MMC adaptive sliding mode control system and method based on two-phase static coordinate system

Abstract

The invention provides an MMC adaptive sliding mode control system and method based on a two-phase static coordinate system, and relates to the field of electric power transmission. The method comprises the following steps: acquiring alpha-beta-axis components of voltage and current of a power grid, and constructing a mathematical model of the modular multilevel converter in a two-phase static coordinate system; calculating a current error value on an alpha-beta axis on the two-phase static coordinate system model; a composite sliding mode surface including a linear term and an integral term is preset and is used for dynamically converging the control target; the error value is used as an independent variable, and a self-adaptive sliding mode reaching rate function is constructed and used for dynamically adjusting the reaching speed according to the size of the error; and generating a control voltage signal on an alpha-beta axis based on the sliding mode surface and the self-adaptive approaching rate, and modulating and outputting the control voltage signal to a power device of the MMC through carrier shifting modulation. The problems that a traditional dq coordinate system needs to apply a large amount of calculation in the PI control technology, the overall processing calculation complexity is high, and control is delayed are solved.

Description

Technical Field

[0001] This application relates to the field of electric drive, and in particular to an MMC adaptive sliding mode control system and method based on a two-phase stationary coordinate system. Background Technology

[0002] With the grid connection of large-scale renewable energy sources such as photovoltaics and wind power, as well as AC / DC loads such as electric vehicles and fuel cells, medium-voltage direct current (MVDC) distribution networks are attracting increasing attention. Existing MVDC systems are characterized by modularity, high scalability, high efficiency, low voltage stress on power semiconductor devices, and good output voltage quality, and have been applied as interconnect converters in many completed AC / DC HDN pilot projects. However, in actual use, the controlled object needs to undergo Clark transformation, Park transformation, and inverse Park transformation to achieve coordinate transformation, and a phase-locked loop is required to ensure phase synchronization. This process requires a large amount of mathematical calculation, increasing the system's computational complexity, demanding high hardware computing power, and easily leading to control delays in scenarios with limited computing power. Summary of the Invention

[0003] This application provides an MMC adaptive sliding mode control system and method based on a two-phase stationary coordinate system, which solves the technical problem that the traditional dq coordinate system requires a large amount of calculation in PI control technology, resulting in high overall computational complexity and control delay.

[0004] To achieve the above objectives, this application adopts the following technical solution: Firstly, the MMC adaptive sliding mode control system based on a two-phase stationary coordinate system includes: a two-phase stationary coordinate system modeling module, which acquires the αβ axis components of the grid voltage and current, and constructs a mathematical model of the modular multilevel converter in the two-phase stationary coordinate system; an error calculation and sliding surface design module, which calculates the current error value on the αβ axis of the two-phase stationary coordinate system model; and presets a composite sliding surface including linear and integral terms for dynamically converging the control target; an adaptive sliding mode approach rate design module, which constructs an adaptive sliding mode approach rate function with the error value as the independent variable, for dynamically adjusting the approach speed according to the error magnitude; and a control signal generation module, which generates a control voltage signal on the αβ axis based on the sliding surface and the adaptive approach rate, and outputs it to the power device of the MMC through carrier shift modulation.

[0005] Based on the above technical solutions, in the MMC adaptive sliding mode control system based on a two-phase stationary coordinate system provided in this application, the two-phase stationary coordinate system modeling module effectively eliminates the phase-locked loop, Park transformation, and inverse Park transformation required by the traditional dq model, fundamentally reducing computational complexity and solving the problems of poor real-time performance and algorithm burden caused by coordinate transformation in PI control. Simultaneously, by using a sliding surface composite of linear and integral terms in the error calculation and sliding surface design module, combined with the nonlinear function of the adaptive sliding mode approach rate design module to dynamically adjust the approach speed, intelligent control of "fast approach for large errors and slow adjustment for small errors" is achieved, significantly improving transient response speed and suppressing overshoot, overcoming the dynamic sluggishness and oscillation problems caused by the linear characteristics and multi-loop series connection of PI controllers. The introduction of the external integral term continuously compensates for small errors, making the steady-state error approach zero, while Lyapunov stability verification ensures the robustness of the system under disturbances, solving the problem of insufficient steady-state accuracy of PI control in nonlinear multi-timescale systems. Finally, the control signal generation module softens the output through a saturation function and combines it with carrier phase-shift modulation to generate a smooth control voltage signal, thereby enhancing overall stability.

[0006] In conjunction with the first aspect mentioned above, in one possible implementation, the construction of the two-phase stationary coordinate system modeling module specifically includes: acquiring the instantaneous voltage and current signals of the three-phase power grid and recording them as three-phase voltage and current signals; converting the three-phase voltage and current signals into α-axis and β-axis components in the two-phase stationary coordinate system through the Clarke transform unit; and establishing a mathematical model of MMC in the two-phase stationary coordinate system.

[0007] In conjunction with the first aspect mentioned above, in one possible implementation, the error calculation and sliding surface design module includes an error calculation unit and a sliding surface design unit: the error calculation unit calculates the current error values ​​on the α-axis and β-axis based on the reference current value and the actual current value; the sliding surface design unit obtains the linear term and integral term through the current error value and presets the composite sliding surface.

[0008] In conjunction with the first aspect mentioned above, in one possible implementation, the process of the adaptive sliding mode approach rate design module outputting the adaptive approach rate value specifically includes: converting the current error value e... α e β and sliding mode surface value s α s β As the independent variable, a nonlinear adaptive approach rate function is constructed. ,in This is the error proportion term. For the sliding mode surface modulation term; the approach rate value is obtained by running the nonlinear adaptive approach rate function. and For the convergence rate value Perform Lyapunov stability condition verification.

[0009] In conjunction with the first aspect mentioned above, in one possible implementation, the process of verifying the Lyapunov stability condition specifically includes: pre-setting a Lyapunov function and embedding radial unboundedness and negative definiteness requirements; and then, based on the convergence rate value... When both radial unboundedness and negative definiteness are satisfied, the approach rate value will be... Feedback is sent to the control signal generation module.

[0010] In conjunction with the first aspect mentioned above, in one possible implementation, the process of the control signal generation module generating the final control signal specifically includes: acquiring the current error value. and Slip mode surface value and and the rate of convergence ; Calculate the control voltage signals on the α and β axes using the control voltage formula. ; control voltage signal The signal is input to the carrier phase-shift modulation module, which generates a PWM modulation signal that is fed back to the MMC power device to drive the MMC bridge arm switch.

[0011] In conjunction with the first aspect mentioned above, one possible implementation also includes a DC-side control module for MMC submodule voltage balancing: the outer-loop voltage regulator unit compares the submodule voltage reference value... The average voltage U of each phase upper and lower bridge arm submodule a Generate circulation reference values The inner loop flow control unit will use the loop reference value. Compared with actual circulation value Compare and generate circulating control signals The signal synthesis unit is based on the circulating reference value. and circulating control signal Obtain the final control signal The pressure equalization control unit calculates the pressure equalization coefficient K and outputs a pressure equalization compensation signal. .

[0012] In conjunction with the first aspect mentioned above, in one possible implementation, the DC-side control module further includes a CPS-PWM modulation unit: based on the final control signal and the voltage equalization compensation signal, it generates multiple triangular carrier signals with the same frequency but sequentially offset phases; it compares the multiple triangular carrier signals with the modulation wave, generates a PWM drive signal, and feeds it back to the sub-module power device of the MMC for sub-module voltage equalization and circulating current suppression.

[0013] Secondly, an adaptive sliding mode control system for a modular multilevel converter (MMC) based on a two-phase stationary coordinate system is provided, including: an AC side and a DC side; on the DC side, the αβ axis components of the grid voltage and current are acquired, and a mathematical model of the modular multilevel converter in the two-phase stationary coordinate system is constructed; the current error value on the αβ axis of the two-phase stationary coordinate system model is calculated; and a composite sliding surface including linear and integral terms is preset for dynamically converging the control target; an adaptive sliding mode approach rate function is constructed with the error value as the independent variable to dynamically adjust the approach speed according to the error magnitude; based on the sliding surface and the adaptive approach rate, a control voltage signal on the αβ axis is generated and output to the power device of the MMC through carrier shift modulation. The DC side of the MMC includes: comparing submodule voltage reference values Average voltage of each phase upper and lower bridge arm submodule Generate circulation reference values ; Circulation reference value Compared with actual circulation value Compare and generate circulating control signals Based on circulation reference values and circulating control signal Obtain the final control signal Calculate the equalization coefficient K and output the equalization compensation signal. .

[0014] This application provides an MMC adaptive sliding mode control system based on a two-phase stationary coordinate system. The two-phase stationary coordinate system modeling module effectively eliminates the phase-locked loop, Park transformation, and inverse Park transformation required by traditional dq models, fundamentally reducing computational complexity and solving the problems of poor real-time performance and algorithmic burden caused by coordinate transformation in PI control. Simultaneously, by employing a sliding surface combining linear and integral terms in the error calculation and sliding surface design modules, and combining this with the nonlinear function of the adaptive sliding mode approach rate design module to dynamically adjust the approach speed, intelligent control is achieved, characterized by "fast approach for large errors and slow adjustment for small errors." This significantly improves transient response speed and suppresses overshoot, overcoming the dynamic sluggishness and oscillation problems caused by the linear characteristics and multi-loop series connection of PI controllers. The introduction of the external integral term continuously compensates for small errors, bringing the steady-state error close to zero, while Lyapunov stability verification ensures the system's robustness under disturbances, addressing the deficiency of insufficient steady-state accuracy in nonlinear multi-timescale systems under PI control. Finally, the control signal generation module softens the output through a saturation function and combines it with carrier phase-shift modulation to generate a smooth control voltage signal, thereby enhancing overall stability.

[0015] It should be understood that the descriptions of technical features, technical solutions, beneficial effects, or similar language in this application do not imply that all features and advantages can be achieved in any single embodiment. Rather, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution, or beneficial effect is included in at least one embodiment. Therefore, the descriptions of technical features, technical solutions, or beneficial effects in this specification do not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions, and beneficial effects described in this embodiment can be combined in any suitable manner. Those skilled in the art will understand that embodiments can be implemented without one or more specific technical features, technical solutions, or beneficial effects of a particular embodiment. In other embodiments, additional technical features and beneficial effects may be identified in specific embodiments that do not embody all embodiments. Attached Figure Description

[0016] Figure 1 An architecture diagram of an MMC adaptive sliding mode control system based on a two-phase stationary coordinate system provided in an embodiment of this application; Figure 2 AC side control diagram of MMC adaptive sliding mode control system based on two-phase stationary coordinate system provided in the embodiments of this application; Figure 3 A flowchart illustrating the MMC adaptive sliding mode control system based on a two-phase stationary coordinate system provided in this application embodiment; Figure 4 A flowchart illustrating the MMC adaptive sliding mode control system based on a two-phase stationary coordinate system provided in this application embodiment; Figure 5 Phase plane diagram of adaptive approach rate in MMC adaptive sliding mode control system based on two-phase stationary coordinate system provided in the embodiments of this application; Figure 6 A flowchart illustrating the MMC adaptive sliding mode control system based on a two-phase stationary coordinate system provided in this application embodiment; Figure 7 A control diagram of the DC side of an MMC adaptive sliding mode control system based on a two-phase stationary coordinate system provided in an embodiment of this application; Figure 8 The overall control diagram of the MMC adaptive sliding mode control system based on a two-phase stationary coordinate system provided in the embodiments of this application; Figure 9 MMC circulating current equalization suppression diagram provided in the embodiments of this application for an MMC adaptive sliding mode control system based on a two-phase stationary coordinate system; Figure 10 A flowchart illustrating the MMC adaptive sliding mode control system based on a two-phase stationary coordinate system provided in this application embodiment; Figure 11A flowchart illustrating the MMC adaptive sliding mode control system based on a two-phase stationary coordinate system provided in this application embodiment; Figure 12 This is a flowchart illustrating the MMC adaptive sliding mode control method based on a two-phase stationary coordinate system provided in an embodiment of this application.

[0017] In the diagram: 100, MMC system; 101, AC side; 1011, two-phase stationary coordinate system modeling module; 1012, error calculation and sliding mode surface design module; 1013, adaptive sliding mode approach rate design module; 1014, control signal generation module; 102, DC side; 1021, outer loop voltage regulator unit; 1022, inner loop circulating current control unit; 1023, signal synthesis unit; 1024, CPS-PWM modulation unit; 1025, voltage equalization control unit. Detailed Implementation

[0018] In the description of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. The "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, "at least one" means one or more, and "multiple" means two or more. The terms "first," "second," etc., do not limit the quantity or order of execution, and "first," "second," etc., do not necessarily imply differences.

[0019] It should be noted that, in this application, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0020] To address the challenges of existing technologies that require Clark, Park, and inverse Park transformations of the controlled object for coordinate transformation, coupled with phase-locked loops (PLLs) to ensure phase synchronization, the extensive mathematical calculations increase system computational complexity and demand high hardware computing power. This can lead to control delays in scenarios with limited computing power. Furthermore, the linear characteristics and multi-loop series structure of the PI controller are incompatible with the nonlinearity, multi-timescale, and strong coupling characteristics of the MMC. This results in the system response speed being limited by the outer loop control with the lowest bandwidth. To ensure robust stability, conservative low-bandwidth parameters must be selected, leading to slow transient responses to disturbances or changes in control commands, and a longer recovery time to a stable state. Under conditions such as MMC pre-start and grid voltage fluctuations, the PI controller's bandwidth has an inherent upper limit, preventing its output from promptly adapting to drastic system state changes. This can easily cause grid current overshoot, and the integral term is prone to saturation during dynamic processes. When commands are reversed or the system recovers, the integrator needs time to exit saturation, further exacerbating the transient overshoot problem. Furthermore, as a nonlinear, strongly coupled system, the operating parameters of MMC fluctuate significantly with changes in power level, DC voltage, and AC system strength. The linear, time-invariant PI controller parameter design is based on specific operating conditions and cannot maintain optimal performance across the entire operating range. Additionally, unfavorable interactions easily arise between control loops of different bandwidths, making it difficult to eliminate steady-state errors. Moreover, this scheme relies on a phase-locked loop for phase synchronization, making it sensitive to non-ideal conditions such as grid frequency fluctuations and phase jumps. Under complex operating conditions, control accuracy easily decreases, robustness is insufficient, and it struggles to adapt to the technical problems of dynamic load changes brought about by renewable energy grid integration. This application provides an MMC adaptive sliding mode control system based on a two-phase stationary coordinate system. This method, through the two-phase stationary coordinate system modeling module 1011, effectively eliminates the phase-locked loop, Park transformation, and inverse Park transformation stages required by the traditional dq model, fundamentally reducing computational complexity and solving the problems of poor real-time performance and algorithmic burden caused by coordinate transformation in PI control. Meanwhile, the error calculation and sliding surface design module 1012 employs a sliding surface combining linear and integral terms, and the adaptive sliding surface approach rate design module 1013 dynamically adjusts the approach speed using a nonlinear function. This achieves intelligent control characterized by "fast approach for large errors and slow adjustment for small errors," significantly improving transient response speed and suppressing overshoot. It overcomes the dynamic sluggishness and oscillation problems caused by the linear characteristics and multi-loop series connection of PI controllers. The introduction of the external integral term continuously compensates for small errors, bringing the steady-state error close to zero. Lyapunov stability verification ensures the system's robustness under disturbances, resolving the deficiency of insufficient steady-state accuracy in nonlinear multi-timescale systems. Finally, the control signal generation module 1014 softens the output through a saturation function and combines it with carrier phase-shift modulation to generate a smooth control voltage signal, enhancing overall stability.

[0021] like Figure 1As shown in the embodiment of this application, the MMC adaptive sliding mode control system based on a two-phase stationary coordinate system includes: The two-phase stationary coordinate system modeling module 1011 obtains the αβ axis components of the grid voltage and current, and constructs a mathematical model of the modular multilevel converter in the two-phase stationary coordinate system. The two-phase stationary coordinate system modeling module 1011 is responsible for signal conversion and model building. It effectively eliminates the need for phase-locked loops (PLLs), Park transforms, and inverse Park transforms, thereby reducing the computational burden. Furthermore, it ensures that the model accurately describes the dynamic behavior of the MMC in the αβ coordinate system, with particular attention to robustness against grid frequency fluctuations.

[0022] In laboratory simulations or actual power grid applications, three-phase voltage and current signals are acquired using LEM LV25-P voltage sensors and LEM LA55-P current sensors. The sampling frequency is set to 10kHz. Then, a Clarke transform is performed by a digital signal processor such as TI TMS320F28379D to convert the three-phase signals into αβ axis components. Finally, a mathematical model is established based on the parameters of the MMC submodule.

[0023] In some implementations, the three-phase grid voltage and current signals of the MMC are first converted into components in a two-phase stationary coordinate system (αβ coordinate system) using Clarke transformation, mainly including voltage components (such as...). , ) and current components (such as , The mathematical model of MMC (i.e., the current differential equation) can be directly constructed based on the known components, and the reference current value under the αβ axis can be output (e.g., ...). , This allows it to effectively avoid cross-coupling terms in rotating coordinate systems. However, in actual operation, it requires a hardware platform such as a digital signal processor (DSP) or FPGA to process the transformation calculations in real time.

[0024] The error calculation and sliding surface design module 1012 calculates the current error value on the αβ axis of the two-phase stationary coordinate system model; and presets a composite sliding surface including linear and integral terms for dynamic convergence control objectives. Among them, the error calculation and sliding surface design module 1012 enables the composite sliding surface to provide a fast transient response through linear terms, and combines linear terms and integral terms to eliminate steady-state errors and improve tracking accuracy.

[0025] In some implementations, the error value can be directly calculated by obtaining the αβ axis reference current and actual current values ​​from the two-phase stationary coordinate system modeling module 1011. Simultaneously, by combining linear and integral terms, a composite sliding surface is designed, thereby eliminating steady-state errors and improving dynamic convergence performance.

[0026] It should be noted that the design of sliding surfaces is mainly based on Lyapunov stability theory, and then a dynamic convergence law is formed by combining linear and integral terms.

[0027] The adaptive sliding mode approach rate design module 1013 constructs an adaptive sliding mode approach rate function with the error value as the independent variable, which is used to dynamically adjust the approach speed according to the error magnitude. The adaptive sliding mode approach rate design module 1013 can dynamically adjust the approach speed with the error as the independent variable, achieving a balance between fast response and low chattering. Simultaneously, the internal adaptive mechanism effectively enables the system to track quickly under disturbances and adjust smoothly in steady state, resolving the contradiction between speed and low chattering in traditional sliding mode control.

[0028] In some implementations, an adaptive approach rate function is constructed based on the error value and sliding surface value of the error calculation and sliding surface design module 1012. This allows the adaptive approach rate function to achieve the effect of "fast approach for large errors and slow adjustment for small errors". After verifying its stability through the Lyapunov function, the adaptive approach rate value is output for control signal generation.

[0029] It should be noted that the adaptive approach rate is based on nonlinear control theory and dynamically adjusts the gain through error feedback.

[0030] The control signal generation module 1014 generates control voltage signals on the αβ axis based on the sliding surface and adaptive approach rate, and outputs them to the power devices of the MMC through carrier shift modulation.

[0031] The control signal generation module 1014 is responsible for generating the final control signal based on the sliding mode surface and adaptive approach rate. Its control signal generation is primarily based on sliding mode control theory, using the approach rate to drive the system state to slide along the sliding mode surface. The internal saturation function is derived from the boundary layer method, used to soften the control signal. Combined with modulation principles, this ensures efficient energy conversion, effectively suppressing chattering and ensuring precise driving of power devices during modulation. This allows the control signal generation module 1014 to achieve smooth control signal output and overall system stability.

[0032] The process by which the control signal generation module 1014 generates the final control signal specifically includes: Obtain current error value Slip mode surface value and the rate of convergence ; Calculate the control voltage signals on the α and β axes using the control voltage formula. ; Control voltage signal The signal is input to the carrier phase-shift modulation module, which generates a PWM modulation signal that is fed back to the MMC power device to drive the MMC bridge arm switch.

[0033] In some implementations, the real-time current error value is obtained. and and sliding surface value and And obtain the adaptive convergence rate value. and The embedded processor (such as the TI TMS320F28379D) is invoked to read the inductor parameter L and resistor parameter R of the MMC AC side 101 from the system memory, and then combined with the α-axis and β-axis components of the grid voltage input from the grid voltage sensor. and And obtain the actual current value. and The differential value of the current is obtained by differential calculation of the current signal. and (Sampling time Ts = 100 μs) All the data can be substituted into the control voltage formula for real-time calculation to obtain the control voltage signals on the α and β axes. The signal is transmitted to the carrier phase-shift modulation submodule via the data bus. The submodule generates a CPS-PWM signal (carrier frequency 2kHz) and finally outputs it to drive the power device switch (such as an IGBT module, model Infineon FF450R17ME4), which drives the MMC bridge arm switch to operate, thus completing the entire process from signal acquisition and calculation to modulation, and ensuring that the system response time is less than 1ms.

[0034] It should be noted that during its operation, the control voltage formula is mainly designed based on system parameters such as inductance L and resistance R, and the saturation function is used to suppress jitter. This ensures that the carrier shift modulation guarantees the voltage balance and circulating current suppression of the submodules. The entire generation process depends on real-time data input and stability verification.

[0035] Based on the above technical solution, using a two-phase stationary coordinate system (αβ coordinate system) as the modeling basis and combining an adaptive sliding mode strategy, intelligent control is achieved through data processing, algorithm optimization, and real-time feedback adjustment. This effectively forms a complete closed-loop control system, thereby effectively eliminating the transformation link and simplifying data processing. It overcomes the problems of high computational complexity, structural limitations, dynamic performance bottlenecks, and insufficient robustness in traditional dq-PI control, achieving a control effect that is computationally efficient, has a fast response, high accuracy, and strong robustness.

[0036] In one possible implementation of the embodiments of this application, combined with Figure 1-3 As shown, the two-phase stationary coordinate system modeling module 1011 can be implemented through the following steps 201 to 203, which are explained in detail below: Step 201: Obtain the instantaneous voltage and current signals of the three-phase power grid and record them as three-phase voltage and current signals.

[0037] Data acquisition equipment configuration: High-precision sensors such as voltage transformers (e.g., LEM LV 25-P type) and current transformers (e.g., LEM LA 55-P type) are directly connected to the three-phase power grid lines and integrated into the control cabinet of the MMC system 100 to acquire instantaneous values ​​in a non-intrusive or intrusive manner through a sampling frequency greater than 10kHz.

[0038] Data Acquisition Environment Conditions: The data acquisition environment is set to the actual power grid operation site (such as renewable energy grid connection point or urban distribution network) or laboratory simulation platform. The ambient temperature range is generally -40°C to +85°C to adapt to industrial standards, and the humidity needs to be controlled within a certain range to prevent condensation. At the same time, the power grid may have non-ideal conditions such as frequency fluctuations, voltage sags or harmonic interference. The acquisition unit must have anti-interference capabilities to ensure signal purity.

[0039] The type of signal to be acquired is the instantaneous voltage and current signal originating from the AC side 101 of the three-phase power grid. It is usually a high voltage and high current environment at power frequency (such as 50Hz or 60Hz), such as a medium voltage distribution network (MVDC) scenario, where the voltage level can reach more than 10kV and the current can reach hundreds of amperes.

[0040] Step 202: Convert the instantaneous voltage and current signals of the three-phase power grid into α-axis and β-axis components in a two-phase stationary coordinate system using a Clarke transform unit; In some implementations, the voltage of the three-phase power grid is collected in real time using sensors. , and The instantaneous current signal is input into the Clarke converter unit and transformed using the formula... This allows the three-phase signal to be converted into αβ axis components. and The coefficients of the transformation matrix ensure power conservation.

[0041] Step 203: Establish the mathematical model of MMC in a two-phase stationary coordinate system; In some implementations, MMC circuit parameters (such as bridge arm resistance R and inductance L) are obtained through electromagnetic shielding laboratory or field shielding cabinet experiments or by design specifications (MMC electrical design specifications or submodule datasheets), combined with αβ axis current components. and αβ axis grid voltage components and and control voltage component Establish a differential equation model and This method describes the current dynamics, allowing the model to be built directly in a two-phase stationary coordinate system, eliminating the need for phase-locked loops, Park transformations, and inverse Park transformations, thereby reducing computational complexity and achieving essential decoupling of the control system.

[0042] Based on the above technical solution, by eliminating the complex transformation steps in the traditional rotating coordinate system (dq coordinate system), the control system can be directly constructed in the two-phase stationary coordinate system (αβ coordinate system), which can significantly reduce the computational complexity, thereby simplifying the system structure and improving its robustness.

[0043] In one possible implementation of the embodiments of this application, combined with Figure 1-4 As shown, the error calculation and sliding surface design module 1012 can be implemented through the following steps 301 to 302, which are explained in detail below: Step 301: The error calculation unit calculates the current error values ​​on the α-axis and β-axis based on the reference current value and the actual current value.

[0044] In some implementations, the actual current value is obtained by measuring it in real time using a current sensor. and And from the two-phase stationary coordinate system modeling module 1011, through active power command P, reactive power command Q, and grid voltage... and You can pass and The reference current value was calculated. and At this point, the error calculation unit can be directly activated, through... and Calculate the current error values ​​on the α-axis and β-axis. and .

[0045] Among them, the current sensor can be a Hall effect sensor such as LEM LAH 100-P.

[0046] Step 302: The sliding surface design unit obtains the linear term and integral term through the current error value and presets the composite sliding surface.

[0047] In some implementations, the coefficients of the linear terms are set by the time constant τ. and (Usually chosen from 1 to 1000) and coefficient of the integral term (according to Time selection ), and make it satisfy By using the coefficients of the linear terms Multiplying the result by the error value yields the linear part. Simultaneously, the error value is integrated using an integrator, and the result is compared with the coefficient of the integral term. Multiplying yields the integral, which is obtained by... and The final superimposed output sliding surface value The signal is then transmitted to the adaptive sliding mode approach rate design module 1013 to generate a control voltage signal.

[0048] The digital signal processor can be implemented using the TI TMS320F28379D to ensure continuous data flow and logical progression.

[0049] Based on the above technical solution, a composite sliding surface structure integrates linear and integral terms. This ensures a fast transient response for the linear term while eliminating steady-state error for the integral term, thus solving the technical problems of large overshoot, slow response, and large steady-state error in traditional PI control. Simultaneously, the structure where the error calculation unit directly processes signals from two stationary coordinate systems effectively avoids the step of coordinate transformation, reducing computational complexity. This allows the sliding surface design unit to achieve dynamic optimization through adaptive parameter configuration, improving system robustness and control accuracy.

[0050] In one possible implementation of the embodiments of this application, combined with Figure 1-6 As shown, the adaptive sliding mode approach rate design module 1013 can be implemented through the following steps 401 to 404, which are explained in detail below: Step 401: Set the current error value and sliding surface value As the independent variable, a nonlinear adaptive approach rate function is constructed. ,in This is the error proportion term. This is a sliding surface modulation term.

[0051] Among them, the sliding surface modulation term It directly reflects the magnitude of the system tracking error; the larger the error, the greater the gain, ensuring a fast response. Adaptive approach rate coefficient. Calculated based on the system sampling period Ts or time constant τ (e.g.) or And it depends on the system scale (such as in motor control). (Usually 10-50), and satisfy The amplitude adjustment parameter λ is used to control the non-negative range of the function value, and satisfies... In dependence During adjustment, the amplitude adjustment parameter λ decreases, depending on... During adjustment, the amplitude adjustment parameter λ is increased. The smoothness parameter η is switched to influence the transition characteristics of the function as the sliding surface changes, and satisfies the following conditions: Increase η when performing rapid switching and decrease η when performing smooth switching.

[0052] Step 402: Run the nonlinear adaptive approach rate function to obtain the approach rate value. .

[0053] In some implementations, the current error values ​​on the α-axis and β-axis are received in real time via an internal data bus. and sliding surface value The received data is used as input to call a stored nonlinear function program to first calculate the absolute value of the error. This can be combined with the adaptive approach rate coefficient. The modulation term is calculated using the amplitude adjustment parameter λ and the switching smoothness parameter η, and finally the convergence rate value is obtained. .

[0054] Step 403: Approach rate value Perform Lyapunov stability condition verification.

[0055] Pre-defined Lyapunov functions, with embedded radial unboundedness and negative definiteness requirements; The Lyapunov function is a positive definite scalar function used to measure the energy of the system state or the deviation from the equilibrium point.

[0056] Radial unboundedness requirement is This is used to ensure that the function is unbounded at infinity, preventing the system state from diverging.

[0057] Negative qualitative requirements are that the time derivative of the Lyapunov function is less than 0 and all This is used to indicate that the system's energy decreases over time and the state approaches an equilibrium point.

[0058] In some implementations, a Lyapunov function is defined to receive the sliding surface value s. And satisfying V≥0, and V=0 when s=0. The expression for the sliding surface derivative being adjusted by the adaptive function is obtained by analyzing the nonlinear adaptive reaching rate function. Thus, through <0 Directly calculate the time derivative of the Lyapunov function .

[0059] It should be noted that sign(s) is the sign function. Let be the derivative of the sliding surface s. sat(s) is a saturation function used to replace the sign function to reduce chattering, and is defined as... Δ is the boundary layer thickness determined through simulation optimization (typically between 0.01 and 0.5), and the specific calculation formula is as follows: .

[0060] In the rate of convergence When both radial unboundedness and negative definiteness are satisfied, the approach rate value will be... Feedback is sent to the control signal generation module 1014; In some implementations, the nonnegativity of f(e,s) is checked in real time. The positive aspect, when Time verification If the value is less than 0, a stability confirmation signal will be output to the control signal generation module 1014 to ensure that the system state approaches the sliding surface. The entire process is executed continuously in each control cycle to form a closed-loop verification.

[0061] For example, assume that the current error e = 0.1A and the sliding mode surface value s = 0.5 when the system starts up, and the parameters... =10, λ=0.7, η=7, then calculate Substitute =−0.243×0.5=−0.1215<0, satisfying the stability condition; when the system approaches steady state, e=0.001A, s=0.01, then f(e,s)≈0.0001. ≈0, which verifies a smooth transition.

[0062] Based on the above technical solution, by constructing a nonlinear approach rate function to dynamically adjust the approach speed, the error and sliding surface feedback can be effectively utilized to achieve the effect of "fast approach for large errors and slow adjustment for small errors," thus resolving the contradiction between speed and low chattering in traditional sliding mode control. This allows for the effective use of parameter adaptive mechanisms to improve the system's transient response speed, suppress steady-state errors and overshoot, reduce computational complexity, and avoid the multi-loop coupling problem of traditional PI control. Consequently, the robustness and dynamic performance of the MMC system 100 can be effectively enhanced, making it more suitable for computationally limited scenarios.

[0063] In one possible implementation of the embodiments of this application, combined with Figure 1-10 As shown, the DC side 102 control module can be implemented through the following steps 701 to 704, which are explained in detail below: Step 701: The outer loop voltage regulator unit 1021 compares the voltage reference value of the submodule. The average voltage U of each phase upper and lower bridge arm submodule a Generate circulation reference values .

[0064] Submodule voltage stabilization refers to maintaining the capacitor voltage of each submodule near its rated value through control, avoiding overvoltage or undervoltage. The voltage sensor can be an LV 25-P type voltage sensor, the current sensor can be an LA 55-P type current sensor, and the controller can be a TI TMS320F28379D digital signal processor.

[0065] In some implementations, after the DC side 102 control module is started, the sub-module voltage acquisition unit obtains the capacitor voltage value U of each sub-module in real time through voltage sensors. cj [The subscript j represents the submodule index (j=1,2,n, where n is the total number of submodules)], and is transmitted to the voltage outer loop regulator unit 1021 and the voltage equalization control unit 1025. The voltage outer loop regulator unit 1021 can then calculate the average voltage U of each phase's upper and lower bridge arm submodules. a and the average value U a Compared with the system-set submodule voltage reference value The comparison is performed to generate a circulating current reference value via the PI controller. As an input to the inner loop circulation control unit 1022.

[0066] Step 702: The inner loop flow control unit 1022 transmits the circulation reference value. Compared with actual circulation value Compare and generate circulating control signals .

[0067] The digital signal processor can be a TI TMS320F28379D, and the overall sampling period is T. s It is typically set to the microsecond level (e.g., 100μs) to ensure a fast dynamic response.

[0068] In some implementations, the actual circulating current value is measured using a current sensor. Calculate the actual circulation value Compared with circulation reference value The circulating error e between circ =i circ,re f−i circ This allows the circulation error to be directly calculated. As input to the PI controller, it activates the proportional-integral control unit, obtains the proportional coefficient Kp (ranging from 0.1 to 10) which determines the system response speed, and then modulates the proportional coefficient Kp with the error... Multiplying them yields the proportional term P, and simultaneously the integral coefficient Ki (ranging from 0.01 to 1) and the error integral are calculated. Multiplying the terms gives the integral term I. Then, adding the proportional and integral terms together yields the circulating control signal. This signal serves as a voltage-type output modulated PWM pulse.

[0069] It should be noted that the proportional term provides fast tracking capability and reduces response delay during transient processes. The integral term, on the other hand, accumulates small errors and eliminates deviations in steady state, thereby improving the overall robustness and accuracy of the system. However, the design of a PI controller must consider the nonlinear characteristics of the system, thus requiring a synergistic approach combined with anti-saturation mechanisms (such as integral separation).

[0070] Step 703: Signal synthesis unit 1023 based on circulating reference value and circulating control signal Obtain the final control signal .

[0071] Step 704: The equalization control unit 1025 calculates the equalization coefficient K and outputs the equalization compensation signal. .

[0072] In some implementations, through Calculate the absolute deviation ΔU between the voltage and average value of each submodule, and obtain the reference coefficient kbase (usually set to 0.5), adjustment gain θ (range 0.5-1.0), and normalization factor δ (value 5-10V). Then, you can... Calculate the voltage equalization factor K. This allows direct access to the system's maximum permissible voltage deviation rating, Umax, thus enabling... Calculate the compensation signal This allows the system to compensate for excessive voltage in a submodule by using a compensation signal. This reduces the amplitude of the modulation wave and the conduction time, thereby causing the voltage to drop.

[0073] The tanh function is used to smooth nonlinear transitions, ensuring that the coefficient K can respond quickly to large deviations while maintaining smooth control with small deviations.

[0074] Based on the above technical solution, a hierarchical control structure is formed through outer-loop voltage regulation, inner-loop circulating current, and voltage equalization control, with each layer processing in the same direction. The outer-loop voltage regulation unit 1021 macroscopically adjusts the circulating current reference through voltage comparison to solve voltage fluctuation problems; the inner-loop circulating current control unit 1022 achieves rapid tracking and suppresses transient oscillations; and the voltage equalization control unit 1025 solves the problem of uneven voltage distribution in submodules by dynamically compensating for voltage deviations. Thus, through progressive data connections (such as voltage acquisition, outer loop, inner loop, and synthesis), rapid voltage stabilization and efficient voltage equalization of submodules are effectively achieved, overcoming the shortcomings of traditional single control methods, such as slow response and poor voltage equalization.

[0075] In one possible implementation of the embodiments of this application, combined with Figure 1-11 As shown, the CPS-PWM modulation unit 1024 inside the DC side 102 control module can be implemented through the following steps 801 to 802, which are explained in detail below: Step 801: Based on the final control signal and the voltage equalization compensation signal, generate multiple triangular carrier signals with the same frequency but sequentially shifted phases.

[0076] In some implementations, a basic triangular carrier signal is generated by an internal oscillation circuit or a digital signal processor (such as the TI TMS320F28379D), with its frequency set to a fixed value (e.g., 2kHz) according to system requirements. This basic triangular carrier signal can then be copied and multiplexed using a phase shifting circuit or algorithm, so that the phase of each carrier signal is shifted uniformly according to the number of sub-modules (e.g., for n sub-modules, the phase is shifted sequentially by 360 / n degrees).

[0077] Step 802: Compare the multi-channel triangular carrier signal with the modulation wave to generate a PWM drive signal and feed it back to the sub-module power device of the MMC for sub-module voltage equalization and circulating current suppression.

[0078] The submodule power device refers to the insulated gate bipolar transistor (IGBT) used for switching control in the MMC, such as the IGBT module with model number FF450R12KE4.

[0079] In some implementations, multiple triangular carrier signals with the same frequency but sequentially shifted phases are compared with the modulation wave in real time. When the amplitude of the modulation wave is higher than that of the carrier wave, a high-level PWM pulse is generated; otherwise, it is low-level, thereby generating a PWM drive signal. The PWM drive signal can then be fed back to the power devices of the MMC submodule through the drive circuit to control their switching action. Voltage balancing is achieved by adjusting the switching on and off of the submodule, while suppressing the circulating current of the bridge arm.

[0080] Based on the above technical solution, the carrier generation, comparison and signal distribution subunits can effectively compare the carrier signal with the modulation wave with the phase-uniformly shifted signal, which solves the technical problems of voltage imbalance and circulating current oscillation in the submodules of traditional PWM modulation, and realizes fine modulation based on carrier phase shift, thereby improving voltage balance and reducing switching losses. At the same time, by integrating the equalization compensation signal, the robustness and dynamic response capability of the system are enhanced.

[0081] The above primarily describes the solutions of the embodiments of this application from the perspective of device implementation. It is understood that each device, such as the MMC adaptive sliding mode control system based on a two-phase stationary coordinate system, includes at least one of the hardware structures and software modules corresponding to the execution of each function in order to achieve the above-mentioned functions. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0082] This application provides an MMC adaptive sliding mode control method based on a two-phase stationary coordinate system, combined with... Figure 1-12 As shown, it includes an AC side 101 and a DC side 102: the AC side 101 includes: Step 901: Obtain the αβ axis components of the grid voltage and current, and construct a mathematical model of the modular multilevel converter in a two-phase stationary coordinate system; Step 902: Calculate the current error value on the αβ axis of the two-phase stationary coordinate system model; and preset a composite sliding surface including linear and integral terms for dynamic convergence control target; construct an adaptive sliding mode approach rate function with the error value as the independent variable to dynamically adjust the approach speed according to the error magnitude. Step 903: Based on the sliding mode surface and adaptive approach rate, generate control voltage signals on the αβ axis and output them to the power devices of the MMC via carrier shift modulation; AC side 101, compare the submodule voltage reference value U aref The average voltage U of each phase upper and lower bridge arm submodule a Generate circulation reference values ; Step 904: Set the circulation reference value Compared with actual circulation value Compare and generate circulating control signals Based on circulation reference values and circulating control signal Obtain the final control signal Calculate the equalization coefficient K and output the equalization compensation signal. .

[0083] In one possible implementation, the AC side 101 is also used to construct the two-phase stationary coordinate system modeling module 1011, specifically including: acquiring the instantaneous voltage and current signals of the three-phase power grid; converting the three-phase voltage and current signals into α-axis and β-axis components in the two-phase stationary coordinate system through the Clarke transformation unit; and establishing a mathematical model of MMC in the two-phase stationary coordinate system.

[0084] In one possible implementation, the AC side 101 is also used for error calculation and sliding surface design module 1012, which includes an error calculation unit and a sliding surface design unit: the error calculation unit calculates the current error values ​​on the α axis and β axis based on the reference current value and the actual current value; the sliding surface design unit obtains the linear term and integral term through the current error value and presets the composite sliding surface.

[0085] In one possible implementation, the AC side 101 is also used for the process of the adaptive sliding mode approach rate design module 1013 outputting an adaptive approach rate value, specifically including: converting the current error value... and sliding surface value As the independent variable, a nonlinear adaptive approach rate function is constructed. ,in This is the error proportion term. For the sliding mode surface modulation term; the approach rate value is obtained by running the nonlinear adaptive approach rate function. and For the convergence rate value Perform Lyapunov stability condition verification.

[0086] In one possible implementation, the communication side 101 is also used for the process of verifying Lyapunov stability conditions, specifically including: presetting the Lyapunov function and embedding radial unboundedness requirements and negative definiteness requirements; and considering the convergence rate value... When both radial unboundedness and negative definiteness are satisfied, the approach rate value will be... Feedback is sent to the control signal generation module 1014.

[0087] In one possible implementation, the AC side 101 is also used to control the process of the signal generation module 1014 generating the final control signal, specifically including: acquiring the current error value. and Slip mode surface value and and the rate of convergence ; Calculate the control voltage signals on the α and β axes using the control voltage formula. ; control voltage signal The signal is input to the carrier phase-shift modulation module, which generates a PWM modulation signal that is fed back to the MMC power device to drive the MMC bridge arm switch.

[0088] In one possible implementation, a DC-side 102 control module is also included for MMC submodule voltage balancing: the outer-loop voltage regulator 1021 compares the submodule voltage reference value... The average voltage U of each phase upper and lower bridge arm submodule a Generate circulation reference values The inner loop flow control unit 1022 will display the loop reference value. Compared with actual circulation value Compare and generate circulating control signals The signal synthesis unit 1023 is based on the circulating reference value. and circulating control signal Obtain the final control signal The equalization control unit 1025 calculates the equalization coefficient K and outputs the equalization compensation signal. .

[0089] In one possible implementation, the DC side 102 control module also includes a CPS-PWM modulation unit 1024: based on the final control signal and the voltage equalization compensation signal, it generates multiple triangular carrier signals with the same frequency but sequentially offset phases; it compares the multiple triangular carrier signals with the modulation wave, generates a PWM drive signal, and feeds it back to the sub-module power device of the MMC for sub-module voltage equalization and circulating current suppression.

[0090] In implementation, each step of the method provided in this embodiment can be completed by integrated logic circuits in the processor or by instructions in software form. The steps of the method disclosed in the embodiments of this application can be directly manifested as being executed by a hardware processor, or being executed by a combination of hardware and software modules in the processor.

[0091] The processor in this application may include, but is not limited to, at least one of the following: a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a microcontroller unit (MCU), or an artificial intelligence processor, etc., and other computing devices that run software. Each computing device may include one or more cores for executing software instructions to perform calculations or processing. The processor may be a standalone semiconductor chip or integrated with other circuits into a single semiconductor chip. For example, it may form a System-on-a-Chip (SoC) with other circuits (such as encoding / decoding circuits, hardware acceleration circuits, or various bus and interface circuits), or it may be integrated as a built-in processor within an ASIC. The ASIC of this integrated processor may be packaged separately or together with other circuits. In addition to the cores for executing software instructions to perform calculations or processing, the processor may further include necessary hardware accelerators, such as field-programmable gate arrays (FPGAs), PLDs (programmable logic devices), or logic circuits that implement dedicated logic operations.

[0092] The memory in the embodiments of this application may include at least one of the following types: read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions; random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions; or electrically erasable programmable-only memory (EEPROM). In some scenarios, the memory may also be a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures that can be accessed by a computer, but is not limited thereto.

[0093] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software programs, implementation can be, in whole or in part, in the form of a computer program product. This computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device containing one or more servers, data centers, etc., that can be integrated with the medium. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state disks (SSDs)).

[0094] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, the disclosure, and the appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple components. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0095] Although this application has been described in conjunction with specific features and embodiments, it is apparent that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely illustrative examples of the application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from the spirit and scope of this application. Thus, if such modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is also intended to include such modifications and variations.

Claims

1. An adaptive sliding mode control system for MMC based on two-phase stationary coordinate system, characterized in that, Comprise: Two-phase stationary coordinate system modeling module, obtain the αβ axis components of grid voltage and current, build the mathematical model of modular multilevel converter in two-phase stationary coordinate system; Error calculation and sliding mode surface design module, calculate the current error value on the αβ axis of the two-phase stationary coordinate system model; And preset the composite sliding mode surface including linear term and integral term, used for dynamic convergence control target; Adaptive sliding mode reaching rate design module, with the error value as the independent variable, build the adaptive sliding mode reaching rate function, used for dynamically adjusting the reaching speed according to the error size; The control signal generation module generates the control voltage signal on the αβ axis based on the sliding mode surface and adaptive reaching rate, and outputs to the power device of MMC through carrier shift modulation.

2. The two-phase stationary frame based MMC adaptive sliding mode control system of claim 1, wherein, The construction of the two-phase stationary coordinate system modeling module specifically includes: Obtain the instantaneous value signals of three-phase grid voltage and current, denoted as three-phase voltage and current signals; Convert the three-phase voltage and current signals into α axis and β axis components in two-phase stationary coordinate system through Clarke transformation unit; Establish the mathematical model of MMC in two-phase stationary coordinate system.

3. The two-phase stationary frame based MMC adaptive sliding mode control system according to claim 2, wherein, The error calculation and sliding mode surface design module includes error calculation unit and sliding mode surface design unit: The error calculation unit calculates the current error value on the αβ axis based on the reference current value and the actual current value; The sliding mode surface design unit obtains the linear term and integral term through the current error value, and presets the composite sliding mode surface.

4. The two-phase stationary frame based MMC adaptive sliding mode control system of claim 3, wherein, The process of the adaptive sliding mode reaching rate design module outputting adaptive reaching rate value specifically includes: The current error value e α is calculated as β the difference between the desired value i α and the actual value i β as argument, a non-linear adaptive approach rate function is constructed, wherein is an error proportion term, is a sliding surface modulation term; running a nonlinear adaptive approach rate function to obtain an approach rate value and ; on the approach rate value Lyapunov stability condition verification is performed.

5. The two-phase stationary frame based MMC adaptive sliding mode control system of claim 4, wherein, The process of verifying the Lyapunov stability condition specifically includes: Preset Lyapunov function, and embed the radial unboundedness requirement and negative definiteness requirement; The approach rate value When the radial unboundedness requirement and the negative definiteness are both satisfied, the approach rate value is fed back to the control signal generation module.

6. The two-phase stationary frame based MMC adaptive sliding mode control system of claim 5, wherein, The process of the control signal generation module generating the final control signal specifically includes: obtaining the current error value and a sliding surface value and and a reaching rate value ; calling a control voltage formula to calculate the control voltage signals on the alpha and beta axes ; The control voltage signal is input to a carrier phase-shift modulation module to generate a PWM modulation signal, which is fed back to drive the MMC bridge arm switches of the MMC power device.

7. The two-phase stationary frame based MMC adaptive sliding mode control system of claim 6, wherein, It also includes a DC side control module for MMC submodule voltage balance: The outer ring voltage stabilizing unit compares the voltage reference value of the sub-module with the average value U of the upper and lower bridge arm sub-modules of each phase a , and generates a circulating current reference value ; The inner loop circulation control unit compares the circulation reference value with the actual circulation value and generates a circulation control signal ; The signal synthesis unit derives a final control signal based on the circulating current reference value and a circulating current control signal The signal synthesis unit derives a final control signal based on the circulating current reference value ; The equalization control unit calculates an equalization coefficient K and outputs an equalization compensation signal .

8. The two-phase stationary coordinate system based MMC adaptive sliding mode control system according to claim 7, characterized in that, The DC side control module further includes a CPS-PWM modulation unit: Based on the final control signal and the voltage compensation signal, generate multiple triangular carrier signals with the same frequency but with phase offset in turn; Compare the multiple triangular carrier signals with the modulation wave to generate PWM drive signal and feedback to the submodule power device of MMC, used for submodule voltage balance and circulating current suppression.

9. The MMC adaptive sliding mode control method based on two-phase stationary coordinate system, characterized in that, The AC side of MMC includes: Obtain the αβ axis components of grid voltage and current, build the mathematical model of modular multilevel converter in two-phase stationary coordinate system; Calculate the current error value on the αβ axis of the two-phase stationary coordinate system model; and preset the composite sliding mode surface including linear term and integral term, used for dynamic convergence control target; With the error value as the independent variable, build the adaptive sliding mode reaching rate function, used for dynamically adjusting the reaching speed according to the error size; Generate the control voltage signal on the αβ axis based on the sliding mode surface and adaptive reaching rate, and output to the power device of MMC through carrier shift modulation.

10. The two-phase stationary coordinate system-based adaptive sliding mode control method of MMC according to claim 9, characterized in that, The DC side of MMC includes; by comparing the submodule voltage reference values to the average of the upper and lower bridge arm submodule voltages of each phase to generate a circulating current reference value ; comparing the ring flow reference value with an actual ring flow value to generate a ring flow control signal ; based on the circulating current reference value and a circulating current control signal to obtain a final control signal ; A uniformity coefficient K is calculated, and a uniformity compensation signal is output .

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