MPC wind power converter power prediction control method based on hierarchical dynamic weight adjustment

Abstract

The application discloses an MPC wind power converter power prediction control method based on hierarchical dynamic weight adjustment, relates to the wind power converter power prediction technical field, and comprises the following steps: constructing a current prediction model based on the mathematical model of the output current of a converter in a first preset coordinate system; introducing an MPC control system to the current prediction model, and setting mutually independent first and second target functions for two control layers of the MPC control system; setting and adjusting the extended weight coefficients of the first and second target functions according to power grid working condition requirements; and establishing the mapping relationship of the extended weight coefficients between the two control layers to realize the dynamic fusion between the two control layers, so that the output power of the converter is controlled; and the control method can realize the flexible adjustment of active and reactive power, and significantly improve the power tracking performance and the dynamic response capability of the system.

Description

A Power Prediction Control Method for Wind Power Converters Based on Hierarchical Dynamic Weight Adjustment Technical Field

[0001] This application generally relates to the field of wind power converter power prediction and control technology, and specifically to an MPC wind power converter power prediction and control method based on hierarchical dynamic weight adjustment. Background Technology

[0002] Wind energy, as a clean and renewable energy source, has become an important component of the global energy structure transformation. With the continuous development of wind power technology, installed wind power capacity and power generation have increased year by year, especially in regions such as Europe, North America, and China, where installed wind power capacity has reached world-leading levels. The widespread adoption of wind power has driven continuous innovation in wind turbine control technology, among which full-power converter (FPC) control technology is crucial for ensuring the stable, economical, and efficient operation of wind power systems.

[0003] The main function of a full-power wind power converter is to convert the AC power generated by the wind turbine generator into stable AC power that meets the requirements of the power grid, and to ensure that the wind turbine can maximize its power generation efficiency under different wind speeds and load conditions, while maintaining synchronization with the power grid. Due to the randomness and volatility of wind energy, traditional control methods (such as PI control and PID control) often fail to provide ideal control results under some complex operating conditions, especially when wind speed changes rapidly or the power grid fluctuates significantly.

[0004] Model predictive control (MPC) is an advanced control method based on system dynamics models. It achieves control objectives by predicting the future state of the system and optimizing the control input. However, traditional MPC control methods typically cannot independently track and regulate active power P and reactive power Q. Due to the strong nonlinearity, high coupling, and complex operating conditions of wind power systems, traditional MPC struggles to achieve optimal control performance under different operating conditions. Summary of the Invention

[0005] In view of the above-mentioned defects or deficiencies in the prior art, it is desirable to provide a power prediction control method for MPC wind power converters based on hierarchical dynamic weight adjustment.

[0006] This application provides a power prediction control method for MPC wind power converters based on hierarchical dynamic weight adjustment, including the following steps:

[0007] A current prediction model is constructed based on the mathematical model of the converter's output current in the first preset coordinate system.

[0008] An MPC control system is introduced into the current prediction model, and a first objective function and a second objective function that are independent of each other are set for the two control layers of the MPC control system.

[0009] Based on the power grid operating conditions, set and adjust the extended weight coefficients of the first objective function and the second objective function;

[0010] An extended weight coefficient mapping relationship is established between the two control layers to achieve dynamic fusion between the two control layers, thereby controlling the output power of the converter;

[0011] The first objective function is used to optimize the predicted power reference value output by the current prediction model in combination with the power grid operating conditions. The second objective function is used to track and control the actual current and voltage of the converter based on the optimized predicted power reference value. The predicted power reference value includes: active power reference value and reactive power reference value.

[0012] According to the technical solution provided in this application, a current prediction model is constructed based on a mathematical model of the converter's output current in a first preset coordinate system, specifically including:

[0013] According to Kirchhoff's laws, the three-phase voltage and current equations of the converter are established through inductance to confirm the mathematical model of the converter's output current in the first preset coordinate system.

[0014] The mathematical model is transformed into a second preset coordinate system to obtain the transformation components corresponding to each axis of the second preset coordinate system.

[0015] Within a preset sampling period, the conversion components corresponding to each axis are discretized, and a phase-locked loop is introduced to obtain the current prediction model.

[0016] According to the technical solution provided in this application, the control layer includes a high control layer and a low control layer, wherein the high control layer and the low control layer correspond to the first objective function and the second objective function, which are independent of each other;

[0017] The first objective function As shown in Formula (I), the second objective function The following formula (II);

[0018] Formula (1);

[0019] Formula (II);

[0020] in, and These represent the active power and reactive power at time k+j after minimizing the next N time points (from K+1 to K+N) within the prediction time domain N. and These represent the active power reference value and reactive power reference value at time k+j, respectively.

[0021] and These represent the current components corresponding to the d-axis and q-axis in the second preset coordinate system at time k+j, after minimizing the N future time points (from K+1 to K+N) within the prediction time domain N. and These represent the current reference values ​​on the d-axis and q-axis at time k+j, respectively; λ is the weighting coefficient. This represents the change in the converter control input voltage along the d-axis at time k+j.

[0022] According to the technical solution provided in this application, the extended weighting coefficient is a fixed coefficient;

[0023] Based on the power grid operating conditions, the extended weight coefficients of the first objective function and the second objective function are set and adjusted, specifically including:

[0024] By setting fixed expansion weight coefficients for the first objective function and the second objective function respectively, the first objective expansion function and the second objective expansion function are obtained.

[0025] Based on the power grid operating conditions, adjust the weights of the two fixed extended weighting coefficients; the power grid operating conditions include at least: the actual energy demand of the power grid load, wind speed status, and power grid voltage status.

[0026] According to the technical solution provided in this application, the first target expansion function As shown in Formula (III), the second objective expansion function The following formula (IV);

[0027] Formula (3);

[0028] Formula (IV);

[0029] in, and These represent the fixed expansion weight coefficients for active power and reactive power in the first objective expansion function, respectively. and These represent the fixed expansion weighting coefficients for the d-axis and q-axis current shunting in the second objective expansion function, respectively.

[0030] Based on the power grid operating conditions, the weights of the two fixed extended weight coefficients are adjusted, specifically including:

[0031] When the actual energy demand increases or the wind speed fluctuates, a fixed expansion weighting coefficient for the power consumption in the first target expansion function is set. Fixed extended weighting factor greater than reactive power Furthermore, a fixed expansion weighting coefficient is set for the d-axis current shunt in the second objective expansion function. Fixed extended weighting factor greater than q-axis current shunt ;

[0032] When the grid voltage is abnormal, a fixed expansion weighting coefficient for the power consumption in the first target expansion function is set. Fixed extended weighting factor less than reactive power Furthermore, a fixed expansion weighting coefficient is set for the d-axis current shunt in the second objective expansion function. Fixed extended weighting factor less than q-axis current shunt .

[0033] According to the technical solution provided in this application, the extended weighting coefficient is a dynamic coefficient;

[0034] Based on the power grid operating conditions, the extended weight coefficients of the third objective function and the fourth objective function are set and adjusted, specifically including:

[0035] Dynamic expansion weight coefficients are set for the first objective function and the second objective function respectively, and the dynamic expansion weight coefficients in the fourth objective function are weighted by proportional allocation to obtain the third objective expansion function and the fourth objective expansion function;

[0036] Based on the power grid operating conditions, two dynamic adjustment ranges for the extended weighting coefficients are set; the power grid operating conditions include at least: power grid load, wind speed fluctuations, and power grid voltage deviation.

[0037] According to the technical solution provided in this application, the third target expansion function The fourth objective expansion function is described in formula (v) below. The following formula (VI);

[0038] Formula (5);

[0039] Formula (VI);

[0040] in, and These are the dynamic expansion weight coefficients for active power and reactive power in the third objective expansion function, respectively. and These represent the dynamic expansion weighting coefficients for the d-axis and q-axis current shunting in the fourth objective expansion function, respectively.

[0041] According to the technical solution provided in this application, an extended weight coefficient mapping relationship is established between the two control layers to achieve dynamic fusion between the two control layers, specifically including:

[0042] Based on the dynamic extended weighting coefficients of the active power and reactive power and the dynamic extended weighting coefficients of the d-axis and q-axis current shunting, a mapping function containing a fusion coefficient is established.

[0043] Based on the power grid operating conditions, different fusion coefficients are set to adjust the control priorities of the high-level control and the low-level control.

[0044] In summary, this technical solution specifically discloses a power prediction control method for wind power converters based on hierarchical dynamic weight adjustment (MPC), comprising: constructing a current prediction model based on a mathematical model of the converter's output current in a first preset coordinate system; introducing an MPC control system into the current prediction model, and setting independent first and second objective functions for the two control layers of the MPC control system; setting and adjusting the extended weight coefficients of the first and second objective functions according to the grid operating conditions; establishing a mapping relationship of the extended weight coefficients between the two control layers to achieve dynamic fusion between the two control layers, thereby controlling the output power of the converter; wherein, the first objective function is used to optimize the predicted power reference value output by the current prediction model in combination with the grid operating conditions, and the second objective function is used to track and control the actual current and voltage of the converter based on the optimized predicted power reference value; the predicted power reference value includes: active power reference value and reactive power reference value.

[0045] Traditional MPC control methods typically cannot independently track and regulate active power P and reactive power Q. However, wind power systems often exhibit strong nonlinearity, high coupling, and complex operating conditions, making it difficult for traditional MPC to achieve optimal control performance under different operating conditions. In this application, by setting a first objective function and a second objective function with extended weight coefficients for the two control layers of the MPC control system, the active power P and reactive power Q can be controlled according to the grid operating conditions by adjusting the extended weight coefficients. At the same time, dynamic fusion is achieved between the two control layers, coordinating the control objectives of the high and low control layers, effectively avoiding conflicts, and thus achieving precise control of the converter's output power. Attached Figure Description

[0046] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0047] Figure 1 is a flowchart illustrating a power prediction control method for MPC wind power converters based on hierarchical dynamic weight adjustment.

[0048] Figure 2 is a schematic diagram of the unfolded process of step S100 in an MPC wind power converter power prediction control method based on hierarchical dynamic weight adjustment.

[0049] Figure 3 is a schematic diagram of the first unfolding process of step S300 in an MPC wind power converter power prediction control method based on hierarchical dynamic weight adjustment.

[0050] Figure 4 is a schematic diagram of the second unfolding process of step S300 in an MPC wind power converter power prediction control method based on hierarchical dynamic weight adjustment.

[0051] Figure 5 is a schematic diagram of the hierarchical structure of the MPC control system.

[0052] Figure 6 shows the global parameters in the objective function corresponding to the lower layer. A schematic diagram showing the influence curves on the smoothness of control inputs and the range of values ​​under different operating conditions.

[0053] Figure 7 shows the weight mapping relationship between the high and low control layers of MPC.

[0054] Figure 8 shows the relationship between the weights of the high and low control layers of MPC and the fusion coefficient.

[0055] Figure 9 shows a comparison of the effects of MPC fixed extended weight coefficient and dynamic extended weight coefficient in active power P reference value tracking.

[0056] Figure 10 shows a comparison of the effects of MPC fixed extended weight coefficient and dynamic extended weight coefficient in reactive power Q reference value tracking. Detailed Implementation

[0057] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0058] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0059] Example 1

[0060] To make the technical solutions of the embodiments of this application clearer and easier to understand, the application background of the embodiments of this application is introduced below.

[0061] Model predictive control (MPC) is an advanced control method based on system dynamics models. It achieves control objectives by predicting the future state of the system and optimizing the control input. MPC has been widely used in industrial control, especially in power electronics and energy management. Compared with traditional feedback control methods, MPC can handle constraints in complex systems (such as voltage, current, and power limits) and optimize multi-objective control. For wind power converters, MPC can optimize the converter's control strategy in real time based on the dynamic characteristics of the wind turbine and the operating conditions of the power grid. MPC can not only perform maximum power point tracking (MPPT) and power regulation, but also effectively improve the stability and reliability of the system under complex conditions such as wind speed fluctuations and power grid frequency changes.

[0062] Wind power systems operate under complex and variable conditions. For example, under light loads, the demand for reactive power may be relatively small. However, under heavy loads or grid faults, priority may be given to ensuring a stable supply of active power to maintain basic system operation, or reactive power may need to be quickly adjusted to stabilize voltage. Traditional MPC (Multi-Process Control) methods typically cannot independently track and regulate active power (P) and reactive power (Q). Due to the strong nonlinearity, high coupling, and complex operating conditions of wind power systems, traditional MPC methods struggle to achieve optimal control performance under different conditions. In some scenarios, such as when the priorities of voltage support and frequency regulation change dynamically, the controller may fail to respond quickly to grid demands, resulting in power deviations and a decline in dynamic performance.

[0063] In view of this, this application provides a hierarchical dynamic weight adjustment MPC power prediction control method for wind power converters. The target object can be a full-power wind power converter. The control method includes: constructing a current prediction model based on a mathematical model of the converter's output current in a first preset coordinate system; introducing an MPC control system into the current prediction model, and setting a first objective function and a second objective function for the two control layers of the MPC control system respectively; setting and adjusting the extended weight coefficients of the first and second objective functions according to the grid operating conditions; establishing a mapping relationship of the extended weight coefficients between the two control layers to achieve dynamic fusion between the two control layers, thereby controlling the output power of the converter; wherein, the first objective function is used to optimize the predicted power reference value output by the current prediction model in combination with the grid operating conditions, and the second objective function is used to track and control the actual current and voltage of the converter based on the optimized predicted power reference value; the predicted power reference value includes: active power reference value and reactive power reference value.

[0064] Therefore, to overcome the limitations of traditional MPC control systems, this invention proposes a hierarchical dynamic weight adjustment-based MPC power prediction control method for wind power converters. Hierarchical dynamic weight MPC can achieve flexible allocation and priority adjustment of power targets (active and reactive power) by constructing a dynamic weight adjustment mechanism between high and low layers. Specifically, one control layer of the MPC control system (the high control layer, or high layer) is responsible for optimizing the global target, such as optimizing the reference values ​​and dynamic weights of the active power P and reactive power Q of the wind power converter, and dynamically adjusting the priority weights of active and reactive power in conjunction with grid demand. The other control layer (the low control layer, or low layer) performs real-time control based on the reference values ​​provided by the high layer and the set extended weight coefficients, combined with the current operating state, to ensure rapid tracking of active and reactive power, while simultaneously achieving smooth control input and strict satisfaction of system constraints. In addition, this invention introduces a weighted fusion mechanism, which combines the global objective of the high-level layer and the local adaptive control of the low-level layer by setting mapping parameters with fusion coefficients in the high-level and low-level layers. Under different operating conditions, different fusion coefficients are set to dynamically adjust the control priorities of the high-level and low-level layers, thereby balancing the global objective and the local response.

[0065] Specifically, please refer to Figure 1, which shows a flowchart of the MPC wind power converter power prediction control method based on hierarchical dynamic weight adjustment provided in this embodiment, including the following steps:

[0066] S100. Based on the mathematical model of the converter's output current in the first preset coordinate system, a current prediction model is constructed.

[0067] In order to more accurately describe the dynamic characteristics of the converter output current, promptly identify potential unstable factors in the system, adjust the control strategy in advance, and ensure the stable operation of the power system, a current prediction model is constructed here based on the mathematical relationship presented by the mathematical model of the converter output current in the first preset coordinate system. This current prediction model can provide forward-looking information for the control strategy, helping the system to effectively improve the stability and reliability of the system under complex conditions such as wind speed fluctuations and grid frequency changes.

[0068] Specifically, referring to Figure 2, the expansion of step S100 includes the following steps:

[0069] S101. According to Kirchhoff's laws, the three-phase voltage and current equations of the converter are established through the inductor to confirm the mathematical model of the converter's output current in the first preset coordinate system.

[0070] In the abc coordinate system, let the three-phase windings of the converter be phase A, phase B, and phase C, and the inductance of each phase winding be L. f The three-phase currents are i a ib i c The three-phase grid voltages are U ia U ib U ic The components of the three-phase grid voltage on each axis in the coordinate system are U Ida U Idc U Iac ;

[0071] Based on Kirchhoff's laws, the three-phase voltage and current equations can be obtained as follows (Formula VII):

[0072] Formula (VII);

[0073] Next, in the output current model of the full-power converter, the current dynamics are determined by the converter's output voltage U. ia and U iβ Therefore, the mathematical model of the converter output current in the first preset coordinate system (αβ coordinate system) is as follows (Eight):

[0074] Formula (8);

[0075] In this formula, i α and i β The output current axis components of the three-phase bridge arms in the αβ coordinate system are U. Idα U Idβ It is the component of the grid voltage along the α and β axes in the αβ coordinate system, U iα and U iβ It is the output voltage axis component of the three-phase bridge arm in the coordinate system.

[0076] S102. Transform the mathematical model to the second preset coordinate system to obtain the transformation components corresponding to each axis of the second preset coordinate system;

[0077] Furthermore, since the current cannot be decoupled and controlled in the αβ coordinate system, in order to perform PQ decoupling control and achieve independent control of active and reactive power, it is necessary to use the Park transformation to transform the mathematical model to the second preset coordinate system (dq coordinate system), and convert it into the d and q axis components in the dq coordinate system. See the following formula (IX) for details:

[0078] (Nine);

[0079] S103. Discretize the conversion components corresponding to each axis within the preset sampling period and introduce a phase-locked loop to obtain the current prediction model.

[0080] By discretizing formula (IX), and with a preset sampling period of T, s This will yield the following formula (10):

[0081] Formula (10);

[0082] In this formula, , These are the predicted values ​​of the d-axis and q-axis currents at time k+1. , These are the sampled values ​​of the d-axis and q-axis currents at time k. The angular frequency of the power grid. , Let be the components of the grid voltage in the dq coordinate system. , It is the component of the converter's output voltage in the dq coordinate system at the current moment, used for control. and This allows for power control to meet the system's power requirements. From formula (x), it can be seen that the d-axis component of the bridge arm inductor current... Not only affected by the d-axis component of the grid connection point d-axis component of converter output voltage It is also affected by the q-axis component of the bridge arm inductor current. The influence of the grid-connected current, specifically the d and q components, means that in addition to being affected by the voltage across the inductor, they are also affected by the coupling effect between them. Furthermore, the active power P mainly depends on... But it is also affected The impact (through) × The reactive power Q depends on But it is also affected The impact (through) × This coupling relationship means that adjusting... or This will affect both P and Q simultaneously. Therefore, the goal of decoupling is to achieve... and Independent control is required to meet the requirements of wind power grid connection for independent regulation of active and reactive power.

[0083] In view of this, a phase-locked loop (PLL) is introduced in the embodiments of this application. The PLL tracks the phase θ and frequency of the grid voltage in real time. In power decoupling, the phase-locked loop ensures that the dq coordinate system is synchronized with the grid voltage, so that the grid voltage in the dq coordinate system will satisfy the following characteristics: (1) =0, that is, the q-axis component of the grid voltage is 0; (2) That is, the d-axis component of the grid voltage is close to the grid voltage amplitude.

[0084] In the dq coordinate system, the instantaneous active power P and reactive power Q can be expressed by the following formula (XI):

[0085] Formula (XI):

[0086] After introducing a phase-locked loop, formula (XI) simplifies to formula (XII):

[0087] Formula (12);

[0088] As can be seen from formula (12), the active power P is only related to... Related to, with Irrelevant; reactive power Q is only related to Related to, with Irrelevant. This indicates that, theoretically, P and Q, after synchronization via a phase-locked loop, are mathematically decoupled. Therefore, the final discretized dynamic current prediction model after introducing the phase-locked loop is the final current prediction model, as shown in the following formula (XIII):

[0089] Formula (XIII);

[0090] Among them, for and The current prediction at subsequent time points relies on the predicted value at the previous time point, and is calculated recursively step by step. Furthermore, the prediction model will... and With control input and Dynamic association was implemented.

[0091] It should be noted that although phase-locked loops can theoretically achieve decoupling in the power formula, in the discretized current dynamic equation shown in equation (xiii), and The coupling terms still exist. This is because the second preset coordinate system (dq coordinate system) is a synchronously rotating reference system, and the rotation of the reference system introduces cross terms, i.e. and These items led to and They will still influence each other. Therefore, this application's embodiments introduce an MPC control system to compensate for the cross terms in the subsequent first and second objective functions, achieving dynamic decoupling.

[0092] Therefore, the following steps follow after step S100:

[0093] S200, introduce the MPC control system into the current prediction model, and set independent first and second objective functions for the two control layers of the MPC control system respectively;

[0094] After introducing the MPC control system, the objective functions of each layer of the MPC will be designed according to the functions of the hierarchical structure. The basic idea of ​​hierarchical control is to divide the control task into two parts: a high control layer (slow global optimization) and a low control layer (fast local tracking). Although each layer has an independent first objective function and a second objective function, they will cooperate with each other to achieve the overall optimization of the system. The first objective function is used to optimize the predicted power reference value output by the current prediction model in combination with the grid operating conditions. The second objective function is used to track and control the actual current and voltage of the converter based on the optimized predicted power reference value. The predicted power reference value includes active power reference value and reactive power reference value.

[0095] The hierarchical control structure is shown in Figure 5. It is clear that the higher layer is responsible for global optimization, while the lower layer is responsible for real-time tracking. The higher-level power mapping module inputs the predicted power reference value into the objective function of the higher layer, and then optimizes the active and reactive power reference values ​​to provide a reference for the lower layer. Therefore, the higher control layer is responsible for global optimization, considering the system's operating state (such as wind speed fluctuations, grid demand, and energy storage systems) and the dynamics of external disturbances, and calculates the converter output current reference value. and Its optimization objective is global power management, namely active power P and reactive power Q; while the lower control layer is responsible for quickly responding to the reference value calculated by the higher layer, realizing converter current tracking and output voltage control. Its main task is to accurately track the reference value and ensure the dynamic performance and stability of the system.

[0096] Specifically, considering that high-level control mainly focuses on the global optimization objective of the system, the first objective function corresponding to the high-level control is designed. The following formula (I):

[0097] Formula (1);

[0098] The first item is the optimization of Maximum Power Point Tracking (MPPT). Wind turbines typically need to operate in MPPT mode to maximize wind energy utilization. The second item considers the grid's needs for reactive power and voltage regulation, and tracks a reference value for reactive power.

[0099] At the same time, according to , Based on the current voltage sampling value, calculate the dq-axis reference current, and calculate... and The following formula (XIV):

[0100] Formula (XIV);

[0101] Considering that the main task of lower-level control is to quickly track the predicted power reference value provided by higher levels and ensure the dynamic response and stability of the system, a second objective function corresponding to the lower level is designed. Formula (II) is as follows:

[0102] Formula (II);

[0103] in, and These represent the active power and reactive power at time k+j after minimizing the next N time points (from K+1 to K+N) within the prediction time domain N. and These represent the active power reference value and reactive power reference value at time k+j, respectively.

[0104] and These represent the current components corresponding to the d-axis and q-axis in the second preset coordinate system at time k+j, after minimizing the N future time points (from K+1 to K+N) within the prediction time domain N. and These represent the current reference values ​​on the d-axis and q-axis at time k+j, respectively; λ is a weighting coefficient used to balance the priority between the first term (current tracking error) and the second term (control input smoothness) of the second objective function. This represents the change in the converter control input voltage along the d-axis at time k+j.

[0105] Furthermore, to ensure that the objective function can be implemented in a real system, the second objective function is optimized during the optimization process. Set the following constraints:

[0106] Constraint 1:

[0107] Constraint 2:

[0108] Constraint 3:

[0109] Among them, constraint 1 ensures that the optimized control input and The voltage output capacity of the inverter must not be exceeded, and control signals must not exceed hardware limits, which could lead to system failure. Constraint 1 is the maximum output voltage of the converter; Constraint 2 is to avoid excessive input fluctuations, protect system stability and hardware equipment, and control the maximum rate of change of the input; Constraint 3 is to ensure that the current does not exceed the rated value of the converter, avoid overcurrent damage to the system, and meet the grid's specifications for current amplitude.

[0110] The first term in Formula (II) is the primary control objective, which represents minimizing the current at N future times (from K+1 to K+N) within the prediction time domain N. and Relative to reference value and The error makes and Accurate tracking reference value and ;because and It is by and The calculated result is then tracked. and Essentially, it controls P and Q, and the objective function optimizes the current tracking error. When calculating the current prediction value, the model will naturally include cross terms.

[0111] The second term in Formula (II) is used to limit the amount of change in the control input by penalizing it. and The rate of change of the control input is used to achieve smoothness of the control input and stability of the system. In converter control, the control input... and These are the dq components of the converter output voltage, and these voltage signals are applied to filters or loads through their modulation strategies. If the control input changes too rapidly, it can cause unnecessary fluctuations in active power P and reactive power Q, affecting the stability of the power grid. and These represent the converter control inputs. and The change in is expressed by the following formula (XV):

[0112] Formula (XV);

[0113] It should be explained that, as shown in Figure 6, if λ is large, the optimization process will place greater emphasis on the smoothness of changes in the control input; if λ is small, the optimization process will focus more on the current tracking accuracy, which may lead to more drastic changes in the control input. Figure 6 shows the range of values ​​for λ in three cases: the red area in the figure represents the area prioritizing fast response, within which... The control input responds faster to the reference value, but has lower smoothness, making it suitable for scenarios requiring rapid dynamic adjustment; the green area in Figure 6 prioritizes stability, and within this range... The control input changes more smoothly and the stability is higher, but it may sacrifice some response speed, making it suitable for scenarios with high requirements for smooth control; the yellow area in Figure 6 represents the balanced operating condition, within which... This approach balances response speed and smoothness, making it suitable for applications requiring a balance between the two. Figure 6 illustrates how to select a suitable range of values ​​for λ based on specific needs, finding the optimal balance between fast response and stability.

[0114] Meanwhile, the second objective function It is a comprehensive performance index for the entire prediction time domain, taking into account current errors and control input variations at both the current and future time points. The goal is to find a set of control input sequences. and , so that the second objective function Minimize the input while considering system constraints during optimization; after optimization, extract the first value from the control input sequence. and This is applied to the converter; however, it is important to note the future control inputs. These are just predicted values; they will be recalculated during actual use. Finally, the above process is repeated in the next sampling period through rolling updates. Based on this, the above mechanism ensures that the control input at the current moment is the global optimum.

[0115] In summary, the MPC control system ultimately achieves power control through the cooperation of two objective functions. The PQ control principle based on hierarchical model prediction is shown in Figure 7. After a series of online calculations and optimizations, the switching quantity is directly output to the converter.

[0116] However, due to the formula (ii) These are global weights, and their function is to limit and control the input. and The rate of change was not specifically targeted at... and The ability to adjust. Therefore, through adjustment It can affect the overall dynamic performance of the system, but it cannot adjust the active power P and reactive power Q separately. Therefore, in order to solve the above problems, in the embodiments of this application, the first objective function and the second objective function of the high layer and the low layer are extended respectively, and different extended weight coefficients are introduced to weight them respectively. In this way, by introducing extended weight coefficients at both the high layer and the low layer, the system is optimized in all aspects from global power management to local dynamic control. The high layer is responsible for setting the global priority, and the low layer can accurately realize the specific current control based on this. That is, step S300: according to the grid operating conditions, set and adjust the extended weight coefficients of the first objective function and the second objective function;

[0117] Depending on the specific operating conditions, for example, in scenarios where dynamic response speed and accuracy requirements are not high, or in situations requiring balanced resource allocation, the extended weighting coefficient can be set to a fixed coefficient, as shown in Figure 3. In this case, step S300 specifically includes the following steps:

[0118] S301a. Set fixed expansion weight coefficients for the first objective function and the second objective function respectively to obtain the first objective expansion function and the second objective expansion function;

[0119] For high-level applications, considering the different demands on active power P and reactive power Q, weighting coefficients can be added to these two items respectively, and the first objective function can be expanded accordingly. The following formula (3):

[0120] Formula (3);

[0121] in, and These are the fixed expansion weighting coefficients for active power and reactive power in the first objective expansion function, respectively. In other words... Assigning importance weights to active power tracking The importance weight of reactive power tracking is adjusted by... and It can control the optimization priority of active or reactive power at higher levels.

[0122] For the lower layers, since they are responsible for current tracking, the weights can be applied directly to them. and The tracking error term, because and The second extended objective function corresponds to P and Q respectively. The following formula (four):

[0123] Formula (IV);

[0124] in, and These represent the fixed expansion weighting coefficients for the d-axis and q-axis current shunting in the second objective expansion function, respectively; in the first term, the fixed expansion weighting coefficients... Adjustment The importance of tracking error, fixed extended weighting coefficients Adjustment The importance of tracking error.

[0125] S302a. Adjust the weight of the two fixed extended weight coefficients according to the grid operating conditions; the grid operating conditions include at least: the actual energy demand of the grid load, wind speed status, and grid voltage status.

[0126] Specifically, based on the foregoing, different terms in the two objective expansion functions each participate in the adjustment. , and , This means that under different power grid operating conditions, the fixed extended weight coefficients can be set in different ways to make the optimal control decision under each operating condition. Therefore, step S302 includes the following steps:

[0127] Step 1: When the actual energy demand increases or the wind speed fluctuates, set a fixed expansion weighting coefficient for the power consumption in the first objective expansion function. Fixed extended weighting factor greater than reactive power Furthermore, a fixed expansion weighting coefficient is set for the d-axis current shunt in the second objective expansion function. Fixed extended weighting factor greater than q-axis current shunt ;

[0128] In such a situation, since the main function of active power is to meet the actual energy demand of the grid load, when the load demand increases and the wind speed fluctuates, the adjustment objective of the fixed extended weighting coefficient is to prioritize smoothing the output of active power, ensure the stability of grid power transmission, and appropriately weaken the priority of reactive power.

[0129] Therefore, the following settings are available:

[0130] (1) Set the weights of the high-level objective function as follows: > ;

[0131] (2) Set the weights of the lower-level objective function as follows: > .

[0132] Step 2: When the grid voltage is abnormal, set a fixed expansion weighting coefficient for the working power in the first objective expansion function. Fixed extended weighting factor less than reactive power Furthermore, a fixed expansion weighting coefficient is set for the d-axis current shunt in the second objective expansion function. Fixed extended weighting factor less than q-axis current shunt .

[0133] In such circumstances, since the main function of reactive power is to regulate voltage levels, support the stable operation of the power grid, and provide electromagnetic energy for inductive or capacitive loads, the system needs to prioritize the regulation of reactive power when the power grid voltage is abnormal.

[0134] Therefore, the following settings are available:

[0135] (1) Set the weights of the high-level objective function as follows: > ;

[0136] (2) Set the weights of the lower-level objective function as follows: > .

[0137] Furthermore, since setting the extended weighting coefficients to fixed coefficients is also applicable to operating conditions requiring balanced resource allocation, under normal grid operation, the responses of P and Q must be dynamically consistent, and the balance control and... and To ensure synchronous response of P and Q, it is also possible to configure... = , = .

[0138] Furthermore, while the aforementioned fixed extended weight coefficient design is suitable for scenarios with low requirements for dynamic response speed and accuracy, or for operating conditions requiring balanced resource allocation, the MPC control system with a fixed extended weight coefficient cannot flexibly cope with changes in operating conditions due to the randomness and fluctuation of wind energy in actual scenarios. Therefore, in this embodiment, a dynamic weight adjustment MPC control system is also introduced, where the extended weight coefficient is a dynamic coefficient. Its advantage lies in its ability to flexibly switch priorities, adapt to multiple operating conditions, and prioritize the allocation of resources to critical targets, making resource utilization more efficient. It can also improve response speed and accuracy, and prioritize the adjustment of critical needs. Referring to Figure 4, step S300 specifically includes the following steps:

[0139] S301b: Set dynamic expansion weight coefficients for the first objective function and the second objective function respectively, and distribute the weights of the dynamic expansion weight coefficients in the fourth objective function through proportional allocation to obtain the third objective expansion function and the fourth objective expansion function;

[0140] Specifically, for the first objective function corresponding to the higher level, the third objective extension function can be designed as shown in Formula (V). :

[0141] Formula (5);

[0142] in, and These represent the dynamic expansion weight coefficients of active power and reactive power in the third objective expansion function, respectively.

[0143] For the second objective function corresponding to the lower layer, the fourth objective expansion function can be designed as shown in formula (VI). :

[0144] Formula (VI);

[0145] in, and These represent the dynamic expansion weighting coefficients for the d-axis and q-axis current shunting in the fourth objective expansion function, respectively.

[0146] S302b. Based on the power grid operating conditions, set the dynamic adjustment range of two dynamic extended weight coefficients; the power grid operating conditions include at least: power grid load, wind speed fluctuation, and power grid voltage deviation.

[0147] Due to the third objective expansion function and the fourth objective expansion function It is designed to flexibly respond to changes in operating conditions, therefore, for the third objective extension function In and The following update rules can be followed:

[0148] (1) Wind speed fluctuation

[0149] Wind speed fluctuations can cause fluctuations in the active power output P of a wind power system. Additional weights are needed to smooth the active power output, and the update rule is as follows (Formula XVI):

[0150] Formula (XVI);

[0151] In formula (xvii), The basic weight for active power. This is the wind speed fluctuation adjustment coefficient. This represents the standard deviation of wind speed fluctuations as measured by a wind speed sensor.

[0152] (2) Increased grid load

[0153] When the grid load increases, leading to increased active power demand When increasing, it is necessary to prioritize outputting more active power and increase [the output]. The weights are updated according to the following formula (XVII):

[0154] Formula (XVII);

[0155] In formula (xvii), This is the load demand adjustment factor when the grid load increases. Increase the amount to meet load requirements.

[0156] (3) Grid voltage deviation

[0157] When the grid voltage Deviation from reference value At this time, it is necessary to adjust the voltage through reactive power Q to increase it. The weights are updated according to the following rules:

[0158] Formula (18)

[0159] In formula (18), As the basic weight for reactive power, This is the adjustment factor for grid voltage deviation. This refers to the voltage deviation.

[0160] Based on the above update rules, we can obtain the comprehensive update rules shown in formula (19) below:

[0161] Formula (19).

[0162] Under such update rules, formula (19) and The dynamic adjustment range should follow the following setting principles:

[0163] (1) Update rule parameter settings

[0164] The settings should be based on the overall goals of the system. If the system prioritizes power generation economics, that is, to ensure active power output first, then... The setting range is If the system needs to consider reactive power, i.e., grid voltage stability, then The setting range is .

[0165] Should be based on wind speed fluctuations The impact on system operation can be set. If wind speed fluctuations significantly affect active power output, then... The setting range is If the system is not sensitive to wind speed fluctuations, then The setting range is .

[0166] Should be based on load changes Sensitivity settings for system active power demand. If the load demand places significant pressure on active power, then... The setting range is If the load change has a small impact on the system pressure, then The setting range is .

[0167] (2) Update rule parameter settings

[0168] According to the system's operational goals, Should be .

[0169] The settings should be based on the impact of grid voltage fluctuations on the system. If grid voltage fluctuations are significant or the system is connected to a weak grid, then... The setting range is If the grid voltage is relatively stable or not sensitive to voltage fluctuations, then The setting range is .

[0170] After introducing the third objective extension function After setting the principles, the fourth objective function will be expanded below. Explanation of setting principles:

[0171] First, in the embodiments of this application, in the fourth target expansion function The objective function designed here The reason for not participating in the dynamic expansion weight coefficient adjustment is that the core objective of dynamic weight adjustment is to dynamically change the weight based on the operating conditions. and This allows for the priority adjustment of active power P or reactive power Q. These are the weighting parameters for the input smoothing term, which affect the converter output voltage u. d and u q The magnitude of change (smoothness), but it does not directly affect i. d and i q Priority allocation. Therefore, in dynamic weight adjustment design, the focus is on... and Dynamic adjustments, but not involving Dynamic adjustment.

[0172] Secondly, dynamic weight adjustment requires the use of runtime status awareness and dynamic weight calculation functions to dynamically allocate and expand weight coefficients in real time. and This allows for flexible and prioritized adjustment of active power P and reactive power Q. Specifically, the following is a detailed implementation of the allocation of the lower-level dynamic expansion weighting coefficients:

[0173] (1) Operation status awareness, obtaining key parameters:

[0174] Sensing the operating status is the core of the dynamic weight adjustment method, which relies on real-time monitoring and analysis of key parameters in the wind power converter and power grid operating environment.

[0175] A. Power grid frequency deviation

[0176] The grid frequency deviation is a direct reflection of the active power balance in the grid: an increase in frequency indicates an excess of active power in the grid, while a decrease in frequency indicates an insufficient active power. Therefore, the grid frequency deviation is calculated by measuring the grid frequency in real time using a phase-locked loop and then using the following formula (20):

[0177] Formula (20);

[0178] Among them, f n This is the rated frequency of the power grid, typically 50Hz.

[0179] B. Grid voltage deviation

[0180] Grid voltage deviation reflects the reactive power balance in the grid: a rise in voltage usually indicates excess reactive power, while a drop in voltage usually indicates insufficient reactive power. The grid voltage is measured in real time by the voltage sensor in the converter. The voltage deviation is calculated using formula (21):

[0181] Formula (21);

[0182] C. Deviation of active power reference value

[0183] Active power reference value deviation This indicates whether the active power P of the wind farm meets the real-time dispatch instructions of the power grid: if P < It is necessary to increase the active power if P > Active power needs to be limited, and the deviation of the active power reference value is required. The following formula (22) is used to calculate:

[0184] Formula (22);

[0185] in, This is the active power reference value. The current is calculated in real time based on the converter output current and the grid voltage.

[0186] D. Deviation of active power reference value

[0187] Active power reference value deviation This indicates whether the reactive power Q of the wind farm meets the real-time dispatch instructions of the power grid: if Q < It is necessary to increase reactive power if Q> It is necessary to limit reactive power and the deviation of active power reference values. The following formula (23) is used to calculate:

[0188] Formula (23);

[0189] in, This is a reactive power reference value. The current is calculated in real time based on the converter output current and the grid voltage.

[0190] Subsequently, this application embodiment also employs a proportional allocation method to dynamically allocate weights. This method is simple and easy to implement, with weight allocation determined by the magnitude of the deviation, making it intuitive and easy to understand. Simultaneously, the weights are dynamically adjusted based on the deviation to adapt to different operating conditions, requiring minimal computation and avoiding the calculation or optimization of weight coefficients, thus reducing the burden of online computation. Specifically, this invention uses a weighted fusion approach to guide the dynamic adjustment of lower-level targets through higher-level weights, while preserving the adaptability of lower-level weights. This fusion requires incorporating higher-level information into the weight allocation and ensuring consistency between higher-level and lower-level targets.

[0191] It can be seen that the dynamic weights allocated proportionally to the lower levels are as follows (Twenty-Four):

[0192] Formula (24);

[0193] and This is based on the proportional allocation of weights at the lower levels. It's important to note that if these deviations are directly used in weight calculations, variables with larger deviations (such as P or Q) will dominate the weight allocation, while variables with smaller deviations (such as...) will... or The deviations may be ignored, and the dimensional differences between different deviations make the calculation results unable to reflect the actual adjustment needs. Therefore, the deviations in the above formula are normalized to obtain the following formula (25):

[0194] Formula (25);

[0195] Substituting the normalized bias into the weighting formula, we obtain the following formula (XXVI):

[0196] Formula (26);

[0197] Formula (26) above demonstrates the weights. and Will vary with deviation , , and Adjusted according to dynamic changes.

[0198] In order to achieve a balance between the global and local aspects of the two high and low control layers, and to coordinate the control objectives of the high and low layers and avoid conflicts between them, this embodiment of the application also introduces a fusion coefficient to make the combination of the high-level weight and the low-level weight more flexible. That is, after step S300, it also includes: S400, establishing an extended weight coefficient mapping relationship between the two control layers to achieve dynamic fusion between the two control layers, thereby controlling the output power of the converter.

[0199] Specifically, please refer to Figure 7, which illustrates the mapping relationship and fusion coefficient between high-level weights and low-level weights. The role of high-level weight and This is the optimized output, used to guide the allocation of lower-level weights; it is combined with the fusion coefficients through a mapping function. This enables the dynamic combination of high-level and low-level weights; the fusion coefficient... Adjusting the influence ratio of high-level and low-level weights, low-level weights and It combines high-level global optimization with its own local needs, as shown in Figure 7 with fusion coefficients. The mapping function is shown in the following formula (XXVII):

[0200] Formula (27);

[0201] In formula (27) This is used as a fusion coefficient for the weights of high-level and low-level layers to adjust the relative influence of high-level and low-level layers. Its setting range is [range missing]. .

[0202] Figure 8 shows the mapping relationship curve between high-level and low-level weights, illustrating the mapping relationship between high-level and low-level weights and demonstrating the influence of the fusion coefficient on this relationship. The blue dashed line represents the high-level weights. and This reflects the priority of target allocation at the higher levels; the solid red line represents the weight of lower levels. and This reflects the lower-level priority of the fusion result based on the higher-level weights. Simultaneously, the fusion coefficient... Controlling the degree of weighting between high-level and low-level layers, when At times, they completely depended on the higher-ups. The timing is entirely determined autonomously by the lower-level weights. The green markers in Figure 8 show different fusion coefficients. The values ​​below and the corresponding lower-level weights are adjusted by... The mapping relationship between high and low layer weights can be flexibly changed.

[0203] Specifically, due to and The objective function i is determined d and i q The importance of the error term is determined by adjusting... and This allows us to focus more on minimizing a particular error, thus enabling the regulation of active power P and reactive power Q to be carried out independently.

[0204] Through the above steps, the weight of the higher levels... and Directly participating in the allocation of low-level weights ensures that high-level objectives guide low-level control. This is achieved through the fusion coefficient. Different settings can be configured for different operating conditions. The control priorities of the high-level and low-level layers can be changed, and the weight of the low-level layer retains the characteristics based on deviation allocation, still has real-time performance, can quickly respond to dynamic changes in the system, and does not completely lose the guidance role of the high-level layer.

[0205] Based on the above content and Figures 9 and 10, it can be seen that the MPC wind power converter power prediction control method based on hierarchical dynamic weight adjustment proposed in this application is of great significance in the field of power prediction control for full-power wind power converters. Specifically, as shown in Figures 9 and 10, the performance differences between fixed extended weight coefficients and dynamic extended weight coefficients in P and Q tracking are illustrated. Figure 9 shows the active power tracking comparison, with the black dashed line representing P. ref The red curve represents the tracking result P under fixed weights, showing lag and deviation; the blue curve represents the tracking result P under dynamic weights, which can more accurately follow the reference value and reduce lag and deviation; Figure 10 shows the reactive power tracking comparison, with the black dashed line representing Q. ref The red curve represents the tracking result Q under fixed weights, which has a larger tracking error; the blue curve represents the tracking result Q under dynamic weights, which can better adapt to changes in the reference value.

[0206] This control method, through dynamic adjustment of weight allocation, enables flexible regulation of active and reactive power, significantly improving power point tracking performance and system dynamic response capabilities. Its hierarchical optimization architecture provides technical support for wind power control under complex operating conditions and offers crucial assurance for the development of smart grids with high wind power integration. In the future, with the further expansion of wind power scale, the hierarchical dynamic weighted MPC method will play an increasingly important role, providing a solid technical foundation for the stable operation of wind farms and the safe and reliable operation of the power grid.

[0207] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application 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 inventive concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A power prediction control method for MPC wind power converters based on hierarchical dynamic weight adjustment, characterized in that, The process includes the following steps: Constructing a current prediction model based on a mathematical model of the converter's output current in a first preset coordinate system; introducing an MPC control system into the current prediction model, and setting independent first and second objective functions for the two control layers of the MPC control system; setting and adjusting the extended weight coefficients of the first and second objective functions according to grid operating conditions; establishing a mapping relationship between the extended weight coefficients of the two control layers to achieve dynamic fusion between the two control layers, thereby controlling the output power of the converter; wherein, the first objective function is used to optimize the predicted power reference value output by the current prediction model in conjunction with grid operating conditions, and the second objective function is used to track and control the actual current and voltage of the converter based on the optimized predicted power reference value; the predicted power reference value includes: active power reference value and reactive power reference value.

2. The MPC wind power converter power prediction control method based on hierarchical dynamic weight adjustment according to claim 1, characterized in that, Based on the mathematical model of the converter's output current in a first preset coordinate system, a current prediction model is constructed. Specifically, this includes: establishing the three-phase voltage and current equations of the converter through an inductor according to Kirchhoff's laws to confirm the mathematical model of the converter's output current in the first preset coordinate system; transforming the mathematical model to a second preset coordinate system to obtain the transformation components corresponding to each axis of the second preset coordinate system; discretizing the transformation components corresponding to each axis within a preset sampling period and introducing a phase-locked loop to obtain the current prediction model.

3. The MPC wind power converter power prediction control method based on hierarchical dynamic weight adjustment according to claim 1, characterized in that, The control layer includes a high control layer and a low control layer, wherein the high control layer and the low control layer correspond to the first objective function and the second objective function, which are independent of each other, respectively; the first objective function... As shown in Formula (I), the second objective function The following formula (II); Formula (1); Formula (II); where, and These represent the active power and reactive power at time k+j after minimizing the next N time points (from K+1 to K+N) within the prediction time domain N. and These represent the active power reference value and reactive power reference value at time k+j, respectively. and These represent the current components corresponding to the d-axis and q-axis in the second preset coordinate system at time k+j, after minimizing the N future time points (from K+1 to K+N) within the prediction time domain N. and These represent the current reference values ​​on the d-axis and q-axis at time k+j, respectively; λ is the weighting coefficient. This represents the change in the converter control input voltage along the d-axis at time k+j.

4. The MPC wind power converter power prediction control method based on hierarchical dynamic weight adjustment according to claim 3, characterized in that, The extended weighting coefficients are fixed coefficients. Based on the power grid operating conditions, the extended weighting coefficients of the first objective function and the second objective function are set and adjusted. Specifically, this includes: setting fixed extended weighting coefficients for the first objective function and the second objective function respectively to obtain the first objective extended function and the second objective extended function; adjusting the weights of the two fixed extended weighting coefficients according to the power grid operating conditions; the power grid operating conditions include at least: the actual energy demand of the power grid load, wind speed status, and power grid voltage status.

5. The MPC wind power converter power prediction control method based on hierarchical dynamic weight adjustment according to claim 4, characterized in that, The first target expansion function As shown in Formula (III), the second objective expansion function The following formula (IV); Formula (3); Formula (IV); where, and These represent the fixed expansion weight coefficients for active power and reactive power in the first objective expansion function, respectively. and These represent the fixed expansion weight coefficients for the d-axis and q-axis current shunting in the second objective expansion function, respectively. Based on the power grid operating conditions, the weights of these two fixed expansion weight coefficients are adjusted, specifically including: when the actual energy demand increases or the wind speed fluctuates, the fixed expansion weight coefficient for the power consumption in the first objective expansion function is set. Fixed extended weighting factor greater than reactive power Furthermore, a fixed expansion weighting coefficient is set for the d-axis current shunt in the second objective expansion function. Fixed extended weighting factor greater than q-axis current shunt When the grid voltage is abnormal, a fixed expansion weighting coefficient for the power consumption in the first target expansion function is set. Fixed extended weighting factor less than reactive power Furthermore, a fixed expansion weighting coefficient is set for the d-axis current shunt in the second objective expansion function. Fixed extended weighting factor less than q-axis current shunt 。 6. The MPC wind power converter power prediction control method based on hierarchical dynamic weight adjustment according to claim 3, characterized in that, The extended weighting coefficient is a dynamic coefficient; Based on the power grid operating conditions, the extended weight coefficients of the first objective function and the second objective function are set and adjusted. Specifically, this includes: setting dynamic extended weight coefficients for the first objective function and the second objective function respectively, and distributing the weights of the dynamic extended weight coefficients in the second objective function through proportional allocation to obtain a third objective extended function and a fourth objective extended function; and setting the dynamic adjustment range of the two dynamic extended weight coefficients based on the power grid operating conditions. The power grid operating conditions include at least: power grid load, wind speed fluctuations, and power grid voltage deviation.

7. The MPC wind power converter power prediction control method based on hierarchical dynamic weight adjustment according to claim 6, characterized in that, The third objective expansion function The fourth objective expansion function is described in formula (v) below. The following formula (VI); Formula (5); Formula (VI); where, and These are the dynamic expansion weight coefficients for active power and reactive power in the third objective expansion function, respectively. and These represent the dynamic expansion weighting coefficients for the d-axis and q-axis current shunting in the fourth objective expansion function, respectively.

8. The MPC wind power converter power prediction control method based on hierarchical dynamic weight adjustment according to claim 7, characterized in that, Establishing an extended weight coefficient mapping relationship between the two control layers to achieve dynamic fusion between the two control layers specifically includes: establishing a mapping function containing fusion coefficients based on the dynamic extended weight coefficients of the active power and reactive power and the dynamic extended weight coefficients of the d-axis and q-axis current shunting; and setting different fusion coefficients according to the power grid operating conditions to adjust the control priority of the high control layer and the low control layer.

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