Method and device for determining control parameters of photovoltaic power generation system

Abstract

The present application provides a method and an apparatus for determining control parameters of a photovoltaic power generation system. The photovoltaic power generation system has an inverter, and the method includes: obtaining a plurality of control parameters to be determined of the inverter; calculating the absolute sensitivities of the plurality of control parameters to be determined respectively when a preset condition is satisfied. The preset condition is to perturb different observed variables of the inverter, and each of the plurality of control parameters to be determined has a corresponding absolute sensitivity under each perturbation; judging the identifiability of the plurality of control parameters to be determined according to the absolute sensitivities corresponding to the plurality of control parameters to be determined. The identifiability characterizes the degree of ease of determining the control parameters to be determined; determining the plurality of control parameters to be determined according to the identifiability; and controlling the operation of the photovoltaic power generation system according to the control parameters. The problem that all parameters of the inverter of the photovoltaic power generation system cannot be accurately obtained, and thus the photovoltaic power generation system cannot be controlled to operate stably is solved.

Description

Technical Field

[0001] The present application relates to the field of photovoltaic power generation, and in particular, to a method for determining control parameters of a photovoltaic power generation system, a determining device, a computer-readable storage medium, and an electronic device. Background Technique

[0002] At present, renewable new energy power generation technology has received extensive attention. Photovoltaic power generation can promote the active consumption of widely distributed solar energy resources and is an important means for building a modern intelligent clean power grid. With the continuous increase in the grid-connected capacity and scale of photovoltaic power generation, it may trigger major technical problems in the current power grid, such as the stability and security of the photovoltaic power generation grid-connected system. The photovoltaic inverter is an important device for photovoltaic grid connection, and its dynamic performance affects the safe and stable operation of the entire photovoltaic power generation grid-connected system.

[0003] In practical engineering applications, the photovoltaic grid-connected inverter controller usually includes a phase-locked loop, a voltage and current double closed-loop controller, etc. However, equipment manufacturers usually do not provide all accurate photovoltaic inverter control parameters. Currently, most methods use methods such as setting a voltage dip on the grid-connected side or setting a step disturbance of the controller reference value to stimulate the controller dynamics, and then use heuristic algorithms such as the particle swarm algorithm and the genetic algorithm to obtain the controller model parameters.

[0004] However, due to the high coupling between the phase-locked loop of the photovoltaic inverter and the current inner loop, and the tight cascade of the controller voltage outer loop and the current inner loop, the previous methods are difficult to solve the identifiability problem of all controller parameters. Even if heuristic algorithms with strong global optimization capabilities are used, it is impossible to stably and accurately obtain all the parameters of the controller. Summary of the Invention

[0005] The main purpose of the present application is to provide a method for determining control parameters of a photovoltaic power generation system, a determining device, a computer-readable storage medium, and an electronic device, so as to at least solve the problem in the prior art that due to the high coupling of different types of parameters of the inverter in the photovoltaic power generation system, it is impossible to accurately obtain all the parameters of the inverter in the photovoltaic power generation system, and thus it is impossible to control the stable operation of the photovoltaic power generation system.

[0006] To achieve the above object, according to one aspect of the present application, a method for determining control parameters of a photovoltaic power generation system is provided. The photovoltaic power generation system has an inverter, and the method includes: obtaining a plurality of control parameters to be determined of the inverter; calculating the absolute sensitivities of the plurality of control parameters to be determined respectively when a preset condition is satisfied. The preset condition is to perturb different observed variables of the inverter, and each time a perturbation occurs, the plurality of control parameters to be determined each have a corresponding absolute sensitivity; judging the identifiability of the plurality of control parameters to be determined according to the absolute sensitivities corresponding to the plurality of control parameters to be determined. The identifiability represents the degree of ease of determining the control parameters to be determined; determining the plurality of control parameters to be determined according to the identifiability; and controlling the operation of the photovoltaic power generation system according to the control parameters.

[0007] Optionally, the determination method further includes: establishing a control model of the inverter of the photovoltaic power generation system: , where and are the conversion values of the three-phase measured voltages at the PCC on the grid-connected side of the photovoltaic power generation system in the dq0 coordinate system; and are the conversion values of the three-phase measured currents at the PCC on the grid-connected side of the photovoltaic power generation system in the dq0 coordinate system; and are the conversion values of the three-phase output voltages of the inverter in the dq0 coordinate system; L is the inductance between the inverter output and the PCC measurement point; is the output of the phase-locked loop angular frequency; determining the control parameters to be determined according to the control model.

[0008] Optionally, calculating the absolute sensitivities of the plurality of control parameters to be determined respectively includes: calculating according to the formula of the absolute sensitivity to determine the absolute sensitivities of the plurality of control parameters to be determined. The formula of the absolute sensitivity is: , where S is the absolute sensitivity of the parameter, O(x) is the simulation output of the control model of the inverter of the photovoltaic power generation system, is the parameter to be analyzed, is the increment size of the parameter, is the actual value of the parameter.

[0009] Optionally, determining the plurality of control parameters to be determined according to the identifiability includes: perturbing different observed variables, selecting the control parameter with the highest identifiability among the plurality of control parameters to be determined as the determination object, and determining the plurality of control parameters to be determined in sequence.

[0010] Optionally, determining the multiple control parameters to be determined includes: obtaining random combination samples of the multiple control parameters to be determined, where the random combination samples include the object to be determined; identifying the random combination samples according to the standard particle swarm algorithm to obtain parameter samples of multiple groups of the object to be determined; and iteratively screening the parameter samples according to the iterative algorithm to obtain the identification result of the object to be determined, thereby completing the determination of the control parameters to be determined.

[0011] Optionally, iteratively screening the parameter samples according to the iterative algorithm to obtain the identification result of the object to be determined includes: an obtaining step: obtaining the preset clustering number of the iterative algorithm; a control step: grouping multiple groups of the random combination samples according to the preset clustering number, and setting the group of the random combination samples with the most parameters among the grouped multiple groups of random combination samples as the reference class sample; a calculation step: respectively calculating multiple concentration degrees of the reference class sample and other multiple random combination samples; a first judgment step: judging whether each concentration degree is greater than a first preset threshold to obtain a first judgment result, and when the first judgment result indicates yes, eliminating the random combination samples with the concentration degree greater than the first preset threshold, and merging the remaining random combination samples and the reference class sample to form a new random combination sample; a second judgment step: judging whether the distance between the maximum parameter and the central parameter of the new random combination sample is greater than or equal to a second preset threshold, and whether the distance between the minimum parameter and the central parameter of the random combination sample is greater than or equal to the second preset threshold to obtain a second judgment result, where the central parameter is the parameter with the intermediate value in the random combination sample; a loop step: when the second judgment result indicates no, looping through at least one of the obtaining step, the control step, the calculation step, the first judgment step, and the second judgment step until the second judgment result indicates yes, thereby obtaining the identification result of the object to be determined.

[0012] Optionally, the multiple control parameters to be determined include: a phase-locked loop proportional coefficient, a phase-locked loop integral coefficient, a voltage outer loop proportional coefficient, a voltage outer loop integral coefficient, a current inner loop proportional coefficient, and a current inner loop integral coefficient.

[0013] According to another aspect of the present application, there is provided an apparatus for determining control parameters of a photovoltaic power generation system, where the photovoltaic power generation system has an inverter, including: an acquisition module, configured to acquire a plurality of control parameters to be determined of the inverter; a first control module, configured to calculate the absolute sensitivity of the plurality of control parameters to be determined respectively when a preset condition is satisfied, where the preset condition is to perturb different observed variables of the inverter, and the plurality of control parameters to be determined each have a corresponding absolute sensitivity in each case of perturbation; a judgment module, configured to judge the identifiability of the plurality of control parameters to be determined according to the absolute sensitivities corresponding to the plurality of control parameters to be determined, where the identifiability represents the degree of ease of determining the control parameters to be determined; a first determination module, configured to determine the plurality of control parameters to be determined according to the identifiability; and a second control module, configured to control the operation of the photovoltaic power generation system according to the control parameters.

[0014] According to another aspect of the present application, there is provided a computer-readable storage medium, where the computer-readable storage medium includes a stored program, and when the program runs, it controls the device where the computer-readable storage medium is located to execute any one of the methods for determining control parameters of the photovoltaic power generation system.

[0015] According to another aspect of the present application, there is provided an electronic device, including: one or more processors, a memory, and one or more programs, where the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include those for executing any one of the methods for determining control parameters of the photovoltaic power generation system.

[0016] Applying the technical solution of the present application, in the above method for determining control parameters of a photovoltaic power generation system, first, a plurality of control parameters to be determined of the inverter of the photovoltaic power generation system are acquired, observed variables of the inverter are selected, and the observed variables of the inverter are controlled to be perturbed. When the observed variables are perturbed, the sensitivity of the plurality of control parameters to be determined is calculated, and the identifiability of the control parameters to be determined is judged according to the sensitivity, that is, the degree of ease of determining and selecting the control parameters to be determined is judged. The control parameter to be determined with the highest identifiability in each perturbation process is selected, and the selected control parameter to be determined is determined. After multiple perturbations, all the control parameters to be determined of the inverter can be determined, and according to the control parameters, the photovoltaic power generation system can be controlled to operate more stably. This solves the problem in the prior art that due to the high coupling of different types of parameters of the inverter of the photovoltaic power generation system, all the parameters of the inverter of the photovoltaic power generation system cannot be accurately acquired, and thus the photovoltaic power generation system cannot be controlled to operate stably. Description of the Drawings

[0017] The accompanying drawings of the specification, which form a part of this application, are used to provide a further understanding of this application. The schematic embodiments and descriptions thereof of this application are used to explain this application and do not constitute an improper limitation to this application. In the drawings:

[0018] Figure 1 A hardware structure block diagram of a mobile terminal for implementing a method for determining control parameters of a photovoltaic power generation system provided in an embodiment of this application is shown;

[0019] Figure 2 A schematic flowchart of a method for determining control parameters of a photovoltaic power generation system provided in an embodiment of this application is shown;

[0020] Figure 3 A waveform schematic diagram of the q-axis component of the grid-connected side voltage of a photovoltaic power generation system provided in an embodiment of this application is shown with respect to time - amplitude;

[0021] Figure 4 A schematic flowchart of the execution of a disturbance generating device of a photovoltaic power generation system provided in an embodiment of this application is shown;

[0022] Figure 5 A grid-connected side current of a photovoltaic power generation system provided in an embodiment of this application is shown with respect to a time - amplitude waveform schematic diagram;

[0023] Figure 6 A waveform schematic diagram of the grid-connected side reactive power measurement signal Q of a photovoltaic power generation system provided in an embodiment of this application with respect to time - amplitude is shown;

[0024] Figure 7 A waveform schematic diagram of the voltage measurement of the DC capacitor on the grid-connected side of a photovoltaic power generation system provided in an embodiment of this application with respect to time - amplitude is shown;

[0025] Figure 8 An actual voltage measurement signal of the DC capacitor on the grid-connected side of a photovoltaic power generation system provided in an embodiment of this application is shown with respect to a time - amplitude waveform schematic diagram;

[0026] Figure 9 A screening result schematic diagram of the phase - locked loop control parameters of a photovoltaic power generation system provided in an embodiment of this application is shown of;

[0027] Figure 10 A screening result schematic diagram of the phase - locked loop control parameters of a photovoltaic power generation system provided in an embodiment of this application is shown of;

[0028] Figure 11 Shows the screening result schematic diagram of the current inner loop control parameters of a photovoltaic power generation system provided according to an embodiment of the present application. ;

[0029] Figure 12 Shows the screening result schematic diagram of the current inner loop control parameters of a photovoltaic power generation system provided according to an embodiment of the present application. ;

[0030] Figure 13 Shows the schematic diagram of a device for determining the control parameters of a photovoltaic power generation system provided according to an embodiment of the present application.

[0031] Among them, the above-mentioned drawings include the following reference numerals:

[0032] 102, processor; 104, memory; 106, transmission device; 108, input / output device. Detailed implementation manners

[0033] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0034] In order to enable those skilled in the art to better understand the solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0035] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so as to describe the embodiments of the present application here. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these process, method, product or device.

[0036] As introduced in the background art, in the prior art, the phase-locked loop of the photovoltaic inverter and the current inner loop are highly coupled, and the controller voltage outer loop and the current inner loop are closely cascaded. It is difficult for the conventional methods to solve the identifiability problem of all controller parameters. Even if a heuristic algorithm with strong global optimization ability is adopted, it is impossible to stably and accurately obtain all the parameters of the controller.

[0037] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present invention.

[0038] The method embodiments provided in the embodiments of the present application can be executed on a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 is a hardware structural block diagram of a mobile terminal for a method of determining control parameters of a photovoltaic power generation system according to an embodiment of the present invention. As Figure 1 shown, the mobile terminal may include one or more ( Figure 1 only one is shown in Figure 1 a processor 102 (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data. Among them, the above mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those of ordinary skill in the art can understand that Figure 1 the structure shown in Figure 1 is only schematic and does not limit the structure of the above mobile terminal. For example, the mobile terminal may further include more or fewer components than

[0039] The memory 104 can be used to store computer programs, such as software programs and modules of application software, such as the computer program corresponding to the method for determining the control parameters of the photovoltaic power generation system in the embodiments of the present invention. The processor 102 executes various functional applications and data processing by running the computer programs stored in the memory 104, that is, implements the above-mentioned method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some instances, the memory 104 may further include a memory remotely disposed relative to the processor 102, and these remote memories can be connected to the mobile terminal through a network. Examples of the above-mentioned network include but are not limited to the Internet, enterprise intranet, local area network, mobile communication network, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the above-mentioned network may include the wireless network provided by the communication provider of the mobile terminal. In one instance, the transmission device 106 includes a network adapter (Network Interface Controller, abbreviated as NIC), which can be connected to other network devices through a base station and thus can communicate with the Internet. In one instance, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0040] In this embodiment, a method for determining the control parameters of a photovoltaic power generation system running on a mobile terminal, a computer terminal, or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0041] Figure 2 It is a flowchart of the method for determining the control parameters of the photovoltaic power generation system according to the embodiments of the present application. As Figure 2 shown, the above-mentioned photovoltaic power generation system has an inverter, and the method includes the following steps:

[0042] Step S201, obtaining a plurality of control parameters to be determined of the above-mentioned inverter;

[0043] Specifically, obtain the parameters of the inverter that need to be determined, so as to screen the above-mentioned control parameters to be determined subsequently.

[0044] Step S202: When the preset conditions are met, calculate the absolute sensitivities of the multiple control parameters to be determined respectively. The preset conditions are to perturb different observed variables of the inverter, and for each perturbation of the multiple control parameters to be determined, there is a corresponding absolute sensitivity.

[0045] Specifically, set the perturbations of multiple types of systems to fully stimulate the dynamic characteristics of the inverter phase-locked loop parameters, voltage outer-loop parameters, and current inner-loop parameters, and observe the dynamic outputs of the observed variables of the system inverter. The steps of the perturbation include: S2021, set the grid-connected side voltage dip perturbation 1 of the photovoltaic power generation system connected to the inverter. Set a three-phase short-circuit fault with a duration of 0.05 s on the grid-connected side transmission line to cause the system voltage to drop and stimulate the dynamic of the inverter phase-locked loop. Figure 3 The q-axis component of the grid-connected side voltage is given The waveform of time-amplitude; S2022, set the grid-connected side current measurement perturbation 2. First, use the digital signal processor (DSP, Digital Signal Processing) in the perturbation generating device as shown Figure 4 to obtain the actual grid-connected side current input from the actual signal of the generating device, the steady-state component, and through the digital-to-analog converter (D / A), preset a step measurement current perturbation signal (preset perturbation signal) with an amplitude of and a duration of 0.05 s, and transmit the preset perturbation signal to the channel switching switch. The actual measurement signal input from the actual signal is also transmitted to the channel switching switch. Then, the generating device issues a channel conversion instruction, and the DSP outputs the set measurement current perturbation signal from the output end of the perturbation generating device to the input end of the inverter current inner loop through the channel switching switch. At the same time, to decouple the simultaneous operation of the inner and outer loops of the inverter and stabilize the inverter voltage, the voltage of the signal at the input end of the outer loop is to shield the action of the inverter voltage outer loop, and the grid-connected side current of perturbation 2 The time-amplitude waveform is as shown Figure 5 in the figure. Figure 6 The waveform diagram of the grid-connected side reactive power measurement signal Q with respect to time-amplitude is given; S2023, set the voltage measurement perturbation 3 of the DC capacitor. Use the perturbation generating device as shown Figure 4 to obtain the steady-state signal of the DC side capacitor measurement, and through the D / A converter, a step perturbation with an amplitude of and a duration of 0.05 s. Then, the generating device issues a channel conversion instruction, and the DSP conveys the set voltage perturbation signal of the capacitor from the output end of the perturbation generating device to the input end of the outer loop of the inverter voltage through the channel switching switch, as shown Figure 7As shown, the waveform of the voltage measurement of the disturbance 3 DC capacitor with respect to time - amplitude. Figure 8 The actual measured signal of the voltage of the DC capacitor is given Schematic diagram of the waveform with respect to time - amplitude.

[0046] Step S203: According to the above absolute sensitivities corresponding to the multiple control parameters to be determined, determine the identifiability of the multiple control parameters to be determined. The identifiability characterizes the degree of ease of determination of the control parameters to be determined.

[0047] Specifically, calculate the absolute sensitivities of each control parameter of the inverter under different disturbances and observed variables. The parameter offset in the parameter absolute sensitivity calculation formula is set to 10%. At the same time, 3 actually easily measurable observed variables are selected for the sensitivity analysis of the inverter control parameters and the distinguishable identifiability of multiple control parameters is analyzed. A step - by - step identification strategy for the control parameters is designed. The observed variables include the q - axis component of the grid - connected side voltage , the reactive power measurement signal Q and the actual measured signal of the voltage of the DC capacitor .

[0048] Step S204: According to the above identifiability, determine the multiple control parameters to be determined;

[0049] Specifically, during the process of disturbing each group of the observed variables, observe the identifiability of the control parameters to be determined. For example, in the case of the above disturbance 1, the phase - locked loop control parameter has a relatively large or obvious sensitivity with respect to the control parameter of the current inner loop and the control parameter of the voltage outer loop, and the phase - locked loop control parameter can be clearly screened out. Then, in the case of the above disturbance 1, the phase - locked loop control parameter has a high identifiability.

[0050] Step S205: According to the above control parameters, control the operation of the photovoltaic power generation system.

[0051] Specifically, equipment manufacturers usually do not provide all the accurate photovoltaic inverter control parameters. After all the above control parameters are confirmed, the photovoltaic power generation system can operate more stably and accurately.

[0052] Through the above method for determining the control parameters of the photovoltaic power generation system, first, obtain multiple control parameters to be determined of the inverter of the photovoltaic power generation system, select the observed variables of the inverter, and control the observed variables of the inverter to perform perturbations. Under the condition of the perturbation of the observed variables, calculate the sensitivities of the multiple control parameters to be determined, and judge the identifiability of the control parameters to be determined according to the sensitivities, that is, judge the degree to which the control parameters to be determined are easily determined and selected. Select the control parameters to be determined with the highest identifiability during each perturbation process, determine the selected control parameters to be determined. After multiple perturbations, all the control parameters to be determined of the inverter can be determined. According to the above control parameters, the photovoltaic power generation system can be controlled to operate more stably. This solves the problem in the prior art that due to the high coupling of different types of parameters of the inverter of the photovoltaic power generation system, it is impossible to accurately obtain all the parameters of the inverter of the photovoltaic power generation system, and thus it is impossible to control the stable operation of the photovoltaic power generation system.

[0053] In some alternative embodiments, the above determination method further includes: establishing a control model of the inverter of the photovoltaic power generation system:

[0054] ,

[0055] wherein, and are the conversion values of the three-phase measured voltages at the PCC on the grid-connected side of the photovoltaic power generation system in the dq0 coordinate system; and are the conversion values of the three-phase measured currents at the PCC on the grid-connected side of the photovoltaic power generation system in the dq0 coordinate system; and are the conversion values of the three-phase voltages output by the inverter in the dq0 coordinate system; L is the inductance between the output of the inverter and the PCC measurement point; is the output of the phase-locked loop angular frequency; according to the above control model, determine the control parameters to be determined.

[0056] In the above alternative embodiments, the control parameters to be determined include phase-locked loop control parameters, current inner-loop control parameters, and voltage outer-loop control parameters, wherein,

[0057] Phase-locked loop transfer function:

[0058] ,

[0059] wherein, and are respectively the proportional coefficient and integral coefficient of the phase-locked loop PI controller.

[0060] Voltage outer-loop control transfer function:

[0061] ,

[0062] wherein, is the d-axis reference value of the grid-connected current generated by the voltage outer-loop PI controller; is the DC capacitor voltage, is the reference value of the DC capacitor voltage, with a value of 500V; and are the proportional coefficient and integral coefficient of the voltage outer-loop PI controller respectively.

[0063] Current inner-loop control transfer function:

[0064] ,

[0065] wherein, and are the input voltages of the three-phase rectifier bridge in the dq0 coordinate system respectively; is the system steady-state frequency, with a value of 60Hz; is the q-axis reference value of the grid-connected current, with a value of 0; and are the proportional coefficient and integral coefficient of the current inner-loop PI controller respectively.

[0066] In the photovoltaic power generation system with an inverter connected, there are a total of 6 control parameters to be identified, which are: the PLL PI controller parameters and , the voltage outer-loop PI controller parameters and , the current inner-loop PI controller parameters and .

[0067] In some alternative embodiments, the absolute sensitivities of the above-mentioned multiple control parameters to be determined are calculated respectively, including: calculating according to the formula of the above-mentioned absolute sensitivity to determine the absolute sensitivities of the above-mentioned multiple control parameters to be determined, and the formula of the above-mentioned absolute sensitivity is:

[0068] ,

[0069] wherein, S is the absolute sensitivity of the parameter, O(x) is the simulation output of the control model of the photovoltaic power generation system inverter, is the parameter to be analyzed, is the increment size of the parameter, is the actual value of the parameter.

[0070] In the above-mentioned alternative embodiments, the parameter offset Set to 10%, and at the same time, three actually easy-to-measure observation variables are selected for the sensitivity analysis of the inverter control parameters. Table 1 shows the sensitivity calculation results of the inverter control parameters.

[0071] Table 1

[0072]

[0073] In some alternative embodiments, according to the above-mentioned identifiability, the multiple control parameters to be determined are determined, including: perturbing different ones of the above-mentioned observation variables, selecting the control parameter with the highest identifiability among the multiple control parameters to be determined as the determination object, and determining the multiple control parameters to be determined in sequence.

[0074] In the above alternative embodiment, the distinguishable identifiability of multiple control parameters is analyzed, and a step-by-step identification strategy for the control parameters is designed. As can be seen from Table 1, the sensitivity values of the control parameters of the phase-locked loop, the outer voltage loop, and the inner current loop have large differences under three perturbations. The control parameter of the phase-locked loop has a relatively large sensitivity under perturbation 1, the control parameter of the inner current loop has a relatively large sensitivity under perturbation 2, while the sensitivity of the parameters of the outer voltage loop has no large gap compared with other parameters. Therefore, to improve the identifiability of the six control parameters of the inverter, a step-by-step identification strategy for the control parameters is designed. Specifically: First, under perturbation 1, taking the q-axis component signal of the grid-connected side voltage as the observation variable, identify the control parameters of the phase-locked loop and ; then, under perturbation 2, taking the measured signal Q of the grid-connected side reactive power as the observation variable, identify the control parameters of the inner current loop and ; finally, under perturbation 3, taking the actually measured signal of the DC capacitor voltage as the observation variable, identify the control parameters of the outer voltage loop and .

[0075] In some alternative embodiments, determining the multiple control parameters to be determined includes: obtaining a random combination sample of the multiple control parameters to be determined, where the random combination sample includes the determination object; identifying the random combination sample according to the standard particle swarm algorithm to obtain parameter samples of multiple groups of the determination object; and iteratively screening the parameter samples according to the iterative algorithm to obtain the identification result of the determination object, thereby completing the determination of the control parameters to be determined.

[0076] In the above optional embodiment, under the grid-connected side voltage dip disturbance (disturbance 1), the steps of randomly assigning values to the control parameters of the voltage outer loop and the current inner loop, identifying the phase-locked loop control parameters multiple times, and screening out the best identification results through the iterative K-means algorithm include the following sub-steps:

[0077] S11. Since the true values of the inverter control parameters are actually unknown, 50 random combinations of the control parameters of the voltage outer loop and the current inner loop are set to reduce the influence of inaccurate parameter values of the voltage outer loop and the current inner loop on the independent identification of the phase-locked loop control parameters.

[0078] S12. Under 50 sets of samples, the standard particle swarm algorithm is used to independently identify the phase-locked loop control parameters. The number of particles is taken as 20 and the iteration is 10 times to obtain a total sample of 50 sets of identification results of the phase-locked loop control parameters.

[0079] S13. The cyclic K-means algorithm is used to screen out the sample cluster with the highest concentration from the 50 sets of total samples. Then, the average value of the samples in the cluster with the highest concentration is used as the final identification result of the phase-locked loop control parameters. Figure 9 The screening result graph (number of identification results - identification value) of the phase-locked loop control parameters is given, and the screening result graph (number of identification results - identification value) of the phase-locked loop control parameters is given. The final identification results of the two parameters are listed in Table 2. Figure 10 The screening result graph (number of identification results - identification value) of the phase-locked loop control parameters is given, and the screening result graph (number of identification results - identification value) of the phase-locked loop control parameters is given. The final identification results of the two parameters are listed in Table 2.

[0080] Under the set current measurement disturbance at the input end of the inverter, the steps of decoupling and identifying the control parameters of the current inner loop include the following sub-steps:

[0081] S21. The phase-locked loop parameters are taken as the identified results, 50 random combinations of the control parameters of the voltage outer loop are set, and then, under these 50 sets of samples, a total sample of 50 sets of identification results of the control parameters of the current inner loop is identified;

[0082] S22. Similarly, the cyclic K-means algorithm is used to obtain the final identification result of the control parameters of the current inner loop. Figure 11 The screening result graph (number of identification results - identification value) of the control parameters of the current inner loop is given, and the screening result graph (number of identification results - identification value) of the control parameters of the current inner loop is given, Figure 12 The screening result graph (number of identification results - identification value) of the control parameters of the current inner loop is given, and the screening result graph (number of identification results - identification value) of the control parameters of the current inner loop is given. The final identification results of the two parameters are listed in Table 2.

[0083] Under the measurement disturbance of the DC capacitor voltage at the input end of the set inverter, the voltage outer loop control parameters are independently identified once, and the identification results of all the control parameters of the inverter are obtained and listed in Table 2 in full. It can be seen from Table 2 that the identification result accuracy of all the control parameters of the inverter is very high, indicating the feasibility of the method of the present invention.

[0084] Table 2

[0085]

[0086] In some alternative embodiments, according to the iterative algorithm, the above parameter samples are iteratively screened to obtain the identification result of the above determined object, including: acquisition step: acquiring the preset clustering number of the above iterative algorithm; control step: grouping multiple groups of the above randomly combined samples according to the above preset clustering number, and setting the group of the above randomly combined samples with the most parameters in the grouped multiple groups of randomly combined samples as the reference class sample; calculation step: respectively calculating the multiple concentration degrees of the above reference class sample and multiple other above randomly combined samples; first judgment step: judging whether each of the above concentration degrees is greater than a first preset threshold to obtain a first judgment result, and in the case where the first judgment result indicates yes, eliminating the above randomly combined samples with the above concentration degrees greater than the above first preset threshold, and merging the remaining above randomly combined samples and the above reference class sample to form a new above randomly combined sample; second judgment step: judging whether the distance between the maximum parameter in the new above randomly combined sample and the central parameter of the above randomly combined sample is greater than or equal to a second preset threshold, and whether the distance between the minimum parameter and the central parameter of the above randomly combined sample is greater than or equal to the above second preset threshold to obtain a second judgment result, the above central parameter being the parameter with the intermediate value in the above randomly combined sample; loop step: in the case where the second judgment result indicates no, looping through at least one of the above acquisition step, the above control step, the above calculation step, the above first judgment step and the above second judgment step until the second judgment result indicates yes, to obtain the identification result of the above determined object.

[0087] In the above optional embodiment, the step of using the cyclic K-means algorithm to screen out the best identification result includes: obtaining the preset number of clusters of the above iterative algorithm, determining the total sample of identification results of the parameter to be identified; if the number of K-means clusters is set to 3, the initial cluster center is set to a random value, and clustering is started; the class with the most sample individuals is selected as the reference class, and the distance (concentration) to the other two classes is calculated; if the relative distance does not exceed A=1% (the first preset threshold), the three groups are merged into one group, and the K-means algorithm is iterated until the maximum individual and the minimum individual in the new sample are both within B=1% (the second preset threshold) from the sample center, and the average value of the finally retained sample is used as the final identification result of the parameter to be identified; if the condition of whether the maximum individual and the minimum individual in the new sample are both within B=1% from the sample center is not met, the above clustering, reference sample selection and concentration calculation steps are re-executed until the condition is met and the final sample is output. If the relative distance exceeds A=1%, the excess class with a relative distance exceeding A=1% (the first preset threshold) is eliminated, and the remaining classes are merged to form a new sample; the K-means algorithm is iterated until the maximum individual and the minimum individual in the new sample are all within B=1% (the second preset threshold) of the sample center, and the average value of the final retained sample is used as the final identification result of the parameter to be identified; if the condition that the maximum individual and the minimum individual in the new sample are all within B=1% of the sample center is not met, the above steps of clustering, selecting reference samples and calculating concentration are re-executed until the conditions are met and the final sample is output.

[0088] In some optional implementations, the multiple control parameters to be determined include: a phase-locked loop proportional coefficient, a phase-locked loop integral coefficient, a voltage outer loop proportional coefficient, a voltage outer loop integral coefficient, a current inner loop proportional coefficient, and a current inner loop integral coefficient.

[0089] In the above optional embodiment, the photovoltaic inverter is a voltage source inverter, and the inverter main circuit is a three-phase bridge structure. The inverter phase-locked loop is a PI control structure, and the inverter controller adopts a voltage and current dual closed-loop PI control structure. The parameters of the parameters are as described above. The simulation system is built on the Matlab2019a / Simulink software platform. The components and parameters use the photovoltaic grid-connected power generation system example provided by the software. The photovoltaic power generation system operates under standard test conditions. The true value and value range of the inverter control parameters to be identified are shown in Table 3. The control parameters to be determined of the inverter include: phase-locked loop proportional coefficient, phase-locked loop integral coefficient, voltage outer loop proportional coefficient, voltage outer loop integral coefficient, current inner loop proportional coefficient and current inner loop integral coefficient. The above-mentioned control parameters to be determined are also called control parameters to be identified, and their true values and value ranges are shown in Table 3.

[0090] Table 3

[0091]

[0092] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0093] The embodiment of the present application also provides a device for determining control parameters of a photovoltaic power generation system. It should be noted that the device for determining control parameters of the photovoltaic power generation system in the embodiment of the present application can be used to execute the method for determining control parameters of the photovoltaic power generation system provided in the embodiment of the present application. This device is used to implement the above-mentioned embodiments and preferred implementation manners, and those that have been described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0094] The following introduces the device for determining control parameters of the photovoltaic power generation system provided in the embodiment of the present application.

[0095] Figure 13 is a schematic diagram of the device for determining control parameters of the photovoltaic power generation system according to the embodiment of the present application. As Figure 13 shown, the above-mentioned photovoltaic power generation system has an inverter, and the device includes: an acquisition module 100, configured to acquire a plurality of control parameters to be determined of the above-mentioned inverter; a first control module 200, configured to calculate the absolute sensitivity of the above-mentioned plurality of control parameters to be determined respectively when a preset condition is met, the preset condition being to perturb different observation variables of the above-mentioned inverter, and the above-mentioned plurality of control parameters to be determined each have a corresponding absolute sensitivity in each case of perturbation; a judgment module 300, configured to judge the identifiability of the above-mentioned plurality of control parameters to be determined according to the absolute sensitivity corresponding to the above-mentioned plurality of control parameters to be determined, the identifiability characterizing the degree of ease of determination of the control parameters to be determined; a first determination module 400, configured to determine the above-mentioned plurality of control parameters to be determined according to the identifiability; a second control module 500, configured to control the operation of the above-mentioned photovoltaic power generation system according to the above-mentioned control parameters.

[0096] The device for determining the control parameters of the above photovoltaic power generation system includes an acquisition module, a first control module, a judgment module, a first determination module, and a second control module. Among them, the acquisition module is used to acquire multiple control parameters to be determined of the inverter of the above photovoltaic power generation system; the first control module is used to select the observation variables of the above inverter and control the observation variables of the above inverter to be perturbed. Under the condition of the perturbation of the observation variables, calculate the sensitivity of the multiple control parameters to be determined; the judgment module is used to judge the identifiability of the control parameters to be determined according to the sensitivity, that is, judge the degree to which the control parameters to be determined are easily determined and selected; the first determination module is used to select the control parameters to be determined with the highest identifiability during each perturbation process, determine the selected control parameters to be determined. After multiple perturbations, the multiple control parameters to be determined of the above inverter can be all determined; the second control module is used to control the above photovoltaic power generation system to operate more stably according to the above control parameters. It solves the problem in the prior art that due to the high coupling of different types of parameters of the inverter of the photovoltaic power generation system, it is impossible to accurately obtain all the parameters of the inverter of the photovoltaic power generation system, and thus it is impossible to control the stable operation of the photovoltaic power generation system.

[0097] In some alternative solutions, the above determination device further includes a construction module and a second determination module. The construction module is used to establish a control model of the inverter of the photovoltaic power generation system: , where and are the conversion values of the three-phase measured voltages at the PCC on the grid-connected side of the photovoltaic power generation system in the dq0 coordinate system; and are the conversion values of the three-phase measured currents at the PCC on the grid-connected side of the photovoltaic power generation system in the dq0 coordinate system; and are the conversion values of the three-phase output voltages of the inverter in the dq0 coordinate system; L is the inductance between the inverter output and the PCC measurement point; is the output of the phase-locked loop angular frequency; the second determination module is used to determine the above control parameters to be determined according to the above control model.

[0098] In some alternative solutions, the above calculation step includes a first sub-determination module, which calculates the absolute sensitivities of the above multiple control parameters to be determined respectively, including: the first sub-determination module is used to calculate according to the formula of the above absolute sensitivity to determine the absolute sensitivities of the above multiple control parameters to be determined. The formula of the above absolute sensitivity is: , where S is the absolute sensitivity of the parameter, O(x) is the simulation output of the control model of the inverter of the photovoltaic power generation system, is the parameter to be analyzed, is the increment size of the parameter, is the actual value of the parameter.

[0099] In some alternative solutions, the first determination module includes a perturbation module. According to the above-mentioned identifiability, the determination of the multiple control parameters to be determined includes: the perturbation module is used to perturb different observation variables, select the control parameter with the highest identifiability among the multiple control parameters to be determined as the determination object, and sequentially determine the multiple control parameters to be determined.

[0100] In an alternative solution, the second determination module includes a first sub-acquisition module, a first sub-determination module, and a screening module. Among them, the first sub-acquisition module is used to determine the multiple control parameters to be determined, including: obtaining multiple random combination samples of the multiple control parameters to be determined, and the random combination samples include the determination object; the first sub-determination module is used to identify the random combination samples according to the standard particle swarm algorithm to obtain multiple parameter samples of the determination object; the screening module is used to iteratively screen the parameter samples according to the iterative algorithm to obtain the identification result of the determination object, and complete the determination of the control parameters to be determined.

[0101] An alternative solution is that the above screening module includes a second sub-acquisition module, a first sub-control module, a first sub-calculation module, a first sub-judgment module, a second sub-judgment module, and a first sub-loop module. According to the iterative algorithm, the iterative screening of the above parameter samples is completed to obtain the identification result of the above determined object, including: The second sub-acquisition module is used to execute the acquisition step: acquiring the preset clustering number of the above iterative algorithm; The first sub-control module is used to execute the control step: grouping multiple groups of the above randomly combined samples according to the above preset clustering number, and setting the group of randomly combined samples with the most parameters in the grouped multiple groups of randomly combined samples as the reference class sample; The first sub-calculation module is used to execute the calculation step: calculating the plurality of concentration degrees of the above reference class sample and other multiple above randomly combined samples respectively; The first sub-judgment module is used to execute the first judgment step: judging whether each of the above concentration degrees is greater than a first preset threshold to obtain a first judgment result. In the case where the above first judgment result indicates "yes", eliminating the above randomly combined samples with the above concentration degree greater than the above first preset threshold, and merging the remaining above randomly combined samples and the above reference class sample to form a new above randomly combined sample; The second sub-judgment module is used to execute the second judgment step: judging whether the distance between the maximum parameter in the new above randomly combined sample and the central parameter of the above randomly combined sample is greater than or equal to a second preset threshold, and whether the distance between the minimum parameter and the central parameter of the above randomly combined sample is greater than or equal to the above second preset threshold to obtain a second judgment result, where the above central parameter is the parameter with the middle value in the above randomly combined sample; The first sub-loop module is used to execute the loop step: in the case where the above second judgment result indicates "no", looping to execute at least once the above acquisition step, the above control step, the above calculation step, the above first judgment step, and the above second judgment step until the above second judgment result indicates "yes", to obtain the identification result of the above determined object.

[0102] An alternative solution is that the multiple above control parameters to be determined in the above acquisition module include: phase-locked loop proportional coefficient, phase-locked loop integral coefficient, voltage outer loop proportional coefficient, voltage outer loop integral coefficient, current inner loop proportional coefficient, and current inner loop integral coefficient.

[0103] The above device for determining the control parameters of the photovoltaic power generation system includes a processor and a memory. The above first acquisition module, etc. are all stored in the memory as program units, and the processor executes the above program units stored in the memory to implement the corresponding functions. The above modules are all located in the same processor; or, the above respective modules are located in different processors in any combination form.

[0104] The processor contains a kernel, which retrieves the corresponding program units from the memory. One or more kernels can be set, and by adjusting the kernel parameters, the problem in the prior art can be solved that due to the high coupling of different types of parameters of the inverter in the photovoltaic power generation system, all parameters of the inverter in the photovoltaic power generation system cannot be accurately obtained, and thus the stable operation of the photovoltaic power generation system cannot be controlled.

[0105] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or forms such as non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.

[0106] An embodiment of the present invention provides a computer-readable storage medium. The above computer-readable storage medium includes a stored program, wherein when the above program runs, it controls the device where the above computer-readable storage medium is located to execute the above method for determining the control parameters of the photovoltaic power generation system.

[0107] Specifically, the method for determining the control parameters of the photovoltaic power generation system includes:

[0108] Step S201, obtaining a plurality of control parameters to be determined of the above inverter;

[0109] Step S202, when a preset condition is satisfied, calculating the absolute sensitivities of the above plurality of control parameters to be determined respectively. The above preset condition is to perturb different observed variables of the above inverter, and each of the above plurality of control parameters to be determined has a corresponding above absolute sensitivity under each perturbation;

[0110] Step S203, judging the identifiability of the above plurality of control parameters to be determined according to the above absolute sensitivities corresponding to the above plurality of control parameters to be determined. The above identifiability represents the degree of ease of determination of the control parameters to be determined.

[0111] Step S204, determining the above plurality of control parameters to be determined according to the above identifiability; [[ID=,22]]

[0112] Step S205, controlling the operation of the above photovoltaic power generation system according to the above control parameters.

[0113] An embodiment of the present invention provides a processor. The above processor is used to run a program, wherein when the above program runs, it executes the above method for determining the control parameters of the photovoltaic power generation system.

[0114] Specifically, the method for determining the control parameters of the photovoltaic power generation system includes:

[0115] Step S201, obtaining a plurality of control parameters to be determined of the above inverter;

[0116] Step S202, when the preset conditions are met, calculate the absolute sensitivities of the multiple control parameters to be determined respectively. The preset conditions are to perturb different observed variables of the inverter, and each of the multiple control parameters to be determined has a corresponding absolute sensitivity in each perturbation case;

[0117] Step S203, based on the absolute sensitivities corresponding to the multiple control parameters to be determined, determine the identifiability of the multiple control parameters to be determined. The identifiability characterizes the degree of ease of determination of the control parameters to be determined.

[0118] Step S204, based on the identifiability, determine the multiple control parameters to be determined;

[0119] Step S205, control the operation of the photovoltaic power generation system according to the control parameters. [[ID=1))

[0120] An embodiment of the present invention provides an electronic device, which includes a processor, a memory, and a program stored on the memory and executable on the processor. When the processor executes the program, it implements at least the following steps:

[0121] Step S201, obtain multiple control parameters to be determined of the inverter;

[0122] Step S202, when the preset conditions are met, calculate the absolute sensitivities of the multiple control parameters to be determined respectively. The preset conditions are to perturb different observed variables of the inverter, and each of the multiple control parameters to be determined has a corresponding absolute sensitivity in each perturbation case;

[0123] Step S203, based on the absolute sensitivities corresponding to the multiple control parameters to be determined, determine the identifiability of the multiple control parameters to be determined. The identifiability characterizes the degree of ease of determination of the control parameters to be determined.

[0124] Step S204, based on the identifiability, determine the multiple control parameters to be determined;

[0125] Step S205, control the operation of the photovoltaic power generation system according to the control parameters.

[0126] The device in this article can be a server, a PC, a PAD, a mobile phone, etc.

[0127] This application also provides a computer program product, which is suitable for executing a program initialized with at least the following method steps when executed on a data processing device:

[0128] Step S201: Obtain multiple control parameters to be determined of the above inverter;

[0129] Step S202: When the preset conditions are met, calculate the absolute sensitivities of the multiple control parameters to be determined respectively. The preset conditions are to perturb different observed variables of the above inverter, and each of the multiple control parameters to be determined has a corresponding absolute sensitivity under each perturbation;

[0130] Step S203: According to the absolute sensitivities corresponding to the multiple control parameters to be determined, judge the identifiability of the multiple control parameters to be determined. The identifiability represents the degree of ease of determining the control parameters to be determined.

[0131] Step S204: Determine the multiple control parameters to be determined according to the identifiability;

[0132] Step S205: Control the operation of the above photovoltaic power generation system according to the above control parameters.

[0133] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general-purpose computing device. They can be concentrated on a single computing device or distributed on a network composed of multiple computing devices. They can be implemented by program codes executable by the computing device. Thus, they can be stored in a storage device and executed by the computing device. And in some cases, the steps shown or described can be executed in a different order than here, or they can be separately made into individual integrated circuit modules, or multiple of them can be made into a single integrated circuit module to implement. In this way, the present invention is not limited to any specific combination of hardware and software.

[0134] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0135] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and combinations of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device generate means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0136] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0137] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0138] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0139] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash RAM. The memory is an example of computer-readable media.

[0140] A computer-readable medium includes permanent and non-permanent, removable and non-removable media and can implement information storage by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory, or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD), or other optical storage, magnetic cassette tapes, disk storage, or other magnetic storage devices, or any other non-transitory media that can be used to store information that can be accessed by a computing device. As defined herein, a computer-readable medium does not include transitory computer-readable media, such as modulated data signals and carrier waves.

[0141] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or apparatus comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or apparatus. Without further limitation, an element defined by the phrase "comprising a..." does not exclude the presence of additional identical elements in the process, method, article or apparatus comprising the element.

[0142] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0143] 1) In the method for determining the control parameters of the photovoltaic power generation system of the present application, first, a plurality of control parameters to be determined of the inverter of the photovoltaic power generation system are obtained, the observed variables of the inverter are selected, and the observed variables of the inverter are controlled to be perturbed. Under the condition of the perturbation of the observed variables, the sensitivities of the plurality of control parameters to be determined are calculated, and the identifiability of the control parameters to be determined is judged according to the sensitivity, that is, the degree to which the control parameters to be determined are easily determined and selected. The control parameter to be determined with the highest identifiability during each perturbation process is selected, and the selected control parameter to be determined is determined. After multiple perturbations, the plurality of control parameters to be determined of the inverter can be all determined. According to the above control parameters, the photovoltaic power generation system can be controlled to operate more stably. This solves the problem in the prior art that due to the high coupling of different types of parameters of the inverter of the photovoltaic power generation system, all the parameters of the inverter of the photovoltaic power generation system cannot be accurately obtained, and thus the photovoltaic power generation system cannot be controlled to operate stably.

[0144] 2), The device for determining the control parameters of the photovoltaic power generation system of the present application includes an acquisition module, a first control module, a judgment module, a first determination module, and a second control module. Among them, the acquisition module is used to acquire a plurality of control parameters to be determined of the inverter of the photovoltaic power generation system; the first control module is used to select the observed variables of the inverter and control the observed variables of the inverter to be perturbed, and calculate the sensitivity of the plurality of control parameters to be determined under the condition of the perturbation of the observed variables; the judgment module is used to judge the identifiability of the control parameters to be determined according to the sensitivity, that is, to judge the degree to which the control parameters to be determined are easily determined and selected; the first determination module is used to select the control parameters to be determined with the highest identifiability during each perturbation process, determine the selected control parameters to be determined, and after multiple perturbations, all the control parameters to be determined of the inverter can be determined; the second control module is used to control the photovoltaic power generation system to operate more stably according to the control parameters. It solves the problem in the prior art that due to the high coupling of different types of parameters of the inverter of the photovoltaic power generation system, it is impossible to accurately obtain all the parameters of the inverter of the photovoltaic power generation system, and thus it is impossible to control the photovoltaic power generation system to operate stably.

[0145] The above are only the preferred embodiments of the present application and are not used to limit the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for determining control parameters of a photovoltaic power generation system, the photovoltaic power generation system having an inverter, characterized in that, Including: Obtaining a plurality of control parameters to be determined of the inverter; When preset conditions are met, calculating the absolute sensitivities of the plurality of control parameters to be determined respectively, where the preset conditions are to perturb different observed variables of the inverter, and each of the plurality of control parameters to be determined has a corresponding absolute sensitivity in each case of perturbation; Judging the identifiability of the plurality of control parameters to be determined according to the absolute sensitivities corresponding to the plurality of control parameters to be determined, where the identifiability represents the degree of ease of determination of the control parameters to be determined; Determining the plurality of control parameters to be determined according to the identifiability, including: perturbing different ones of the observed variables, selecting the control parameter with the highest identifiability among the plurality of control parameters to be determined as the object to be determined, and successively determining the plurality of control parameters to be determined; Determining the plurality of control parameters to be determined, including: obtaining a plurality of random combination samples of the plurality of control parameters to be determined, where the random combination samples include the object to be determined; identifying the random combination samples according to the standard particle swarm algorithm to obtain a plurality of parameter samples of the object to be determined; and iteratively screening the parameter samples according to the iterative algorithm to obtain the identification result of the object to be determined, thereby completing the determination of the control parameters to be determined; Completing the iterative screening of the parameter samples according to the iterative algorithm to obtain the identification result of the object to be determined, including: Obtaining step: Obtaining the preset clustering number of the iterative algorithm; Controlling step: Grouping the plurality of random combination samples according to the preset clustering number, and setting the group of random combination samples with the most parameters among the grouped plurality of random combination samples as the reference class sample; Calculating step: Calculating a plurality of concentration degrees of the reference class sample and other plurality of random combination samples respectively; First judging step: Judging whether each of the concentration degrees is greater than a first preset threshold to obtain a first judgment result. When the first judgment result indicates yes, eliminating the random combination samples with the concentration degrees greater than the first preset threshold, and merging the remaining random combination samples and the reference class sample to form a new random combination sample; Second judging step: Judging whether the distance between the maximum parameter and the central parameter of the new random combination sample is greater than or equal to a second preset threshold, and whether the distance between the minimum parameter and the central parameter of the random combination sample is greater than or equal to the second preset threshold to obtain a second judgment result, where the central parameter is the parameter with the intermediate value in the random combination sample; Looping step: When the second judgment result indicates no, looping through at least once the obtaining step, the controlling step, the calculating step, the first judging step and the second judging step until the second judgment result indicates yes, to obtain the identification result of the object to be determined; Controlling the operation of the photovoltaic power generation system according to the control parameters.

2. The determination method according to claim 1, wherein The determining method further includes: Establish a control model for the inverter of a photovoltaic power generation system: where, e d and e q are the conversion values of the three-phase measured voltages at the PCC on the grid-connected side of the photovoltaic power generation system in the dq0 coordinate system; i d and i q are the conversion values of the three-phase measured currents at the PCC on the grid-connected side of the photovoltaic power generation system in the dq0 coordinate system; u d and u q are the conversion values of the three-phase output voltages of the inverter in the dq0 coordinate system; L is the inductance between the inverter output and the PCC measurement point; ω PLL is the output of the phase-locked loop angular frequency; Determine the control parameters to be determined according to the control model.

3. The determination method according to claim 1, wherein Calculate the absolute sensitivities of the multiple control parameters to be determined respectively, including: Determine the absolute sensitivities of the multiple control parameters to be determined according to the calculation of the formula of the absolute sensitivity. The formula of the absolute sensitivity is: Where S is the absolute sensitivity of the parameter, O(x) is the simulation output of the control model of the inverter of the photovoltaic power generation system, θ is the parameter to be analyzed, Δθ is the increment size of the parameter, and θ0 is the actual value of the parameter.

4. The determination method according to claim 1, characterized in that, The multiple control parameters to be determined include: phase-locked loop proportional coefficient, phase-locked loop integral coefficient, voltage outer loop proportional coefficient, voltage outer loop integral coefficient, current inner loop proportional coefficient, and current inner loop integral coefficient.

5. A device for determining control parameters of a photovoltaic power generation system, the photovoltaic power generation system having an inverter, characterized in that, Including: An acquisition module for acquiring the multiple control parameters to be determined of the inverter; A first control module for calculating the absolute sensitivities of the multiple control parameters to be determined respectively when a preset condition is satisfied. The preset condition is to perturb different observed variables of the inverter, and each of the multiple control parameters to be determined has a corresponding absolute sensitivity under each perturbation; A judgment module for judging the identifiability of the multiple control parameters to be determined according to the absolute sensitivities corresponding to the multiple control parameters to be determined. The identifiability characterizes the degree of ease of determining the control parameters to be determined; A first determination module for determining the multiple control parameters to be determined according to the identifiability; The first determination module includes a perturbation module. Determining the multiple control parameters to be determined according to the identifiability includes: the perturbation module is used to perturb different observed variables, select the control parameter with the highest identifiability among the multiple control parameters to be determined as the determination object, and determine the multiple control parameters to be determined in sequence; The second determination module includes a first sub-acquisition module, a first sub-determination module, and a screening module. Among them, the first sub-acquisition module is used to determine the multiple control parameters to be determined, including: acquiring multiple random combination samples of the multiple control parameters to be determined, and the random combination samples include the determination object; the first sub-determination module is used to identify the random combination samples according to the standard particle swarm algorithm to obtain multiple parameter samples of the determination object; the screening module is used to iteratively screen the parameter samples according to the iterative algorithm to obtain the identification result of the determination object, and complete the determination of the control parameters to be determined; The screening module includes a second sub-acquisition module, a first sub-control module, a first sub-calculation module, a first sub-judgment module, a second sub-judgment module, and a first sub-loop module. According to the iterative algorithm, it completes the iterative screening of the parameter samples to obtain the identification result of the determined object, including: The second sub-acquisition module is used to perform the acquisition step: acquiring the preset number of clusters of the iterative algorithm; The first sub-control module is used to perform the control step: grouping multiple sets of the random combination samples according to the preset number of clusters, and setting the set of random combination samples with the most parameters in the grouped multiple sets of random combination samples as the reference class samples; The first sub-calculation module is used to perform the calculation step: calculating the concentrations of the reference class samples and multiple other random combination samples respectively; The first sub-judgment module is used to perform the first judgment step: judging whether each concentration is greater than a first preset threshold to obtain a first judgment result. In the case where the first judgment result indicates yes, eliminating the random combination samples with concentrations greater than the first preset threshold, and merging the remaining random combination samples and the reference class samples to form a new random combination sample; The second sub-judgment module is used to perform the second judgment step: judging whether the distance between the maximum parameter and the central parameter of the new random combination sample is greater than or equal to a second preset threshold, and whether the distance between the minimum parameter and the central parameter of the random combination sample is greater than or equal to the second preset threshold to obtain a second judgment result, where the central parameter is the parameter with the middle value in the random combination sample; The first sub-loop module is used to perform the loop step: in the case where the second judgment result indicates no, looping and executing at least once the acquisition step, the control step, the calculation step, the first judgment step, and the second judgment step until the second judgment result indicates yes, to obtain the identification result of the determined object; A second control module, configured to control the operation of the photovoltaic power generation system according to the control parameter.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein when the program runs, it controls the device where the computer-readable storage medium is located to execute the method for determining the control parameter of the photovoltaic power generation system according to any one of claims 1 to 4.

7. An electronic device, characterized in that, Comprising: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include the method for determining the control parameter of the photovoltaic power generation system according to any one of claims 1 to 4.

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