Improved new energy grid-connected inverter robustness prediction control method

Abstract

The invention discloses an improved new energy grid-connected inverter robustness prediction control method, and relates to the technical field of new energy grid-connected inverter control. According to the method, a lookup table (LUT) is designed based on sampled system data (current and voltage) and used for implementation of the robustness predictive control method, and dependence of predictive control on model parameters is reduced. By combining a system variable gradient updating equation, real-time rapid updating of all system variable gradients in the lookup table is realized, the problem that a prediction process and a reference value acquisition process of an existing model prediction control method are sensitive to system parameters and the problem of current gradient updating stagnation are solved, output current ripples are reduced, and the system performance is improved. The control performance of the system is improved, and the output performance of the grid-connected inverter is further improved. The robustness prediction control method adopted by the invention can well solve the problems of strong dependence on model parameters and large output current ripples of a traditional model prediction control method, and is especially suitable for a grid-connected power generation system with frequent line impedance change.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy grid-connected inverter control, and in particular to an improved robustness prediction control method for a new energy grid-connected inverter. Background Art

[0002] Grid-connected inverters are widely used as the key interface between renewable energy systems and independent loads or power grids due to their simple structure and high reliability. As the access capacity of new energy sources continues to increase, the proportion of traditional generators in the power grid is gradually decreasing. In this case, the grid-connected inverter will operate in voltage source mode to provide voltage support and control for the grid, helping to maintain the voltage stability and quality of the grid.

[0003] In order to achieve good control of the output performance of grid-connected inverters, researchers have developed and studied a variety of control methods. Among them, model predictive control (MPC) has been widely used in the current control of grid-connected inverters because of its advantages such as simple implementation, fast response, and multi-objective collaborative control. However, due to the complexity and uncertainty of the power system, new energy grid-connected inverters face problems such as parameter changes and external interference. When the model parameters do not match the system parameters, prediction errors will occur, resulting in deterioration of the control performance of the system. In order to improve the robustness of model predictive control to parameter changes, many improved model prediction methods have been proposed at home and abroad, which are mainly divided into the following two types:

[0004] (1) Online identification of system parameters to compensate for parameter errors;

[0005] (2) Estimate and compensate for time-varying interference caused by parameter uncertainty.

[0006] Both approaches can improve the robustness of model predictive control to parameter variations; however, their implementation requires the design of complex observers and the tuning of multiple observer parameters, which results in a large computational burden.

[0007] Recently, a model-free predictive control method based on a lookup table (LUT) was proposed in the literature [C.Lin, T.Liu, J.Yu, L.Fu and C.Hsiao,"Model-Free Predictive Current Control for Interior Permanent-Magnet Synchronous Motor Drives Based on Current Difference Detection Technique,"IEEE Transactions on Industrial Electronics, vol.61, no.2, pp.667-681, Feb.2014.] and successfully implemented in a permanent magnet synchronous motor drive. This control method is simple in principle and easy to implement, and completely eliminates the dependence of model predictive control on the system model. In this control strategy, the sixteen current gradients caused by eight basic voltage vectors in each control cycle are stored in two lookup tables on the d-axis and q-axis respectively. Therefore, the control performance of this model-free predictive control depends largely on the accuracy of the LUT. However, in each control cycle, only the current gradient caused by the applied voltage vector can be updated, and other gradients will stagnate. Long-term stagnation may lead to unreliable predictions and even affect system stability.

[0008] In order to improve the accuracy of the lookup table, the literature [C.Lin, J.Yu, Y.Lai and H.Yu,"ImprovedModel-Free Predictive Current Control for Synchronous Reluctance MotorDrives,"IEEE Transactions on Industrial Electronics, vol.63, no.6, pp.3942-3953, June 2016.] defines the minimum update frequency of the lookup table to ensure that each gradient is updated within the specified time. In other words, if a voltage vector has not been used within the predefined frequency in the past, the voltage vector must be used in the next cycle, and its corresponding current gradient can be updated. However, frequently applying non-optimal voltage vectors will have a negative impact on control performance, and the update of the lookup table is not timely.

[0009] In the literature [D.Da Rù, M.Polato and S.Bolognani,"Model-free predictive current control for a SynRM drive based on an effective update of measured current responses,"IEEE International Symposium on Predictive Control ofElectrical Drives and Power Electronics(PRECEDE),Pilsen,Czech Republic,2017.] and [PGCarlet, F.Tinazzi, S.Bolognani and M.Zigliotto,"An Effective Model-FreePredictive Current Control for Synchronous Reluctance Motor Drives,"IEEETransactions on Industry Applications,vol.55,no.4,pp.3781-3790,July-Aug.2019.], other current gradients are estimated based on the updated current gradients used in the past three cycles. This strategy can only be implemented when the voltage vectors of the past three consecutive cycles are different from each other. However, the continuous application of three different voltage vectors is a random phenomenon, which may cause the lookup table update to stagnate. Furthermore, this strategy considers all possible vector sequences (up to 210), which increases the computational burden.

[0010] The literature [C.Ma, H.Li, X.Yao, Z.Zhang and F.De Belie,"An Improved Model-Free Predictive Current Control With Advanced Current Gradient Updating Mechanism,"IEEE Transactions on Industrial Electronics, vol.68, no.12, pp.11968-11979, Dec.2021.] analyzed the relationship between the current gradients of different voltage vectors. Then, the current gradients of all unused voltage vectors in the control cycle are estimated based on this relationship. However, when two consecutive basic voltage vectors are the same, there is still a stagnation phenomenon of current gradient update. Therefore, the stagnation problem of current gradient has not been completely solved.

[0011] In addition, model predictive control searches for the optimal voltage vector through the value function, and parameter mismatch will lead to inaccurate reference values. Therefore, it is also necessary to improve the robustness of the predictive control method from the perspective of reference values. Summary of the invention

[0012] For the control of the output performance of existing grid-connected inverters, if the traditional model prediction is used, although the control is relatively simple, it requires accurate model parameters. When the model parameters do not match the control parameters, it is difficult to achieve good control of the output current; therefore, some scholars have proposed a robust control method based on a lookup table, which completely eliminates the dependence of predictive control on system parameters. However, the current model-free predictive control method only outputs one voltage vector in each control cycle, which brings a large current ripple, and its current gradient update strategy cannot guarantee the real-time update of all gradients, which in turn affects the output current control of the inverter. In addition, the existing control method does not consider the impact of parameter mismatch on the accuracy of the reference value. In actual control, the reference value deviation caused by parameter error will affect the system control performance.

[0013] In view of this, the present invention proposes an improved robust predictive control method for new energy grid-connected inverters. A lookup table (LUT) is designed based on sampled system data (current, voltage) for the implementation of the robust predictive control method, which reduces the dependence of predictive control on model parameters. Combined with the system variable gradient update equation, the real-time and rapid update of all system variable gradients in the lookup table of the invention is realized, solving the problem that the prediction process and reference value acquisition process of the existing model predictive control method are more sensitive to system parameters and the problem of stagnant current gradient update, reducing the output current ripple, improving the system control performance, and further improving the output performance of the grid-connected inverter.

[0014] In order to achieve the above object, the technical solution adopted by the present invention is:

[0015] An improved robust predictive control method for a renewable energy grid-connected inverter, the specific steps are as follows:

[0016] Step 1: Establish a mathematical model of the two-level grid-connected inverter in the αβ stationary coordinate system, and obtain its eight basic voltage vectors according to the three-phase switch states of the two-level inverter, which are u0(0,0,0), u1(1,0,0), u2(1,1,0), u3(0,1,0), u4(0,1,1), u5(0,0,1), u6(1,0,1), and u7(1,1,1);

[0017] Step 2: Calculate the voltage vector and system variable gradients applied in the previous cycle, i.e., current gradient and voltage gradient, and store them in the LUT; use the system variable gradients, i.e., current gradient and voltage gradient, to replace the prediction model of the traditional two-level grid-connected inverter;

[0018] Step 3: Based on the mathematical relationship between different voltage vectors, the voltage vector applied in the previous cycle and its corresponding system variable gradient are used to update the system variable gradient data corresponding to other basic voltage vectors in the lookup table (LUT);

[0019] Step 4: Based on the sampled bridge arm side current and capacitor voltage, the capacitor voltage reference value and the bridge arm side current reference value are calculated using the proposed parameter-free reference value setting method;

[0020] Step 5: Add the current gradients corresponding to the 8 voltage vectors to the sampled current to predict the system current and voltage, and substitute the 8 prediction results into the value function equation to obtain eight value function values; select the synthetic voltage vector with the smallest value function value as the optimal voltage vector and apply it to the next control cycle of the two-level grid-connected inverter.

[0021] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0022] 1. The method of the present invention designs a lookup table based on the current data and voltage data obtained by sensor sampling, and implements a fast lookup of the system gradient stored in the table based on the relationship between different voltage vectors and their corresponding system gradients to ensure the accuracy of system prediction. The prediction process does not require any system parameters and eliminates the impact of current gradient update stagnation.

[0023] 2. The present invention adopts a predictive control method based on a lookup table, which eliminates the dependence of the prediction value calculation on parameters in the traditional model predictive control algorithm, and effectively improves the robustness of the predictive control to the changes in the model parameters; by increasing the dimension of the lookup table, the system current and voltage sampling data at different times are stored, and by analyzing the mathematical relationship between different voltage vectors and their corresponding system gradients, the full update formula of the current gradient and voltage gradient of the LCL type grid-connected inverter is constructed, which eliminates the update stagnation of the capacitor voltage gradient of the LCL grid-connected inverter and the current gradient on the bridge arm side, and improves the prediction accuracy.

[0024] 3. The present invention designs a parameter-free reference value calculation method, which eliminates the parameter dependence of the calculation of the capacitor voltage reference value of the LCL type voltage source inverter and the inverter side current reference value. A simplified VSG method is used to provide a capacitor voltage reference for the proposed method. The inverter side current reference is obtained by using the equation relationship between the sampled values ​​of the capacitor current and capacitor voltage and the reference value, eliminating the parameter dependence of the calculation of the capacitor voltage reference value of the LCL type voltage source inverter and the grid-connected current reference value. The stable control of active and reactive power is achieved when the grid-connected inverter is connected to the grid in voltage source mode, providing inertial support for the power grid.

[0025] 4. The robust predictive control method adopted by the present invention can effectively solve the problems of strong dependence on model parameters and large output current ripple of traditional model predictive control methods, and is particularly suitable for grid-connected power generation systems with frequently changing line impedance. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is the grid-connected control block diagram of the grid-connected inverter voltage source mode.

[0027] Figure 2 It is the topological diagram of the two-level grid-connected inverter of the present invention and its basic voltage vector diagram.

[0028] Figure 3 This is a schematic diagram of real-time updating of the current gradient of the present invention.

[0029] Figure 4 This is the control block diagram of the grid-connected inverter voltage source mode VSG.

[0030] Figure 5 The method for obtaining the proposed reference value.

[0031] Figure 6 The figure is a schematic diagram of the control flow implemented by the present invention.

[0032] Figure 7 Diagram of the experimental platform constructed for the present invention.

[0033] Figure 8 The figures are the experimental results of the present invention and the traditional model when the nominal active power Pn is set to 10000W and the reactive power Qn is set to OVar; wherein, (a) is the traditional model predictive control method, and (b) is the improved robust predictive control method proposed in the present invention.

[0034] Fig. 9 The experimental results of power sharing and system frequency response of the present invention and the traditional model under a reference power step; wherein (a) is the traditional model predictive control method, and (b) is the improved robust predictive control method proposed in the present invention.

[0035] Fig.10The experimental results of the present invention and the traditional model under parameter mismatch are shown in Figure 2. The amplitude of active power is set to 12500W, and the mismatched L i * The values ​​of C* vary from 20% to 200% of their actual values; wherein (a) is the traditional model predictive control method, and (b) is the improved robust predictive control method proposed in the present invention. DETAILED DESCRIPTION

[0036] The present invention is further described in detail below in conjunction with embodiments and drawings.

[0037] When the grid-connected inverter is connected to the grid in voltage mode, the inverter effectively injects DC power into the grid by adjusting the amplitude, frequency and phase of the output voltage. In this mode, the output current of the inverter is determined by the grid and affected by the external load. The control block diagram using the traditional model predictive control method is as follows Figure 1 As shown in the figure, it mainly includes two parts: the first part is the virtual synchronous machine (VSG) part, which obtains the virtual angular frequency and amplitude of the reference voltage through active power and reactive power. At the same time, the VSG control has a virtual impedance function, thereby realizing accurate power sharing; the second part is the predictive control part, which uses the discrete model of the inverter to optimize and select the inverter output voltage vector through the model predictive control method, so as to realize accurate and rapid control of the output voltage and meet the steady-state and dynamic response requirements of the inverter grid-connected.

[0038] In this regard, an embodiment of the present invention provides an improved robustness prediction control method for a new energy grid-connected inverter, such as Figure 6 As shown, here, the system current and voltage sampling data of the LCL type grid-connected inverter are used to calculate the system gradient and stored in the lookup table (LUT). By analyzing the relationship between different voltage vectors and their corresponding system gradients, a real-time fast update method of the system gradient is designed, and the system prediction is performed based on the system gradient, eliminating the dependence of traditional predictive control on system parameters and improving the robustness of predictive control. At the same time, a parameter-free reference value calculation method is designed, and a simplified VSG method is used to provide a capacitor voltage reference for the proposed method. The inverter side current reference is obtained by using the equation relationship between the sampling values ​​of the capacitor current and capacitor voltage and the reference value, eliminating the parameter dependence of the calculation of the capacitor voltage reference value and the grid-connected current reference value of the LCL type voltage source inverter. The specific steps are as follows:

[0039] Step 1: Establish a mathematical model of the two-level grid-connected inverter in the αβ stationary coordinate system. According to the three-phase switch states of the two-level inverter, its eight basic voltage vectors are obtained, namely u0(0,0,0), u1(1,0,0), u2(1,1,0), u3(0,1,0), u4(0,1,1), u5(0,0,1), u6(1,0,1), and u7(1,1,1). Please refer to Figure 2 shown.

[0040] The sampled three-phase bridge arm current i iabc , three-phase capacitor voltage v cabc Perform Clarke coordinate transformation to obtain i iαβ 、v cαβ :

[0041]

[0042] Unlike the current-type grid-connected inverter with LCL filtering, although the voltage source inverter filter also uses LCL filter, the output current on the grid side is indirectly controlled by adjusting the power. Therefore, the system model is reduced to the second order, which is expressed as:

[0043]

[0044] Among them, u iαβ =[u iα ,u iβ ] T ,u iα and u iβ Respectively represent the voltage components output by the two-level grid-connected inverter on the α-axis and β-axis; i iαβ =[i iα ,i iβ ] T ,i iα and i iβ They represent the bridge arm current components output by the grid-connected inverter on the α-axis and β-axis respectively; v cαβ =[v cα ,v cβ ] T , v cα and v cβ Respectively represent the voltage components of the capacitor voltage output on the α-axis and β-axis; L i represents the inductance on the bridge arm side of the LCL filter, and C represents the capacitance of the LCL filter.

[0045] When the parameters in the mathematical model of the grid-connected inverter do not match the actual parameters, its mathematical model will become:

[0046]

[0047] Where, ΔL i Both ΔC and ΔC represent the parameter errors of the LCL filter.

[0048] Discretize the above mathematical model to get its prediction model:

[0049]

[0050] Among them, T s Indicates the control period.

[0051] Step 2: Calculate the voltage vector and system variable gradients (current gradient, voltage gradient) applied in the previous cycle and store them in the LUT. Use the system variable gradients (current gradient and voltage gradient) to replace the prediction model of the traditional two-level grid-connected inverter.

[0052] According to the prediction model (4), when there is a parameter error in the mathematical model, the result of the prediction model will change. If equation (4) is rewritten as equation (5), the current gradient Δi corresponding to the voltage vector at the previous moment can be calculated: iαβ (k-1) and voltage gradient Δv cαβ (k-1):

[0053]

[0054] According to the current gradient Δi iαβ (k-1) and voltage gradient Δv cαβ (k-1), rewriting the prediction model (4) as (6) can completely eliminate the impact of parameter mismatch on the prediction model:

[0055]

[0056] Step 3: Based on the mathematical relationship between different voltage vectors, the voltage vector applied in the previous cycle and its corresponding system variable gradient are used to update the system variable gradient data corresponding to other basic voltage vectors in the LUT.

[0057] According to formula (5), only the gradient value under the action of the applied vector in the previous control cycle can be updated through voltage and current sampling. For the unapplied vector, the corresponding gradient value cannot be updated. This phenomenon is called stagnation, which will increase the prediction error and reduce the output voltage performance. In order to eliminate the stagnation phenomenon of gradient update, the following gradient update mechanism is adopted.

[0058] If the voltage vector acting at time (k-1) is u iαβ (k-1), i∈{0,1,…,7}, according to formula (5), the system gradient can be obtained as:

[0059]

[0060] When the vector u used in formula (7) iαβ (k–1) is replaced by u jαβ (k–1), j∈{0,1,…,7} and j≠i, the system gradient on the inverter side will also become the system gradient Δi corresponding to the vector ijαβ (k–1) and Δv cjαβ (k–1), which is expressed as:

[0061]

[0062] Subtracting formula (7) from formula (8) yields the system gradient relationship corresponding to the two vectors, which is expressed as:

[0063]

[0064] According to formula (9), the voltage vector u acting at time (k-1) is iαβ (k-1) corresponds to the system gradient Δi iiαβ (k–1), Δv ciαβ (k–1) and the remaining basic voltage vectors u jαβ (k–1) corresponds to the system gradient Δi ijαβ (k–1), Δv cjαβ (k–1) has an amplitude difference, and this amplitude difference is determined by the amplitude difference of the inverter action vector and the coefficient T s / L i , T s / C are jointly determined. In order to eliminate the coefficient T s / L i , T s The influence of / C on the system gradient update can be expressed by pushing formula (9) forward by one control cycle:

[0065]

[0066] Divide formula (9) by formula (10) to eliminate the coefficient T s / L i , T s / C and is expressed as:

[0067]

[0068] After adjusting formula (11), the updated system gradient is obtained and expressed as:

[0069]

[0070] It should be noted that the validity of the update formula shown in formula (11) will be affected by the value of the denominator. When the denominator in the formula is equal to zero (i.e., u iαβ(k–2)-u jαβ (k–2)=0 or i iαβ (k–2)-i jαβ (k–2)=0), according to formula (9), the updated system gradient is equal to the system gradient obtained by sampling, which is expressed as:

[0071]

[0072] Based on the above derivation, the full update of all voltage vectors corresponding to the system gradient can be achieved without using system parameters, eliminating the update stagnation, such as Figure 3 shown.

[0073] Step 4: Based on the sampled bridge arm side current and capacitor voltage, the capacitor voltage reference value and the bridge arm side current reference value are calculated using the proposed parameter-free reference value setting method.

[0074] After the proposed method is used to replace the traditional prediction model, the influence of parameters on the prediction control can be eliminated. In this section, the present invention uses a simplified VSG method to provide a capacitor voltage reference for the proposed method, realizes the stable control of active and reactive power when the grid-connected inverter is connected to the grid in voltage source mode, and provides inertial support for the power grid. The simplified VSG control block diagram is shown in Figure 4 As shown, it mainly includes the following four parts: ① speed regulator; ② swing equation of inertia simulation; ③ reactive power control; ④ virtual impedance loop.

[0075] ① Speed ​​governor: The main goal of the speed governor is to adjust the active power according to the frequency deviation. The essence of the speed governor is achieved through ω-P droop control, which is expressed as:

[0076] P in =P n -k ω (ω m -ω n ) (14)

[0077] Where P in With ω m are the virtual active power and virtual angular frequency of VSG, respectively, ω is the droop coefficient of ω-P droop control, P n With ω n are the nominal active power and the nominal angular frequency, respectively, where ω n =2πf n .

[0078] ② Swing equation for inertia simulation: The main goal of the swing equation is to simulate the inertia and damping characteristics of the synchronous generator by introducing the rotor motion equation, which is expressed as:

[0079]

[0080]

[0081] Where P out is the output active power of VSG, D is the damping factor of VSG, J is the virtual inertia matrix of VSG, ω c are the cutoff frequency, damping factor and virtual moment of inertia of the low-pass filter.

[0082] ③ Reactive power control: In VSG, the essence of reactive power control is achieved through QV droop control, which is expressed as:

[0083] V ref =V n -k q (Q out -Q n ) (17)

[0084]

[0085] Where V ref , is the voltage reference amplitude, V n is the nominal voltage amplitude, k q is the droop coefficient of QV droop control, Q out is the output reactive power, Q n is the nominal reactive power.

[0086] ④ Virtual impedance loop: Different line impedances may affect the accuracy of power sharing. In order to mitigate this adverse effect, VSG usually uses virtual impedance to correct the output impedance to achieve effective power distribution. Therefore, the inner loop voltage reference value with a virtual impedance loop is expressed as:

[0087]

[0088] In the formula, v cαβ ref is the capacitor voltage reference of the proposed MFPC, Z v The virtual impedance preset for damping synchronous resonance. Since the capacitor voltage reference is a general sinusoidal variable, the virtual impedance is expressed as:

[0089] Z v =R v +jωL v (20)

[0090] It can be seen from formula (21) that the calculation of the inverter side current reference value in the traditional model predictive control is affected by the filter capacitor parameters.

[0091]

[0092] In order to eliminate the influence of system parameters on reference calculation, this section proposes a new reference calculation method. The essence of reference calculation is to obtain the inverter side current reference by using the relationship between capacitor current and capacitor voltage.

[0093] like Figure 5 As shown, the system sampling data of the voltage source grid-connected inverter meets the following requirements:

[0094]

[0095] Formula (21) and formula (22) are transformed and divided to eliminate the coefficient ωC and expressed as:

[0096]

[0097] Generally, the influence of system parameter changes on the inverter-side current reference value can be eliminated by (23). However, when the denominator of (23) is close to zero, it cannot be used to calculate the system reference value. Another system reference value calculation method is needed.

[0098]

[0099] According to formula (23) and formula (24), the calculation process of the proposed inverter side current reference value does not need to use any system parameters, thereby eliminating the influence of parameter errors on the reference calculation and improving the parameter robustness of the system control.

[0100] Step 5: Add the current gradients corresponding to the eight voltage vectors to the sampled current to predict the system current and voltage, and substitute the eight prediction results into the value function equation to obtain eight value function values. Select the synthetic voltage vector with the smallest value function value as the optimal voltage vector and apply it to the next control cycle of the two-level grid-connected inverter.

[0101] The cost function of capacitor voltage tracking and inverter side current tracking is expressed as:

[0102]

[0103] Then, the cost function of the inverter is expressed as:

[0104] J=λ v J v +λ i J i (26)

[0105] The value function values ​​corresponding to the eight basic voltage vectors are calculated according to the value function (26), and the voltage vector with the smallest value function value is selected as the optimal vector to be used in the next control cycle.

[0106] Specific experiments:

[0107] Experimental platform such as Figure 7 As shown in Figure 1, it includes VSI, TMS32F28335, AC voltage source, DC voltage source and LCL filter. The sampling frequency is set to 20kHz. The experimental parameters are shown in Table 1.

[0108] Table 1 System parameters

[0109]

[0110] In order to verify the steady-state performance of the present invention, Figure 8 Given the nominal active power P n is set to 10000W, the reactive power Q n Experimental results with Var set to 0. Figure 8 (a) shows the predicted and reference ripples of the inverter side current and capacitor voltage under the traditional model predictive control method, where the prediction and reference are calculated based on system parameters. It can be seen that in the traditional model predictive control method, the root mean square error (RMSE) of the inverter side current prediction error is 1.81A, and the RMSE of the capacitor voltage prediction error is 4.40V. Figure 8 (b) shows the predicted and reference ripples of the inverter side current and capacitor voltage under the improved robust predictive control method proposed in the present invention, where the prediction and reference are generated by the proposed MFP and PLR methods, respectively. It can be seen that the RMSE of the inverter side current prediction error is 0.54A, and the RMS of the capacitor voltage prediction error is 2.05V, which verifies the effectiveness and correctness of the method proposed in the present invention.

[0111] In order to evaluate the dynamic performance under precise parameters, Fig. 9 The experimental results of the present invention on power sharing and system frequency response under reference power steps are shown; here, the nominal active power is from 0 W to 5000 W, and then from 5000 W to 10000 W. The amplitude of the reactive power is set to 0Var. Fig. 9 (a) shows the experimental results under the traditional model predictive control method. The response times under the nominal power steps from 0W to 5000W and 5000W to 10000W are 237ms and 216ms, respectively. Fig. 9 (b) shows the experimental results of the improved robust predictive control method proposed in the present invention. The response time of the proposed method under the nominal power step from 0W to 5000W and from 5000W to 10000W is 251ms and 238ms respectively. It is apparent that the dynamic response effect of the method proposed in the present invention is similar to that of the traditional model predictive control method.

[0112] Fig.10The experimental results of different control methods under the condition of parameter mismatch are shown. The amplitude of active power is set to 12500W and the mismatched L i * and C* values ​​vary from 20% to 200% of their actual values. Fig.10 (a) shows the grid connection point voltage v under the traditional model predictive control method c It can be observed that when the mismatch value is large, the grid voltage v c The ripple is obviously distorted. When the parameter mismatch is 200% of the actual value (L i * =2L i , C*=2C), the grid voltage prediction error is 35.59V, and THD increases from 0.75% in steady state to 6.24%. When the mismatch value is 20% of the actual value (L i * =0.2L i , C*=0.2C), the grid connection point voltage prediction error is 28.94, and THD increases from 0.75% in steady state to 3.55%. Fig.10 (b) shows the grid connection point voltage v under the improved robust predictive control method proposed in the present invention. c It can be seen that the grid-connected point voltage prediction error is always less than 2V, and the THD is always less than 1%. Under parameter mismatch, it has more stable control effect and lower error than the traditional model predictive control method, and has excellent parameter robustness.

[0113] In summary, the present invention proposes an improved robust predictive control method for a new energy grid-connected inverter, which aims to enhance the parameter robustness of traditional predictive control and reduce current ripple. The method uses the current data and voltage data obtained by sensor sampling to design a lookup table, and based on the relationship between different voltage vectors and their corresponding system gradients, the system gradient is stored in a fast lookup table to ensure the accuracy of system prediction. The prediction process does not require any system parameters, and eliminates the impact of the stagnation of current gradient update. At the same time, the present invention also designs a parameter-free reference value calculation method, which adopts a simplified VSG method to provide a capacitor voltage reference. By utilizing the equation relationship between the sampled values ​​of capacitor current and capacitor voltage and the reference value, the inverter side current reference is obtained, thereby eliminating the parameter dependence of the LCL type voltage source inverter capacitor voltage reference value and the grid-connected current reference value calculation. The robust predictive control method proposed by the present invention overcomes the problems of strong dependence on model parameters and large output current ripple of the traditional model predictive control method. It is particularly suitable for grid-connected power generation systems with frequent changes in line impedance. By introducing an improved control algorithm and a parameter-free reference value calculation method, this method can improve the stability and control accuracy of the system, thereby providing a reliable solution for the application of new energy grid-connected inverters.

[0114] The above contents are merely examples and explanations of the concept of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. An improved robust predictive control method for a renewable energy grid-connected inverter, characterized in that: The specific steps are as follows: Step 1: Establish a mathematical model of the two-level grid-connected inverter in the αβ stationary coordinate system, and obtain its eight basic voltage vectors according to the three-phase switch states of the two-level inverter, which are u0(0,0,0), u1(1,0,0), u2(1,1,0), u3(0,1,0), u4(0,1,1), u5(0,0,1), u6(1,0,1), and u7(1,1,1); Step 2: Calculate the voltage vector and system variable gradients applied in the previous cycle, i.e., current gradient and voltage gradient, and store them in the LUT; Using system variable gradients, i.e., current gradient and voltage gradient, to replace the prediction model of the traditional two-level grid-connected inverter; Step 3: Based on the mathematical relationship between different voltage vectors, the voltage vector applied in the previous cycle and its corresponding system variable gradient are used to update the system variable gradient data corresponding to other basic voltage vectors in the lookup table (LUT); Step 4: Based on the sampled bridge arm side current and capacitor voltage, the capacitor voltage reference value and the bridge arm side current reference value are calculated using the proposed parameter-free reference value setting method; Step 5: Add the current gradients corresponding to the 8 voltage vectors to the sampled current to predict the system current and voltage, and substitute the 8 prediction results into the value function equation to obtain eight value function values; select the synthetic voltage vector with the smallest value function value as the optimal voltage vector and apply it to the next control cycle of the two-level grid-connected inverter.

2. The improved robustness predictive control method for a new energy grid-connected inverter according to claim 1, characterized in that: Step 1 The detailed steps are as follows: The sampled three-phase bridge arm current i iabc , three-phase capacitor voltage v cabc Perform Clarke coordinate transformation to obtain i iαβ 、v cαβ : Unlike the current-type grid-connected inverter with LCL filtering, although the voltage source inverter filter also uses LCL filter, the output current on the grid side is indirectly controlled by adjusting the power. Therefore, the system model is reduced to the second order, which is expressed as: Among them, u iαβ =[u iα ,u iβ ] T ,u iα and u iβ Respectively represent the voltage components output by the two-level grid-connected inverter on the α-axis and β-axis; i iαβ =[i iα ,i iβ ] T ,i iα and i iβ They represent the bridge arm current components output by the grid-connected inverter on the α-axis and β-axis respectively; v cαβ =[v cα ,v cβ ] T , v cα and v cβ Respectively represent the voltage components of the capacitor voltage output on the α-axis and β-axis; L i represents the inductance of the bridge arm side of the LCL filter, and C represents the capacitance of the LCL filter; When the parameters in the mathematical model of the grid-connected inverter do not match the actual parameters, its mathematical model will become: Among them, ΔL i and ΔC both represent the parameter errors of the LCL filter; Discretize the above mathematical model to get its prediction model: Among them, T s Indicates the control period.

3. The improved robustness predictive control method for a new energy grid-connected inverter as claimed in claim 2, characterized in that: Step 2: The detailed steps are as follows: According to the prediction model (4), when there is a parameter error in the mathematical model, the result of the prediction model will change. If equation (4) is rewritten as equation (5), the current gradient Δi corresponding to the voltage vector at the previous moment can be calculated: iαβ (k-1) and voltage gradient Δv cαβ (k-1): According to the current gradient Δi iαβ (k-1) and voltage gradient Δv cαβ (k-1), rewriting the prediction model (4) as (6) can completely eliminate the impact of parameter mismatch on the prediction model:

4. The improved robustness prediction control method for a new energy grid-connected inverter as claimed in claim 3, characterized in that: Step 3: Detailed steps are as follows: In order to eliminate the stagnation phenomenon of gradient update, the following gradient update mechanism is adopted; If the voltage vector acting at time (k-1) is u iαβ (k-1), i∈{0,1,…,7}, according to formula (5), the system gradient is: When the vector u used in formula (7) iαβ (k–1) is replaced by u jαβ (k–1), j∈{0,1,…,7} and j≠i, the system gradient on the inverter side will also become the system gradient Δi corresponding to the vector ijαβ (k–1) and Δv cjαβ (k–1), which is expressed as: Subtracting formula (7) from formula (8) yields the system gradient relationship corresponding to the two vectors, which is expressed as: According to formula (9), the voltage vector u acting at time (k-1) is iαβ (k-1) corresponds to the system gradient Δi iiαβ (k–1), Δv ciαβ (k–1) and the remaining basic voltage vectors u jαβ (k–1) corresponds to the system gradient Δi ijαβ (k–1), Δv cjαβ (k–1) has an amplitude difference, and this amplitude difference is determined by the amplitude difference of the inverter action vector and the coefficient T s / L i , T s / C jointly determined; in order to eliminate the coefficient T s / L i , T s The influence of / C on the system gradient update is to push forward formula (9) by one control cycle and express it as: Divide formula (9) by formula (10) to eliminate the coefficient T s / L i , T s / C and is expressed as: After adjusting formula (11), the updated system gradient is obtained and expressed as: The validity of the update formula shown in formula (11) will be affected by the value of the denominator. When the denominator in the formula is equal to zero (i.e., u iαβ (k–2)-u jαβ (k–2)=0 or i iαβ (k–2)-i jαβ (k–2)=0), according to formula (9), the updated system gradient is equal to the system gradient obtained by sampling, which is expressed as: Based on the above derivation, it is possible to fully update the system gradients corresponding to all voltage vectors without using system parameters, eliminating their update stagnation.

5. The improved robustness prediction control method for a new energy grid-connected inverter as claimed in claim 4, characterized in that: Step 4: The detailed steps are as follows: The simplified VSG control realizes the stable control of active and reactive power when the grid-connected inverter is connected to the grid in voltage source mode, providing inertial support for the grid; it includes the following four parts: ① speed regulator; ② swing equation for inertia simulation; ③ reactive power control; ④ virtual impedance loop; ① Speed ​​governor: The main goal of the speed governor is to adjust the active power according to the frequency deviation. The essence of the speed governor is achieved through ω-P droop control, which is expressed as: P in =P n -k ω (oh m -oh n ) (14) Where P in With ω m are the virtual active power and virtual angular frequency of VSG, respectively, ω is the droop coefficient of ω-P droop control, P n With ω n are the nominal active power and the nominal angular frequency, respectively, where ω n =2πf n ; ② Swing equation for inertia simulation: The main goal of the swing equation is to simulate the inertia and damping characteristics of the synchronous generator by introducing the rotor motion equation, which is expressed as: Where P out is the output active power of VSG, D is the damping factor of VSG, J is the virtual inertia matrix of VSG, ω c are the cutoff frequency, damping factor and virtual moment of inertia of the low-pass filter; ③ Reactive power control: In VSG, the essence of reactive power control is achieved through QV droop control, which is expressed as: V ref =V n -k q (Q out -Q n )(17) Where V ref , is the voltage reference amplitude, V n is the nominal voltage amplitude, k q is the droop coefficient of QV droop control, Q out is the output reactive power, Q n is the nominal reactive power; ④ Virtual impedance loop: Different line impedances may affect the accuracy of power sharing. In order to mitigate this adverse effect, VSG usually uses virtual impedance to correct the output impedance to achieve effective power distribution; therefore, the inner loop voltage reference value with a virtual impedance loop is expressed as: In the formula, is the capacitor voltage reference of the proposed MFPC, Z v The virtual impedance preset for damping synchronous resonance; wherein, since the capacitor voltage reference is a general sinusoidal variable, the virtual impedance is expressed as: Z v =R v +jωL v (20) It can be seen from formula (21) that the calculation of the inverter side current reference value in the traditional model predictive control is affected by the filter capacitor parameters; In order to eliminate the influence of system parameters on reference calculation, a new reference calculation method is proposed, which essentially uses the relationship between capacitor current and capacitor voltage to obtain the inverter side current reference; The system sampling data of the voltage source grid-connected inverter meets the following requirements: Formula (21) and formula (22) are transformed and divided to eliminate the coefficient ωC and expressed as: Generally, the influence of system parameter changes on the inverter-side current reference value can be eliminated by (23); however, when the denominator of (23) is close to zero, it cannot be used to calculate the system reference value; another system reference value calculation method is needed; According to formula (23) and formula (24), the calculation process of the proposed inverter side current reference value does not need to use any system parameters, thereby eliminating the influence of parameter errors on the reference calculation and improving the parameter robustness of the system control.

6. The improved robustness prediction control method for a new energy grid-connected inverter as claimed in claim 5, characterized in that: Step 5 The detailed steps are as follows: The cost function of capacitor voltage tracking and inverter side current tracking is expressed as: Then, the cost function of the inverter is expressed as: J=λ v J v +λ i J i (26) The value function values ​​corresponding to the eight basic voltage vectors are calculated according to the value function (26), and the voltage vector with the smallest value function value is selected as the optimal vector to be used in the next control cycle.

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