Self-adaptive control method of inertia synchronization PMSG (Permanent Magnet Synchronous Generator) based on online impedance estimation

Abstract

The invention relates to an inertia synchronization PMSG self-adaptive control method based on online impedance estimation, and belongs to the field of new energy grid connection. The method comprises the following steps: S1, constructing an RLS-GA hybrid strategy algorithm to quickly and accurately identify power grid impedance, and determining the strength type of a power grid; s2, establishing a direct-drive fan grid-connected system control strategy, and adopting a power control ring, a current control ring and an inertia transmission control ring at a machine side; the grid side adopts a direct-current voltage synchronous loop, a reactive voltage control loop and a stabilization control loop; s3, linearizing the control equation, and based on a linearized small signal equation, obtaining a state variable of the direct-current voltage synchronous control grid-connected system of the grid-forming type direct-driven wind turbine generator; s4, calculating control parameters of a reactive voltage control loop and a direct-current voltage synchronous loop; and S5, carrying out adaptive parameter setting on an existing permanent magnet fan inertia synchronization strategy so as to adapt to the change of strong and weak power grids and improve the stability performance of the PMSG under different power grid short circuit ratios.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of new energy grid connection and relates to an adaptive control method for inertia synchronous PMSG based on online impedance estimation. BACKGROUND

[0002] With the increasing proportion of renewable energy in the global energy structure, new energy technologies represented by wind power generation are experiencing rapid development. Currently, wind power converters mostly use grid-following control strategies based on phase-locked loops (PLL) when connected to the grid. This strategy relies on accurate tracking of the grid voltage phase to achieve synchronization, and it does not have independent frequency and voltage support capabilities. When large-scale wind farms replace traditional synchronous generators, the total rotational inertia of the power system decreases significantly, i.e., the "inertia deficiency" problem occurs. This weakens the system's ability to resist frequency disturbances, making it prone to frequency instability when the grid is disturbed, which threatens the safe and stable operation of the power system.

[0003] To address the above challenges, grid-forming control strategies have emerged. Grid-forming control simulates the operating mechanism and dynamic characteristics of synchronous generators, enabling converters to synchronize with the grid independently and presenting as controllable voltage source characteristics. This control method not only provides inertia and damping support for the grid, but also is less likely to cause oscillation instability problems when interacting with weak grids, so it is considered a key technology for supporting new power systems dominated by new energy.

[0004] In the application of permanent magnet synchronous generators (PMSG), existing grid-forming control strategies are mainly divided into two categories according to their different core control objectives: active power control mode and direct current voltage control mode. Among them, grid-forming PMSGs using active power control mode will have a dramatic change in output power when facing high load fluctuations. This easily causes abnormal interaction between the machine-side converter and the grid-side converter, leading to system oscillation or even instability, so its adaptability under complex working conditions is poor.

[0005] In contrast, grid-forming PMSGs using direct current voltage control mode exhibit superior performance. This mode indirectly controls power balance by stabilizing the direct current bus voltage, eliminating the risk of direct current side interaction instability. It not only maintains stable operation under weak grid conditions, but also has the ability to establish a grid (i.e., "networking") in island mode and resist high load and wind power fluctuations, making it more widely applicable.

[0006] However, although the direct voltage control mode has obvious advantages, in order to further improve the grid-connection performance and support capability of the PMSG to different intensity power grids, it is a technical problem to be solved at present. SUMMARY

[0007] Therefore, the purpose of the present application is to provide an adaptive control method of inertia synchronous PMSG based on online impedance estimation, which quickly and accurately identifies the grid impedance through the RLS-GA hybrid strategy algorithm, and determines the strong or weak type of the grid. On this basis, the existing inertia synchronous strategy of the permanent magnet wind turbine is adaptively parameter set, so as to adapt to the change of the strong or weak grid and improve the stability performance of the PMSG under different grid short-circuit ratios.

[0008] To achieve the above purpose, the present application provides the following technical solutions:

[0009] An adaptive control method of inertia synchronous PMSG based on online impedance estimation, specifically comprising the following steps:

[0010] S1: constructing a grid impedance identification model based on the RLS-GA hybrid strategy algorithm, specifically: first, estimating the initial value of the impedance parameter through the recursive least square method, greatly reducing the search range; then, dynamically adjusting the crossover rate and mutation rate through the adaptive genetic operator to speed up the convergence speed, and estimating the grid impedance online; wherein, RLS represents the recursive least square method, and GA represents the genetic algorithm;

[0011] S2: establishing a grid-connected system control strategy of the direct-driven wind turbine, adopting a power control loop, a current control loop and an inertia transmission control loop on the machine side; and adopting a direct current voltage synchronous loop, a reactive voltage control loop and a stabilizing control loop on the grid side;

[0012] S3: linearizing the equation of the control strategy in step S2, and obtaining the state variables of the direct current voltage synchronous control grid-connected system of the grid-connected direct-driven wind turbine based on the linearized small signal equation;

[0013] S4: calculating the control parameters K s and K pq of the reactive voltage control loop and the control parameters K iq of the direct current voltage synchronous loop;

[0014] S5: online adaptive coordinated control: under grid-connected operation, the RLS-GA hybrid strategy algorithm in step S1 is immediately activated, and the estimated values of the impedance components R g and X g are initialized through RLS; the obtained estimated values are used to calculate the power items of the small signal linearization; then, the control parameters K s , K pq and K iq; By continuously reading and monitoring the voltage value of the DC bus, when the voltage value changes and exceeds a certain threshold, the system automatically generates a signal, which makes the RLS-GA hybrid strategy algorithm start, re-estimates the grid impedance parameters; Finally, the updated impedance value is used to recalculate K s 、K pq and K iq , and then compatible with different working conditions of strong and weak power grids.

[0015] Further, in step S1, the grid impedance characteristics are represented by the voltage-current relationship at the point of common coupling (PCC), as shown in the following formula:

[0016]

[0017] Where v PCC (t) is the PCC point voltage measurement value at time t; e g (t) is the grid voltage; i g (t) is the grid current instantaneous value; R g , L g is the equivalent resistance and inductance of the grid to be estimated;

[0018] The RLS parameter update formula is as follows:

[0019]

[0020] Where y=v PCC -e g , the regression vector The parameter estimation vector The forgetting factor λ∈(0,1]; P is the error covariance matrix; K(k) is the gain vector, e(k) is the prediction error, and k is the update iteration number;

[0021] Based on the RLS estimation result, the search range of the genetic algorithm is compressed:

[0022]

[0023] Where, is the equivalent resistance of the grid based on RLS estimation, is the equivalent inductance of the grid based on RLS estimation;

[0024] The initial population based on the RLS estimation result is:

[0025] P0={θ i ∣θ i =[R g,i ,L g,i ] T ,i=1,...,N} (4)

[0026] wherein P0 is the initial population, θ i is the ith individual in the population, and N is the total number of individuals in the initial population;

[0027] The fitness function is:

[0028]

[0029] wherein, is the grid-connected point estimated voltage, and M is the number of sampling points in the current data window;

[0030] The selection operator is used to perform the survival of the fittest operation on the individuals in the population:

[0031]

[0032] wherein P select (x i ) is the probability of the individual being selected, x i is the fitness of the individual, i.e., x i =F(θ i );

[0033] An adaptive crossover probability mechanism is used, i.e., the crossover probability P c is dynamically adjusted according to the relative relationship between the maximum fitness f' and the average fitness f of the current population, and then the crossover operation is:

[0034] L child =βX1+(1-β)X2, β∈[0,1] (7)

[0035] wherein L child is the offspring individual, β is the weight coefficient, and X1 and X2 are the parent individuals;

[0036] An adaptive mutation strategy with time-decreasing type is used, and the mutation probability changes linearly with the generation number, so as to give consideration to global exploration and local convergence; the mutation amplitude is attenuated according to the generation number to perform the mutation operation:

[0037]

[0038] wherein δ i (g) is the mutation step size of the ith individual in the gth generation, η i is the mutation strength coefficient, g is the current iteration number, G is the maximum iteration number, are the upper and lower boundaries of the ith individual, respectively.

[0039] Further, in step S2, the machine side adopts a power control loop, a current control loop and an inertia transmission control loop, specifically including: the calculation formula of the q-axis current reference value i mqref is:

[0040]

[0041] wherein k p1 is a power control loop proportional coefficient, k i1 is a power control loop integral coefficient, P mref is a power reference value, P m is a direct-drive motor output power, P m * is an inertia transmission power, y1 is an output of a machine-side converter (MSC) active power control loop integral regulator, and s is a complex frequency;

[0042] The inertia transmission power P m * is calculated according to the following formula:

[0043]

[0044] wherein H m (s) is an inertia controller, K m is an inertia transmission coefficient, T m is a filter time constant, and u dc is a direct-current voltage unit value;

[0045] The current control loop tracks the direct-drive wind turbine output current i m based on a PI current controller in a current control loop, as shown in formula (11):

[0046]

[0047] wherein k p2 , k p3 are current control loop proportional coefficients, k i2 , k i3 are current control loop integral coefficients, i mqref is a q-axis current reference value, i md , i mq are direct-drive wind turbine output dq-axis currents, L d , L q are permanent magnet motor dq-axis inductances, ω m is a permanent magnet synchronous motor electrical angular velocity, and ψ is a permanent magnet synchronous motor flux linkage; y2 is an output of a machine-side converter current control loop q-axis integral regulator; and y3 is an output of a machine-side converter current control loop d-axis integral regulator.

[0048] Further, in step S2, the grid side adopts a direct-current voltage synchronization loop, a reactive voltage control loop, and a stabilization control loop, specifically including: considering that the grid voltage rotates at a rated angular frequency ω g , therefore the grid voltage angle θ gThe phase difference between the grid-side converter (GSC) output electromotive force angle θ gsc may be defined as:

[0049]

[0050] where δ is the phase angle difference between the converter output voltage and the grid voltage, ω g is the rated angular frequency, and ω gsc is the grid-side angular velocity.

[0051] The DC voltage synchronization loop is shown in equation (13):

[0052]

[0053] where U dc is the DC voltage instantaneous value, and U dcref is the DC voltage steady-state value.

[0054] The reactive power voltage control loop consists of a PI controller, as shown in equation (14):

[0055]

[0056] where E ref is the rated voltage, k pq and k iq are the proportional coefficient and integral coefficient of the reactive power voltage control loop, Q ref is the reactive power reference value, which is 0, Q g is the actual reactive power value, U s is the stabilizing voltage, K s is the stabilizing control coefficient, T g is the grid-side filter time constant, and u dc is the DC voltage unit value, and s is the complex frequency.

[0057] Further, in step S2, the main circuit Thevenin equivalent model of the direct-drive wind turbine grid-connected system is:

[0058]

[0059] where E g = [E gd E gq ] T is the grid dq-axis voltage, u pcc = [u pccd u pccq ] T is the dq-axis voltage at the PCC point, and i s = [i sd i sq ] T is the grid-side dq-axis current. and R g L and R are the equivalent inductance and resistance of the grid side; ω g ω is the rated angular frequency;

[0060] The dynamic equation of the capacitor voltage is shown in equation (16):

[0061]

[0062] where C dc is the capacitance, U dcref is the steady-state DC voltage, U dc is the DC voltage instantaneous value, P g is the grid-side converter output active power;

[0063] The machine-side active power of the grid-connected system, i.e., the direct-drive motor output power P m is:

[0064]

[0065] where u md and u mq are the machine-side output dq-axis voltages, i md and i mq are the machine-side output dq-axis currents;

[0066] The grid-connected point output active power and reactive power are:

[0067]

[0068] where P pcc and Q pcc are the grid-connected point output active power and reactive power, V i and V j are the effective values of the system grid-connected point and grid-side voltages, X g is the total impedance between the grid-side converter and the grid, and δ is the phase angle difference between the converter output voltage and the grid voltage.

[0069] Further, in step S3, the state variable Δx of the grid-constructing type direct-drive wind turbine DC voltage synchronous control grid-connected system is obtained as:

[0070] Δx = [ΔU dc ΔδΔi md Δi mq Δy1Δy2Δy3Δx m Δx g Δi sd Δi sq Δy4] T (19)

[0071] where ΔUdc is the variation of the DC side (grid side) voltage; Δδ is the variation of the phase of the grid side converter (GSC) output voltage; Δi md is the variation of the d-axis output current of the machine side converter (MSC); Δi mq is the variation of the q-axis output current of the machine side converter (MSC); Δy1 is the variation of the output of the active power control loop integral regulator of the machine side converter (MSC); Δy2 is the variation of the output of the q-axis integral regulator of the current control loop of the machine side converter (MSC); Δy3 is the variation of the output of the d-axis integral regulator of the current control loop of the machine side converter (MSC); Δx m is the variation of the output of the inertia transfer control loop first order low pass filter of the machine side converter (MSC); Δx g is the variation of the output of the grid stabilization control loop first order low pass filter of the grid side converter (GSC); Δi sd is the variation of the d-axis component of the output current of the grid side converter (GSC); Δi sq is the variation of the q-axis component of the output current of the grid side converter (GSC); Δy4 is the variation of the output of the reactive power loop integral regulator of the grid side converter (GSC).

[0072] Further, in step S4, the control parameter K s Specifically, the response performance of the dynamic process of the DC voltage and the grid frequency is mainly determined by the damping ratio of the second order function, and the damping ratio of the second order response function of the DC voltage variation in response to the grid frequency disturbance is equal to 0.707, and the optimal stabilization coefficient K s , as shown in equation (20):

[0073]

[0074] where ζ is the damping ratio, C dc is the capacitance, U dc0 is the DC voltage steady state value, k pdc is the inertia gain; where A 11 is the correlation coefficient of the active power and the phase angle difference of the grid connection point under small disturbance, V i0 is the effective value of the system grid connection point voltage when operating in steady state, V j0 is the effective value of the grid side voltage when operating in steady state, δ0 is the steady state value of the phase angle difference between the converter output voltage and the grid voltage; A 12 is the correlation coefficient of the active power and the voltage of the grid connection point under small disturbance;

[0075] The maximum value of the stabilization coefficient K smax , as shown in equation (21):

[0076]

[0077] wherein, U i is the output voltage amplitude of the AC side of the receiving end converter station.

[0078] Further, in step S4, the control parameters K pq and K iq of the DC voltage synchronization loop are calculated, and the expression is:

[0079]

[0080] wherein, τ is a time constant.

[0081] The present application has the following advantages: based on the PMSG inertia synchronization control, the present application performs adaptive adjustment of the control parameters under different short-circuit ratio conditions, and realizes the dynamic stability of the permanent magnet wind turbine under different degrees of short-circuit ratio. The main advantages are as follows:

[0082] (1) The stability and adaptability of the system under different grid strengths are significantly improved.

[0083] The parameters of the traditional control strategy are fixed once they are set, and it is difficult to adapt to the changes of the grid conditions. The present application can accurately determine the strength of the grid (i.e. different short-circuit ratios) by online real-time estimation of the grid impedance, and adaptively adjust the key control parameters accordingly. The simulation results show that when the grid is weak or changes drastically, the traditional strategy may oscillate or even become unstable, while the present application method can always keep the permanent magnet synchronous wind turbine (PMSG) running stably, effectively compatible with strong and weak grid conditions, and greatly enhances the grid connection adaptability of the unit.

[0084] (2) The fast and accurate online identification of the grid impedance is realized.

[0085] The present application creatively adopts a hybrid strategy combining the recursive least square method (RLS) and the genetic algorithm (GA). The RLS algorithm can quickly provide a relatively accurate initial estimate of the impedance, greatly reducing the search space for subsequent optimization; and the improved genetic algorithm accelerates the convergence speed by adapting the crossover rate and mutation rate, and realizes the accurate optimization of the impedance parameters. This "coarse tuning + fine tuning" mode ensures the rapidity and accuracy of the impedance estimation, and is the key prerequisite for realizing the adaptive adjustment of the control parameters.

[0086] (3) The dynamic response performance of the system is optimized.

[0087] Based on the accurate impedance estimate and the small signal model of the system, the present application optimizes the PI parameters (K s , K pq , K iq ) of the damping coefficient (K s ) and the reactive voltage control loop.) to perform online adaptive tuning. This makes the system not only maintain stability, but also show better dynamic performance when it suffers from disturbances (such as grid frequency variation). Simulation comparison results show that, compared with the traditional fixed parameter strategy, the system using the method of the application has faster response speed, smaller DC voltage drop depth, and shorter time to restore stability, thereby improving power quality and transient stability of the system.

[0088] (4) provides a clear theoretical basis for control parameter tuning. The application deeply reveals the mathematical relationship between the control parameters (especially Ks) and the system damping characteristics by establishing a full-order small-signal model containing grid impedance, and gives an analytical calculation method of Ks based on the optimal damping ratio (0.707). This provides clear theoretical guidance for the parameter design of the controller, avoids the tedious trial-and-error process in the traditional method, and improves the scientificity and reliability of the design.

[0089] Other advantages, objects, and features of the application will be in part apparent and in part pointed out hereinafter. The objects and other advantages of the application will be realized and attained by the structure particularly pointed out in the specification and claims hereof as well as the appended drawings. BRIEF DESCRIPTION OF DRAWINGS

[0090] In order to make the objects, technical solutions and advantages of the application clearer, the preferred embodiments of the application will be described in detail below with reference to the accompanying drawings, in which:

[0091] Figure 1 Control structure schematic diagram for direct-drive wind turbine grid-connected system;

[0092] Figure 2 Adaptive control flowchart;

[0093] Figure 3 Simulation results of adaptive group and control group. DETAILED DESCRIPTION

[0094] The embodiments of the application are described below by way of specific examples. Those skilled in the art can easily understand other advantages and effects of the application from the disclosure herein. The application can also be implemented or applied by different specific embodiments, and the details in the specification can be modified or changed based on different views and applications without departing from the spirit of the application. It should be noted that the diagrams provided in the following examples only illustrate the basic concept of the application in a schematic manner, and the following examples and features in the examples can be combined with each other without conflict.

[0095] Please refer to Figure 1The embodiment of the application provides an adaptive control method for inertia synchronous PMSG based on online impedance estimation, comprising the following steps:

[0096] S1: constructing a power grid impedance identification model based on an RLS-GA hybrid strategy algorithm. First, the initial value of the impedance parameter is estimated by a recursive least square method (RLS), so that the search range is greatly reduced. Then, the crossover rate and the mutation rate are dynamically adjusted by an adaptive genetic operator to accelerate the convergence speed, and the power grid impedance is estimated online.

[0097] The power grid impedance characteristic is represented by the voltage-current relationship of a point of common coupling (PCC), as shown in formula (1):

[0098]

[0099] Wherein, v PCC (t) is the PCC point voltage measurement value at t; e g (t) is the power grid voltage; i g (t) is the grid-connected current instantaneous value; R g and L g are the equivalent resistance and inductance of the power grid to be estimated.

[0100] The RLS parameter update formula is shown in formula (2):

[0101]

[0102] Wherein, y=v PCC -e g , the regression vector is the parameter estimation vector , the forgetting factor λ is in (0, 1]; P is the error covariance matrix; K(k) is the gain vector, e(k) is the prediction error, and k is the update iteration number.

[0103] Based on the RLS estimation result, the search range of the genetic algorithm is compressed:

[0104]

[0105] Wherein, is the equivalent resistance of the power grid based on the RLS pre-estimation, is the equivalent resistance of the power grid based on the RLS pre-estimation.

[0106] The initial population based on the RLS estimation result is generated as:

[0107] P0={θ i ∣θ i =[R g,i ,L g,i ] T , i=1,...,N} (4)

[0108] Where P0 is the initial population, θ i Let N be the i-th individual in the population, and N be the total number of individuals in the initial population.

[0109] The fitness function is:

[0110]

[0111] in, The voltage at the grid connection point is estimated, where M is the number of sampling points in the current data window.

[0112] Selection operators are used to perform a survival-of-the-fittest operation on individuals in a population:

[0113]

[0114] Among them, P select (x i Let x be the probability that an individual is selected. i The fitness of an individual, i.e., x i =F(θ) i ).

[0115] An adaptive crossover probability mechanism is adopted, which dynamically adjusts the crossover probability P based on the relative relationship between the maximum fitness f′ and the average fitness f of the current population. c Then, the crossover operation is performed as follows:

[0116] L child =βX1+(1-β)X2, β∈[0,1] (7)

[0117] Among them, L child X1 and X2 are the offspring individuals, β is the weighting coefficient, and X1 and X2 are the parent individuals.

[0118] A time-decreasing adaptive mutation strategy is adopted, where the mutation probability changes linearly with the number of algebras to balance global exploration and local convergence; the mutation amplitude is decayed according to the number of algebras.

[0119]

[0120] Where, δ i (g) represents the variable asynchronous length of the i-th individual in the g-th generation, η i Here, g is the variation intensity coefficient, g is the current iteration number, and G is the maximum iteration number. These are the upper and lower boundaries of the i-th individual, respectively.

[0121] S2: Establish a control strategy for the direct-drive wind turbine grid-connected system, such as... Figure 1 As shown, where P mref P is the power reference value. mP is the output power of the direct drive motor m * u is the inertia transmission power dc H is the DC voltage unit value m (s) is the inertia controller, K m is the inertia transmission coefficient. i md , i mq L is the output dq-axis current of the direct drive fan d , L q is the dq-axis inductance of the permanent magnet motor. ω gsc and θ gsc are the grid-side angular velocity and angle, respectively, U ref is the virtual electromotive force provided by the reactive voltage control, U dc is the DC voltage instantaneous value, U dcref is the DC voltage steady-state value, E ref is the rated voltage; Q ref is the reactive power reference value, which is 0; Q g is the actual value of the reactive power; H g (s) is the stabilizing controller.

[0122] The machine side uses a power control loop, a current control loop, and an inertia transmission control loop. The q-axis current reference value i mqref as shown in equation (9):

[0123]

[0124] where k p1 is the power control loop proportional coefficient, k i1 is the power control loop integral coefficient, P mref is the power reference value, P m is the output power of the direct drive motor, P m * is the inertia transmission power, y1 is the output of the integral regulator of the active power control loop of the machine-side converter (MSC), and s is the complex frequency.

[0125] Inertia transmission power P m * as shown in equation (10):

[0126]

[0127] where H m (s) is the inertia controller, K m is the inertia transmission coefficient, T m is the filter time constant.

[0128] The current control loop controls the output current i mThe tracking is performed as shown in equation (11):

[0129]

[0130] wherein k p2 , k p3 is a current control loop proportional coefficient, k i2 , k i3 is a current control loop integral coefficient, i mqref is a q-axis current reference value, i md , i mq is a direct-drive wind turbine output dq-axis current, L d , L q is a permanent magnet motor dq-axis inductance, ω m is a permanent magnet synchronous motor electrical angular velocity, ψ is a permanent magnet synchronous motor flux linkage; y2 is an output of a q-axis integral regulator of a machine-side converter current control loop; and y3 is an output of a d-axis integral regulator of the machine-side converter current control loop.

[0131] The grid side uses a DC voltage synchronization loop, a reactive voltage control loop, and a stabilizing control loop. Considering that the grid voltage rotates at a rated angular frequency ω g , the phase difference between the grid voltage angle θ g and the grid-side converter (GSC) output electromotive force angle θ gsc can be defined as:

[0132]

[0133] The DC voltage synchronization loop is shown in equation (13):

[0134]

[0135] wherein U dc is a DC voltage instantaneous value, and U dcref is a DC voltage steady-state value.

[0136] The reactive voltage control loop is composed of a PI controller, as shown in equation (14):

[0137]

[0138] wherein E ref is a rated voltage, k pq and k iq are a reactive voltage control loop proportional coefficient and an integral coefficient; Q ref is a reactive power reference value, which is 0; Q g is an actual reactive power value, U s is a stabilizing voltage; K s is a stabilizing control coefficient; and T g is a grid-side filter time constant, udc is the per-unit value of DC voltage, and s is the complex frequency.

[0139] The equivalent model of the main circuit Thevenin is:

[0140]

[0141] Among them, E g =[E gd E gq ] T The dq-axis voltage of the power grid; u pcc =[u pccd u pccq ] T The dq-axis voltage at point pcc; i s =[i sd i sq ] T For the grid-side dq-axis current; and R g For the equivalent inductance and resistance on the grid side; ω g This is the rated angular frequency.

[0142] The dynamic equation for the capacitor voltage is shown in equation (16):

[0143]

[0144] Among them, C dc For capacitor, U dcref For steady-state DC voltage, U dc P is the instantaneous value of the DC voltage. g It outputs active power to the grid-side converter.

[0145] The active power on the machine side of the grid-connected system is the output power P of the direct drive motor. m for:

[0146]

[0147] Among them, u md u mq For the machine-side output dq axis voltage, i md i mq This is the output dq-axis current on the machine side.

[0148] The active and reactive power outputs at the grid connection point are as follows:

[0149]

[0150] Among them, P pcc and Q pcc For the grid connection point to output active and reactive power, V i and V jare the effective values of the system point of common coupling and grid side voltage respectively, X g is the total impedance between grid side converter and grid, and δ is the phase angle difference between converter output voltage and grid voltage.

[0151] S3: Linearize the control equation in step S2, and based on the linearized small signal equation, obtain the state variable Δx of the grid-connected type direct-driven wind turbine direct current voltage synchronous control grid-connected system.

[0152] Δx = [ΔU dc ΔδΔi md Δi mq Δy1Δy2Δy3Δx m Δx g Δi sd Δi sq Δy4] T (19)

[0153] wherein, ΔU dc is the change of the direct current side (grid side) voltage; Δδ is the phase change of the grid side converter (GSC) output voltage; Δi md is the change of the d-axis current output by the machine side converter (MSC); Δi mq is the change of the q-axis current output by the machine side converter (MSC); Δy1 is the change of the output of the active power control loop integral regulator of the machine side converter (MSC); Δy2 is the change of the output of the q-axis integral regulator of the current control loop of the machine side converter (MSC); Δy3 is the change of the output of the d-axis integral regulator of the current control loop of the machine side converter (MSC); Δx m is the change of the output of the inertia transfer control loop first-order low-pass filter of the machine side converter (MSC); Δx g is the change of the output of the grid stabilization control loop first-order low-pass filter of the grid side converter (GSC); Δi sd is the change of the d-axis component of the output current of the grid side converter (GSC); Δi sq is the change of the q-axis component of the output current of the grid side converter (GSC); and Δy4 is the change of the output of the reactive power loop integral regulator of the grid side converter (GSC).

[0154] S4: The active power small signal expression of formula (18) is shown as formula (20); and the small signal expression of formula (16) is shown as formula (21).

[0155]

[0156] wherein, the subscript 0 is a steady state value; A 11 is the correlation coefficient of the active power and phase angle difference of the point of common coupling under small disturbance, V i0V is the effective value of the grid-connected point voltage when the system is in steady operation j0 V is the effective value of the grid-connected point voltage when the system is in steady operation 12 V is the effective value of the grid-connected point voltage when the system is in steady operation pcc V is the effective value of the grid-connected point voltage when the system is in steady operation dc V is the effective value of the grid-connected point voltage when the system is in steady operation

[0157] Combining formula (20) and formula (21), the machine-side input power is constant, and the transfer function of the dynamic change of the DC voltage and the change of the grid frequency is as formula (22). The response performance of the dynamic process of the DC voltage and the grid frequency is mainly determined by the damping ratio of the second-order function, so that the damping ratio of the second-order response function of the DC voltage change amount responding to the grid frequency disturbance is equal to 0.707, and the optimal stabilizing coefficient K is solved at this time s , as formula (23). The maximum value of the stabilizing coefficient K smax , as formula (24).

[0158]

[0159] S5: the relationship between the reactive power and the DC voltage control loop after small-signal linearization of formula (18) and the reactive power reference value is as formula (25), and ignoring the influence of the DC voltage loop, it can be written as formula (26).

[0160] ΔQ pcc = H 21 Δu dc + H 22 ΔQ ref (25)

[0161] ΔQ pcc = H 22 ΔQ ref (26)

[0162] Among them, H 21 is the correlation coefficient of the grid-connected point reactive power and the DC voltage under small disturbance, A 22 is the correlation coefficient of the grid-connected point reactive power and the voltage under small disturbance. In order to eliminate the steady-state error, the gain of the reactive power loop is 1, and K pq is selected. In order to make the reactive power response speed faster and avoid the influence of high-frequency noise, K iq is: Wherein, τ is the time constant.

[0163] S6: online adaptive coordinated control, such as Figure 2As shown. Under grid-connected operation, the RLS-GA hybrid strategy algorithm in step S1 is immediately activated, initializing the impedance component R. g and X g The estimated values ​​are then used to calculate the power term for small-signal linearization. The control parameters K of the grid-side converter (MSC) are then calculated. s K pq and K iq By continuously reading and monitoring the DC bus voltage, when the signal changes and exceeds a certain threshold, the system automatically generates a signal that triggers the RLS-GA hybrid strategy algorithm to re-estimate the grid impedance parameters. Finally, K is recalculated using the updated impedance values. s K pq and K iq This allows it to be compatible with different operating conditions of both strong and weak power grids.

[0164] Verification experiment:

[0165] In the simulation model, the system outputs 4MW of active power and a reactive power reference value of 0. Online adaptive coordinated control is initiated at t=0.5s, and the grid frequency drops by 0.1Hz at t=1s. Figure 3 As shown, adaptive K-axis was observed under different SCR values ​​(1.5, 3, 8, 15). s and K pq and K iq The DC voltage waveform is shown. Using the adaptive group and the traditional strategy as a control group, the adaptive group reaches stability faster and has a smaller voltage drop depth. When the SCR exceeds 8, the traditional strategy exhibits instability and oscillation, while the adaptive group maintains stability. Therefore, the impedance estimation method of this invention demonstrates better applicability and more accurate parameter tuning compared to traditional methods in maintaining overall system stability.

[0166] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An adaptive control method of inertia synchronous PMSG based on online impedance estimation, characterized in that, The method specifically comprises the following steps: S1: constructing a power grid impedance identification model based on an RLS-GA hybrid strategy algorithm, specifically: firstly estimating an initial value of an impedance parameter through a recursive least square method; then dynamically adjusting a crossover rate and a mutation rate through an adaptive genetic operator to estimate the power grid impedance online; wherein, RLS represents the recursive least square method, and GA represents the genetic algorithm; S2: establishing a control strategy of the direct-driven wind turbine grid-connected system, adopting a power control loop, a current control loop and an inertia transmission control loop on the machine side; and adopting a direct-current voltage synchronization loop, a reactive voltage control loop and a stabilizing control loop on the grid side; S3: linearizing an equation of the control strategy in the step S2, and obtaining state variables of the direct-driven wind turbine grid-connected system based on a linearized small signal equation; S4: calculating the control parameter K of the reactive voltage control loop s and the control parameter K of the direct current voltage synchronous loop pq and K iq ; S5: Online adaptive coordinated control: under grid-connected operation, the RLS-GA hybrid strategy algorithm in step S1 is activated immediately, and the impedance component R g and the estimation value of X g are initialized by RLS; the obtained estimation values are used to calculate the power terms of small-signal linearization; then the control parameters K s , K pq and K iq of the grid-side converter are calculated respectively; by continuously reading and monitoring the voltage value of the DC bus, when the voltage value changes and exceeds the threshold value, the system automatically generates a signal which makes the RLS-GA hybrid strategy algorithm start and re-estimate the grid impedance parameters; finally, K s , K pq and K iq are recalculated using the updated impedance values, thereby being compatible with different operating conditions of strong and weak power grids.

2. The adaptive control method of inertia synchronous PMSG based on online impedance estimation according to claim 1, characterized in that, In the step S1, a power grid impedance characteristic is represented through a voltage-current relationship of a point of common coupling (PCC), as shown in the following formula: Wherein, v PCC (t) is the PCC point voltage measurement value at time t; e g (t) is the grid voltage; i g (t) is the grid current instantaneous value; R g , L g is the grid equivalent resistance and inductance to be estimated; An RLS parameter updating formula is as shown in the following formula: where y = v PCC - e g , regression vector parameter estimation vector forgetting factor λ ∈ (0, 1]; P is the error covariance matrix; K(k) is the gain vector, e(k) is the prediction error, and k is the update iteration number; Based on an RLS estimation result, a search range of a compressed genetic algorithm is as follows: wherein, RLS is the grid equivalent resistance based on RLS estimation, RLS is the grid equivalent inductance based on RLS estimation; Based on the RLS estimation result, an initial population is generated as follows: where P0is the initial population, θ i is the ith individual in the population, and N is the total number of individuals in the initial population. An adaptability function is as follows: wherein, is the voltage estimated for the point of common coupling, M is the number of sampling points in the current data window. A selection operator is adopted to perform a survival of the fittest operation on individuals in the population: where P select (x i ) is the probability of selection of the individual, x i is the fitness of the individual, i.e. x i = F(θ i ); An adaptive crossover probability mechanism is used, i.e. the crossover probability P is dynamically adjusted according to the relative relationship between the maximum fitness f' and the average fitness f of the current population c The crossover operation is then performed as follows: L child = βX1+ (1 - β)X2, β ∈ [0, 1] (7) wherein L child is the offspring individual, β is a weight coefficient, and X1and X2are the parent individuals; A time-decreasing adaptive mutation strategy is adopted, and a mutation probability linearly changes with an algebra; and a mutation amplitude is attenuated according to the algebra to perform a mutation operation: where δ i (g) is the mutation step size of the i-th individual in the g-th generation, η i is the mutation intensity coefficient, g is the current iteration number, G is the maximum iteration number, are the upper and lower bounds of the i-th individual, respectively.

3. The adaptive control method of inertia synchronous PMSG based on online impedance estimation according to claim 1, characterized in that, In step S2, the machine side adopts a power control loop, a current control loop and an inertia transfer control loop, specifically including: a q-axis current reference value i mqref The calculation formula is: wherein k p1 is a power control loop proportional coefficient, k i1 is a power control loop integral coefficient, P mref is a power reference value, P m is a direct-drive motor output power, P m * is an inertia transmission power, y1 is an output of a machine-side converter active power control loop integral regulator, and s is a complex frequency. Inertial transfer power P m * The formula for calculating the power P is: where H m (s) is an inertia controller, K m is an inertia transfer coefficient, T m is a filter time constant, u dc is a DC voltage unit; The current control loop is based on a PI current controller in the current control loop for the direct drive fan output current i m Tracking is performed as shown in equation (11): wherein, k p2 , k p3 is a current control loop proportional coefficient, k i2 , k i3 is a current control loop integral coefficient, i mqref is a q-axis current reference value, i md , i mq is a direct-drive wind turbine output dq-axis current, L d , L q is a permanent magnet motor dq-axis inductance, ω m is a permanent magnet synchronous motor electrical angular velocity, ψ is a permanent magnet synchronous motor flux linkage; y2 is an output of a q-axis integral regulator of a machine-side converter current control loop; y3 is an output of a d-axis integral regulator of a machine-side converter current control loop.

4. The adaptive control method of inertia synchronous PMSG based on online impedance estimation according to claim 3, characterized in that, In step S2, the grid side adopts a direct-current voltage synchronous ring, a reactive voltage control ring and a stabilizing control ring, specifically including: considering that the grid voltage has a rated angular frequency ω g rotates, so the phase difference between the grid voltage angle θ g and the grid side converter output electromotive force angle θ gsc can be defined as: where δ is the phase angle difference between the converter output voltage and the grid voltage, ω g is the rated angular frequency, ω gsc is the grid-side angular velocity; A direct-current voltage synchronization loop is as shown in the formula (13): wherein U dc is the instantaneous value of the direct voltage, U dcref is the steady-state value of the direct voltage; A reactive voltage control loop is composed of a PI controller, as shown in the formula (14): wherein E ref is the rated voltage, k pq and k iq are the proportional and integral coefficients of the reactive voltage control loop; Q ref is the reactive power reference value, which is 0; Q g is the actual value of the reactive power, U s is the stabilizing voltage; K s is the stabilizing control coefficient; T g is the time constant of the grid-side filter, u dc is the DC voltage reference value, and s is the complex frequency.

5. The adaptive control method of inertia synchronous PMSG based on online impedance estimation according to claim 4, characterized in that, In the step S2, a Thevenin equivalent model of a main circuit of the direct-driven wind turbine grid-connected system is as follows: where E g = [E gd E gq ] T is the grid dq-axis voltage; u pcc = [u pccd u pccq ] T is the PCC point dq-axis voltage; i s = [i sd i sq ] T is the grid-side dq-axis current; and R g is the grid-side equivalent inductance and resistance; ω g is the rated angular frequency; A dynamic equation of a capacitor voltage is as shown in the formula (16): where C dc is the capacitance, U dcref is the steady-state DC voltage, U dc is the DC voltage instantaneous value, P g is the grid-side converter output active power; The active power of the machine side of the grid-connected system, i.e. the output power P of the direct-drive motor m is: wherein u md , u mq is the machine-side output dq-axis voltage, i md , i mq is the machine-side output dq-axis current; Output active power and reactive power of a grid-connected point are as follows: where P pcc and Q pcc are the grid-connected point output active power and reactive power, V i and V j are the voltage effective values of the system grid-connected point and the grid side, respectively, X g is the total impedance between the grid-side converter and the grid, and δ is the phase angle difference between the converter output voltage and the grid voltage.

6. The adaptive control method of inertia synchronous PMSG based on online impedance estimation according to claim 5, characterized in that, In the step S3, state variables Δx of the direct-driven wind turbine grid-connected system are as follows: Δx = [ΔU dc ΔδΔi md Δi mq Δy1Δy2Δy3Δx m Δx g Δi sd Δi sq Δy4] T (19) wherein, ΔU dc is the change of the DC side voltage; Δδ is the phase change of the grid side converter output voltage; Δi md is the change of the machine side converter output d-axis current; Δi mq is the change of the machine side converter output q-axis current; Δy1 is the change of the machine side converter active power control loop integral regulator output; Δy2 is the change of the machine side converter current control loop q-axis integral regulator output; Δy3 is the change of the machine side converter current control loop d-axis integral regulator output; Δx m is the change of the machine side converter inertia transfer control loop first order low pass filter output; Δx g is the change of the grid side converter stability control loop first order low pass filter output; Δi sd is the change of the grid side converter output current d-axis component; Δi sq is the change of the grid side converter output current q-axis component; Δy4 is the change of the grid side converter reactive power loop integral regulator output.

7. The adaptive control method of inertia synchronous PMSG based on online impedance estimation according to claim 6, characterized in that, In step S4, the control parameter K of the reactive voltage control loop is calculated s Specifically, the response performance of the dynamic process of the DC voltage and the grid frequency is determined by the damping ratio of the second-order function, and the damping ratio of the second-order response function of the DC voltage variation to the grid frequency disturbance is equal to 0.707, and the optimal stabilizing coefficient K at this time is calculated s As formula (20): where ζ is the damping ratio, C dc is the capacitance, U dc0 is the DC voltage steady-state value, k pdc is the inertia gain; the subscript 0 is the steady-state value, where A 11 is the correlation coefficient of the active power and the phase angle difference of the grid-connected point under small disturbance, V i0 is the effective value when the system grid-connected point voltage is in steady-state operation, V j0 is the effective value when the grid-side voltage is in steady-state operation, and δ0 is the steady-state value of the phase angle difference between the converter output voltage and the grid voltage; A 12 is the correlation coefficient of the active power and the voltage of the grid-connected point under small disturbance; Maximum value of stabilizing coefficient K smax As in equation (21): wherein U i is the output voltage amplitude of the AC side of the receiving converter station.

8. The adaptive control method of inertia synchronous PMSG based on online impedance estimation according to claim 7, characterized in that, In step S4, the control parameter K of the direct-current voltage synchronous loop is calculated pq and K iq , and the expression is: Wherein, τ is a time constant.

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