Virtual synchronous generator parameter adaptive control method and medium

Abstract

The invention relates to the field of power electronic control, and discloses a virtual synchronous generator parameter adaptive control method and a medium, and the method comprises the steps: calculating the active power and reactive power outputted by a VSG; establishing a small signal model based on the VSG, obtaining a function relationship between the damping ratio and the natural angular frequency of the VSG and the rotational inertia and the damping coefficient, and determining value boundaries of the rotational inertia and the damping coefficient; a meta-heuristic optimization algorithm is adopted to carry out optimization in the value boundary to solve the reference rotational inertia and the reference damping coefficient of the output system under the steady-state working condition; setting an angular velocity change rate threshold value and an angular velocity variation threshold value; monitoring the angular velocity change rate and the angular velocity change quantity of the system in real time; when the angular velocity change rate or the angular velocity variable quantity exceeds a corresponding threshold value, obtaining a final rotational inertia and a final damping coefficient according to a preset self-adaptive control rule; and generating a voltage reference signal of the VSG based on the final rotational inertia and the damping coefficient. According to the invention, the problem of slow convergence of adaptive control is avoided.

Description

Technical Field

[0001] This invention relates to the field of power electronic control, specifically to a method and medium for adaptive control of virtual synchronous generator parameters. Background Technology

[0002] In recent years, the proportion of new energy power generation, represented by wind power and photovoltaic power, in the power system has continued to grow. However, these energy sources are usually connected to the grid through power electronic devices, whose inherent low inertia and weak damping lead to a decrease in the grid's frequency regulation capability and increase the risk of system instability. To address this issue, Virtual Synchronous Generator (VSG) technology has been proposed. Its core idea is to use control algorithms to enable the converter to simulate the dynamic characteristics of a synchronous generator. This technology can simulate the characteristics of a synchronous generator, giving the grid-connected inverter both "inertia" and "damping" characteristics, thereby improving the robustness and stability of the system.

[0003] Traditional VSG control uses constant moments of inertia and damping coefficients, resulting in poor dynamic adjustment performance. However, the types of disturbances and operating states of the actual power grid are constantly changing. Fixed moment of inertia and damping coefficient control strategies not only fail to meet the optimal output indicators under different disturbances, but also easily lead to risks such as system instability and performance degradation, affecting power supply quality and equipment lifespan, and even posing safety hazards.

[0004] To further improve power quality, reduce safety risks, and enhance the dynamic regulation capability of the power grid, enabling the VSG system to be reliably and efficiently applied under different operating conditions, a VSG parameter adaptive control method based on an improved tuna swarm optimization algorithm is proposed. This method allows the rotational inertia and damping coefficient of the VSG system to be dynamically adjusted according to changes in the system, satisfying the optimal output indicators under various disturbances.

[0005] An improved tuna swarm optimization algorithm is derived by introducing the Lévy flight mechanism into the spiral foraging phase of the traditional algorithm. This effectively overcomes the tuna swarm optimization algorithm's tendency to get trapped in local optima in the later stages of iteration, significantly enhancing the algorithm's global exploration capability. It can effectively solve VSG parameter optimization problems under complex conditions. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of existing VSG technology and provide a method and medium for adaptive control of virtual synchronous generator parameters. This method provides optimal steady-state initial parameters to the system through an improved tuna swarm optimization algorithm and adjusts the parameters in real time based on the system's dynamic response, thereby comprehensively improving the dynamic performance, steady-state accuracy, and disturbance rejection capability of the VSG under different operating conditions.

[0007] This invention is achieved through the following technical solution:

[0008] A method for adaptive control of virtual synchronous generator parameters includes:

[0009] Sample the voltage and current signals of the virtual synchronous generator (VSG), and calculate the active and reactive power output of the VSG.

[0010] Based on the active power and reactive power, and combining the rotor motion equation and power transmission equation of the virtual synchronous generator (VSG), a nonlinear state-space model of the VSG is established; the nonlinear model is linearized at the equilibrium point to establish a small-signal model of the VSG.

[0011] Based on the small-signal model, the damping ratio of the virtual synchronous generator (VSG) is obtained. and natural angular frequency The functional relationship between the moment of inertia J and the damping coefficient D is determined, and based on the functional relationship and the system stability requirements, the value boundaries of the moment of inertia J and the damping coefficient D are determined.

[0012] A metaheuristic optimization algorithm is employed, aiming to minimize the combined frequency and voltage deviations of the system. The optimization solution is performed within the specified value boundaries, outputting the system's reference moment of inertia under steady-state conditions. and reference damping coefficient ;

[0013] Set the threshold for the rate of change of angular velocity. and threshold of angular velocity change ; Rate of change of angular velocity of the real-time monitoring system and angular velocity change ;

[0014] When the rate of change of angular velocity or the amount of change of angular velocity exceeds its corresponding threshold, according to the preset adaptive control rules, the reference moment of inertia is... and reference damping coefficient Based on this, the final moment of inertia J and damping coefficient D are obtained by real-time adjustment; otherwise, the reference parameters are directly used as the final parameters.

[0015] Based on the final moment of inertia J and damping coefficient D, a voltage reference signal for the VSG is generated, and the inverter is driven to output the required electrical energy through closed-loop control and PWM modulation.

[0016] As an optimization, active power is calculated. and reactive power Specifically, through the following formula... Performed in a two-phase stationary coordinate system:

[0017] ;

[0018] in, and These represent the active and reactive power outputs of the virtual synchronous generator (VSG), respectively. and The fundamental frequency of the inverter bridge arm midpoint voltage is respectively at... Expression in coordinate system and The inverter-side inductor current is respectively Expression in coordinate system The coordinate system is a two-phase stationary coordinate system using Clarke transformation, which is used to simplify the analysis of three-phase systems.

[0019] As an optimization, the damping ratio and natural angular frequency The functional relationship between the moment of inertia J and the damping coefficient D is as follows:

[0020] ;

[0021] ;

[0022] In the formula: E is the input and output voltage of the virtual synchronous generator VSG; U is the output voltage of the virtual synchronous generator VSG. D is the equivalent impedance; D is the damping coefficient; J is the moment of inertia. This refers to the angular velocity on the power grid side.

[0023] As an optimization, the determination of the boundary values ​​for the moment of inertia J and the damping coefficient D specifically involves:

[0024] The upper limit of the moment of inertia J satisfies ;

[0025] The lower limit of the damping coefficient D satisfies ;

[0026] in, This is the maximum cutoff frequency; This represents the system's maximum output power. The angular velocity of the system during operation; E represents the rate of change of angular velocity; U represents the input and output voltages of the virtual synchronous generator VSG; and U represents the output voltage of the virtual synchronous generator VSG. The angular velocity is the one on the grid side. This is the equivalent impedance.

[0027] As an optimization, the metaheuristic optimization algorithm is an improved tuna swarm optimization algorithm.

[0028] As an optimization, the improved tuna swarm optimization algorithm is improved by introducing the Levy flight mechanism during the position update process, specifically including:

[0029] Assign a probability to each individual in the population. And generate random numbers randp;

[0030] when At that time, the individual position is updated according to the rules introduced for Levi's flight;

[0031] when At that time, the individual's location is updated according to the spiral foraging rule.

[0032] As an optimization, the objective function is:

[0033] ;

[0034] In the formula: For frequency deviation, For voltage deviation; The frequency deviation weighting coefficient; The weighting factor for voltage deviation; It is the derivative with respect to time; T is the iteration period.

[0035] As an optimization, the adjustment rule for the real-time rotational inertia J in the adaptive control rule is as follows:

[0036] ;

[0037] In the formula: The threshold for the rate of change of angular velocity; The threshold for the change in angular velocity; This is the adjustment coefficient for the moment of inertia.

[0038] As an optimization, the adjustment rule for the real-time damping coefficient (D) in the adaptive control rule is as follows:

[0039] ;

[0040] in, This refers to the change in angular velocity. The threshold for the change in angular velocity; This is the adjustment coefficient for the damping coefficient.

[0041] This invention discloses a storage medium storing a computer program that, when executed by a processor, implements a virtual synchronous generator parameter adaptive control method as described above.

[0042] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0043] This invention obtains the optimal steady-state parameters through an improved tuna swarm optimization algorithm. and This provides the system with a high-starting-point initial configuration, avoiding the initial overshoot, oscillation and slow convergence problems caused by adaptive control adjusting from zero, and the system is stable from startup.

[0044] The adaptive control of this invention only requires fine-tuning based on excellent steady-state parameters, making the design of adaptive rules simpler, parameter estimation more accurate, dynamic response faster, and effectively suppressing frequency and power oscillations.

[0045] The improved tuna swarm algorithm in this invention effectively balances global exploration and local exploitation capabilities by introducing the Lévy flight mechanism, avoiding getting trapped in local optima. It can reliably solve the VSG parameter optimization problem under complex working conditions, providing a solid and high-quality starting point for adaptive control.

[0046] This invention combines the global optimality of optimization algorithms with the dynamic adaptability of adaptive control, enabling VSG to achieve simultaneous and comprehensive improvements in steady-state accuracy, dynamic response speed, disturbance rejection capability, and support for the power grid. Attached Figure Description

[0047] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0048] Figure 1 The diagram shows the structure of the VSG main circuit and control system provided in the embodiment of the present invention.

[0049] Figure 2 A flowchart illustrating the improved tuna swarm optimization algorithm provided in an embodiment of the present invention.

[0050] Figure 3 This is a schematic diagram of the system angular velocity oscillation curve and adaptive adjustment interval division in an embodiment of the present invention. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0052] 1. Power Calculation and System Modeling

[0053] The main circuit and control structure diagram of the VSG of this invention are attached. Figure 1 As shown, the three-phase output voltage is obtained through sensor sampling. , , and output current , , Its value is obtained through Clarke transform. Components in two-phase stationary coordinate systems , and , The active power output of the VSG is calculated using the instantaneous power theory formula. and reactive power :

[0054] ;

[0055] In this embodiment, the number of pole pairs is taken as 1, so the electrical angular velocity equals the mechanical angular velocity. Using a traditional second-order synchronous generator model, the equivalent VSG rotor motion equation is obtained as follows:

[0056] ;

[0057] J is the moment of inertia; VSG angular velocity; Rated angular velocity; For mechanical torque; Electromagnetic torque; D is the damping torque; D is the damping coefficient; This refers to the angular velocity deviation. Mechanical power; Electromagnetic power; For the angle of attack.

[0058] In actual operation, it can be approximated as = Then the electromagnetic equations of VSG can be expressed as:

[0059] ;

[0060] In the formula: , , These are the three-phase output voltages of the VSG. , , These are the three-phase output currents of the VSG.

[0061] From the grid-connected equivalent circuit diagram of the virtual synchronous generator, the power angle of the virtual synchronous generator can be obtained. for:

[0062] ;

[0063] Due to the angle of attack Very small, can be considered , Then the active power and reactive power of VSG are:

[0064] ;

[0065] In the formula: E is the input voltage of the VSG; U is the output voltage of the VSG. This is the equivalent impedance.

[0066] According to the VSG power frequency characteristic curve, we can obtain: ,in It is the droop coefficient. It is a given reference value for active power.

[0067] A small-signal model of the VSG is established, and the above equations are linearized near the equilibrium point to obtain the closed-loop transfer function G(s) of the active power of the VSG system containing the moment of inertia J and the damping coefficient D:

[0068] ;

[0069] in, , These represent the electromagnetic power and mechanical power in the complex frequency domain, respectively; s is the complex frequency domain variable; E is the VSG input port voltage and output voltage; U is the VSG output port voltage. D is the equivalent impedance; D is the damping coefficient; J is the moment of inertia. This refers to the angular velocity on the power grid side.

[0070] By obtaining the functional relationship between the damping ratio and natural angular frequency and the moment of inertia and damping coefficient, the range of the system's damping ratio, settling time, and overshoot are considered, and the range of the moment of inertia and damping coefficient are determined.

[0071] The natural angular frequency of the system can be derived from the standard second-order system transfer function. Damping ratio Functional relationship with J and D:

[0072] ;

[0073] .

[0074] 2. Determining the range of key parameters

[0075] To ensure system stability and good dynamic performance, it is necessary to determine a reasonable range of values ​​for J and D.

[0076] Based on dynamic performance: the VSG system is selected with a settling time of less than 0.1s and an error band of... Then the overshoot σ and settling time and and The relationship is:

[0077] ,

[0078] Since VSG systems typically operate in an underdamped state, the damping ratio is... The range is between (0,1).

[0079] Based on stability margin: Considering the stability margin of the active power loop, the phase margin is required. satisfy:

[0080] ;

[0081] ;

[0082] In the formula, h represents the gain margin.

[0083] Based on power and frequency constraints: The real part of the closed-loop poles of the system should satisfy... Meanwhile, the system open-loop transfer function is: For the system to be stable, the following conditions must be met:

[0084] ;

[0085] ;

[0086] ;

[0087] ;

[0088] ;

[0089] ;

[0090] In the formula: This is the system's maximum output power. The angular cutoff frequency; This is the maximum cutoff frequency; This is the maximum cutoff frequency; This is the adjustment coefficient.

[0091] 3. Steady-state parameter optimization: Based on the ranges of J and D obtained in the above steps, an improved tuna swarm optimization algorithm is used, with the minimum frequency deviation and voltage deviation as objective functions, to optimize and solve for the optimal reference moment of inertia of the system under steady-state conditions. and reference damping coefficient .

[0092] The improved tuna swarm optimization algorithm is as follows: Figure 2 As shown, the specific steps are as follows:

[0093] A1: Initialize the population size NP and the maximum number of iterations. And set the upper and lower limits of the solution space based on the calculated ranges of J and D.

[0094] A2: Calculate the fitness value for each individual (i.e., a set of J, D), record the current best individual and its position, and generate random numbers randp. The fitness function y is: ;in, For frequency deviation, For voltage deviation; The frequency deviation weighting coefficient; The weighting factor for voltage deviation; It is the derivative with respect to time; T is the iteration period.

[0095] A3: Position Update. Based on spiral foraging, a Levy flight mechanism is introduced and the optimal value is output. The implementation is as follows: A probability is set for each individual tuna. .

[0096] when At that time, execute the Levi flight update:

[0097] ;

[0098] in, This refers to the i-th individual in the (t+1)th iteration; The current optimal individual; Let be the i-th individual in the current iteration; t is the current iteration number; , U and V are random variables that follow a normal distribution, i.e. , and Calculated using the Lévy index n, ; ; s is the random step size, n is the Lévy exponent, and N represents a normal distribution.

[0099] when At that time, perform spiral foraging update:

[0100] ;

[0101] in, , , , Here, 'a' is the weighting coefficient, and 'a' is a constant value that determines the closeness between two individuals. It is the helical factor; l is the helical parameter. b is a random number in the range [0,1]. This represents the maximum number of iterations.

[0102] A4: Calculate the fitness value of the updated population and retain the historical best individual.

[0103] A5: Determine if the iteration count has been reached. If so, output the optimal individual. and Otherwise, return to step A2 and continue iterating.

[0104] A6: Determine if the restart conditions are met:

[0105] ;

[0106] This is the current optimal solution. This is the optimal solution from the previous iteration. Set a threshold. If yes, return to step 4.1 to reinitialize the population and perform the search; otherwise, end the iteration.

[0107] 4. Dynamic parameter adaptation and control execution

[0108] Set a threshold to distinguish the magnitude of the rate of change of angular velocity. and the threshold for distinguishing changes in angular velocity These two thresholds are fixed values ​​set based on actual operating conditions.

[0109] When the system changes, its angular velocity change and rate of change also change. Based on the real-time changes in its angular velocity change and rate of change, the moment of inertia J and damping coefficient D are dynamically adjusted, and according to the attached... Figure 3 When the system is operating, the angular velocity oscillation curve is set to an adaptive control strategy:

[0110] interval d / dt Moment of inertia Damping coefficient 1 very small very small Select Select 2 >0 >0 Increase Increase 3 >0 <0 Decrease Decrease 4 <0 >0 Increase Increase 5 <0 <0 Decrease Decrease

[0111] When the VSG system operates in interval 1, it is less susceptible to disturbances, and the changes in angular velocity and rate of change in angular velocity are very small. The system is approximately considered stable, and the optimized steady-state moment of inertia JW and damping coefficient DW are selected. When the system operates in intervals 2, 3, 4, and 5, it is more susceptible to disturbances and cannot maintain a stable state. In this case, J and D need to be adaptively adjusted based on JW and DW in real time.

[0112] Adaptive rule for real-time rotational inertia J:

[0113] ;

[0114] In the formula: The threshold for the rate of change of angular velocity; The threshold for the change in angular velocity; This is the adjustment coefficient for the moment of inertia.

[0115] Adaptive rule for real-time damping coefficient D:

[0116] ;

[0117] in, This refers to the change in angular velocity. The threshold for the change in angular velocity; This is the adjustment coefficient for the damping coefficient.

[0118] Finally, using the determined real-time J and D values, the rotor motion equations and voltage equations of the VSG are calculated to determine the phase and amplitude of the VSG output voltage, generating a voltage reference signal. This signal is passed through a voltage-current dual closed-loop controller to output the required modulation wave, which is compared with a triangular carrier wave to generate PWM signals. These PWM signals directly drive the switching on and off of the inverter's power switches, and after being filtered by an LC filter, generate voltage and current that meet power quality requirements before being fed into the grid.

[0119] In summary, the technical effects of this invention are as follows:

[0120] (1) The initial moment of inertia and damping coefficient of the system may be far from the optimal value when it starts operating. The adaptive controller needs to make significant parameter adjustments from the beginning to correct the huge initial error. This process may lead to: large overshoot and strong oscillation in the initial stage; slow convergence speed and instability of the system in the initial stage. However, the optimized steady-state moment of inertia and damping coefficient is a parameter combination that performs well in terms of stability. The adaptive controller only needs to make fine adjustments based on these parameters to adapt to dynamic changes, which speeds up the adaptive convergence process and avoids adjusting from zero. This improves the stability of the system in the initial stage.

[0121] (2) Adaptive control is mainly used to deal with time-varying and unknown system parameters when the system is disturbed, and to optimize the rotational inertia and damping coefficient in steady state. Essentially, it improves the inherent dynamic characteristics of the system that are determinable in steady state to a better level. Adaptive control can deal with only the time-varying problems of the system, rather than dealing with the problems of poor system basic design and time-varying parameters at the same time. This makes the design of adaptive control rules simpler and the estimation more accurate.

[0122] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for adaptive control of virtual synchronous generator parameters, characterized in that, include: Sample the voltage and current signals of the virtual synchronous generator (VSG), and calculate the active and reactive power output of the VSG. Based on the active power and reactive power, and combining the rotor motion equation and power transmission equation of the virtual synchronous generator (VSG), a nonlinear state-space model of the VSG is established; the nonlinear model is linearized at the equilibrium point to establish a small-signal model of the VSG. Based on the small-signal model, the damping ratio of the virtual synchronous generator (VSG) is obtained. and natural angular frequency The functional relationship between the moment of inertia J and the damping coefficient D is determined, and based on the functional relationship and the system stability requirements, the value boundaries of the moment of inertia J and the damping coefficient D are determined. A metaheuristic optimization algorithm is employed, aiming to minimize the combined frequency and voltage deviations of the system. The optimization solution is performed within the specified value boundaries, outputting the system's reference moment of inertia under steady-state conditions. and reference damping coefficient ; Set the threshold for the rate of change of angular velocity. and threshold of angular velocity change ; Rate of change of angular velocity of the real-time monitoring system and angular velocity change ; When the rate of change of angular velocity or the amount of change of angular velocity exceeds its corresponding threshold, according to the preset adaptive control rules, the reference moment of inertia is... and reference damping coefficient Based on this, the final moment of inertia J and damping coefficient D are obtained by real-time adjustment; otherwise, the reference parameters are directly used as the final parameters. Based on the final moment of inertia J and damping coefficient D, a voltage reference signal for the VSG is generated, and the inverter is driven to output the required electrical energy through closed-loop control and PWM modulation.

2. The virtual synchronous generator parameter adaptive control method according to claim 1, characterized in that, Calculate active power and reactive power Specifically, through the following formula... Performed in a two-phase stationary coordinate system: ； in, and These represent the active and reactive power outputs of the virtual synchronous generator (VSG), respectively. and The fundamental frequency of the inverter bridge arm midpoint voltage is respectively at... Expression in coordinate system and The inverter-side inductor current is respectively Expression in coordinate system The coordinate system is a two-phase stationary coordinate system using Clarke transformation, which is used to simplify the analysis of three-phase systems.

3. The virtual synchronous generator parameter adaptive control method according to claim 1, characterized in that, The damping ratio and natural angular frequency The functional relationship between the moment of inertia J and the damping coefficient D is as follows: ； ； In the formula: E is the input and output voltage of the virtual synchronous generator VSG; U is the output voltage of the virtual synchronous generator VSG. D is the equivalent impedance; D is the damping coefficient; J is the moment of inertia. This refers to the angular velocity on the power grid side.

4. The virtual synchronous generator parameter adaptive control method according to claim 1, characterized in that, The determination of the boundary values ​​for the moment of inertia J and the damping coefficient D is specifically as follows: The upper limit of the moment of inertia J satisfies ; The lower limit of the damping coefficient D satisfies ; in, This is the maximum cutoff frequency; This represents the system's maximum output power. The angular velocity of the system during operation; E represents the rate of change of angular velocity; U represents the input and output voltages of the virtual synchronous generator VSG; and U represents the output voltage of the virtual synchronous generator VSG. The angular velocity on the grid side; This is the equivalent impedance.

5. The virtual synchronous generator parameter adaptive control method according to claim 1, characterized in that, The metaheuristic optimization algorithm is an improved tuna swarm optimization algorithm.

6. The virtual synchronous generator parameter adaptive control method according to claim 5, characterized in that, The improved tuna swarm optimization algorithm is enhanced by introducing the Levy flight mechanism during the position update process, specifically including: Assign a probability to each individual in the population. And generate random numbers randp; when At that time, the individual position is updated according to the rules introduced for Levi's flight; when At that time, the individual's location is updated according to the spiral foraging rule.

7. The virtual synchronous generator parameter adaptive control method according to claim 5, characterized in that, The objective function is: ； In the formula: For frequency deviation, For voltage deviation; These are the frequency deviation weighting coefficients; The weighting factor for voltage deviation; It is the derivative with respect to time; T is the iteration period.

8. The virtual synchronous generator parameter adaptive control method according to claim 1, characterized in that, In the adaptive control rules, the adjustment rule for the real-time moment of inertia J is as follows: ； In the formula: The threshold for the rate of change of angular velocity; The threshold for the change in angular velocity; This is the adjustment coefficient for the moment of inertia.

9. The virtual synchronous generator parameter adaptive control method according to claim 1, characterized in that, In the adaptive control rule, the adjustment rule for the real-time damping coefficient D is as follows: ； in, This refers to the change in angular velocity. The threshold for the change in angular velocity; This is the droop coefficient of the damping coefficient.

10. A storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements a virtual synchronous generator parameter adaptive control method as described in any one of claims 1 to 9.

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