A photovoltaic micro-grid system grid-connected control method and device based on a predetermined time

Abstract

The application discloses a photovoltaic micro-grid system grid-connected control method and device based on a predetermined time, and the method comprises the following steps: designing a time-varying function of the pre-defined time control based on the initial state of the photovoltaic micro-grid system; considering the grid-connected photovoltaic inverter model of the photovoltaic micro-grid system which is not accurately modeled and disturbed, introducing the time-varying function into the backstepping control framework to construct a composite state tracking error, designing a virtual controller, and solving the differential calculation problem of the virtual controller through dynamic surface processing and filter error processing, designing the state tracking error of the D-axis current and the Q-axis current, and introducing adaptive control law processing to output the controller of the system; selecting appropriate control parameters for the controller of the photovoltaic micro-grid system, and guaranteeing the global pre-defined time stability of the closed-loop system by using the Lyapunov theory to realize the pre-defined time design of the photovoltaic micro-grid system and realize the tracking control of the reference signal within the pre-defined time. The device comprises a processor and a memory.

Description

Technical Field

[0001] This invention relates to the field of grid-connected control of photovoltaic microgrid systems, and more particularly to a method and apparatus for grid-connected control of photovoltaic microgrid systems based on a predetermined time. Background Technology

[0002] With economic development, the demand for energy is constantly increasing, raising the need for energy transition. [1] Traditional fossil fuels are not only limited in reserves but also pollute the environment. In contrast, renewable energy sources, led by photovoltaics, are safe, pollution-free, widely distributed, and adaptable to local conditions. Therefore, increasing the grid connection rate of renewable energy in the power system has become a new trend. [2] Furthermore, the rapid development of power electronics technology has greatly facilitated the utilization of renewable energy. [3] In recent years, photovoltaics, as a renewable and clean energy source, has been the subject of extensive research in many countries.

[0003] The large-scale grid connection trend of photovoltaic power generation places new demands on control stability and system convergence, which in turn facilitates the planning and management of photovoltaic grid connection. [4]Currently, backstepping control has many applications in the stability control of photovoltaic inverters. In terms of convergence time control, a finite-time control algorithm has been proposed, which can track the reference command within a finite time. For photovoltaic inverters in islanded systems, the controller adopts an adaptive finite-time sliding mode design to achieve smooth power control and ensure the stability and convergence of the tracking error within a finite time. In reference [5], in order to enhance the stability of grid-connected photovoltaic arrays and the robustness of tracking, a finite-time continuous sliding mode controller is proposed to solve the modeling uncertainty caused by the changes in converter parameters, system frequency and external factors. In order to suppress the power oscillation in photovoltaic inverters of master-slave islanded microgrid systems, reference [6] studies the adaptive sliding mode backstepping control method to ensure finite-time stability, good robustness and shorten the convergence time of the system state. In addition, fixed-time control algorithms have attracted attention, which can ensure that the tracking error converges within a fixed time range and is independent of the initial state. Compared with the finite-time control algorithm, fixed-time control further improves the convergence speed, but the specific convergence time still cannot be determined by the control parameters. To address the system-level frequency deviation problem of photovoltaic power generation systems, reference [7] completed the control of bus overvoltage or random output, ensuring the response of the battery energy storage system within a fixed time. Considering the impact of intermittent power and frequent load changes of renewable energy on the dynamic performance of microgrid clusters, reference [8] proposed a fixed-time fuzzy backstepping control strategy to address the modeling uncertainty and dynamic performance problem of islanded inverters. The filter parameters and output current are taken as unknowns, and the controller is designed to approximate the unknowns in the system by combining the fixed-time Lyapunov stability principle, fuzzy logic control and backstepping control. The output voltage of the inverter is adjusted to ensure that the fixed-time stability of the system is independent of the initial state of the system. In recent years, the concept of predefined time control algorithm has been proposed based on fixed time. Compared with fixed-time control, the predefined time control algorithm allows the user to arbitrarily design the preset convergence time of the controlled object, and this time can be determined by the controller parameters, realizing the predefined time design of the system state. [9] Reference

[10] designed a global predetermined time terminal sliding mode controller in the independent photovoltaic system model, which realized the tracking of the reference voltage and the maximum power point tracking within the predetermined time.

[0004] Currently, the main methods for controlling the grid-connected settling-off time of photovoltaic microgrid systems are finite-time control and fixed-time control. However, these methods cannot provide quantitative analysis of the settling-off time, and they rely on precise models. Furthermore, the control parameters are often too complex for practical engineering applications. Therefore, this paper considers a grid-connected control method and device for photovoltaic microgrid systems based on a predetermined time, aiming to improve the control performance of photovoltaic inverters.

[0005] References

[0006] [1] Gao Biao. Analysis of control technology for distributed photovoltaic grid-connected power generation system [J]. Electrical Technology and Economy, 2023(06):138-140.

[0007] [2] Li Feng, Ding Jie, Zhou Caiqi, Yong Weizhen, Huang Yuehui, Wang Jianxue, Xu Xiao. Discussion on key technologies for large-scale grid-connected operation of distributed photovoltaic power under new power system [J / OL]. Power System Technology, 2023(0771):1-16.

[0008] [3] Li Lin, Ren Xiaona, Song Peiwu, et al. Research on composite control of photovoltaic grid-connected inverters [J]. China Equipment Engineering, 2023(17):101-104.

[0009] [4] Gao Ziyuan. A brief analysis of photovoltaic power generation grid connection technology [J]. Modern Industrial Economy and Information Technology, 2022, 12(07):265-267.

[0010] [5]Dhar S,Dash P KA new backstepping finite time sliding modecontrol of grid connected PV system using multivariabledynamic VSCmodel.International Journal of Electrical Power&Energy Systems,2016,82:314-330.

[0011] [6]Yang CS,Ni SF,Dai YC,Huang XN,Zhang D D.Anti-DisturbanceFinite-Time Adaptive Sliding Mode Backstepping Control for PV Inverter inMaster-Slave-Organized Islanded Microgrid.Energies,2020,13(17).

[0012] [7] Xiong LY, Huang SH, Zhou Y, Li PH, Wang ZQ, et al. Voltage and frequency regulation with WT-PV-BESS in remote weak grids via fixed-Timecontainment control [J], IEEE Transactions on Power Systems, 2023, 38: 2719-273.

[0013] [8] He Guofeng, Zhou Zichun, Zhang Guoyong, et al. Fixed-time fuzzy backstepping control strategy for isolated photovoltaic parallel inverter system [J]. Power System Protection and Control, 2023, 51(18):147-156.

[0014] [9]Zhang Y, Wang YW, Xiao JW, Liu XKPredefined-time secondarycontrol for DC microgrid. IEEE Transactions on Industrial Electronics, 2021,69(12):13504-13513.

[0015]

[10] Wang Yan, Liu Chongxin. Independent photovoltaic MPPT based on global predetermined time terminal sliding mode control [J]. Electrical Engineering, 2023(04):6-11+52. Summary of the Invention

[0016] This invention provides a grid-connected control method and device for a photovoltaic microgrid system based on a predetermined time. To improve the control effect of the photovoltaic inverter, this invention quantitatively designs the system's settling time and designs a predefined time controller based on the initial state of the photovoltaic microgrid system. To reduce the dependence of the control algorithm on an accurate model, the invention addresses modeling uncertainties and disturbances in practical applications, and studies the predefined time backstepping control of the photovoltaic microgrid system inverter, enabling users to predefine the upper limit of the settling time. Details are described below:

[0017] A first aspect is a grid-connected control method for a photovoltaic microgrid system based on a predetermined time, the method comprising:

[0018] Design a time-varying function with predefined time control based on the initial state of a photovoltaic microgrid system;

[0019] Considering the grid-connected photovoltaic inverter model of the photovoltaic microgrid system with inaccurate modeling and disturbances, a time-varying function is introduced into the backstepping control framework to construct a composite state tracking error. A virtual controller is designed, and the differential calculation problem of the virtual controller is solved through dynamic surface processing and filtering error processing. The state tracking errors of the D-axis current and Q-axis current are designed and processed by adaptive control law to output the controller of the system.

[0020] Appropriate control parameters are selected for the controller of the photovoltaic microgrid system, and Lyapunov theory is used to ensure the global predefined time stability of the closed-loop system, thereby realizing the predefined time design of the photovoltaic microgrid system and achieving tracking control of the reference signal within the predefined time.

[0021] The time-varying function for the predefined time control of the initial state design of the photovoltaic microgrid system is as follows:

[0022] The time-varying function ρ(t) of the D-axis satisfies the following at the initial moment:

[0023] ρ(0)=m=u dc (0)-y d (0)

[0024]

[0025] In the formula, ρ(0) is the value of ρ(t) at the initial time, which is determined by the initial DC voltage u. dc (t) and D-axis reference signal y d (t) is obtained; This represents the first derivative of ρ(t) at the initial time.

[0026]

[0027] In the formula, T1 is the user-defined time, and t is the real-time time; for a first-order system along the Q-axis, the time-varying function υ(t) satisfies the following at the initial time:

[0028] υ(0)=l=i sq (0)-y q (0)

[0029] In the formula, y q The reference signal for the Q-axis is used; υ(t) is designed as follows:

[0030]

[0031] Among them, the introduction of a time-varying function ρ(t) into the backstepping control framework to construct the D-axis voltage composite state tracking error e1 is as follows:

[0032] e1=u dc -y d(t)-ρ(t)

[0033] Among them, u dc y is the DC side voltage of the inverter. d Let ρ(t) be the DC voltage reference value along the D-axis, and ρ(t) be the time-varying function along the D-axis. The virtual controller α2 is designed as follows:

[0034]

[0035] Among them, u sd C is the voltage value along the D-axis. dc Here, k1 is the DC-side capacitor value of the inverter, and u is the backstepping control parameter. sq i is the Q-axis voltage value. sq i is the Q-axis current value. dc This is the DC side current value. The first derivative of the D-axis reference signal. It is the first derivative of the D-axis time-varying function.

[0036] Furthermore, after dynamic surface processing and filtering error processing, the state tracking errors of the D-axis current and Q-axis current are designed as follows:

[0037] Dynamic surface control is introduced, and the signal is obtained by filtering the virtual controller α2.

[0038]

[0039] Filtering error is defined as The state tracking errors for the D-axis current and Q-axis current are designed as follows:

[0040]

[0041] Where μ is the design parameter; e2 is the D-axis current state tracking error; e3 is the Q-axis current state tracking error; i sd The current is the D-axis current; y q (t) is the current tracking value; υ(t) is the time-varying function of the Q axis.

[0042] The process of introducing adaptive control laws results in the output system's controller:

[0043] The adaptive control law is designed as follows:

[0044]

[0045]

[0046] Where r1, r2, σ1, and σ2 are adaptive design parameters. γ > 0 is a design constant;

[0047] The controller is designed as follows:

[0048]

[0049]

[0050] Where, τ d For D-axis controller; τ q For the Q-axis controller; L is the inductance value; k2 and k3 are the backstepping control parameters; R is the resistance value; ω is the system angular frequency; The first derivative of the filtered virtual control signal; The first derivative of the Q-axis reference signal; This is the adaptive estimate of the disturbance signal d1 in the model; u is the first derivative of the Q-axis time-varying function; sd This represents the voltage value along the D-axis. This is the adaptive estimate of the disturbance signal d2 in the model.

[0051] Secondly, a grid-connected control device for a photovoltaic microgrid system based on a predetermined time, the device comprising:

[0052] The design module is used to design a time-varying function for predefined time control based on the initial state of the photovoltaic microgrid system;

[0053] The output module is used to consider the grid-connected photovoltaic inverter model of the photovoltaic microgrid system that is not accurately modeled and has disturbances. It introduces a time-varying function into the backstepping control framework to construct a composite state tracking error, designs a virtual controller, and solves the differential calculation problem of the virtual controller through dynamic surface processing and filtering error processing. It designs the state tracking error of the D-axis current and Q-axis current and introduces an adaptive control law to process it, and outputs the controller of the system.

[0054] The tracking control module is used to select appropriate control parameters for the controller of the photovoltaic microgrid system and use Lyapunov theory to ensure the global predefined time stability of the closed-loop system, thereby realizing the predefined time design of the photovoltaic microgrid system and achieving tracking control of the reference signal within the predefined time.

[0055] Thirdly, a grid-connected control device for a photovoltaic microgrid system based on a predetermined time, the device comprising: a processor and a memory, the memory storing program instructions, the processor calling the program instructions stored in the memory to cause the device to execute the method described in any one of the first aspects.

[0056] Fourth aspect, a computer-readable storage medium storing a computer program, the computer program including program instructions that, when executed by a processor, cause the processor to perform the method described in any one of the first aspects.

[0057] The beneficial effects of the technical solution provided by this invention are:

[0058] 1. This invention reduces the dependence of control algorithms on accurate models, designs for modeling uncertainties and disturbances in practical applications, and uses adaptive control to estimate and compensate for disturbances;

[0059] 2. This invention takes into account the randomness and volatility of photovoltaic microgrid systems, sets temperature and irradiance variations, and verifies the control effect;

[0060] 3. This invention ensures the stability and boundedness of the globally predefined time of the photovoltaic microgrid system, and realizes the user's predefined setting of the upper limit of the stable time of the photovoltaic microgrid system. Attached Figure Description

[0061] Figure 1 This is a framework model diagram of the photovoltaic microgrid system provided by the present invention;

[0062] Figure 2 This is a signal flow topology diagram of the control algorithm provided by the present invention;

[0063] Figure 3 A diagram showing the settings for irradiance and temperature in a photovoltaic microgrid system;

[0064] Figure 4 The tracking effect of DC voltage under different preset time settings is shown in the figure;

[0065] Figure 5 A diagram showing the effect of the output three-phase voltage of a photovoltaic system;

[0066] Figure 6 A diagram showing the effect of the photovoltaic system's output three-phase current;

[0067] Figure 7 This is a harmonic analysis diagram of the bus voltage;

[0068] Figure 8 This is a harmonic analysis diagram of the bus current. Detailed Implementation

[0069] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below.

[0070] Example 1

[0071] This invention provides a grid-connected control method for a photovoltaic microgrid system based on a predetermined time. The method includes: first, designing a time-varying function for predefined time control based on the initial state of the photovoltaic microgrid system; second, introducing the time-varying function into the backstepping control framework based on a model of the grid-connected photovoltaic inverter; third, employing dynamic surface control in the recursive design of the backstepping control to solve the differential calculation explosion problem caused by the analytical calculation of the virtual controller; and finally, selecting appropriate control parameters and utilizing Lyapunov theory to ensure the global predefined time stability of the closed-loop system. By considering the influence of system parameters and other factors, users can achieve predefined time design for the photovoltaic microgrid system, enabling the system to track and control the reference signal within the predefined time.

[0072] Step 1: Set the temperature and irradiance of the photovoltaic microgrid system, and use the incremental conductivity method to achieve maximum power point tracking control of the photovoltaic microgrid DC converter, so that the photovoltaic microgrid system can output maximum power;

[0073] Step 2: For the voltage source converter inverter, neglecting the impedance of the DC-side capacitor and the inverter in the photovoltaic microgrid system, establish a mathematical model under the DQ coordinate axis;

[0074] Step 3: Measure the three-phase grid voltage u bus Sampling and phase-locking are performed on the DC-side voltage u of the inverter. dc The three-phase voltage u measured by AC in the photovoltaic microgrid system sabc and three-phase current i sabc Perform sampling;

[0075] Step 4: Design time-varying functions for the D-axis and Q-axis based on the initial state of the photovoltaic microgrid system;

[0076] Step 5: Test the three-phase voltage u sabc and three-phase current i sabc Perform coordinate transformations to obtain the parameters u. sd u sq i sd and i sq The parameters are input into the inverter controller, and the controller is designed and the parameters are selected.

[0077] Step 6: After the output voltage of the photovoltaic system is regulated by a transformer, it is connected to the AC power grid to achieve tracking control of the reference signal within a predefined time.

[0078] Furthermore, the method also includes feasibility verification of the control parameters. Specifically, considering the randomness and volatility of photovoltaic power generation, the irradiance and temperature changes of the photovoltaic microgrid system are set, and appropriate control parameters are selected to verify the control effect of the controller under different predefined times. Simulation analysis verifies that when irradiance and temperature change, the AC current changes but the AC voltage remains unchanged, and the controller can achieve tracking control of the reference signal within a predetermined time.

[0079] The specific process of step 2 in this embodiment of the invention is as follows:

[0080] According to Kirchhoff's voltage and current laws, the relationship between the DC side voltage and the AC measured voltage is as follows:

[0081]

[0082] According to the modulated wave signal τ dq The inverter model is derived from the relationship between the three-phase balanced AC voltage and current:

[0083]

[0084] In the formula, L is the inductor, R is the resistor, and C is the inductor. dc d1(t) represents the DC-side capacitor of the inverter; d1(t) and d2(t) represent the total interference on the D-axis and Q-axis of the photovoltaic system, and d1(t)≤D1 and d2(t)≤D2, where D1 and D2 are the upper limits of d1(t) and d2(t), respectively.

[0085] The specific process of step 4 in this embodiment of the invention is as follows:

[0086] The time-varying function ρ(t) of the D-axis satisfies:

[0087]

[0088] In the formula, ρ(0) is the value of ρ(t) at the initial time, which is determined by the initial DC voltage u. dc (t) and D-axis reference signal y d (t) is obtained; Let denot be the first derivative of ρ(t) at the initial time.

[0089]

[0090] In the formula, T1 is the user-defined time, and t is the real-time time.

[0091] Furthermore, for a first-order system along the Q-axis, the time-varying function υ(t) satisfies:

[0092] υ(0)=l=i sq (0)-y q (0) (5)

[0093] In the formula, y q This is the Q-axis reference signal.

[0094] Furthermore, υ(t) is designed as follows:

[0095]

[0096] The specific process of step 5 in this embodiment of the invention is as follows:

[0097] First, based on the backstepping control framework, a time-varying function is introduced to construct a composite state tracking error:

[0098] e1=u dc -y d (t)-ρ(t) (7)

[0099] Furthermore, the virtual controller is designed as follows:

[0100]

[0101] Furthermore, dynamic surface control is introduced to solve the explosion problem of differential calculation in the backstepping method, that is, by filtering the virtual controller α2 to obtain the signal.

[0102]

[0103] Filtering error is defined as

[0104] Furthermore, the state tracking errors for the D-axis current and Q-axis current are designed:

[0105]

[0106] Furthermore, the adaptive control law is designed as follows:

[0107]

[0108] in,

[0109] Furthermore, design the system's controller:

[0110]

[0111] For step 6, select appropriate control parameters to ensure the stability of the predefined time control of the photovoltaic microgrid system.

[0112] Furthermore, when designing a predefined settling time, users should note that the selection of the predefined time must take into account factors such as system parameters.

[0113] In summary, the embodiments of the present invention reduce the dependence of the control algorithm on the accurate model, and design for modeling uncertainties and disturbances in practical applications. Adaptive control is used to estimate and compensate for disturbances, thus meeting the needs of practical applications.

[0114] Example 2

[0115] The following is combined with Figures 1-8 The solution in Example 1 will be further described below:

[0116] Figure 1 This is a framework model diagram of a photovoltaic microgrid system provided in an embodiment of the present invention. Figure 1 As can be seen, a photovoltaic microgrid system consists of a photovoltaic array, a boost converter, and a DC / AC converter, and is connected to the power grid via a transformer. The main working principle of a photovoltaic microgrid system is as follows: the photovoltaic array generates direct current (DC) based on the irradiance and temperature, and outputs maximum power through a DC-AC converter. The DC power generated by the photovoltaic array is controlled by maximum power point tracking (MPPT) using the incremental conductance method, effectively utilizing photovoltaic power generation. The DC power is then converted to alternating current (AC) by a voltage source converter, which then supplies power to the three-phase grid voltage u. abc Sampling and phase-locking are performed on the DC-side voltage u of the inverter. dc The three-phase voltage u measured by AC in the photovoltaic microgrid system sabc and three-phase current i sabc The voltage and current values ​​under the DQ axis are sampled and obtained through coordinate transformation and input into the controller. The inverter controller outputs a modulated wave signal, which is controlled by a pulse width modulator (PWM) to maintain the DC side voltage tracking the reference signal. This enables the inverter to achieve stable control within a user-defined time and meet grid connection requirements.

[0117] This invention provides a predefined time-backstep adaptive control strategy, which is applied to the control of grid-connected inverters in photovoltaic microgrid systems.

[0118] The following section explains the inverter control for a photovoltaic microgrid system. (See also...) Figure 2 This is the signal flow topology diagram for the control algorithm.

[0119] Depend on Figure 2 It can be seen that the inverter control strategy mainly includes the design of the time-varying function, the design of the inverter controller, and the selection of control parameters. The time-varying function is designed based on the initial state of the system and is incorporated into the design process of the backstepping controller. The inverter controller design adopts a backstepping control framework and adaptive control to achieve stable control and disturbance tracking compensation. The selection of control parameters ensures the stability and boundedness of predefined time control and achieves stable control of user-defined time.

[0120] In this embodiment of the invention, for inverter model design, based on the relationship between the modulation wave signal and the AC side voltage of the inverter, and taking into account the disturbance problem in actual engineering, a mathematical model of the inverter is designed:

[0121]

[0122] Where ω is the system angular frequency, τ d and τ q The modulated output signal of the controller is input to the PWM generator to control the inverter to generate the required AC power. The first two formulas constitute the D-axis system, and the third formula constitutes the Q-axis system. d1(t) and d2(t) represent the total disturbances of the D-axis and Q-axis in the photovoltaic microgrid system, and satisfy d1(t)≤D1 and d2(t)≤D2, where D1 and D2 are the upper limits of d1(t) and d2(t), respectively, and their estimated values ​​are... The error is and

[0123] See Figure 3 Considering the randomness and volatility of actual photovoltaic microgrid systems, the temperature and irradiance of the photovoltaic microgrid system are set. The grid-connected backstepping adaptive control method for the voltage source converter inverter of a photovoltaic microgrid system based on predefined time control, as described in this embodiment of the invention, is as follows:

[0124] In this embodiment of the invention, an adaptive law is designed to achieve disturbance estimation and compensation:

[0125]

[0126] In this embodiment of the invention, to achieve predefined time control, a time-varying function based on the system's initial state is designed. For an n-order system, the time-varying function should satisfy that itself and its (n+1)th derivative are bounded and continuous at t≥0. After the predefined time T1, the value of the time-varying function is 0. The initial value of the D-axis time-varying function is designed as follows:

[0127]

[0128] In the formula, the time-varying function is determined by the initial state of the DC voltage and the reference signal along the D-axis, and the first derivative of the time-varying function is determined by the first derivative state of the DC voltage and the reference signal.

[0129] Select the time-varying function ρ(t) on the D-axis:

[0130]

[0131] The initial values ​​for the Q-axis time-varying function are designed as follows:

[0132] υ(0)=l=i sq (0)-yq (0) (17)

[0133] In the formula, the time-varying function is determined by the Q-axis current and the initial state of the reference signal.

[0134] Select the Q-axis time-varying function υ(t):

[0135]

[0136] In this embodiment of the invention, the design process of the predefined time controller is the design of a virtual controller and a DQ axis controller.

[0137] Define the D-axis voltage composite state tracking error:

[0138] e1=u dc -y d (t)-ρ(t) (19)

[0139] Design a virtual controller:

[0140]

[0141] In the formula, k1 represents the controller parameters. Backstepping is prone to differential calculation explosion; therefore, dynamic surface control is introduced.

[0142]

[0143] In the formula, filtering the virtual controller α2 yields the signal. μ is the filtering parameter, and the designed filtering error is...

[0144] Design the current state tracking error for the D-axis and Q-axis:

[0145]

[0146] Design of a DQ axis controller for a photovoltaic microgrid system:

[0147]

[0148] In the formula, k2 and k3 are the control parameters for the D-axis and Q-axis, respectively.

[0149] In this embodiment of the invention, in order to achieve the stability and boundedness of the predefined time, it is necessary to perform stability analysis and select appropriate parameters for parameter design to achieve predefined time control.

[0150] Choose a Lyapunov function V1:

[0151]

[0152] Simplify its differential form using the following inequality:

[0153]

[0154] Furthermore, we obtain:

[0155]

[0156] Furthermore, to complete the stability proof, we define the Lyapunov function V2:

[0157]

[0158] Its derivative equation can be simplified by the following inequality:

[0159]

[0160] Furthermore, it is proved that:

[0161]

[0162] Furthermore, to complete the stability proof, we define the Lyapunov function V3:

[0163]

[0164] Simplifying its derivative, we obtain:

[0165]

[0166] Furthermore, we prove the global signal stability and design the Lyapunov function V:

[0167] V = V1 + V2 + V3 (31)

[0168] The derivative with respect to time satisfies:

[0169]

[0170] In the formula,

[0171] Furthermore, define vectors The compact set Ω is:

[0172]

[0173] The controller parameters are selected as follows:

[0174]

[0175] Furthermore, we can conclude that:

[0176]

[0177] Finally, for the above model and controller, the embodiments of the present invention select parameters that meet the conditions for design and perform simulation analysis.

[0178] A simulation analysis of a photovoltaic microgrid system is performed, with the reference signal set to y. d (t) = 500V and y q (t) = 0A. The photovoltaic microgrid system operates at an irradiance of 1000 (W / m²). 2 Maximum power is achieved when the temperature is set to 25°C. MPPT control operation begins at 0.05 seconds. Table 1 lists the physical parameters of the photovoltaic microgrid system, and Table 2 lists the controller parameter design.

[0179] Table 1: Physical parameters of photovoltaic systems

[0180] Table 1 Physical parameters of photovoltaic system

[0181]

[0182]

[0183] Table 2: Parameter Design of Predefined Time Controller

[0184] Table 2Parameter design of predefined time controller

[0185]

[0186] DC voltage tracking performance under different preset time settings, such as Figure 4 As shown, the control system achieved stable tracking control of the DC voltage within different predetermined time periods; the output three-phase voltage and current of the photovoltaic microgrid system are as follows. Figure 5 and Figure 6 As shown, the robustness of the control was verified under extreme temperature and radiation conditions on the photovoltaic cells; the bus voltage and current harmonic analysis is as follows. Figure 7 and Figure 8 As shown, it meets the grid connection requirements for voltage and current.

[0187] Example 3

[0188] A grid-connected control device for a photovoltaic microgrid system based on a predetermined time, the device comprising:

[0189] The design module is used to design a time-varying function for predefined time control based on the initial state of the photovoltaic microgrid system;

[0190] The output module is used to consider the grid-connected photovoltaic inverter model of the photovoltaic microgrid system that is not accurately modeled and has disturbances. It introduces a time-varying function into the backstepping control framework to construct a composite state tracking error, designs a virtual controller, and solves the differential calculation problem of the virtual controller through dynamic surface processing and filtering error processing. It designs the state tracking error of the D-axis current and Q-axis current and introduces an adaptive control law to process it, and outputs the controller of the system.

[0191] The tracking control module is used to select appropriate control parameters for the controller of the photovoltaic microgrid system and use Lyapunov theory to ensure the global predefined time stability of the closed-loop system, thereby realizing the predefined time design of the photovoltaic microgrid system and achieving tracking control of the reference signal within the predefined time.

[0192] In summary, the embodiments of this invention quantify the system's settling time and design a predefined time controller based on the initial state of the photovoltaic microgrid system. To reduce the dependence of the control algorithm on the precise model, the design addresses modeling uncertainties and disturbances in practical applications, and studies the predefined time backstepping control of the photovoltaic microgrid system inverter, enabling users to predefine the upper limit of the settling time.

[0193] Example 4

[0194] A grid-connected control device for a photovoltaic microgrid system based on a predetermined time, the device comprising: a processor and a memory, wherein the memory stores program instructions, and the processor calls the program instructions stored in the memory to cause the device to execute the following method steps in Embodiment 1:

[0195] Design a time-varying function with predefined time control based on the initial state of a photovoltaic microgrid system;

[0196] Considering the grid-connected photovoltaic inverter model of the photovoltaic microgrid system with inaccurate modeling and disturbances, a time-varying function is introduced into the backstepping control framework to construct a composite state tracking error. A virtual controller is designed, and the differential calculation problem of the virtual controller is solved through dynamic surface processing and filtering error processing. The state tracking errors of the D-axis current and Q-axis current are designed and processed by adaptive control law to output the controller of the system.

[0197] By selecting appropriate control parameters for the controller of the photovoltaic microgrid system and using Lyapunov theory to ensure the global predefined time stability of the closed-loop system, the predefined time design of the photovoltaic microgrid system is realized, and the tracking control of the reference signal is achieved within the predefined time.

[0198] The time-varying function for predefined time control based on the initial state design of the photovoltaic microgrid system is as follows:

[0199] The time-varying function ρ(t) of the D-axis satisfies the following at the initial moment:

[0200] ρ(0)=m=u dc (0)-y d (0)

[0201]

[0202] In the formula, ρ(0) is the value of ρ(t) at the initial time, which is determined by the initial DC voltage u. dc (t) and D-axis reference signal y d (t) is obtained; This represents the first derivative of ρ(t) at the initial time.

[0203]

[0204] In the formula, T1 is the user-defined time, and t is the real-time time; for a first-order system along the Q-axis, the time-varying function υ(t) satisfies the following at the initial time:

[0205] υ(0)=l=i sq (0)-y q (0)

[0206] In the formula, y q The Q-axis reference signal is used; υ(t) is designed as follows:

[0207]

[0208] In the backstepping control framework, a time-varying function ρ(t) is introduced to construct the D-axis voltage composite state tracking error e1:

[0209] e1=u dc -y d (t)-ρ(t)

[0210] Among them, u dc The inverter's DC-side voltage, y d Let ρ(t) be the DC voltage reference value along the D-axis, and ρ(t) be the time-varying function along the D-axis. The virtual controller α2 is designed as follows:

[0211]

[0212] Among them, u sd C is the voltage value along the D-axis. dc Here, k1 is the DC-side capacitor value of the inverter, and u is the backstepping control parameter. sq i is the Q-axis voltage value. sq i is the Q-axis current value. dc This is the DC side current value. The first derivative of the D-axis reference signal. It is the first derivative of the D-axis time-varying function.

[0213] Furthermore, after dynamic surface processing and filtering error processing, the state tracking errors of the D-axis current and Q-axis current are designed as follows:

[0214] Dynamic surface control is introduced, and the signal is obtained by filtering the virtual controller α2.

[0215]

[0216] Filtering error is defined as The state tracking errors for the D-axis current and Q-axis current are designed as follows:

[0217]

[0218] Where μ is the design parameter; e2 is the D-axis current state tracking error; e3 is the Q-axis current state tracking error; i sd The current is the D-axis current; y q (t) is the current tracking value; υ(t) is the time-varying function of the Q axis.

[0219] In this process, adaptive control laws are introduced to process the output system's controller:

[0220] The adaptive control law is designed as follows:

[0221]

[0222]

[0223] Where r1, r2, σ1, and σ2 are adaptive design parameters. γ > 0 is a design constant;

[0224] The controller is designed as follows:

[0225]

[0226]

[0227] Where, τ d For D-axis controller; τ q For the Q-axis controller; L is the inductance value; k2 and k3 are the backstepping control parameters; R is the resistance value; ω is the system angular frequency; The first derivative of the filtered virtual control signal; The first derivative of the Q-axis reference signal; This is the adaptive estimate of the disturbance signal d1 in the model; u is the first derivative of the Q-axis time-varying function; sd This represents the voltage value along the D-axis. This is the adaptive estimate of the disturbance signal d2 in the model.

[0228] It should be noted that the device descriptions in the above embodiments correspond to the method descriptions in the embodiments, and the embodiments of the present invention will not be repeated here.

[0229] The execution entities of the processor 1 and memory 2 mentioned above can be devices with computing functions such as computers, microcontrollers, and microcontrollers. In specific implementation, the embodiments of the present invention do not limit the execution entity and can select it according to the needs of actual application.

[0230] Data signals are transmitted between memory 2 and processor 1 via bus 3, which will not be described in detail in this embodiment of the invention.

[0231] Based on the same inventive concept, embodiments of the present invention also provide a computer-readable storage medium, the storage medium including a stored program, which, when the program is running, controls the device where the storage medium is located to execute the method steps in the above embodiments.

[0232] The computer-readable storage medium includes, but is not limited to, flash memory, hard disk, solid-state drive, etc.

[0233] It should be noted that the description of the readable storage medium in the above embodiments corresponds to the description of the method in the embodiments, and the embodiments of the present invention will not be repeated here.

[0234] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated.

[0235] A computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in or transmitted through a computer-readable storage medium. A computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be magnetic or semiconductor, etc.

[0236] Unless otherwise specified, the model numbers of the various devices in this embodiment of the invention are not limited, and any device that can perform the above functions is acceptable.

[0237] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0238] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A grid-connected control method for a photovoltaic microgrid system based on a predetermined time, characterized in that, The method includes: Design a time-varying function with predefined time control based on the initial state of a photovoltaic microgrid system; Considering the grid-connected photovoltaic inverter model of the photovoltaic microgrid system with inaccurate modeling and disturbances, a time-varying function is introduced into the backstepping control framework to construct a composite state tracking error. A virtual controller is designed, and the differential calculation problem of the virtual controller is solved through dynamic surface processing and filtering error processing. The state tracking errors of the D-axis current and Q-axis current are designed and processed by adaptive control law to output the controller of the system. Appropriate control parameters are selected for the controller of the photovoltaic microgrid system, and Lyapunov theory is used to ensure the global predefined time stability of the closed-loop system, thereby realizing the predefined time design of the photovoltaic microgrid system and achieving tracking control of the reference signal within the predefined time.

2. The grid-connected control method for a photovoltaic microgrid system based on a predetermined time, as described in claim 1, is characterized in that... The time-varying function for the predefined time control of the initial state design of the photovoltaic microgrid system is as follows: The time-varying function ρ(t) of the D-axis satisfies the following at the initial moment: p(0)=m=u dc (0)-y d (0) In the formula, ρ(0) is the value of ρ(t) at the initial time, which is determined by the initial DC voltage u. dc (t) and D-axis reference signal y d (t) is obtained; This represents the first derivative of ρ(t) at the initial time. In the formula, T1 is the user-defined time, and t is the real-time time; for a first-order system along the Q-axis, the time-varying function υ(t) satisfies the following at the initial time: υ(0)=l=i sq (0)-y q (0) In the formula, y q The reference signal for the Q-axis is used; υ(t) is designed as follows:

3. The grid-connected control method for a photovoltaic microgrid system based on a predetermined time, as described in claim 1, is characterized in that... The time-varying function ρ(t) is introduced into the backstepping control framework to construct the D-axis voltage composite state tracking error e1: e1=u dc -y d (t)-ρ(t) Among them, u dc y is the DC side voltage of the inverter. d Let ρ(t) be the DC voltage reference value along the D-axis, and ρ(t) be the time-varying function along the D-axis. The virtual controller α2 is designed as follows: Among them, u sd C is the voltage value along the D-axis. dc Here, k1 is the DC-side capacitor value of the inverter, and u is the backstepping control parameter. sq i is the Q-axis voltage value. sq i is the Q-axis current value. dc This is the DC side current value. The first derivative of the D-axis reference signal. It is the first derivative of the D-axis time-varying function.

4. The grid-connected control method for a photovoltaic microgrid system based on a predetermined time, as described in claim 1, is characterized in that... After dynamic surface processing and error filtering, the state tracking errors for the D-axis current and Q-axis current are designed as follows: Dynamic surface control is introduced, and the signal is obtained by filtering the virtual controller α2. Filtering error is defined as The state tracking errors for the D-axis current and Q-axis current are designed as follows: Where μ is the design parameter; e2 is the D-axis current state tracking error; e3 is the Q-axis current state tracking error; i sd The current is the D-axis current; y q (t) is the current tracking value; υ(t) is the time-varying function of the Q axis.

5. The grid-connected control method for a photovoltaic microgrid system based on a predetermined time, as described in claim 4, is characterized in that... The process of introducing adaptive control laws results in the output system's controller: The adaptive control law is designed as follows: Where r1, r2, σ1, and σ2 are adaptive design parameters. γ>0 is a design constant; The controller is designed as follows: Where, τ d For D-axis controller; τ q For the Q-axis controller; L is the inductance value; k2 and k3 are the backstepping control parameters; R is the resistance value; ω is the system angular frequency; The first derivative of the filtered virtual control signal; The first derivative of the Q-axis reference signal; This is the adaptive estimate of the disturbance signal d1 in the model; u is the first derivative of the Q-axis time-varying function; sd This represents the voltage value along the D-axis. This is the adaptive estimate of the disturbance signal d2 in the model.

6. A grid-connected control device for a photovoltaic microgrid system based on a predetermined time, characterized in that, The device includes: The design module is used to design a time-varying function for predefined time control based on the initial state of the photovoltaic microgrid system; The output module is used to consider the grid-connected photovoltaic inverter model of the photovoltaic microgrid system that is not accurately modeled and has disturbances. It introduces a time-varying function into the backstepping control framework to construct a composite state tracking error, designs a virtual controller, and solves the differential calculation problem of the virtual controller through dynamic surface processing and filtering error processing. It designs the state tracking error of the D-axis current and Q-axis current and introduces an adaptive control law to process it, and outputs the controller of the system. The tracking control module is used to select appropriate control parameters for the controller of the photovoltaic microgrid system and use Lyapunov theory to ensure the global predefined time stability of the closed-loop system, thereby realizing the predefined time design of the photovoltaic microgrid system and achieving tracking control of the reference signal within the predefined time.

7. A grid-connected control device for a photovoltaic microgrid system based on a predetermined time, characterized in that, The device includes a processor and a memory, the memory storing program instructions, the processor invoking the program instructions stored in the memory to cause the device to perform the method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions that, when executed by a processor, cause the processor to perform the method described in any one of claims 1-5.

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