Virtual synchronous generator parameter adaptive control method and system based on genetic algorithm and simulated annealing hybrid algorithm

Abstract

The invention discloses a virtual synchronous generator parameter adaptive control method and system based on a hybrid algorithm of a genetic algorithm and simulated annealing, and relates to the technical field of virtual synchronous generators, and the method comprises the steps: constructing a hybrid optimization algorithm based on the genetic algorithm and simulated annealing, and outputting optimization variables; constructing a two-dimensional threshold criterion system based on the rotational inertia, the damping coefficient and a threshold for distinguishing the change of the angular velocity change rate; voltage and current double-loop PI control is adopted, a PWM signal is generated, and the PWM signal is amplified by a driving circuit and then controls on-off of a full-bridge switch of a three-phase inverter. According to the method, dynamic adaptive adjustment of the VSG parameters J and D is realized, the frequency stability, the dynamic response performance and the anti-interference capability of the VSG in a new energy high-proportion power grid are remarkably improved, the stability and the reliability of a power system are effectively enhanced, and the method has the beneficial effects of good engineering application prospect and popularization value.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of virtual synchronous generators, in particular to a virtual synchronous generator parameter adaptive control method and system based on a genetic algorithm and simulated annealing hybrid algorithm. BACKGROUND

[0002] As a core technology of new energy grid connection, the virtual synchronous generator (VSG) can effectively improve the frequency stability and dynamic response capability of the power grid with a high proportion of renewable energy by simulating the inertia and damping characteristics of the traditional synchronous generator. Among them, the virtual inertia J and the damping coefficient D are the key parameters that determine the performance of the VSG: J affects the inertia support capability of the system, and D determines the suppression effect of frequency fluctuations. However, the existing VSG control methods generally use fixed parameters or offline optimization strategies, which are difficult to adapt to complex working conditions such as sudden changes in grid load and intermittent output of new energy, resulting in insufficient performance of the system in terms of frequency deviation suppression, oscillation decay speed, etc.

[0003] Currently, the research on VSG parameter optimization mainly focuses on single intelligent algorithms or traditional optimization methods. For example, the genetic algorithm (GA) is applied in parameter optimization due to its global search capability and good population diversity, but it has the problems of weak local search capability and slow convergence speed; the simulated annealing (SA) algorithm can effectively avoid falling into local optimum through the probability jump feature, but its convergence speed is greatly affected by the initial temperature and cooling strategy, and its exploration ability for global search space is limited. In addition, the existing technology lacks effective dynamic adaptive mechanism, and cannot automatically trigger parameter optimization according to the real-time operating state of the power grid (such as frequency deviation and power fluctuation), resulting in a disconnection between the optimization process and the actual working conditions, making it difficult to achieve precise control in engineering.

[0004] In actual engineering, VSG parameter optimization needs to consider frequency stability, system oscillation suppression and parameter physical constraints, and traditional single algorithms are difficult to balance multiple target requirements. At the same time, high-proportion new energy power grids have higher requirements for the real-time performance and robustness of VSG, and there is an urgent need for a hybrid optimization algorithm that combines global search and local refinement and has dynamic adaptive capability to realize online real-time optimization of J and D, and improve the stability and reliability of power grid operation. SUMMARY

[0005] In view of the above problems, the present application is proposed.

[0006] Therefore, the technical problem solved by the present application is that the existing reinforcement learning and imitation learning methods have low sample efficiency, sensitive reward shaping, delayed feedback, and the problem of how to combine imitation learning with reinforcement learning.

[0007] To solve the above technical problems, the application provides the following technical scheme: a virtual synchronous generator parameter adaptive control method based on a genetic algorithm and a simulated annealing hybrid algorithm, comprising: constructing a hybrid optimization algorithm based on a genetic algorithm and simulated annealing to output optimization variables; constructing a two-dimensional threshold criterion system based on a moment of inertia, a damping coefficient, and a threshold value changing in size of an angular velocity change rate; adopting a voltage-current double-loop PI control to generate a PWM signal, which is amplified by a driving circuit to control the on-off of a three-phase inverter full-bridge switch; the hybrid optimization algorithm based on the genetic algorithm and simulated annealing takes the moment of inertia, the damping coefficient, and the angular velocity change rate threshold value under a steady state condition as optimization variables, and determines real-time moment of inertia and damping coefficient based on the two-dimensional threshold criterion system.

[0008] As a preferred scheme of the virtual synchronous generator parameter adaptive control method based on the genetic algorithm and the simulated annealing hybrid algorithm, the construction of the hybrid optimization algorithm based on the genetic algorithm and simulated annealing comprises: based on a three-phase full-bridge inverter topology, obtaining instantaneous active power and reactive power of system output by Park transformation decoupling to construct a small-signal dynamic model of a virtual synchronous generator control strategy, establishing a parameter mapping relationship between system damping ratio and second-order inherent angular frequency and the moment of inertia and the damping coefficient based on pole placement characteristics of a transfer function, determining the value range of the moment of inertia and the damping coefficient in combination with power system second-order oscillation mode stability criteria, and constructing the hybrid optimization algorithm.

[0009] As a preferred scheme of the virtual synchronous generator parameter adaptive control method based on the genetic algorithm and the simulated annealing hybrid algorithm, the construction of the hybrid optimization algorithm based on the genetic algorithm and simulated annealing comprises: taking a steady-state frequency deviation, a regulation time, and a power oscillation weighted function as an objective function, and a multi-objective adaptive function is expressed as: , wherein, is a multi-objective function, is a weight coefficient of the steady-state frequency deviation, is the steady-state frequency deviation, is a weight coefficient of the regulation time, is the regulation time, is a weight coefficient of the power oscillation, is the power oscillation.

[0010] As a preferred scheme of the virtual synchronous generator parameter adaptive control method based on the genetic algorithm and the simulated annealing hybrid algorithm, the output optimization variables include the moment of inertia, the damping coefficient and the angular velocity change rate threshold in the steady state working condition as the optimization variables, system modeling and parameter initialization are performed, the mathematical model of the virtual synchronous generator is established and the multi-objective fitness function is defined, the population and parameters of the genetic algorithm are initialized, the simulated annealing algorithm is triggered regularly in the optimization process of the genetic algorithm, the global optimal solution is compared and updated, whether to start a new round of online adaptive optimization to meet the termination condition is judged according to the real-time feedback data of the power grid, and the optimal parameters are output.

[0011] As a preferred scheme of the virtual synchronous generator parameter adaptive control method based on the genetic algorithm and the simulated annealing hybrid algorithm, the moment of inertia and the damping coefficient are key control parameters of the virtual synchronous generator, the values are determined based on the parameter mapping relationship of the damping ratio and the second-order natural angular frequency in the virtual synchronous generator signal dynamic model, and multi-objective optimization is performed in a preset constraint range through the genetic algorithm and the simulated annealing hybrid algorithm.

[0012] As a preferred scheme of the virtual synchronous generator parameter adaptive control method based on the genetic algorithm and the simulated annealing hybrid algorithm, the construction of the double-dimension threshold criterion system includes defining the angular velocity deviation threshold and establishing the angular velocity change rate grading threshold, subdividing the angular velocity change and the angular velocity change rate interval, and determining the real-time moment of inertia and the damping coefficient.

[0013] As a preferred scheme of the virtual synchronous generator parameter adaptive control method based on the genetic algorithm and the simulated annealing hybrid algorithm, the real-time moment of inertia and the damping coefficient include the optimal moment of inertia and the damping coefficient based on the steady state, which are determined through the double-dimension threshold criterion system, and the voltage given value is generated through the cooperative action of the virtual governor and the excitation controller based on the real-time moment of inertia and the damping coefficient.

[0014] As a preferred scheme of the virtual synchronous generator parameter adaptive control method based on the genetic algorithm and the simulated annealing hybrid algorithm, the voltage and current double-loop PI control includes the three-phase voltage generated based on the real-time moment of inertia and the damping coefficient as the voltage outer loop reference value, the deviation is processed through the voltage PI controller after being compared with the actual output voltage, and the reference value of the current inner loop is generated.

[0015] As a preferred scheme of the virtual synchronous generator parameter adaptive control method based on the genetic algorithm and the simulated annealing hybrid algorithm, the control of the on-off of the three-phase inverter full-bridge switch comprises: taking the output three-phase voltage as a voltage outer loop reference value, comparing the actual output voltage, generating a current inner loop reference value through a PI controller, comparing the actual output current through a point current PI controller to generate a modulation signal, obtaining a three-phase modulation wave through dq inverse transformation, comparing the three-phase modulation wave with a high-frequency triangular carrier to generate a PWM signal, and amplifying the PWM signal through a drive circuit to control the on-off of the three-phase inverter full-bridge switch.

[0016] Another object of the present application is to provide a virtual synchronous generator parameter adaptive control system based on a genetic algorithm and a simulated annealing hybrid algorithm, which can solve the problem of insufficient global search or low local precision efficiency of a single optimization algorithm of a current virtual synchronous generator under complex power grid conditions by constructing a two-dimensional threshold criterion system based on the moment of inertia, the damping coefficient and the threshold value changing in size according to the angular velocity change rate.

[0017] To solve the above technical problems, the present application provides the following technical scheme: a virtual synchronous generator parameter adaptive control system based on a genetic algorithm and a simulated annealing hybrid algorithm, which comprises an optimization output module, a parameter adjustment module and an inverter control module; the optimization output module is used to collect filter inductance current signals and filter capacitance voltage signals based on a three-phase full-bridge inverter topology, calculate the instantaneous active power and the reactive power of system output through Park transformation decoupling, construct a small signal dynamic model of a virtual synchronous generator control strategy, establish a parameter mapping relationship between the system damping ratio and the second-order natural angular frequency and the moment of inertia and the damping coefficient, construct a genetic algorithm and simulated annealing hybrid optimization algorithm, and perform multi-objective global optimization; the parameter adjustment module is used to output the real-time moment of inertia and the damping coefficient suitable for the current operating condition by constructing a two-dimensional threshold criterion system of the angular velocity deviation and the angular velocity change rate, performing nonlinear mapping by using a function, and determining and outputting the real-time moment of inertia and the damping coefficient; and the inverter control module is used to generate a voltage given value through a virtual governor and an excitation controller by taking the real-time moment of inertia and the damping coefficient output by the parameter adjustment module as a voltage outer loop reference value, generate a modulation signal after the voltage given value is processed by a voltage and current double PI controller, generate a PWM signal through dq inverse transformation and comparison with a triangular carrier, and amplify the PWM signal through a drive circuit to control the on-off of the three-phase inverter full-bridge switch.

[0018] The virtual synchronous generator parameter adaptive control method based on the genetic algorithm and the simulated annealing hybrid algorithm provided by the application realizes online adaptive optimization of virtual inertia and damping coefficients of the virtual synchronous generator by constructing a closed-loop control system of global search, local refinement and real-time feedback, maintains population diversity by selection, crossover and mutation operations, avoids premature convergence, performs fine adjustment on the global solution output by the GA through SA probability jump, improves parameter optimization precision and robustness, designs a multi-objective fitness function containing frequency stability, oscillation suppression and parameter rationality to dynamically balance control requirements under different working conditions, establishes a triggering mechanism based on real-time states such as power grid frequency deviation and power fluctuation, enables the algorithm to automatically start the optimization process according to working condition changes, and updates the virtual inertia and damping coefficients in real time, effectively solves problems such as frequency response lag and insufficient oscillation suppression of the traditional control strategy under scenes such as load mutation and new energy output fluctuation, significantly improves the dynamic performance and robustness of the virtual synchronous generator, and provides an efficient and reliable parameter adaptive control scheme for stable operation of a power grid with a high proportion of renewable energy access. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0020] Figure 1 A whole flowchart of the virtual synchronous generator parameter adaptive control method based on the genetic algorithm and the simulated annealing hybrid algorithm provided for the embodiment 1 of the application.

[0021] Figure 2 A hybrid algorithm flowchart of the virtual synchronous generator parameter adaptive control method based on the genetic algorithm and the simulated annealing hybrid algorithm provided for the embodiment 1 of the application.

[0022] Figure 3 A whole schematic diagram of the virtual synchronous generator parameter adaptive control method based on the genetic algorithm and the simulated annealing hybrid algorithm provided for the embodiment 2 of the application. DETAILED DESCRIPTION

[0023] In order to make the above-mentioned purposes, features and advantages of the application more obvious and easy to understand, the specific embodiments of the application will be described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor should be within the protection scope of the application.

[0024] Embodiment 1, reference Figures 1-2 For an embodiment of the present application, a virtual synchronous generator parameter adaptive control method based on a genetic algorithm and simulated annealing hybrid algorithm is provided, the method comprising: S1: constructing a hybrid optimization algorithm based on genetic algorithm and simulated annealing to output optimization variables.

[0025] Specifically, constructing a hybrid optimization algorithm based on genetic algorithm and simulated annealing includes, based on the three-phase full-bridge inverter topology, obtaining the instantaneous active power and reactive power of the system output by Park transformation decoupling, constructing a small signal dynamic model of the virtual synchronous generator (VSG) control strategy, based on the pole placement characteristics of the transfer function, establishing the parameter mapping relationship between the system damping ratio and the second order inherent angular frequency and the moment of inertia, the damping coefficient, combining the power system second order oscillation mode stability criterion, determining the value range of the moment of inertia and the damping coefficient, and constructing a hybrid optimization algorithm.

[0026] The instantaneous active power and reactive power of the system output obtained by Park transformation decoupling is expressed as: , , , Among them, is the measured AC capacitor voltage on the shaft, is the measured AC capacitor voltage on the shaft, is the mechanical angular velocity, is the time, is the measured AC capacitor voltage on the shaft, is the measured AC capacitor voltage on the shaft, is the measured AC capacitor voltage on the shaft, is the measured AC filter inductance current on the shaft, is the measured AC filter inductance current on the shaft, is the measured AC load current on the shaft, is the measured AC load current on the shaft, is the measured AC load current on the shaft, is the active power, is the reactive power.

[0027] ​​​​​​​​​​According to the obtained active power and reactive power values, under the second-order model, the relationship between the moment of inertia and the damping coefficient and the mechanical torque and the electromagnetic torque is obtained by the VSG droop characteristic and is expressed as: , wherein, is the mechanical torque, is the electromagnetic torque, is the virtual mechanical power, is the inverter output active power, is the mechanical angular velocity, is the moment of inertia, is the damping coefficient, is the grid source side angular velocity.

[0028] According to the power frequency characteristic curve of the synchronous generator, the droop control can be obtained and is expressed as: , , wherein, is the virtual mechanical power, is the active power given value, is the active droop coefficient, is the angular velocity deviation, is the output voltage reference value, is the VSG voltage given value, is the reactive droop coefficient, is the reactive power rated value.

[0029] Small signal analysis is performed on the VSG power loop, the parameter mapping relationship between the system damping ratio and the second-order natural angular frequency and the moment of inertia and the damping coefficient is established and is expressed as: , wherein, is the natural frequency, is the VSG input port voltage, is the VSG output port voltage, is the moment of inertia, is the grid source side angular velocity, is the equivalent impedance, is the damping ratio, is the damping coefficient, is the control coefficient related to the frequency deviation.

[0030] According to the obtained function relationship between the damping ratio and the second-order natural angular frequency, the expression of the moment of inertia and the damping coefficient is determined, the damping coefficient and the value range of the moment of inertia are determined by considering the damping ratio range of the system and the stability and the regulation time of the active power loop, and the damping ratio range of the system is expressed as: , , , wherein, is a negative margin, is a phase margin, is a damping ratio.

[0031] The stability of the active power loop and the regulation time are expressed as: , wherein, is a lower limit of the moment of inertia, is the moment of inertia, is an upper limit of the moment of inertia, is a minimum allowed damping ratio, is an active droop coefficient, is a damping compensation coefficient, is a maximum allowed damping ratio, is a natural frequency, is a maximum allowed regulation time.

[0032] It should be noted that the hybrid optimization algorithm based on genetic algorithm and simulated annealing also includes taking the weighted function of the steady-state frequency deviation, the regulation time and the power oscillation as the objective function, and the multi-objective fitness function is expressed as: , wherein, is a multi-objective function, is a weight coefficient of the steady-state frequency deviation, is a steady-state frequency deviation, is a weight coefficient of the regulation time, is a regulation time, is a weight coefficient of the power oscillation, is a power oscillation.

[0033] It should also be noted that the output optimization variables include the moment of inertia, the damping coefficient and the angular velocity change rate threshold in the steady-state condition as the optimization variables, the system modeling and parameter initialization are performed, the mathematical model of the virtual synchronous generator is established and the multi-objective fitness function is defined, the population and parameters of the genetic algorithm are initialized, the simulated annealing algorithm is triggered regularly in the optimization genetic algorithm process, the global optimal solution is compared and updated, whether to start a new round of online adaptive optimization is determined according to the real-time feedback data of the power grid to meet the termination condition, and the optimal parameters are output.

[0034] It should be noted that by constructing a hybrid optimization algorithm of genetic algorithm and simulated annealing, combining Park transformation decoupling and VSG small signal model, the technical problems of parameter setting difficulty, system oscillation suppression and dynamic response optimization in virtual synchronous generator control are solved, the precise configuration of damping ratio and natural frequency is realized, the steady-state frequency deviation is reduced, the adjustment time is shortened, the power oscillation is suppressed, and the stability and reliability of the power system are improved.

[0035] S2: Based on the moment of inertia, the damping coefficient and the threshold value changing with the angular velocity change rate, a two-dimensional threshold criterion system is constructed.

[0036] Specifically, the moment of inertia and the damping coefficient are key control parameters of the virtual synchronous generator, the values are determined based on the parameter mapping relationship of the damping ratio and the second-order natural angular frequency in the virtual synchronous generator signal dynamic model, and multi-objective optimization is performed in the preset constraint range by the genetic algorithm and simulated annealing hybrid algorithm.

[0037] The parameter setting algorithm process is shown in Figure 2 First, the VSG mathematical model is established, including the inertia link, the damping link and the droop control equation to describe the dynamic characteristics of the system, then the parameter optimization range is set, the moment of inertia value range is , the damping coefficient value range is , finally the multi-objective fitness function is defined, which is the weighted sum of the steady-state frequency deviation, the adjustment time and the power oscillation, and the weight coefficient is initialized to quantify the parameter performance.

[0038] The parameters of the genetic algorithm (GA) are initialized, including setting the population size, the maximum number of iterations, the crossover probability and the mutation probability, using real number coding to randomly generate the initial population in the parameter space, calculating the fitness value of each individual, and outputting the global optimal solution; update the iteration counter and start the GA iteration process, use the roulette selection method, select good individuals into the next generation according to the fitness, perform arithmetic crossover on the selected individuals with a certain probability to generate new individuals, perform uniform coding on the new individuals with a certain probability, introduce random disturbance, perform boundary processing to ensure that the new individual parameters are within the constraint range, then calculate the fitness value of the new population and update the global historical optimal solution Gbest.

[0039] Simulated annealing (SA) local optimization trigger and execution, first to determine whether to meet the SA trigger conditions, such as the current GA iteration number is an integer multiple of the preset value, if the trigger condition is met, with the current global optimal solution Gbest as the initial solution, initialization of SA parameters, including the initial temperature, cooling coefficient, termination temperature, the length of the Markov chain at each temperature, in the SA optimization process, for each temperature, generate new solution and calculate the fitness difference, according to the Metropolis criterion to accept the inferior solution with a probability, thus local fine adjustment, gradually reduce the temperature, repeat the process until the temperature is lower than , output the optimal solution of SA optimization .

[0040] The fitness of the SA output solution is compared with the fitness of the current Gbest. If the SA solution is better, the global optimal solution is updated The optimized parameters of moment of inertia and damping coefficient are applied to the VSG controller to realize adaptive adjustment of parameters. Real-time feedback data such as power grid frequency and power are collected to determine whether re-optimization is needed. If the frequency deviation is greater than the threshold value, the power fluctuation is greater than the threshold value or the preset time interval is reached, return to the GA algorithm initialization to start a new round of iteration. When the termination condition is met, such as reaching the maximum iteration number or the fitness improvement is less than the threshold value for a continuous number of times, the final optimized parameters of moment of inertia and damping coefficient are output, and the optimization process is completed.

[0041] It should be noted that the construction of the two-dimensional threshold criterion system includes defining the angular velocity deviation threshold and establishing the angular velocity change rate classification threshold, subdividing the angular velocity change and angular velocity change rate interval, and determining the real-time moment of inertia and damping coefficient.

[0042] It should also be noted that the real-time moment of inertia and damping coefficient include the moment of inertia and damping coefficient based on the steady-state optimum, determined by the two-dimensional threshold criterion system, and the voltage set value generated by the coordinated action of the virtual governor and the excitation controller based on the real-time moment of inertia and damping coefficient.

[0043] The real-time moment of inertia and damping coefficient are specifically represented as: , , , wherein, is a sign function, is any real number, is the moment of inertia, is the moment of inertia when the system is stable, is the minimum value of the moment of inertia when running stably, is the maximum value of the moment of inertia when running stably, is an inverse tangent function, is an angular velocity deviation, is an angular velocity differential, is a time differential, is a threshold value of an angular frequency change rate, is a damping coefficient, is a damping coefficient when the system is stable, is a minimum value of the moment of inertia when the system is stable, is a maximum value of the moment of inertia when the system is stable.

[0044] It should also be noted that by constructing a two-dimensional threshold criterion system, combining the steady-state parameters optimized offline by the hybrid algorithm, the problem that the fixed parameters of the VSG cannot simultaneously consider the dynamic response speed and stability under large disturbances is solved, the online adaptive adjustment of the moment of inertia and the damping coefficient is realized, and the optimization accuracy, robustness and efficiency of the virtual inertia and the damping coefficient are significantly improved.

[0045] S3: A voltage and current double-loop PI control is adopted to generate a PWM signal, which is amplified by a driving circuit to control the on-off of the three-phase inverter full-bridge switches.

[0046] Specifically, the voltage and current double-loop PI control includes that the three-phase voltage generated based on the real-time moment of inertia and the damping coefficient is taken as the voltage outer loop reference value, the deviation after comparison with the actual output voltage is processed by the voltage PI controller to generate the reference value of the current inner loop.

[0047] It should be noted that controlling the on-off of the three-phase inverter full-bridge switches includes that the three-phase voltage output is taken as the voltage outer loop reference value, the reference value of the current inner loop is generated by the PI controller after comparison with the actual output voltage, the modulation signal is generated by the point current PI controller after comparison with the actual output current, the three-phase modulation wave is obtained by dq inverse transformation, the PWM signal is generated by comparison with the high-frequency triangular carrier, and the on-off of the three-phase inverter full-bridge switches is controlled after amplification by the driving circuit.

[0048] It should also be noted that by the voltage and current double-PI control structure, the adaptively optimized VSG parameters are converted into voltage reference instructions, the coordination problem of the switching control accuracy of the power device and the dynamic response speed of the system is solved, the precise and rapid control of the inverter output is realized, the voltage and current harmonics are reduced, and the stable operation of the system is ensured.

[0049] Embodiment 2, refer to Figure 3 As an embodiment of the present application, a virtual synchronous generator parameter adaptive control system based on a genetic algorithm and a simulated annealing hybrid algorithm is provided, which includes an optimization output module 100, a parameter adjustment module 200 and an inverter control module 300.

[0050] The optimization output module 100 is configured to collect the filter inductance current signal and the filter capacitance voltage signal based on the three-phase full-bridge inverter topology, obtain the instantaneous active power and the reactive power of the system output through Park transformation decoupling calculation, construct a small signal dynamic model of the VSG control strategy, establish a parameter mapping relationship between the system damping ratio and the second-order inherent angular frequency and the moment of inertia and the damping coefficient, construct a hybrid optimization algorithm of the genetic algorithm and the simulated annealing, and perform global optimization on multiple targets.

[0051] The parameter adjustment module 200 is configured to output the real-time moment of inertia and the damping coefficient suitable for the current operating condition by constructing a two-dimensional threshold criterion system of the angular velocity deviation and the angular velocity change rate, performing nonlinear mapping by using a function, and determining and outputting the real-time moment of inertia and the damping coefficient based on the steady-state optimal parameters of the moment of inertia and the damping coefficient and the angular velocity change rate threshold output by the optimization output module 100.

[0052] The inverter control module 300 is configured to generate a voltage given value by using the virtual governor and the excitation controller based on the real-time moment of inertia and the damping coefficient output by the parameter adjustment module 200, take the voltage given value as a voltage outer loop reference, generate a modulation signal after processing a deviation by using a voltage-current double PI controller, generate a PWM signal by using dq inverse transformation and comparison with a triangular carrier, and control the on-off of the three-phase inverter full-bridge switch after amplification by a driving circuit.

[0053] The embodiment also provides a computer device including a memory and a processor, the memory stores a computer program, and the processor implements the virtual synchronous generator parameter adaptive control method based on the genetic algorithm and the simulated annealing hybrid algorithm as proposed in the above embodiment when executing the computer program.

[0054] The embodiment also provides a computer readable storage medium having a computer program stored thereon, and the computer program is executed by a processor to implement the virtual synchronous generator parameter adaptive control method based on the genetic algorithm and the simulated annealing hybrid algorithm as proposed in the above embodiment.

[0055] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the part of the technical solutions that essentially contribute to the prior art or the part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0056] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered a list of executable instructions for implementing logic functions, and can be specifically embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processor, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions, or in conjunction with these instructions execution systems, apparatuses, or devices. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport programs for use by an instruction execution system, apparatus, or device, or in conjunction with these instruction execution systems, apparatuses, or devices.

[0057] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection having one or more wires (electrical devices), a portable computer diskette (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CD ROM). In addition, the computer readable medium can even be paper or other suitable medium on which the program can be printed, as the program can be electronically obtained, for example, by optical scanning of the paper or other medium, followed by editing, interpreting, or otherwise processing, if necessary, in other suitable ways to be electronically obtained, and then stored in the computer memory.

[0058] It should be understood that portions of the present application can be implemented in hardware, software, firmware, or combinations thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, implementation can be with any or a combination of the following technologies, which are all well known in the art: a discrete logic circuit having logic gates for implementing logic functions upon an application of data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0059] It should be noted that the above examples are only used to illustrate the technical solutions of the present application but not limit the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and all of them should be covered in the scope of the claims of the present application.

Claims

1. A virtual synchronous generator parameter adaptive control method based on a genetic algorithm and a simulated annealing hybrid algorithm, characterized in that, The application relates to a method for determining real-time inertia and damping coefficient of a virtual synchronous generator (VSG) based on a hybrid optimization algorithm of a genetic algorithm and simulated annealing. A two-dimensional threshold criterion system is constructed based on the inertia and the damping coefficient and the threshold value of the angular velocity change rate; A voltage-current double-loop PI control is adopted to generate a PWM signal, and the PWM signal is amplified through a driving circuit to control the on-off of a three-phase inverter full-bridge switch. The hybrid optimization algorithm of the genetic algorithm and simulated annealing takes the inertia, the damping coefficient and the threshold value of the angular velocity change rate under a steady state as optimization variables, and determines the real-time inertia and the damping coefficient based on the two-dimensional threshold criterion system. The hybrid optimization algorithm of the genetic algorithm and simulated annealing comprises the following steps:

2. The virtual synchronous generator parameter adaptive control method based on the genetic algorithm and simulated annealing hybrid algorithm according to claim 1, characterized in that: Based on the three-phase full-bridge inverter topology, the instantaneous active power and the reactive power of the system output are obtained through Park transformation decoupling, a small signal dynamic model of a virtual synchronous generator control strategy is constructed, the parameter mapping relationship between the system damping ratio and the second-order inherent angular frequency and the inertia and the damping coefficient is established based on the pole placement characteristics of the transfer function, the value range of the inertia and the damping coefficient is determined in combination with the second-order oscillation mode stability criterion of the power system, and the hybrid optimization algorithm is constructed. The hybrid optimization algorithm of the genetic algorithm and simulated annealing comprises the following steps:

3. The virtual synchronous generator parameter adaptive control method based on the genetic algorithm and simulated annealing hybrid algorithm according to claim 2, characterized in that: A weighted function of the steady state frequency deviation, the regulation time and the power oscillation is taken as a target function, and a multi-objective adaptive function is expressed as: The optimization variables comprise the following steps: ， wherein, is a multi-objective function, is a weight coefficient of the steady-state frequency deviation, is a steady-state frequency deviation, is a weight coefficient of the regulation time, is a regulation time, is a weight coefficient of the power oscillation, is a power oscillation.

4. The method of claim 1 to 3, wherein the method is characterized in that: The inertia, the damping coefficient and the threshold value of the angular velocity change rate under a steady state are taken as optimization variables, system modeling and parameter initialization are carried out, a mathematical model of the virtual synchronous generator is established and a multi-objective fitness function is defined, the population and parameters of the genetic algorithm are initialized, the simulated annealing algorithm is triggered regularly in the optimization process of the genetic algorithm, the global optimal solution is compared and updated, whether a new round of online adaptive optimization is started is judged according to real-time feedback data of the power grid, and the optimal parameters are output until a termination condition is met. The inertia and the damping coefficient are key control parameters of the virtual synchronous generator, the values of the inertia and the damping coefficient are determined based on the parameter mapping relationship between the damping ratio and the second-order inherent angular frequency in the signal dynamic model of the virtual synchronous generator, and the inertia and the damping coefficient are multi-objectively optimized in a preset constraint range through the hybrid algorithm of the genetic algorithm and simulated annealing.

5. The method of claim 1-3, wherein the method is characterized in that: The two-dimensional threshold criterion system comprises the following steps:

6. The method of claim 1, wherein the method is characterized by: An angular velocity deviation threshold value is defined, and an angular velocity change rate grading threshold value is established, the angular velocity change and the angular velocity change rate interval are subdivided, and the real-time inertia and the damping coefficient are determined. The real-time inertia and the damping coefficient comprise the following steps:

7. The method of adaptive control of parameters of a virtual synchronous generator based on a hybrid algorithm of genetic algorithm and simulated annealing according to claim 1, 2, 3 or 6, characterized in that: Based on the optimal inertia and the damping coefficient under a steady state, the real-time inertia and the damping coefficient are determined through the two-dimensional threshold criterion system, and the voltage given value is generated through the synergistic effect of a virtual governor and an excitation controller based on the real-time inertia and the damping coefficient. The voltage-current double-loop PI control comprises the following steps:

8. The method of claim 1-3 and 6, wherein the method is characterized in that: The three-phase voltage generated based on the real-time inertia and the damping coefficient is taken as a voltage outer loop reference value, the deviation is processed through a voltage PI controller after being compared with the actual output voltage, and a current inner loop reference value is generated. The three-phase inverter full-bridge switch is controlled to turn on and off.

9. The method of claim 1-3 and 6, wherein the method is characterized in that: ​ The three-phase voltage based on the output is taken as the voltage outer loop reference value, and the current inner loop reference value is generated after comparison with the actual output voltage through a PI controller, and the modulation signal is generated through a point current PI controller after comparison with the actual output current, the three-phase modulation wave is obtained through dq inverse transformation, the PWM signal is generated after comparison with the high-frequency triangular carrier, and the on-off of the three-phase inverter full-bridge switch is controlled after amplification through the drive circuit.

10. A virtual synchronous generator parameter adaptive control system based on a genetic algorithm and simulated annealing hybrid algorithm, adopting a virtual synchronous generator parameter adaptive control method based on a genetic algorithm and simulated annealing hybrid algorithm according to any one of claims 1-9, characterized in that: The method comprises an optimization output module (100), a parameter adjustment module (200), and an inverter control module (300). The optimization output module (100) is used for collecting filter inductance current signals and filter capacitance voltage signals based on a three-phase full-bridge inverter topology, obtaining instantaneous active power and reactive power of system output through Park transformation decoupling calculation, constructing a small signal dynamic model of a virtual synchronous generator control strategy, establishing a parameter mapping relationship between system damping ratio and second-order inherent angular frequency and moment of inertia and damping coefficient, constructing a hybrid optimization algorithm of genetic algorithm and simulated annealing, and performing multi-objective global optimization. The parameter adjustment module (200) is used for outputting real-time moment of inertia and damping coefficient suitable for the current operating condition by constructing a two-dimensional threshold criterion system of angular velocity deviation and angular velocity change rate, performing nonlinear mapping through a function, and determining and outputting the real-time moment of inertia and damping coefficient based on the steady-state optimal parameters of moment of inertia and damping coefficient and the angular velocity change rate threshold value output by the optimization output module (100). The inverter control module (300) is used for generating a voltage given value through a virtual governor and an excitation controller based on the real-time moment of inertia and damping coefficient output by the parameter adjustment module (200), taking the voltage given value as a voltage outer loop reference value, generating a modulation signal after deviation processing through voltage and current double PI controllers, generating a PWM signal through dq inverse transformation and comparison with a triangular carrier, and controlling the on-off of the three-phase inverter full-bridge switch after amplification through a drive circuit.

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