Model parameter identification method and device and computer device

Abstract

The application discloses a model parameter identification method and device and computer equipment. The method comprises the following steps: based on the structural components included in a photovoltaic power generation system, a grid-connected simulation model of the photovoltaic power generation system is constructed; based on predetermined test condition parameters and the grid-connected simulation model, test data of the photovoltaic power generation system at a grid-connected point is generated; based on the test data, the working state of the photovoltaic power generation system is changed, and change result data of the photovoltaic power generation system at the grid-connected point is obtained; based on the change result data, an equivalent circuit model of the grid-connected simulation model is determined; based on the change result data, a non-mechanism black box method is used to identify the parameters of the equivalent circuit model, and model parameters of the equivalent circuit model corresponding to the photovoltaic power generation system are obtained. The application solves the technical problem that research errors are large due to incorrect power system load model parameters, thereby affecting the stability of the power system.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a model parameter identification method, device and computer equipment. Background Art

[0002] In related technologies, the growing demand for electricity, increasing environmental pressures, and technological advancements have led to significant progress in the application of renewable energy in power systems. Among renewable energy generation projects, solar photovoltaics have seen rapid growth. However, the integration of large numbers of distributed photovoltaic systems into medium- and low-voltage distribution networks has also brought with it certain challenges. Distributed photovoltaic power generation is affected by environmental conditions such as sunlight, temperature, and weather, resulting in random and fluctuating output. Furthermore, when distributed photovoltaic systems are integrated into distribution networks characterized by load characteristics, they can cause changes in the overall load characteristics of the regional power grid. However, changes in power system load characteristics have a significant impact on overall system stability. Many power system studies, such as those on voltage stability, relay protection, and load shedding, require careful consideration of dynamic load models. Incorrect load models not only lead to divergence in power flow calculations but also significantly impact short-circuit current calculations, power system transient analysis, and voltage stability calculations. The power consumed by the combined load of a power system generally varies with system operating parameters (primarily voltage U or frequency f). The curve or mathematical expression that reflects this variation is called the load characteristic. The static characteristic reflects the load power change characteristics when the voltage and frequency change slowly, and the dynamic characteristic reflects the load power change characteristics when the voltage and frequency change sharply.

[0003] However, in related technologies, the distribution network incorporates the characteristics of power sources such as wind turbines and photovoltaics on the basis of traditional loads. It is even possible that the load nodes will transmit power back to the grid. The traditional load model will no longer be able to accurately describe the dynamic model of the distribution network.

[0004] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0005] The embodiments of the present invention provide a model parameter identification method, apparatus and computer equipment to at least solve the technical problem that large research errors caused by incorrect power system load model parameters affect the stability of the power system.

[0006] According to one aspect of an embodiment of the present invention, a model parameter identification method is provided, comprising: constructing a grid-connected simulation model of a photovoltaic power generation system based on structural components included in the photovoltaic power generation system; generating test data of the photovoltaic power generation system at a grid-connected point based on predetermined test condition parameters and the grid-connected simulation model; changing the working state of the photovoltaic power generation system based on the test data to obtain change result data of the photovoltaic power generation system at the grid-connected point; determining an equivalent circuit model of the grid-connected simulation model based on the change result data; and identifying parameters of the equivalent circuit model using a non-mechanistic black box method based on the change result data to obtain model parameters of the equivalent circuit model corresponding to the photovoltaic power generation system.

[0007] Optionally, the predetermined test condition parameters include: the light intensity of the photovoltaic array in the photovoltaic power generation system, and the temperature of the photovoltaic array.

[0008] Optionally, based on the test data, the working state of the photovoltaic power generation system is changed to obtain the change result data of the photovoltaic power generation system at the grid-connected point, including: disturbing the test data so that the bus voltage at the grid-connected point drops, and obtaining the changed voltage and changed power of the photovoltaic power generation system at the grid-connected point; and / or adjusting the test data so that the active power output by the photovoltaic array in the photovoltaic power generation system is reduced, and obtaining the changed voltage and changed power of the photovoltaic power generation system at the grid-connected point; wherein the change result data includes the changed voltage and the changed power.

[0009] Optionally, determining the equivalent circuit model of the grid-connected simulation model based on the change result data includes: determining a transfer function between the changing voltage and the changing power based on the changing voltage and the changing power; constructing a first-order or multi-order RLC series-parallel model based on the transfer function; constructing a parallel switch RLC branch based on a voltage drop in the bus voltage at the grid-connected point and a reduction in the active power output of the photovoltaic array; and generating an equivalent circuit model of the grid-connected simulation model based on the RLC series-parallel model and the parallel switch RLC branch.

[0010] Optionally, determining the transfer function between the changing voltage and the changing power based on the changing voltage and the changing power includes: obtaining an initial function including variables, wherein a transfer order is set in the initial function; based on the changing voltage and the changing power, solving the variables in the initial function to obtain the transfer function between the changing voltage and the changing power.

[0011] Optionally, based on the change result data, a non-mechanism black box method is used to identify the parameters of the equivalent circuit model to obtain the model parameters of the equivalent circuit model corresponding to the photovoltaic power generation system, including: using the grid connection point voltage in the change result data as input, the grid connection point power in the change result data as output, and using the non-mechanism black box method to iterate the model parameters of the equivalent circuit model multiple times until the difference between the magnitude and waveform of the power output by the equivalent circuit model and the magnitude and waveform of the power output by the grid-connected simulation model is less than a predetermined difference threshold, thereby obtaining the model parameters of the equivalent circuit model corresponding to the photovoltaic power generation system.

[0012] According to another aspect of an embodiment of the present invention, a model parameter identification device is also provided, including: a construction module for constructing a grid-connected simulation model of the photovoltaic power generation system based on the structural components included in the photovoltaic power generation system; a generation module for generating test data of the photovoltaic power generation system at the grid-connected point based on predetermined test condition parameters and the grid-connected simulation model; a processing module for changing the working state of the photovoltaic power generation system based on the test data to obtain change result data of the photovoltaic power generation system at the grid-connected point; a determination module for determining an equivalent circuit model of the grid-connected simulation model based on the change result data; and an identification module for identifying the parameters of the equivalent circuit model based on the change result data using a non-mechanistic black box method to obtain model parameters of the equivalent circuit model corresponding to the photovoltaic power generation system.

[0013] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is further provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the model parameter identification method described in any one of the above methods.

[0014] According to another aspect of an embodiment of the present invention, a computer device is provided, comprising: a memory and a processor, wherein the memory stores a computer program; the processor is configured to execute the computer program stored in the memory, wherein when the computer program is executed, the processor executes the model parameter identification method described in any one of the above methods.

[0015] In an embodiment of the present invention, a grid-connected simulation model is constructed based on the structural components of a photovoltaic power generation system, and test data is obtained using predetermined test condition parameters and the grid-connected simulation model. Based on the test data, the working state of the photovoltaic power generation system is changed to obtain change result data of the grid-connected point and determine the equivalent circuit model. By using a non-mechanism black box method, the purpose of obtaining the correct equivalent circuit model parameters corresponding to the photovoltaic power generation system is achieved, thereby achieving the technical effect of establishing a correct power load model, and further solving the technical problem of large research errors caused by incorrect power system load model parameters, thereby affecting the stability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0017] Figure 1 is a flow chart of a model parameter identification method according to an embodiment of the present invention;

[0018] Figure 2 This is a flow chart of adaptive parameter identification of an equivalent circuit of a photovoltaic power generation system based on a non-mechanism black box RLC model according to an embodiment of the present invention;

[0019] Figure 3 is an optional photovoltaic power grid-connected topology diagram according to an embodiment of the present invention;

[0020] Figure 4 is a control structure diagram of a photovoltaic system according to an embodiment of the present invention;

[0021] Figure 5 is an equivalent circuit diagram of G(s) according to an embodiment of the present invention;

[0022] Figure 6 4 is a structural block diagram of a model parameter identification device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0024] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] According to an embodiment of the present invention, a method embodiment of a model parameter identification method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0026] Figure 1 is a flow chart of a model parameter identification method according to an embodiment of the present invention. Figure 1 As shown, the method includes the following steps:

[0027] Step S102, constructing a grid-connected simulation model of the photovoltaic power generation system based on the structural components included in the photovoltaic power generation system;

[0028] As an optional embodiment, the execution entity of the above-mentioned model parameter identification method can be a terminal or a server. The above-mentioned terminal can be various types of terminals, for example, it can be a computer terminal, a mobile terminal, a virtual terminal, etc., but regardless of the type of terminal, it needs to have a certain computing power to meet the computing requirements and the ability to establish a simulation model. The above-mentioned server can also be in various forms, for example, it can be a single computer device, it can also be a computer cluster including multiple computers, it can be a local computing unit, it can also be a remote cloud server, etc.

[0029] As an optional embodiment, the above photovoltaic power generation system may include a variety of structural components, such as solar cell components, DC / AC inverters, photovoltaic back panels, junction boxes, distribution room designs, etc., which constitute the photovoltaic power generation system can be the structural components of the photovoltaic power generation system.

[0030] Step S104, generating test data of the photovoltaic power generation system at the grid connection point based on predetermined test condition parameters and the grid connection simulation model;

[0031] As an optional embodiment, the predetermined test condition parameters may include: the light intensity of the photovoltaic array in the photovoltaic power generation system, and the temperature of the photovoltaic array. In a photovoltaic power generation system, the light intensity and the temperature of the photovoltaic array are important factors affecting the output current of the photovoltaic power generation system. Based on the desired predetermined test conditions, the relevant parameters are adjusted to obtain appropriate test data for the photovoltaic power generation system at the grid connection point.

[0032] Step S106, based on the test data, changing the working state of the photovoltaic power generation system to obtain the change result data of the photovoltaic power generation system at the grid connection point;

[0033] As an optional embodiment, the above-mentioned method of changing the working state of the photovoltaic power generation system based on the test data to obtain the change result data of the photovoltaic power generation system at the grid-connected point can be used to obtain the change result data of the photovoltaic power generation system at the grid-connected point in the following manner: by disturbing the test data, causing the bus voltage at the grid-connected point to drop, and obtaining the changed voltage and changed power of the photovoltaic power generation system at the grid-connected point; and / or, by adjusting the test data, reducing the active power output by the photovoltaic array in the photovoltaic power generation system, and obtaining the changed voltage and changed power of the photovoltaic power generation system at the grid-connected point; wherein the change result data includes the changed voltage and the changed power.

[0034] As an optional embodiment, active power control can be a closed-loop control system that controls the active power of generators within the jurisdiction to achieve high-quality electricity and to meet the real-time balance of power supply and demand. At critical moments, the stability of the photovoltaic power generation system can be improved by controlling the active power of the photovoltaic power generation system. By adjusting the test data as described above, reducing the active power output of the photovoltaic array in the photovoltaic power generation system to obtain the changing voltage and changing power, it can provide more accurate and more realistic changing result data for model building.

[0035] Step S108, determining an equivalent circuit model of the grid-connected simulation model based on the change result data;

[0036] As an optional embodiment, determining the equivalent circuit model of the grid-connected simulation model based on the change result data can be achieved in the following manner: determining the transfer function between the changing voltage and the changing power based on the changing voltage and the changing power; constructing a first-order or multi-order RLC series-parallel model based on the obtained transfer function; constructing a parallel switch RLC branch based on the voltage drop of the bus voltage at the grid connection point and the reduction of the active power output of the photovoltaic array; generating an equivalent circuit model of the grid-connected simulation model based on the RLC series-parallel model and the parallel switch RLC branch. Based on the obtained transfer function, constructing a first-order or multi-order RLC series-parallel model. Since the transfer function takes into account most of the test data under normal conditions, the constructed RLC series-parallel model is equivalent to taking into account most of the normal conditions. Based on the voltage drop of the busbar voltage at the grid connection point and the reduction of active power output by the photovoltaic array, a parallel switch RLC branch is constructed. The voltage drop of the busbar voltage at the grid connection point and the reduction of active power output by the photovoltaic array are disturbances to the normal network. Therefore, the parallel switch RLC branch constructed using the corresponding test data takes into account the disturbance of normal conditions. Therefore, the method of jointly generating an equivalent circuit model based on the RLC series-parallel model and the parallel switch RLC branch not only considers most normal conditions, but also considers abnormal conditions after the normal conditions are disturbed. The equivalent circuit model generated based on these two can more realistically reflect the actual conditions of the entire power grid, making the simulation of the entire power grid more comprehensive.

[0037] As an optional embodiment, the above-mentioned determination of the transfer function between the changing voltage and the changing power based on the changing voltage and the changing power includes: obtaining an initial function including variables, wherein the initial function is provided with a transfer order; and solving the variables in the initial function based on the changing voltage and the changing power to obtain the transfer function between the changing voltage and the changing power. Obtaining the transfer function between the changing voltage and the changing power by solving the function is faster and more accurate. It should be noted that there are multiple ways to solve the function, for example, a corresponding solver can be used for solving, or a corresponding solving software can be used for solving, and no specific restrictions are made here.

[0038] In step S110 , based on the change result data, a non-mechanism black box method is used to identify the parameters of the equivalent circuit model to obtain the model parameters of the equivalent circuit model corresponding to the photovoltaic power generation system.

[0039] As an optional embodiment, the grid connection point voltage in the change result data is used as input, and the grid connection point power in the change result data is used as output. A non-mechanism black box method is used to iterate the model parameters of the equivalent circuit model multiple times until the difference between the magnitude and waveform of the power output of the equivalent circuit model and the magnitude and waveform of the power output of the grid-connected simulation model is less than a predetermined difference threshold, thereby obtaining the model parameters of the equivalent circuit model corresponding to the photovoltaic power generation system. The method of iterating the model parameters of the equivalent circuit model multiple times using the non-mechanism black box method until the output of the model meets the requirements is more intelligent and faster, and therefore, the model parameters of the equivalent circuit can also be obtained more intelligently and quickly.

[0040] As an optional embodiment, the black box method, also known as the black box system identification method, explores the internal structure and mechanism of the black box by observing the changing relationship between external input information and the black box output information. The grid connection point voltage in the above-mentioned change result data can be the input information of the black box method, and the grid connection point power in the above-mentioned change result data can be the output information of the black box method. The changes in input and output information are observed until the equivalent circuit model corresponding to the photovoltaic power generation system is obtained.

[0041] As an optional embodiment, the equivalent circuit model can be verified by the following method: obtaining verification data; based on the verification data, obtaining a first result of the equivalent circuit model and a second result of the grid-connected simulation model; based on the first result and the second result, determining the verification result of the equivalent circuit model.

[0042] As an optional embodiment, after determining the parameters in the photovoltaic power generation system equivalent circuit model and the grid-connected simulation model, the equivalent circuit model is verified, the input voltage data in the photovoltaic power generation system equivalent circuit model is changed, the deviation between the output data and the actual data in the equivalent circuit model is tested, and the accuracy of the equivalent circuit model and parameters is evaluated.

[0043] Through the above steps, a grid-connected simulation model is constructed based on the structural components of the photovoltaic power generation system, and test data is obtained using predetermined test condition parameters and the grid-connected simulation model. Based on the test data, the working state of the photovoltaic power generation system is changed to obtain the change result data of the grid-connected point and determine the equivalent circuit model. By using a non-mechanism black box method, the purpose of obtaining the correct equivalent circuit model parameters corresponding to the photovoltaic power generation system is achieved, and the technical effect of establishing a correct power load model can be achieved.

[0044] Based on the above embodiments and preferred embodiments, an optional implementation is provided.

[0045] Photovoltaic power generation systems are characterized by volatility, intermittency, and instability, making them the most difficult component of power systems to accurately model. Based on the inputs, outputs, and dynamic processes of photovoltaic power generation systems, the static and dynamic characteristics of photovoltaic systems, as well as their internal structure and control strategies, can be quantitatively or qualitatively understood. The purpose of establishing a mathematical model through identification is to estimate the key parameters that characterize system behavior, establish a model that mimics real-world system behavior, use currently measurable system inputs and outputs to predict the future evolution of system outputs, and design controllers that reflect the static and dynamic characteristics of photovoltaic power generation systems at grid connection. This involves establishing an equivalent circuit model with fewer electrical parameters and a simple circuit structure to characterize the photovoltaic power generation system's ability to correctly output active and reactive power under varying voltage input conditions. This is essential for power load modeling.

[0046] To address the above issues, this optional embodiment provides an adaptive parameter identification method for the equivalent circuit of a photovoltaic power generation system using a non-mechanical black-box RLC model, tailored to the requirements of existing photovoltaic power generation system grid-connected technologies. This method is simple and easy to use, with a primarily graphical interface. Estimation is performed by building an equivalent circuit model in Simulink based on simulation or actual data, and can perform parameter identification for both linear and nonlinear systems.

[0047] According to an embodiment of the present invention, a flow chart of adaptive parameter identification of an equivalent circuit of a photovoltaic power generation system based on a non-mechanism black box RLC model is provided, such as Figure 2 The specific implementation steps are as follows:

[0048] Taking into account the high and low voltage characteristics of photovoltaic power generation under voltage drop and the grid-connected output characteristics of photovoltaic arrays when they are blocked, a complete grid-connected simulation model is built by measuring the structural parameters of the battery components, inverters, filters, box transformers, etc. of the actual engineering photovoltaic power generation system. The voltage, active power, and reactive power data of the grid-connected point of the PV power generation system under different states are obtained by testing. The output characteristics of the photovoltaic power generation system under different operating states are fully considered. An equivalent circuit model that can characterize the dynamic and static characteristics of the photovoltaic power generation system when it is grid-connected is proposed. A non-mechanism black box RLC model is established in MATLAB Simulink that can be used for equivalent circuit model of photovoltaic power generation system for power load modeling. The variables in the model are identified, and the model and parameter verification and generalization ability verification are completed. The equivalent circuit model of the photovoltaic power generation system obtained by the present invention is more scientific and more accurate.

[0049] The method provided by the present invention can be implemented using computer software technology. Figure 3The photovoltaic power grid-connected topology shown in the figure is as follows: Cdc is the DC voltage stabilizing capacitor, Lf and Cf are the filter inductor and filter capacitor, respectively; PCC is the common coupling point connecting the photovoltaic system and the grid; R1 and L1 represent the equivalent line resistance and inductance from the photovoltaic system to the PCC point, respectively. The following example illustrates the process of the present invention using a 25MW photovoltaic power generation system with a grid-connected voltage level of 35kV as an example:

[0050] Step 1: Build a complete grid-connected simulation model based on the structural parameters of the actual photovoltaic power generation system, including battery components, inverters, filters, and box transformers. Select an irradiance of 1000W / m2 and a temperature of 25°C for testing to obtain the grid-connected point data. Generate voltage and current test data for the photovoltaic system at the grid-connected point and combine them into a test sequence. When step 1 is executed for the first time, the photovoltaic power generation system can be connected to the grid and generate electricity normally.

[0051] The specific implementation process of the embodiment is described as follows:

[0052] Based on the EMTDC PSCAD software environment, an electromagnetic transient model of the photovoltaic power generation system was established, and modular modeling was performed based on the main circuit topology of the photovoltaic power generation system. A photovoltaic power generation system with an actual capacity of 25MW and a grid-connected voltage level of 35kV was used, including the parameters of the photovoltaic cells and the parameters of the AC system. This is the main interface of the constructed photovoltaic grid-connected system. The modeling adopts a modular modeling concept, dividing the photovoltaic grid-connected system into several modules, including: photovoltaic array module, BOOST circuit module, DC / AC inverter module, boost grid-connected module, transmission line module, etc. All modules are linked together according to the main photovoltaic power generation circuit. For the photovoltaic power grid system, the electricity generated by the photovoltaic array is direct current, which needs to be converted into electricity through an inverter and sent to the grid.

[0053] Set the relevant parameters for the photovoltaic grid-connected circuit. The inverter circuit uses the most common three-phase voltage-type bridge inverter circuit, which utilizes PWM control technology. The PV array outputs DC power, which is then converted to AC power via a three-phase bridge circuit. The AC power is then boosted and connected to the grid. The inverter parameters include DC input parameters, AC output parameters, and protection functions.

[0054] There are many control strategies for photovoltaic grid-connected systems. The present invention is based on a grid voltage-oriented vector control strategy, and its control objective is to ensure that the photovoltaic tracking maximum power grid-connected active and reactive power are stable. From the perspective of control objectives. For photovoltaic power generation systems, the Parker transformation method, a coordinate transformation method often used in electrical engineering, is introduced to transform the stationary three-phase ABC coordinate system into the synchronously rotating dq coordinate system, which can realize the transformation from three-phase AC quantity to the control of two-phase DC dq. Before introducing the entire control system, the expressions of active power and reactive power output by the photovoltaic power grid system in the rotating dq coordinate system are as follows:

[0055]

[0056] In expression (1), ed is the dq component of the grid connection point voltage; id and iq are the dq components of the grid connection point current, respectively.

[0057] According to expression (1), when the grid voltage is stable, the active power and reactive power are proportional to id and iq, respectively. For a three-phase debug inverter circuit, if the current pulsation on the DC side is relatively small when the switching frequency is high, the DC side is approximately regarded as a stable value. Ignoring the IGBT loss, the active power input to the inverter on the DC side and the active power output to the AC grid by the inverter remain equal. The power input to the inverter on the DC side is determined by multiplying Vpv by Ipv. When Ipv remains constant, the grid-connected active power is proportional to the Vpv on the DC side, that is, the grid-connected active power is proportional to the d-axis component of the grid-connected point current and the DC voltage on the DC side, and the grid-connected reactive power is proportional to the q-axis current at the grid-connected point. By controlling the stability of the DC-side Vpv and the grid-connected point iq, the grid-connected active power and reactive power are controlled.

[0058] The overall control process is as follows Figure 4 As shown in the figure, the three-phase voltage and current at the grid connection point (ua, ub, uc and ia, ib, ic) are collected using a measuring device. A PLL phase-locked loop (PLL) performs a Park transform, using the grid connection point voltage phase as the reference phase. This transforms the three-phase AC quantities at the grid connection point into DC quantities in a dq rotating coordinate system. The entire photovoltaic power grid system is controlled by controlling these DC quantities. The voltage outer loop control module first determines the DC voltage Vpv and reactive power Q. Through PI control and negative feedback of the DC voltage and grid-connected reactive power, the d-axis and q-axis reference components of the grid-connected current, Idref and Iqref, are obtained.

[0059] Among them, the DC voltage reference value Vpvref is determined by the maximum power tracking link of the photovoltaic array link, which inputs DC current and DC voltage, and outputs the DC voltage reference value through the MPPT link. The working principle is to disturb the output voltage of the photovoltaic cell and observe the change in the output power of the photovoltaic cell. According to the trend of power change, the direction of the disturbance voltage is continuously changed so that the photovoltaic array finally works at the maximum power point. The reference Qref of the grid-connected reactive power is based on the power factor requirements of the power grid for the electric energy of the photovoltaic power generation system. The reactive output range adjusts the reactive output according to the grid voltage level, and participates in the grid voltage regulation. In the present invention, it is provided that when the grid voltage rises or drops to above 1.15pu or below 0.85pu, the photovoltaic power generation system automatically adjusts its absorption or emission of reactive power to support the grid voltage, so that it has the ability to cross high and low voltages.

[0060] The d-axis and q-axis reference components of the grid-connected current, obtained from the outer voltage control loop, are introduced into the inner current control loop for the active and reactive currents (id and iq). This yields the reference values ​​for the d-axis and q-axis components of the grid-connected point voltage (uid and uiq). Using the inverse Park transform, the reference values ​​for the three-phase AC voltages (uia, uib, and uic) at the grid-connected point are then derived. PWM control is used to generate control signals for the switches in the three-phase bridge inverter circuit, ultimately achieving closed-loop control of the entire photovoltaic power grid system.

[0061] In step 1, the primary circuit and control circuit of the photovoltaic power generation system are modeled, and the voltage and power data of the photovoltaic power generation system when it is normally connected to the grid are obtained.

[0062] The grid-connected data of the photovoltaic power generation system in different states obtained in step 2, i.e., the voltage, active power, and reactive power data (all in per-unit values) of the PV power generation system grid-connected point when the grid parameters change (U changes when the bus voltage at the grid-connected point experiences a voltage drop, and P changes when the active power of the photovoltaic array decreases). The present invention further provides a specific method for changing the voltage and power in real time in an embodiment as follows:

[0063] A test module is set at the AC-side grid connection point to ensure the PV system enters the high and low voltage ride-through range. The test duration is 1 second, and the voltage and power data at the PV system's grid connection point are recorded. Based on the relevant technical regulations for distributed power generation access to the grid and the control strategy in step 1, the PV system automatically adjusts its reactive power absorption or output to support the grid voltage, ensuring high and low voltage ride-through capability. The voltage and power data at the PV system's grid connection point are recorded.

[0064] When the photovoltaic power generation system is operating normally, reduce the irradiance of the photovoltaic array for 5 seconds and then return to the irradiance during normal operation. Record the voltage and power data of the photovoltaic power generation system grid connection point.

[0065] Step 3: The voltage, active power, and reactive power data of the stable grid-connected point of the photovoltaic power generation system obtained in step 2 are imported into MATLAB Simulink. The transfer function between voltage and power is obtained using the parameter identification module. The circuit model between the excitation and output is analyzed based on the transfer function, and series-parallel models of RLCs of different orders are constructed. When the voltage drops and is blocked, the parallel switch RLC branch can be used to represent the different operating states of the photovoltaic power generation system to characterize the equivalent circuit model of the photovoltaic power generation system when it is grid-connected.

[0066] Example Specific implementation plan is:

[0067] In MATLAB Simulink, a transfer function module with single input and single output is established. The input is the grid voltage data, and the output is the variable grid power data. The transfer function is set. For intermediate order numbers and variables, we can obtain the transfer function between power and voltage;

[0068] Decompose the zeros and poles of the transfer function. For example, the transfer function can be decomposed into the form of G(s) = sL + R or Available as Figure 5 The RLC circuit shown is equivalent.

[0069] According to the transfer function between voltage and power, series-parallel models of RLC with different orders are built. When the voltage changes, the switching function is used to switch the equivalent circuit and set the switching action time. Different RLC series-parallel branches can be used to represent different operating states of the photovoltaic power generation system.

[0070] Step 4: Perform parameter identification on the equivalent circuit model from Step 3. Determine the number of independent variable parameters in the equivalent circuit model for the grid-connected PV power generation system, using the voltage data from Step 2 as input and the active and reactive power data as output. Continuously iterate the variable parameters in the equivalent circuit model until the output active and reactive power values ​​and waveforms closely match the output power waveform.

[0071] The variables that need to be identified in the photovoltaic grid-connected equivalent circuit built in step 3 are defined in the Simulink workspace. The input signal parameters in the equivalent model come from the voltage data in step 2, and the output data is the power data in step 2.

[0072] In the Simulink app, find the parameter estimator, select the parameters to be identified in the equivalent model, click New Experiment to create an estimation task, and select power data as the output parameter for parameter identification in Outputs.

[0073] MATLAB provides several optimization algorithms. The present invention uses nonlinear minimum variance, among which the parameter cutoff error and function cutoff error are more important. It is only necessary to determine that the parameters of two iterations do not exceed the cutoff error to stop the iteration, so that the power output waveform in the equivalent circuit model of the photovoltaic power generation system is infinitely close to the simulation model waveform in step 2.

[0074] After the parameter identification is completed, the identification values ​​of each parameter in the photovoltaic equivalent circuit model are obtained.

[0075] Step 5: Verify the equivalent circuit model and parameters for the grid-connected PV power generation system and verify its generalization capabilities. The model is fed with the same (raw data) or different data inputs. The calculated results are compared with the detailed PV grid-connected system simulation model, and the average error is calculated to determine the most accurate set of identification parameters. Based on this set of parameters, the model's response under different voltage fluctuation ranges is verified to determine the error between the response and the detailed PV grid-connected system simulation model.

[0076] That is, after determining the parameters in the equivalent circuit model of the photovoltaic power generation system, change the input voltage data in the equivalent circuit model of the photovoltaic power generation system, test the deviation between the output data and the actual data in the equivalent circuit model, and evaluate the accuracy of the equivalent circuit model and parameters.

[0077] In an embodiment of the present invention, a model parameter identification device is also provided. Figure 6 This is a structural block diagram of a model parameter identification device provided according to an embodiment of the present invention. As shown in the figure, the device includes: a construction module 60, a generation module 62, a processing module 64, a determination module 66 and an identification module 68. The device is described below.

[0078] A construction module 60 is used to construct a grid-connected simulation model of a photovoltaic power generation system based on the structural components included in the photovoltaic power generation system; a generation module 62 is connected to the above-mentioned construction module 60, and is used to generate test data of the photovoltaic power generation system at the grid-connected point based on predetermined test condition parameters and the grid-connected simulation model; a processing module 64 is connected to the above-mentioned generation module 62, and is used to change the working state of the photovoltaic power generation system based on the test data, and obtain the change result data of the photovoltaic power generation system at the grid-connected point; a determination module 66 is connected to the above-mentioned processing module 64, and is used to determine the equivalent circuit model of the grid-connected simulation model based on the change result data; an identification module 68 is connected to the above-mentioned determination module 66, and is used to identify the parameters of the equivalent circuit model based on the change result data using a non-mechanism black box method, and obtain the model parameters of the equivalent circuit model corresponding to the photovoltaic power generation system.

[0079] In an embodiment of the present invention, a computer-readable storage medium is also provided, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute a model parameter identification method of any one of the above methods.

[0080] In an embodiment of the present invention, a computer device is also provided, including: a memory and a processor, the memory storing a computer program; the processor is used to execute the computer program stored in the memory, and when the computer program is running, the processor executes any one of the above-mentioned model parameter identification methods.

[0081] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0082] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0083] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0084] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0085] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0086] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc. Various media that can store program codes.

[0087] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A model parameter identification method, characterized in that: include: Based on the structural components included in the photovoltaic power generation system, a grid-connected simulation model of the photovoltaic power generation system is constructed; Generating test data of the photovoltaic power generation system at the grid connection point based on predetermined test condition parameters and the grid connection simulation model; Based on the test data, changing the working state of the photovoltaic power generation system to obtain change result data of the photovoltaic power generation system at the grid connection point; Determining an equivalent circuit model of the grid-connected simulation model based on the change result data; Based on the change result data, a non-mechanism black box method is used to identify the parameters of the equivalent circuit model to obtain model parameters of the equivalent circuit model corresponding to the photovoltaic power generation system; Wherein, changing the working state of the photovoltaic power generation system based on the test data to obtain change result data of the photovoltaic power generation system at the grid-connected point includes: adjusting the test data so that the active power output by the photovoltaic array in the photovoltaic power generation system is reduced, and obtaining the changed voltage and changed power of the photovoltaic power generation system at the grid-connected point; wherein the change result data includes the changed voltage and the changed power; The determining of the equivalent circuit model of the grid-connected simulation model based on the change result data includes: determining a transfer function between the changing voltage and the changing power based on the changing voltage and the changing power; constructing a first-order or multi-order RLC series-parallel model based on the transfer function; constructing a parallel switch RLC branch based on a voltage drop in the bus voltage at the grid connection point and a reduction in the active power output of the photovoltaic array; and generating an equivalent circuit model of the grid-connected simulation model based on the RLC series-parallel model and the parallel switch RLC branch.

2. The method according to claim 1, characterized in that The predetermined test condition parameters include: the light intensity of the photovoltaic array in the photovoltaic power generation system, and the temperature of the photovoltaic array.

3. The method according to claim 1, characterized in that The step of changing the working state of the photovoltaic power generation system based on the test data to obtain change result data of the photovoltaic power generation system at the grid connection point includes: By disturbing the test data, a voltage drop occurs in the bus voltage at the grid connection point, and a changed voltage and a changed power of the photovoltaic power generation system at the grid connection point are obtained.

4. The method according to claim 1, wherein The determining, based on the changed voltage and the changed power, a transfer function between the changed voltage and the changed power, comprises: Acquire an initial function including variables, wherein the initial function is provided with a transfer order; Based on the changing voltage and the changing power, the variables in the initial function are solved to obtain the transfer function between the changing voltage and the changing power.

5. The method according to claim 1, characterized in that The method of identifying the parameters of the equivalent circuit model based on the change result data using a non-mechanism black box method to obtain the model parameters of the equivalent circuit model corresponding to the photovoltaic power generation system includes: The grid-connected point voltage in the change result data is used as input, and the grid-connected point power in the change result data is used as output. The non-mechanism black box method is used to iterate the model parameters of the equivalent circuit model multiple times until the difference between the magnitude and waveform of the power output by the equivalent circuit model and the magnitude and waveform of the power output by the grid-connected simulation model is less than a predetermined difference threshold, thereby obtaining the model parameters of the equivalent circuit model corresponding to the photovoltaic power generation system.

6. The method according to any one of claims 1 to 5, characterized in that After identifying the parameters of the equivalent circuit model based on the change result data using a non-mechanism black box method to obtain the model parameters of the equivalent circuit model corresponding to the photovoltaic power generation system, the method further includes: Get verification data; Based on the verification data, respectively obtain a first result of the equivalent circuit model and a second result of the grid-connected simulation model; A verification result of the equivalent circuit model is determined based on the first result and the second result.

7. A model parameter identification device, characterized in that: include: A construction module, configured to construct a grid-connected simulation model of the photovoltaic power generation system based on the structural components included in the photovoltaic power generation system; A generating module, configured to generate test data of the photovoltaic power generation system at a grid connection point based on predetermined test condition parameters and the grid connection simulation model; a processing module, configured to change the working state of the photovoltaic power generation system based on the test data, and obtain change result data of the photovoltaic power generation system at the grid connection point; A determination module, configured to determine an equivalent circuit model of the grid-connected simulation model based on the change result data; an identification module, configured to identify the parameters of the equivalent circuit model based on the change result data using a non-mechanism black box method to obtain model parameters of the equivalent circuit model corresponding to the photovoltaic power generation system; The processing module is further configured to adjust the test data so that the active power output by the photovoltaic array in the photovoltaic power generation system is reduced, thereby obtaining a changed voltage and a changed power of the photovoltaic power generation system at the grid connection point; wherein the change result data includes the changed voltage and the changed power; The determination module is further configured to determine a transfer function between the changing voltage and the changing power based on the changing voltage and the changing power; construct a first-order or multi-order RLC series-parallel model based on the transfer function; construct a parallel switch RLC branch based on a voltage drop in the bus voltage at the grid connection point and a reduction in the active power output by the photovoltaic array; and generate an equivalent circuit model of the grid-connected simulation model based on the RLC series-parallel model and the parallel switch RLC branch.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the model parameter identification method according to any one of claims 1 to 6.

9. A computer device, characterized in that: include: memory and processor, The memory stores a computer program; The processor is configured to execute a computer program stored in the memory, and when the computer program is run, the processor is enabled to execute the model parameter identification method according to any one of claims 1 to 6.

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