Shared energy storage virtual inertia compensation control method for wind power cluster collection

Abstract

The invention relates to a shared energy storage virtual inertia compensation control method for wind power cluster collection, and belongs to the technical field of power system control. In order to solve the problem of insufficient cooperative control of a wind power system and an energy storage system in the prior art, a dynamic model is established by obtaining operation parameters, and the dynamic model comprises a wind turbine generator active power control model and an energy storage system charging and discharging model; designing a virtual inertia control strategy, and introducing virtual inertia control to simulate inertia response characteristics of the synchronous generator; shared energy storage coordination control is carried out, and a strategy is designed by using an MPC method according to an inertia demand and an SOC state; establishing a frequency dynamic response model and calculating inertia response time as a virtual inertia control start-stop condition; and optimizing controller parameters and designing locking logic by adopting an intelligent algorithm. The method can improve the system performance and efficiency, enhance the frequency stability, improve the energy utilization efficiency of the energy storage system, optimize the system performance, and improve the system stability and reliability.

Description

Technical Field

[0001] The present invention relates to a virtual inertia compensation control method for shared energy storage gathered by a wind power cluster, and belongs to the technical field of power system control, and in particular to technologies related to the coordinated control of wind power and energy storage systems. Background Art

[0002] In terms of the coordinated control of wind power clusters and energy storage systems, existing technologies have many deficiencies. First, the wind power system is affected by its own characteristics such as wind speed volatility. When the grid frequency is disturbed, it cannot provide stable inertial support like traditional synchronous generators, which can easily lead to unstable grid frequency. Secondly, the control strategy of the energy storage system does not fully consider factors such as the inertia requirements of the wind power cluster and its own state of charge (SOC), resulting in low energy utilization efficiency of the energy storage system and difficulty in effectively coordinating with the wind power system to stabilize the grid frequency. Existing solutions, some existing technologies attempt to control the wind power system or the energy storage system separately through simple control algorithms, but do not comprehensively consider the coordination between the two and the impact of various operating conditions.

[0003] The shortcomings of existing solutions are that independent control cannot achieve effective coordination between wind power systems and energy storage systems, cannot reasonably allocate charging and discharging tasks according to the inertia requirements of wind power clusters and the SOC status of energy storage systems, and cannot simulate the inertial response characteristics of synchronous generators to enhance the stability of the system under frequency disturbances. At the same time, the differences and changes in the operating conditions of wind turbines and energy storage units are not fully considered, resulting in inaccurate and inefficient control.

[0004] To this end, we propose a virtual inertia compensation control method for shared energy storage in wind power clusters. Summary of the invention

[0005] In view of the above deficiencies in the prior art, the purpose of the present invention is to solve the deficiencies in the prior art and improve the performance and efficiency of the ventilation system by adopting advanced data acquisition technology, intelligent algorithms and optimized control strategies.

[0006] In order to achieve the above objectives, the present invention adopts the following technical solutions:

[0007] A virtual inertia compensation control method for shared energy storage in a wind power cluster comprises the following steps:

[0008] S1. System parameter acquisition and modeling: obtain the operating parameters of the wind power cluster and the energy storage system, and use the acquired operating parameters to establish a dynamic model of the wind power cluster and the energy storage system. The dynamic model includes the active power control model of the wind turbine and the charging and discharging model of the energy storage system.

[0009] S2. Design of virtual inertia control strategy. Using the operating parameters obtained in S1 and the established dynamic model, virtual inertia control is introduced into the active power control system of the wind turbine to simulate the inertial response characteristics of the synchronous generator. A phase-locked loop (PLL) and proportional-integral (PI) controller are used to design a virtual inertia control strategy, where PLL is used to track the grid frequency and PI controller is used to adjust the active power output.

[0010] S3. Shared energy storage coordinated control. Based on S2, according to the inertia demand of the wind power cluster and the SOC state of the energy storage system, the model predictive control (MPC) method is used to design a coordinated control strategy for shared energy storage by considering the differences and changes in the operating conditions of wind turbines and energy storage units.

[0011] S4. Establish a frequency dynamic response model, based on the virtual inertia control strategy designed in S2 and the shared energy storage coordination control strategy designed in S3, and establish a frequency dynamic response model of wind and energy storage combined with virtual inertia, calculate the inertia response time of the frequency dynamic response model, and use the inertia response time as the start and stop condition of the virtual inertia control;

[0012] S5. Controller parameter optimization and locking: an intelligent algorithm is used to optimize the controller parameters of the frequency dynamic response model, and the controller locking logic is designed using the optimized controller parameters. When the frequency of the frequency dynamic response model returns to the normal range, the virtual inertia control is turned off in time to avoid frequency overshoot and system instability.

[0013] Furthermore, the operating parameters include wind rotor speed, generator speed, wind speed, state of charge (SOC) of the energy storage system, etc.

[0014] Furthermore, in S1, the method for establishing the active power control model of the wind turbine generator set is as follows:

[0015] S11. The simplified power system frequency model is given, and the simplified power system frequency model is expressed as:

[0016]

[0017] Among them, ΔP m is the mechanical power change, ΔE is the energy storage change, α, T, L, M, and D are system parameters;

[0018] S12. Considering the aerodynamic characteristics of the wind turbine, the aerodynamic torque model T of the wind rotor is introduced a , T a It is related to factors such as wind speed v, rotor radius R, air density ρ, tip speed ratio λ and pitch angle β of the rotor, and can be expressed as:

[0019]

[0020] Among them, C p (λ, β) is the wind energy utilization coefficient, which is a function of the tip speed ratio λ and the pitch angle β;

[0021] S13. Considering the torsion model of the transmission shaft, the torque balance equation of the transmission shaft is:

[0022]

[0023] Among them, J t is the moment of inertia of the drive shaft, ω t is the speed of the transmission shaft, T g is the electromagnetic torque of the generator, D t is the damping coefficient of the drive shaft;

[0024] S14. Consider establishing the relationship between power and speed:

[0025] According to the electromagnetic torque T of the generator g With the generator output power P g and the generator speed ω g The relationship P g =T g ω g , under ideal conditions (ignoring some losses), the wind turbine output power P out Equal to the generator output power P g ;

[0026] From the torque balance equation of the drive shaft Solve T g Substituting into P g =T g ω g get Right now

[0027] Considering that during stable operation, the wind wheel speed ω and the transmission shaft speed ω t and the generator speed ω g There is a proportional relationship between them, namely ω t =k1ω,ω g =k2ω, k1 and k2 are both proportional coefficients, then:

[0028]

[0029] Will Substitution The active power control model of wind turbine is obtained as follows:

[0030]

[0031] Further, in said S1, the charging and discharging model of the energy storage system includes an equivalent circuit model and a charging and discharging power relationship;

[0032] Among them, the equivalent circuit model considers the electrochemical characteristics of the energy storage system and establishes an equivalent circuit model based on the electrochemical principle of the battery. The equivalent circuit model is expressed as:

[0033]

[0034] U b is the battery terminal voltage, E0 is the battery open circuit voltage, R0 is the battery ohmic internal resistance, C b is the equivalent capacitance of the battery, I b is the battery charge and discharge current; the battery open circuit voltage E0 is related to the battery SOC state, and E0 is expressed as:

[0035] E0=k1-k2SOC-k3ln(SOC)+k4ln(1-SOC);

[0036] Among them, k1, k2, k3, and k4 are constants related to battery characteristics, and SOC is the state of charge of the battery; the -k2SOC term indicates that as the SOC decreases (i.e., the battery power decreases), the open circuit voltage will decrease accordingly, and the magnitude of the decrease is related to the values ​​of k2 and SOC; the -k3 ln(SOC) term and the k4 ln(1-SOC) term further consider the impact of the logarithmic relationship on the open circuit voltage. This logarithmic relationship is based on the electrochemical characteristics of the battery and can more accurately describe the nonlinear relationship of the open circuit voltage with changes;

[0037] The charge and discharge power relationship is expressed as:

[0038] P b =ηU b I b ;

[0039] P b is the actual charge and discharge power; η is the charge and discharge efficiency; U b is the battery terminal voltage; I b is the battery charging and discharging current.

[0040] Furthermore, S2 includes:

[0041] S21. Determine the virtual inertia coefficient according to the equivalent inertia time constant H of the wind turbine ω Calculate the virtual inertia coefficient K I , the formula is:

[0042]

[0043] Among them, P ris the rated power of the wind turbine, f r is the system rated frequency;

[0044] S22. Design droop control. Droop control is used to couple the output power of the wind turbine with the grid frequency to achieve frequency regulation. The formula for droop control is:

[0045] P out =P opt +K D Δf;

[0046] Where P out is the output power of the wind turbine; P opt is the optimal power output under maximum power point tracking (MPPT); K D is the droop coefficient, which determines the degree of response of the droop control to the grid frequency deviation, that is, the proportional relationship of the droop control to adjust the output power according to the frequency deviation; Δf is the grid frequency deviation;

[0047] S23. Implement a phase-locked loop. The phase-locked loop (PLL) is a synchronous frame phase-locked loop (SRF-PLL) structure. The principle of the synchronous frame phase-locked loop (SRF-PLL) is to convert the grid voltage from a three-phase stationary coordinate system to a two-phase rotating coordinate system (dq coordinate system), and to achieve phase tracking by controlling the q-axis voltage component to zero. In the dq coordinate system, the expression of the grid voltage is:

[0048]

[0049] Where T is the coordinate transformation matrix; u a 、u b 、u c is the three-phase voltage of the power grid; u d is the d-axis voltage component; u q is the q-axis voltage component;

[0050] The control equation of the PLL is:

[0051]

[0052] In the formula, u q (s) is the q-axis voltage component; u′ q (s) is the reference value of q-axis voltage, u′ q (s) = 0; K p is the proportional gain of the PLL; K i is the integral gain of PLL; s is the complex variable in Laplace transform;

[0053] S24. Design a proportional-integral (PI) controller. The PI controller is used to adjust the active power output of the wind turbine in response to changes in the grid frequency. The transfer function of the PI controller is:

[0054]

[0055] K′ p is the proportional gain of the (PI) controller; K′ i is the integral gain of the (PI) controller;

[0056] S25. An integrated control strategy is provided to integrate virtual inertia control, droop control and PI controller into the active power control system of the wind turbine; wherein the virtual inertia control provides the initial frequency response, the droop control adjusts the output power to maintain the grid frequency, and the PI controller refines the power regulation to reduce the steady-state error;

[0057] The power adjustment calculated by the virtual inertia control needs to be passed to the droop control and PI controller as a reference for subsequent calculations; the droop control calculates its own power adjustment command based on the frequency deviation and the output of the virtual inertia control, and passes it to the PI controller and the actuator of the wind turbine; the PI controller comprehensively considers all input information, calculates the final power control signal, and controls the active power output of the wind turbine; the final power control signal is:

[0058]

[0059] ΔP f is the final power control signal, which is used to control the active power output of the wind turbine; ΔP D The power adjustment command calculated for droop control; ΔP VI is the power adjustment calculated by virtual inertia control, ΔP PI-D is the power adjustment of the PI controller to the droop control output; Δf is the grid frequency deviation, that is, the difference between the actual grid frequency and the rated frequency; K D is the droop coefficient; K′ p is the proportional gain of the (PI) controller; K i ′ is the integral gain of the (PI) controller.

[0060] Further, the S3 includes:

[0061] S31. Analyze the inertia demand of wind power cluster and the SOC state of energy storage system, according to the grid frequency deviation allowable value Δf′ and the coefficient k related to grid characteristics f , through formula I d =k f Δf′ estimates the inertia requirement I d ;

[0062] SOC status monitoring, obtain real-time state of charge (SOC) information through the built-in monitoring device of the energy storage system. The calculation formula of SOC is: Where Q c is the current charge, Q t is the total charge;

[0063] S32.MPC model is established, which includes the output power P of wind turbines w , the charging and discharging power of the energy storage system, the grid frequency f g And inertia related variables J, etc.; the dynamic equation of the model can be expressed as:

[0064]

[0065] In the formula, a1, a2, and a3 are coefficients determined according to the characteristics of the wind turbine; b1, b2, and b3 are coefficients determined according to the characteristics of the energy storage system;

[0066] S33. Objective function setting, considering the energy utilization rate E of the energy storage system u The relevant target term λ E ·E u , where λ E Energy utilization rate E of energy storage system u The weight coefficient, while considering the number of charge and discharge cycles N of the energy storage system c The influence term λ on the objective function N ·N c , where λ N N is the number of charge and discharge cycles of the energy storage system c The weight coefficient of ; the objective function is:

[0067] F=λ ω ·∑(Δω r,i -Δω r,ave ) 2 +λ B ·∑|f L (k) 2 +λ E ·E u +λ N ·N c

[0068] λ ω is the weight coefficient related to the balance of wind turbine rotor speed; Δω r,i Indicates the difference between the rotor speed of the i-th wind turbine and a certain reference speed; Δω r,ave is the average value of the rotor speed of all wind turbines; B is the weight coefficient of the target related to the grid frequency; f L(k) is a function related to the grid frequency and the operating parameters of the wind turbine and the energy storage system;

[0069] In the process of solving the objective function, it is necessary to consider the differences and changes in the operating conditions of wind turbines and energy storage units; the output power of wind turbines will change with the change of wind speed, and the charging and discharging power of the energy storage system will be affected by the SOC state and the charging and discharging efficiency; through comprehensive consideration of these factors, the optimal control sequence that minimizes the objective function is found, including the power adjustment instructions of the wind turbines and the charging and discharging instructions of the energy storage system.

[0070] Further, the S4 includes:

[0071] S41. Model establishment: Based on the virtual inertia control strategy designed in S2 and the shared energy storage coordination control strategy designed in S3, a frequency dynamic response model of the wind-storage combined system with virtual inertia is established; Frequency dynamic response model of the wind-storage combined system with virtual inertia:

[0072]

[0073] Among them, J v is the virtual inertia; P ω is the output power of the wind turbine; P s is the charging and discharging power of the energy storage system; f g is the grid frequency; I d is the inertia requirement; c1-c6 are coefficients determined according to system characteristics; Q c is the current charge of the energy storage system; Q t is the total charge; Q o It is the actual amount of electricity released during discharge;

[0074] S42. Inertia response time calculation: according to the system's dynamic equation and initial conditions, calculate the time required for the system frequency to change from the initial value to the stable value, i.e., the inertia response time;

[0075] The change of system frequency conforms to the first-order linear differential equation: s ′(t)+αf(t) s =β,f s ′ is the system frequency change rate, f s is the system frequency, α and β are constants related to the system parameters; solving this first-order equation, the solution is:

[0076]

[0077] When the system reaches a stable state, that is, t→∞, The inertial response time is defined as the time required for the system frequency to change from an initial value to a near-stable value. By solving the above equation, the inertial response time is obtained:

[0078]

[0079] Among them, f s (0) is the system frequency from the initial value; T w is the frequency stability value; T r is the inertial response time;

[0080] S43. Setting the start and stop conditions of virtual inertia control, and setting the inertia response time T r As the start and stop conditions of virtual inertia control, when the system frequency changes and the inertia response time T r When the system frequency returns to the normal range and the inertia response time T r When it is greater than the set threshold, the virtual inertia control is turned off;

[0081] Consider the impact of the start-up time of the wind turbine and the response time of the energy storage system on the inertial response time; when the system frequency changes and the start-up time of the wind turbine is less than the set threshold, start the virtual inertia control; when the system frequency returns to the normal range and the start-up time of the wind turbine is greater than the set threshold, turn off the virtual inertia control.

[0082] Further, the S5 includes:

[0083] S51. Parameter optimization, using the particle swarm optimization algorithm to take the objective function in S3 as the fitness function, and calculate the fitness value according to the particle position vector; by iteratively updating the particle position and velocity, find the particle position that minimizes the fitness function, that is, the optimal controller parameters;

[0084] S52. Lockout logic design, using the optimized controller parameters to design the controller lockout logic; the lockout logic is: when the system frequency returns to the normal range, that is, when the system frequency range belongs to [f min ,f max ], turn off virtual inertia control to avoid frequency overshoot and system instability; when the system frequency range is within [f min ,f max ], turn off virtual inertia control to avoid frequency overshoot and system instability, [f min ,f max ].

[0085] In summary, due to the adoption of the above technical solution, the beneficial technical effects of the invention are:

[0086] The description of system behavior characteristics is more accurate. By obtaining the operating parameters of the wind power cluster and the energy storage system, a dynamic model is established, including the active power control model of the wind turbine and the charging and discharging model of the energy storage system. These models can accurately describe the behavior characteristics of the wind turbine and the energy storage system, provide a theoretical basis for subsequent control strategies, and improve the accuracy and effectiveness of control.

[0087] Enhance system frequency stability, the virtual inertia control strategy simulates the inertial response characteristics of the synchronous generator. When the grid frequency is disturbed, the energy storage system can provide virtual inertia according to this strategy, enhance the stability of the system under frequency disturbance, and help maintain the stability of the grid frequency.

[0088] Improve the energy utilization efficiency of the energy storage system. The shared energy storage coordinated control strategy uses the model predictive control method to reasonably allocate the charging and discharging tasks according to the inertia requirements of the wind power cluster and the SOC state of the energy storage system. This avoids unreasonable charging and discharging of the energy storage system, improves the energy utilization efficiency of the energy storage system, and ensures the efficient operation of the system.

[0089] Optimizing system performance, establishing a frequency dynamic response model, calculating the inertial response time, and determining the start and stop conditions of virtual inertial control further optimized system performance. By reasonably controlling the start and stop of virtual inertial control, frequency overshoot and system instability were avoided.

[0090] Improve system stability and reliability. The controller parameter optimization and locking link uses intelligent algorithms to find the optimal controller parameters and design reasonable locking logic. This further improves the stability and reliability of the system and ensures that the system can operate stably under various working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] Figure 1 A logic block diagram of a virtual inertia compensation control method for shared energy storage in a wind power cluster;

[0092] Figure 2 The logical block diagram of the method for establishing the active power control model of the wind turbine generator set;

[0093] Figure 3 The logic block diagram of the virtual inertia control strategy design method;

[0094] Figure 4 A logical block diagram of a coordinated control strategy method for shared energy storage;

[0095] Figure 5 A logical block diagram of the method for establishing a frequency dynamic response model;

[0096] Figure 6 This is the logic block diagram of the controller parameter optimization and locking method. DETAILED DESCRIPTION

[0097] In order to make the purpose, technical solution and advantages of the invention more clear, the invention is further described in detail below in combination with the embodiments. It should be understood that the specific embodiments described here are only used to explain the invention and are not used to limit the invention.

[0098] Overall idea:

[0099] A virtual inertia compensation control method for shared energy storage in wind power clusters first focuses on the acquisition of operating parameters of wind power clusters and energy storage systems, which are the basis for subsequent modeling and control strategy design. By establishing an accurate dynamic model, the behavioral characteristics of wind turbines and energy storage systems can be described, providing a theoretical basis for virtual inertia control and shared energy storage coordinated control. The virtual inertia control strategy aims to use the energy storage system to simulate the inertia response and enhance the stability of the system under frequency disturbances. The shared energy storage coordinated control strategy reasonably allocates charging and discharging tasks according to the inertia demand and the SOC state of the energy storage system to ensure the efficient operation of the system. After the frequency dynamic response model is established, the dynamic changes of the system frequency can be analyzed, and the start and stop conditions of the virtual inertia control can be determined by calculating the inertia response time, further optimizing the system performance. Finally, the controller parameter optimization and locking link finds the optimal controller parameters through intelligent algorithms, and designs reasonable locking logic to avoid frequency overshoot and system instability.

[0100] Specific implementation steps:

[0101] In order to make the purpose, technical solution and advantages of the invention more clear, the invention is further described in detail below in combination with the embodiments. It should be understood that the specific embodiments described here are only used to explain the invention and are not used to limit the invention.

[0102] like Figure 1 As shown, a virtual inertia compensation control method for shared energy storage of wind power clusters includes the following steps:

[0103] S1. System parameter acquisition and modeling: obtain the operating parameters of the wind power cluster and the energy storage system, and use the acquired operating parameters to establish a dynamic model of the wind power cluster and the energy storage system. The dynamic model includes the active power control model of the wind turbine and the charging and discharging model of the energy storage system.

[0104] S2. Design of virtual inertia control strategy. Using the operating parameters obtained in S1 and the established dynamic model, virtual inertia control is introduced into the active power control system of the wind turbine to simulate the inertial response characteristics of the synchronous generator. A phase-locked loop (PLL) and proportional-integral (PI) controller are used to design a virtual inertia control strategy, where PLL is used to track the grid frequency and PI controller is used to adjust the active power output.

[0105] S3. Shared energy storage coordinated control. Based on S2, according to the inertia demand of the wind power cluster and the SOC state of the energy storage system, the model predictive control (MPC) method is used to design a coordinated control strategy for shared energy storage by considering the differences and changes in the operating conditions of wind turbines and energy storage units.

[0106] S4. Establish a frequency dynamic response model, based on the virtual inertia control strategy designed in S2 and the shared energy storage coordination control strategy designed in S3, and establish a frequency dynamic response model of wind and energy storage combined with virtual inertia, calculate the inertia response time of the frequency dynamic response model, and use the inertia response time as the start and stop condition of the virtual inertia control;

[0107] S5. Controller parameter optimization and locking: an intelligent algorithm is used to optimize the controller parameters of the frequency dynamic response model, and the controller locking logic is designed using the optimized controller parameters. When the frequency of the frequency dynamic response model returns to the normal range, the virtual inertia control is turned off in time to avoid frequency overshoot and system instability.

[0108] The operating parameters include wind rotor speed, generator speed, wind speed, state of charge (SOC) of the energy storage system, etc.

[0109] like Figure 2 As shown, in S1, the method for establishing the active power control model of the wind turbine generator set is:

[0110] S11. The simplified power system frequency model is given, and the simplified power system frequency model is expressed as:

[0111]

[0112] Among them, ΔP m is the mechanical power change, ΔE is the energy storage change, α, T, L, M, and D are system parameters;

[0113] S12. Considering the aerodynamic characteristics of the wind turbine, the aerodynamic torque model T of the wind rotor is introduced a , T a It is related to factors such as wind speed v, rotor radius R, air density ρ, tip speed ratio λ and pitch angle β of the rotor, and can be expressed as:

[0114]

[0115] Among them, C p (λ, β) is the wind energy utilization coefficient, which is a function of the tip speed ratio λ and the pitch angle β;

[0116] S13. Considering the torsion model of the transmission shaft, the torque balance equation of the transmission shaft is:

[0117]

[0118] Among them, J t is the moment of inertia of the drive shaft, ω t is the speed of the transmission shaft, T g is the electromagnetic torque of the generator, D t is the damping coefficient of the drive shaft;

[0119] S14. Consider establishing the relationship between power and speed:

[0120] According to the electromagnetic torque T of the generator g With the generator output power P g and the generator speed ω g The relationship P g =T g ω g , under ideal conditions (ignoring some losses), the wind turbine output power P out Equal to the generator output power P g ;

[0121] From the torque balance equation of the drive shaft Solve T g Substituting into P g =T g ω g get Right now

[0122] Considering that during stable operation, the wind wheel speed ω and the transmission shaft speed ω t and the generator speed ω g There is a proportional relationship between them, namely ω t =k1ω,ω g =k2ω, k1 and k2 are both proportional coefficients, then:

[0123]

[0124] Will Substitution The active power control model of wind turbine is obtained as follows:

[0125]

[0126] In S1, the charge and discharge model of the energy storage system includes an equivalent circuit model and a charge and discharge power relationship;

[0127] Among them, the equivalent circuit model considers the electrochemical characteristics of the energy storage system and establishes an equivalent circuit model based on the electrochemical principle of the battery. The equivalent circuit model is expressed as:

[0128]

[0129] U b is the battery terminal voltage, E0 is the battery open circuit voltage, R0 is the battery ohmic internal resistance, C b is the equivalent capacitance of the battery, I b is the battery charge and discharge current; the battery open circuit voltage E0 is related to the battery SOC state, and E0 is expressed as:

[0130] E0=k1-k2 / SOC-k3ln(SOC)+k4ln(1-SOC);

[0131] Among them, k1, k2, k3, and k4 are constants related to battery characteristics, and SOC is the state of charge of the battery; the -k2 / SOC term indicates that as the SOC decreases (i.e., the battery power decreases), the open circuit voltage will decrease accordingly, and the magnitude of the decrease is related to the values ​​of k2 and SOC; the -k3ln(SOC) term and the k4ln(1-SOC) term further consider the impact of the logarithmic relationship on the open circuit voltage. This logarithmic relationship is based on the electrochemical characteristics of the battery and can more accurately describe the nonlinear relationship between the open circuit voltage and the change;

[0132] The charge and discharge power relationship is expressed as:

[0133] P b =ηU b I b ;

[0134] P b is the actual charge and discharge power; η is the charge and discharge efficiency; U b is the battery terminal voltage; I b is the battery charging and discharging current.

[0135] like Figure 3 As shown, the S2 includes:

[0136] S21. Determine the virtual inertia coefficient according to the equivalent inertia time constant H of the wind turbine ω Calculate the virtual inertia coefficient K I , the formula is:

[0137]

[0138] Among them, P r is the rated power of the wind turbine, f r is the system rated frequency;

[0139] S22. Design droop control. Droop control is used to couple the output power of the wind turbine with the grid frequency to achieve frequency regulation. The formula for droop control is:

[0140] P out =Popt +K D Δf;

[0141] Where P out is the output power of the wind turbine; P opt is the optimal power output under maximum power point tracking (MPPT); K D is the droop coefficient, which determines the degree of response of the droop control to the grid frequency deviation, that is, the proportional relationship of the droop control to adjust the output power according to the frequency deviation; Δf is the grid frequency deviation;

[0142] S23. Implement a phase-locked loop. The phase-locked loop (PLL) is a synchronous frame phase-locked loop (SRF-PLL) structure. The principle of the synchronous frame phase-locked loop (SRF-PLL) is to convert the grid voltage from a three-phase stationary coordinate system to a two-phase rotating coordinate system (dq coordinate system), and to achieve phase tracking by controlling the q-axis voltage component to zero. In the dq coordinate system, the expression of the grid voltage is:

[0143]

[0144] Where T is the coordinate transformation matrix; u a 、u b 、u c is the three-phase voltage of the power grid; u d is the d-axis voltage component; u q is the q-axis voltage component;

[0145] The control equation of the PLL is:

[0146]

[0147] In the formula, u q (s) is the q-axis voltage component; u′ q (s) is the reference value of q-axis voltage, u′ q (s) = 0; K p is the proportional gain of the PLL; K i is the integral gain of PLL; s is the complex variable in Laplace transform;

[0148] K p Determines the response speed and strength of the PLL to the q-axis voltage deviation. In the PLL control equation, when the q-axis voltage component u q (s) deviates from the reference value u′(s) q When the proportional gain K p The control link will adjust the output proportionally according to the deviation to promote u q (s) approaches the reference value faster; larger K pA high value will make the PLL more sensitive to deviations and correct them faster, but it will cause system stability to decrease and oscillation to occur.

[0149] K i The integral gain is used to eliminate the steady-state error. In the control process of PLL, the integral gain k i The q-axis voltage deviation will be integrated; as time goes by, even if the proportional control link cannot completely eliminate the deviation, the integral action will continue to accumulate deviation information, so that the q-axis voltage can be more accurately controlled at the reference value u′ q (s), thereby achieving accurate tracking of the grid frequency; however, if the value is too large, the system response speed will be slow, and problems such as integral saturation may occur, affecting the normal operation of the system;

[0150] S24. Design a proportional-integral (PI) controller. The PI controller is used to adjust the active power output of the wind turbine in response to changes in the grid frequency. The transfer function of the PI controller is:

[0151]

[0152] K′ p is the proportional gain of the (PI) controller, K′ p Determines the initial response speed and amplitude of the PI controller to the grid frequency deviation. The larger K′ p The controller will be more sensitive to frequency deviation, but it will increase the overshoot of the system. p The system response will be slower, but relatively more stable; K i ′ is the integral gain of the (PI) controller, K i ′ affects the strength of the integral link of the PI controller. A larger K i ′ can eliminate steady-state errors faster, but it will make the dynamic process of the system worse, or even cause instability; a smaller K i ′ will slow down the elimination of steady-state errors, but the stability will be better;

[0153] S25. An integrated control strategy is provided to integrate virtual inertia control, droop control and PI controller into the active power control system of the wind turbine; wherein the virtual inertia control provides the initial frequency response, the droop control adjusts the output power to maintain the grid frequency, and the PI controller refines the power regulation to reduce the steady-state error;

[0154] The power adjustment calculated by the virtual inertia control needs to be passed to the droop control and PI controller as a reference for their subsequent calculations; the droop control calculates its own power adjustment command based on the frequency deviation and the output of the virtual inertia control, and passes it to the PI controller and the actuator of the wind turbine; the PI controller comprehensively considers all input information, calculates the final power control signal, and controls the active power output of the wind turbine; the final power control signal is:

[0155]

[0156] ΔP f is the final power control signal, which is used to control the active power output of the wind turbine; ΔP D The power adjustment command calculated for droop control; ΔP VI is the power adjustment calculated by virtual inertia control, ΔP PI-D is the power adjustment of the PI controller to the droop control output; Δf is the grid frequency deviation, that is, the difference between the actual grid frequency and the rated frequency; K D is the droop coefficient; K′ p is the proportional gain of the (PI) controller; K i ′ is the integral gain of the (PI) controller.

[0157] like Figure 4 As shown, the S3 includes:

[0158] S31. Analyze the inertia demand of the wind power cluster and the SOC status of the energy storage system, and continuously collect the operating data of the wind power cluster, including the output power, speed and grid frequency of each wind turbine; combine the dynamic model of the wind turbine (such as the active power control model established in step S1) and the grid frequency stability requirements to analyze the inertia demand of the wind power cluster under different working conditions; for example, according to the grid frequency deviation allowable value Δf′ and the coefficient k related to the grid characteristics f , through formula I d =k f Δf′ estimates the inertia requirement I d ;

[0159] SOC status monitoring, obtain real-time state of charge (SOC) information through the built-in monitoring device of the energy storage system. The calculation formula of SOC is: Where Q c is the current charge, Q t is the total charge;

[0160] S32.MPC model is established, which includes the output power P of wind turbines w , the charging and discharging power of the energy storage system, the grid frequency f gAnd inertia related variables J, etc.; the dynamic equation of the model can be expressed as:

[0161]

[0162] In the formula, a1, a2, and a3 are coefficients determined according to the characteristics of the wind turbine; b1, b2, and b3 are coefficients determined according to the characteristics of the energy storage system;

[0163] S33. Objective function setting, considering the energy utilization rate E of the energy storage system u The relevant target term λ E ·E u , where λ E Energy utilization rate E of energy storage system u The weight coefficient, while considering the number of charge and discharge cycles N of the energy storage system c The influence term λ on the objective function N ·N c , where λ N N is the number of charge and discharge cycles of the energy storage system c The weight coefficient of ; the objective function is:

[0164] F=λ ω ·∑(Δω r,i -Δω r,ave ) 2 +λ B ·∑|f L (k) 2 +λ E ·E u +λ N ·N c

[0165] λ ω is the weight coefficient related to the balance of wind turbine rotor speed; Δω r,i Indicates the difference between the rotor speed of the i-th wind turbine and a certain reference speed; Δω r,ave is the average value of the rotor speed of all wind turbines; B is the weight coefficient of the target related to the grid frequency; f L (k) is a function related to the grid frequency and the operating parameters of the wind turbine and the energy storage system;

[0166] In the solution process, the differences and changes in the operating conditions of wind turbines and energy storage units need to be considered. For example, the output power of wind turbines will change with the change of wind speed, and the charging and discharging power of energy storage systems will be affected by the SOC state and charging and discharging efficiency. By comprehensively considering these factors, the optimal control sequence that minimizes the objective function is found, including the power adjustment instructions of wind turbines and the charging and discharging instructions of energy storage systems. By implementing such a coordinated control strategy, while meeting the inertia requirements of wind power clusters, the energy of energy storage systems is reasonably utilized to improve the operating efficiency and stability of the entire system.

[0167] like Figure 5 As shown, the S4 includes:

[0168] S41. Model establishment: Based on the virtual inertia control strategy designed in S2 and the shared energy storage coordination control strategy designed in S3, a frequency dynamic response model of the wind-storage combined system with virtual inertia is established;

[0169] Based on the virtual inertia control strategy designed by S2 and the shared energy storage coordination control strategy designed by S3, a frequency dynamic response model of the wind-storage combined system with virtual inertia is established.

[0170] Consider the impact of virtual inertia control strategy (S2) on the model

[0171] In S2, the virtual inertia control strategy simulates the inertial response characteristics of the synchronous generator by introducing virtual inertia control in the active power control system of the wind turbine. This means that in the frequency dynamic response model, the virtual inertia will affect the change of the system frequency. Specifically, when the system frequency changes, the virtual inertia will adjust the output power of the wind turbine according to its set control strategy, thereby affecting the dynamic change of the system frequency. The virtual inertia will cause the wind turbine to increase its output power when the frequency drops, in order to provide additional inertial support and slow down the speed of frequency drop.

[0172] At the same time, the phase-locked loop (PLL) and proportional-integral (PI) controller in S2 will also have an impact on the model. The phase-locked loop is used to track the grid frequency, and its control equations and parameters will affect the system's response speed and accuracy to grid frequency changes. The proportional-integral controller is used to adjust the active power output of the wind turbine, and its transfer function and parameters will adjust the output power of the wind turbine according to the grid frequency deviation, thereby affecting the dynamic changes of the system frequency.

[0173] Considering the impact of shared energy storage coordinated control strategy (S3) on the model

[0174] In S3, the shared energy storage coordination control strategy adjusts the charging and discharging power of the energy storage system according to the inertia requirements of the wind power cluster and the SOC status of the energy storage system. In the frequency dynamic response model, the charging and discharging power of the energy storage system will directly affect the dynamic changes of the system frequency. When the system frequency decreases and the SOC status of the energy storage system allows, the energy storage system will discharge, increase the total power of the system, and thus increase the system frequency.

[0175] In addition, the MPC model establishment and objective function setting in S3 take into account the differences and changes in the operating conditions of wind turbines and energy storage units. The output power of wind turbines will change with factors such as wind speed and pitch angle, and the charge and discharge power of energy storage systems will be affected by factors such as SOC state, charge and discharge efficiency, and temperature. The relationship between these factors and their comprehensive impact on system frequency need to be accurately described in the model.

[0176] Frequency dynamic response model of wind-storage combined system with virtual inertia:

[0177]

[0178] Among them, J v is the virtual inertia; P ω is the output power of the wind turbine; P s is the charging and discharging power of the energy storage system; f g is the grid frequency; I d is the inertia requirement; c1-c6 are coefficients determined according to system characteristics; Q c is the current charge of the energy storage system; Q t is the total charge; Q o It is the actual amount of electricity released during discharge;

[0179] S42. Inertia response time calculation: according to the system's dynamic equation and initial conditions, calculate the time required for the system frequency to change from the initial value to the stable value, i.e., the inertia response time;

[0180] Assume that the frequency variation of the system complies with the first-order linear differential equation: s ′(t)+αf(t) s =β,f s ′ is the system frequency change rate, f s is the system frequency, α and β are constants related to the system parameters; for this first-order equation, the solution is:

[0181]

[0182] When the system reaches a stable state, that is, t→∞, The inertial response time is defined as the time required for the system frequency to change from an initial value to a value close to the stable value (for example, to 95% of the stable value). By solving the above equation, the inertial response time is obtained:

[0183]

[0184] Among them, f s (0) is the system frequency from the initial value; T w is the frequency stability value; T r is the inertial response time;

[0185] S43. Setting the start and stop conditions of virtual inertia control, and setting the inertia response time T r As the start and stop conditions of virtual inertia control, when the system frequency changes and the inertia response time T r When the system frequency returns to the normal range and the inertia response time T r When it is greater than the set threshold, the virtual inertia control is turned off;

[0186] Consider the impact of the start-up time of the wind turbine and the response time of the energy storage system on the inertial response time. Assume that the total inertial response time of the system is: when the system frequency changes and the start-up time of the wind turbine is less than the set threshold, start the virtual inertial control; when the system frequency returns to the normal range and the start-up time of the wind turbine is greater than the set threshold, turn off the virtual inertial control.

[0187] like Figure 6 As shown, the S5 includes:

[0188] S51. Parameter optimization, using the particle swarm optimization algorithm to take the objective function in S3 as the fitness function, and calculate the fitness value according to the particle position vector; by iteratively updating the particle position and velocity, find the particle position that minimizes the fitness function, that is, the optimal controller parameters;

[0189] S52. Lockout logic design, using the optimized controller parameters to design the controller lockout logic; the lockout logic is: when the system frequency returns to the normal range, that is, when the system frequency range belongs to [f min ,f max ], turn off virtual inertia control to avoid frequency overshoot and system instability; when the system frequency range is within [f min ,f max ], turn off virtual inertia control to avoid frequency overshoot and system instability, [f min ,f max ].

[0190] The above description is a preferred embodiment of the invention and is not intended to limit the invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the invention should be included in the protection scope of the invention.

Claims

1. A virtual inertia compensation control method for shared energy storage in wind power clusters, characterized in that: The following steps are involved: S1. System parameter acquisition and modeling: obtain the operating parameters of the wind power cluster and the energy storage system, and use the acquired operating parameters to establish a dynamic model of the wind power cluster and the energy storage system. The dynamic model includes the active power control model of the wind turbine and the charging and discharging model of the energy storage system. S2. Design of virtual inertia control strategy. Using the operating parameters obtained in S1 and the established dynamic model, virtual inertia control is introduced into the active power control system of the wind turbine to simulate the inertial response characteristics of the synchronous generator. The virtual inertia control strategy is designed using a phase-locked loop (PLL) and a proportional-integral (PI) controller. The PLL is used to track the grid frequency, and the PI controller is used to adjust the active power output. S3. Shared energy storage coordinated control. Based on S2, according to the inertia requirements of the wind power cluster and the SOC status of the energy storage system, the model predictive control method is used to design a coordinated control strategy for shared energy storage taking into account the differences and changes in the operating conditions of wind turbines and energy storage units. S4. Establish a frequency dynamic response model, based on the virtual inertia control strategy designed in S2 and the shared energy storage coordination control strategy designed in S3, and establish a frequency dynamic response model of wind and energy storage combined with virtual inertia, calculate the inertia response time of the frequency dynamic response model, and use the inertia response time as the start and stop condition of the virtual inertia control; S5. Controller parameter optimization and locking: an intelligent algorithm is used to optimize the controller parameters of the frequency dynamic response model, and the controller locking logic is designed using the optimized controller parameters. When the frequency of the frequency dynamic response model returns to the normal range, the virtual inertia control is turned off in time to avoid frequency overshoot and system instability.

2. The virtual inertia compensation control method for shared energy storage of wind power clusters according to claim 1 is characterized in that: The operating parameters include wind rotor speed, generator speed, wind speed, and state of charge (SOC) of the energy storage system.

3. The virtual inertia compensation control method for shared energy storage of wind power clusters according to claim 1 is characterized in that: In S1, the method for establishing the active power control model of the wind turbine generator set is as follows: S11. The simplified power system frequency model is given, and the simplified power system frequency model is expressed as: Among them, ΔP m is the mechanical power change, ΔE is the energy storage change, α, T, L, M, and D are system parameters; S12. Considering the aerodynamic characteristics of the wind turbine, the aerodynamic torque model T of the wind rotor is introduced a , T a It is related to wind speed v, rotor radius R, air density ρ, tip speed ratio λ and pitch angle β of the rotor, and can be expressed as: Among them, C p (λ, β) is the wind energy utilization coefficient, which is a function of the tip speed ratio λ and the pitch angle β; S13. Considering the torsion model of the transmission shaft, the torque balance equation of the transmission shaft is: Among them, J t is the moment of inertia of the drive shaft, ω t is the speed of the transmission shaft, T g is the electromagnetic torque of the generator, D t is the damping coefficient of the drive shaft; S14. Establish the relationship between power and speed; According to the electromagnetic torque T of the generator g With the generator output power P g and the generator speed ω g The relationship P g =T g ω g , under ideal conditions, the wind turbine output power P out Equal to the generator output power P g ; From the torque balance equation of the drive shaft Solve Substituting Tg into P g =T g ω g get Right now Considering that during stable operation, the wind wheel speed ω and the transmission shaft speed ω t and the generator speed ω g There is a proportional relationship between them, namely ω t =k1ω,ω g =k2ω, k1 and k2 are both proportional coefficients, then: Will Substitution The active power control model of wind turbine is obtained as follows:

4. The virtual inertia compensation control method for shared energy storage of wind power clusters according to claim 1 is characterized in that: In S1, the charge and discharge model of the energy storage system includes an equivalent circuit model and a charge and discharge power relationship; Among them, the equivalent circuit model considers the electrochemical characteristics of the energy storage system and establishes an equivalent circuit model based on the electrochemical principle of the battery. The equivalent circuit model is expressed as: U b is the battery terminal voltage, E0 is the battery open circuit voltage, R0 is the battery ohmic internal resistance, C b is the equivalent capacitance of the battery, I b is the battery charge and discharge current; the battery open circuit voltage E0 is related to the battery SOC state, and E0 is expressed as: E0=k1-k2SOC-k3ln(SOC)+k4ln(1-SOC); Among them, k1, k2, k3, and k4 are constants related to battery characteristics, and SOC is the state of charge of the battery; the -k2SOC term indicates that as the SOC decreases, the open circuit voltage will decrease accordingly, and the magnitude of the decrease is related to the values ​​of k2 and SOC; the -k3ln(SOC) term and the k4ln(1-SOC) term further consider the impact of the logarithmic relationship on the open circuit voltage; The charge and discharge power relationship is expressed as: P b =ηU b AND b ; P b is the actual charge and discharge power; η is the charge and discharge efficiency; U b is the battery terminal voltage; I b is the battery charging and discharging current.

5. The virtual inertia compensation control method for shared energy storage of wind power clusters according to claim 1 is characterized in that: The S2 includes: S21. Determine the virtual inertia coefficient according to the equivalent inertia time constant H of the wind turbine ω Calculate the virtual inertia coefficient K I , the formula is: Among them, P r is the rated power of the wind turbine, f r is the system rated frequency; S22. Design droop control. Droop control is used to couple the output power of the wind turbine with the grid frequency to achieve frequency regulation. The formula for droop control is: P out =P opt +K D ·Δf; Where P out is the output power of the wind turbine; P opt is the optimal power output under maximum power point tracking; K D is the droop coefficient, which determines the degree of response of the droop control to the grid frequency deviation, that is, the proportional relationship of the droop control to adjust the output power according to the frequency deviation; Δf is the grid frequency deviation; S23. Implement a phase-locked loop. The phase-locked loop is a phase-locked loop structure based on a synchronous coordinate system. The principle of the phase-locked loop based on a synchronous coordinate system is to convert the grid voltage from a three-phase stationary coordinate system to a two-phase rotating coordinate system, and to achieve phase tracking by controlling the q-axis voltage component to zero. In the dq coordinate system, the expression of the grid voltage is: Where T is the coordinate transformation matrix; u a 、u b 、u c is the three-phase voltage of the power grid; u d is the d-axis voltage component; u q is the q-axis voltage component; The control equation of the PLL is: In the formula, u q (s) is the q-axis voltage component; u′ q (s) is the reference value of q-axis voltage, u′ q (s) = 0; K p is the proportional gain of the PLL; K i is the integral gain of PLL; s is the complex variable in Laplace transform; S24. Design a proportional-integral PI controller. The PI controller is used to adjust the active power output of the wind turbine in response to changes in the grid frequency. The transfer function of the PI controller is: K′ p is the proportional gain of the PI controller; K′ i is the integral gain of the PI controller; S25. An integrated control strategy is provided to integrate virtual inertia control, droop control and PI controller into the active power control system of the wind turbine; wherein the virtual inertia control provides the initial frequency response, the droop control adjusts the output power to maintain the grid frequency, and the PI controller refines the power regulation to reduce the steady-state error; The power adjustment calculated by the virtual inertia control needs to be passed to the droop control and PI controller as a reference for subsequent calculations; the droop control calculates its own power adjustment command based on the frequency deviation and the output of the virtual inertia control, and passes it to the PI controller and the actuator of the wind turbine; the PI controller comprehensively considers all input information, calculates the final power control signal, and controls the active power output of the wind turbine; the final power control signal is: ΔP f is the final power control signal, which is used to control the active power output of the wind turbine; ΔP D The power adjustment command calculated for droop control; ΔP VI is the power adjustment calculated by virtual inertia control, ΔP PI-D is the power adjustment of the PI controller to the droop control output; Δf is the grid frequency deviation, that is, the difference between the actual grid frequency and the rated frequency; K D is the droop coefficient; K′ p is the proportional gain of the (PI) controller; K′ i is the integral gain of the PI controller.

6. The virtual inertia compensation control method for shared energy storage of wind power clusters according to claim 1 is characterized in that: The S3 includes: S31. Analyze the inertia demand of wind power cluster and the SOC state of energy storage system, according to the grid frequency deviation allowable value Δf′ and the coefficient k related to grid characteristics f , through formula I d =k f Δf′ estimates the inertia requirement I d ; SOC status monitoring, obtain real-time state of charge (SOC) information through the built-in monitoring device of the energy storage system. The calculation formula of SOC is: Where Q c is the current charge, Q t is the total charge; S32.MPC model is established, which includes the output power P of wind turbines w , the charging and discharging power of the energy storage system, the grid frequency f g And inertia related variables J; the dynamic equation of the model is expressed as: In the formula, a1, a2, and a3 are coefficients determined according to the characteristics of the wind turbine; b1, b2, and b3 are coefficients determined according to the characteristics of the energy storage system; S33. Objective function setting, considering the energy utilization rate E of the energy storage system u The relevant target term λ E ·E u , where λ E Energy utilization rate E of energy storage system u The weight coefficient, while considering the number of charge and discharge cycles N of the energy storage system c The influence term λ on the objective function N ·N c , where λ N N is the number of charge and discharge cycles of the energy storage system c The weight coefficient of ; the objective function is: F=λ ω ·∑(See r,i -See r,ave ) 2 +λ B ·∑|f L (k)| 2 +λ E ·E u +λ N ·N c ; λ ω is the weight coefficient related to the balance of wind turbine rotor speed; Δω r,i Indicates the difference between the rotor speed of the i-th wind turbine and a certain reference speed; Δω r,ave is the average value of the rotor speed of all wind turbines; B is the weight coefficient of the target related to the grid frequency; f L (k) is a function related to the grid frequency and the operating parameters of the wind turbine and the energy storage system; In the process of solving the objective function, it is necessary to consider the differences and changes in the operating conditions of wind turbines and energy storage units; the output power of wind turbines will change with the change of wind speed, and the charging and discharging power of the energy storage system will be affected by the SOC state and the charging and discharging efficiency; through comprehensive consideration of these factors, the optimal control sequence that minimizes the objective function is found, including the power adjustment instructions of the wind turbines and the charging and discharging instructions of the energy storage system.

7. The virtual inertia compensation control method for shared energy storage of wind power clusters according to claim 1 is characterized in that: The S4 includes: S41. Model establishment: Based on the virtual inertia control strategy designed in S2 and the shared energy storage coordination control strategy designed in S3, a frequency dynamic response model of the wind-storage combined system with virtual inertia is established; Frequency dynamic response model of the wind-storage combined system with virtual inertia: Among them, J v is the virtual inertia; P ω is the output power of the wind turbine; P s is the charging and discharging power of the energy storage system; f g is the grid frequency; I d is the inertia requirement; c1-c6 are coefficients determined according to system characteristics; Q c is the current charge of the energy storage system; Q t is the total charge; Q o It is the actual amount of electricity released during discharge; S42. Inertia response time calculation: according to the system's dynamic equation and initial conditions, calculate the time required for the system frequency to change from the initial value to the stable value, i.e., the inertia response time; The change of system frequency conforms to the first-order linear differential equation: s ′(t)+αf(t) s =β,f s ′ is the system frequency change rate, f s is the system frequency, α and β are constants related to the system parameters; solving this first-order equation, the solution is: When the system reaches a stable state, that is, t→∞, The inertial response time is defined as the time required for the system frequency to change from an initial value to a near-stable value. By solving the above equation, the inertial response time is obtained: Among them, f s (0) is the system frequency from the initial value; T w is the frequency stability value; T r is the inertial response time; S43. Setting the start and stop conditions of virtual inertia control, and setting the inertia response time T r As the start and stop conditions of virtual inertia control, when the system frequency changes and the inertia response time T r When the system frequency returns to the normal range and the inertia response time T r When it is greater than the set threshold, the virtual inertia control is turned off; Consider the impact of the start-up time of the wind turbine and the response time of the energy storage system on the inertial response time; when the system frequency changes and the start-up time of the wind turbine is less than the set threshold, start the virtual inertia control; when the system frequency returns to the normal range and the start-up time of the wind turbine is greater than the set threshold, turn off the virtual inertia control.

8. The virtual inertia compensation control method for shared energy storage of wind power clusters according to claim 1 is characterized in that: The S5 includes: S51. Parameter optimization, using the particle swarm optimization algorithm to take the objective function in S3 as the fitness function, and calculate the fitness value according to the particle position vector; by iteratively updating the particle position and velocity, find the particle position that minimizes the fitness function, that is, the optimal controller parameters; S52. Lockout logic design, using the optimized controller parameters to design the controller lockout logic; the lockout logic is: when the system frequency returns to the normal range, that is, when the system frequency range belongs to [f min ,f max ], turn off virtual inertia control to avoid frequency overshoot and system instability; when the system frequency range is within [f min ,f max ], turn off virtual inertia control to avoid frequency overshoot and system instability, [f min ,f max ].

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