic array, the grid-connected inverter model consists of an IGBT module to form a three-phase full-bridge structure, and the inverted AC current is connected to the grid through a three-phase LCL filter.

[0051] Inverter output current vector control is a typical photovoltaic grid-connected inverter control strategy, which can control the grid-side active and reactive power. The current loop is implemented in the dq coordinate system. Using the two coordinate transformations of abc / αβ and αβ / dq, the grid-connected inverter output current i a 、i b 、i c It becomes the DC quantity i in the synchronous rotating coordinate system d 、i q , and then respectively with the current reference value i d *、i q * In comparison, the PI regulator is used to complete the d 、i q The zero-static-error control is finally achieved, and the switch drive signal of the grid-connected inverter is finally obtained to control the on-off of each IGBT.

[0052] Therefore, the steady-state model of the photovoltaic grid-connected inverter is established in this application, and the working state described is within the linear range near the normal working point. Therefore, each dynamic system in the model can be set to be approximately a single-input single-output linear time invariant (SISO LTI) model (such as Figure 3 However, when the converter has a wider operating range, the nonlinear structure of the system must be considered.

[0053] In one embodiment, for ease of understanding, the present application uses Figure 2 The main circuit topology of the photovoltaic power generation system shown in Figure 4 The d-axis current control model block diagram is shown for explanation. In the entire model, the main circuit structure, parameters and control circuit structure can be obtained from the equipment manufacturer or in other ways, and are all included in the current control model information. The manufacturer will not disclose the unknown control parameters that mainly affect the external characteristics, which are what this application needs to identify or obtain.

[0054] Determining a theoretical transfer function according to the current control model information includes: determining a voltage calculation formula of the output voltage of the control system according to the current control model information, the voltage calculation formula being expressed as:

[0055]

[0056] In formula (1), K p and K i are the proportional and integral coefficients of the d-axis current controller, which are unknown control parameters; L2 is the filter inductor on the bridge arm side, i 2dref is the d-axis current reference value, u cd and i 2d are the d-axis voltage and current after passing through the inductor L2; ω is the current system frequency output by the phase-locked loop. The bridge arm side filter inductance, d-axis current reference value, d-axis voltage, d-axis current and current system frequency can be directly acquired and are all included in the current control model information.

[0057] Considering the actual situation, the grid-side current is easy to measure, so the grid-side current i is selected 1d As the output for identification, after eliminating the coupling variables, the process of determining the d-axis current control expression is as follows: First, determine the control system output voltage u d :

[0058]

[0059] Then determine the calculation formula for the d-axis current:

[0060]

[0061] Substituting equation (2) into equation (3), the d-axis current control expression can be determined as follows:

[0062]

[0063] The d-axis current reference value is used as the input of the single-input single-output linear time-invariant model, and the grid-side current is used as the output of the single-output linear time-invariant model, where the d-axis current reference value is i 2dref is a measurable known value, which can be substituted into formula (4) to obtain:

[0064]

[0065] After sorting, we can deduce 2dref to 1d The theoretical transfer function is expressed as:

[0066]

[0067] In one embodiment, constructing a sensitivity solution of an unknown control parameter includes: changing the complex frequency s in the theoretical transfer function into a jω form, and constructing an amplitude sensitivity solution and a phase sensitivity solution respectively.

[0068] In one embodiment, according to formula (6), after the main circuit and the control circuit are determined, the parameters that need to be identified are the current loop control parameters, specifically, the unknown control parameters including the proportional coefficient and the integral coefficient. The difficulty analysis of parameter identification can be obtained by calculating the sensitivity of the parameters.

[0069] In one embodiment, the unknown control parameters include a proportional coefficient and an integral coefficient; the unknown control parameters are obtained using a sensitivity solution, including: calculating the sensitivity of the proportional coefficient and the integral coefficient respectively according to the sensitivity calculation formula; selecting an unknown control parameter with a greater sensitivity as a first parameter, and an unknown control parameter with a smaller sensitivity as a second parameter; first solving for the value of the first parameter, and then solving for the value of the second parameter after solving for the value of the first parameter.

[0070] In one embodiment, the sensitivity analysis method can be used to accurately identify key parameters under specific working conditions. Generally, parameters with high sensitivity are easy to identify and have high identification accuracy, while parameters with low sensitivity are difficult to identify and have low identification accuracy. For linear systems, the amplitude sensitivity and phase sensitivity can be analyzed by transfer function. That is, for i 2dre f to i 1d The theoretical transfer function of the unknown control parameter θ is K p , K i . Change the s in the theoretical transfer function to jω form, and the H(θ,s) in the original theoretical transfer function becomes H(θ,j2πf). And select Kp , K i , when K i No change, give K p A very small increment, by finding the limit we can get K p Amplitude sensitivity and phase sensitivity. K p The sensitivity of changes with the frequency f and is a function of f. Based on this, the amplitude sensitivity solution and phase sensitivity solution are established respectively. The amplitude sensitivity solution is expressed as:

[0071]

[0072] The phase sensitivity solution is expressed as:

[0073]

[0074] θ is the unknown control parameter, including the proportional coefficient K p and the integral coefficient K i ; S M is the amplitude sensitivity of the unknown control parameter, S θ is the phase sensitivity of the unknown control parameter.

[0075] In one embodiment, generally speaking, parameters with large amplitude sensitivity and phase sensitivity are easy to identify, while parameters with small amplitude sensitivity and phase sensitivity are difficult to identify. In order to make the system identifiable, the input signal must be continuously excited during the experiment, and the spectral density function of the input signal must be able to cover all modes of the system, and the spectrum must take non-zero values ​​in the full frequency range, but its power or amplitude must be limited to prevent the system from operating outside the linear range near the steady-state operating point.

[0076] Step S120: When the photovoltaic power generation system reaches a stable state, a preset disturbance signal is applied to the input current of the photovoltaic power generation system, the input and output signals of the photovoltaic power generation system after the preset disturbance signal is applied are obtained, and the system frequency response is calculated according to the input and output signals.

[0077] In one embodiment, the system frequency response is calculated based on the input and output signals, including: obtaining a steady-state value when the photovoltaic power generation system is in a stable state, removing the steady-state value in the input and output signals to obtain a disturbance input and output signal; filtering the input and output signals using a moving average filtering algorithm to obtain a filtered input and output signal; converting the filtered input and output signals in the form of time domain signals into recorded data of frequency domain signals using a fast Fourier transform; and calculating the system frequency response based on the recorded data.

[0078] In one embodiment, calculating the system frequency response based on the recorded data includes: calculating the autopower spectrum of the photovoltaic power generation system input signal and the cross-power spectrum of the input and output signals based on the recorded data; and calculating the system frequency response based on the autopower spectrum and the cross-power spectrum.

[0079] In an optional embodiment of the present application, the unknown control parameters include a proportional coefficient and an integral coefficient; the unknown control parameters are obtained using a sensitivity solution formula, including: calculating the sensitivities of the proportional coefficient and the integral coefficient respectively according to the sensitivity calculation formula; selecting an unknown control parameter with a greater sensitivity as a first parameter, and an unknown control parameter with a smaller sensitivity as a second parameter; first solving for the value of the first parameter, and then solving for the value of the second parameter after solving for the value of the first parameter.

[0080] In one embodiment, after the theoretical model is constructed, the actual model can be tested to identify unknown control parameters. The specific process can be that after the system reaches a stable state, the input current command (that is, the d-axis current reference value i 2dref ) is applied with a preset disturbance signal. Specifically, the preset disturbance signal is a multi-amplitude step sequence (composed of multiple step signals) disturbance signal.

[0081] Synchronous sampling system input signal i 2dref and the output signal i 1d , remove the steady state (steady state value before the disturbance) of the input and output signals to obtain the disturbance input and output signals. The disturbance input and output signals specifically include and grid-side current d-axis component disturbance The numbers with “^” are small signal disturbances. In order to reduce the influence of certain frequency components and noise on the identification results, the collected data can be filtered using filtering algorithms such as moving average filtering, and the time domain signal can be converted into a frequency domain signal using FFT (fast Fourier transform) to obtain the recorded data.

[0082] According to the recorded data, the auto-power spectrum of the input signal and the cross-power spectrum of the input and output signals are calculated respectively, and the system frequency response can be calculated. The calculation method is expressed as:

[0083]

[0084] In formula (9), H(ω) is the system frequency response; G XX (ω) is the auto-power spectrum; G XY (ω) is the cross power spectrum.

[0085] Step S130: Fit the system frequency response to an actual transfer function, compare the actual transfer function with the theoretical transfer function, and use the sensitivity solution to obtain unknown control parameters.

[0086] In one embodiment, after obtaining the system frequency response, an optimization algorithm is further used to fit the transfer function sequence, that is, the system frequency response is fitted into the actual transfer function. The actual transfer function obtained by fitting is compared with the theoretical transfer function obtained by equation (6). Therefore, the theoretical transfer function has two unknowns (K p , K i ), and the actual transfer function obtained by identification is a complete expression. The unknown number can be obtained by corresponding the coefficients of the numerator and denominator. After comparison, the unknown control parameter K can be obtained. p With K i .

[0087] By comparing the fusion model with the detailed model, it is verified that the fusion modeling method of photovoltaic power generation system based on mechanism and data drive can reflect the control characteristics, simplify the modeling process, and has strong applicability. This modeling method can simplify the model and maintain the simulation accuracy, which is of great significance for simulating the operation status of the photovoltaic power generation system, predicting the system behavior, and evaluating the overall performance indicators.

[0088] In one embodiment, the method provided in the present application also includes: obtaining a known mechanism model, the mechanism model is determined by the main circuit topology and control method of the photovoltaic power generation system; substituting the solved unknown control parameters into the mechanism model to construct a simulation model of the photovoltaic power generation system.

[0089] Therefore, the present application can find easily identifiable parameters for the unknown parameters of the photovoltaic power generation system by applying appropriate disturbance signals and sampling periods, reasonably selecting data length, performing data preprocessing and parameter sensitivity analysis. By applying appropriate identification tools and algorithms, the unknown parameters can be effectively obtained, and the accuracy and applicability of the simulation model can be improved.

[0090] Figure 5The internal functional module relationship diagram of an unknown control parameter identification device for a photovoltaic power generation system in one embodiment is shown. The unknown control parameter identification device 50 for a photovoltaic power generation system includes: a construction module A510, a disturbance module A520 and an identification module A530. The construction module A510 is used to obtain the current control model information of the photovoltaic power generation system, determine the theoretical transfer function according to the current control model information, and the theoretical transfer function includes the unknown control parameters to be identified. Construct a sensitivity solution for the unknown control parameters. The disturbance module A520 is used to apply a preset disturbance signal to the input current of the photovoltaic power generation system after the photovoltaic power generation system reaches a stable state, obtain the input and output signals of the photovoltaic power generation system after applying the preset disturbance signal, and calculate the system frequency response according to the input and output signals. The identification module A530 is used to fit the system frequency response to the actual transfer function, compare the actual transfer function with the theoretical transfer function, and use the sensitivity solution to obtain the unknown control parameters.

[0091] Figure 6 FIG. 1 shows an internal structure diagram of a computer device in an embodiment. The computer device may be a terminal or a server. Figure 6 As shown, the computer device includes a processor, a memory and a network interface connected via a system bus. The memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program. When the computer program is executed by the processor, the processor may implement a method for identifying unknown control parameters of a photovoltaic power generation system. The internal memory may also store a computer program. When the computer program is executed by the processor, the processor may implement a method for identifying unknown control parameters of a photovoltaic power generation system. Those skilled in the art will appreciate that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0092] In one embodiment, the present application further proposes a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of the method described in any of the aforementioned embodiments.

[0093] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0094] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0095] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for identifying unknown control parameters of a photovoltaic power generation system, characterized in that: The steps include: Acquire current control model information of the photovoltaic power generation system, determine a theoretical transfer function according to the current control model information, wherein the theoretical transfer function includes unknown control parameters to be identified; and construct a sensitivity solution formula for the unknown control parameters; When the photovoltaic power generation system reaches a stable state, applying a preset disturbance signal to the input current of the photovoltaic power generation system, acquiring input and output signals of the photovoltaic power generation system after applying the preset disturbance signal, and calculating the system frequency response according to the input and output signals; The system frequency response is fitted into an actual transfer function, the actual transfer function is compared with the theoretical transfer function, and the unknown control parameter is obtained using the sensitivity solution.

2. The method for identifying unknown control parameters of a photovoltaic power generation system according to claim 1, characterized in that: The determining of the theoretical transfer function according to the current control model information comprises: The voltage calculation formula of the control system output voltage is determined according to the current control model information, and the voltage calculation formula is expressed as: In the formula, K p and K i are the proportional and integral coefficients of the d-axis current controller, which are the unknown control parameters; L2 is the filter inductance on the bridge arm side, i 2dref is the d-axis current reference value, u cd and i 2d They are the d-axis voltage and d-axis current after passing through the inductor L2, respectively. The bridge arm side filter inductor, the d-axis current reference value, the d-axis voltage and the d-axis current can be directly acquired; The d-axis current control expression is determined according to the voltage calculation formula, which is expressed as: The d-axis current reference value is used as the input of the single-input single-output linear time-invariant model, the grid-side current is used as the output of the single-output linear time-invariant model, and the theoretical transfer function is determined according to the d-axis current control expression, which is expressed as:

3. The method for identifying unknown control parameters of a photovoltaic power generation system according to claim 2, characterized in that: The sensitivity solution formula for constructing the unknown control parameter includes: The complex frequency s in the theoretical transfer function is changed into the jω form, and the amplitude sensitivity solution and the phase sensitivity solution are constructed respectively; The amplitude sensitivity solution is expressed as: The phase sensitivity solution is expressed as: θ is the unknown control parameter, including the proportional coefficient K p and the integration coefficient K i ; S M is the amplitude sensitivity of the unknown control parameter, S θ is the phase sensitivity of the unknown control parameter.

4. The method for identifying unknown control parameters of a photovoltaic power generation system according to claim 1, characterized in that: The step of calculating the system frequency response according to the input and output signals comprises: Acquire a steady-state value when the photovoltaic power generation system is in a stable state, and remove the steady-state value from the input-output signal to obtain a disturbance input-output signal; Filtering the input and output signals using a moving average filtering algorithm to obtain filtered input and output signals; Converting the filtered input and output signals in the form of time domain signals into recorded data of frequency domain signals using fast Fourier transform; The system frequency response is calculated based on the recorded data.

5. The method for identifying unknown control parameters of a photovoltaic power generation system according to claim 4, characterized in that: The calculating the system frequency response according to the recorded data comprises: Calculating the autopower spectrum of the input signal of the photovoltaic power generation system and the cross-power spectrum of the input and output signals according to the recorded data; The system frequency response is calculated according to the auto-power spectrum and the cross-power spectrum, and the calculation method is expressed as: Where H(ω) is the frequency response of the system; G XX (ω) is the autopower spectrum; G XY (ω) is the cross power spectrum.

6. The method for identifying unknown control parameters of a photovoltaic power generation system according to claim 1, characterized in that: The unknown control parameters include a proportional coefficient and an integral coefficient; The step of obtaining the unknown control parameter by using the sensitivity solution formula includes: Calculating the sensitivity of the proportional coefficient and the integral coefficient respectively according to the sensitivity calculation formula; The unknown control parameter with greater sensitivity is selected as the first parameter, and the unknown control parameter with less sensitivity is selected as the second parameter; The value of the first parameter is first solved, and then the value of the second parameter is solved after the value of the first parameter is solved.

7. The method for identifying unknown control parameters of a photovoltaic power generation system according to claim 1, characterized in that: The method for identifying unknown control parameters of a photovoltaic power generation system further comprises: Acquire a known mechanism model, wherein the mechanism model is determined by a main circuit topology and a control method of the photovoltaic power generation system; The unknown control parameters obtained by solving the problem are substituted into the mechanism model to construct a simulation model of the photovoltaic power generation system.

8. A device for identifying unknown control parameters of a photovoltaic power generation system, characterized in that: include: A construction module is used to obtain current control model information of the photovoltaic power generation system, determine a theoretical transfer function according to the current control model information, wherein the theoretical transfer function includes unknown control parameters to be identified; and construct a sensitivity solution formula for the unknown control parameters; A disturbance module is used to apply a preset disturbance signal to the input current of the photovoltaic power generation system after the photovoltaic power generation system reaches a stable state, obtain the input and output signals of the photovoltaic power generation system after the preset disturbance signal is applied, and calculate the system frequency response according to the input and output signals; The identification module is used to fit the system frequency response into an actual transfer function, compare the actual transfer function with the theoretical transfer function, and obtain the unknown control parameter using the sensitivity solution.

9. A computer device, characterized in that: including a processor and a memory; The processor is configured to execute the computer program stored in the memory to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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