Online monitoring method for rotational inertia of network construction type energy storage converter

Abstract

The invention discloses an online monitoring method for rotational inertia of a network-forming type energy storage converter, which directly calculates the rotational inertia by fitting a transfer function, does not depend on response of a single frequency band, and comprehensively considers the overall dynamic characteristics of the network-forming type energy storage converter. Even if a filter is adopted for damping design of the net-forming type energy storage converter, the method can still accurately capture inertia characteristics, measurement failure caused by the influence of the filter in a low-frequency asymptote approximation method is avoided, and meanwhile the error problem of a peak response method in a complex system is solved. Besides, the method for fitting the transfer function can adapt to system models of different orders, is suitable for more complex systems of three orders and above, has higher precision, robustness and adaptability, can more stably and accurately carry out online monitoring of the virtual rotational inertia, and provides more reliable inertia support for a power grid system.

Description

Technical Field

[0001] The present invention belongs to the technical field of new energy grid connection, and specifically introduces an online monitoring method for the rotational inertia of a grid-forming energy storage converter. Background Art

[0002] Traditional power systems mainly rely on mechanical generator sets to provide stable power output. Their rotational inertia and damping characteristics can balance frequency fluctuations in a short time, thus ensuring the stability of the power grid. However, the large-scale access of new energy power generation systems, especially wind energy, solar energy, etc., often lacks sufficient rotational inertia because most of these new energy power generation devices rely on power electronic devices (such as converters) to directly connect electrical energy to the grid and lack the inertial support of traditional generator sets. This makes the power grid prone to frequency fluctuations when facing load fluctuations, wind speed changes, etc., and may even lead to the collapse of the power system in severe cases. To address this challenge, grid-forming energy storage systems are considered one of the key regulatory resources in modern power systems. Energy storage systems can provide functions such as peak shaving, frequency modulation, and voltage regulation through charging and discharging in a short time, compensating for the lack of stability caused by the lack of inertia in the power system. Especially with the continuous development of energy storage technology, virtual inertia technology has gradually become an effective means to improve the stability of the power grid and cope with the challenges of new energy access.

[0003] Virtual rotational inertia adjusts the operating state of energy storage devices through a power electronic converter, thereby simulating the rotational inertia function of a traditional generator. Through this technology, the energy storage system can provide inertial support when the power grid frequency fluctuates, helping the power system maintain frequency stability and providing an inertial response similar to that of a traditional generator.

[0004] However, the detection and evaluation of virtual rotational inertia are crucial for the safety of the power grid. First, the implementation of virtual rotational inertia is affected by the converter control strategy. In particular, different damping designs will directly affect the inertial response of the system. Different dynamic effects will be generated by proportional-derivative compensation and additional filter damping implementation methods, which require precise monitoring methods to analyze and optimize. Second, the calculation of virtual rotational inertia is usually based on a certain mathematical model. In practical applications, due to changes in system parameters, there will be a certain error between the model and the actual operation. These errors may lead to inaccurate estimation of virtual rotational inertia, thereby affecting the frequency regulation ability of the power grid. Therefore, regular online monitoring can help detect and correct these deviations in a timely manner to ensure that the system can provide stable inertial support.

[0005] The existing online monitoring methods for virtual moment of inertia mainly include the low-frequency asymptote approximation method and the peak response method. The low-frequency asymptote approximation method estimates the virtual moment of inertia by analyzing the low-frequency response of the power grid. Its advantages are simple implementation and applicability to steady-state conditions. However, the additional filter damping implementation strategy will affect the frequency response characteristics of the system, causing changes in the equivalent transfer function of the system. Since the low-frequency asymptote method assumes that the response of the system in the low-frequency range can be used to calculate the inertia, the introduction of the filter will cause the response curve to shift, breaking this assumption and making it impossible for the low-frequency asymptote method to obtain accurate inertia estimation values. The peak response method calculates the moment of inertia by analyzing the peak value of the power grid frequency fluctuation, which has good real-time performance and sensitivity and is applicable to relatively fast dynamic changes. However, its disadvantage is that it is greatly affected by the nonlinear characteristics of the system and disturbance factors, which may lead to large measurement errors and the accuracy cannot be effectively guaranteed. Summary of the Invention

[0006] The purpose of the present invention is to address the problem of a large error between the actual inertia of a grid-forming energy storage system and the set virtual inertia value, and perform online measurement of the actual inertia based on the frequency response. This method accurately analyzes the dynamic behavior of the grid-forming energy storage converter, effectively overcomes the measurement errors caused by factors such as different damping implementation methods and system parameter perturbations, and thus realizes more accurate inertia detection. In addition, this method has strong adaptability, can be applied to system models of different orders, and performs excellently in terms of accuracy, robustness, and real-time performance. With its stable and accurate online monitoring ability, this method can provide more reliable inertial support for the power grid and further improve the stability and security of the system.

[0007] The technical solution of the present invention to solve the above technical problems is: Design an online monitoring method for the moment of inertia of a grid-forming energy storage converter, characterized in that the specific steps of this method are as follows:

[0008] Step 1: For a grid-forming energy storage converter based on the virtual synchronous generator control strategy (VSG), according to its grid-connected equivalent circuit, perform Laplace transform on the equation after disturbance separation and linearization of the active power control loop of the VSG to establish a small-signal model of the VSG power control loop and obtain the calculation formula of the transfer function model

[0009] Step 1: For a grid-forming energy storage converter based on the virtual synchronous generator control strategy (VSG), according to its grid-connected equivalent circuit, perform Laplace transform on the equation after disturbance separation and linearization of the active power control loop of the VSG to establish a small-signal model of the VSG power control loop and obtain the calculation formula of the transfer function model, and this calculation formula contains at least the unknown parameter virtual moment of inertia; The calculation formula contains at least the unknown parameter virtual moment of inertia;

[0010] Step 2: Inject angular frequencies ω mod1 ω mod2 ω mod3 …, ω modk …, ωmodn The sine perturbation signal, where the sine perturbation signal p modk = Asin(2πf modk ), A is the amplitude of the sine perturbation signal, f modk is the frequency, ω modk = 2πf modk , that is, the injected sine perturbation signal only changes the frequency and does not change the amplitude, and n is the number of injected angular frequencies; synchronously measure the grid connection point voltage and grid connection current signals at each angular frequency, and calculate the instantaneous active power signal; perform FFT analysis on the injected sine perturbation signal and the instantaneous active power signal respectively, and extract the amplitude-frequency response characteristics of the active power of the power grid system to the power grid frequency perturbation at the current angular frequency through spectrum analysis, so as to obtain the amplitude gain and phase shift data of the active power corresponding to this frequency point relative to the amplitude of the perturbation signal; the amplitude gain is shown in Equation (12), and the phase shift is shown in Equation (13);

[0011]

[0012] Step 3: According to the amplitude gain and phase shift data of the active power corresponding to different frequency points obtained in Step 2 relative to the sine perturbation signal, obtain the measured frequency domain response, R Ak = |G(jω modk )|∠(jω modk ), R Ak is the measured frequency domain response at the frequency point f modk ; replace the complex frequency variable in the calculation formula of the transfer function model established in Step 1 with jω modk , where j is the imaginary unit of the complex number, and ω modk is the angular frequency corresponding to the frequency point, and obtain the theoretical complex response R Lk at this frequency point, R Lk = |G1(jω modk )|∠(jω modk ); the theoretical complex response is a calculation formula containing at least the unknown parameter virtual moment of inertia. Set the error function as the difference between the measured frequency domain response and the theoretical complex response at each frequency point, and set the objective function as the sum of the square of the imaginary part and the square of the real part of the error function at each frequency point. Use the nonlinear least squares method to minimize the value of the objective function to optimize and solve the value of the unknown parameter in the theoretical complex response, set the optimization termination condition, and when the termination condition is satisfied, the optimal solution is obtained. The value of the virtual moment of inertia in the optimal solution is the value sought, that is, the online monitoring of the moment of inertia of the grid-forming energy storage converter is realized.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: The method of the present invention directly calculates the moment of inertia by fitting the transfer function, without relying on the response of a single frequency band, but comprehensively considering the overall dynamic characteristics of the grid-forming energy storage converter. Even if a filter is used for damping design in the grid-forming energy storage converter, this method can still accurately capture the inertial characteristics, avoiding the measurement failure caused by the influence of the filter in the low-frequency asymptote approximation method, and at the same time overcoming the error problem of the peak response method in a complex system. In addition, the method of fitting the transfer function can adapt to system models of different orders, is applicable to more complex third-order and higher-order systems, and has higher accuracy, robustness and adaptability, and can perform online monitoring of virtual moment of inertia more stably and accurately, providing more reliable inertial support for the power grid system. Description of the Drawings

[0014] Figure 1 It is the active power control block diagram of the grid-forming energy storage converter based on the virtual synchronous generator control strategy.

[0015] Figure 2 It is the equivalent circuit diagram of grid connection of the grid-forming energy storage converter based on the virtual synchronous generator control strategy.

[0016] Figure 3 It is the schematic diagram of the test principle of the amplitude gain and phase shift data of the active power relative to the sine disturbance signal in an embodiment of the online monitoring method for the moment of inertia of the grid-forming energy storage converter of the present invention.

[0017] Figure 4 It is the curve graph of the theoretical model and the swept-frequency model of the amplitude gain and phase shift data of the active power relative to the sine disturbance signal in an embodiment of the online monitoring method for the moment of inertia of the grid-forming energy storage converter of the present invention.

[0018] Figure 5 It is the curve graph of the fitting model and the theoretical model of the transfer function obtained after fitting in an embodiment of the online monitoring method for the moment of inertia of the grid-forming energy storage converter of the present invention.

[0019] Figure 6 It is the main Simulink simulation circuit diagram for the simulation test using the online monitoring method for the moment of inertia of the grid-forming energy storage converter of the present invention.

[0020] Figure 7 For using Figure 6 The inertia test results obtained from the simulation test using the online monitoring method for the moment of inertia of the grid-forming energy storage converter of the present invention in the main Simulink simulation circuit diagram in Detailed Embodiment

[0021] To enable those skilled in the art to better understand the solution of the present invention, the technical solution of the present invention will be further described below in conjunction with specific embodiments.

[0022] The present invention proposes an online monitoring method for the rotational inertia of a grid-forming energy storage converter. The specific steps of this method are as follows:

[0023] Step 1: For a grid-forming energy storage converter based on the virtual synchronous generator control strategy (VSG), according to its grid-connected equivalent circuit, perform Laplace transform on the equation after disturbance separation and linearization of the active power control loop of the VSG, establish a small-signal model of the VSG power control loop, and obtain the calculation formula of the transfer function model, and this calculation formula contains at least the unknown parameter virtual rotational inertia J.

[0024] Figure 1 is the active power control block diagram of the grid-forming energy storage converter. This grid-forming energy storage converter adopts a strategy based on the virtual synchronous generator (VSG) control, mainly including three core parts: transient damping, primary frequency modulation, and simulated inertia. First, the transient damping part (transfer formula ) processes the instantaneous dynamic changes of the frequency in the system through a high-pass filter and a damping control strategy, effectively reducing the grid frequency fluctuations and the oscillations caused by load changes, and eliminating the steady-state error brought by the damping control under steady state. Thus, ensuring the stable operation of the system. Secondly, the primary frequency modulation part, namely K f , dynamically adjusts the system output power according to the deviation between the angular frequency output by the grid-forming energy storage converter and the actual grid angular frequency, quickly responds and synchronizes with the grid frequency. When the grid frequency fluctuates, this part can timely adjust the output power to ensure the stability of the grid. Finally, the simulated inertia part (transfer formula ) helps balance the grid frequency change and reduce the frequency deviation caused by load fluctuations or emergencies by simulating the inertial characteristics of traditional synchronous generators and introducing a response mechanism similar to mechanical inertia, thereby enhancing the frequency stability of the system. Through the synergistic effect of these three parts, the system can efficiently regulate the active power and achieve synchronization with the grid frequency, obtaining the output angular frequency ω of the actual grid-forming energy storage converter. s in each formula is a complex frequency variable.

[0025] According to Figure 2 shown in the grid-connected equivalent circuit diagram of the grid-forming energy storage converter, the instantaneous active power P e is solved. First, assume that the grid voltage is V g ∠0 and take the grid voltage as the reference value. The grid-forming energy storage converter is equivalent to a voltage source E∠δ at the grid connection point through Thevenin's theorem. Secondly, the line resistance and line reactance between the grid connection point and the grid are R and X respectively. That is, the equivalent voltage source E∠δ passes through the line impedance and the grid voltage V g∠0 is connected in series to form a loop. Finally, through KVL (Kirchhoff's voltage law), the current I in the grid-connected equivalent circuit diagram is obtained as shown in Equation 1. Then, by solving the complex power to obtain the single-phase power as shown in Equation 3. Since both the grid voltage and the equivalent voltage use the amplitude, and the total power is the sum of the three-phase active powers, the instantaneous active power P e , as shown in Equation 4.

[0026]

[0027]

[0028] In the above formula, δ is the power angle, that is, the angle between the grid-connected point voltage and the grid voltage; j is the imaginary part unit of the complex number.

[0029] Subsequently, the active control loop of its VSG is subjected to disturbance separation and linearization processing, and based on the linearized equation, a Laplace transform is performed to establish a small-signal model of the VSG power control loop.

[0030]

[0031] δ = ∫(ω - ω g )dt (6)

[0032] In the formula, J is the virtual moment of inertia, ω N is the grid rated angular frequency, ω is the output angular frequency of the grid-forming energy storage converter, ω g is the grid angular frequency, P ref is the given reference power, P e is the instantaneous output active power, f c is the HPF cut-off frequency (the cut-off frequency of the HPF refers to the frequency at which the signal amplitude decays to the maximum attenuation amount in the frequency response of the filter), D is the damping coefficient, K f is the primary frequency regulation droop coefficient, E is the peak value of the grid-connected point phase voltage, V g is the peak value of the grid phase voltage, δ is the power angle, and s is the complex frequency variable.

[0033] Assume X >> R and consider the circuit to be inductive;

[0034] Assume that during small-signal disturbances, the change in δ is very small, sinδ ≈ δ, then the instantaneous active power is:

[0035]

[0036] Apply small disturbances to the state variables in Equations (5), (6), and (7), that is:

[0037]

[0038] Substitute Equation (8) into (5), (6), and (7), and linearize to obtain the small-signal model of the VSG power control loop as follows:

[0039]

[0040] Let Obtain the transfer function according to Equations (9) and (10) That is:

[0041]

[0042] ω c is the cut-off angular frequency of the HPF, and its value is equal to 2πf c .

[0043] Step 2: Inject sinusoidal perturbation signals with angular frequencies ω mod1 , ω mod2 , ω mod3 , …, ω modk , …, ω modn at the grid voltage source in sequence, where the sinusoidal perturbation signal p modk = Asin(2πf modk ), A is the amplitude of the sinusoidal perturbation signal, f modk is the frequency, ω modk = 2πf modk , that is, the injected sinusoidal perturbation signal only changes the frequency and does not change the amplitude, and n is the number of injected angular frequencies. The frequency range of the sinusoidal perturbation signal is from 0.1 Hz to 10 Hz. Synchronously measure the grid-connected point voltage and grid-connected current signals at each angular frequency, and calculate the instantaneous active power signal. Perform FFT (Fast Fourier Transform) analysis on the injected sinusoidal perturbation signal and the instantaneous active power signal respectively, and extract the amplitude-frequency response characteristics of the active power of the power grid system to the grid frequency perturbation at the current angular frequency through spectrum analysis, so as to obtain the amplitude gain and phase shift data of the active power corresponding to this frequency point relative to the amplitude of the sinusoidal perturbation signal. The amplitude gain is shown in Equation (12), and the phase shift is shown in Equation (13).

[0044]

[0045] arg is a complex function representing the argument of a complex number, that is, the phase shift in this embodiment.

[0046] As Figure 3 shown, inject a sinusoidal perturbation signal with an angular frequency of ω modkThe sine disturbance signal is used to synchronously measure the grid-connected point voltage and grid-connected current signals, and the instantaneous active power signal is calculated. The FFT analysis is respectively performed on the injected sine disturbance signal and the instantaneous active power signal. Through spectral analysis and extraction, the amplitudes of the sine disturbance signal and the instantaneous active power signal at the current frequency are obtained respectively. Substituting them into formulas (12) and (13), the amplitude gain and phase shift data of the active power corresponding to this frequency point relative to the disturbance signal are obtained.

[0047] Under the action of the disturbance signal, the grid-forming energy storage converter will generate an active power response. In order to analyze this response in detail, the Fourier transform can be used to convert the time-domain response of the system into the frequency-domain response. The Fourier transform can decompose the time-domain signal into different frequency components, and then analyze the amplitude and phase of each frequency component. By passing the sine disturbance signals with different angular frequencies and the corresponding instantaneous active power signals through this conversion and calculating according to formulas (12) and (13), the amplitude gain and phase shift data of the grid-forming energy storage converter at different frequencies can be obtained.

[0048] Step 3: According to the amplitude gain and phase shift data of the active power corresponding to different frequency points relative to the sine disturbance signal obtained in Step 2, the measured frequency-domain response R Ak =|G(jω modk )|∠(jω modk ) is obtained. R Ak is the measured frequency-domain response at the frequency point f modk . Replace the complex frequency variable s in the calculation formula of the transfer function model established in Step 1 with jω modk , that is, s = jω modk , where j is the imaginary part unit of the complex number, and ω modk is the angular frequency corresponding to the frequency point, and the theoretical complex response R Lk at this frequency point is obtained. R Lk =|G1(jω modk )|∠(jω modk ). The theoretical complex response is a calculation formula that contains at least the unknown parameter virtual moment of inertia J. In this implementation, the unknown parameters in the theoretical complex response are J, D, f c , K f , K. Set the error function as the difference between the measured frequency-domain response and the theoretical complex response at each frequency point, and set the objective function as the sum of the square of the imaginary part and the square of the real part of the error function at each frequency point. Use the nonlinear least squares method to minimize the value of the objective function to optimize and solve the values of the unknown parameters in the theoretical complex response, set the optimization termination condition, and when the termination condition is satisfied, the optimal solution is obtained. The value of the virtual moment of inertia in the optimal solution is the value sought, that is, the online monitoring of the moment of inertia of the grid-forming energy storage converter is realized.

[0049] The termination condition is to stop the iteration when the variation of the values of all unknown parameters is less than 0.01 or the change rate of the objective function is less than 1%.

[0050] In the t-th round, the error function ε(θ) and the objective function are as shown in formulas (14) and (15):

[0051] ε(θ i ) k = R Ak - R Lk (14)

[0052]

[0053] where θ i is the value of the vector composed of unknown parameters in the theoretical complex response at the i-th iteration of the t-th round. ε(θ i ) k is the difference between the measured frequency-domain response and the theoretical complex response at the angular frequency ω modk at the i-th iteration of the t-th round; ε(θ t ) k is the difference between the measured frequency-domain response and the theoretical complex response at the value of the unknown parameters when the t-th round of iteration is completed and at the angular frequency ω modk . Re[ε(θ t ) k represents taking the real part of this difference, and Im[ε(θ i ) k represents taking the imaginary part of this difference.

[0054] The specific process is as follows: when t = 1, that is, the first round, the unknown parameters are initialized with random values, and then the data of the first angular frequency ω mod1 is input, the first iteration calculation is performed, and the current error and the unknown parameter increment Δθ are calculated. The nonlinear least squares method updates the values of the unknown parameters by solving the unknown parameter increment Δθ through the Jacobian matrix J, and the Jacobian matrix J is a matrix composed of the results of differentiating the theoretical complex response with respect to the unknown parameters.

[0055] Δθ i = [J k (θ i-1 ) T J k (θ i-1 )] -1 J k (θ i-1 ) T ε(θ i-1 ) k

[0056] Δθ i is the increment of the unknown parameter generated for the i-th iteration operation, and θ i-1 is the value of the unknown parameter at the end of the (i - 1)-th iteration, and J k (θ i-1 ) is the Jacobian matrix at the angular frequency ω modk at the end of the (i - 1)-th iteration, and ε(θ i-1 ) k is the difference between the measured frequency-domain response and the theoretical complex response at the angular frequency ω modk at the (o - 1)-th iteration in the first round. The value of the unknown parameter θ i at the end of the i-th iteration is θ i-1 +Δθ i . Substitute the value of the unknown parameter at the end of the previous iteration into the error function and the Jacobian matrix, and input the next angular frequency data to calculate the increment of the unknown parameter generated for this iteration operation, and thus obtain the value of the unknown parameter for completing this iteration. Each angular frequency data is iterated once, and the value of the unknown parameter is updated once based on the result of the previous angular frequency iteration. Repeat continuously until the last angular frequency data is iterated, completing one round of training; use the value of the unknown parameter at the end of the previous round of training as the initial value for the next round of training, and repeat the training process for one round; after each round of training is completed, calculate the sum of the square of the real part and the square of the imaginary part of the error function at each frequency point under the value of the unknown parameter at the end of this round, that is, the value of the objective function. When the change amount of the value of all unknown parameters is less than 0.01 or the change rate of the objective function is less than 1%, stop the iteration. At this time, the value of the unknown parameter is optimal, that is, the values of the parameters J, D, f c 、K f 、K are obtained. Among them, the value of the virtual moment of inertia J is the value sought, that is, the online monitoring of the moment of inertia of the grid-forming energy storage converter is realized.

[0057] The verification method of the present invention is as follows:

[0058] Taking the grid-forming energy storage converter as the object, establish a simulation model and use Simulink for simulation. Its main circuit diagram is as Figure 6 shown, where R1 and L1 are the parasitic resistance and inductance value of the inductor on the inverter side, R2 and L2 are the parasitic resistance and inductance value of the inductor on the grid-connected side, Rc and C are the parasitic resistance and capacitance value of the filter capacitor, and Rg and Lg are the line resistance and inductance value from the grid connection point to the power grid. Let the control parameters of the simulation experiment be: V dc = 1500V, J = 4.2, K f is 6366.2, D is 2400, f c is 0.35, ω Nis 314.15926, and K is 1.908×10 5 .

[0059] In this process, first, according to the transfer function form established in Step 1, it is assumed that the coefficients of each term of the numerator polynomial and the denominator polynomial of the transfer function are unknown. Next, the experimental data in Step 2 is used to solve these unknown parameters by the least squares method, so that the output of the transfer function model is as close as possible to the experimental data.

[0060] Taking the amplitude gain and phase shift data as the y-axis data respectively, and the corresponding frequency as the x-axis data, the obtained results are as Figure 4 shown, where the scanning model curve of the amplitude gain and frequency obtained by Fourier transform is approximated to the theoretical model curve of the transfer function model calculated according to the set value , indicating that the theoretical derivation model is correct.

[0061] The coefficients of the fitted transfer function obtained by the experiment are shown in formula (14). And it is verified whether the fitted transfer function (i.e., the fitted model) obtained according to the calculated coefficients is consistent with the theoretical model (calculated according to the set value) as Figure 5 shown.

[0062]

[0063] After fitting, it is obtained that: K is 1.8525×10 5 , K f is 6593.46, J is 4.242, D is 2310.886, f c is 0.3176; ω N is 314.15926. That is, the actual moment of inertia of the grid-forming energy storage converter is 4.242. Through these fitted parameters, the virtual inertia time constant of the system can be further analyzed, and the actual virtual inertia time constant is 3.72 obtained by formula (18).

[0064] The active power increment generated by the inertia support is provided by the virtual moment of inertia J and the transient damping respectively, as shown in formula (17). Simplify the formula to obtain the actual virtual inertia time constant of the system, as shown in formula (18).

[0065]

[0066] Substitute the values of J, D, f c obtained in Step 3, as well as the rated angular frequency ω N , rated active power P N , and rated frequency f N of the power grid into formula (18) to obtain the actual virtual inertia time constant.

[0067] Verify whether the inertia time constant is correct through the inertia support test. If the active power increment generated by the inertia support test is the same as that of the virtual inertia time constant obtained by fitting, it indicates that the actual rotational inertia of the network-forming energy storage converter is 4.242, that is, the method of the present invention is effective.

[0068] At t = 2s, the grid frequency decreases from 50Hz to 48.1Hz at a rate of -0.633Hz / s, and the primary frequency regulation function is turned off. Verify whether the virtual inertia time constant is accurate through the active power increment. According to the grid inertia test requirements, if the virtual inertia time constant is 3.72 and the frequency change rate is -0.6333, it can be known that the active power increment is 4711.752W. Figure 7 It can be seen that the active power increment corresponding to the detected virtual inertia time constant is almost the same as the simulation, that is, the actual rotational inertia is 4.242. This proves the feasibility of the present method.

[0069] The above is an exemplary description of the present invention. It should be noted that without departing from the core of the present invention, any simple deformation, modification or equivalent replacement that can be made by those skilled in the art without creative labor falls within the protection scope of the present invention.

[0070] Where the present invention is not described shall be applicable to the prior art.

[0071] The parts not described in the present invention are applicable to the prior art.

Claims

1. An online monitoring method for the rotational inertia of a grid-forming energy storage converter, characterized in that, The specific steps of this method are as follows: Step 1: For the grid-forming energy storage converter based on the virtual synchronous generator control strategy (VSG), according to its grid-connected equivalent circuit, perform Laplace transform on the equation after disturbance separation and linearization of the active power control loop of VSG, establish a small-signal model of the VSG power control loop, and obtain the transfer function model of the calculation formula, and the calculation formula contains at least the unknown parameter virtual moment of inertia; Step 2: Inject sinusoidal disturbance signals with angular frequencies ω mod1 , ω mod2 , ω mod3 , …, ω modk , …, ω modn at the grid voltage source, where the sinusoidal disturbance signal p modk = Asin(2πf modk ), A is the amplitude of the sinusoidal disturbance signal, f modk is the frequency, ω modk = 2πf modk , that is, the injected sinusoidal disturbance signal only changes the frequency and does not change the amplitude, and n is the number of injected angular frequencies; synchronously measure the grid connection point voltage and grid connection current signals at each angular frequency, and calculate the instantaneous active power signal; perform FFT analysis on the injected sinusoidal disturbance signal and the instantaneous active power signal respectively, and extract the amplitude-frequency response characteristics of the active power of the power grid system to the grid frequency disturbance at the current angular frequency through spectrum analysis, so as to obtain the amplitude gain and phase shift data of the active power corresponding to this frequency point relative to the amplitude of the disturbance signal; the amplitude gain is shown in Equation (12), and the phase shift is shown in Equation (13); Step 3: Obtain the measured frequency-domain response, R, based on the amplitude gain and phase shift data of the active power corresponding to different frequency points obtained in Step 2 with respect to the sinusoidal perturbation signal Ak = |G(jω modk )| ∠(jω modk ), where R Ak is the measured frequency-domain response at frequency point f modk ; Replace the complex frequency variable in the calculation formula of the transfer function model established in Step 1 with jω modk , where j is the imaginary unit of the complex number and ω modk is the angular frequency corresponding to the frequency point, to obtain the theoretical complex response R Lk at this frequency point. R Lk = |G1(jω modk )| ∠(jω modk ); The theoretical complex response is a calculation formula that at least contains the unknown parameter virtual moment of inertia. Set the error function as the difference between the measured frequency-domain response and the theoretical complex response at each frequency point, and set the objective function as the sum of the square of the imaginary part and the square of the real part of the error function at each frequency point. Use the nonlinear least squares method to minimize the value of the objective function to optimize and solve the value of the unknown parameter in the theoretical complex response. Set the optimization termination condition. When the termination condition is met, the optimal solution is obtained, and the value of the virtual moment of inertia in the optimal solution is the value sought, that is, the online monitoring of the moment of inertia of the grid-forming energy storage converter is realized.

2. The online monitoring method for the moment of inertia of a grid-forming energy storage converter according to claim 1, wherein In Step 1, the grid-forming energy storage converter based on the virtual synchronous generator control strategy includes a transient damping part, a primary frequency regulation part, and an analog inertia part. The transfer formula of the transient damping part is The primary frequency regulation droop coefficient of the primary frequency regulation part is K f , and the transfer formula of the analog inertia part is where D is the damping coefficient, f c is the HPF cut-off frequency, J is the virtual moment of inertia, ω N is the grid rated angular frequency, and s is the complex frequency variable.

3. The online monitoring method for the moment of inertia of a grid-forming energy storage converter according to claim 2, characterized in that, Obtain the transfer function model The specific process is as follows: Solve for the instantaneous active power P according to the grid-connected equivalent circuit diagram of the grid-forming energy storage converter e ; First, assume that the grid voltage is V g ∠0 and use the grid voltage as the reference value. The grid-forming energy storage converter is equivalent to a voltage source E∠δ at the grid connection point by Thevenin's theorem; Second, the line resistance and line reactance between the grid connection point and the grid are R and X respectively; that is, the equivalent voltage source E∠δ forms a loop in series with the grid voltage V g ∠0 through the line impedance; Finally, through KVL, the current I in the grid-connected equivalent circuit diagram is obtained as shown in Equation 1; then the single-phase power is obtained by solving the complex power as shown in Equation 3; Since the grid voltage and the equivalent voltage both use amplitudes, and the total power is the sum of the three-phase active powers, the instantaneous active power P e , as shown in Equation 4; In the above formula, δ is the power angle; j is the imaginary unit of a complex number; Subsequently, disturbance separation and linearization processing are performed on the active power control loop of the VSG, and Laplace transform is performed based on the linearized equation to establish a small-signal model of the VSG power control loop; δ = ∫(ω - ω g )dt (6) Where J is the virtual moment of inertia, ω N is the rated angular frequency, ω is the output angular frequency of the grid-forming energy storage converter, ω g is the grid angular frequency, P ref is the given reference power, P e is the instantaneous output active power, f c is the HPF cut-off frequency, D is the damping coefficient, K f is the primary frequency regulation droop coefficient, E is the peak value of the grid connection point phase voltage, V g is the peak value of the grid phase voltage, δ is the power angle, s is the complex frequency variable; Assume that X >> R, considering the circuit as inductive; Assume that when there is a small-signal disturbance, the change in δ is very small, sinδ ≈ δ, then the instantaneous active power is Apply small disturbances to the state variables in equations (5), (6), and (7), that is: Substitute equation (8) into (5), (6), and (7) to obtain the small-signal model of the VSG power control loop after linearization as; Let Obtain the transfer function according to Equations (9) and (10) That is: ω c is the cut-off angular frequency of the HPF, and its value is equal to 2πf c .

4. A method for online monitoring of the moment of inertia of a grid-forming energy storage converter according to claim 1, characterized in that, In step two, the frequency range of the sine disturbance signal is from 0.1 Hz to 10 Hz.

5. The online monitoring method for the moment of inertia of a grid-forming energy storage converter according to claim 3, characterized in that, The unknown parameters in the theoretical complex response are J, D, f c , K f , K 6. The online monitoring method for the moment of inertia of a network-forming energy storage converter according to claim 1, wherein In step three, the specific process of using the nonlinear least squares method to minimize the value of the objective function to optimize and solve the values of the unknown parameters in the theoretical complex response is: When t = 1, that is, the first round, first initialize the unknown parameters, then input the data of the first angular frequency, perform the first iterative calculation, and calculate the current error and the unknown parameter increment Δθ; the nonlinear least squares method solves the unknown parameter increment Δθ through the Jacobian matrix J to update the values of the unknown parameters, and the Jacobian matrix J is a matrix composed of the results of differentiating the theoretical complex response with respect to the unknown parameters; Δθ i = [J k (θ i-1 ) T J k (θ i-1 )] -1 J k (θ i-1 ) T ε(θ i-1 ) k Δθ i is the increment of the unknown parameter generated for the i-th iteration operation, θ i-1 is the value of the unknown parameter at the end of the (i - 1)-th iteration, J k (θ i-1 ) is the Jacobian matrix at the angular frequency ω modk at the end of the (i - 1)-th iteration, ε(θ i-1 ) k is the difference between the measured frequency-domain response and the theoretical complex response at the angular frequency ω modk at the first (i - 1)-th iteration of the first round. The value of the unknown parameter θ i at the end of the i-th iteration is θ i-1 +Δθ i ; Substitute the value of the unknown parameter at the end of the previous iteration into the error function and the Jacobian matrix, input the next angular frequency data, calculate the increment of the unknown parameter generated for this iteration operation, and thus obtain the value of the unknown parameter for completing this iteration. Each angular frequency data is iterated once, and the value of the unknown parameter is updated once based on the result of the previous angular frequency iteration. Repeat continuously until the last angular frequency data is iterated, completing one round of training; Use the value of the unknown parameter at the end of the previous round of training as the initial value for the next round of training, and repeat the training process of one round; After each round of training is completed, calculate the sum of the square of the real part and the square of the imaginary part of the error function at each frequency point under the value of the unknown parameter at the end of this round, that is, the value of the objective function; When the termination condition is met, the optimal solution is obtained, and the value of the virtual moment of inertia in the optimal solution is the required value.

7. The online monitoring method for the moment of inertia of a grid-forming energy storage converter according to claim 6, characterized in that The unknown parameters are initialized with random values.

8. A method for on-line monitoring of the moment of inertia of a network-forming energy storage converter according to claim 1 or 6, characterized in that The termination condition is to stop the iteration when the change in the values of all unknown parameters is less than 0.01 or the change rate of the objective function is less than 1%.

9. The online monitoring method for the moment of inertia of a network-forming energy storage converter according to claim 6, characterized in that, In the t-th round, the error function ε(θ) and the objective function are as shown in equations (14) and (15): ε(θ i ) k =R Ak -R Lk (14) where θ i is the value at the i-th iteration of the t-th round of the vector composed of unknown parameters in the theoretical complex response; ε(θ i ) k is the difference between the measured frequency-domain response and the theoretical complex response at the angular frequency ω modk at the i-th iteration of the t-th round; ε(θ t ) k is the difference between the measured frequency-domain response and the theoretical complex response at the value of the unknown parameter at the end of the t-th round of iteration and at the angular frequency ω modk , Re[ε(θ t ) k represents taking the real part of this difference, and Im(ε(θ t ) k represents taking the imaginary part of this difference.

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