A network-constructing type VSG inertia damping cooperative adaptive control method and system

Abstract

The application discloses a kind of network type VSG inertia damping collaborative adaptive control method and system, belong to virtual synchronous generator control technical field, the real-time acquisition energy storage battery operating information of the present application, and determine battery charge-discharge state, adopt continuous smooth function to combine hysteresis loop logic calculation state of charge smooth constraint factor;Again according to frequency variation rate, frequency deviation, and superimposed constraint factor adaptive adjustment virtual moment of inertia and damping coefficient;The parameter is substituted into virtual synchronous generator ontology control logic to generate phase signal, drives network type inverter operation after pulse width modulation, and executes safety control logic in combination with battery state of charge interval and operating state.This application realizes the smooth continuous adjustment of virtual moment of inertia, damping coefficient, eliminates the system oscillation caused by parameter mutation, effectively prevents overcharge, overdischarge of energy storage battery through state of charge closed loop constraint, prolongs battery service life, and gives consideration to power grid frequency stability support and energy storage system safe operation.

Description

Technical Field

[0001] This invention relates to the field of virtual synchronous generator control technology, and in particular to a network-type VSG inertia damping cooperative adaptive control method and system. Background Technology

[0002] Under the "dual carbon" target, the high proportion of new energy sources such as photovoltaics and wind power connected to the grid has led to a decrease in the proportion of traditional synchronous generators, a significant decrease in the equivalent inertia of the power system, increased frequency fluctuations, and weakened anti-disturbance capabilities. Therefore, grid-based control technology is urgently needed to improve system stability.

[0003] Grid-based energy storage VSG is a core solution to address the stability issues of low-inertia power grids. However, the charging and discharging capabilities of energy storage batteries are strictly limited by the State of Charge (SOC). Traditional VSG control does not incorporate SOC into the adaptive closed loop, resulting in a lack of coordination between frequency support and energy storage security, which limits its engineering applications.

[0004] The closest existing technology to this invention is the VSG rotational inertia and damping coefficient coordinated adaptive control strategy, which is specifically implemented by: establishing the VSG rotor motion equation and second-order small-signal model, and analyzing the influence of J and D on frequency / power response;

[0005] The dynamic process is divided into 4 intervals based on the synchronous generator power angle oscillation, and J and D are adjusted in segments according to the frequency deviation Δω and the frequency change rate dω / dt.

[0006] A fixed threshold piecewise function is used to achieve J and D cooperative adaptive control, suppressing frequency fluctuations and power oscillations;

[0007] The strategy can be verified through MATLAB / Simulink simulation to reduce power overshoot and shorten the settling time.

[0008] Another similar approach is VSG control that only considers SOC limiting: SOC is used as a boundary constraint, and the frequency modulation function is simply cut off when SOC is below 20% or above 80%, without smooth adaptive adjustment logic.

[0009] Disadvantages of existing technology

[0010] (1) No energy storage SOC closed-loop constraint: Only the frequency signal is used as input, and the SOC state of the energy storage battery is completely ignored. The battery is prone to overcharging / over-discharging, which cannot meet the safe operation requirements of grid-type energy storage.

[0011] (2) Parameter adjustment has sharp changes: The fixed threshold hard segment switching is used, and the instantaneous changes in J and D cause power oscillation and DC bus voltage fluctuation.

[0012] (3) Conflict between frequency support and energy storage safety: The inertial damping cannot be flexibly adjusted in real time according to the SOC, and the supporting power is still forcibly output under extreme SOC, which damages the battery;

[0013] (4) Poor adaptability: It is designed only for strong grid connection scenarios and does not consider mainstream application scenarios of grid-type energy storage such as weak grids and isolated grids.

[0014] (5) Insufficient stability guarantee: J and D regulation has no physical constraints, which can easily lead to system instability due to parameter exceeding the limit.

[0015] (6) Solve the problem that the existing VSG-SOC constraint does not distinguish between battery charging and discharging states, and the overcharge and over-discharge protection logic is chaotic and does not match the actual operating characteristics of the battery. Summary of the Invention

[0016] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a network-type VSG inertia damping cooperative adaptive control method and system. This invention constructs a VSG inertia damping cooperative adaptive mechanism with dual inputs of SOC and frequency, achieving coordinated optimization of frequency support and energy storage safety.

[0017] This invention employs a continuous smooth function and incorporates hysteresis to eliminate abrupt spikes in the J and D parameters, thereby improving the dynamic stability of the system.

[0018] This invention enables flexible adjustment of J and D parameters under real-time SOC constraints, completely avoiding overcharging and over-discharging of energy storage batteries;

[0019] This invention is adaptable to all scenarios of grid-type energy storage, including strong / weak power grids and isolated grids, improves the practicality of strategy engineering, adds parameter saturation constraints, and ensures stable operation of the VSG system under all operating conditions.

[0020] This invention is achieved through the following technical solution:

[0021] A network-type VSG inertia damping cooperative adaptive control method includes:

[0022] S1. Real-time acquisition of the real-time state of charge of the energy storage battery, battery operating power, and grid angular frequency; calculation of grid angular frequency deviation and frequency change rate based on the acquired grid angular frequency; and determination of whether the energy storage battery is currently in charging, discharging, or standby state based on the battery operating power.

[0023] S2. Based on the state of the energy storage battery, calculate the corresponding state of charge smoothing constraint factor, and introduce hysteresis control logic to correct the state of charge smoothing constraint factor. When the energy storage battery is in the discharge state, calculate the discharge constraint factor based on the real-time state of charge value. When the energy storage battery is in the charging state, calculate the charging constraint factor based on the real-time state of charge value. When the energy storage battery is in the standby state, set the standby constraint factor to a fixed reference value.

[0024] S3. Combining the grid angular frequency deviation, frequency change rate, and the corrected state of charge smoothing constraint factor, the virtual moment of inertia and damping coefficient of the virtual synchronous generator are adaptively calculated, and upper and lower limit amplitude constraints are set on the calculated virtual moment of inertia and damping coefficient to limit the virtual moment of inertia and damping coefficient within the preset safety range.

[0025] S4. Substitute the virtual moment of inertia and damping coefficient of the virtual synchronous generator into the rotor motion equation of the virtual synchronous generator to generate the phase and voltage commands of the grid-type inverter, and drive the grid-type inverter to run after pulse width modulation.

[0026] S5. During the operation of the grid-type inverter, the safety control logic is executed in real time by combining the state of charge range and charging and discharging state of the energy storage battery to flexibly constrain the output power of the grid-type inverter.

[0027] In S1, the real-time state of charge of the energy storage battery is calculated by integrating the energy. The charging and discharging states are distinguished by the positive and negative values ​​of the battery operating power. When the battery operating power is greater than zero, it is determined to be in the charging state. When the battery operating power is less than zero, it is determined to be in the discharging state. When the battery operating power is equal to zero, it is determined to be in the standby state.

[0028] In step S2, when the energy storage battery is in a discharging state, a first state of charge threshold is preset. Based on the first state of charge threshold, two state of charge intervals are divided. When the real-time state of charge of the energy storage battery is higher than the first state of charge threshold, the discharge constraint factor remains at a fixed value. When the real-time state of charge of the energy storage battery is lower than or equal to the first state of charge threshold, the discharge constraint factor decreases smoothly as the real-time state of charge decreases.

[0029] When the energy storage battery is in a charging state, a second state of charge threshold is preset. Based on the second state of charge threshold, two state of charge intervals are divided. When the real-time state of charge of the energy storage battery is lower than the second state of charge threshold, the charging constraint factor remains at a fixed value. When the real-time state of charge of the energy storage battery is higher than or equal to the second state of charge threshold, the charging constraint factor decreases smoothly as the real-time state of charge increases.

[0030] In S2, a discharge state hysteresis logic and a third state of charge threshold are set. When the real-time state of charge drops to the first state of charge threshold, the discharge state hysteresis logic is activated. When the real-time state of charge rises back to above the third state of charge threshold, the discharge state hysteresis logic is deactivated. At the same time, a charging state hysteresis logic and a fourth state of charge threshold are set. When the real-time state of charge rises to the second state of charge threshold, the charging state hysteresis logic is activated. When the real-time state of charge drops to below the fourth state of charge threshold, the charging state hysteresis logic is deactivated.

[0031] The discharge constraint factor and the charging constraint factor are both calculated using a continuous smooth function.

[0032] In S3, the adjustment of virtual rotational inertia is divided into two operating conditions. When the rate of change of frequency is less than or equal to the set critical value, or when the frequency is in the recovery stage, the virtual rotational inertia is the product of the reference virtual rotational inertia and the state of charge smoothing constraint factor. When the rate of change of frequency is greater than the set critical value, or when the frequency is in the deterioration stage, the virtual rotational inertia is adjusted incrementally based on the reference virtual rotational inertia and the rate of change of frequency, and then multiplied by the state of charge smoothing constraint factor to obtain the virtual rotational inertia.

[0033] For the adjustment of the damping coefficient, two operating conditions are distinguished. When the grid angular frequency deviation is less than or equal to the set deviation threshold, the damping coefficient is the product of the reference damping coefficient and the state of charge smoothing constraint factor. When the grid angular frequency deviation is greater than the set deviation threshold, the damping coefficient is adjusted incrementally based on the reference damping coefficient and the grid angular frequency deviation, and then multiplied by the state of charge smoothing constraint factor to obtain the damping coefficient.

[0034] In S4, the rotor motion equation is: the product of the virtual moment of inertia and the rate of change of frequency equals the difference between the mechanical power and the electromagnetic power divided by the rated angular frequency, and then the product of the damping coefficient and the deviation of the grid angular frequency is subtracted.

[0035] In S5, the safety control logic is as follows: when the energy storage battery is in a discharging state and the real-time state of charge is higher than the first state of charge threshold, the state of charge constraint factor is maximized, and the virtual moment of inertia and damping coefficient are output at full scale; when the energy storage battery is in a discharging state and the real-time state of charge is lower than or equal to the first state of charge threshold, the virtual moment of inertia and damping coefficient are rapidly reduced; when the energy storage battery is in a charging state and the real-time state of charge is lower than the second state of charge threshold, the state of charge constraint factor is maximized, and the virtual moment of inertia and damping coefficient are output at full scale; when the energy storage battery is in a charging state and the real-time state of charge is higher than or equal to the second state of charge threshold, the virtual moment of inertia and damping coefficient are gradually reduced; when the energy storage battery is in a standby state, no constraints are applied.

[0036] A network-type VSG inertia damping cooperative adaptive control system includes:

[0037] Data acquisition unit: Real-time acquisition of the energy storage battery's real-time state of charge, battery operating power, and grid angular frequency; Calculation of grid angular frequency deviation and frequency change rate based on the acquired grid angular frequency; Determination of the energy storage battery's current state of charging, discharging, or standby based on the battery operating power.

[0038] State of Charge (SCO) Smoothing Constraint Factor Calculation Unit: Based on the state of the energy storage battery, calculate the corresponding SCO smoothing constraint factor and introduce hysteresis control logic to correct the SCO smoothing constraint factor. When the energy storage battery is in the discharging state, calculate the discharging constraint factor based on the real-time SCO value. When the energy storage battery is in the charging state, calculate the charging constraint factor based on the real-time SCO value. When the energy storage battery is in the standby state, set the standby constraint factor to a fixed reference value.

[0039] Virtual moment of inertia and damping coefficient calculation unit: Combining the grid angular frequency deviation, frequency change rate and the corrected state of charge smoothing constraint factor, it adaptively calculates the virtual moment of inertia and damping coefficient of the virtual synchronous generator, and sets upper and lower limit amplitude constraints on the calculated virtual moment of inertia and damping coefficient to limit the virtual moment of inertia and damping coefficient within the preset safety range.

[0040] Control unit: Substitutes the virtual moment of inertia and damping coefficient of the virtual synchronous generator into the rotor motion equation of the virtual synchronous generator to generate the phase and voltage commands of the grid-type inverter, which are then driven to operate after pulse width modulation;

[0041] Safety control unit: During the operation of the grid-connected inverter, it executes safety control logic in real time by combining the state of charge range and charging / discharging state of the energy storage battery, and flexibly constrains the output power of the grid-connected inverter.

[0042] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the network-type VSG inertia damping cooperative adaptive control method.

[0043] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the network-type VSG inertia-damped cooperative adaptive control method.

[0044] The advantages of this invention are: Energy storage safety assurance: This invention introduces SOC closed-loop real-time constraint, which effectively avoids battery overcharging / over-discharging and extends service life;

[0045] Dynamic response smoothing: This invention employs a piecewise variable slope S-shaped function to eliminate abrupt spikes in J and D parameters, significantly reducing power / frequency oscillations;

[0046] Dual-objective synergistic optimization: This invention simultaneously achieves grid frequency support and safe operation of energy storage, overcoming the single-objective shortcomings of traditional technologies;

[0047] Full-scenario adaptability: This invention is adaptable to all operating conditions of grid-type energy storage, including strong grids, weak grids, and isolated grids, and has strong engineering practicality;

[0048] Higher stability: This invention adds J and D saturation limiting to improve stable operation under all working conditions, and the control accuracy and robustness are superior to the existing technology;

[0049] Compatible with existing architectures: This invention can be directly ported to existing grid-type energy storage VSG systems without hardware modifications, resulting in low transformation costs;

[0050] More precise protection logic: This invention strictly distinguishes between the charging and discharging states of the battery. It only prevents over-discharge when discharging and only prevents over-charge when charging, which perfectly matches the physical characteristics of lithium batteries and has no redundant constraints.

[0051] More stable operation: This invention adopts an asymmetric piecewise variable slope S-shaped function + hysteresis logic to avoid the SOC jittering near the threshold, which causes frequent J / D jumps. The system is oscillating-free and more robust. Attached Figure Description

[0052] Figure 1 The overall framework diagram of the network-type VSG inertia damping cooperative adaptive control considering the energy storage SOC in this invention is shown below;

[0053] Figure 2 This is the overall framework of the control algorithm of this invention;

[0054] Figure 3 This is an image of the SOC protection function of the present invention;

[0055] Figure 4 This is a judgment diagram for the protection function K(SOC) of this invention. Detailed Implementation

[0056] like Figure 1 As shown, this invention proposes a grid-type VSG inertia-damping coordinated adaptive control method considering the energy storage's state of charge (SOC). This method uses an energy storage battery pack, a bidirectional DC / DC converter, a grid-type inverter, an LC filter, and the power grid to form the main power circuit. By real-time acquisition of information such as battery SOC, battery power, grid frequency, voltage, and current, it calculates frequency deviation and frequency change rate, and generates an SOC smoothing constraint factor based on the battery's charge / discharge state. On this basis, the controller adaptively adjusts the virtual rotational inertia J and damping coefficient D according to frequency dynamics and the SOC constraint factor, enabling the energy storage system to provide inertia and damping support during grid frequency disturbances. Simultaneously, it automatically reduces the support strength when the SOC approaches the safety boundary to prevent overcharging or over-discharging of the battery. Finally, the adaptive J and D parameters are fed into the VSG control loop, driving the grid-type inverter through PWM modulation, achieving coordinated control of grid frequency stability and safe operation of the energy storage battery.

[0057] Relationship between J / D and rate of change of frequency

[0058] Based on the fundamental principles of VSG, the rotor mechanical equation of VSG is:

[0059]

[0060] in, and These are the mechanical torque and electromagnetic torque of the synchronous generator, respectively. For input power, For output power, Where ω is the rated angular frequency, and J is the virtual moment of inertia. Here, ω represents the angular frequency deviation of the power grid, and t represents time t.

[0061] The above equation, neglecting the damping term, can be transformed to obtain:

[0062]

[0063] The rate of change of frequency;

[0064] From this relationship, we can deduce that when the power deficit is constant, the rate of change of angular frequency is inversely proportional to the moment of inertia, and consequently, the rate of change of frequency is inversely proportional to the moment of inertia.

[0065]

[0066] From the above formula, it can be seen that, theoretically, when the system is subjected to disturbance, the larger the value of J, the better. The smaller the moment of inertia, the better it is at suppressing frequency fluctuations. However, the moment of inertia cannot be set too large, as an excessively large moment of inertia will worsen the system's dynamic characteristics and even cause instability. Therefore, the moment of inertia can be flexibly changed according to the rate of change of frequency during the transient process to ensure... When there are significant changes, the J value can change quickly and promptly.

[0067] The virtual moment of inertia of a traditional synchronous generator is determined by the rotor mass and structure, and is a physically fixed parameter; however, the virtual moment of inertia J in a VSG is not a real mechanical inertia, but a control parameter set by the controller software. Therefore, in grid-connected VSG energy storage, J can be adjusted online according to the system operating status.

[0068] Relationship between battery power, virtual moment of inertia J, damping coefficient D and frequency dynamics

[0069] In a grid-connected VSG (Variable Renewable Energy Storage) system, the energy storage battery provides active power to the grid-connected inverter via a bidirectional DC / DC converter or a DC bus. The AC output power of the inverter is determined by the VSG rotor motion equations; therefore, changes in the virtual moment of inertia J and the damping coefficient D directly alter the inverter's active power command, thereby changing the charging and discharging power of the energy storage battery.

[0070] Let the angular frequency deviation of the power grid be: The rate of change of frequency is:

[0071] The motion equation of the VSG rotor is:

[0072] In the formula, The angular frequency of the power grid. The rated angular frequency, For input power, This refers to the active power output of the inverter on the AC side.

[0073] The active power output of the inverter on the AC side can be obtained by summarizing:

[0074] When the system frequency decreases And usually At this time, both of the above power components cause P e Increased frequency means that grid-based energy storage releases more active power to the grid, corresponding to battery discharge; when the system frequency increases... And usually At this time P e The energy storage capacity decreases or even becomes negative, absorbing excess active power and correspondingly charging the battery.

[0075] If we define battery power P bat >0 indicates charging, P bat If the value is less than 0, then ignoring converter losses, we have:

[0076]

[0077] Therefore, J and D have the following relationship with battery power:

[0078]

[0079] Therefore, increasing J enhances the battery's instantaneous power response to frequency change rate, while increasing D enhances the battery's damped power response to frequency deviation. Thus, this invention uses SOC to smoothly constrain J and D, essentially providing a smooth constraint on the battery's charging and discharging power, thereby simultaneously achieving frequency support and battery safety protection.

[0080] The overall block diagram of the control algorithm of this invention is as follows: Figure 2 As shown, the present invention provides a network-type VSG inertia damping cooperative adaptive control method, comprising:

[0081] S1. Real-time acquisition of the real-time state of charge of the energy storage battery, battery operating power, and grid angular frequency; calculation of grid angular frequency deviation and frequency change rate based on the acquired grid angular frequency; and determination of whether the energy storage battery is currently in charging, discharging, or standby state based on the battery operating power.

[0082] In S1, the real-time state of charge of the energy storage battery is calculated by integrating the energy. The charging and discharging states are distinguished by the positive and negative values ​​of the battery operating power. When the battery operating power is greater than zero, it is determined to be in the charging state. When the battery operating power is less than zero, it is determined to be in the discharging state. When the battery operating power is equal to zero, it is determined to be in the standby state.

[0083] Real-time data collection of energy storage batteries Calculated by integration:

[0084]

[0085] Collect the angular frequency ω of the power grid and calculate the frequency deviation. Rate of change of frequency SOC refers to the current real-time state of charge of the battery, while SOC0 refers to the initial state of charge of the battery. The rated capacity of the energy storage battery, This refers to the rated DC terminal voltage of the energy storage battery. The active power for real-time charging and discharging of the battery.

[0086] Collect battery power P bat Determine the charging / discharging state: P bat >0 indicates a charging state; P bat <0 indicates a discharge state; P bat =0 indicates standby mode.

[0087] S2. Based on the state of the energy storage battery, calculate the corresponding state of charge smoothing constraint factor, and introduce hysteresis control logic to correct the state of charge smoothing constraint factor. When the energy storage battery is in the discharge state, calculate the discharge constraint factor based on the real-time state of charge value. When the energy storage battery is in the charging state, calculate the charging constraint factor based on the real-time state of charge value. When the energy storage battery is in the standby state, set the standby constraint factor to a fixed reference value.

[0088] In step S2, when the energy storage battery is in a discharging state, a first state of charge threshold is preset. Based on the first state of charge threshold, two state of charge intervals are divided. When the real-time state of charge of the energy storage battery is higher than the first state of charge threshold, the discharge constraint factor remains at a fixed value. When the real-time state of charge of the energy storage battery is lower than or equal to the first state of charge threshold, the discharge constraint factor decreases smoothly as the real-time state of charge decreases.

[0089] When the energy storage battery is in a charging state, a second state of charge threshold is preset. Based on the second state of charge threshold, two state of charge intervals are divided. When the real-time state of charge of the energy storage battery is lower than the second state of charge threshold, the charging constraint factor remains at a fixed value. When the real-time state of charge of the energy storage battery is higher than or equal to the second state of charge threshold, the charging constraint factor decreases smoothly as the real-time state of charge increases.

[0090] In S2, a discharge state hysteresis logic and a third state of charge threshold are set. When the real-time state of charge drops to the first state of charge threshold, the discharge state hysteresis logic is activated. When the real-time state of charge rises back to above the third state of charge threshold, the discharge state hysteresis logic is deactivated. At the same time, a charging state hysteresis logic and a fourth state of charge threshold are set. When the real-time state of charge rises to the second state of charge threshold, the charging state hysteresis logic is activated. When the real-time state of charge drops to below the fourth state of charge threshold, the charging state hysteresis logic is deactivated.

[0091] The discharge constraint factor and the charging constraint factor are both calculated using a continuous smooth function.

[0092] Independent constraint factors are designed for charging and discharging using the hyperbolic tangent tanh function, and hysteresis logic is added to prevent jitter. It should be noted that the choice of the SOC limit is not unique. Different batteries have different SOC lower limits, which depends on the characteristics of the actual battery. In order to quantitatively analyze the corresponding power output of the energy storage battery when the SOC deteriorates, the values ​​are set to 0.4 (first state of charge threshold) and 0.8 (second state of charge threshold). Figure 3 For charging and discharging protection curve K dis and K chg ;

[0093] (1) Battery discharge

[0094]

[0095] The tanh function is a hyperbolic tangent function. During the discharge process, a discharge constraint factor K is used. dis (SOC) adjusts the virtual moment of inertia J and damping coefficient D. When SOC > 0.4, the battery is considered to have sufficient remaining charge. dis (SOC), the system maintains complete inertia support and damping support capabilities; when SOC∈[0,0.4], the battery gradually approaches the low charge range, K dis (SOC) decreases smoothly as SOC decreases, thereby gradually weakening the adjustment range of J and D, limiting the battery from continuing to discharge at high power, and avoiding over-discharge of the energy storage battery due to frequency support.

[0096] (2) Battery charging

[0097]

[0098] During the charging process, a charging constraint factor K is used. chg (SOC) adjusts the virtual moment of inertia J and damping coefficient D. When SOC < 0.8, the battery is considered to still have charging margin, K chg When SOC = 1, the system maintains full inertia and damping support capabilities; when SOC ∈ (0.8, 1], the battery gradually approaches full charge. chg The state of charge (SOC) decreases smoothly as the SOC increases, thereby reducing the charging power demand generated by the VSG control and avoiding the risk of overcharging caused by the energy storage battery continuing to charge at high power in the high SOC range.

[0099] (3) Final SOC constraint factor (with hysteresis stabilization):

[0100]

[0101] Final SOC constraint factor K soc Selection is based on the direction of battery power: When the system is in a discharging state, K is used. dis (SOC) performs over-discharge protection; when P bat When the value is greater than 0, the system is in a charging state, using K. chg (SOC) performs overcharge protection; when P bat When K = 0, the system is in standby or has no obvious charging / discharging state. soc =1, no additional constraints are imposed on J and D. Meanwhile, to avoid frequent control mode switching due to small fluctuations in SOC near the threshold, this invention sets up hysteresis judgment logic: discharge protection enters when SOC is below 0.40 and exits when it is above 0.42 (the third state of charge threshold); charging protection enters when SOC is above 0.80 and exits when it is below 0.78 (the fourth state of charge threshold), thereby improving the smoothness and stability of the control process. The specific judgment segment logic is as follows: Figure 4 As shown.

[0102] S3. Combining the grid angular frequency deviation, frequency change rate, and the corrected state of charge smoothing constraint factor, the virtual moment of inertia and damping coefficient of the virtual synchronous generator are adaptively calculated, and upper and lower limit amplitude constraints are set on the calculated virtual moment of inertia and damping coefficient to limit the virtual moment of inertia and damping coefficient within the preset safety range.

[0103] In S3, the adjustment of virtual rotational inertia is divided into two operating conditions. When the rate of change of frequency is less than or equal to the set critical value, or when the frequency is in the recovery stage, the virtual rotational inertia is the product of the reference virtual rotational inertia and the state of charge smoothing constraint factor. When the rate of change of frequency is greater than the set critical value, or when the frequency is in the deterioration stage, the virtual rotational inertia is adjusted incrementally based on the reference virtual rotational inertia and the rate of change of frequency, and then multiplied by the state of charge smoothing constraint factor to obtain the virtual rotational inertia.

[0104] For the adjustment of the damping coefficient, two operating conditions are distinguished. When the grid angular frequency deviation is less than or equal to the set deviation threshold, the damping coefficient is the product of the reference damping coefficient and the state of charge smoothing constraint factor. When the grid angular frequency deviation is greater than the set deviation threshold, the damping coefficient is adjusted incrementally based on the reference damping coefficient and the grid angular frequency deviation, and then multiplied by the state of charge smoothing constraint factor to obtain the damping coefficient.

[0105] Adaptive Formula for Virtual Moment of Inertia J

[0106]

[0107] Here, N represents the set critical value of the rate of change of frequency. Based on experimental experience, this value can be selected. When the frequency change rate is less than the critical value, or during the frequency recovery period, J0 remains constant. When the frequency deteriorates and the frequency change rate exceeds the critical value, J activates the adaptive mode, while also considering the protection factor K. soc The effect is used to obtain the final moment of inertia J.

[0108] Adaptive formula for damping coefficient D

[0109]

[0110] Here, M represents the set critical value for frequency deviation, which can be selected based on experimental experience. When the frequency deviation is small, D0 remains unchanged; when the frequency is greater than 0.1, D activates adaptive mode, while also considering the protection factor K. soc This process is used to obtain the final moment of inertia D.

[0111] Parameter settings: J0=0.2, D0=10, J limit [0.05,0.5], D limit [5,30].

[0112] Where J0 and D0 are the moment of inertia and damping coefficient K of the virtual synchronous generator during stable operation, respectively. j and K d These are the adjustment coefficients for the moment of inertia and the damping coefficient, respectively. Based on experimental experience, K can be taken as... j =0.2, K d =10.

[0113] S4. Substitute the virtual moment of inertia and damping coefficient of the virtual synchronous generator into the rotor motion equation of the virtual synchronous generator to generate the phase and voltage commands of the grid-type inverter, and drive the grid-type inverter to run after pulse width modulation.

[0114] In S4, the rotor motion equation is: the product of the virtual moment of inertia and the rate of change of frequency equals the difference between the mechanical power and the electromagnetic power divided by the rated angular frequency, and then the product of the damping coefficient and the deviation of the grid angular frequency is subtracted.

[0115] Substituting adaptive J and D into the rotor motion equation:

[0116]

[0117] The output phase signal is modulated by PWM to drive the grid-type inverter.

[0118] S5. During the operation of the grid-type inverter, the safety control logic is executed in real time by combining the state of charge range and charging and discharging state of the energy storage battery to flexibly constrain the output power of the grid-type inverter.

[0119] In S5, the safety control logic is as follows: when the energy storage battery is in a discharging state and the real-time state of charge is higher than the first state of charge threshold, the state of charge constraint factor is maximized, and the virtual moment of inertia and damping coefficient are output at full scale; when the energy storage battery is in a discharging state and the real-time state of charge is lower than or equal to the first state of charge threshold, the virtual moment of inertia and damping coefficient are rapidly reduced; when the energy storage battery is in a charging state and the real-time state of charge is lower than the second state of charge threshold, the state of charge constraint factor is maximized, and the virtual moment of inertia and damping coefficient are output at full scale; when the energy storage battery is in a charging state and the real-time state of charge is higher than or equal to the second state of charge threshold, the virtual moment of inertia and damping coefficient are gradually reduced; when the energy storage battery is in a standby state, no constraints are applied.

[0120] 1. Discharge state: When SOC∈(0.4,1], K SOC =1, J / D full-scale output; when SOC∈[0,0.4], J / D is rapidly reduced to forcibly limit discharge power and prevent over-discharge;

[0121] 2. Charging state: When SOC∈[0,0.8), K SOC =1, J / D full-scale output; when SOC∈[0.8,1], J / D decreases gradually to limit charging power and prevent overcharging;

[0122] 3. Standby mode: No constraints are applied, maintaining system response speed.

[0123] A network-type VSG inertia damping cooperative adaptive control system includes:

[0124] Data acquisition unit: Real-time acquisition of the energy storage battery's real-time state of charge, battery operating power, and grid angular frequency; Calculation of grid angular frequency deviation and frequency change rate based on the acquired grid angular frequency; Determination of the energy storage battery's current state of charging, discharging, or standby based on the battery operating power.

[0125] State of Charge (SCO) Smoothing Constraint Factor Calculation Unit: Based on the state of the energy storage battery, calculate the corresponding SCO smoothing constraint factor and introduce hysteresis control logic to correct the SCO smoothing constraint factor. When the energy storage battery is in the discharging state, calculate the discharging constraint factor based on the real-time SCO value. When the energy storage battery is in the charging state, calculate the charging constraint factor based on the real-time SCO value. When the energy storage battery is in the standby state, set the standby constraint factor to a fixed reference value.

[0126] Virtual moment of inertia and damping coefficient calculation unit: Combining the grid angular frequency deviation, frequency change rate and the corrected state of charge smoothing constraint factor, it adaptively calculates the virtual moment of inertia and damping coefficient of the virtual synchronous generator, and sets upper and lower limit amplitude constraints on the calculated virtual moment of inertia and damping coefficient to limit the virtual moment of inertia and damping coefficient within the preset safety range.

[0127] Control unit: Substitutes the virtual moment of inertia and damping coefficient of the virtual synchronous generator into the rotor motion equation of the virtual synchronous generator to generate the phase and voltage commands of the grid-type inverter, which are then driven to operate after pulse width modulation;

[0128] Safety control unit: During the operation of the grid-connected inverter, it executes safety control logic in real time by combining the state of charge range and charging / discharging state of the energy storage battery, and flexibly constrains the output power of the grid-connected inverter.

[0129] Smoothing function alternatives: Replacing the tanh function with the sigmoid function, exponential function, or Gaussian function can all achieve SOC smoothing constraints and achieve the same technical effect;

[0130] Alternative parameter optimization methods: Online optimization of K using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). SOC K j K d coefficients improve adaptive accuracy;

[0131] Alternative energy storage solutions: Replace lithium batteries with supercapacitors or vanadium redox flow batteries, only modifying the SOC safety range; the control logic remains the same.

[0132] Control structure alternative: Combine J / D cooperative adaptive control with fuzzy control and model predictive control (MPC) to further improve adaptability to complex operating conditions;

[0133] Threshold alternative: Replace the fixed threshold with an adaptive threshold, dynamically adjusting T based on grid strength (SCR). j T d .

[0134] Terminology Explanation:

[0135] Virtual Synchronous Generator (VSG): A smart inverter technology that simulates the operating characteristics of a traditional synchronous generator through power electronics and control algorithms. It reproduces the core characteristics of a synchronous generator, such as rotor motion equations and excitation regulation, through software algorithms, enabling new energy power generation equipment to have inertial response, damping characteristics, primary frequency regulation and voltage regulation capabilities, thereby enhancing grid stability.

[0136] Moment of inertia ( Moment of inertia is the ability of an object to resist changes in angular velocity. It reflects the magnitude of the inertia of a rotating system in maintaining its original rotational state. In power systems, the moment of inertia of synchronous generators is crucial to the stability of grid frequency and can mitigate frequency abrupt changes. Virtual synchronous generators simulate this characteristic through algorithms, thereby enhancing the stability of new energy grid connection.

[0137] Damping ( Damping is a mechanism by which a system suppresses oscillations by consuming energy, allowing the dynamic process to decay rapidly to a steady state. In power systems, damping can mitigate frequency or power fluctuations and prevent instability; virtual synchronous generators actively simulate this characteristic through control algorithms.

[0138] State of charge (SOC): A state quantity that represents the remaining capacity of an energy storage battery. Its value ranges from 0 to 1. It is a core indicator for measuring whether a battery can be safely charged and discharged and avoiding overcharging and over-discharging.

Claims

1. A network-type VSG inertia-damped cooperative adaptive control method, characterized in that: Including: S1. Real-time acquisition of the real-time state of charge of the energy storage battery, battery operating power, and grid angular frequency; calculation of grid angular frequency deviation and frequency change rate based on the acquired grid angular frequency; and determination of whether the energy storage battery is currently in charging, discharging, or standby state based on the battery operating power. S2. Based on the state of the energy storage battery, calculate the corresponding state of charge smoothing constraint factor and introduce hysteresis control logic to correct the state of charge smoothing constraint factor. When the energy storage battery is in the discharge state, calculate the discharge constraint factor based on the real-time state of charge value. When the energy storage battery is in the charging state, calculate the charging constraint factor based on the real-time state of charge value. When the energy storage battery is in the standby state, set the standby constraint factor to a fixed reference value. S3. Combining the grid angular frequency deviation, frequency change rate, and the corrected state of charge smoothing constraint factor, the virtual moment of inertia and damping coefficient of the virtual synchronous generator are adaptively calculated, and upper and lower limit amplitude constraints are set on the calculated virtual moment of inertia and damping coefficient to limit the virtual moment of inertia and damping coefficient within the preset safety range. S4. Substitute the virtual moment of inertia and damping coefficient of the virtual synchronous generator into the rotor motion equation of the virtual synchronous generator to generate the phase and voltage commands of the grid-type inverter, and drive the grid-type inverter to run after pulse width modulation. S5. During the operation of the grid-type inverter, the safety control logic is executed in real time by combining the state of charge range and charging and discharging state of the energy storage battery to flexibly constrain the output power of the grid-type inverter.

2. The method for coordinated adaptive control of inertia damping in a network-type VSG according to claim 1, characterized in that: In S1, the real-time state of charge of the energy storage battery is calculated by integrating the energy. The charging and discharging states are distinguished by the positive and negative values ​​of the battery operating power. When the battery operating power is greater than zero, it is determined to be in the charging state. When the battery operating power is less than zero, it is determined to be in the discharging state. When the battery operating power is equal to zero, it is determined to be in the standby state.

3. The method for coordinated adaptive control of inertia damping in a network-type VSG according to claim 1, characterized in that: In step S2, when the energy storage battery is in a discharging state, a first state of charge threshold is preset. Based on the first state of charge threshold, two state of charge intervals are divided. When the real-time state of charge of the energy storage battery is higher than the first state of charge threshold, the discharge constraint factor remains at a fixed value. When the real-time state of charge of the energy storage battery is lower than or equal to the first state of charge threshold, the discharge constraint factor decreases smoothly as the real-time state of charge decreases. When the energy storage battery is in a charging state, a second state of charge threshold is preset. Based on the second state of charge threshold, two state of charge intervals are divided. When the real-time state of charge of the energy storage battery is lower than the second state of charge threshold, the charging constraint factor remains at a fixed value. When the real-time state of charge of the energy storage battery is higher than or equal to the second state of charge threshold, the charging constraint factor decreases smoothly as the real-time state of charge increases.

4. The network-type VSG inertia damping cooperative adaptive control method according to claim 3, characterized in that: In S2, a discharge state hysteresis logic and a third state of charge threshold are set. When the real-time state of charge drops to the first state of charge threshold, the discharge state hysteresis logic is activated. When the real-time state of charge rises back to above the third state of charge threshold, the discharge state hysteresis logic is deactivated. At the same time, a charging state hysteresis logic and a fourth state of charge threshold are set. When the real-time state of charge rises to the second state of charge threshold, the charging state hysteresis logic is activated. When the real-time state of charge drops to below the fourth state of charge threshold, the charging state hysteresis logic is deactivated. The discharge constraint factor and the charging constraint factor are both calculated using a continuous smooth function.

5. The network-type VSG inertia damping cooperative adaptive control method according to claim 1, characterized in that: In S3, the adjustment of virtual rotational inertia is divided into two operating conditions. When the rate of change of frequency is less than or equal to the set critical value, or when the frequency is in the recovery stage, the virtual rotational inertia is the product of the reference virtual rotational inertia and the state of charge smoothing constraint factor. When the rate of change of frequency is greater than the set critical value, or when the frequency is in the deterioration stage, the virtual rotational inertia is adjusted incrementally based on the reference virtual rotational inertia and the rate of change of frequency, and then multiplied by the state of charge smoothing constraint factor to obtain the virtual rotational inertia. For the adjustment of the damping coefficient, two operating conditions are distinguished. When the grid angular frequency deviation is less than or equal to the set deviation threshold, the damping coefficient is the product of the reference damping coefficient and the state of charge smoothing constraint factor. When the grid angular frequency deviation is greater than the set deviation threshold, the damping coefficient is adjusted incrementally based on the reference damping coefficient and the grid angular frequency deviation, and then multiplied by the state of charge smoothing constraint factor to obtain the damping coefficient.

6. The network-type VSG inertia damping cooperative adaptive control method according to claim 1, characterized in that: In S4, the rotor motion equation is: the product of the virtual moment of inertia and the rate of change of frequency equals the difference between the mechanical power and the electromagnetic power divided by the rated angular frequency, and then the product of the damping coefficient and the deviation of the grid angular frequency is subtracted.

7. The method for coordinated adaptive control of inertia damping in a network-type VSG according to claim 3, characterized in that: In S5, the safety control logic is as follows: when the energy storage battery is in a discharging state and the real-time state of charge is higher than the first state of charge threshold, the state of charge constraint factor is maximized, and the virtual moment of inertia and damping coefficient are output at full scale; when the energy storage battery is in a discharging state and the real-time state of charge is lower than or equal to the first state of charge threshold, the virtual moment of inertia and damping coefficient are rapidly reduced; when the energy storage battery is in a charging state and the real-time state of charge is lower than the second state of charge threshold, the state of charge constraint factor is maximized, and the virtual moment of inertia and damping coefficient are output at full scale; when the energy storage battery is in a charging state and the real-time state of charge is higher than or equal to the second state of charge threshold, the virtual moment of inertia and damping coefficient are gradually reduced; when the energy storage battery is in a standby state, no constraints are applied.

8. A network-type VSG inertia-damped cooperative adaptive control system, characterized in that: Including: Data acquisition unit: Real-time acquisition of the energy storage battery's real-time state of charge, battery operating power, and grid angular frequency; Calculation of grid angular frequency deviation and frequency change rate based on the acquired grid angular frequency; Determination of the energy storage battery's current state of charging, discharging, or standby based on the battery operating power. State of Charge (SCO) Smoothing Constraint Factor Calculation Unit: Based on the state of the energy storage battery, calculate the corresponding SCO smoothing constraint factor and introduce hysteresis control logic to correct the SCO smoothing constraint factor. When the energy storage battery is in the discharging state, calculate the discharging constraint factor based on the real-time SCO value. When the energy storage battery is in the charging state, calculate the charging constraint factor based on the real-time SCO value. When the energy storage battery is in the standby state, set the standby constraint factor to a fixed reference value. Virtual moment of inertia and damping coefficient calculation unit: Combining the grid angular frequency deviation, frequency change rate and the corrected state of charge smoothing constraint factor, it adaptively calculates the virtual moment of inertia and damping coefficient of the virtual synchronous generator, and sets upper and lower limit amplitude constraints on the calculated virtual moment of inertia and damping coefficient to limit the virtual moment of inertia and damping coefficient within the preset safety range. Control unit: Substitutes the virtual moment of inertia and damping coefficient of the virtual synchronous generator into the rotor motion equation of the virtual synchronous generator to generate the phase and voltage commands of the grid-type inverter, which are then driven to operate after pulse width modulation; Safety control unit: During the operation of the grid-connected inverter, it executes safety control logic in real time by combining the state of charge range and charging / discharging state of the energy storage battery, and flexibly constrains the output power of the grid-connected inverter.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the network-type VSG inertia damping cooperative adaptive control method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the network-type VSG inertia damping cooperative adaptive control method according to any one of claims 1-7.

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