Self-adaptive stability control method and system for wide short-circuit ratio of constructed network type energy storage

Abstract

The invention provides a network construction type energy storage wide short circuit ratio adaptive stability control method and system, and the method comprises the steps: carrying out the calculation based on a power grid energy storage parameter, power grid impedance and energy storage impedance, and obtaining a dynamic short circuit ratio; according to the dynamic short-circuit ratio, switching the power grid operation condition mode to a power grid operation condition mode corresponding to the dynamic short-circuit ratio; in a power grid operation working condition mode, obtaining optimized virtual inductance resistance parameters or frequency-variable admittance regulator parameters; a corresponding control instruction is generated according to the virtual inductance resistance parameters or the frequency-variable admittance regulator parameters, and self-adaptive stability control is completed; real-time identification and dynamic adjustment of the power grid impedance and the energy storage impedance are achieved, traditional fixed parameter control limitation is broken through, the dynamic short-circuit ratio is obtained through calculation, the problem of power angle instability under the wide short-circuit ratio is solved, corresponding switching of power grid operation working condition modes can be conducted based on the dynamic short-circuit ratio, differential control requirements of a weak power grid and a strong power grid are considered, and the method is suitable for large-scale popularization and application. And the full-working-condition adaptive capacity is improved.

Description

Technical Field

[0001] The patent application of this invention belongs to the technical field of power system stability control, and specifically relates to a grid-type energy storage wide short-circuit ratio adaptive stability control method and system. Background Art

[0002] With the large-scale integration of renewable energy into the power system, grid stability and reliability are facing challenges. Grid-connected energy storage, as a key technology for improving power system stability, has gradually attracted widespread attention. However, in actual operation, the uncertainty of renewable energy output and the fluctuations in load that cause changes in grid strength pose numerous challenges to the stable operation of grid-connected energy storage systems.

[0003] Under weak grid conditions and high power angles, a significant dynamic coupling effect exists between the active and reactive power of grid-connected energy storage systems, which can easily trigger synchronous frequency resonance. The relationship between power angle changes, impedance ratio changes, and the dynamic coupling coefficient clearly shows that reactive coupling significantly reduces the system's stability margin. Under strong grid conditions, the voltage control of grid-connected energy storage introduces a capacitive negative damping region. As the grid short-circuit ratio continues to increase, the system's stability margin continues to decline. Furthermore, grid impedance interaction can lead to subsynchronous oscillations, seriously threatening the safe and stable operation of the power system.

[0004] Existing impedance reshaping methods, such as the virtual impedance method and the virtual admittance method, can increase the equivalent output impedance of the converter to a certain extent. The virtual impedance method introduces the voltage components of virtual resistance and virtual inductance on the voltage loop reference voltage, and the virtual admittance method replaces the voltage control link with virtual admittance to achieve the conversion of voltage reference value to current reference value. However, most of these methods use constant parameters or fixed control frameworks, which are difficult to adapt to complex and variable wide short-circuit ratio conditions. In addition, the powerful impedance regulation potential of grid-type energy storage itself has not yet been fully explored, resulting in its role in the stability control of the power system not being fully utilized. Therefore, there is an urgent need to develop a wide short-circuit ratio adaptive stability control method and system for grid-type energy storage. Summary of the Invention

[0005] In order to overcome the above-mentioned deficiencies of the prior art, the present invention patent application proposes a method for adaptive stabilization control of a wide short-circuit ratio of a grid-type energy storage, comprising:

[0006] Based on the grid energy storage parameters, grid impedance and energy storage impedance, the dynamic short circuit ratio is calculated using the dynamic short circuit ratio calculation formula;

[0007] According to the dynamic short-circuit ratio, the power grid operation mode is switched to the power grid operation mode corresponding to the dynamic short-circuit ratio;

[0008] Under the grid operation mode, a dynamic impedance matching algorithm is used to obtain optimized virtual inductor and resistor parameters or frequency-variable admittance regulator parameters;

[0009] According to the optimized virtual inductor resistance parameters or the frequency-variable admittance regulator parameters, corresponding control instructions are generated, and based on the control instructions, an execution control strategy is adopted to complete adaptive stable control.

[0010] Preferably, the dynamic short-circuit ratio is calculated based on the grid energy storage parameters, the grid impedance and the energy storage impedance using a dynamic short-circuit ratio calculation formula, including:

[0011] Based on the grid energy storage parameters, the grid impedance and energy storage impedance are calculated respectively by using a pre-built grid impedance model and energy storage impedance model;

[0012] Calculating a dynamic short circuit ratio using a dynamic short circuit ratio calculation formula based on the grid impedance, the energy storage impedance, and the grid energy storage parameter;

[0013] Among them, the grid energy storage parameters include one or more of the following: effective value of grid voltage, dq axis voltage of grid-type energy storage, current signal, voltage signal, actual grid frequency, grid phase and energy storage system state parameters; the grid impedance model is constructed by performing Fourier analysis on the grid sample voltage and current signals to extract harmonic components of different frequency bands; the energy storage impedance model is constructed by performing Fourier analysis on the energy storage sample voltage and current signals to extract harmonic components of different frequency bands.

[0014] Preferably, the calculation formula of the dynamic short-circuit ratio is as follows:

[0015]

[0016] Among them, SCR is the dynamic short circuit ratio, V g is the effective value of the grid voltage, Z ess is the energy storage impedance, V dq is the dq axis voltage of the grid-type energy storage, Z g is the grid impedance.

[0017] Preferably, switching the grid operation mode to the grid operation mode corresponding to the dynamic short-circuit ratio according to the dynamic short-circuit ratio includes:

[0018] When the dynamic short-circuit ratio is less than a preset dynamic short-circuit threshold, switching the grid-type energy storage to enter the weak grid mode;

[0019] When the dynamic short-circuit ratio is greater than or equal to the dynamic short-circuit threshold, the grid-forming energy storage is switched to enter the strong grid mode.

[0020] Preferably, the dynamic impedance matching algorithm includes a fuzzy logic algorithm and an intelligent algorithm; in the power grid operation mode, the dynamic impedance matching algorithm is used to obtain optimized virtual inductance and resistance parameters or frequency-variable admittance regulator parameters, including:

[0021] When the grid-type energy storage enters the weak grid mode, the input variables of the fuzzy logic controller are calculated based on the grid energy storage parameters, the superimposed converter output voltage command and the rated grid frequency;

[0022] Based on the input variables, the fuzzy logic controller is used to perform fuzzification to calculate the adjustment coefficient of the virtual inductor and resistor;

[0023] According to the adjustment coefficient of the virtual inductor and resistor, the virtual inductor and resistor parameters are optimized using an optimized virtual inductor and resistor parameter calculation formula to obtain optimized virtual inductor and resistor parameters;

[0024] When the grid-type energy storage enters the strong grid mode, a current deviation signal is calculated based on the grid energy storage parameters through the frequency-variable admittance regulator;

[0025] Adopting an intelligent algorithm, based on the current deviation signal and the grid energy storage parameter, optimizing the proportional coefficient, integral coefficient and differential coefficient of the frequency-variable admittance regulator, and obtaining the optimized proportional coefficient, integral coefficient and differential coefficient as the optimized frequency-variable admittance regulator parameters;

[0026] The superimposed converter output voltage command is obtained by superimposing virtual inductance and resistance parameters based on the set converter output voltage command; the input variables include one or more of the following: voltage amplitude deviation, frequency deviation, and frequency deviation change rate; the virtual inductance and resistance parameters include: virtual inductance value and virtual resistance value; the virtual inductance and resistance adjustment coefficients include: virtual inductance adjustment coefficient and virtual resistance adjustment coefficient; and the intelligent algorithm includes a particle swarm optimization algorithm.

[0027] Preferably, the optimized virtual inductor and resistance parameter calculation formula is as follows:

[0028]

[0029] in, is the optimized virtual inductance value, k L is the adjustment coefficient of virtual inductance, is the virtual inductance value before optimization, is the optimized virtual resistance value, k R Adjustment coefficient of virtual resistance, is the virtual resistance value before optimization.

[0030] Preferably, generating corresponding control instructions according to the optimization parameters, and implementing the adaptive stability control by adopting the execution control strategy based on the control instructions, includes:

[0031] generating compensation voltage and current instructions based on the optimized virtual inductor and resistance parameters;

[0032] generating a reverse damping instruction based on the optimized frequency-variable admittance regulator parameters;

[0033] The compensation voltage and current command or the reverse damping command is output to a multi-channel pulse width modulation (PWM) modulator after power limiting to complete adaptive stability control.

[0034] Preferably, generating corresponding control instructions according to the optimized virtual inductance and resistance parameters or the frequency-variable admittance regulator parameters, and executing a control strategy based on the control instructions to complete the adaptive stability control further includes:

[0035] Based on the grid energy storage parameters, Lyapunov is used to perform stability correction on the pre-built digital twin model to ensure that the derivative of the energy function is less than 0;

[0036] When the derivative of the energy function is less than 0, hierarchical intelligent protection monitoring is completed according to the voltage amplitude deviation and the frequency deviation;

[0037] The digital twin model is constructed based on the grid impedance and energy storage impedance.

[0038] Preferably, the expression of the energy function is as follows:

[0039] V(x)=x T Px

[0040] Among them, V(x) is the energy function, x T is the transpose of the state variable, P is the system matrix, and x is the state variable.

[0041] Based on the same inventive concept, the present invention patent application also provides a grid-type energy storage wide short-circuit ratio adaptive stability control system, including: a dynamic short-circuit ratio calculation module, a grid operating condition switching module, an optimization parameter module and an adaptive stability control module;

[0042] The dynamic short circuit ratio calculation module is used to calculate the dynamic short circuit ratio based on the grid energy storage parameters, the grid impedance and the energy storage impedance using a dynamic short circuit ratio calculation formula;

[0043] The grid operating condition switching module is used to switch the grid operating condition mode to the grid operating condition mode corresponding to the dynamic short circuit ratio according to the dynamic short circuit ratio;

[0044] The parameter optimization module is used to obtain optimized virtual inductance and resistance parameters or frequency-variable admittance regulator parameters by using a dynamic impedance matching algorithm under the power grid operation mode;

[0045] The adaptive stability control module is used to generate corresponding control instructions according to the optimized virtual inductance and resistance parameters or the frequency-variable admittance regulator parameters, and to implement adaptive stability control based on the control instructions and an execution control strategy.

[0046] Preferably, the dynamic short-circuit ratio calculation module is specifically used to:

[0047] Based on the grid energy storage parameters, the grid impedance and energy storage impedance are calculated respectively by using a pre-built grid impedance model and energy storage impedance model;

[0048] Calculating a dynamic short circuit ratio using a dynamic short circuit ratio calculation formula based on the grid impedance, the energy storage impedance, and the grid energy storage parameter;

[0049] Among them, the grid energy storage parameters include one or more of the following: effective value of grid voltage, dq axis voltage of grid-type energy storage, current signal, voltage signal, actual grid frequency, grid phase and energy storage system state parameters; the grid impedance model is constructed by performing Fourier analysis on the grid sample voltage and current signals to extract harmonic components of different frequency bands; the energy storage impedance model is constructed by performing Fourier analysis on the energy storage sample voltage and current signals to extract harmonic components of different frequency bands.

[0050] Preferably, the calculation formula of the dynamic short-circuit ratio is as follows:

[0051]

[0052] Among them, SCR is the dynamic short circuit ratio, V g is the effective value of the grid voltage, Z ess is the energy storage impedance, V dq is the dq axis voltage of the grid-type energy storage, Z g is the grid impedance.

[0053] Preferably, the power grid operating condition switching module is specifically used to:

[0054] When the dynamic short-circuit ratio is less than a preset dynamic short-circuit threshold, switching the grid-type energy storage to enter the weak grid mode;

[0055] When the dynamic short-circuit ratio is greater than or equal to the dynamic short-circuit threshold, the grid-forming energy storage is switched to enter the strong grid mode.

[0056] Preferably, the optimization parameter module is specifically used to:

[0057] When the grid-type energy storage enters the weak grid mode, the input variables of the fuzzy logic controller are calculated based on the grid energy storage parameters, the superimposed converter output voltage command and the rated grid frequency;

[0058] Based on the input variables, the fuzzy logic controller is used to perform fuzzification to calculate the adjustment coefficient of the virtual inductor and resistor;

[0059] According to the adjustment coefficient of the virtual inductor and resistor, the virtual inductor and resistor parameters are optimized using an optimized virtual inductor and resistor parameter calculation formula to obtain optimized virtual inductor and resistor parameters;

[0060] When the grid-type energy storage enters the strong grid mode, a current deviation signal is calculated based on the grid energy storage parameters through the frequency-variable admittance regulator;

[0061] Adopting an intelligent algorithm, based on the current deviation signal and the grid energy storage parameter, optimizing the proportional coefficient, integral coefficient and differential coefficient of the frequency-variable admittance regulator, and obtaining the optimized proportional coefficient, integral coefficient and differential coefficient as the optimized frequency-variable admittance regulator parameters;

[0062] The superimposed converter output voltage command is obtained by superimposing virtual inductance and resistance parameters based on the set converter output voltage command; the input variables include one or more of the following: voltage amplitude deviation, frequency deviation, and frequency deviation change rate; the virtual inductance and resistance parameters include: virtual inductance value and virtual resistance value; the virtual inductance and resistance adjustment coefficients include: virtual inductance adjustment coefficient and virtual resistance adjustment coefficient; and the intelligent algorithm includes a particle swarm optimization algorithm.

[0063] Preferably, the optimized virtual inductor and resistance parameter calculation formula is as follows:

[0064]

[0065] in, is the optimized virtual inductance value, k L is the adjustment coefficient of virtual inductance, is the virtual inductance value before optimization, is the optimized virtual resistance value, k R Adjustment coefficient of virtual resistance, is the virtual resistance value before optimization.

[0066] Preferably, the adaptive stability control module is specifically used to:

[0067] generating compensation voltage and current instructions based on the optimized virtual inductor and resistance parameters;

[0068] generating a reverse damping instruction based on the optimized frequency-variable admittance regulator parameters;

[0069] The compensation voltage and current command or the reverse damping command is output to a multi-channel pulse width modulation (PWM) modulator after power limiting to complete adaptive stability control.

[0070] Preferably, the system further comprises: a monitoring module; the monitoring module is configured to:

[0071] Based on the grid energy storage parameters, Lyapunov is used to perform stability correction on the pre-built digital twin model to ensure that the derivative of the energy function is less than 0;

[0072] When the derivative of the energy function is less than 0, hierarchical intelligent protection monitoring is completed according to the voltage amplitude deviation and the frequency deviation;

[0073] The digital twin model is constructed based on the grid impedance and energy storage impedance.

[0074] Preferably, the expression of the energy function is as follows:

[0075] V(x)=x T Px

[0076] Among them, V(x) is the energy function, x T is the transpose of the state variable, P is the system matrix, and x is the state variable.

[0077] Based on the same inventive concept, the present invention patent application also provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0078] The memory is used to store one or more programs;

[0079] When the one or more programs are executed by the at least one processor, the aforementioned grid-type energy storage wide short-circuit ratio adaptive stability control method is implemented.

[0080] Based on the same inventive concept, the patent application of the present invention also provides a readable storage medium on which a computer program and an execution program are stored. When the execution program is executed, a grid-type energy storage wide short-circuit ratio adaptive stability control method as described above is implemented.

[0081] Compared with the closest prior art, the patent application of this invention has the following beneficial effects:

[0082] The patent application of the present invention provides a method and system for adaptive stability control of a grid-type energy storage with a wide short-circuit ratio, including: based on the grid energy storage parameters, the grid impedance and the energy storage impedance, a dynamic short-circuit ratio calculation formula is used to calculate the dynamic short-circuit ratio; according to the dynamic short-circuit ratio, the grid operation mode is switched to the grid operation mode corresponding to the dynamic short-circuit ratio; under the grid operation mode, a dynamic impedance matching algorithm is used to obtain optimized virtual inductance and resistance parameters or frequency-variable admittance regulator parameters; according to the optimized virtual inductance and resistance parameters or the frequency-variable admittance regulator parameters, corresponding control instructions are generated, and based on the control instructions, an execution control strategy is adopted to complete adaptive stability control; the present invention is based on real-time identification of grid energy storage parameters and dynamic adjustment of grid impedance and energy storage impedance, breaking through the limitations of traditional fixed parameter control, calculating the dynamic short-circuit ratio, solving the problem of power angle instability under a wide short-circuit ratio, and based on the dynamic short-circuit ratio, corresponding switching of the grid operation mode can be performed, taking into account the differentiated control requirements of weak grids and strong grids, and improving the adaptability to all working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 A flow chart of a method for adaptive stabilization control of a wide short-circuit ratio of grid-type energy storage provided in the patent application of this invention;

[0084] Figure 2 A specific flow chart of the grid-type energy storage wide short-circuit ratio adaptive stability control method provided in the patent application of this invention;

[0085] Figure 3 A schematic diagram of a grid-type energy storage wide short-circuit ratio adaptive stability control system provided in the patent application of this invention;

[0086] Figure 4 A schematic diagram of the structure of an electronic device provided for the patent application of this invention. DETAILED DESCRIPTION

[0087] The specific implementation methods of the patent application of the present invention are further described in detail below with reference to the accompanying drawings.

[0088] Example 1:

[0089] The invention patent application provides a method for adaptive stabilization control of wide short-circuit ratio of network energy storage. Figure 1 Shown, including:

[0090] Step 1: Based on the grid energy storage parameters, grid impedance and energy storage impedance, the dynamic short-circuit ratio is calculated using the dynamic short-circuit ratio calculation formula;

[0091] Step 2: According to the dynamic short-circuit ratio, the grid operation mode is switched to the grid operation mode corresponding to the dynamic short-circuit ratio;

[0092] Step 3: Under the grid operation mode, a dynamic impedance matching algorithm is used to obtain optimized virtual inductor and resistor parameters or frequency-variable admittance regulator parameters;

[0093] Step 4: Generate corresponding control instructions according to the optimized virtual inductor and resistor parameters or the frequency-variable admittance regulator parameters, and implement the adaptive stability control based on the control instructions using the execution control strategy.

[0094] In one implementation, the above step 1 uses a dynamic short-circuit ratio calculation formula based on the grid energy storage parameters, the grid impedance, and the energy storage impedance to calculate the dynamic short-circuit ratio, including:

[0095] Based on the grid energy storage parameters, the grid impedance and energy storage impedance are calculated respectively by using a pre-built grid impedance model and energy storage impedance model;

[0096] For example, the grid voltage, current, frequency and energy storage system state parameters are collected in real time, and the grid impedance and energy storage impedance are calculated using a pre-built grid impedance model and energy storage impedance model. Establishing the grid impedance model and energy storage impedance model specifically includes the following steps:

[0097] High-precision voltage / current sensors are used to collect the three-phase voltage, current, frequency, and phase parameters of the power grid in real time, and the power grid frequency is obtained in real time through a phase-locked loop (PLL).

[0098] Fourier analysis is performed on the pre-collected sample voltage and current signals to extract the harmonic components of different frequency bands. The grid impedance model and energy storage impedance model are established respectively. The sample voltage and current signals include the grid sample voltage and current signals and the energy storage sample voltage and current signals. The mathematical expression of the grid impedance model is as follows:

[0099]

[0100] Among them, Z g is the grid impedance, R g is the grid resistive component, j is an imaginary number, ω is the grid angular frequency, L g is the inductive component of the power grid, C g is the capacitive component of the power grid;

[0101] The mathematical expression of the energy storage impedance model is as follows:

[0102] Z ess =R ess +jωL ess +K PQ V dc

[0103] Among them, Zess is the energy storage impedance, R ess is the resistive component of the grid-type energy storage, j is an imaginary number, ω is the grid angular frequency, L ess is the inductive component of the network energy storage, V dc is the voltage of the grid-type energy storage battery, K PQ is the active-reactive coupling coefficient.

[0104] Calculating a dynamic short circuit ratio using a dynamic short circuit ratio calculation formula based on the grid impedance, the energy storage impedance, and the grid energy storage parameter;

[0105] Among them, the grid energy storage parameters include one or more of the following: effective value of grid voltage, grid-type energy storage dq axis voltage, current signal, voltage signal, actual grid frequency, grid phase and energy storage system state parameters; the grid impedance model is constructed by performing Fourier analysis on the grid sample voltage and current signals to extract harmonic components of different frequency bands; the energy storage impedance model is constructed by performing Fourier analysis on the energy storage sample voltage and current signals to extract harmonic components of different frequency bands; based on the constructed grid impedance model and energy storage impedance model, the present invention can dynamically adjust the virtual impedance parameters in a timely manner, breaking through the limitations of traditional fixed parameter control, and on this basis, quickly complete the calculation of the dynamic short-circuit ratio.

[0106] In one implementation, the dynamic short-circuit ratio is calculated as follows:

[0107]

[0108] Among them, SCR is the dynamic short circuit ratio, V g is the effective value of the grid voltage, Z ess is the energy storage impedance, V dq is the dq axis voltage of the grid-type energy storage, Z g is the grid impedance.

[0109] In one implementation, the step 2 above of switching the grid operation mode to the grid operation mode corresponding to the dynamic short-circuit ratio according to the dynamic short-circuit ratio includes:

[0110] When the dynamic short-circuit ratio is less than a preset dynamic short-circuit threshold, switching the grid-type energy storage to enter the weak grid mode;

[0111] When the dynamic short-circuit ratio is greater than or equal to the dynamic short-circuit threshold, the grid-forming energy storage is switched to enter the strong grid mode.

[0112] For example, the operating mode is judged based on the dynamic short circuit ratio (SCR) value to determine the grid strength. If SCR < 3, it enters the weak grid mode, and SCR ≥ 3, it enters the strong grid mode. The details are as follows:

[0113] The threshold is judged based on the dynamic short-circuit ratio. When SCR < 3, the grid-connected energy storage enters the weak grid mode, triggering virtual impedance compensation. When SCR ≥ 3, the grid-connected energy storage enters the strong grid mode, triggering frequency-variable admittance adjustment. The lower limit S1 and upper limit S2 of the SCR control bandwidth are set. When S1 ≤ SCR ≤ S2, no parameter adjustment is performed. The mode switching logic of the present invention is based on the dynamic short-circuit ratio and the dynamic short-circuit threshold, taking into account the differentiated control requirements of weak and strong grids, and improving the adaptability to all working conditions.

[0114] In one implementation, the dynamic impedance matching algorithm includes a fuzzy logic algorithm and an intelligent algorithm. In step 3 above, in the power grid operation mode, the dynamic impedance matching algorithm is used to obtain optimized virtual inductance and resistance parameters or frequency-variable admittance regulator parameters, including:

[0115] When the grid-type energy storage enters the weak grid mode, the input variables of the fuzzy logic controller are calculated based on the grid energy storage parameters, the superimposed converter output voltage command and the rated grid frequency;

[0116] Based on the input variables, the fuzzy logic controller is used to perform fuzzification to calculate the adjustment coefficient of the virtual inductor and resistor;

[0117] According to the adjustment coefficient of the virtual inductor and resistor, the virtual inductor and resistor parameters are optimized using an optimized virtual inductor and resistor parameter calculation formula to obtain optimized virtual inductor and resistor parameters;

[0118] For example, the dynamic impedance matching algorithm is executed. In the weak grid mode, the virtual inductor / resistor parameters are optimized by the fuzzy logic algorithm as follows:

[0119] (1) Introducing virtual inductance / resistance components, in the dq rotating coordinate system, the voltage drop V generated by the virtual inductance Lv The calculation formula is as follows:

[0120] V Lv =jωL v I dq

[0121] Among them, V Lv is the voltage drop caused by virtual inductance, j is an imaginary number, ω is the grid angular frequency, L v is the virtual inductance value, I dq is the dq axis current of the network energy storage;

[0122] The voltage drop V generated by the virtual resistor Rv The calculation formula is as follows:

[0123] V Rv =R v I dq

[0124] Among them, V Rv is the voltage drop caused by the virtual resistor, R v is the virtual resistance value, I dq is the dq axis current of the grid-type energy storage.

[0125] By software programming, virtual inductance / resistance control is added to the grid-type energy storage converter control program, and the calculated V Lv With V Rv Superimposed on the converter output voltage command V dq * In the superposition, V dqnew The calculation formula is as follows:

[0126] V dqnew =V dq * +V Lv +V Rv

[0127] Among them, V dqnew is the output voltage command of the converter after superposition, V dq * is the output voltage command of the converter before superposition, V Lv is the voltage drop caused by the virtual inductor, V Rv is the voltage drop caused by the virtual resistor;

[0128] Thereby changing the equivalent output impedance of the energy storage system.

[0129] (2) Initialization of fuzzy logic algorithm parameters, for input variables grid voltage amplitude deviation ΔV, frequency deviation Δf and their change rate Quantify it to the fuzzy domain, and the quantification formula is:

[0130]

[0131] Among them, x is the actual input variable value, x min is the lower limit of the actual input variable value range, x max is the upper limit of the range of the actual input variable value, n is the quantization level, x q is the actual input variable value after quantization. The adjustment coefficient of the output variable virtual inductance / resistance is also quantized in this way. After the fuzzy rule table is established, the proportional factor K of the fuzzy logic controller is initialized. p , quantization factor Ke and other parameters.

[0132] (3) Real-time data acquisition and processing, the collected voltage signal and current signal coordinates are transformed into the dq rotating coordinate system to obtain V dq , I dq Calculate the voltage amplitude deviation, frequency deviation and frequency deviation change rate; the voltage amplitude deviation calculation formula is as follows:

[0133] ΔV=V dqnew -V dq

[0134] Where ΔV is the voltage amplitude deviation, V dqnew is the output voltage command of the converter after superposition, V dq is the dq axis voltage of the grid-type energy storage.

[0135] Frequency deviation calculation formula:

[0136] Δf=f * -f

[0137] Where Δf is the frequency deviation, f * is the rated frequency, f is the actual frequency;

[0138] The frequency deviation change rate is calculated as follows:

[0139]

[0140] in, is the frequency deviation change rate, Δf(k) is the actual frequency at time k, Δf(k-1) is the actual frequency at time k-1, T s is the sampling period, which serves as the input of the fuzzy logic controller.

[0141] (4) Fuzzy logic algorithm optimizes parameters, sends the quantized input data to the fuzzy logic controller, performs fuzzy reasoning through fuzzification, uses the center of gravity method to defuzzify, and calculates the adjustment coefficient k of the virtual inductance / resistance L 、k R ;

[0142] (5) Control effect feedback and iteration, calculation of the total harmonic distortion (THD) of the energy storage system output current I :

[0143]

[0144] Among them, THD I is the total harmonic distortion rate, I h is the effective value of the hth harmonic current, and I1 is the effective value of the fundamental current.

[0145] Calculate the current ripple ΔIripple :

[0146] ΔI ripple =I max -I min

[0147] Among them, ΔI ripple is the current ripple, I max is the maximum current within a sampling period, I min It is the minimum value of the current in one sampling period.

[0148] If THD I or ΔI ripple If the expected indicators are not achieved, readjust the parameters of the fuzzy logic controller, such as the proportional factor and quantization factor, and repeat the above steps until the system operates stably.

[0149] When the grid-type energy storage enters the strong grid mode, a current deviation signal is calculated based on the grid energy storage parameters through the frequency-variable admittance regulator;

[0150] Adopting an intelligent algorithm, based on the current deviation signal and the grid energy storage parameter, optimizing the proportional coefficient, integral coefficient and differential coefficient of the frequency-variable admittance regulator, and obtaining the optimized proportional coefficient, integral coefficient and differential coefficient as the optimized frequency-variable admittance regulator parameters;

[0151] For example, in the strong power grid mode, the intelligent algorithm is used to optimize the frequency-variable admittance regulator parameters, as follows:

[0152] (1) Design a frequency-variable admittance regulator based on the transfer function. Assume that the transfer function of the frequency-variable admittance regulator is:

[0153]

[0154] Among them, G(s) is the transfer function, K p is the proportionality coefficient, K i is the integral coefficient, K d is the differential coefficient, s is the Laplace operator; according to the frequency characteristics of the power grid and the dynamic response requirements of the energy storage system, the initial parameter K is set p0 , K i0 , K d0 .

[0155] (2) Initialization of intelligent algorithm. Taking the particle swarm optimization algorithm as an example, the population size N is initialized and the position vector X of each particle is initialized. i :

[0156] X i =[K pi ,K ii ,K di ]

[0157] Among them, X i is the position vector of each particle, K pi is the proportional coefficient of the i-th particle, K ii is the integral coefficient of the i-th particle, K di is the differential coefficient of the i-th particle; (i=1,2,…,N).

[0158] Set the number of iterations M, learning factors c1, c2. Define the optimization objective function J:

[0159]

[0160] Among them, J is the optimization objective function, ω1, ω2, ω3 are weight coefficients, ΔP k is the deviation of active power at the kth sampling moment, ΔQ k is the deviation of reactive power at the kth sampling moment, N s is the number of sampling points, ξ is the actual damping ratio of the system, ref is the reference damping ratio.

[0161] (3) Calculation and feedback of reverse damping current, based on the dq axis voltage V of the grid-type energy storage dq , grid-type energy storage dq axis current I dq Real-time data, calculate the reverse damping current command through the frequency-variable admittance regulator

[0162]

[0163] in, is the reverse damping current command, G(s) is the transfer function, V dq is the dq axis voltage of the grid-type energy storage, is the voltage command value;

[0164] The calculated reverse damping current With the grid-type energy storage dq axis current I dq By comparison, we can get the current deviation signal ΔI:

[0165]

[0166] Among them, ΔI is the current deviation signal, is the reverse damping current, I dq is the dq axis current of the network energy storage;

[0167] (4) The intelligent algorithm optimizes the parameters, taking the current deviation signal ΔI, grid frequency f and other information as the input of the particle swarm optimization algorithm to update the speed and position of the particles:

[0168] Vi (t+1)=λV i (t)+r1(gbest i (t)-X i (t))+r2(gbest(t)-X i (t))

[0169] Among them, V i (t+1) is the velocity of the i-th particle at time t+1, λ is the inertia weight, V i (t) is the velocity of the i-th particle at the moment, r1r2 is a random number between [0,1], gbest i (t) is the individual optimal position of the i-th particle at time t, gbest(t) is the global optimal position at time t, X i (t) is the position of the i-th particle at the moment;

[0170] The particle position is calculated as follows:

[0171] X i (t+1)=X i (t)+V i (t+1)

[0172] Among them, X i (t+1) is the position of the i-th particle at time t+1, X i (t) is the position of the i-th particle at the moment, V i (t+1) is the velocity of the i-th particle at time t+1.

[0173] After each iteration, the performance of the system under the current parameters is evaluated according to the optimization objective function, and the individual optimum and global optimum are updated until the set number of iterations is reached or the convergence condition is met.

[0174] (5) Parameter update and system operation, the optimal parameters obtained by the particle swarm optimization algorithm are updated Update to the frequency-variable admittance regulator so that the frequency-variable admittance regulator outputs a more reasonable reverse damping current instruction, suppresses system oscillation, and improves the dynamic performance and stability of the energy storage system under a strong power grid.

[0175] Among them, the superimposed converter output voltage instruction is obtained by superimposing virtual inductance and resistance parameters based on the set converter output voltage instruction; the input variables include one or more of the following: voltage amplitude deviation, frequency deviation and frequency deviation change rate; the virtual inductance and resistance parameters include: virtual inductance value and virtual resistance value; the adjustment coefficient of the virtual inductance and resistance includes: virtual inductance adjustment coefficient and virtual resistance adjustment coefficient; the intelligent algorithm includes a particle swarm optimization algorithm; the present invention introduces a fuzzy logic algorithm to dynamically optimize the virtual inductance and resistance parameters, which significantly improves the adaptability and stability of the grid-type energy storage in the weak grid mode; based on the grid energy storage parameters and the current deviation signal, the present invention uses an intelligent algorithm to dynamically adjust the proportional, integral and differential coefficients, breaking through the adjustment lag and steady-state error problems caused by the solidification of traditional PID (proportional integral derivative) control parameters;

[0176] In one implementation, the optimized virtual inductor and resistance parameter calculation formula is as follows:

[0177]

[0178] in, is the optimized virtual inductance value, k L is the adjustment coefficient of virtual inductance, is the virtual inductance value before optimization, is the optimized virtual resistance value, k R Adjustment coefficient of virtual resistance, is the virtual resistance value before optimization.

[0179] In one implementation, the step 4 generates corresponding control instructions according to the optimization parameters, and executes a control strategy based on the control instructions to complete the adaptive stability control, including:

[0180] generating compensation voltage and current instructions based on the optimized virtual inductor and resistance parameters;

[0181] generating a reverse damping instruction based on the optimized frequency-variable admittance regulator parameters;

[0182] The compensation voltage and current command or the reverse damping command is output to a multi-channel pulse width modulation (PWM) modulator after power limiting to complete adaptive stability control.

[0183] For example, a differentiated impedance reshaping strategy is implemented based on the grid mode, and the algorithm output is converted into an executable PWM control signal. The FPGA (Field Programmable Gate Array) hardware quickly generates compensation voltage / current instructions or reverse damping instructions, which are embedded in the power limiting module and then output to the multi-channel PWM modulator.

[0184] The power limiting mode is:

[0185]

[0186] in, is the maximum current of the grid-type energy storage, I rated is the rated current of the grid-type energy storage, P max is the maximum power of grid-type energy storage, V dc is the voltage of the grid-type energy storage battery.

[0187] In one implementation, after generating corresponding control instructions based on the optimized virtual inductance and resistance parameters or the frequency-variable admittance regulator parameters, and executing a control strategy based on the control instructions to complete the adaptive stability control, the method further includes:

[0188] Based on the grid energy storage parameters, Lyapunov is used to perform stability correction on the pre-built digital twin model to ensure that the derivative of the energy function is less than 0;

[0189] When the derivative of the energy function is less than 0, hierarchical intelligent protection monitoring is completed according to the voltage amplitude deviation and the frequency deviation;

[0190] The digital twin model is constructed based on the grid impedance and energy storage impedance.

[0191] For example, during operation, grid fluctuations and energy storage system status are continuously monitored, stability is pre-verified through the digital twin model, and parameters are corrected using the Lyapunov criterion, as follows:

[0192] (1) Real-time monitoring through multi-source data fusion, continuously collecting key parameters such as grid voltage, current, frequency, and energy storage system battery terminal voltage, charge and discharge current, state of charge (SOC), temperature, and power converter output power;

[0193] (2) Dynamic deduction and risk prediction of digital twin model, based on the grid impedance Z g and storage impedance Z ess Parameters are used to construct a digital twin model. In weak grid mode, the virtual inductor L v and virtual resistor R v Incorporating the digital twin model, the impedance calculation formula is as follows:

[0194] Z v =jωL v +R v

[0195] Among them, Z v is the virtual impedance, j is an imaginary number, ω is the grid angular frequency, L v is the virtual inductance value, R v is the virtual resistance value; in the strong grid mode, the frequency-variable admittance regulator transfer function is integrated into the model, and the frequency-variable admittance regulator transfer function is as follows:

[0196] G(s)=K p +K i / s+K d s

[0197] Among them, G(s) is the transfer function, K p is the proportionality coefficient, K i is the integral coefficient, K d is the differential coefficient, s is the Laplace operator;

[0198] (3) Lyapunov is used for stability correction, and the energy function derivative is ensured to be less than 0 by adjusting the matrix online. is the first-order derivative of the energy function; when When the system is stable, otherwise the control parameters are adjusted by the optimization algorithm to make Satisfy the stability conditions.

[0199] (4) Hierarchical intelligent protection and rapid self-healing: the abnormality level is divided according to the size of voltage fluctuation ΔV and frequency deviation Δf. Minor abnormality: 0.05<ΔV≤0.1 and 0.2<Δf≤0.5; Intermediate abnormality: ΔV>0.1 or Δf>0.5; Severe fault: three-phase short circuit occurs in the power grid. In case of minor abnormality, the control parameters are adjusted according to the Lyapunov criterion; in case of intermediate abnormality, the output power of the energy storage system is limited.

[0200] P limit =P rated ×η

[0201] Among them, P rated is the rated power value, P limit is the power limit value η is the power limit coefficient, 0<η<1;

[0202] In case of serious fault, fast mechanical switch action time t switch Satisfy t switch <t fault (fault tolerance duration), cut off the connection; the present invention pre-verifies the strategy stability through the digital twin model and dynamically corrects the control parameters.

[0203] In one implementation, the energy function is expressed as follows:

[0204] V(x)=x T Px

[0205] Among them, V(x) is the energy function, x T is the transpose of the state variable, P is the system matrix, and x is the state variable.

[0206] The present invention breaks through the limitations of traditional fixed parameter control and solves the problem of power angle instability under wide short-circuit ratio by real-time coupling coefficient identification and dynamic adjustment of virtual impedance parameters. The mode switching logic based on the short-circuit ratio threshold takes into account the differentiated control requirements of weak and strong power grids and improves the adaptability to all working conditions. In response to the insufficient stability margin of the grid-type energy storage system under wide short-circuit ratio conditions in the new energy high penetration power grid, and the synchronous frequency resonance and subsynchronous oscillation caused by the dynamic coupling effect, the present invention proposes a dynamic decoupling and adaptive impedance reshaping collaborative control scheme, such as Figure 2 The method shown is based on the real-time detection of the short-circuit ratio to divide the weak / strong grid mode: in a weak grid, the active-reactive dynamic coupling effect is eliminated by a dynamic decoupling control strategy, and an adaptive virtual impedance is introduced to compensate for the impedance mismatch; in a strong grid, a frequency-variable admittance regulator is designed to optimize the parameters of the capacitive negative damping zone to suppress subsynchronous oscillations. The present invention breaks through the limitations of traditional fixed parameter control, solves the problem of power angle instability under a wide short-circuit ratio, and takes into account the differentiated control requirements of weak and strong grids, thereby improving the adaptability to all working conditions. Based on the real-time identification of grid energy storage parameters and the dynamic adjustment of grid impedance and energy storage impedance, the present invention breaks through the limitations of traditional fixed parameter control, calculates the dynamic short-circuit ratio, solves the problem of power angle instability under a wide short-circuit ratio, and based on the dynamic short-circuit ratio, can perform corresponding switching of the grid operating mode, taking into account the differentiated control requirements of weak and strong grids, thereby improving the adaptability to all working conditions.

[0207] Example 2:

[0208] Based on the same inventive concept, the present invention patent application also provides a grid-type energy storage wide short-circuit ratio adaptive stability control system such as Figure 3 As shown, it includes: a dynamic short-circuit ratio calculation module, a power grid operating condition switching module, an optimization parameter module and an adaptive stability control module;

[0209] The dynamic short circuit ratio calculation module is used to calculate the dynamic short circuit ratio based on the grid energy storage parameters, the grid impedance and the energy storage impedance using a dynamic short circuit ratio calculation formula;

[0210] The grid operating condition switching module is used to switch the grid operating condition mode to the grid operating condition mode corresponding to the dynamic short circuit ratio according to the dynamic short circuit ratio;

[0211] The parameter optimization module is used to obtain optimized virtual inductance and resistance parameters or frequency-variable admittance regulator parameters by using a dynamic impedance matching algorithm under the power grid operation mode;

[0212] The adaptive stability control module is used to generate corresponding control instructions according to the optimized virtual inductance and resistance parameters or the frequency-variable admittance regulator parameters, and to implement adaptive stability control based on the control instructions and an execution control strategy.

[0213] Preferably, the dynamic short-circuit ratio calculation module is specifically used to:

[0214] Based on the grid energy storage parameters, the grid impedance and energy storage impedance are calculated respectively by using a pre-built grid impedance model and energy storage impedance model;

[0215] Calculating a dynamic short circuit ratio using a dynamic short circuit ratio calculation formula based on the grid impedance, the energy storage impedance, and the grid energy storage parameter;

[0216] Among them, the grid energy storage parameters include one or more of the following: effective value of grid voltage, dq axis voltage of grid-type energy storage, current signal, voltage signal, actual grid frequency, grid phase and energy storage system state parameters; the grid impedance model is constructed by performing Fourier analysis on the grid sample voltage and current signals to extract harmonic components of different frequency bands; the energy storage impedance model is constructed by performing Fourier analysis on the energy storage sample voltage and current signals to extract harmonic components of different frequency bands.

[0217] Preferably, the calculation formula of the dynamic short-circuit ratio is as follows:

[0218]

[0219] Among them, SCR is the dynamic short circuit ratio, V g is the effective value of the grid voltage, Z ess is the energy storage impedance, V dq is the dq axis voltage of the grid-type energy storage, Z g is the grid impedance.

[0220] Preferably, the power grid operating condition switching module is specifically used to:

[0221] When the dynamic short-circuit ratio is less than a preset dynamic short-circuit threshold, switching the grid-type energy storage to enter the weak grid mode;

[0222] When the dynamic short-circuit ratio is greater than or equal to the dynamic short-circuit threshold, the grid-forming energy storage is switched to enter the strong grid mode.

[0223] Preferably, the optimization parameter module is specifically used to:

[0224] When the grid-type energy storage enters the weak grid mode, the input variables of the fuzzy logic controller are calculated based on the grid energy storage parameters, the superimposed converter output voltage command and the rated grid frequency;

[0225] Based on the input variables, the fuzzy logic controller is used to perform fuzzification to calculate the adjustment coefficient of the virtual inductor and resistor;

[0226] According to the adjustment coefficient of the virtual inductor and resistor, the virtual inductor and resistor parameters are optimized using an optimized virtual inductor and resistor parameter calculation formula to obtain optimized virtual inductor and resistor parameters;

[0227] When the grid-type energy storage enters the strong grid mode, a current deviation signal is calculated based on the grid energy storage parameters through the frequency-variable admittance regulator;

[0228] Adopting an intelligent algorithm, based on the current deviation signal and the grid energy storage parameter, optimizing the proportional coefficient, integral coefficient and differential coefficient of the frequency-variable admittance regulator, and obtaining the optimized proportional coefficient, integral coefficient and differential coefficient as the optimized frequency-variable admittance regulator parameters;

[0229] The superimposed converter output voltage command is obtained by superimposing virtual inductance and resistance parameters based on the set converter output voltage command; the input variables include one or more of the following: voltage amplitude deviation, frequency deviation, and frequency deviation change rate; the virtual inductance and resistance parameters include: virtual inductance value and virtual resistance value; the virtual inductance and resistance adjustment coefficients include: virtual inductance adjustment coefficient and virtual resistance adjustment coefficient; and the intelligent algorithm includes a particle swarm optimization algorithm.

[0230] Preferably, the optimized virtual inductor and resistance parameter calculation formula is as follows:

[0231]

[0232] in, is the optimized virtual inductance value, k L is the adjustment coefficient of virtual inductance, is the virtual inductance value before optimization, is the optimized virtual resistance value, k R Adjustment coefficient of virtual resistance, is the virtual resistance value before optimization.

[0233] Preferably, the adaptive stability control module is specifically used to:

[0234] generating compensation voltage and current instructions based on the optimized virtual inductor and resistance parameters;

[0235] generating a reverse damping instruction based on the optimized frequency-variable admittance regulator parameters;

[0236] The compensation voltage and current command or the reverse damping command is output to a multi-channel pulse width modulation (PWM) modulator after power limiting to complete adaptive stability control.

[0237] Preferably, the system further comprises: a monitoring module; the monitoring module is configured to:

[0238] Based on the grid energy storage parameters, Lyapunov is used to perform stability correction on the pre-built digital twin model to ensure that the derivative of the energy function is less than 0;

[0239] When the derivative of the energy function is less than 0, hierarchical intelligent protection monitoring is completed according to the voltage amplitude deviation and the frequency deviation;

[0240] The digital twin model is constructed based on the grid impedance and energy storage impedance.

[0241] Preferably, the expression of the energy function is as follows:

[0242] V(x)=x T Px

[0243] Among them, V(x) is the energy function, x T is the transpose of the state variable, P is the system matrix, and x is the state variable.

[0244] Example 3

[0245] like Figure 4 As shown, the present invention also provides an electronic device, which may be a computer, a single-chip microcomputer, a smart mobile device, or the like. The electronic device in this embodiment may include a processor, a memory, a transceiver component, and the like. The memory, processor, and transceiver component are connected via a bus; the memory may be used to store an execution program, which may include instructions; and the processor may be used to execute the instructions stored in the memory. The memory may also be used to store data, which may be accessed and / or modified during the execution of the instructions.

[0246] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the storage medium to implement the corresponding method flow or corresponding function, so as to realize the steps of a grid-type energy storage wide short-circuit ratio adaptive stability control method in the above embodiment.

[0247] Example 4

[0248] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It can be understood that the storage medium here can include both the built-in storage medium in the electronic device and, of course, the extended storage medium supported by the electronic device. The storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space. These instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk memory. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of a grid-type energy storage wide short-circuit ratio adaptive stabilization control method in the above embodiment.

[0249] It will be understood by those skilled in the art that the embodiments of the present invention patent application may be provided as a method, system, or computer program product. Therefore, the present invention patent application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention patent application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0250] The present invention patent application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present invention patent application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0251] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0252] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0253] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the patent application of the present invention and are not intended to limit its scope of protection. Although the patent application of the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that after reading the patent application of the present invention, those skilled in the art can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the application, but these changes, modifications or equivalent substitutions are all within the scope of protection of the claims to be approved.

Claims

1. A method for adaptive stabilization control of a wide short-circuit ratio of a grid-type energy storage, characterized in that: include: Based on the grid energy storage parameters, grid impedance and energy storage impedance, the dynamic short circuit ratio is calculated using the dynamic short circuit ratio calculation formula; According to the dynamic short-circuit ratio, the power grid operation mode is switched to the power grid operation mode corresponding to the dynamic short-circuit ratio; Under the grid operation mode, a dynamic impedance matching algorithm is used to obtain optimized virtual inductor and resistor parameters or frequency-variable admittance regulator parameters; According to the optimized virtual inductor resistance parameters or the frequency-variable admittance regulator parameters, corresponding control instructions are generated, and based on the control instructions, an execution control strategy is adopted to complete adaptive stable control.

2. The method according to claim 1, wherein The dynamic short-circuit ratio is calculated based on the grid energy storage parameters, the grid impedance and the energy storage impedance using a dynamic short-circuit ratio calculation formula, including: Based on the grid energy storage parameters, the grid impedance and energy storage impedance are calculated respectively by using a pre-built grid impedance model and energy storage impedance model; Calculating a dynamic short circuit ratio using a dynamic short circuit ratio calculation formula based on the grid impedance, the energy storage impedance, and the grid energy storage parameter; Among them, the grid energy storage parameters include one or more of the following: effective value of grid voltage, dq axis voltage of grid-type energy storage, current signal, voltage signal, actual grid frequency, grid phase and energy storage system state parameters; the grid impedance model is constructed by performing Fourier analysis on the grid sample voltage and current signals to extract harmonic components of different frequency bands; the energy storage impedance model is constructed by performing Fourier analysis on the energy storage sample voltage and current signals to extract harmonic components of different frequency bands.

3. The method according to claim 2, wherein The calculation formula of the dynamic short-circuit ratio is as follows: Among them, SCR is the dynamic short circuit ratio, V g is the effective value of the grid voltage, Z ess is the energy storage impedance, V dq is the dq axis voltage of the grid-type energy storage, Z g is the grid impedance.

4. The method according to claim 1 or 2, wherein: The step of switching the grid operation mode to the grid operation mode corresponding to the dynamic short-circuit ratio according to the dynamic short-circuit ratio includes: When the dynamic short-circuit ratio is less than a preset dynamic short-circuit threshold, switching the grid-type energy storage to enter the weak grid mode; When the dynamic short-circuit ratio is greater than or equal to the dynamic short-circuit threshold, the grid-forming energy storage is switched to enter the strong grid mode.

5. The method according to claim 4, wherein The dynamic impedance matching algorithm includes a fuzzy logic algorithm and an intelligent algorithm; in the grid operation mode, the dynamic impedance matching algorithm is used to obtain optimized virtual inductance and resistance parameters or frequency-variable admittance regulator parameters, including: When the grid-type energy storage enters the weak grid mode, the input variables of the fuzzy logic controller are calculated based on the grid energy storage parameters, the superimposed converter output voltage command and the rated grid frequency; Based on the input variables, the fuzzy logic controller is used to perform fuzzification to calculate the adjustment coefficient of the virtual inductor and resistor; According to the adjustment coefficient of the virtual inductor and resistor, the virtual inductor and resistor parameters are optimized using an optimized virtual inductor and resistor parameter calculation formula to obtain optimized virtual inductor and resistor parameters; When the grid-type energy storage enters the strong grid mode, a current deviation signal is calculated based on the grid energy storage parameters through the frequency-variable admittance regulator; Adopting an intelligent algorithm, based on the current deviation signal and the grid energy storage parameter, optimizing the proportional coefficient, integral coefficient and differential coefficient of the frequency-variable admittance regulator, and obtaining the optimized proportional coefficient, integral coefficient and differential coefficient as the optimized frequency-variable admittance regulator parameters; The superimposed converter output voltage command is obtained by superimposing virtual inductance and resistance parameters based on the set converter output voltage command; the input variables include one or more of the following: voltage amplitude deviation, frequency deviation, and frequency deviation change rate; the virtual inductance and resistance parameters include: virtual inductance value and virtual resistance value; the virtual inductance and resistance adjustment coefficients include: virtual inductance adjustment coefficient and virtual resistance adjustment coefficient; and the intelligent algorithm includes a particle swarm optimization algorithm.

6. The method according to claim 5, wherein The optimized virtual inductor and resistance parameter calculation formula is as follows: in, is the optimized virtual inductance value, k L is the adjustment coefficient of virtual inductance, is the virtual inductance value before optimization, is the optimized virtual resistance value, k R Adjustment coefficient of virtual resistance, is the virtual resistance value before optimization.

7. The method according to claim 1, wherein Generating corresponding control instructions according to the optimization parameters, and implementing a control strategy based on the control instructions to complete adaptive stability control, including: generating compensation voltage and current instructions based on the optimized virtual inductor and resistance parameters; generating a reverse damping instruction based on the optimized frequency-variable admittance regulator parameters; The compensation voltage and current command or the reverse damping command is output to a multi-channel pulse width modulation (PWM) modulator after power limiting to complete adaptive stability control.

8. The method according to claim 1, wherein After generating corresponding control instructions according to the optimized virtual inductance and resistance parameters or the frequency-variable admittance regulator parameters, and executing a control strategy based on the control instructions to complete the adaptive stability control, the method further includes: Based on the grid energy storage parameters, Lyapunov is used to perform stability correction on the pre-built digital twin model to ensure that the derivative of the energy function is less than 0; When the derivative of the energy function is less than 0, hierarchical intelligent protection monitoring is completed according to the voltage amplitude deviation and the frequency deviation; The digital twin model is constructed based on the grid impedance and energy storage impedance.

9. The method according to claim 8, wherein The expression of the energy function is as follows: V(x)=x T Px Among them, V(x) is the energy function, x T is the transpose of the state variable, P is the system matrix, and x is the state variable.

10. A grid-type energy storage wide short-circuit ratio adaptive stability control system, characterized in that: include: Dynamic short-circuit ratio calculation module, grid operating condition switching module, parameter optimization module and adaptive stability control module; The dynamic short circuit ratio calculation module is used to calculate the dynamic short circuit ratio based on the grid energy storage parameters, the grid impedance and the energy storage impedance using a dynamic short circuit ratio calculation formula; The grid operating condition switching module is used to switch the grid operating condition mode to the grid operating condition mode corresponding to the dynamic short circuit ratio according to the dynamic short circuit ratio; The parameter optimization module is used to obtain optimized virtual inductance and resistance parameters or frequency-variable admittance regulator parameters by using a dynamic impedance matching algorithm under the power grid operation mode; The adaptive stability control module is used to generate corresponding control instructions according to the optimized virtual inductance and resistance parameters or the frequency-variable admittance regulator parameters, and to implement adaptive stability control based on the control instructions and an execution control strategy.

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