Grid-connected MMC variable priority control method and system for deep sea wind power

Abstract

The present invention belongs to the technical field of flexible direct current transmission of deep-sea wind power, and specifically discloses a grid-connected MMC variable priority control method and system for deep-sea wind power, the method comprising: respectively calculating the relative deviation of current control and the relative deviation of circulating current control; calculating the initial submodule input number combination of the next cycle based on the relative deviation of current control and the relative deviation of circulating current control; selecting three candidate combinations including the initial submodule input number combination, and obtaining the output current of the system in the next cycle through the current prediction equation; selecting the first submodule input number combination that minimizes the first cost function; selecting three candidate combinations including the first submodule input number combination, and obtaining the circulating current prediction value of the system in the next cycle through the circulating current prediction equation; selecting the second submodule input number combination that minimizes the second cost function. The present invention can enhance the control degree as the relative deviation of the control target increases.

Description

Technical Field

[0001] The present invention relates to the technical field of flexible direct current transmission for deep sea wind power, and in particular to a grid-connected MMC variable priority control method and system for deep sea wind power. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Offshore wind power is currently showing a trend of large-scale wind turbines (above 10MW), large-scale wind farms (above 1GW), and development towards deep sea (above 100km). However, the harsh environment at sea (especially deep sea) leads to poor power quality, and the bidding for offshore wind power also has more stringent requirements on the cost of electricity, thus placing higher performance requirements on the entire power conversion system.

[0004] In terms of grid connection, modular multilevel converter (MMC) has become the preferred topology type for (multi-terminal) flexible direct current transmission converter stations due to its modular design, strong scalability of power and voltage levels, and fault tolerance. In the (multi-terminal) flexible direct current transmission system, MMC is responsible for controlling the DC voltage and the transmitted power, so the study of efficient control of MMC is of great significance to the reliable and stable operation of the entire system. Improving the control efficiency of MMC means improving the overall performance of the entire system.

[0005] Finite set model predictive control (FCS-MPC) has the advantages of simple design, good dynamic performance and flexible structure. It is particularly suitable for processing multi-input and multi-output nonlinear power electronic converter systems such as grid-connected MMC. It uses the cost function to flexibly include multiple objectives and nonlinear constraints, and directly selects the switch state combination that minimizes the tracking error of the system state variables in the set by minimizing the cost function. Traditional FCS-MPC mixes multiple control objectives in one objective function and determines the priority of the objectives through weight coefficients. However, since the control objectives are usually coupled with each other and span multiple time scales and multiple orders of magnitude, it is difficult to set the optimal weight coefficient to weigh the priority of each control objective.

[0006] To solve this problem, the academic community proposed an MPC method based on sequential optimization. This method manages multiple control objectives by sorting them according to their importance and then selecting the switch vector according to the priority order. For the grid-connected MMC system, the MPC algorithm based on sequential optimization is optimized in the order of current tracking, circulating current suppression, and capacitor voltage balance. The cost function of current tracking and circulating current suppression is designed. In each stage, the number of sub-modules in each bridge arm is used as the control quantity, and all candidate sub-modules are traversed to merge and minimize the cost function. The number of sub-modules that make the control variable optimal is selected. Finally, the switch signal of each sub-module is determined and output through the capacitor voltage sorting algorithm. The weight coefficient setting problem is solved. However, the disadvantage of this method is that the number of traversal optimizations for the grid-connected current control part of each phase MPC is N+1 times. In the field of high-voltage direct current transmission (HVDC), the number of sub-modules N in each bridge arm of the grid-connected MMC may reach hundreds, and the MPC controller still faces a very large computational burden, which is difficult to implement and has a heavy computational burden.

[0007] To this end, the prior art proposes an improved simplified optimization algorithm. In the grid-connected current prediction control of the kth cycle, only three combinations near the optimal number of submodules in the previous cycle are used as candidates, instead of traversing all possible combinations. That is, the number of MPC traversals is limited to a very small range, and the number of traversals is reduced from N+1 to 3, which is no longer related to the total number of bridge arm submodules N. In flexible DC transmission situations with a large number of submodules, the calculation burden can be greatly reduced; however, the priority order of the control objectives in the improved simplified optimization algorithm is fixed, that is, it is always optimized in the order of current tracking and circulating current suppression. In the next cycle, the optimal number of inputs determined by circulating current suppression is substituted into the cycle. Therefore, it is difficult to adapt to the control requirements under all working conditions, and it will lead to poor control performance of lower priority objectives. Summary of the invention

[0008] In order to solve the above problems, the present invention proposes a grid-connected MMC variable priority control method and system for deep sea wind power, introduces the concept of relative deviation, calculates the relative deviation of the control target, and uses a weighted method to adaptively select the number of sub-modules to be brought into the next cycle, so that the control priority is variable and the effect of circulation suppression is improved.

[0009] In some embodiments, the following technical solutions are adopted:

[0010] A grid-connected MMC variable priority control method for deep sea wind power, comprising:

[0011] Calculate grid-connected current reference value and circulating current reference value;

[0012] Based on the cost function of the selected candidate switch vector, as well as the grid-connected current reference value and the circulating current reference value, a relative deviation of current control and a relative deviation of circulating current control are calculated respectively;

[0013] Based on the relative deviation of current control and the relative deviation of circulating current control, the combination of the initial number of submodules put into operation in the next cycle is calculated;

[0014] Select three candidate combinations including the initial submodule input number combination, and obtain the output current of the system in the next cycle through the current prediction equation; select the first submodule input number combination that minimizes the first cost function from the three candidate combinations;

[0015] Select three candidate combinations including the first submodule input number combination, and obtain the circulation prediction value of the next cycle of the system through the circulation prediction equation; from the three candidate combinations, select the second submodule input number combination that minimizes the second cost function.

[0016] As a further solution, the relative deviation of current control and the relative deviation of circulating current control are calculated respectively, specifically:

[0017] ;

[0018] in, Indicates control objectives The relative deviation of For the x Phase control target No. i The cost function of candidate switch vectors is Reference value for control target; control target It is current control or circulating current control.

[0019] As a further approach, control objectives For current control, the selected candidate switching vectors are:

[0020] and ;

[0021] Control Target When the circulating current is controlled, the candidate switching vectors are selected as follows:

[0022] and ;

[0023] in, and The optimal number of sub-modules selected for the upper bridge arm and the lower bridge arm in the kth cycle respectively.

[0024] As a further solution, based on the relative deviation of current control and the relative deviation of circulating current control, the initial submodule input number combination of the next cycle is calculated, specifically:

[0025] ;

[0026] ;

[0027] in, , are the initial number of submodules put into use in the upper bridge arm and the lower bridge arm in the next cycle, , are the relative deviation of current control and the relative deviation of circulating current control respectively, N is the total number of half-bridge submodules in each phase bridge arm, The optimal number of upper bridge arm submodules selected for the current cycle current control phase. The optimal number of upper bridge arm submodules selected for the current cycle circulation suppression phase.

[0028] As a further solution, three candidate combinations including the initial submodule input number combination are selected, specifically:

[0029] , and ;

[0030] in, Input number combinations for the initial submodule.

[0031] As a further solution, three candidate combinations including the first submodule input number combination are selected, specifically:

[0032] , and ;

[0033] in, Input number combination for the first submodule.

[0034] As a further option, it also includes:

[0035] Based on the obtained first submodule input number combination and second submodule input number combination, capacitor voltage balancing is achieved using a submodule capacitor voltage sorting algorithm, and the switch state of each submodule is output.

[0036] In other embodiments, the following technical solutions are adopted:

[0037] A grid-connected MMC variable priority control system for deep sea wind power, comprising:

[0038] A reference value calculation module, used for calculating a grid-connected current reference value and a circulating current reference value;

[0039] A relative deviation calculation module, used for calculating the relative deviation of current control and the relative deviation of circulating current control respectively based on the cost function of the selected candidate switch vector, and the grid-connected current reference value and the circulating current reference value;

[0040] An initial input number calculation module is used to calculate the initial submodule input number combination of the next cycle based on the relative deviation of the current control and the relative deviation of the circulating current control;

[0041] A current prediction module is used to select three candidate combinations including an initial submodule input number combination, and obtain the output current of the system in the next cycle through a current prediction equation; and select a first submodule input number combination that minimizes a first cost function from the three candidate combinations;

[0042] The circulation prediction module is used to select three candidate combinations including the first submodule input number combination, and obtain the circulation prediction value of the next cycle of the system through the circulation prediction equation; from the three candidate combinations, select the second submodule input number combination that minimizes the second cost function.

[0043] In other embodiments, the following technical solutions are adopted:

[0044] A terminal device comprises a processor and a memory, wherein the processor is used to implement instructions; the memory is used to store a plurality of instructions, wherein the instructions are suitable for being loaded by the processor and executing the above-mentioned grid-connected MMC variable priority control method for deep sea wind power.

[0045] In other embodiments, the following technical solutions are adopted:

[0046] A computer-readable storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor of a terminal device and executing the above-mentioned grid-connected MMC variable priority control method for deep sea wind power.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] (1) The present invention associates the relative deviation with the number of sub-modules put into operation in the next cycle, and adopts a weighted approach so that the number of sub-modules put into operation in the next cycle tends to give priority to controlling the control target with a large deviation from the reference value, thereby ensuring the control effect of current tracking and circulating current suppression, and being able to enhance the control degree as the relative deviation of the control target increases.

[0049] (2) The present invention improves the priority of the control target with large relative deviation by changing the selection method of the number of sub-modules to be put into use in the next cycle without changing the cascade order. Through variable priority, the proposed method can adaptively adjust the priority of the control target according to the different relative deviations of the control target under different working conditions to ensure that the performance requirements of the control target under various working conditions are met.

[0050] Other features and advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a grid-connected MMC topology diagram in the prior art;

[0052] Figure 2 It is a block diagram of the grid-connected MMC model prediction control based on sequential optimization in the prior art;

[0053] Figure 3 It is a schematic diagram of the variable priority control process of the grid-connected MMC for deep sea wind power in an embodiment of the present invention. DETAILED DESCRIPTION

[0054] It should be noted that the following detailed descriptions are illustrative and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used in the present invention have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0055] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0056] Embodiment 1

[0057] In one or more embodiments, a variable priority control method for grid-connected MMC for deep sea wind power is disclosed, so that the system can adaptively adjust the priority of the control target according to the deviation of the control target. In addition, a weighted method is used to ensure that the number of submodules put into use does not change significantly, so as to balance the control effects of the two control targets.

[0058] The grid-connected MMC topology of this embodiment is as follows Figure 1 As shown, each phase It consists of two bridge arms (the upper bridge arm and the lower bridge arm are marked with subscripts , Each phase bridge arm consists of N half-bridge sub-modules SM and a bridge arm reactor. The half-bridge sub-module SM consists of two IGBTs and a voltage Capacitor composition.

[0059] Assuming that the capacitor voltages of each SM in the bridge arm are balanced, the output voltage of the bridge arm ( , ) is completely determined by the number of half-bridge sub-modules SM put into use ( , ). The AC output side of the grid-connected MMC is connected to the AC grid through the RL filter. and are the inductance and resistance of the filter respectively, and are the grid current and voltage respectively.

[0060] The dynamic equations of the AC and DC equivalent circuits are:

[0061] (1)

[0062] (2)

[0063] In the formula, is the equivalent inductance on the AC output side. is the circulating current flowing in the x phase.

[0064] Assume that the capacitor voltage of SM is balanced. From (1) and (2), we can know that the relationship model between grid current and circulating current and SM input number is as follows:

[0065] (3)

[0066] (4)

[0067] Where k represents the kth sampling period. , , , and is the average capacitor voltage of the x-phase SM (Submodular).

[0068] The control objectives of the grid-connected MMC are:

[0069] ① DC voltage (or active power), reactive power;

[0070] ②Circulation suppression;

[0071] ③Capacitor voltage balance.

[0072] 1. The control implementation of each part of the finite set model predictive control (FCS-MPC) of the grid-connected MMC is introduced below.

[0073] (1) DC voltage control:

[0074] When the grid-connected MMC converter station is in DC voltage control mode, a proportional-integral PI DC voltage controller is used:

[0075] (5)

[0076] Here It is a PI controller in discrete z domain. From the DC voltage controller, the active power reference is obtained .

[0077] (2) Current reference generation

[0078] The control of active power and reactive power is indirectly completed by grid-connected current control. The following describes how to obtain the grid-connected current reference value based on the active and reactive power references.

[0079] First, the instantaneous value of the three-phase grid voltage obtained by sampling The instantaneous value of the three-phase grid voltage at time k+1 can be obtained by the recursive formula:

[0080] (6)

[0081] Then, using Clarke transformation, we get . Using instantaneous power theory, obtain the grid-connected current reference value:

[0082] (7)

[0083] Finally, the three-phase grid-connected current reference value is obtained by using Clarke inverse transformation .

[0084] (3) Predictive current control

[0085] ① Delay compensation

[0086] FCS-MPC requires a certain amount of computation time when implemented in a digital controller, that is, the optimal switch state calculated after a certain period of time (which should be applied to the entire kth cycle) can only be output to the MMC at the beginning of the k+1th cycle. Therefore, there is a one-step delay, which will greatly affect the dynamic and steady-state performance of predictive control. To solve this problem, the academic community has proposed a delay compensation algorithm.

[0087] First, at the beginning of the kth cycle, the grid-connected current and circulating current values ​​obtained by sampling are , , and kThe switch status of each submodule in the cycle and , substitute the discrete mathematical model of grid-connected current and circulating current to obtain the predicted values ​​of grid-connected current and circulating current at time k+1 , Then, in the kth cycle, the predicted values ​​at the k+1 moments are used to further predict the values ​​of the grid-connected current circulation at the k+2 moment under all submodule switch state combinations: , .

[0088] ② Minimize the cost function

[0089] The cost function of the grid-connected MMC finite set model predictive control phase is established below:

[0090] (8)

[0091] (9)

[0092] The grid-connected current reference Obtained from DC voltage or power external loop, the capacitor voltage reference is set to its rated value Since the circulating current contains DC and AC components, the DC component must exist and the AC component needs to be suppressed, so the circulating current reference DC component The reference values ​​of the above system variables at time k+2 can be obtained by the following recursive algorithm:

[0093] (10)

[0094] (11)

[0095] here x for or . The corresponding combination of all submodule switch states obtained by the previous delay compensation , Substitute into equations (8) and (9), calculate the corresponding cost function, and select the submodule switch state combination that minimizes the cost function.

[0096] ③Capacitor voltage balance control

[0097] The capacitor voltage sorting algorithm is used to balance the submodule voltages. Specifically, after obtaining the optimal number of submodules to be put into operation for the upper and lower bridge arms of each phase, the capacitor voltages of all submodules of the bridge arm are sorted in ascending or descending order, and then the submodules with the highest or lowest order are selected according to the current direction to be put into operation first, so that the submodules with high capacitor voltage are discharged first, and the submodules with low voltage are charged first to achieve voltage balance. Then, the corresponding switching signals are output to all submodules. In addition, when implementing this algorithm in a digital controller, an FPGA-based PWM hardware module is often used to output the switching signals (0 or 1) of the submodules.

[0098] 2. The improved and simplified optimization algorithm of the grid-connected MMC based on sequential optimization is described below.

[0099] Figure 2 The control block diagram of the improved simplified optimization algorithm based on sequential optimization for grid-connected MMC is given. Its priority is fixed and there is no weight coefficient. It uses multiple cost functions in the order of grid-connected current tracking and circulating current suppression, so that each cost function contains only one control target.

[0100] This method is in k The delay compensation algorithm is used in each cycle to obtain the grid-connected current. k +1 cycle prediction value. Then take the three combinations near the optimal submodule input number of the previous cycle as candidates, namely and Substitute the grid-connected current prediction value and the candidate number of submodules into formula (3) to calculate the grid-connected current in all cases. k Then, substitute the predicted values ​​in all cases into equation (8), calculate all corresponding grid-connected current cost function values, and select the one that minimizes equation (8). k+ Combination of submodule input numbers in 1 cycle and .

[0101] Then the circulating current control is predicted. From equations (3) and (4), it can be seen that the current of the grid-connected MMC is controlled by the differential mode voltage, that is, , the circulating current is controlled by the common mode voltage, that is Therefore, in order to provide control freedom for circulating current prediction control, the total number of upper and lower bridge arm submodules in each phase needs to be changed dynamically. At the same time, +1, -1 and +0 (since these three combinations only change the common mode voltage of the upper and lower bridge arms, and will not affect the differential mode voltage, they will not affect the first step of grid-connected current prediction control), then three new candidate submodule input number combinations can be obtained. Then substitute the three new candidate combinations and the circulating current reference value obtained by delay compensation into formula (4) to calculate the circulating current prediction values ​​corresponding to the three candidate combinations Similarly, the three predicted values ​​are substituted into the cost function of the circulation (9), and the optimal combination of submodule input numbers for the k+1th cycle is selected by minimizing the cost function. and .

[0102] Then, the capacitor voltage sorting algorithm is used to select the submodule to be put into operation, and then the corresponding switching signals are output to all submodules.

[0103] In the next cycle, the optimal input number determined by circulating current suppression is and Substituted into the loop as initial value.

[0104] 3. The following describes the implementation process of the grid-connected MMC variable priority control method for deep sea wind power in this embodiment.

[0105] The disadvantage of the existing grid-connected MMC model predictive control algorithm based on sequential optimization is that it is impossible to adjust the priority of the control target.

[0106] The method proposed in this embodiment still adopts the cost function of the cascade structure and maintains the order of current control and circulating current control. The difference is that: by using the feature of "changing the number of submodules put into the next cycle, the priority of the control target can be adjusted". If the optimal number of inputs determined by current tracking is adopted, that is, Substituted into the next cycle, the current tracking effect is better; if the optimal input number determined by circulating current suppression is used, that is, Substituted into the next cycle, the circulating current suppression effect is better; the deviation of the two control targets of current and circulating current is used as the adjustment coefficient, the number of submodules substituted into the next cycle is changed, and a weighted method is used to change the priority of the control target with the deviation, so that the control target with a larger deviation from the reference value can be controlled first. This method can adjust the control priority according to different working conditions.

[0107] The specific implementation process of the method in this embodiment is as follows:

[0108] S101: Calculate the grid-connected current reference value and the circulating current reference value;

[0109] Combination Figure 3, the measured value of the outer loop DC voltage is compared with the reference value through equation (5), and the proportional integral PI DC voltage controller is used to obtain the active power reference ; Through the active power reference, reactive power reference, and grid voltage, the grid-connected current is calculated using formula (7): refer to.

[0110] S102: Calculating a relative deviation of current control and a relative deviation of circulating current control based on a cost function of the selected candidate switch vector, a grid-connected current reference value, and a circulating current reference value;

[0111] (12)

[0112] (13)

[0113] (14)

[0114] In the formula, For the x Phase control target , The grid current Control or circulation control; i The cost function of the candidate switching vectors; when the control target is the grid current When controlling, the three candidate switch vectors are selected as follows: and ; When the control target is circulation When , the three candidate switch vectors are selected as and .

[0115] is the reference value of the control target, where the reference value of current control is the grid-connected current Reference, the reference value of circulating current control is the DC component of the circulating current.

[0116] S103: Calculating the initial submodule input number combination for the next cycle based on the relative deviation of the current control and the relative deviation of the circulating current control;

[0117] The specific calculation formula is as follows:

[0118] (15)

[0119] (16)

[0120] in, , are the initial number of submodules put into use in the upper bridge arm and the lower bridge arm in the next cycle, , are the relative deviation of current control and the relative deviation of circulating current control respectively, N is the total number of half-bridge submodules in each phase bridge arm, The optimal number of upper bridge arm submodules selected for the current cycle current control phase. The optimal number of upper bridge arm submodules selected for the current cycle circulation suppression phase.

[0121] This embodiment uses a weighted method to adaptively select the initial number of sub-modules to be brought into the next cycle according to the relative deviation between output current tracking and circulating current suppression. This can increase the priority of control targets with large relative deviations and reduce the priority of control targets with small relative deviations without changing the order of sequential optimization. This allows the system to adaptively adjust the priority of control targets under different operating conditions to ensure that the performance requirements for control targets under various operating conditions are met.

[0122] S104: Select three candidate combinations including the initial sub-module input number combination, and obtain the output current of the system in the next cycle through the current prediction equation; from the three candidate combinations, select the first sub-module input number combination that minimizes the first cost function as the optimal sub-module input number combination for the current control stage of the next cycle.

[0123] Specifically, this embodiment selects three candidate combinations including the initial submodule input number combination: , and ;

[0124] Substituting the three candidate combinations into the current prediction equation of formula (3), we can obtain the output current of the system at the next moment: (after delay compensation).

[0125] The controller then iterates over the three candidate combinations, namely , , choose the cost function in formula (8) Minimum first submodule input number combination .

[0126] S105: Select three candidate combinations including the first submodule input number combination, and obtain the circulation prediction value of the next cycle of the system through the circulation prediction equation; from the three candidate combinations, select the second submodule input number combination that minimizes the second cost function as the optimal submodule input number combination for the circulation suppression stage of the next cycle.

[0127] Specifically, the three candidate combinations including the first submodule input number combination selected in this embodiment are: , and ; Substitute the three candidate combinations into the circulation prediction equation of formula (4) to obtain the circulation of the system at the next moment (after delay compensation).

[0128] The controller then traverses the three candidate combinations and selects the second submodule input number combination with the smallest cost function in equation (9) .

[0129] S106: Based on the obtained first submodule input number combination and second submodule input number combination, capacitor voltage balancing is achieved using a submodule capacitor voltage sorting algorithm, and a switch state of each submodule is output.

[0130] The capacitor voltage sorting algorithm refers to the method of sorting the capacitor voltage after knowing the input number of each bridge arm submodule. Under the premise of Sort in ascending or descending order. Then sort by current The submodules with the front or back direction selection order are put into operation first, so that the submodules with high capacitor voltage are discharged first, and the submodules with low voltage are charged first, so as to achieve voltage balance; this method is a prior art and will not be described in detail.

[0131] This embodiment proposes a method for calculating the relative deviation amount, and adopts a weighted method so that the priority of the control target is determined by the relative deviation between the actual value of the target and the set reference value. This method can adjust the priority order of control according to different working conditions, so that the number of submodules put into use in the next cycle tends to give priority to the control target with a large deviation from the reference value; it solves the problem of poor control performance of the target with a lower priority due to the use of fixed priority in the prior art.

[0132] Embodiment 2

[0133] In one or more embodiments, a grid-connected MMC variable priority control system for deep sea wind power is disclosed, comprising:

[0134] A reference value calculation module, used for calculating a grid-connected current reference value and a circulating current reference value;

[0135] A relative deviation calculation module, used for calculating the relative deviation of current control and the relative deviation of circulating current control respectively based on the cost function of the selected candidate switch vector, and the grid-connected current reference value and the circulating current reference value;

[0136] An initial input number calculation module is used to calculate the initial submodule input number combination of the next cycle based on the relative deviation of the current control and the relative deviation of the circulating current control;

[0137] A current prediction module is used to select three candidate combinations including an initial submodule input number combination, and obtain the output current of the system in the next cycle through a current prediction equation; and select a first submodule input number combination that minimizes a first cost function from the three candidate combinations;

[0138] The circulation prediction module is used to select three candidate combinations including the first submodule input number combination, and obtain the circulation prediction value of the next cycle of the system through the circulation prediction equation; from the three candidate combinations, select the second submodule input number combination that minimizes the second cost function.

[0139] It should be noted that the specific implementation method of the above modules is the same as that in Example 1 and will not be described in detail.

[0140] Embodiment 3

[0141] In one or more embodiments, a terminal device is disclosed, which includes a processor and a memory, the processor is used to implement instructions; the memory is used to store multiple instructions, and the instructions are suitable for being loaded by the processor and executing the grid-connected MMC variable priority control method for deep sea wind power described in Example 1.

[0142] It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0143] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0144] In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in a processor or an instruction in the form of software.

[0145] Embodiment 4

[0146] In one or more embodiments, a computer-readable storage medium is disclosed, in which a plurality of instructions are stored, wherein the instructions are suitable for being loaded by a processor of a terminal device and executing the grid-connected MMC variable priority control method for deep sea wind power described in Example 1.

[0147] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A grid-connected MMC variable priority control method for deep sea wind power, characterized in that: include: Calculate grid-connected current reference value and circulating current reference value; Based on the cost function of the selected candidate switch vector, as well as the grid-connected current reference value and the circulating current reference value, a relative deviation of current control and a relative deviation of circulating current control are calculated respectively; Based on the relative deviation of current control and the relative deviation of circulating current control, the combination of the initial number of submodules put into operation in the next cycle is calculated; Select three candidate combinations including the initial submodule input number combination, and obtain the output current of the system in the next cycle through the current prediction equation; select from the three candidate combinations the combination that makes the first cost function The smallest combination of the number of inputs to the first submodule; Select three candidate combinations including the first submodule input number combination, and obtain the circulation prediction value of the next cycle of the system through the circulation prediction equation; select from the three candidate combinations so that the second cost function The smallest second submodule input number combination; in, and are the predicted value and reference value of the grid-connected current at time k+2 respectively; and are the predicted value and reference value of circulation at time k+2 respectively; The relative deviation of current control and the relative deviation of circulating current control are calculated respectively, specifically: ； in, Indicates control objectives The relative deviation of For the x Phase control target No. i The cost function of candidate switch vectors is Reference value for control target; control target It is current control or circulating current control; Based on the relative deviation of current control and the relative deviation of circulating current control, the initial submodule input number combination of the next cycle is calculated, specifically: ； ； in, , are the initial number of submodules put into use in the upper bridge arm and the lower bridge arm in the next cycle, , are the relative deviation of current control and the relative deviation of circulating current control respectively, N is the total number of half-bridge submodules in each phase bridge arm, The optimal number of upper bridge arm submodules selected for the current cycle current control phase. The optimal number of upper bridge arm submodules selected for the current cycle circulation suppression phase.

2. A method for controlling the grid-connected MMC variable priority for deep sea wind power as claimed in claim 1, characterized in that: Control Target For current control, the selected candidate switching vectors are: and ; Control Target When the circulating current is controlled, the candidate switching vectors are selected as follows: and ; in, and They are the optimal number of sub-modules selected for the upper bridge arm and the lower bridge arm in the kth cycle respectively.

3. A method for controlling the grid-connected MMC variable priority for deep sea wind power as claimed in claim 1, characterized in that: Three candidate combinations including the initial submodule input number combination are selected, specifically: , and ; in, Input number combinations for the initial submodule.

4. A grid-connected MMC variable priority control method for deep sea wind power as claimed in claim 1, characterized in that: Three candidate combinations including the first submodule input number combination are selected, specifically: , and ; in, Input number combination for the first submodule.

5. A grid-connected MMC variable priority control method for deep sea wind power as claimed in claim 1, characterized in that: Also includes: Based on the obtained first submodule input number combination and second submodule input number combination, capacitor voltage balancing is achieved using a submodule capacitor voltage sorting algorithm, and the switch state of each submodule is output.

6. A system for implementing the grid-connected MMC variable priority control method for deep sea wind power as claimed in claim 1, characterized in that: include: A reference value calculation module, used for calculating a grid-connected current reference value and a circulating current reference value; A relative deviation calculation module, used for calculating the relative deviation of current control and the relative deviation of circulating current control respectively based on the cost function of the selected candidate switch vector, and the grid-connected current reference value and the circulating current reference value; An initial input number calculation module is used to calculate the initial submodule input number combination of the next cycle based on the relative deviation of the current control and the relative deviation of the circulating current control; A current prediction module is used to select three candidate combinations including an initial submodule input number combination, and obtain the output current of the system in the next cycle through a current prediction equation; and select a first submodule input number combination that minimizes a first cost function from the three candidate combinations; The circulation prediction module is used to select three candidate combinations including the first submodule input number combination, and obtain the circulation prediction value of the next cycle of the system through the circulation prediction equation; from the three candidate combinations, select the second submodule input number combination that minimizes the second cost function.

7. A terminal device, comprising a processor and a memory, wherein the processor is used to implement instructions; and the memory is used to store multiple instructions, characterized in that: The instructions are suitable for being loaded by a processor and executing the grid-connected MMC variable priority control method for deep sea wind power as described in any one of claims 1-5.

8. A computer-readable storage medium storing a plurality of instructions, characterized in that: The instructions are suitable for being loaded by a processor of a terminal device and executing the grid-connected MMC variable priority control method for deep sea wind power as described in any one of claims 1-5.

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