High-permeability photovoltaic inverter system indifference control method based on model predictive control

Abstract

The invention relates to the technical field of photovoltaic inversion, in particular to a high-permeability photovoltaic inversion system indifference control method based on model predictive control. The method comprises the following steps: S1, establishing a comprehensive mathematical model of the photovoltaic inverter system; s2, designing a model prediction control strategy based on a discretization prediction model, and predicting the future state of the system; s3, setting a cost function and constraint conditions, and solving optimal control input by adopting an optimization algorithm; s4, applying control input and adjusting the system; and S5, analyzing the stability of the whole control system. According to the design, the state change of the photovoltaic inverter system in a period of time in the future is predicted in real time, the optimal control input sequence of the minimum cost function is solved through an optimization algorithm, and the photovoltaic inverter system is optimized through detailed modeling of key components such as a photovoltaic array, a direct-current link and an inverter. The system parameters can be automatically adjusted under different environment conditions such as illumination intensity, temperature and wind speed.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic inverters, and in particular to a zero-difference control method for a high-penetration photovoltaic inverter system based on model predictive control. Background Art

[0002] Photovoltaic power generation depends on sunlight, and its output is intermittent and uncertain. When the penetration rate of photovoltaic systems in the power grid increases, this uncertainty will have a negative impact on the stability and reliability of the power grid; traditional PID controllers and other simple feedback control systems may not be able to fully cope with the precise control needs of photovoltaic inverter systems in complex dynamic environments; in order to improve the efficiency of photovoltaic systems, it is necessary to ensure that photovoltaic panels always operate at their maximum power point (MPP). However, due to changes in environmental conditions (such as light intensity and temperature), the maximum power point will move accordingly, and traditional maximum power point tracking (MPPT) technology may not be flexible or accurate enough, especially under rapidly changing conditions. In summary, a method for zero-error control of high-penetration photovoltaic inverter systems based on model predictive control is provided. Summary of the invention

[0003] The purpose of the present invention is to provide a method for zero-difference control of a high-penetration photovoltaic inverter system based on model predictive control to solve the problems raised in the above background technology that photovoltaic power generation depends on the intermittent output of solar illumination, the limitations of traditional control methods and the low accuracy of maximizing energy capture.

[0004] To achieve the above object, the present invention provides a method for zero-difference control of a high-penetration photovoltaic inverter system based on model predictive control, comprising the following steps: S1. Establish a comprehensive mathematical model of the photovoltaic inverter system; S2. Design a model predictive control strategy based on a discretized prediction model and predict the future state of the system; S3, setting the cost function and constraints, and using the optimization algorithm to solve the optimal control input; S4, apply control input and adjust the system; S5. Analyze the stability of the entire control system.

[0005] As a further improvement of the technical solution, in S1, the comprehensive mathematical model of the photovoltaic inverter system is established as follows: S1.1. Identify the main components of the photovoltaic inverter system and collect technical parameters; S1.2, establish a dynamic model of the boost converter; S1.3. Establish a photovoltaic array model and introduce environmental factors; S1.4, establish DC link and inverter models; S1.5. Combine all models to form a complete discrete prediction model for the photovoltaic inverter system.

[0006] As a further improvement of the technical solution, the specific steps of establishing the dynamic model of the boost converter are: S1.2.1. According to the characteristics of inductance and capacitance, establish the equivalent circuit model of the switch device when it is turned on and off; When conducting: ; When shutting down: ; in, Indicates the photovoltaic panel voltage; Represents the photovoltaic panel current; Indicates the inductance value; Indicates the rate of change of the inductor current of the photovoltaic panel; Indicates the DC side bus current; Indicates the DC side bus voltage; Indicates the capacitance of the capacitor; Indicates the rate of change of the DC bus voltage; Indicates DC input current; S1.2.2. Introducing the switch function , unify the models when conducting and shutting down; Switch Function The value is 1 or 0, and the unified model is: ; S1.2.3, using the forward Euler difference method to discretize the dynamic model; The discretized model is: ; in, Indicates the period of the circuit sampling signal; express The photovoltaic array output current at the moment; express The DC bus voltage at the moment; express The photovoltaic panel voltage at the moment; express The photovoltaic panel current at the moment; express The DC bus current at the moment; express The DC bus voltage at the moment.

[0007] As a further improvement of the technical solution, the photovoltaic array model is established and the environmental factors are introduced as follows: S1.3.1, Basic model of photovoltaic array; Output current of photovoltaic array With voltage The relationship is: ; in, represents the photogenerated current; represents the reverse saturation current of the diode, represents the series resistance, represents parallel resistance; S1.3.2, introduce the dynamic equations of light intensity, temperature and wind speed; Introduce the light intensity parameter and modify the photovoltaic array model: ; The temperature dynamic equation is: ; The dynamic equation of wind speed is: ; The DC link model is: ; The inverter model is: ; in, represents the ideality factor of the diode; represents thermal voltage; Represents the capacitance of the DC link; Indicates the voltage of the DC link; Indicates the current flowing into the DC link; Indicates the current flowing out of the DC link; Indicates the output voltage of the inverter; represents the duty cycle, which varies with time; represents the time variable; Express the output current of the photovoltaic array at time 𝑡 by introducing the light intensity parameter; represents the light intensity at time 𝑡; Indicates the reference light intensity; represents the charge of an electron; represents the Boltzmann constant; represents heat capacity; Indicates at time The solar radiation power density; It represents the convective heat transfer coefficient when there is no wind; represents the increment of convective heat transfer coefficient related to wind speed; Indicates wind speed; Represents the surface area of ​​the photovoltaic panel; Indicates the ambient light intensity; The time constant representing the change of wind speed; Represents external wind speed disturbance.

[0008] As a further improvement of the technical solution, in S2, a model predictive control strategy based on a discretized prediction model is designed to predict the future state of the system, including the following steps: S2.1, initialize the prediction model and set the initial conditions; S2.2, define different control input sequences; S2.3, for each defined control input sequence, use the dynamic model of the boost converter, the photovoltaic array model, the DC link model and the inverter model established in step S1 to perform simulation calculations; S2.4, for each time step, calculating the update of the photovoltaic inverter system state; S2.5. Record the simulation results of each step, including the time series data of the state variables of the photovoltaic inverter system.

[0009] As a further improvement of the technical solution, in S3, setting the cost function and constraint conditions includes the following steps: S3.1. Define a cost function to measure the control effect, and add atmospheric transparency and sunlight angle parameters to the cost function; S3.2, determine the constraints; S3.3. Construct an optimization problem and integrate the cost function and constraints into the optimization problem.

[0010] As a further improvement of the technical solution, in S3.1, the cost function is: ; Adding atmospheric transparency and sunlight angle parameters to the cost function is: ; in, represents the cost function; Represents the cost function after adding atmospheric transparency and sunlight angle parameters; The index representing the time step number; represents the time variable; Indicates the number of future time steps predicted from the current time point; Indicates that the photovoltaic inverter system is The predicted output of time steps; Indicates A reference signal of time steps; A weight factor representing the difference between the output of the photovoltaic inverter system and the reference signal; represents the weight factor for adjusting the rate of change of the control input; Indicates at time and Control input for each time step; represents the weight factor that adjusts the difference between the atmospheric transparency and the reference transparency; Indicates the angle of sunlight; Indicates atmospheric transparency; Indicated in Atmospheric transparency at each time step; Indicates the reference atmospheric transparency; A weight factor representing the difference between the adjusted sunlight angle and the optimal angle; Indicated in The sunshine angle at each time step; Indicates the optimal sunlight angle; Represents the weight factor that adjusts the difference between the PV inverter system output and the reference signal at the final time step.

[0011] As a further improvement of the technical solution, in S3.2, the constraints include: boost converter output limitation, photovoltaic array output limitation, inverter output limitation, DC link voltage limitation and control signal limitation.

[0012] As a further improvement of the technical solution, in S3, an optimization algorithm is used to solve the optimal control input, including the following steps: S3.4. Convert the optimization problem into a standard quadratic programming form; The quadratic programming form is: ; In the formula, represents control input; Represents the coefficient matrix of the quadratic term in the cost function; represents a vector; S3.5, construct corresponding matrices and vectors according to the cost function; S3.6, use the QP solver to solve the optimization problem; S3.7. Optimal control input sequence that minimizes the cost function from the solution results.

[0013] As a further improvement of the technical solution, in S4, applying the control input and adjusting the system includes the following steps: S4.1, extracting a first control input from the optimal control input sequence, and applying the first control input to the system; S4.2, after applying the control input, the PV inverter system responds and enters a new state; S4.3, use the new state information as the initial state of the next control cycle, and re-predict and optimize; S4.4. Use the first control input of the newly found optimal control sequence as the actual control input of the next control cycle, and repeat steps S4.1-S4.3.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. In the high-penetration photovoltaic inverter system error-free control method based on model predictive control, the state changes of the photovoltaic inverter system in the future are predicted in real time, and the optimal control input sequence that minimizes the cost function is solved by the optimization algorithm, so that the system can quickly and accurately track the required reference signal. This method can not only reduce the delay of system response, but also improve the tracking accuracy, ensure that the output is consistent with the expected target, and thus improve the stability and reliability of the entire power system.

[0015] 2. In the high-penetration photovoltaic inverter system error-free control method based on model predictive control, through detailed modeling of key components such as photovoltaic arrays, DC links and inverters, the system parameters can be automatically adjusted under different environmental conditions such as light intensity, temperature and wind speed, so that it always operates in the best working state. This helps to maximize the energy capture efficiency of the photovoltaic system and reduce energy waste. Through reasonable control strategies, it can optimize resource allocation, extend equipment life and reduce maintenance costs while meeting power demand. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The figure is a flow chart of the overall method of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments 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 creative work are within the scope of protection of the present invention.

[0018] Example: See Figure 1 As shown, this embodiment provides a high penetration photovoltaic inverter system zero-difference control method based on model predictive control, including the following steps: S1. Establish a comprehensive mathematical model of the photovoltaic inverter system; In this embodiment, the comprehensive mathematical model of the photovoltaic inverter system is established as follows: S1.1. Identify the main components included in the photovoltaic inverter system, such as photovoltaic arrays, DC / DC converters, inverters, filters, grid interfaces, etc., and collect the technical parameters of all relevant components, such as the open circuit voltage, short circuit current, maximum power point voltage and current, temperature coefficient, etc. of photovoltaic cells; the maximum output power and efficiency curve of the inverter, etc.; S1.2. Build a dynamic model of the boost converter (describe the electrical characteristics of the boost converter in the photovoltaic inverter system and its behavior in different working states. Specifically, it describes how the inductor current and capacitor voltage change when the switching device (such as a transistor) is in the on or off state); The specific steps of establishing the dynamic model of the boost converter are as follows: S1.2.1. According to the characteristics of inductance and capacitance, establish the equivalent circuit model of the switch device when it is turned on and off; When conducting: ; When shutting down: ; in, Indicates the photovoltaic panel voltage; Represents the photovoltaic panel current; Indicates the inductance value; Indicates the rate of change of the inductor current of the photovoltaic panel; Indicates the DC side bus current; Indicates the DC side bus voltage; Indicates the capacitance of the capacitor; Indicates the rate of change of the DC bus voltage; Indicates DC input current; S1.2.2. Introducing the switch function , unify the models when conducting and shutting down; Switch Function The value is 1 or 0, and the unified model is: ; S1.2.3. Use the forward Euler difference method to discretize the dynamic model (the main purpose is to convert the differential equations in the continuous time domain into differential equations in the discrete time domain to facilitate computer numerical simulation and real-time control. Discretization can simplify the solution process of differential equations, so that the mathematical model of the originally complex continuous time system can be approximated by simple difference operations; the forward Euler method is based on an intuitive principle, that is, using the current state and speed to predict the state of the next step, which is very useful for understanding and debugging the algorithm); The discretized model is: ; in, Indicates the period of the circuit sampling signal; express The photovoltaic array output current at the moment; express The DC bus voltage at the moment; express The photovoltaic panel voltage at the moment; express The photovoltaic panel current at the moment; express The DC bus current at the moment; express The DC bus voltage at the moment; S1.3. Establish a photovoltaic array model (describing the electrical characteristics of the photovoltaic array, especially the relationship between its output current and voltage (i.e., IV characteristics), and how it is affected by changes in light intensity and temperature. Through this model, the output performance of the photovoltaic array under different environmental conditions can be predicted), a DC link model (describing the energy storage characteristics of the DC link, especially the changes in the DC bus voltage. The DC link is an important part connecting the photovoltaic panel and the inverter. The stability of its voltage is crucial to the normal operation of the system. Through this model, the changes in the DC link voltage under different input currents and output currents can be predicted) and an inverter model (describing how the inverter converts DC power into AC power that meets grid standards, including the control logic of the inverter and the dynamic response of the AC side, to achieve efficient energy conversion, while ensuring the stability of the inverter under various operating conditions) to simulate the overall behavior of the photovoltaic inverter system under different conditions, and introduce light intensity parameters into the photovoltaic array model, and combine the photovoltaic array model with the temperature dynamic equation and the wind speed dynamic equation; S1.4, establish DC link and inverter models; Furthermore, a photovoltaic array model is established and environmental factors are introduced as follows: S1.3.1, Basic model of photovoltaic array; Output current of photovoltaic array With voltage The relationship is: ; in, represents the photogenerated current; represents the reverse saturation current of the diode, represents the series resistance, represents parallel resistance; S1.3.2, introduce the dynamic equations of light intensity, temperature and wind speed; Light intensity directly affects the output current and voltage of the photovoltaic array. Adding light intensity parameters to the model can make the model more realistically reflect the electrical characteristics of the photovoltaic array under actual operating conditions, thereby improving the prediction accuracy of the model. By real-time monitoring and inputting light intensity data, the control system can dynamically adjust the operating state of the photovoltaic system according to the current light conditions to ensure that the system is always near the optimal working point and improve energy conversion efficiency. Since light intensity changes with time and weather, the introduction of light intensity parameters can make the model more flexible and able to adapt to changes in light intensity in different time periods, which is very important for improving the stability and reliability of the system. The output performance of the photovoltaic array is greatly affected by the ambient temperature, and the temperature is affected by many factors such as solar radiation, ambient temperature and wind speed. By combining the temperature dynamic equation and The wind speed dynamic equation can more comprehensively consider the impact of these environmental factors on the photovoltaic array, thereby improving the prediction accuracy and reliability of the model. The working efficiency of the photovoltaic array will decrease as the temperature increases. The temperature dynamic equation can help predict the changing trend of the photovoltaic array surface temperature, while the wind speed will affect the heat dissipation efficiency of the photovoltaic panel. Combining these two dynamic equations can better adapt to changes in environmental conditions and ensure that the photovoltaic system can operate efficiently in various environments. By accurately predicting the output power of the photovoltaic array under different environmental conditions, the design and operation of the energy management system can be optimized. For example, when the temperature is predicted to rise, measures can be taken in advance to reduce the impact of thermal effects, or when the wind speed increases and the temperature decreases, the load distribution can be adjusted to make full use of the additional power generation capacity; introduce the light intensity parameter to correct the photovoltaic array model: ; The temperature dynamic equation is: ; The dynamic equation of wind speed is: ; The DC link model is: ; The inverter model is: ; in, represents the ideality factor of the diode; represents thermal voltage; Represents the capacitance of the DC link; Indicates the voltage of the DC link; Indicates the current flowing into the DC link; Indicates the current flowing out of the DC link; Indicates the output voltage of the inverter; represents the duty cycle, which varies with time; represents the time variable; Express the output current of the photovoltaic array at time 𝑡 by introducing the light intensity parameter; represents the light intensity at time 𝑡; Indicates the reference light intensity; represents the charge of an electron; represents the Boltzmann constant; represents heat capacity; Indicates at time The solar radiation power density; It represents the convective heat transfer coefficient when there is no wind; represents the increment of convective heat transfer coefficient related to wind speed; Indicates wind speed; Represents the surface area of ​​the photovoltaic panel; Indicates the ambient light intensity; The time constant representing the change of wind speed; Represents external wind speed disturbance.

[0019] S1.5. Combine all models to form a complete discrete prediction model for the photovoltaic inverter system.

[0020] S2. Design a model predictive control strategy based on a discretized prediction model and predict the future state of the system; In this embodiment, a model predictive control strategy based on a discretized prediction model and predicting the future state of the system includes the following steps: S2.1. Initialize the prediction model and set initial conditions, including the initial temperature of the photovoltaic array, the light intensity, the initial voltage of the DC link, the inductor current of the boost converter, the capacitor voltage and other related state variables; S2.2. Define different control input sequences, including different duty cycle changes, light intensity changes, or other factors that may affect the system state. These input sequences represent different operating scenarios or strategies. S2.3, for each defined control input sequence, use the dynamic model of the boost converter, the photovoltaic array model, the DC link model and the inverter model established in step S1 to perform simulation calculations; S2.4. For each time step, the update of the PV inverter system state is calculated. The update of the system state includes the boost converter output voltage, the PV array output current, the DC link voltage and the inverter output voltage, etc. The results of these calculations reflect the behavior of the system in the future time period under consideration; S2.5. Record the simulation results of each step. The simulation results include the time series data of the state variables of the photovoltaic inverter system. These data can be used to evaluate the effects of different control strategies and provide a basis for minimizing the cost function in the next step.

[0021] S3, setting the cost function and constraints, and using the optimization algorithm to solve the optimal control input; In this embodiment, setting the cost function and constraint conditions includes the following steps: S3.1. Define a cost function to measure the control effect, and add atmospheric transparency and sunlight angle parameters to the cost function; Among them, the cost function is: ; Atmospheric transparency (referring to the degree of cloud cover and air pollution level in the sky) and sunshine angle (the angle of the sun relative to the photovoltaic panel) are important factors affecting the energy output of the photovoltaic system. Incorporating these parameters into the cost function can help the model predict the output of the photovoltaic system more accurately, thereby improving the accuracy of the control strategy. Atmospheric transparency directly affects the amount of solar radiation reaching the ground. Introducing this parameter can more accurately predict the output power of the photovoltaic system, especially in cloudy or hazy weather conditions, because changes in atmospheric transparency will cause fluctuations in solar radiation intensity. By considering atmospheric transparency, the energy output of the photovoltaic system can be more accurately estimated, thereby better planning the use of energy storage systems, such as battery charging and discharging plans, to ensure the balance of power supply and demand. The sunshine angle determines the incident angle of sunlight on the surface of the photovoltaic panel, which directly affects the amount of radiation received by the photovoltaic panel. By adjusting the direction or tilt angle of the photovoltaic panel, the energy capture efficiency of the photovoltaic system can be optimized, especially when the position of the sun changes greatly in the morning and evening. The sunshine angle changes with time, especially in different time periods of the day. Introducing this parameter can help the system dynamically adjust the control strategy to ensure that the photovoltaic system always works in the best state. Adding atmospheric transparency and sunshine angle parameters to the cost function is: ; in, represents the cost function; Represents the cost function after adding atmospheric transparency and sunlight angle parameters; The index representing the time step number; represents the time variable; Indicates the number of future time steps predicted from the current time point; Indicates that the photovoltaic inverter system is The predicted output of time steps, i.e. time series data; Indicates A reference signal of time steps; A weight factor representing the difference between the output of the photovoltaic inverter system and the reference signal; represents the weight factor for adjusting the rate of change of the control input; Indicates at time and Control input for each time step; represents the weight factor that adjusts the difference between the atmospheric transparency and the reference transparency; Indicates the angle of sunlight; Indicates atmospheric transparency; Indicated in Atmospheric transparency at each time step; Indicates the reference atmospheric transparency; A weight factor representing the difference between the adjusted sunlight angle and the optimal angle; Indicated in The sunshine angle at each time step; Indicates the optimal sunlight angle; A weight factor representing the difference between the PV inverter system output and the reference signal at the final time step of adjustment; S3.2, determine the constraints; Furthermore, the constraints include: boost converter output limitation (the inductor current does not exceed its design capacity; the voltage across the capacitor does not exceed its withstand voltage; the switching frequency is within a safe range to prevent additional losses and noise caused by high frequency), photovoltaic array output limitation (the photovoltaic inverter system should always operate near the maximum power point to ensure that the output power of the photovoltaic panel is maximized; the output current and voltage of the photovoltaic panel should be within the range specified by the equipment), inverter output limitation (the voltage of the DC link should be within the allowable range to prevent overvoltage from damaging the equipment; the current flowing through the DC link should not exceed the maximum rated current of the equipment; the capacitance of the DC link should be large enough to ensure that the voltage fluctuation is not too large), DC link voltage limitation (the AC voltage, current, switching frequency and duty cycle of the inverter output should be within the allowable range) and control signal limitation (the upper and lower limits of the control signal (such as duty cycle) to prevent the inverter or other control equipment from exceeding its operating range; the change rate of the control signal is limited to prevent rapid changes from causing system instability or equipment damage); S3.3. Construct an optimization problem and integrate the cost function and constraints into the optimization problem. The goal of optimization is to minimize the cost function while satisfying all constraints.

[0022] Furthermore, an optimization algorithm (quadratic programming algorithm QP) is used to solve the optimal control input, including the following steps: In this embodiment, in model-based predictive control, various physical and engineering constraints often need to be considered, such as the upper and lower limits of the input signal, the state range of the system, etc. The QP algorithm can directly process these linear or quadratic constraints, making the optimization problem more practical; the QP problem is a special form of convex optimization. When the problem is a convex problem, the QP algorithm can find the global optimal solution, which means that in the application scenario of MPC, if the cost function and the constraints are properly defined, the QP algorithm can reliably find the best control input sequence that minimizes the cost function; the QP algorithm can ensure that the optimal solution is found under the premise of satisfying all constraints, which is crucial to maintaining the stable operation of the system. The control sequence obtained by optimization can keep the system output within the desired range, avoiding system instability caused by improper control; using a quadratic programming algorithm to solve the cost function and find the optimal control input sequence that minimizes the cost function includes the following steps: S3.4. Convert the optimization problem into a standard quadratic programming form; The quadratic programming form is: ; In the formula, represents control input; Represents the coefficient matrix of the quadratic term in the cost function; Represents a vector, representing the coefficient vector of the first-order term in the cost function; S3.5, construct corresponding matrices and vectors according to the cost function; Among them, construct the quadratic coefficient matrix The process is: Item, we can construct a diagonal , and add negative The value represents the difference between adjacent control inputs; if there is no other complex interaction, the matrix will be a sparse matrix where most elements are zero; Construct the coefficient vector of the first-order term The process is: vector Includes all items directly related to control input, such as and ; For each time step ,if With reference signal The difference between is weighted, then The corresponding elements in will reflect this; S3.6, use the QP solver to solve the optimization problem; S3.7. Optimal control input sequence that minimizes the cost function from the solution results.

[0023] S4, apply control input and adjust the system; In this embodiment, applying control input and adjusting the system includes the following steps: S4.1. extracting a first control input from the optimal control input sequence and applying the first control input to the system (involving sending a control signal to an actuator (such as an inverter) to adjust the operating state of the system); S4.2. After applying the control input, the PV inverter system responds and enters a new state (measuring or estimating a new state vector, using sensors to obtain key system parameters, such as the output current of the PV array, the voltage of the DC link, etc.); S4.3, use the new state information as the initial state of the next control cycle, and re-predict and optimize; S4.4. Use the first control input of the newly found optimal control sequence as the actual control input of the next control cycle, and repeat steps S4.1-S4.3.

[0024] S5. Analyze the stability of the entire control system; In this embodiment, the stability of the entire control system is analyzed as follows: confirm the initial conditions set in step S2.1, including the initial temperature of the photovoltaic array, the light intensity, the initial voltage of the DC link, the inductor current of the boost converter, the capacitor voltage and other related state variables; confirm whether the configuration of all models (photovoltaic array model, boost converter model, DC link model, inverter model) is correct; calculate the partial derivative matrix (Jacobian matrix) of each state variable with respect to the state variable and the control input, which will help understand the behavior of the system under small signal disturbances; calculate the eigenvalues ​​of the Jacobian matrix to determine the system The stability of the system at the equilibrium point is considered. If the real parts of all eigenvalues ​​are negative, the system is locally asymptotically stable. A positive definite Lyapunov function is constructed, and the global stability of the system is proved by proving that the Lyapunov function is less than zero. The stability of the system in the presence of uncertainty and interference is considered to ensure that the system can remain stable under a certain range of uncertainty. Based on the above analysis results, the stability of the entire control system is evaluated. If the system is found to be unstable or has poor performance, the control parameters (such as weight factors in the cost function, controller gains, etc.) are adjusted, and steps S5.2 to S5.4 are repeated.

[0025] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only preferred examples of the present invention, and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A method for zero-difference control of a high-penetration photovoltaic inverter system based on model predictive control, characterized in that: The following steps are involved: S1. Establish a comprehensive mathematical model of the photovoltaic inverter system; S2. Design a model predictive control strategy based on a discretized prediction model and predict the future state of the system; S3, setting the cost function and constraints, and using the optimization algorithm to solve the optimal control input; S4, apply control input and adjust the system; S5. Analyze the stability of the entire control system.

2. The method for zero-difference control of a high-penetration photovoltaic inverter system based on model predictive control according to claim 1, characterized in that: In S1, the comprehensive mathematical model of the photovoltaic inverter system is established as follows: S1.

1. Identify the main components of the photovoltaic inverter system and collect technical parameters; S1.2, establish a dynamic model of the boost converter; S1.

3. Establish a photovoltaic array model and introduce environmental factors; S1.4, establish DC link and inverter models; S1.

5. Combine all models to form a complete discrete prediction model for the photovoltaic inverter system.

3. When shutting down: ； in, Indicates the photovoltaic panel voltage; Represents the photovoltaic panel current; Indicates the inductance value; Indicates the rate of change of the inductor current of the photovoltaic panel; Indicates the DC side bus current; Indicates the DC side bus voltage; Indicates the capacitance of the capacitor; Indicates the rate of change of the DC bus voltage; Indicates DC input current; S1.2.

2. Introducing the switch function , unify the models for on and off times; Switch Function The value is 1 or 0, and the unified model is: ; S1.2.3, using the forward Euler difference method to discretize the dynamic model; The discretized model is: ； in, Indicates the period of the circuit sampling signal; express The photovoltaic array output current at the moment; express The DC bus voltage at the moment; express The photovoltaic panel voltage at the moment; express The photovoltaic panel current at the moment; express The DC bus current at the moment; express The DC bus voltage at the moment.

4. The method for zero-difference control of a high-penetration photovoltaic inverter system based on model predictive control according to claim 1, characterized in that: The photovoltaic array model is established and the environmental factors are introduced as follows: S1.3.1, Basic model of photovoltaic array; Output current of photovoltaic array With voltage The relationship is: ； in, represents the photogenerated current; represents the reverse saturation current of the diode, represents the series resistance, represents parallel resistance; S1.3.2, introduce the dynamic equations of light intensity, temperature and wind speed; Introduce the light intensity parameter and modify the photovoltaic array model: ； The temperature dynamic equation is: ; The dynamic equation of wind speed is: ; The DC link model is: ； The inverter model is: ； in, represents the ideality factor of the diode; represents thermal voltage; Represents the capacitance of the DC link; Indicates the voltage of the DC link; Indicates the current flowing into the DC link; Indicates the current flowing out of the DC link; Indicates the output voltage of the inverter; represents the duty cycle, which varies with time; represents the time variable; Express the output current of the photovoltaic array at time 𝑡 by introducing the light intensity parameter; represents the light intensity at time 𝑡; Indicates the reference light intensity; represents the charge of an electron; represents the Boltzmann constant; represents heat capacity; Indicates at time The solar radiation power density; It represents the convective heat transfer coefficient when there is no wind; represents the increment of convective heat transfer coefficient related to wind speed; Indicates wind speed; Represents the surface area of ​​the photovoltaic panel; Indicates the ambient light intensity; The time constant representing the change of wind speed; Represents external wind speed disturbance.

5. The method for zero-difference control of a high-penetration photovoltaic inverter system based on model predictive control according to claim 4, characterized in that: In S2, a model predictive control strategy based on a discretized prediction model is designed to predict the future state of the system, including the following steps: S2.1, initialize the prediction model and set the initial conditions; S2.2, define different control input sequences; S2.3, for each defined control input sequence, use the dynamic model of the boost converter, the photovoltaic array model, the DC link model and the inverter model established in step S1 to perform simulation calculations; S2.4, for each time step, calculating the update of the photovoltaic inverter system state; S2.

5. Record the simulation results of each step, including the time series data of the state variables of the photovoltaic inverter system.

6. The method for zero-difference control of a high-penetration photovoltaic inverter system based on model predictive control according to claim 5, characterized in that: In S3, setting the cost function and constraint conditions includes the following steps: S3.

1. Define a cost function to measure the control effect, and add atmospheric transparency and sunlight angle parameters to the cost function; S3.2, determine the constraints; S3.

3. Construct an optimization problem and integrate the cost function and constraints into the optimization problem.

7. The method for zero-difference control of a high-penetration photovoltaic inverter system based on model predictive control according to claim 6, characterized in that: In S3.1, the cost function is: ； Adding atmospheric transparency and sunlight angle parameters to the cost function is: ； in, represents the cost function; Represents the cost function after adding atmospheric transparency and sunlight angle parameters; The index representing the time step number; represents the time variable; Indicates the number of future time steps predicted from the current time point; Indicates that the photovoltaic inverter system is The predicted output of time steps; Indicates A reference signal of time steps; A weight factor representing the difference between the output of the photovoltaic inverter system and the reference signal; represents the weight factor for adjusting the rate of change of the control input; Indicates at time and Control input for each time step; represents the weight factor that adjusts the difference between the atmospheric transparency and the reference transparency; Indicates the angle of sunlight; Indicates atmospheric transparency; Indicated in Atmospheric transparency at each time step; Indicates the reference atmospheric transparency; A weight factor representing the difference between the adjusted sunlight angle and the optimal angle; Indicated in The sunshine angle at each time step; Indicates the optimal sunlight angle; Represents the weight factor that adjusts the difference between the PV inverter system output and the reference signal at the final time step.

8. The method for zero-difference control of a high-penetration photovoltaic inverter system based on model predictive control according to claim 7, characterized in that: In S3.2, the constraints include: boost converter output limitation, photovoltaic array output limitation, inverter output limitation, DC link voltage limitation and control signal limitation.

9. The method for zero-difference control of a high-penetration photovoltaic inverter system based on model predictive control according to claim 8, characterized in that: In S3, an optimization algorithm is used to solve the optimal control input, including the following steps: S3.

4. Convert the optimization problem into a standard quadratic programming form; The quadratic programming form is: ； in, represents control input; Represents the coefficient matrix of the quadratic term in the cost function; represents a vector; S3.5, construct corresponding matrices and vectors according to the cost function; S3.6, use the QP solver to solve the optimization problem; S3.

7. Optimal control input sequence that minimizes the cost function from the solution results.

10. The method for zero-difference control of a high-penetration photovoltaic inverter system based on model predictive control according to claim 9, characterized in that: In S4, applying the control input and adjusting the system includes the following steps: S4.1, extracting a first control input from the optimal control input sequence, and applying the first control input to the system; S4.2, after applying the control input, the PV inverter system responds and enters a new state; S4.3, use the new state information as the initial state of the next control cycle, and re-predict and optimize; S4.

4. Use the first control input of the newly found optimal control sequence as the actual control input of the next control cycle, and repeat steps S4.1-S4.3.

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