Grid-connected inverter harmonic suppression method and system based on double-vector model predictive control

Abstract

The invention provides a grid-connected inverter harmonic suppression method based on double-vector model predictive control, and the method comprises the steps: building an inverter prediction model under an alpha-beta static coordinate system, separating an output current into a fundamental component and a harmonic component, and carrying out the prediction control of the fundamental component and the harmonic component; a dual-target optimization function containing a fundamental wave weight coefficient eta and a harmonic wave weight coefficient gamma is constructed, minimization of an error area between PCC harmonic current and a zero axis is taken as a harmonic suppression target, and a dual-vector model is combined to predict and control a dynamic optimal voltage vector and action time thereof, so that coordinated optimization of fundamental wave power transmission and harmonic suppression is realized. The method can effectively reduce the total harmonic distortion rate at the PCC, improves the electric energy quality of a multi-inverter parallel system, and is suitable for an alternating current bus system containing multiple harmonic sources.

Description

Technical Field

[0001] This invention relates to the field of AC power generation, transmission, distribution and consumption, and specifically to a harmonic mitigation method for grid-connected inverters based on dual-vector model predictive control. Background Technology

[0002] In recent years, nonlinear loads, represented by power electronic devices, have been widely used in power systems. These devices generate a large number of harmonics during operation, which seriously affect the power quality of the system and may endanger the safe and reliable operation of equipment. Therefore, improving power quality and effectively suppressing harmonics has become an important goal of research on new power systems. To mitigate harmonics, existing technologies typically use active power filters (APFs) for compensation. However, these filters usually require high switching frequencies to achieve ideal compensation effects, thereby increasing system losses and filtering costs.

[0003] In systems with multiple inverters in parallel and multiple harmonic sources coexisting, the common bus harmonic problem is more prominent, and traditional single-machine control strategies struggle to achieve high-precision harmonic suppression in a multi-machine collaborative environment. Existing dual-vector model predictive control (MPC) strategies, by rationally switching between the two vectors within a control cycle, enable the inverter output current to more accurately approximate the control reference current, achieving higher tracking accuracy compared to single-vector control. However, existing methods do not distinguish between the inverter output fundamental and harmonic components, limiting their application to single-machine systems. Furthermore, their effect on common bus harmonic suppression is limited, only reducing harmonics in the inverter's own output current and failing to effectively manage power quality issues from other inverters or harmonic sources within the system.

[0004] Existing harmonic suppression technologies mainly rely on active power filters (APFs), which compensate for harmonic components by generating harmonic currents with equal amplitude but opposite polarity to the system's harmonic components, thereby reducing the harmonic content at the point of common coupling (PCC). However, in high-frequency harmonic scenarios, APFs have high requirements for switching frequency, leading to increased system losses. Furthermore, in scenarios with multiple converters in parallel and multiple harmonic sources, the compensation performance is highly sensitive to grid impedance and harmonic detection accuracy, making it difficult to maintain stable and effective harmonic suppression in complex power grids.

[0005] The paper "Xu Yanping, Zhang Baocheng, Zhou Qin. Dual-vector model predictive current control for permanent magnet synchronous motors [J]. Journal of Electrical Engineering, 2017, 32(20):222-230" discloses a dual-vector model predictive current control scheme for permanent magnet synchronous motors. This paper addresses the problem of large current fluctuations in duty cycle model predictive current control by proposing a dual-vector model predictive current control method. The core idea is to perform two voltage vector selections within each sampling period, selecting a non-zero voltage vector in the second selection, thereby expanding the range of selectable voltage vectors. Simultaneously, the paper introduces the influence of the action time into the cost function, selecting the voltage vector combination and its action time corresponding to the current prediction value closest to the current setpoint through a value function, thus considering the optimization effect of the action time during vector selection. Experimental results show that this method can effectively reduce current fluctuations and improve the dynamic response performance of the system. However, the research object of this literature is mainly permanent magnet synchronous motors, and the control objectives are focused on the transient suppression and steady-state accuracy improvement of stator current. It does not conduct in-depth research on the harmonic energy distribution and common bus harmonic mitigation in multi-motor parallel systems. In addition, the q-axis deadbeat method used to allocate the two vector action times within a sampling period can improve dynamic performance, but cannot guarantee the minimization of the THD of the output current. Summary of the Invention

[0006] To address the shortcomings of existing technologies, the purpose of this invention is to provide a harmonic mitigation method for grid-connected inverters based on dual-vector model predictive control.

[0007] A harmonic mitigation method for grid-connected inverters based on dual-vector model predictive control, according to the present invention, includes the following steps: A mathematical model of the grid-connected inverter in the αβ stationary coordinate system is established and discretized to obtain the current prediction model. The inverter output current is decomposed into fundamental and harmonic components, and a prediction model for the fundamental current and the harmonic current is established respectively. Obtain the harmonic current at PCC and construct an optimization objective function that includes the fundamental current deviation target and the harmonic current deviation target; Based on the aforementioned objective function, the optimal voltage vector combination and its application time are determined through two-vector model predictive control. By controlling the inverter according to the optimal voltage vector combination and its operating time, effective suppression of harmonics at the PCC can be achieved.

[0008] Preferably, the fundamental current prediction model is as follows:

[0009] in, i α (k +1) and i β ( k +1) is k +1 instant inverter α shaft and β Shaft-mounted output current; T c To control the cycle, R f For filtering resistors, L f For filtering inductors, i α ( k )and i β ( k )for k Inverter α shaft and β Shaft-mounted output current; u α ( k )and u β ( k )for k time α shaft and β Off-axis inverter output voltage, u' α ( k )and u' β ( k )for k time α shaft and β Axis grid voltage.

[0010] Preferably, the prediction model for the harmonic current at the PCC is:

[0011] in, and For the predicted PCC harmonic current, and This is to output harmonic current for the inverter.

[0012] Preferably, the optimization objective function is:

[0013] in, The target for fundamental current tracking error is... For harmonic suppression, η and γ are the weighting coefficients for the fundamental wave and harmonics, respectively.

[0014] Preferably, the fundamental current tracking error target Defined as minimizing the fundamental current reference tracking error:

[0015] in, i * a,1 ( k+ 1) and i * β,1 ( k+ 1) In the αβ coordinate system k The fundamental reference current component at time +1 i a,1 ( k+ 1) and i β,1 ( k+ 1) The predicted fundamental current component; The harmonic suppression target Defined as minimizing the amplitude of PCC harmonic components;

[0016] in, i’ a,h ( k+ 1) and i' β,h ( k+ 1) The harmonic current components at PCC predicted in the αβ coordinate system.

[0017] Preferably, in the dual-vector model predictive control, the duration of action of the dual vectors is determined by minimizing the error area between the PCC harmonic current and the zero axis.

[0018] Preferably, for the β-axis harmonic current, the objective function is to minimize the area enclosed by the reference value zero.

[0019] in, This is the β-axis harmonic current at PCC. For reference only.

[0020] Preferably, the fundamental wave weighting coefficient η and the harmonic weighting coefficient γ can be dynamically adjusted according to the system operating state: η is increased during steady-state operation to ensure power transmission, and γ is increased during harmonic disturbances to enhance harmonic suppression.

[0021] Preferably, the method is applied to a common AC bus system with multiple harmonic sources and multiple converters connected in parallel.

[0022] A harmonic mitigation system for a grid-connected inverter based on dual-vector model predictive control, according to the present invention, includes the following modules: Module M1: Establish a mathematical model of the grid-connected inverter in the αβ stationary coordinate system, and discretize it to obtain the current prediction model; Module M2: Decomposes the inverter output current into fundamental and harmonic components, and establishes a fundamental current prediction model and a harmonic current prediction model respectively. Module M3: Obtain the harmonic current at PCC and construct an optimization objective function that includes the fundamental current deviation target and the harmonic current deviation target; Module M4: Based on the aforementioned optimization objective function, the optimal voltage vector combination and its application time are determined through dual-vector model predictive control; Module M5: Controls the inverter based on the optimal voltage vector combination and its operating time to effectively suppress harmonics at the PCC.

[0023] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention utilizes the multi-functional multiplexing characteristics of inverters. By introducing a harmonic suppression strategy based on dual-vector model predictive control (MPC) into one of the inverters in the system, it achieves effective elimination of harmonics at the PCC by separating and controlling the fundamental and harmonic components. On one hand, this invention uses the harmonic current at the PCC as a control input, causing the inverter's output harmonic current to interact with the PCC harmonics, minimizing the error area between the harmonic current component at the PCC and the zero axis. Theoretically, minimizing this area is equivalent to minimizing the integral of the harmonic current's fluctuation in the time domain, indirectly reducing the amplitude of each harmonic, thereby reducing total harmonic distortion (THD). On the other hand, by using the inverter's fundamental current as an input, MPC is used to make the output fundamental frequency as close as possible to the set reference value, thus ensuring the inverter's normal power transmission function while suppressing harmonics.

[0024] 2. The objective function of this invention consists of two parts: a fundamental frequency objective and a harmonic objective. A fundamental frequency and harmonic frequency trade-off mechanism is introduced, with adjustments made by introducing fundamental frequency weighting coefficients and harmonic frequency weighting coefficients. This invention effectively reduces the harmonic level on the common AC bus while ensuring fundamental frequency power quality. Compared with the traditional APF method, this invention avoids excessive reliance on high switching frequencies, improves harmonic mitigation capabilities in multi-converter systems, simplifies the control process, and enhances overall robustness.

[0025] 3. This invention is the first to extend dual-vector model predictive control from a single machine to a multi-machine system. It proposes a harmonic suppression strategy based on dual-vector model prediction, taking the minimum error area between the harmonic current component at the PCC and the zero axis as the optimization objective. Compared with the method of only pursuing harmonics to be close to zero, it more effectively reduces the amplitude of each harmonic and achieves precise bus harmonic control.

[0026] 4. This invention is the first to apply the dual-vector model predictive control strategy to harmonic control in a multi-machine parallel system. It innovatively proposes a harmonic suppression approach with the minimum area of ​​PCC harmonic current and zero-axis error as the optimization objective, thereby achieving the minimization of harmonic energy control.

[0027] 5. This invention introduces dual-weighted coefficients for fundamental and harmonic frequencies into the objective function. By adjusting their ratio, a flexible trade-off between fundamental power transmission and harmonic suppression is achieved. When the system is in steady-state operation, priority is given to ensuring the inverter's power output accuracy; when the system is subjected to harmonic disturbances, the harmonic suppression capability can be adaptively enhanced, significantly improving the system's power quality.

[0028] 6. The dual-vector "vector-time" dual-layer optimization method proposed in this invention effectively reduces the dependence of harmonic mitigation on high switching frequencies, reduces system switching losses, and maintains good harmonic suppression effect. Attached Figure Description

[0029] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a structural diagram of the strategy system proposed in the embodiments of the present invention; Figure 2(a) is a schematic diagram of the current trajectory in the traditional zero steady-state error strategy in the embodiment of the present invention; Figure 2(b) is a schematic diagram of the current trajectory when the area is minimized in an embodiment of the present invention; Figure 3(a) is a current waveform diagram of the traditional zero steady-state error strategy in an embodiment of the present invention; Figure 3(b) is a schematic diagram of THD of the traditional zero steady-state error strategy in the embodiment of the present invention; Figure 4(a) is a current waveform diagram of the strategy proposed in the embodiment of the present invention; Figure 4(b) is a schematic diagram of the THD of the strategy proposed in the embodiment of the present invention. Detailed Implementation

[0030] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0031] This invention proposes a harmonic mitigation method for grid-connected inverters based on dual-vector model predictive control. This method independently models and predictively controls the fundamental and harmonic frequencies of the inverter output current, minimizing the error area between the harmonic current component at the PCC and the zero axis as the optimization objective. This effectively reduces the amplitude of each harmonic in the time domain, improving the total harmonic distortion (THD). Simultaneously, by separating the control of the fundamental and harmonic frequencies, direct coupling interference between them can be avoided, achieving multi-functional multiplexing of harmonic suppression and fundamental frequency power supply while ensuring normal power transmission of the inverter.

[0032] The harmonic mitigation strategy based on dual-vector MPC proposed in this invention can achieve precise suppression of common bus harmonics while ensuring stable system operation, providing a high-efficiency, reliable and engineering-approved harmonic mitigation technology path for multi-converter systems.

[0033] Example 1 This invention discloses a harmonic mitigation method for grid-connected inverters based on dual-vector model predictive control. The system's block diagram is shown below. Figure 1 As shown in the figure u' abc The voltage at PCC. i' abc The current at PCC; i PLL The phase angle is output by the phase-locked loop; i' α,h and i' β,h They are respectively α and β Current harmonics at the PCC under the shaft; i * abc,1 This is the fundamental reference value for the inverter output current. i * α,1 for α The fundamental reference value of the output current of the shaft-mounted inverter. i * β,1 for β The fundamental reference value of the output current of the shaft-mounted inverter; i α,1 for α The fundamental value of the output current of the shaft-mounted inverter. i β,1 for β Reference value for the fundamental frequency of the inverter output current.

[0034] This invention, based on standard Finite Control Set Model Predictive Control (FCS-MPC), introduces a dual-vector modulation stage and models and independently regulates the fundamental and harmonic frequencies of the inverter output current. Unlike traditional control methods, this invention explicitly prioritizes harmonic suppression as the primary objective and fundamental frequency tracking as a secondary objective. This allows for efficient suppression of common AC bus harmonics while ensuring normal power transmission of the inverter. The specific implementation methods are as follows: A. Theoretical Modeling for Obtaining Optimal Switching States In FCS-MPC, the system predicts the tracking error of each possible switching state in the next sampling period based on a discrete-time model and selects the switching state that minimizes the error. When the current contains harmonic components, if a rotating coordinate system is used, the harmonics will introduce coupled oscillations, increasing the complexity of the controller design. Therefore, this invention establishes a model in the αβ stationary coordinate system to achieve effective separation and control of the fundamental and harmonic components.

[0035] According to Kirchhoff's voltage and current laws, the mathematical model of a grid-connected inverter in the αβ coordinate system can be expressed as follows: (1) in, express α Down-shaft inverter output current, Indicates the filter resistor. express α Off-axis inverter output voltage, express α The voltage of the power grid below the shaft; express β Down-shaft inverter output current, express β Off-axis inverter output voltage, express β Voltage of the power grid below the shaft.

[0036] After discretizing equation (1), we can obtain αβ The prediction model in the coordinate system is (2) In the formula L f For filtering inductors, T c To control the cycle, R f For filtering resistors, i α ( k )and i β (k )for k Inverter α shaft and β Shaft-mounted output current; i α ( k +1) and i β ( k +1) is k +1 instant inverter α shaft and β Shaft-mounted output current; u α ( k )and u β ( k )for k time α shaft and β Off-axis inverter output voltage, u' α ( k )and u' β ( k )for k time α shaft and β Axis grid voltage.

[0037] Based on this, the present invention designs a dual-vector modulation and harmonic-fundamental separation control strategy. First, the harmonic current component of the PCC is detected and used as a control input to predict the future trend of the harmonic current under different switching vectors. Then, with the goal of minimizing the area enclosed by the harmonic current component and its zero reference value, the optimal dual vector and its application time are selected, thereby achieving priority control of harmonic suppression. Simultaneously, the inverter's fundamental current is used as another input reference, causing its output fundamental current to approach the set reference value, ensuring the inverter's normal power transmission function.

[0038] Therefore, by combining equation (2), the current is decomposed into fundamental and harmonic components. (3) Wherein, the subscript 1 represents the fundamental component, and the subscript... h Represents harmonic components.

[0039] Based on this, the fundamental wave prediction model can be obtained as follows: (4) Harmonic current at PCC i' h For grid-side harmonic current i grid,h With inverter i hSum of injected harmonic currents (5) The prediction model for inverter-side harmonic current is as follows: (6) Because the sampling period is very short, the external network side harmonics are considered approximately constant. (7) therefore, k The harmonic current at PCC at time +1 is (8) Simplifying, we can obtain the harmonic current at PCC as follows: (9) Based on the above prediction model, a control optimization objective can be further constructed. For the fundamental current, minimizing the reference tracking error is adopted as the optimization objective: (10) in, i * a,1 ( k+ 1) and i * β,1 ( k+ 1) In the αβ coordinate system k The fundamental reference current component at time +1 i a,1 ( k+ 1) and i β,1 ( k+ 1) The predicted fundamental current component.

[0040] For harmonic currents, the objective is to minimize the amplitude of the PCC harmonic components: (11) in, i’ a,h ( k+ 1) and i' β,h ( k+ 1) The harmonic current components at PCC predicted in the αβ coordinate system.

[0041] To achieve the above dual-objective control, this invention proposes the following objective function: (12) in, J f The target is the fundamental current deviation. J hFor harmonic current deviation target, or and c These are the weighting factors for the fundamental frequency and harmonics, respectively. These can be flexibly adjusted. or and c It can prioritize harmonic suppression while ensuring inverter power transmission, thereby improving the power quality of the common bus.

[0042] B. Harmonic mitigation method for grid-connected inverters based on dual-vector model predictive control In power quality analysis, the total harmonic distortion (THD) rate is commonly used to measure the relative abundance of harmonic components in current or voltage. In a stationary coordinate system, using... β Taking the shaft as an example, its traditional definition is as follows: (13) in, i * β ( t () represents the sinusoidal reference current at PCC. i β ( t ) for PCC β Actual shaft current, I β,1 yes β RMS value of axial fundamental wave T This is the fundamental frequency period. The numerator represents the mean square error between the total current and the fundamental current, which physically represents the degree of deviation of the current waveform from the fundamental frequency.

[0043] PCC β shaft current i' β ( t ) and sinusoidal reference current i * ' β ( t The area enclosed by the enclosure can be expressed as: (14) From equations (9) and (10) above, it can be seen that the sum of the root mean square values ​​of the current harmonics and the area S β Proportional.

[0044] The actual current at the PCC consists of two parts: the fundamental component and the harmonic component, which can be expressed as: (15) Similarly, the reference current consists of two parts: the fundamental reference current and the harmonic reference current, which can be expressed as: (16) The actual fundamental current can approximately perfectly track the reference current, i.e. i' β,1 ( t )≈ i * ' β,1 ( t Then equation (14) can be simplified to (17) Therefore, the sum of the root mean square values ​​of current harmonics is proportional to the area of ​​error between the harmonic current component and the zero axis. Based on this proportional relationship, this invention, when constructing the optimization objective, chooses to minimize the area enclosed by the harmonic component at the PCC and the reference value zero, to more intuitively reflect the magnitude of harmonic energy, thereby achieving direct suppression of harmonic components at the control level. This optimization objective not only maintains consistency with traditional THD but also provides a clearer harmonic energy constraint in control design, enabling precise adjustment of harmonics.

[0045] (1) Traditional method without steady-state error In the traditional two-vector model predictive current control framework, the controller is typically optimized to ensure that the current accurately reaches the reference value at the next sampling time. The basic idea is: within one control cycle... T c Two different voltage vectors are applied sequentially to make the current change along two different slopes, so as to achieve current tracking without steady-state error at the end of the cycle.

[0046] As shown in Figure 2(a), the system first determines the current state. i jo m and the slope of the current change corresponding to the candidate vector k j1 , k j2 The trajectory of the current under the action of two vectors is predicted. Then, the action time of the two vectors is adjusted. t j1 , t j2 This ensures that the current at the end of the cycle is equal to the reference value. The above process can be described by the following constraint relationships. (18) in, i * β,h For reference current, i m β,h For the initial current, k j1 and k j2These represent the slopes of the current change under the action of the two voltage vectors. t j1 The duration of action of the first vector. t j2 This is the duration of action of the second vector.

[0047] (2) The proposed method for minimizing harmonic area The area of ​​the error integral between the trajectory of the harmonic current on the PCC and zero reflects the magnitude of its THD. When this area is large, the THD is relatively high, and the power quality of the current on the PCC is poor. To further reduce THD, this invention is based on a dual-vector MPC framework, recalculating the action time of the dual vectors to minimize the area enclosed between the harmonic current trajectory of the PCC and the reference value, thereby achieving more accurate harmonic suppression. Specifically, in this invention... β The optimization objective is to minimize the error integral area of ​​the shaft harmonic current component. By dynamically adjusting the vector action time, the output current harmonics can be precisely suppressed.

[0048] As shown in Figure 2(b), in one control cycle T c Inside, β The axis harmonic current trajectory consists of two voltage vectors V j1 and V j2 The combined effect of these factors can be expressed as follows: (19) To minimize the area enclosed by the harmonic current and the reference value zero, the following function can be defined. (20) i' β,h ( t ) and reference value i*' β,h When the two lines intersect at =0, the intersection points are denoted as follows: P 1 and P 2, the corresponding time is t 1 and t 2, its calculation formula is (twenty one) Substituting equation (21) into equation (20), we can obtain the function with respect to... t j1 The parsing expression is (twenty two) In the control strategy of this invention, the two vectors have opposite directions, that is... k j1 kj2 The value <0 causes the current to fold back within one sampling period to approximate the reference current trajectory. At this time, there is... A >0, therefore f ( t j1 A convex quadratic function has a unique minimum. This can be determined by taking the derivative of the function and setting... f ′( t j1 When )=0, the optimal switching time can be obtained. t j1 Its expression is (twenty three) Finding the optimal switching time t j1 Subsequently, the optimal switching time for the dual-vector method was selected based on... β The optimization criterion is to minimize the area enclosed by the axis harmonic components and the zero axis, thereby achieving harmonic minimization control in the time domain. The selection of the dual vector combination comprehensively considers the dual objectives of fundamental current tracking and harmonic suppression. Equation (12) is used as the objective function, and the dynamic trade-off between the fundamental and harmonic components is achieved by adjusting the target weights. When the system is operating in steady state, the fundamental weight is increased to ensure the accurate transmission of the inverter's output power; when the system is subjected to harmonic disturbances, the harmonic weight is increased to enhance the harmonic suppression effect.

[0049] Finding the optimal switching time t j1 Subsequently, the dual-vector modulation strategy of this invention includes two core aspects: the switching time selection principle and the vector combination selection principle. Firstly, the selection of the switching time is based on... β The optimization criterion is to minimize the error area between the axis harmonic components and the zero axis. This is achieved through minimization in the time-domain integral sense, effectively suppressing harmonic energy. This optimization criterion causes the harmonic components of the PCC current to approach zero mean within one sampling period, thereby reducing the overall THD and achieving harmonic minimization control in the time domain. Secondly, the selection of the vector combination is determined by the objective function in equation (12). J The decision is based on a comprehensive consideration of the dual objectives of fundamental current tracking and harmonic suppression. This is achieved by adjusting the weighting factors for the fundamental and harmonic frequencies in the objective function. or and c This allows for a dynamic trade-off between the two: when the system is in steady-state operation, the fundamental frequency weight is appropriately increased to ensure accurate transmission of the inverter's output power; when the system is subjected to harmonic disturbances, the harmonic weight is increased to enhance the harmonic suppression effect. Through the above-mentioned "vector-time" dual-layer optimization, this invention significantly improves the power quality of the common bus while ensuring stable inverter operation, achieving a coordinated unity of harmonic suppression and power transmission functions. For a detailed analysis, see Implementation Example 1.

[0050] Example 2 This invention proposes a harmonic mitigation method for grid-connected inverters based on dual-vector model predictive control. This method introduces a dual-vector modulation mechanism into the traditional finite control set model predictive control framework, decomposing the inverter output current into fundamental and harmonic components, which are then modeled and optimized separately. Through a dual-objective predictive regulation process, the method can improve harmonic suppression capability while simultaneously considering the inverter's active and reactive power transmission performance, achieving coordinated mitigation of common bus harmonics and improving overall operational quality. To verify the effectiveness of this method, corresponding simulation tests were conducted.

[0051] This invention discloses a harmonic mitigation method for grid-connected inverters based on dual-vector model predictive control. This method, building upon the traditional finite control set model predictive control framework, introduces a dual-vector modulation mechanism and decomposes the inverter output current into fundamental and harmonic components, modeling and controlling them separately. This achieves effective suppression of common bus harmonics and coordinated protection of inverter power transmission. Simulation verification was conducted to validate the effectiveness of this harmonic mitigation method.

[0052] The DC bus voltage of the grid-connected inverter applying the proposed control strategy is 700V, and the filter inductor in the filter circuit... L f 2mH, grid voltage amplitude U 311V, mains frequency f 1. The frequency is 50Hz, the reference current amplitude for the three-phase grid-connected current is 50A, and the sampling frequency is 20kHz. In the MPC control loop, the fundamental frequency weighting factor is selected as follows: or =1, Harmonic weighting factor selection c =5, in order to simultaneously take into account fundamental wave tracking performance and harmonic suppression capability.

[0053] The expression for the harmonic current at PCC is as follows, and the THD calculated from this is 4.243%.

[0054] (twenty four) When using the traditional zero steady-state error control strategy, the current waveform at PCC is shown in Figure 3(a), and its harmonic distortion rate (THD) is 3.57%, as shown in Figure 3(b). In contrast, after adopting the dual-vector model predictive control strategy proposed in this invention, the current waveform at PCC is shown in Figure 4(a), and its THD is reduced to 2.66%, as shown in Figure 4(b). This indicates that the proposed method can effectively control harmonics at PCC and has superior control capabilities.

[0055] This invention establishes a harmonic prediction model and switching time optimization criteria under dual-vector action, providing a control basis for minimizing harmonic energy from a time-domain perspective, and for the first time incorporating fundamental current tracking and harmonic suppression objectives into the optimization mechanism. By rationally designing the fundamental and harmonic weighting factors, a better vector combination and switching strategy can be obtained, significantly reducing the harmonics of the common bus and providing an effective control means for the high-quality grid-connected operation of the inverter system.

[0056] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features of the present invention can be arbitrarily combined with each other.

Claims

1. A harmonic mitigation method for grid-connected inverters based on dual-vector model predictive control, characterized in that, Includes the following steps: A mathematical model of the grid-connected inverter in the αβ stationary coordinate system is established and discretized to obtain the current prediction model. The inverter output current is decomposed into fundamental and harmonic components, and a prediction model for the fundamental current and the harmonic current is established respectively. Obtain the harmonic current at PCC and construct an optimization objective function that includes the fundamental current deviation target and the harmonic current deviation target; Based on the aforementioned objective function, the optimal voltage vector combination and its application time are determined through two-vector model predictive control. By controlling the inverter according to the optimal voltage vector combination and its operating time, effective suppression of harmonics at the PCC can be achieved.

2. The harmonic mitigation method for grid-connected inverters based on dual-vector model predictive control according to claim 1, characterized in that, The fundamental current prediction model is as follows: in, i α ( k +1) and i β ( k +1) is k +1 instant inverter α shaft and β Shaft-mounted output current; T c To control the cycle, R f For filtering resistors, L f For filtering inductors, i α ( k )and i β ( k )for k Inverter α shaft and β Shaft-mounted output current; u α ( k )and u β ( k )for k time α shaft and β Off-axis inverter output voltage, u' α ( k )and u' β ( k )for k time α shaft and β Axis grid voltage.

3. The harmonic mitigation method for grid-connected inverters based on dual-vector model predictive control according to claim 1, characterized in that, The prediction model for the harmonic current at the PCC is as follows: in, and For the predicted PCC harmonic current, and This is to output harmonic current for the inverter.

4. The harmonic mitigation method for grid-connected inverters based on dual-vector model predictive control according to claim 1, characterized in that, The optimization objective function is: in, The target for fundamental current tracking error is... For harmonic suppression, η and γ are the weighting coefficients for the fundamental wave and harmonics, respectively.

5. The harmonic mitigation method for grid-connected inverters based on dual-vector model predictive control according to claim 4, characterized in that, Fundamental current tracking error target Defined as minimizing the fundamental current reference tracking error: in, i * a,1 ( k+ 1) and i * β,1 ( k+ 1) In the αβ coordinate system k The fundamental reference current component at time +1 i a,1 ( k+ 1) and i β,1 ( k+ 1) The predicted fundamental current component; The harmonic suppression target Defined as minimizing the amplitude of PCC harmonic components; in, i’ a,h ( k+ 1) and i' β,h ( k+ 1) The harmonic current components at PCC predicted in the αβ coordinate system.

6. The harmonic mitigation method for grid-connected inverters based on dual-vector model predictive control according to claim 1, characterized in that, In the dual-vector model predictive control, the duration of action of the dual vectors is determined by minimizing the error area between the PCC harmonic current and the zero axis.

7. The harmonic mitigation method for grid-connected inverters based on dual-vector model predictive control according to claim 6, characterized in that, For the β-axis harmonic current, minimizing the area enclosed by it and the reference value zero is the objective function: in, This refers to the β-axis harmonic current at the PCC. For reference only.

8. The harmonic mitigation method for grid-connected inverters based on dual-vector model predictive control according to claim 4, characterized in that, The fundamental wave weighting coefficient η and harmonic weighting coefficient γ can be dynamically adjusted according to the system operating status: η is increased during steady-state operation to ensure power transmission, and γ is increased during harmonic disturbances to enhance harmonic suppression.

9. The harmonic mitigation method for grid-connected inverters based on dual-vector model predictive control according to claim 1, characterized in that, The method is applied to a common AC bus system with multiple harmonic sources and multiple converters connected in parallel.

10. A harmonic mitigation system for a grid-connected inverter based on dual-vector model predictive control, characterized in that, Includes the following modules: Module M1: Establish a mathematical model of the grid-connected inverter in the αβ stationary coordinate system, and discretize it to obtain the current prediction model; Module M2: Decomposes the inverter output current into fundamental and harmonic components, and establishes a fundamental current prediction model and a harmonic current prediction model respectively. Module M3: Obtain the harmonic current at PCC and construct an optimization objective function that includes the fundamental current deviation target and the harmonic current deviation target; Module M4: Based on the aforementioned optimization objective function, the optimal voltage vector combination and its application time are determined through dual-vector model predictive control; Module M5: Controls the inverter based on the optimal voltage vector combination and its operating time to effectively suppress harmonics at the PCC.

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