Dynamic control method, system and equipment for photovoltaic inverter
By establishing an inductor current model of the photovoltaic inverter and performing simulation control records and comparative analysis, the problems of insufficient dynamic response speed, control accuracy and stability of the photovoltaic inverter under complex working conditions were solved, and a more efficient dynamic control effect was achieved.
Patent Information
- Application Number
- CN202511165243.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-20
- Publication Date
- 2025-09-30
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Existing photovoltaic inverter control methods are difficult to adapt to complex and changeable operating conditions due to fixed parameters, resulting in insufficient dynamic response speed, control accuracy and stability.
By collecting multi-dimensional characteristic parameters, an inductor current model is established, simulation control records are made and simulation outputs are extracted, and target comparison deviations are analyzed to achieve dynamic control.
The dynamic response speed, control accuracy and stability of photovoltaic inverters under complex working conditions are improved.
Smart Images
Figure CN120729063A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of inverter control, and in particular to a dynamic control method, system, and device for photovoltaic inverters. Background Art
[0002] In photovoltaic power generation systems, dynamic control of photovoltaic inverters is crucial for improving system efficiency, stability, and power quality, and is directly related to the performance and economic benefits of the entire photovoltaic power generation system. Currently, the main approach to addressing the dynamic control of photovoltaic inverters is to adopt a fixed-parameter control strategy, which presets and maintains certain key parameters for the inverter to achieve control and regulation. However, due to the complex and variable operating conditions of photovoltaic inverters, such as the constant changes in environmental factors such as light intensity and temperature, the current methods make it difficult for the preset fixed parameters to adapt to actual operating conditions. This, in turn, leads to significant deficiencies in the inverter's dynamic response speed, control accuracy, and stability.
[0003] Among the current related technologies, the control of photovoltaic inverters is unable to adapt to complex and changeable working conditions, resulting in obvious technical problems in the inverter's dynamic response speed, control accuracy and stability. Summary of the Invention
[0004] The present application provides a dynamic control method, system and equipment for photovoltaic inverters, adopts the collection of multi-dimensional characteristic parameters to establish an inductor current model, extracts the simulation output based on the simulation control record and compares and analyzes it to obtain the target comparison deviation, and then performs dynamic control of the target time zone and other technical means to solve the technical problem that the existing control for photovoltaic inverters cannot adapt to complex and changeable working conditions, resulting in obvious deficiencies in the dynamic response speed, control accuracy and stability of the inverter, and achieves the technical effect of improving the dynamic response speed, control accuracy and stability of the photovoltaic inverter under complex working conditions.
[0005] The present application provides a dynamic control method for a photovoltaic inverter, comprising: collecting and obtaining multidimensional characteristic parameters of a photovoltaic inverter, and establishing an inductor current model of the photovoltaic inverter based on the multidimensional characteristic parameters; performing simulation control on the inductor current model to obtain a simulation control record; extracting output terminal simulation information in the simulation control record based on a predetermined extraction frequency to obtain a simulated output; comparing a first output quantity time sequence with a second output quantity time sequence in the simulated output to obtain a target comparison deviation in a target time zone, wherein the target time zone has a corresponding relationship with the second output quantity time sequence; and dynamically controlling the photovoltaic inverter in the target time zone based on the target comparison deviation.
[0006] In a possible implementation, the following processing is performed: the multi-dimensional characteristic parameters include at least a DC bus voltage, a grid-side inductance, and a grid-side resistance.
[0007] In a possible implementation, the output end simulation information in the simulation control record is extracted based on a predetermined extraction frequency to obtain a simulation output quantity, and the following processing is performed: extract the simulation current parameters in the output end simulation information; perform parameter extraction on the simulation current parameters according to the predetermined extraction frequency to obtain a current signal timing; and use the current signal timing as the simulation output quantity.
[0008] In a possible implementation, before comparing the first output quantity timing with the second output quantity timing in the simulation output to obtain the target comparison deviation of the target time zone, the following processing is performed: obtaining the time zone of the adjacent previous cycle of the target time zone, recorded as the previous time zone; matching the output quantity timing of the previous time zone in the simulation output, recorded as the first output quantity timing.
[0009] In a possible implementation, the first output quantity time series and the second output quantity time series in the simulation output are compared to obtain a target comparison deviation in the target time zone, and the following processing is performed: based on the uniform sampling principle, the first output quantity time series and the second output quantity time series are sampled in sequence to obtain a first sample point set and a second sample point set, respectively; the first sample point in the first sample point set is extracted, and the corresponding sample point of the first sample point is matched in the second sample point set, recorded as the second sample point; a predetermined comparison deviation table is obtained, and the predetermined comparison deviation table is filled and analyzed according to the first sample point and the second sample point to obtain the target comparison deviation.
[0010] In a possible implementation, a predetermined contrast deviation table is obtained, and a filling analysis is performed on the predetermined contrast deviation table based on the first sample point and the second sample point to obtain the target contrast deviation, and the following processing is performed: a first table space of the predetermined contrast deviation table is obtained; a first deviation distance between the first sample point and the second sample point is obtained, and the first deviation distance is filled into the first table space to obtain a first filling result; a second table space of the predetermined contrast deviation table is obtained; a filling process is performed on the second table space using the first filling result as a filling constraint to obtain a second filling result; and a filling value corresponding to a predetermined unit point in the second filling result is used as the target contrast deviation.
[0011] In a possible implementation, the following processing is performed: the first table space includes the unit points in the first row and first column of the predetermined comparison deviation table, and the second table space includes the unit points in the predetermined comparison deviation table except the unit points in the first table space.
[0012] In a possible implementation, the second table space is filled with the first filling result as the filling constraint to obtain a second filling result, and the following processing is performed: extract any unit point in the second table space, and calculate any deviation distance of the arbitrary unit point; form an arbitrary constraint unit point set of the arbitrary unit point, and filter the maximum deviation distance in the arbitrary constraint unit point set; take the sum of the arbitrary deviation distance and the maximum deviation distance, fill and update the arbitrary unit point, and obtain any target deviation distance; based on the arbitrary target deviation distance, form the second filling result.
[0013] The present application also provides a dynamic control system for a photovoltaic inverter, including: an inductor current model establishment module, used to collect and obtain multidimensional characteristic parameters of the photovoltaic inverter, and establish the inductor current model of the photovoltaic inverter based on the multidimensional characteristic parameters; a simulation control module, used to simulate and control the inductor current model to obtain a simulation control record; a simulation output quantity extraction module, used to extract output terminal simulation information in the simulation control record based on a predetermined extraction frequency to obtain a simulation output quantity; a timing comparison module, used to compare the first output quantity timing with the second output quantity timing in the simulation output quantity to obtain a target comparison deviation of a target time zone, wherein the target time zone and the second output quantity timing have a corresponding relationship; a dynamic control module, used to dynamically control the target time zone of the photovoltaic inverter based on the target comparison deviation.
[0014] The present application also provides an electronic device, comprising: a memory for storing executable instructions; and a processor for implementing a dynamic control method for a photovoltaic inverter when executing the executable instructions stored in the memory.
[0015] The dynamic control method, system, and device for photovoltaic inverters proposed in this application first collect and obtain the multi-dimensional characteristic parameters of the photovoltaic inverter, and establish the inductor current model of the photovoltaic inverter based on the multi-dimensional characteristic parameters. Then, the inductor current model is simulated and controlled to obtain a simulation control record. Then, based on a predetermined extraction frequency, the output terminal simulation information in the simulation control record is extracted to obtain a simulated output quantity. Then, the first output quantity timing and the second output quantity timing in the simulated output quantity are compared to obtain a target comparison deviation of the target time zone, wherein the target time zone has a corresponding relationship with the second output quantity timing. Finally, the photovoltaic inverter is dynamically controlled in the target time zone based on the target comparison deviation. The technical effect of improving the dynamic response speed, control accuracy, and stability of the photovoltaic inverter under complex working conditions is achieved. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.
[0017] Figure 1 A schematic flow chart of a dynamic control method for a photovoltaic inverter provided in an embodiment of the present application.
[0018] Figure 2 A schematic diagram of the structure of a dynamic control system for a photovoltaic inverter provided in an embodiment of the present application.
[0019] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.
[0020] Explanation of the accompanying symbols: inductor current model building module 10, simulation control module 20, simulation output quantity extraction module 30, timing comparison module 40, dynamic control module 50, input device 301, processor 302, memory 303, output device 304. DETAILED DESCRIPTION
[0021] 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 in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.
[0022] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.
[0023] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or that are inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.
[0024] The present application provides a dynamic control method for photovoltaic inverters, such as Figure 1 As shown, the method includes: Step S100: Collect and obtain multi-dimensional characteristic parameters of the photovoltaic inverter, and establish an inductor current model of the photovoltaic inverter based on the multi-dimensional characteristic parameters, wherein the multi-dimensional characteristic parameters include at least DC bus voltage, grid-side inductance, and grid-side resistance.
[0025] Specifically, sensors and data acquisition cards are used to collect multidimensional characteristic parameters of the PV inverter, including but not limited to: PV cell output voltage and current (collected via voltage and current sensors), PV inverter input DC bus voltage (collected via DC voltage sensors), PV inverter output AC voltage and current (collected via AC voltage sensors and current transformers), PV cell and inverter internal temperatures (collected via temperature sensors), PV panel light intensity (collected via light intensity sensors), grid-side inductance (collected via an inductance tester), and grid-side resistance (collected via a multimeter). These multidimensional characteristic parameters are filtered, denoised, and normalized to ensure data accuracy and consistency. Based on these multidimensional characteristic parameters, mathematical modeling methods (such as system identification and machine learning) are used to build an inductor current model to describe the dynamic characteristics of the inductor current. For example, system identification methods use input and output data to model the system and determine the dynamic characteristics of the inductor current. Machine learning methods use algorithms such as neural networks or support vector machines (SVMs) to train models based on historical data and predict the inductor current. The sensor configuration is shown in Table 1.
[0026] Table 1: Sensor configuration table
[0027] Step S200 , performing simulation control on the inductor current model to obtain a simulation control record.
[0028] Specifically, the established inductor current model is simulated using simulation software (such as MATLAB / Simulink, ANSYS Maxwell, etc.). Control algorithms, such as PID control, are implemented within the simulation environment, regulating the inductor current using proportional and integral controllers. An improved insine delay feedback control (ICDFC) is employed, using the difference between the controlled system's output and its own delay over one cycle as feedback. The control signal is generated after a cosine function and feedback control parameters are applied. Input and output data, including inductor current, control signal, and error signal, are recorded during the simulation. Simulation control data includes timestamps, inductor current values, control signal values, and error signal values.
[0029] Step S300: extracting output terminal simulation information in the simulation control record based on a predetermined extraction frequency to obtain a simulation output quantity.
[0030] Specifically, the predetermined extraction frequency is a pre-set frequency for extracting data from the simulation control record, for example, once per second. Output simulation information is extracted from the simulation control record at the predetermined extraction frequency. The extracted data includes inductor current values, control signal values, error signal values, and the like. The extracted simulation output values are stored in a database or file for subsequent processing.
[0031] In one possible implementation, output terminal simulation information from the simulation control record is extracted based on a predetermined extraction frequency to obtain a simulated output quantity. Step S300 further includes step S310 of extracting simulated current parameters from the output terminal simulation information. Specifically, a data extraction tool (such as a MATLAB script or a data logging module in Simulink) is used to extract output terminal simulated current parameters from the simulation control record. These parameters include the instantaneous value, effective value, and peak value of the inductor current. The extracted current parameters include a timestamp. Examples of the extracted current parameters are shown in Table 2.
[0032] Table 2: Example of simulation current parameters
[0033] Step S320, extract the parameters of the simulation current parameters according to the predetermined extraction frequency, obtain the current signal time series, and use the current signal time series as the simulation output. Specifically, extract data points from the simulation current parameters according to the predetermined extraction frequency (for example, once per second). The extraction frequency can be adjusted according to actual needs, for example, once per second, once every 10 milliseconds, etc. Arrange the extracted current parameters in chronological order to generate a current signal time series for analysis and comparison. For example, an example of a current signal time series extracted once per second is shown in Table 3. This implementation method extracts parameters of the simulation current parameters at a predetermined extraction frequency, which can reduce the amount of data and reduce the complexity of data processing. After reducing the amount of data, subsequent comparative analysis and dynamic control calculations can be completed faster, thereby improving the response speed of the system.
[0034] Table 3: Current signal timing example
[0035] Step S400 : comparing a first output quantity time series with a second output quantity time series in the simulation output quantity to obtain a target comparison deviation of a target time zone, wherein the target time zone has a corresponding relationship with the second output quantity time series.
[0036] Specifically, the target time zone is determined based on the second output time series. For example, eigenvalue trajectory analysis is used to identify key points of system stability and nonlinear behavior, and the time period with the largest deviation (e.g., 0-5 seconds) is selected as the target time zone. Timing analysis is performed on the extracted simulation outputs, and the deviation between the first and second output time series is calculated to obtain a target comparative deviation. The target comparative deviation refers to the final deviation value calculated by comparing the current signals in the previous and target time zones within the target time zone. This deviation value reflects the dynamic changes of the system within the target time zone.
[0037] In one possible implementation, before comparing the first output quantity time series with the second output quantity time series in the simulation output to obtain the target comparison deviation of the target time zone, step S400 further includes step S410, obtaining the time zone of the adjacent previous cycle of the target time zone, recorded as the previous time zone. Specifically, the time range of the target time zone is determined, and the target time zone is the time period that the system needs to focus on. Find the time zone of the adjacent previous cycle of the target time zone, that is, the previous time zone. The time range of the previous time zone is the same as that of the target time zone, but is one cycle ahead in time.
[0038] Step S420, the output time sequence of the preceding time zone is matched in the simulation output, and recorded as the first output time sequence. Specifically, the output time sequence of the preceding time zone is extracted from the simulation output. The extracted output time sequence of the preceding time zone is recorded as the first output time sequence. This implementation method provides a clear comparison benchmark for target comparison deviation calculation by obtaining the output time sequence of the preceding time zone. The data of the preceding time zone reflects the operating status of the system in the previous cycle. By comparing with the data of the current target time zone, the system can better understand the current change trend. Based on this comparison, the system can dynamically adjust the control strategy to adapt to different operating conditions and load changes.
[0039] In one possible implementation, a first output time series and a second output time series in the simulation output are compared to obtain a target comparison deviation for a target time zone. Step S400 further includes step S430, in which the first output time series and the second output time series are sequentially sampled based on a uniform sampling principle to obtain a first sample point set and a second sample point set, respectively. Specifically, uniform sampling is performed on the first output time series (the output time series in the preceding time zone) and the second output time series (the output time series in the target time zone). Uniform sampling refers to collecting data points at fixed time intervals to ensure that the sampling points are evenly distributed along the time axis.
[0040] Step S440: Extract the first sample point from the first sample point set, and match the corresponding sample point of the first sample point in the second sample point set, recording it as the second sample point. Specifically, extract each sample point from the first sample point set, and find the sample point in the second sample point set that corresponds to the timestamp of the first sample point. For example, if the timestamp of the first sample point is 1.000s, then find the sample point with a timestamp of 2.000s in the second sample point set.
[0041] Step S450, obtain a predetermined comparison deviation table, and fill and analyze the predetermined comparison deviation table according to the first sample point and the second sample point to obtain the target comparison deviation. Specifically, create a predetermined comparison deviation table for recording the deviation value of each sample point pair. For each pair of sample points, calculate the deviation between the output of the first sample point and the output of the second sample point, and fill in the comparison deviation table. Fill the deviation values of all sample point pairs into the comparison deviation table, analyze the comparison deviation table, and obtain the target comparison deviation of the target time zone. This implementation method can accurately calculate the current deviation at each time point through uniform sampling and sample point matching. This accurate deviation calculation helps to optimize the control strategy, such as adjusting the parameters of the PID controller or the feedback control parameters of the ICDFC, to further improve the control accuracy.
[0042] In one possible implementation, a predetermined contrast deviation table is obtained and populated and analyzed based on the first sample point and the second sample point to obtain the target contrast deviation. Step 450 further includes step S451, obtaining a first tablespace of the predetermined contrast deviation table. Specifically, the predetermined contrast deviation table is a two-dimensional table for recording the deviation value between the first sample point and the second sample point. The first tablespace includes the unit points in the first row and first column of the predetermined contrast deviation table. That is, the first tablespace includes the unit points in the first row and first column of the predetermined contrast deviation table. These locations are used to store the initial deviation value.
[0043] Step S452: Obtain a first deviation distance between the first sample point and the second sample point, and fill the first deviation distance into the first table space to obtain a first filling result. Specifically, for each pair of the first sample point and the second sample point, calculate the output deviation distance between them. The deviation distance can be the absolute difference in output, for example, deviation distance = Fill the calculated deviation distance into the corresponding position of the first table space.
[0044] Step S453: Acquire a second table space of the predetermined comparison deviation table. Specifically, the second table space includes unit points in the predetermined comparison deviation table except the unit points in the first table space, and these positions are used to store deviation results after further processing.
[0045] In step S454, the second tablespace is populated using the first populating result as a populating constraint to obtain a second populating result. Specifically, the second tablespace is populated using the deviation distance in the first populating result as a constraint. The specific populating process steps are shown in steps S4541 to S4544.
[0046] Step S455: The fill value corresponding to a predetermined unit point in the second fill result is used as the target contrast deviation. The first tablespace includes the unit points in the first row and first column of the predetermined contrast deviation table, and the second tablespace includes the unit points in the predetermined contrast deviation table excluding the unit points in the first tablespace. Specifically, the predetermined unit point is the unit point in the lower right corner of the predetermined contrast deviation table. The fill value corresponding to the predetermined unit point is extracted from the second fill result and used as the target contrast deviation.
[0047] In one possible implementation, the second tablespace is filled using the first filling result as a filling constraint to obtain a second filling result. Step S454 further includes step S4541, extracting any unit point in the second tablespace and calculating any deviation distance for the arbitrary unit point. Specifically, any unit point is extracted from the second tablespace. For the extracted unit point, the corresponding deviation distance is calculated. Unit points in the second tablespace are sequentially extracted and the deviation distance calculation is performed until all unit points are calculated.
[0048] Step S4542: Build an arbitrary constraint unit point set for the arbitrary unit point and filter the maximum deviation distance in the arbitrary constraint unit point set. Specifically, the arbitrary constraint unit point set refers to the values of the upper and left grids of the currently extracted unit point. The maximum deviation distance is filtered out in the arbitrary constraint unit point set.
[0049] Step S4543: Take the sum of the arbitrary deviation distance and the maximum deviation distance, and perform filling and updating on the arbitrary unit point to obtain an arbitrary target deviation distance. Specifically, the deviation distance of the extracted unit point is added to the maximum deviation distance in the set of arbitrary constrained unit points to obtain the target deviation distance. The target deviation distance is then filled into the extracted unit point.
[0050] Step S4544: Based on the arbitrary target deviation distance, the second filling result is constructed. Specifically, all cells in the second tablespace are traversed in row-priority order, and steps S4542-S4543 are repeated to fill and update all unit points in the second tablespace, ultimately forming the second filling result. The final value of the lower right corner cell is the target comparison deviation, which is used to adjust the control strategy. This implementation method ensures that the control strategy is more sensitive to timing fluctuations by superimposing the maximum deviation of the historical path.
[0051] Step S500 : Dynamically controlling the target time zone of the photovoltaic inverter according to the target contrast deviation.
[0052] Specifically, a new control signal is generated based on the deviation between the target and the current. For example, the parameters of the PID controller and the feedback control parameters of the ICDFC control strategy are adjusted based on the deviation to reduce the deviation. The generated control signal is sent to the control unit of the photovoltaic inverter for dynamic adjustment. For example, a digital signal processor (DSP) or microcontroller (MCU) outputs the control signal to the photovoltaic inverter's drive circuit. By monitoring the photovoltaic inverter's output in real time, the control signal is dynamically adjusted to ensure that the inductor current reaches the target. For example, the photovoltaic inverter's output is monitored once a second, and the control signal is adjusted based on the real-time data.
[0053] The embodiment of the present application adopts technical means such as collecting multi-dimensional characteristic parameters to establish an inductor current model, extracting simulation output quantities based on simulation control records and performing comparative analysis to obtain target comparison deviations, and then performing dynamic control of target time zones. This solves the technical problem that existing control systems for photovoltaic inverters cannot adapt to complex and changeable working conditions, resulting in obvious deficiencies in the dynamic response speed, control accuracy, and stability of the inverter, and achieves the technical effect of improving the dynamic response speed, control accuracy, and stability of photovoltaic inverters under complex working conditions.
[0054] In the above, refer to Figure 1 The dynamic control method for photovoltaic inverter according to the embodiment of the present invention is described in detail. Figure 2 A dynamic control system for a photovoltaic inverter according to an embodiment of the present invention is described.
[0055] The dynamic control system for a photovoltaic inverter according to an embodiment of the present invention is designed to address the technical problem that existing photovoltaic inverter controls are unable to adapt to complex and changing operating conditions, resulting in significant deficiencies in the inverter's dynamic response speed, control accuracy, and stability. This improves the dynamic response speed, control accuracy, and stability of the photovoltaic inverter under complex operating conditions. The dynamic control system for a photovoltaic inverter includes: an inductor current model establishment module 10, a simulation control module 20, a simulation output quantity extraction module 30, a timing comparison module 40, and a dynamic control module 50.
[0056] An inductor current model establishment module 10 is used to collect and obtain multidimensional characteristic parameters of a photovoltaic inverter and establish an inductor current model of the photovoltaic inverter based on the multidimensional characteristic parameters; a simulation control module 20 is used to perform simulation control on the inductor current model to obtain a simulation control record; a simulation output quantity extraction module 30 is used to extract output terminal simulation information in the simulation control record based on a predetermined extraction frequency to obtain a simulation output quantity; a timing comparison module 40 is used to compare the first output quantity timing with the second output quantity timing in the simulation output quantity to obtain a target comparison deviation in a target time zone, wherein the target time zone has a corresponding relationship with the second output quantity timing; and a dynamic control module 50 is used to dynamically control the photovoltaic inverter in the target time zone based on the target comparison deviation.
[0057] The following will describe in detail the specific configuration of the inductor current model building module 10. As described above, the inductor current model building module 10 may further include: the multi-dimensional characteristic parameters at least include the DC bus voltage, the grid-side inductance and the grid-side resistance.
[0058] The specific configuration of the simulation output quantity extraction module 30 will be described in detail below. As described above, the output terminal simulation information in the simulation control record is extracted based on a predetermined extraction frequency to obtain a simulation output quantity. The simulation output quantity extraction module 30 may further include: a simulation current parameter extraction unit for extracting simulation current parameters from the output terminal simulation information; and a current signal timing acquisition unit for extracting parameters from the simulation current parameters based on the predetermined extraction frequency to obtain a current signal timing, and using the current signal timing as the simulation output quantity.
[0059] The specific configuration of the timing comparison module 40 will be described in detail below. As described above, before comparing the first output quantity timing with the second output quantity timing in the simulation output to obtain the target comparison deviation of the target time zone, the timing comparison module 40 may further include: a previous time zone determination unit for obtaining the time zone of the previous cycle adjacent to the target time zone, recorded as the previous time zone; and a first output quantity timing matching acquisition unit for matching the simulation output to obtain the output quantity timing of the previous time zone, recorded as the first output quantity timing.
[0060] Among them, the first output quantity timing and the second output quantity timing in the simulation output quantity are compared to obtain the target comparison deviation of the target time zone. The timing comparison module 40 may further include: a uniform sampling unit for sampling the first output quantity timing and the second output quantity timing in sequence based on the uniform sampling principle to obtain a first sample point set and a second sample point set respectively; a sample point extraction unit for extracting the first sample point in the first sample point set, and matching the corresponding sample point of the first sample point in the second sample point set, recorded as the second sample point; a filling analysis unit for obtaining a predetermined comparison deviation table, and performing filling analysis on the predetermined comparison deviation table according to the first sample point and the second sample point to obtain the target comparison deviation.
[0061] Among them, a predetermined contrast deviation table is obtained, and a filling analysis is performed on the predetermined contrast deviation table according to the first sample point and the second sample point to obtain the target contrast deviation. The filling analysis unit may further include: a first table space acquisition subunit for obtaining the first table space of the predetermined contrast deviation table; a first filling result acquisition subunit for obtaining the first deviation distance between the first sample point and the second sample point, and filling the first deviation distance into the first table space to obtain a first filling result; a second table space acquisition subunit for obtaining the second table space of the predetermined contrast deviation table; a second filling result acquisition subunit for filling the second table space with the first filling result as the filling constraint to obtain a second filling result; and a target contrast deviation acquisition subunit for using the filling value corresponding to the predetermined unit point in the second filling result as the target contrast deviation.
[0062] Among them, the filling analysis unit may further include: the first table space includes the unit points in the first row and the first column of the predetermined comparison deviation table, and the second table space includes the unit points in the predetermined comparison deviation table except the unit points in the first table space.
[0063] Among them, the second table space is filled with the first filling result as the filling constraint to obtain the second filling result. The second filling result acquisition subunit may further include: a deviation distance calculation component is used to extract any unit point in the second table space, and calculate the arbitrary deviation distance of the arbitrary unit point; a screening component is used to form an arbitrary constraint unit point set of the arbitrary unit point, and screen the maximum deviation distance in the arbitrary constraint unit point set; a filling update component is used to take the sum of the arbitrary deviation distance and the maximum deviation distance, fill and update the arbitrary unit point, obtain an arbitrary target deviation distance, and form the second filling result based on the arbitrary target deviation distance.
[0064] The dynamic control system for photovoltaic inverters provided by the embodiment of the present invention can execute the dynamic control method for photovoltaic inverters provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.
[0065] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.
[0066] Based on the foregoing embodiments, an embodiment of the present application further provides an electronic device. Figure 3 1 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention, showing a block diagram of an exemplary electronic device suitable for implementing an embodiment of the present invention. Figure 3 The electronic device shown is merely an example and should not limit the functionality and scope of use of the embodiments of the present invention. The electronic device is implemented as a general-purpose computing device, and its components may include, but are not limited to, an input device 301, a processor 302, a memory 303, and an output device 304. The processor 302 may be one or more; the memory 303 may include a computer-readable medium and at least one program product, which has a set (at least one) of program modules configured to perform the functions of the various embodiments of the present application.
[0067] The memory 303 shown in the embodiment of the present invention may adopt any combination of one or more computer-readable media; the computer-readable storage medium may be, but is not limited to, an infrared, semiconductor system, device or component, or any combination thereof, for storing software programs, computer executable programs and modules, such as the program instructions / modules corresponding to the dynamic control method for photovoltaic inverters in the embodiment of the present invention. The processor 302 executes various functional applications and data processing of the computer device by running the software programs, instructions and modules stored in the memory 303, thereby realizing the above-mentioned dynamic control method for photovoltaic inverters.
[0068] The above specific embodiments do not constitute a limitation to the scope of protection of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of this application should be included in the scope of protection of this application. In some cases, the actions or steps recorded in this application can be performed in an order different from that in the embodiments and can still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
Claims
1. A dynamic control method for photovoltaic inverters, characterized in that: include: Collecting multidimensional characteristic parameters of a photovoltaic inverter, and establishing an inductor current model of the photovoltaic inverter according to the multidimensional characteristic parameters; Performing simulation control on the inductor current model to obtain a simulation control record; Extracting output terminal simulation information from the simulation control record based on a predetermined extraction frequency to obtain a simulation output; Comparing a first output quantity time series and a second output quantity time series in the simulation output quantity to obtain a target comparison deviation of a target time zone, wherein the target time zone has a corresponding relationship with the second output quantity time series; Dynamically control the target time zone of the photovoltaic inverter according to the target contrast deviation.
2. The dynamic control method for photovoltaic inverter according to claim 1, characterized in that: The multi-dimensional characteristic parameters include at least DC bus voltage, grid-side inductance and grid-side resistance.
3. The dynamic control method for photovoltaic inverter according to claim 1, characterized in that: Extracting output terminal simulation information from the simulation control record based on a predetermined extraction frequency to obtain simulation output, including: Extracting simulation current parameters from the output end simulation information; Extracting the simulation current parameters according to the predetermined extraction frequency to obtain a current signal time sequence; The current signal timing is used as the simulation output.
4. The dynamic control method for photovoltaic inverter according to claim 3, characterized in that: Before comparing the first output quantity time series and the second output quantity time series in the simulation output quantity to obtain the target comparison deviation of the target time zone, the method includes: Obtain the time zone of the previous cycle adjacent to the target time zone, and record it as the previous time zone; The output time sequence of the preceding time zone is obtained by matching the simulation output, and is recorded as the first output time sequence.
5. The dynamic control method for photovoltaic inverter according to claim 4, characterized in that: Comparing the first output quantity time series and the second output quantity time series in the simulation output quantity to obtain a target comparison deviation of a target time zone includes: Based on the uniform sampling principle, the first output time series and the second output time series are sequentially sampled to obtain a first sample point set and a second sample point set respectively; Extracting a first sample point from the first sample point set, and matching a corresponding sample point of the first sample point in the second sample point set, which is recorded as a second sample point; A predetermined contrast deviation table is obtained, and the predetermined contrast deviation table is filled and analyzed according to the first sample point and the second sample point to obtain the target contrast deviation.
6. The dynamic control method for photovoltaic inverter according to claim 5, characterized in that: Obtaining a predetermined contrast deviation table, and performing a fill analysis on the predetermined contrast deviation table according to the first sample point and the second sample point to obtain the target contrast deviation, including: Obtaining a first table space of the predetermined comparison deviation table; Obtaining a first deviation distance between the first sample point and the second sample point, and filling the first deviation distance into the first table space to obtain a first filling result; Obtaining a second table space of the predetermined comparison deviation table; Using the first filling result as a filling constraint, filling the second tablespace to obtain a second filling result; The filling value corresponding to the predetermined unit point in the second filling result is used as the target contrast deviation.
7. The dynamic control method for photovoltaic inverter according to claim 6, characterized in that: The first table space includes the unit points in the first row and the first column of the predetermined comparison deviation table, and the second table space includes the unit points in the predetermined comparison deviation table except the unit points in the first table space.
8. The dynamic control method for photovoltaic inverter according to claim 6, characterized in that: Using the first filling result as a filling constraint, filling the second tablespace to obtain a second filling result includes: Extracting any unit point in the second table space, and calculating any deviation distance of the arbitrary unit point; Establishing an arbitrary constraint unit point set of the arbitrary unit point, and screening the maximum deviation distance in the arbitrary constraint unit point set; Taking the sum of the arbitrary deviation distance and the maximum deviation distance, filling and updating the arbitrary unit point, and obtaining the arbitrary target deviation distance; Based on the arbitrary target deviation distance, the second filling result is composed.
9. A dynamic control system for photovoltaic inverters, characterized in that: The system is used to implement the dynamic control method for a photovoltaic inverter according to any one of claims 1 to 8, and the system includes: an inductor current model establishment module, configured to collect and obtain multi-dimensional characteristic parameters of a photovoltaic inverter, and establish an inductor current model of the photovoltaic inverter according to the multi-dimensional characteristic parameters; A simulation control module, configured to perform simulation control on the inductor current model to obtain a simulation control record; A simulation output quantity extraction module is used to extract the output terminal simulation information in the simulation control record based on a predetermined extraction frequency to obtain a simulation output quantity; a timing comparison module, configured to compare a first output timing sequence with a second output timing sequence in the simulation output to obtain a target comparison deviation of a target time zone, wherein the target time zone has a corresponding relationship with the second output timing sequence; A dynamic control module is used to dynamically control the target time zone of the photovoltaic inverter according to the target comparison deviation.
10. An electronic device, characterized in that: The electronic device comprises: a memory for storing executable instructions; The processor is configured to implement the dynamic control method for a photovoltaic inverter according to any one of claims 1 to 8 when executing the executable instructions stored in the memory.