Photovoltaic flexible direct current grid-connected system simulation method and device and storage medium

Abstract

The invention discloses a photovoltaic flexible direct current grid-connected system simulation method and device and a storage medium, and relates to the technical field of power system simulation, and the method comprises the steps: building a photovoltaic module, an alternating current system module and a direct current line module on a simulation platform, so as to construct a simulation model; deploying the photovoltaic module on a first device, deploying the AC system module and the DC line module on a second device, and setting each simulation step length; after environment parameters and faults are set, the first device and the second device are operated at the same time based on the simulation step lengths, and data interaction between the first device and the second device is achieved through a data synchronization interface according to a preset data interaction period till a simulation result is output. Compared with the prior art, the method has the advantages that synchronous calculation of the multi-time-scale model can be realized, so that the problems of module calculation failure and simulation result distortion in the prior art are solved.

Description

Technical Field

[0001] This invention relates to the field of power system simulation technology, and in particular to a method, equipment and storage medium for simulating a photovoltaic flexible DC grid-connected system. Background Technology

[0002] After large-scale photovoltaic bases are centrally connected to the power system, their characteristics such as random power output fluctuations, lack of rotational inertia, and weak anti-disturbance capabilities pose severe challenges to the stability of system frequency and voltage. Flexible DC transmission (VSC-HVDC, Voltage Source Converter High Voltage Direct Current) technology has advantages such as dynamic reactive power compensation, active and reactive power decoupling control, ability to supply power to isolated areas, and no commutation failure. Therefore, flexible DC transmission has become the preferred solution for transmitting large-capacity new energy.

[0003] Simulation technology is an important tool for studying power systems. However, when simulating photovoltaic flexible DC grid-connected systems, the required simulation step size varies greatly between different modules. If the entire simulation model is run on a single device, it can easily lead to the failure of some modules, resulting in distorted simulation results. Summary of the Invention

[0004] The purpose of this invention is to provide a simulation method, device, equipment, and storage medium for photovoltaic flexible DC grid-connected systems, which can realize synchronous calculation of multi-timescale models, thereby solving the problems of module calculation failure and simulation result distortion in the prior art.

[0005] To achieve the above objectives, embodiments of the present invention provide a simulation method for a photovoltaic flexible DC grid-connected system, comprising: A simulation model is constructed by building photovoltaic modules, AC system modules, and DC line modules on a simulation platform. The photovoltaic module is deployed on the first device, the AC system module and the DC line module are deployed on the second device, and each simulation step size is set. After setting the environmental parameters and faults, the first device and the second device are run simultaneously based on the simulation step size, and the data interaction between the first device and the second device is realized by using a data synchronization interface according to the preset data interaction cycle until the simulation results are output.

[0006] As an improvement to the above solution, the photovoltaic module includes: The photovoltaic array submodule is used to output operating voltage and operating current according to the environmental parameters. The MPPT control submodule is used to generate control signals based on the short-circuit current and open-circuit voltage of the photovoltaic array panel submodule. A DC boost submodule is used to generate a DC line positive current based on the control signal, the operating voltage, and the operating current. The three-phase inverter submodule is used to generate AC line current and AC line voltage based on the positive current of the DC line and the rated voltage of the DC line. An AC transformer module is used to convert the AC line current and the AC line voltage into rated AC voltage and rated AC current.

[0007] As an improvement to the above solution, the simulation model further includes a converter valve control module, which is deployed on a third device, and: The converter valve control module also includes a first instruction correction submodule, which is used to generate an intermediate negative sequence compensation instruction based on the delay compensation coefficient, historical delay data and initial negative sequence compensation instruction; The photovoltaic module further includes a second instruction correction submodule, which is used to generate a target negative sequence compensation instruction based on the delay compensation coefficient, real-time delay data and the intermediate negative sequence compensation instruction, so as to compensate the q-axis negative sequence current instruction of the three-phase inverter submodule.

[0008] As an improvement to the above scheme, the delay compensation coefficient is iteratively optimized in advance through the following method: Run the simulation model and collect the response time of the photovoltaic module and the response time of the converter valve control module; The coordination error is calculated based on the response time of the photovoltaic module and the response time of the converter valve control module. When the coordination error is greater than the coordination error threshold, the coordination error, the coordination error threshold and the delay compensation coefficient are input into the proportional-integral module to obtain a new delay compensation coefficient. Based on the new delay compensation coefficient, the steps of running the simulation model are iterated until the first iteration termination condition is met.

[0009] As an improvement to the above scheme, the cooperative error is calculated in the following way: Calculate the absolute value of the time difference based on the response time of the photovoltaic module and the response time of the converter valve control module; The coordination error is obtained by dividing the absolute value of the time difference by the rated response time.

[0010] As an improvement to the above solution, the photovoltaic module includes: A power prediction submodule is used to generate predicted power based on the environmental parameters and historical power. A coupling mapping matrix is ​​used to generate excitation signal parameters based on the power fluctuation level and the fault; wherein the power fluctuation level is calculated based on the historical power and the predicted power. The excitation signal generation submodule is used to generate an excitation signal based on the excitation signal parameters.

[0011] As an improvement to the above scheme, the excitation signal parameters include the fault characteristic frequency, attenuation coefficient, and excitation signal amplitude; and: The amplitude of the excitation signal is obtained by multiplying the amplitude correction coefficient, the power fluctuation amplitude, and the fault severity coefficient; wherein the power fluctuation amplitude and the fault severity coefficient are obtained based on the power fluctuation level and the fault type, respectively.

[0012] As an improvement to the above scheme, the amplitude correction coefficient and the fault severity coefficient are iteratively optimized in advance through the following method: Run the simulation model and collect the excitation signal and the measured excitation signal; Calculate the coupling error based on the excitation signal and the measured excitation signal; When the coupling error is greater than the coupling error threshold, the amplitude correction coefficient and the fault severity coefficient are modified, and the simulation model is run again for iteration until the second iteration termination condition is met. The coupling error is calculated in the following way: The difference between the excitation signal and the measured excitation signal is calculated, and the L2 norm is taken as the excitation signal error. The coupling error is obtained by dividing the excitation signal error by the L2 norm of the measured excitation signal.

[0013] To achieve the above objectives, embodiments of the present invention also provide a photovoltaic flexible DC grid-connected system simulation device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the photovoltaic flexible DC grid-connected system simulation method as described in any of the above embodiments.

[0014] To achieve the above objectives, embodiments of the present invention also provide a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the photovoltaic flexible DC grid-connected system simulation method as described in any of the above embodiments.

[0015] Compared with existing technologies, the photovoltaic flexible DC grid-connected system simulation method, equipment, and storage medium provided in this invention construct a simulation model by building a photovoltaic module, an AC system module, and a DC line module on a simulation platform. The photovoltaic module is deployed on a first device, and the AC system module and the DC line module are deployed on a second device, with each simulation step size set. After setting environmental parameters and faults, the first and second devices are run simultaneously based on each simulation step size, and data interaction between the first and second devices is achieved using a data synchronization interface according to a preset data interaction cycle until the simulation results are output. Compared with existing technologies, this invention can achieve synchronous solution of multi-timescale models, thereby solving the problems of module solution failure and simulation result distortion in existing technologies. Attached Figure Description

[0016] Figure 1 This is a flowchart of a simulation method for a photovoltaic flexible DC grid-connected system provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of a simulation model of a photovoltaic flexible DC grid-connected system provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the photovoltaic module provided in the first embodiment of the present invention; Figure 4 This is a schematic diagram of an LSTM unit; Figure 5 This is a schematic diagram of the structure of a photovoltaic flexible DC grid-connected system simulation device provided in an embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0019] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0020] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0021] See Figure 1 This is a flowchart of a photovoltaic flexible DC grid-connected system simulation method provided in an embodiment of the present invention, including steps S1 to S3: S1. Build a photovoltaic module, an AC system module, and a DC line module on the simulation platform to construct a simulation model; S2. Deploy the photovoltaic module on the first device, deploy the AC system module and the DC line module on the second device, and set the simulation step size; S3. After setting the environmental parameters and faults, the first device and the second device are run simultaneously based on the simulation step size, and the data interaction between the first device and the second device is realized by using the data synchronization interface according to the preset data interaction cycle until the simulation results are output.

[0022] For example, see Figure 2 This is a simulation model of a photovoltaic flexible DC grid-connected system provided in an embodiment of the present invention, including a photovoltaic module, an AC system module, and a DC line module. The AC system module includes a receiving-end MMC (Modular Multilevel Converter) converter station submodule, a sending-end MMC converter station submodule, and a receiving-end AC grid submodule. Figure 2In this model, PV represents the photovoltaic module, BUS1 represents the busbar at the receiving end AC port with a rated value of 500kV (kilovolts), BUS2 represents the busbar at the sending end AC port with a rated value of 400V (volts), HVDC (High-Voltage Direct Current) represents high-voltage direct current transmission, and Tline represents the overhead DC line with a rated voltage of ±320kV, i.e., +320 kV at the positive pole, -320 kV at the negative pole, and 640 kV between poles. For example, in some scenarios, the simulation model also includes the MMC converter valve control module (not shown in the figure), and the photovoltaic module includes the photovoltaic converter. However, the power fluctuation of the photovoltaic converter is in the millisecond range, while the control of the converter valve control module is in the microsecond range. The difference in the required simulation step size is orders of magnitude. Running all modules on a single device may lead to calculation failure, resulting in distorted simulation results.

[0023] For example, to adapt to the millisecond-level fluctuations of the photovoltaic converter, the photovoltaic module is deployed on the first device and the simulation step size is set to 50μs; to adapt to the millisecond-level fluctuations of the AC system module and the DC line module, they are deployed on the second device and the simulation step size is set to 50μs; to adapt to the microsecond-level control of the MMC converter valve control model, they are deployed on the third device and the simulation step size is set to 250ns; and the first device, the second device and the third device respectively use a GPU (Graphics Processing Unit), a CPU (Central Processing Unit) cluster and an FPGA (Field-Programmable Gate Array) as the core.

[0024] Furthermore, the three devices interact with each other using a 50μs data exchange cycle and through a data synchronization interface. This data synchronization interface can be a high-speed data bus-based synchronization interface to ensure the timestamp alignment of the simulation data (power data, grid data, and valve control data) of the three devices.

[0025] For example, during data interaction, the data interaction latency and the simulation results of each device are monitored to see if they are consistent. If they are inconsistent, it indicates that the latency exceeds the standard, and the interface communication protocol should be adjusted.

[0026] Compared with the prior art, the embodiments of the present invention deploy each component module of the photovoltaic flexible DC grid-connected system simulation model on different devices and set the simulation duration for each. Then, the data interaction cycle is set based on the minimum simulation duration, and the data interaction between different devices is completed through the data synchronization interface according to the data interaction cycle. This can solve the problems of solution failure and simulation result distortion caused by different time scales in the prior art.

[0027] As one optional implementation, the photovoltaic module includes: The photovoltaic array submodule is used to output operating voltage and operating current based on environmental parameters; The MPPT control submodule is used to generate control signals based on the short-circuit current and open-circuit voltage of the photovoltaic array panel submodule. A DC boost submodule is used to generate a DC line positive current based on the control signal, the operating voltage, and the operating current. The three-phase inverter submodule is used to generate AC line current and AC line voltage based on the positive current of the DC line and the rated voltage of the DC line. An AC transformer module is used to convert the AC line current and the AC line voltage into rated AC voltage and rated AC current.

[0028] For example, see Figure 3 This is a schematic diagram of a photovoltaic module provided in the first embodiment of the present invention, including a photovoltaic array submodule, an MPPT (Maximum Power Point Tracking) control submodule, a DC boost submodule, a three-phase inverter submodule, and an AC transformer submodule.

[0029] For example, the MPPT control submodule also uses a power disturbance algorithm for power prediction; the DC boost submodule uses a BOOST DC boost module; the three-phase inverter submodule is a DC-AC three-phase inverter module (photovoltaic converter), and the control mode is Vdc-Q control; the AC transformer submodule includes two AC transformers and one photovoltaic fusion converter, and the AC transformer submodule changes the turns ratio to make the voltage and current at the PV_OUT point equal to the rated AC voltage and rated AC current designed when building the simulation model. For example, the ratio of the primary side current to the secondary side current of the photovoltaic fusion converter can be 1:307. Furthermore, in Figure 3 In this context, Vpv and Ipv represent the actual operating voltage and current of the photovoltaic array submodule, respectively; Vdc represents the rated voltage between the positive and negative terminals of the DC line; IpvHV represents the current flowing through the positive terminal of the DC line (DC line positive current); Iac represents the AC line current on the line, and Vac represents the AC line voltage on the line. Figure 3In order to clearly express the location of the line voltage, only two lines are used to represent the three-phase AC quantity; PV_OUT represents the power output point of the photovoltaic module; the short-circuit current and open-circuit voltage are data obtained by testing the photovoltaic array panel separately.

[0030] As one optional implementation, the simulation model further includes a converter valve control module, which is deployed on a third device, and: The converter valve control module also includes a first instruction correction submodule, which is used to generate an intermediate negative sequence compensation instruction based on the delay compensation coefficient, historical delay data and initial negative sequence compensation instruction; The photovoltaic module further includes a second instruction correction submodule, which is used to generate a target negative sequence compensation instruction based on the delay compensation coefficient, real-time delay data and the intermediate negative sequence compensation instruction, so as to compensate the q-axis negative sequence current instruction of the three-phase inverter submodule.

[0031] It is worth noting that although the synchronous operation of multiple devices can solve the problem of calculation failure, the physical transmission of simulation data between different devices will still cause response delays. Therefore, the embodiments of the present invention also set up a first instruction correction submodule and a second instruction correction submodule on different modules to further reduce the delay problem caused by the operation of multiple devices.

[0032] For example, each instruction correction submodule corrects the negative order compensation instruction using the following formula: (1) in, This indicates the corrected negative order compensation instruction; This indicates the negative order compensation instruction before correction; Indicates the delay compensation coefficient; This indicates the delay data from the negative sequence compensation command issued by the converter valve control module to the response of the photovoltaic module / photovoltaic converter.

[0033] Furthermore, the first instruction correction submodule is used to implement pre-correction at the instruction sending end. Based on historical delay data and delay compensation coefficient, it corrects the initial negative sequence compensation instruction to obtain an intermediate negative sequence compensation instruction, thereby reducing the initial impact of transmission delay. For example, the historical delay data can be the average of the previous n delay data. Furthermore, the second instruction correction submodule is used to implement final correction at the instruction receiving end. After the photovoltaic module receives the intermediate initial negative sequence compensation instruction, it corrects the intermediate negative sequence compensation instruction based on the measured delay data and delay compensation coefficient to obtain the target negative sequence compensation instruction, which is immediately used to adjust the q-axis negative sequence current instruction to ensure the real-time performance of the compensation.

[0034] Compared with the prior art, the embodiments of the present invention reduce the response latency caused by multiple devices by setting up instruction correction sub-modules for modules running on different devices, enabling instruction correction at both the instruction sending end and the receiving end.

[0035] As one optional implementation, the delay compensation coefficient is iteratively optimized in advance through the following method: Run the simulation model and collect the response time of the photovoltaic module and the response time of the converter valve control module; The coordination error is calculated based on the response time of the photovoltaic module and the response time of the converter valve control module. When the coordination error is greater than the coordination error threshold, the coordination error, the coordination error threshold and the delay compensation coefficient are input into the proportional-integral module to obtain a new delay compensation coefficient. Based on the new delay compensation coefficient, the steps of running the simulation model are iterated until the first iteration termination condition is met.

[0036] For example, multiple operating scenarios can be set up, and simulation models can be run under various operating scenarios. The operating scenarios can include normal operation (no power fluctuation or fault), power fluctuation (5% / 10% / 15% / 20% / 25% / 30% amplitude), AC / DC fault, coupled fault (power fluctuation + fault), and fault recovery (the process of system recovery after fault clearance).

[0037] After each simulation, the response time of the photovoltaic module and the response time of the converter valve control module are collected, and the coordination error is calculated.

[0038] As one optional implementation, the cooperative error is calculated in the following manner: Calculate the absolute value of the time difference based on the response time of the photovoltaic module and the response time of the converter valve control module; The coordination error is obtained by dividing the absolute value of the time difference by the rated response time.

[0039] It is worth noting that the coordination error is used to characterize the synchronization deviation between the operation of the converter valve control module and the photovoltaic module. For example, the coordination error is calculated using the following formula: (2) in, Indicates cooperative error; Indicates the response time of the photovoltaic module; Indicates the response time of the converter valve control module; Indicates the rated response time.

[0040] Furthermore, when the coordination error exceeds the coordination error threshold, the proportional-integral module is used to correct the delay compensation coefficient, resulting in a new delay compensation coefficient: (3) in, This represents the new delay compensation coefficient; This represents the old delay compensation coefficient; Indicates the proportionality coefficient; Indicates the integral coefficient; Indicates the cooperative error deviation ; This represents the integral variable.

[0041] Then, the simulation model is rerun based on the new delay compensation coefficients until the iteration termination condition is met. For example, the iteration termination condition could be... ,in, The threshold value for collaborative error can be set to different values ​​according to actual conditions. Furthermore, the iteration termination condition can also include the number of iterations reaching 20, that is, when any of the above conditions are met, the iteration stops. By setting an upper limit on the number of iterations, this embodiment of the invention can ensure the optimization efficiency of the delay compensation coefficient.

[0042] As one optional implementation, the photovoltaic module includes: A power prediction submodule is used to generate predicted power based on the environmental parameters and historical power. A coupling mapping matrix is ​​used to generate excitation signal parameters based on the power fluctuation level and the fault; wherein the power fluctuation level is calculated based on the historical power and the predicted power. The excitation signal generation submodule is used to generate an excitation signal based on the excitation signal parameters.

[0043] For example, the power prediction submodule consists of LSTM (Long Short-Term Memory) units, and each LSTM unit includes a forget gate, an input gate, and an output gate. See also Figure 4 , is a schematic diagram of an LSTM unit, and its mathematical operations are as follows: Equations (4) to (10), where the forget gate is as follows: (4) in, This represents the output of the forget gate at the current moment; This represents the Sigmoid activation function; Represents the forget gate weight matrix; This represents the output of the hidden layer at the previous time step; The input vector at the current moment includes historical power data, light intensity, and temperature; This represents the forget gate bias term.

[0044] Furthermore, the input gate is as follows: (5) in, This indicates the output of the input gate at the current moment; This represents the Sigmoid activation function; Represents the input gate weight matrix; This represents the output of the hidden layer at the previous time step; This represents the input vector at the current moment; This represents the input gate bias term.

[0045] Furthermore, the candidate cell state is as follows: (6) in, This indicates the current state of the candidate cells; Represents the hyperbolic tangent function; This represents the weight matrix used to calculate the candidate cell states; This represents the output of the hidden layer at the previous time step; This represents the input vector at the current moment; This represents the bias term used to calculate the candidate cell state.

[0046] Furthermore, the cell state is updated as follows: (7) in, Indicates the current state of the cell; This indicates the cell state at the previous moment; This represents the output of the forget gate at the current moment; This indicates the output of the input gate at the current moment; Represents element-wise product; This indicates the current state of the candidate cell.

[0047] Furthermore, the output gate is as follows: (8) in, This indicates the output of the output gate at the current moment; This represents the Sigmoid activation function; This represents the output gate weight matrix; This represents the output of the hidden layer at the previous time step; This represents the input vector at the current moment; This indicates the output gate bias term.

[0048] Furthermore, the output of the hidden layer at the current moment is as follows: (9) in, This represents the output of the hidden layer at the current moment. This indicates the output of the output gate at the current moment; Represents element-wise product; Represents the hyperbolic tangent function; This indicates the current state of the cell.

[0049] Furthermore, the predicted power is as follows: (10) in, This represents the predicted power at the current moment; This represents the output layer weight matrix; Indicates the output layer bias term; This represents the output of the hidden layer at the current moment.

[0050] Furthermore, historical power data, environmental parameter data, and typical fault data of the flexible DC system were collected from the photovoltaic base. After denoising and normalization, the data was divided into training and validation sets for training the power prediction submodule. Historical power data included power fluctuation data; environmental parameters included irradiance and temperature; and typical fault data of the flexible DC system included AC voltage, AC current, power, and phase angle of important system nodes under conditions such as single-phase grounding of the converter valve, DC line short circuit, and three-phase short circuit of the photovoltaic collector line. Important nodes refer to… Figure 2 BUS1, BUS2 and Tline.

[0051] Furthermore, define the coupling mapping matrix. ,in: Represents the real number field; This indicates the power fluctuation level, which is divided according to the power (photovoltaic output) fluctuation rate, such as 0%~5%, 5%~10%, ..., 25%~30%; This indicates the number of fault types, which may include DC single-pole short circuit, DC double-pole short circuit, converter valve single-phase grounding, converter valve three-phase grounding, photovoltaic busbar single-phase short circuit, and photovoltaic busbar three-phase short circuit. The excitation signal parameter dimension is 3. In this embodiment of the invention, the excitation signal parameters include the fault characteristic frequency, the attenuation coefficient and the excitation signal amplitude.

[0052] Furthermore, the matrix elements are defined as follows: (11) in, The coupling mapping matrix represents the first... Line number Column elements; This represents the amplitude of the excitation signal, and... , Indicates the amplitude correction factor; Indicates the first Level power fluctuation amplitude, for example, for the first level power fluctuation "0%~5%", We can take the median of 2.5%; Indicates the first The severity coefficient of a fault type; express The fault characteristic frequency of a type of fault, for example, 100Hz (Hertz) for DC faults and 50Hz fundamental or harmonic frequency for AC faults. Represents the attenuation coefficient, derived from the first... The attenuation characteristics of this type of fault are determined, and , Indicates the first Level power fluctuation and the first The transient decay time constant after fault coupling. Furthermore, the aforementioned excitation signal parameters are determined through statistical coupling analysis of historical fault data and power fluctuation data.

[0053] Then, after retrieving the excitation signal parameters, the excitation signal is generated using the following formula: (12) in, Indicates the excitation signal; Indicates the amplitude of the excitation signal; Indicates the attenuation coefficient; Indicates the characteristic frequency of the fault; Represents the natural constant; Indicates time; Represents pi; This represents the initial phase, determined based on the phase difference between the fault occurrence time and the peak power fluctuation, with a value range of [0, 2π].

[0054] Compared with existing technologies, the photovoltaic module of this invention can generate an equivalent excitation signal that reflects the synergistic effect of "power fluctuation-fault" by constructing a coupling mapping matrix, which solves the defect of traditional models that cannot characterize coupled scenarios, thereby providing more accurate signal input for subsequent simulation calculations.

[0055] As one optional implementation, the excitation signal parameters include the fault characteristic frequency, attenuation coefficient, and excitation signal amplitude; and: The amplitude of the excitation signal is obtained by multiplying the amplitude correction coefficient, the power fluctuation amplitude, and the fault severity coefficient; wherein the power fluctuation amplitude and the fault severity coefficient are obtained based on the power fluctuation level and the fault type, respectively.

[0056] As one optional implementation, the amplitude correction coefficient and the fault severity coefficient are iteratively optimized in advance through the following method: Run the simulation model and collect the excitation signal and the measured excitation signal; Calculate the coupling error based on the excitation signal and the measured excitation signal; When the coupling error is greater than the coupling error threshold, the amplitude correction coefficient and the fault severity coefficient are modified, and the simulation model is run again for iteration until the second iteration termination condition is met. The coupling error is calculated in the following way: The difference between the excitation signal and the measured excitation signal is calculated, and the L2 norm is taken as the excitation signal error. The coupling error is obtained by dividing the excitation signal error by the L2 norm of the measured excitation signal.

[0057] For example, the coupling error is calculated using the following formula: (13) in, Indicates coupling error; This represents the excitation signal generated based on the coupling mapping matrix, etc. This indicates that the measured excitation signal can be obtained from the photovoltaic module of the first embodiment; This represents the L2 norm.

[0058] For example, the iteration termination condition could be ,in, The coupling error threshold can be set to different values ​​according to the actual situation. Furthermore, the iteration termination condition can also include the iteration count reaching 20 times. That is, when any of the above conditions are met, the iteration stops. By setting an upper limit on the number of iterations, this embodiment of the invention can ensure the optimization efficiency of the delay compensation coefficient.

[0059] See Figure 5 This invention also provides a photovoltaic flexible DC grid-connected system simulation device 20, including a processor 21, a memory 22, and a computer program stored in the memory 22 and configured to be executed by the processor 21. When the processor 21 executes the computer program, it implements the steps as described in the above-described photovoltaic flexible DC grid-connected system simulation method embodiment, for example... Figure 1The steps S1 to S3 described above; or, when the processor 21 executes the computer program, it implements the functions of each module in the above-described device embodiments.

[0060] The photovoltaic flexible DC grid-connected system simulation device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The photovoltaic flexible DC grid-connected system simulation device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the schematic diagram is merely an example of a photovoltaic flexible DC grid-connected system simulation device and does not constitute a limitation on the device. It may include more or fewer components than illustrated, or combine certain components, or use different components. For example, the photovoltaic flexible DC grid-connected system simulation device may also include input / output devices, network access devices, buses, etc.

[0061] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the photovoltaic flexible DC grid-connected system simulation equipment, connecting all parts of the equipment via various interfaces and lines.

[0062] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the photovoltaic flexible DC grid-connected system simulation equipment by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created according to the use of the controller, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0063] If the modules integrated into the photovoltaic flexible DC grid-connected system simulation equipment are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0064] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

[0065] Compared with existing technologies, the photovoltaic flexible DC grid-connected system simulation method, equipment, and storage medium provided in this invention construct a simulation model by building a photovoltaic module, an AC system module, and a DC line module on a simulation platform. The photovoltaic module is deployed on a first device, and the AC system module and the DC line module are deployed on a second device, with each simulation step size set. After setting environmental parameters and faults, the first and second devices are run simultaneously based on each simulation step size, and data interaction between the first and second devices is achieved using a data synchronization interface according to a preset data interaction cycle until the simulation results are output. Compared with existing technologies, this invention can achieve synchronous solution of multi-timescale models, thereby solving the problems of module solution failure and simulation result distortion in existing technologies.

Claims

1. A simulation method for a photovoltaic flexible DC grid-connected system, characterized in that, include: A simulation model is constructed by building photovoltaic modules, AC system modules, and DC line modules on a simulation platform. The photovoltaic module is deployed on the first device, the AC system module and the DC line module are deployed on the second device, and each simulation step size is set. After setting the environmental parameters and faults, the first device and the second device are run simultaneously based on the simulation step size, and the data interaction between the first device and the second device is realized by using a data synchronization interface according to the preset data interaction cycle until the simulation results are output.

2. The simulation method for a photovoltaic flexible DC grid-connected system as described in claim 1, characterized in that, The photovoltaic module includes: The photovoltaic array submodule is used to output operating voltage and operating current according to the environmental parameters. The MPPT control submodule is used to generate control signals based on the short-circuit current and open-circuit voltage of the photovoltaic array panel submodule. A DC boost submodule is used to generate a DC line positive current based on the control signal, the operating voltage, and the operating current. The three-phase inverter submodule is used to generate AC line current and AC line voltage based on the positive current of the DC line and the rated voltage of the DC line. An AC transformer module is used to convert the AC line current and the AC line voltage into rated AC voltage and rated AC current.

3. The simulation method for a photovoltaic flexible DC grid-connected system as described in claim 2, characterized in that, The simulation model also includes a converter valve control module, which is deployed on a third device, and: The converter valve control module also includes a first instruction correction submodule, which is used to generate an intermediate negative sequence compensation instruction based on the delay compensation coefficient, historical delay data and initial negative sequence compensation instruction; The photovoltaic module further includes a second instruction correction submodule, which is used to generate a target negative sequence compensation instruction based on the delay compensation coefficient, real-time delay data and the intermediate negative sequence compensation instruction, so as to compensate the q-axis negative sequence current instruction of the three-phase inverter submodule.

4. The simulation method for a photovoltaic flexible DC grid-connected system as described in claim 3, characterized in that, The delay compensation coefficient is pre-optimized iteratively using the following method: Run the simulation model and collect the response time of the photovoltaic module and the response time of the converter valve control module; The coordination error is calculated based on the response time of the photovoltaic module and the response time of the converter valve control module. When the coordination error is greater than the coordination error threshold, the coordination error, the coordination error threshold and the delay compensation coefficient are input into the proportional-integral module to obtain a new delay compensation coefficient. Based on the new delay compensation coefficient, the steps of running the simulation model are iterated until the first iteration termination condition is met.

5. The simulation method for a photovoltaic flexible DC grid-connected system as described in claim 4, characterized in that, The cooperative error is calculated in the following way: Calculate the absolute value of the time difference based on the response time of the photovoltaic module and the response time of the converter valve control module; The coordination error is obtained by dividing the absolute value of the time difference by the rated response time.

6. The simulation method for a photovoltaic flexible DC grid-connected system as described in claim 1, characterized in that, The photovoltaic module includes: A power prediction submodule is used to generate predicted power based on the environmental parameters and historical power. A coupling mapping matrix is ​​used to generate excitation signal parameters based on the power fluctuation level and the fault; wherein the power fluctuation level is calculated based on the historical power and the predicted power. The excitation signal generation submodule is used to generate an excitation signal based on the excitation signal parameters.

7. The simulation method for a photovoltaic flexible DC grid-connected system as described in claim 6, characterized in that, The excitation signal parameters include the fault characteristic frequency, attenuation coefficient, and excitation signal amplitude; and: The amplitude of the excitation signal is obtained by multiplying the amplitude correction coefficient, the power fluctuation amplitude, and the fault severity coefficient; wherein the power fluctuation amplitude and the fault severity coefficient are obtained based on the power fluctuation level and the fault type, respectively.

8. The simulation method for a photovoltaic flexible DC grid-connected system as described in claim 7, characterized in that, The amplitude correction coefficient and the fault severity coefficient are pre-optimized iteratively using the following methods: Run the simulation model and collect the excitation signal and the measured excitation signal; Calculate the coupling error based on the excitation signal and the measured excitation signal; When the coupling error is greater than the coupling error threshold, the amplitude correction coefficient and the fault severity coefficient are modified, and the simulation model is run again for iteration until the second iteration termination condition is met. The coupling error is calculated in the following way: The difference between the excitation signal and the measured excitation signal is calculated, and the L2 norm is taken as the excitation signal error. The coupling error is obtained by dividing the excitation signal error by the L2 norm of the measured excitation signal.

9. A simulation device for a photovoltaic flexible DC grid-connected system, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the photovoltaic flexible DC grid-connected system simulation method as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the photovoltaic flexible DC grid-connected system simulation method as described in any one of claims 1 to 8.

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