Sampling noise compensation-based model-free predictive current control system and method for LCL grid-connected inverter

Abstract

The invention belongs to the technical field of electronic power, and particularly relates to an LCL grid-connected inverter model-free predictive current control system and method based on sampling noise compensation, and the system comprises an inverter main circuit, an LCL filter and a controller. The controller is configured to firstly calculate a current gradient and a voltage gradient according to a collected system operation signal; then, a second-order generalized integrator is used for conducting accurate compensation on sampling noise contained in the gradient; and finally, executing a model-free predictive control algorithm based on the compensated gradient, and preferably selecting an optimal voltage vector through a multi-objective value function integrating inverter side current, capacitor voltage and network side current. According to the method, sampling noise interference is effectively suppressed, the control precision and the anti-interference capability of the system are improved, and stable and efficient operation of the LCL grid-connected inverter can be realized without additional damping.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of electronic power, and particularly relates to a model-free predictive current control system and method for an LCL grid-connected inverter based on sampling noise compensation. BACKGROUND

[0002] As a key device for connecting a new energy power generation system to a power grid, a grid-connected inverter is mainly used to convert direct current into alternating current and feed the alternating current into the power grid. In order to ensure the quality of the grid-connected current and reduce the harmonic content, a filter is usually installed at the output end of the grid-connected inverter. Among various types of filters, an LCL filter is widely used because it can achieve better filtering effect while significantly reducing the size.

[0003] In the control strategy of the grid-connected inverter, model-free predictive current control (MFPCC) is concerned because it does not require accurate system model parameters and has strong robustness to parameter changes. However, in actual application, the MFPCC algorithm is easily disturbed by switching noise in the sampling process. These noises can mix into the sampled voltage and current signals, causing deviations in the calculated current gradient and voltage gradient. This deviation directly affects the accuracy of predictive control, thereby reducing the overall control performance and power quality of the grid-connected inverter system. Specifically, when the sampling signal contains noise, the controller will make predictions and controls based on inaccurate gradient information, which may lead to an increase in output current harmonics, a decrease in system stability, and even cause resonance in some cases. Therefore, how to effectively suppress the influence of sampling noise on the current gradient and voltage gradient, and improve the precision of model-free predictive current control and the anti-interference ability of the system, is a problem that needs to be solved in the current grid-connected inverter technology field. The existing technology needs to be improved. SUMMARY

[0004] The purpose of the present application is to provide a model-free predictive current control system and method for an LCL grid-connected inverter based on sampling noise compensation, to solve the problem of how to overcome the inaccuracy of current gradient and voltage gradient calculation caused by sampling switching noise in model-free predictive current control, thereby affecting control precision and system stability.

[0005] The present application achieves the above-mentioned purposes through the following technical solutions: The present application provides a model-free predictive current control system for an LCL grid-connected inverter based on sampling noise compensation, which comprises: an inverter main circuit for converting direct current into alternating current; an LCL filter connected between the alternating current output side of the inverter main circuit and the power grid; a signal acquisition circuit for acquiring electrical operating signals of the inverter main circuit and the LCL filter; The controller has its input terminal connected to the signal acquisition circuit and its output terminal connected to the switching transistor of the inverter main circuit. The controller includes: The gradient calculation module is used to predict the current at future times and calculate the current gradient and voltage gradient based on the electrical operation signal without system model parameters. The noise compensation module is used to input the current gradient and voltage gradient to a noise compensation module composed of a second-order generalized integrator SOGI, so as to filter out the sampling switching noise contained therein and obtain the compensated gradient. The signal generation module is used to execute a model-free predictive current control algorithm based on the compensated gradient to generate a switching signal that drives the inverter main circuit.

[0006] Furthermore, the method by which the controller calculates the current gradient is as follows: The current gradient Defined as the difference between the sampled current at the current moment and the current at the previous moment, its expression is: ; In the formula, It is the predicted current at time k+1. It is the sampling current at time k.

[0007] Furthermore, the gradient calculation module is configured as follows: Based on the optimal voltage vector of the previous control cycle and its corresponding historical gradient, estimate the current gradient and voltage gradient corresponding to all candidate voltage vectors in the current cycle. The historical gradients are stored in a pre-configured lookup table and updated with each control cycle.

[0008] Furthermore, the controller is configured to perform the following cyclic steps: a. For each candidate voltage vector in the current control cycle, the gradient is estimated using the gradient calculation module, and then compensated by the noise compensation module to obtain the compensated gradient; b. Based on the compensated gradient, predict the inverter-side current, filter capacitor voltage, and grid-side current at the next moment after applying each voltage vector; c. Substitute the predicted value obtained in step b into the pre-constructed multi-objective value function for calculation, and select the voltage vector that minimizes the value of the function as the optimal voltage vector output for the current cycle; d. At the start of the next control cycle, calculate the true gradient corresponding to the optimal voltage vector based on the actual system response and update it in the lookup table.

[0009] Furthermore, the expression for the multi-objective value function is: ; In the formula, Two-phase stationary coordinate system The inverter-side reference current of the shaft. Two-phase stationary coordinate system Predicted current on the inverter side of the shaft. for Reference value of capacitor voltage in axial coordinate system for Predicted capacitor voltage in axial coordinate system for Reference value of grid-side current in axial coordinate system for Predicted grid-side current values ​​in axial coordinate system and This is the weighting factor.

[0010] Furthermore, the transfer function of the second-order generalized integrator is: ; Where K is the gain, The resonant frequency, This represents the current gradient.

[0011] Furthermore, the method by which the controller compensates for noise using the second-order generalized integrator includes: Receive with a specific voltage vector Corresponding original current gradient With the original voltage gradient ; The original current gradient With the original voltage gradient The input gradient signals are fed in parallel into the second-order generalized integrator SOGI; SOGI processes the input gradient signals and outputs filtered current gradients after removing high-frequency noise. With the filtered voltage gradient ; Calculate the sampling noise error estimate of the current gradient , = - ; Calculate the estimated sampling noise error of the voltage gradient , = - ; Calculate the compensated current gradient , = - ; Calculate the compensated voltage gradient , = - .

[0012] This invention also provides a model-free predictive current control method for LCL grid-connected inverters based on sampling noise compensation, implemented through the control system described above. The method is executed by a controller, which includes a gradient calculation module, a noise compensation module, and a signal generation module. The method includes: The gradient calculation module calculates the current gradient and voltage gradient based on the collected operating signals of the LCL grid-connected inverter system. The noise compensation module receives the current gradient and voltage gradient, and uses its internal second-order generalized integrator SOGI to compensate for the sampling noise contained in the gradient, and outputs the compensated gradient. The signal generation module executes a model-free predictive current control algorithm based on the compensated gradient to generate switching signals for controlling the inverter circuit.

[0013] The beneficial effects of this invention are as follows: 1. This invention employs a second-order generalized integrator to perform real-time noise compensation for current and voltage gradients, effectively filtering out switching noise components in the sampled signals and significantly improving the accuracy of the gradient signals. This enables model-free predictive control algorithms to predict current based on more accurate gradient information, thereby improving the system's current tracking accuracy and control performance.

[0014] 2. This invention enhances the system's ability to suppress sampling noise, improves its anti-interference performance and operational stability in complex power grid environments, and effectively avoids the risk of increased current harmonics and system oscillations caused by gradient calculation deviations.

[0015] 3. This invention constructs a multi-objective value function that includes inverter-side current, capacitor voltage, and grid-side current, and sets weighting factors appropriately. This enables stable control of the system without the need to introduce additional damping circuits, thus maintaining the simplicity of the system structure and reducing hardware costs.

[0016] 4. While maintaining the strong robustness of model-free control to parameter changes, this invention effectively solves the problem of sampling noise affecting control accuracy, providing a control scheme with higher control accuracy and stronger anti-interference capability for LCL grid-connected inverters. Attached Figure Description

[0017] Figure 1 This is a system block diagram of a model-free predictive current control system for LCL grid-connected inverters based on sampling noise compensation in this invention. Figure 2This is the main circuit topology diagram of the two-level LCL grid-connected inverter used in this invention; Figure 3 This is a block diagram of the second-order generalized integrator (SOGI) used in this invention. Detailed Implementation

[0018] The following description provides specific application scenarios and requirements for this specification, intended to enable those skilled in the art to make and use the contents of this specification. Various partial modifications to the disclosed embodiments will be apparent to those skilled in the art, and the general principles defined herein can be applied to other embodiments and applications without departing from the spirit and scope of this specification. Therefore, this specification is not limited to the embodiments shown, but rather to the widest scope consistent with the claims.

[0019] The terminology used herein is for the purpose of describing particular exemplary embodiments only and is not restrictive. For example, unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “the” used herein may also include the plural forms. When used in this specification, the terms “comprising,” “including,” and / or “containing” mean that the associated integers, steps, operations, elements, and / or components are present, but do not exclude the presence of one or more other features, integers, steps, operations, elements, components, and / or groups, or that other features, integers, steps, operations, elements, components, and / or groups may be added to the system / method.

[0020] Considering the following description, these and other features of this specification, as well as the operation and function of the related components of the structure, and the economy of assembly and manufacture of the parts, can be significantly improved. All of these form part of this specification with reference to the accompanying drawings. However, it should be clearly understood that the drawings are for illustrative and descriptive purposes only and are not intended to limit the scope of this specification. It should also be understood that the drawings are not drawn to scale.

[0021] The flowcharts used in this specification illustrate operations implemented according to some embodiments of this specification. It should be clearly understood that the operations in the flowcharts may not be implemented in a sequential order. Instead, the operations may be implemented in reverse order or simultaneously. Furthermore, one or more additional operations may be added to the flowcharts. One or more operations may be removed from the flowcharts.

[0022] As a key device for connecting new energy systems to the power grid, the harmonic content of the output current of grid-connected inverters is an important indicator of their performance. To reduce harmonics, LCL filters are widely used in grid-connected inverters due to their excellent filtering effect and small size. However, traditional model predictive control methods are easily affected by system parameter mismatches when applied to LCL grid-connected inverters, leading to a significant reduction in control effectiveness.

[0023] In model-free predictive current control (MFPCC), although the dependence on model parameters is eliminated, switching noise is inevitably introduced into the sampled voltage and current during the actual sampling process. This noise directly affects the accuracy of current and voltage gradient calculations, leading to deviations in predictive control and ultimately impacting the overall performance and stability of the grid-connected inverter system. For example, suppose a grid-connected inverter system, during operation, experiences significant sampling noise in the acquired system operating signals (such as inverter-side current, filter capacitor voltage, and grid-side current) due to complex grid environments or frequent switching of switching devices. If this problem is not addressed, this noise will be directly transmitted to the controller's gradient calculation module, causing the calculated current and voltage gradients to deviate from their true values. This results in the controller executing the model-free predictive current control algorithm based on incorrect gradient information, potentially leading to inverter output current distortion, increased harmonic content in the grid-connected current, and even system instability.

[0024] To address this issue, this application proposes a model-free predictive current control system and method for LCL grid-connected inverters based on sampling noise compensation, aiming to solve the problem of decreased accuracy of model-free predictive current control under the influence of sampling noise in existing technologies. This system effectively compensates for sampling noise in the acquired system operating signals through gradient calculation and noise compensation, thereby improving the calculation accuracy of current and voltage gradients, and ultimately enhancing the performance of the model-free predictive current control algorithm and the system's anti-interference capability.

[0025] System Modeling and Theoretical Foundations To achieve accurate model-free predictive control, a mathematical model of the system is first required. The two-level LCL grid-connected inverter topology constructed in this invention is as follows: Figure 2 As shown.

[0026] exist Figure 2 In the LCL grid-connected inverter shown, in the LCL grid-connected inverter, This refers to the DC-side voltage of the inverter. , , This refers to the three-phase output voltage of the inverter. , , This refers to the voltage of the three-phase filter capacitor. , , This refers to the three-phase output current on the inverter side; , , These are the three-phase power grid currents; , , This refers to the three-phase power grid voltage. For the inverter-side filter inductor, C is the grid-side filter inductor; C is the filter capacitor; For the parasitic resistance of the inverter-side inductor, S1 represents the parasitic resistance of the grid-side inductor; S1~S6 are six switching transistors.

[0027] A two-level LCL grid-connected inverter has three bridge arms, encompassing eight switching states. These eight switching states correspond to eight voltage vectors, namely: , , , , , , , .

[0028] According to Kirchhoff's current and voltage laws, a mathematical model in a three-phase stationary coordinate system (abc) can be derived: The differential equation for the inverter-side current is: ; Similarly, the differential equation for the capacitor voltage can be obtained: ; Differential equation for grid current: ; To reduce the complexity of controller design, the three-phase stationary coordinate system (abc) is transformed to a two-phase stationary coordinate system using Clark transformation. Below. The transformed system mathematical model simplifies to: ; The mathematical model in the two-phase stationary coordinate system is derived as follows: ; The above continuous model is discretized using Euler method to obtain a discrete form, which is easier for digital controllers to process. ; The list can be written in determinant form as follows: ; The core of the Model-Free Predictive Current Control (MFPCC) method based on lookup table (LUT) is defining and utilizing the current gradient. Current gradient Defined as the difference between the sampled currents of adjacent control cycles: ; In the formula, It is the predicted current at time k+1. It is the sampling current at time k. It is the current gradient at time k.

[0029] Because of the control delay, the predicted current at time k+2 is required: ; In MFPCC, the lookup table is updated by distinguishing the gradients corresponding to the optimal and non-optimal voltage vectors. This leads to the complete derivation of the current and voltage gradients used for prediction.

[0030] The gradients corresponding to the optimal voltage vector are as follows: ; The gradients corresponding to the non-optimal voltage vectors are as follows: ; This leads to the derivation formulas for the current gradient and voltage gradient: ; First Embodiment This embodiment discloses a model-free predictive current control system for an LCL grid-connected inverter based on sampling noise compensation. The system mainly includes an inverter circuit, an LCL filter connected to the inverter circuit, and a controller for controlling the inverter circuit. The inverter circuit is responsible for converting DC power into AC power and feeding it into the grid. The LCL filter is used to filter out high-frequency harmonics in the inverter circuit's output current, ensuring the quality of the grid-connected current. The controller is the core of the entire system; its main function is to execute the model-free predictive current control algorithm to achieve precise control of the inverter circuit.

[0031] Please combine Figure 1 This embodiment proposes a model-free predictive current control system for LCL grid-connected inverters based on sampling noise compensation. The system includes: The main circuit of the inverter is used to convert direct current (DC) to alternating current (AC). An LCL-type filter is connected between the AC output side of the inverter's main circuit and the power grid. The signal acquisition circuit is used to acquire the electrical operating signals of the inverter main circuit and the LCL filter; The controller has its input terminal connected to the signal acquisition circuit and its output terminal connected to the switching transistor of the inverter main circuit. The controller includes: The gradient calculation module is used to predict the current at future times and calculate the current gradient and voltage gradient based on the electrical operation signal without system model parameters. The noise compensation module is used to input the current gradient and voltage gradient to a noise compensation module composed of a second-order generalized integrator SOGI, so as to filter out the sampling switching noise contained therein and obtain the compensated gradient. The signal generation module is used to execute a model-free predictive current control algorithm based on the compensated gradient to generate a switching signal that drives the inverter main circuit.

[0032] In this embodiment, the controller can be a digital signal processor (DSP), a microcontroller unit (MCU), or a field-programmable gate array (FPGA). The signal acquisition circuit includes a current sensor and a voltage sensor, used to acquire the electrical operating signals in real time, and convert them into digital signals via an analog-to-digital converter (ADC) before sending them to the controller. The output of the controller is a pulse width modulation (PWM) signal, which, after being amplified by the drive circuit, directly controls the on and off states of the switching transistors (S1-S6) in the inverter's main circuit.

[0033] Preferably, the method by which the controller calculates the current gradient is as follows: The current gradient Defined as the difference between the sampled current at the current moment and the current at the previous moment, its expression is: ; In the formula, It is the predicted current at time k+1. It is the sampling current at time k.

[0034] Preferably, the gradient calculation module is configured as follows: Based on the optimal voltage vector of the previous control cycle and its corresponding historical gradient, estimate the current gradient and voltage gradient corresponding to all candidate voltage vectors in the current cycle. The historical gradients are stored in a pre-configured lookup table and updated with each control cycle.

[0035] Preferably, the controller is configured to perform the following cyclic steps: a. For each candidate voltage vector in the current control cycle, the gradient is estimated using the gradient calculation module, and then compensated by the noise compensation module to obtain the compensated gradient; b. Based on the compensated gradient, predict the inverter-side current, filter capacitor voltage, and grid-side current at the next moment after applying each voltage vector; c. Substitute the predicted value obtained in step b into the pre-constructed multi-objective value function for calculation, and select the voltage vector that minimizes the value of the function as the optimal voltage vector output for the current cycle; Preferably, the expression for the multi-objective value function is: ; In the formula, Two-phase stationary coordinate system The inverter-side reference current of the shaft. Two-phase stationary coordinate system Predicted current on the inverter side of the shaft. for Reference value of capacitor voltage in axial coordinate system for Predicted capacitor voltage in axial coordinate system for Reference value of grid-side current in axial coordinate system for Predicted grid-side current values ​​in axial coordinate system and This is the weighting factor.

[0036] By adjusting the value of the weighting factor, stable control of the LCL grid-connected inverter can be achieved without introducing additional external damping.

[0037] d. At the start of the next control cycle, calculate the true gradient corresponding to the optimal voltage vector based on the actual system response and update it in the lookup table.

[0038] Preferably, the transfer function of the second-order generalized integrator is: ; Where K is the gain, The resonant frequency, This represents the current gradient.

[0039] Preferably, the method by which the controller compensates for noise using the second-order generalized integrator includes: In the MFPCC method, the sampled signal contains switching noise, causing the measured value to deviate from the ideal value, as shown below: ; In the above formula, and For ideal current value, and Is it switching noise? Deviation in the coordinate system.

[0040] ; and For ideal voltage values, and Is it switching noise? The deviation in the coordinate system. This noise causes errors in the calculated current gradient and voltage gradient.

[0041] The noise compensation module compensates for the error using SOGI, specifically as follows: Receive with a specific voltage vector Corresponding original current gradient With the original voltage gradient ; the original current gradient With the original voltage gradient The inputs are fed in parallel into the second-order generalized integrator SOGI (which can be combined with...). Figure 3 The SOGI process the input gradient signal and outputs filtered current gradients after removing high-frequency noise. With the filtered voltage gradient ; Calculate the sampling noise error estimate of the current gradient , = - ; Calculate the estimated sampling noise error of the voltage gradient , = - ; Calculate the compensated current gradient , = - ; Calculate the compensated voltage gradient , = - .

[0042] Second Embodiment This embodiment proposes a model-free predictive current control method for LCL grid-connected inverters based on sampling noise compensation. It is implemented using a control system as described in the first embodiment. The method is executed by a controller, which includes a gradient calculation module, a noise compensation module, and a signal generation module. The method includes: The gradient calculation module calculates the current gradient and voltage gradient based on the collected operating signals of the LCL grid-connected inverter system. The noise compensation module receives the current gradient and voltage gradient, and uses its internal second-order generalized integrator SOGI to compensate for the sampling noise contained in the gradient, and outputs the compensated gradient. The signal generation module executes a model-free predictive current control algorithm based on the compensated gradient to generate switching signals for controlling the inverter circuit.

[0043] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0044] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0045] The embodiments described above are merely examples of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. A model-free predictive current control system for LCL grid-connected inverters based on sampling noise compensation, characterized in that the system... include: The main circuit of the inverter is used to convert direct current (DC) to alternating current (AC). An LCL-type filter is connected between the AC output side of the inverter's main circuit and the power grid. The signal acquisition circuit is used to acquire the electrical operating signals of the inverter main circuit and the LCL filter; The controller has its input terminal connected to the signal acquisition circuit and its output terminal connected to the switching transistor of the inverter main circuit. The controller includes: The gradient calculation module is used to predict the current at future times and calculate the current gradient and voltage gradient based on the electrical operation signal without system model parameters. The noise compensation module is used to input the current gradient and voltage gradient to a noise compensation module composed of a second-order generalized integrator SOGI, so as to filter out the sampling switching noise contained therein and obtain the compensated gradient. The signal generation module is used to execute a model-free predictive current control algorithm based on the compensated gradient to generate a switching signal that drives the inverter main circuit.

2. The model-free predictive current control system for LCL grid-connected inverters based on sampling noise compensation according to claim 1, characterized in that, The method by which the controller calculates the current gradient is as follows: The current gradient Defined as the difference between the sampled current at the current moment and the current at the previous moment, its expression is: ； In the formula, It is the predicted current at time k+1. It is the sampling current at time k.

3. The model-free predictive current control system for LCL grid-connected inverters based on sampling noise compensation according to claim 1, characterized in that, The gradient calculation module is configured as follows: Based on the optimal voltage vector of the previous control cycle and its corresponding historical gradient, estimate the current gradient and voltage gradient corresponding to all candidate voltage vectors in the current cycle. The historical gradients are stored in a pre-configured lookup table and updated with each control cycle.

4. The model-free predictive current control system for LCL grid-connected inverters based on sampling noise compensation according to claim 3, characterized in that, The controller is configured to perform the following cyclic steps: a. For each candidate voltage vector in the current control cycle, the gradient is estimated using the gradient calculation module, and then compensated by the noise compensation module to obtain the compensated gradient; b. Based on the compensated gradient, predict the inverter-side current, filter capacitor voltage, and grid-side current at the next moment after applying each voltage vector; c. Substitute the predicted value obtained in step b into the pre-constructed multi-objective value function for calculation, and select the voltage vector that minimizes the value of the function as the optimal voltage vector output for the current cycle; d. At the start of the next control cycle, calculate the true gradient corresponding to the optimal voltage vector based on the actual system response and update it in the lookup table.

5. The model-free predictive current control system for LCL grid-connected inverters based on sampling noise compensation according to claim 4, characterized in that, The expression for the multi-objective value function is: ； In the formula, Two-phase stationary coordinate system The inverter-side reference current of the shaft. Two-phase stationary coordinate system Predicted current on the inverter side of the shaft. for Reference value of capacitor voltage in axial coordinate system for Predicted capacitor voltage in axial coordinate system for Reference value of grid-side current in axial coordinate system for Predicted grid-side current values ​​in axial coordinate system and This is the weighting factor.

6. The model-free predictive current control system for LCL grid-connected inverters based on sampling noise compensation according to claim 1, characterized in that, The transfer function of the second-order generalized integrator is: ； Where K is the gain, The resonant frequency, This represents the current gradient.

7. The model-free predictive current control system for LCL grid-connected inverters based on sampling noise compensation according to claim 6, characterized in that, The method by which the controller compensates for noise using the second-order generalized integrator includes: Receive with a specific voltage vector Corresponding original current gradient With the original voltage gradient ; The original current gradient With the original voltage gradient The input gradient signals are fed in parallel into the second-order generalized integrator SOGI; SOGI processes the input gradient signals and outputs filtered current gradients after removing high-frequency noise. With the filtered voltage gradient ; Calculate the sampling noise error estimate of the current gradient , = - ; Calculate the estimated sampling noise error of the voltage gradient , = - ; Calculate the compensated current gradient , = - ; Calculate the compensated voltage gradient , = - .

8. A model-free predictive current control method for LCL grid-connected inverters based on sampling noise compensation, characterized in that, Implemented by a control system as described in any one of claims 1-7, the method is executed by a controller, the controller comprising a gradient calculation module, a noise compensation module, and a signal generation module, the method comprising: The gradient calculation module calculates the current gradient and voltage gradient based on the collected operating signals of the LCL grid-connected inverter system. The noise compensation module receives the current gradient and voltage gradient, and uses its internal second-order generalized integrator SOGI to compensate for the sampling noise contained in the gradient, and outputs the compensated gradient. The signal generation module executes a model-free predictive current control algorithm based on the compensated gradient to generate switching signals for controlling the inverter circuit.

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