Improved control method for low voltage ride through of distributed direct-driven wind turbine generator

Abstract

The invention discloses an improved control method for low voltage ride through of a distributed direct-driven wind turbine generator, and belongs to the technical field of grid-connected control of wind turbine generators. And when the voltage of the power grid drops, the distributed direct-driven fan enters a low-voltage ride-through state. The method comprises the following steps: firstly, establishing a dq-axis grid-connected system model of the distributed direct-driven fan; and then, by taking'reduction of active loss and improvement of terminal voltage 'as double control objectives, under the constraint of a grid-connected guide rule, a machine-side / grid-side current limit and a power limit, an improved multi-objective particle swarm optimization algorithm is adopted to solve optimal set values of active and reactive current online in real time, and the optimal set values directly act on a grid-side converter. Compared with an existing proportional reactive power injection method, on the premise that the grid-connected guide rule requirement is met during the fault period, the method can simultaneously provide reactive power support and raise the terminal voltage; active loss is reduced, and the power grid frequency is supported; according to the method, the low-voltage ride-through capability and the power generation economy of the wind power plant are remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind turbine grid-connected control, and relates to an improved low voltage ride-through control method for a distributed direct-drive wind turbine. Background Art

[0002] With the continuous advancement of wind power technology, an increasing number of new energy distributed wind turbines are being connected to distribution networks, leading to a gradual increase in wind turbine penetration in the power system. In some regions, this increasing penetration is hindering the ability of traditional synchronous generators to support the grid's frequency and voltage. When a wind turbine enters a fault ride-through state, grid-connection regulations require it to provide the necessary reactive current to support the grid voltage. However, the system active power shortage caused by fault ride-through can cause fluctuations in grid frequency, and in severe cases, trigger underfrequency protection, leading to wind turbine disconnections. Therefore, it is necessary to fully exploit the potential of distributed wind turbines to provide both voltage and frequency support while meeting grid-connection regulations.

[0003] CN119921412A discloses a method for optimizing the control parameters of a doubly-fed wind turbine generator set based on the lowest frequency point of the power system during low voltage ride-through. The method comprises the following steps: 1. considering the influence of wind speed, pitch angle, and rotational speed on the capture coefficient to solve the capture power; 2. proposing a virtual synchronous machine control strategy for active power compensation of the wind turbine generator set, solving the transfer function between the wind turbine generator set rotational speed and the system frequency, and deriving the wind turbine generator set power-frequency response transfer function; 3. solving the wind turbine generator set equivalent inertia time constant based on the transfer function, considering the influence of different operating conditions of the wind turbine generator set during low voltage ride-through, weighting the calculation of the system equivalent inertia time constant, and quantifying the power-frequency response characteristics of the wind turbine generator set under different operating conditions; 4. solving the frequency variation based on the system equivalent inertia time constant and the power-frequency response characteristics; 5. solving the system trajectory sensitivity, constructing a Hessian matrix based on the sensitivity, and solving the optimal parameter values ​​using the interior point method to improve the lowest frequency point.

[0004] CN120185068A discloses a low voltage ride-through collaborative control method for a doubly fed wind turbine based on power prediction, comprising the following steps: data input; power prediction based on a convolution and long short-term memory neural network (CNN-LSTM); dynamic allocation calculation of converter capacity; and design of an improved direct power control strategy.

[0005] CN120474035A discloses a method for improving the transient voltage support capability of a doubly fed wind turbine generator set, comprising the following steps: step S1: obtaining detailed parameters of a doubly fed wind power grid-connected system; step S2: calculating a phase-locked loop phase-locked error angle based on the detailed parameters; step S3: calculating a current reference value of a doubly fed wind turbine rotor-side converter taking into account phase jumps during low voltage ride-through; step S4: calculating a current reference value of a doubly fed wind turbine rotor-side converter taking into account phase jumps after grid voltage recovery; step S5: calculating a rotor-side converter voltage equation taking into account phase jumps; step S6: designing a control logic strategy based on the obtained detailed parameters of the doubly fed wind power grid-connected system and the calculated phase-locked error angle, and suppressing the influence of the phase-locked error on the power output characteristics of the wind turbine by modifying the current reference value of the rotor-side converter and improving the rotor-side voltage equation.

[0006] The aforementioned existing technologies offer some control methods for low-voltage ride-through (LVRT) of wind turbines. However, these methods fail to fully address the need to enhance reactive power support, increase terminal voltage, and reduce active power losses while meeting grid connection requirements. There is an urgent need for an improved LVRT control method for distributed direct-drive wind turbines. While meeting grid connection guidelines, this method optimizes the active and reactive current reference values ​​of the wind turbines to achieve frequency support while providing the necessary voltage support, thereby promoting more stable and secure grid operation. Summary of the Invention

[0007] The purpose of the present invention is to provide an improved control method for low voltage ride-through of distributed direct-drive wind turbines. When a fault occurs in the power grid, the wind turbine optimizes the active current and reactive current to provide reactive support and raise the terminal voltage while reducing the active power loss during the fault ride-through period to support the grid frequency. Based on these two main control objectives: reducing active power loss and increasing the terminal voltage, a wind turbine grid-connected system model is established. Within the scope of relevant constraints, an improved multi-objective particle swarm optimization algorithm is used to obtain the optimal active / reactive current during the fault period, thereby completing low voltage ride-through. The purpose of the present invention is achieved through the following specific technical solutions.

[0008] An improved low voltage ride-through control method for distributed direct-drive wind turbines includes the following steps: S1: Collect wind turbine system information parameters, obtain grid-connected system parameters, and calculate the current output active power ( P WT ) and reactive power ( Q WT ), calculate the current terminal voltage amplitude ( U WT ); S2: When it is detected that the voltage amplitude at the generator terminal is lower than the threshold value of the wind turbine low voltage ride-through, the system enters low voltage ride-through improved control. The specific process is as follows: S2-1: The unloading circuit (Chopper) starts working, detecting the DC bus voltage and using the Bang-Bang controller to stabilize the DC bus voltage. S2-2: During a grid voltage sag, a dq-axis model of the wind turbine grid-connected system is constructed based on instantaneous power theory, and an expression for the wind turbine output power is given to quantify the terminal voltage amplitude and its relationship with the output power. S2-3: With the dual objectives of "reducing active power loss" and "increasing terminal voltage", and with grid-connection guidelines, current limit, and power limit as constraints, a multi-objective optimization problem is constructed; S2-4: using an improved multi-objective particle swarm optimization algorithm to solve the multi-objective optimization problem, and obtaining the optimal active current setting value and reactive current setting value during the fault period online and in real time; S2-5: Inputting the optimal active current setting value and the reactive current setting value into the current inner loop of the grid-side converter, so that the converter outputs corresponding control signals; S3: Change the grid-side converter operating state according to the real-time control signal to achieve the control target; S4: Detect that the voltage at the grid connection point has returned to normal, exit the low voltage ride-through improved control, and resume normal control.

[0009] The improved control method for low voltage ride-through of distributed direct-drive wind turbines provided by the present invention realizes the coordinated optimization of reactive power support, voltage rise and minimization of active power loss during faults through a model-algorithm-control closed loop, thereby directly improving the unit's support capability for grid frequency and voltage.

[0010] Furthermore, the wind turbine system information parameters in step S1 include the three-phase terminal voltage ( V WTabc ) and machine-end current ( I WTabc ), DC bus voltage ( V dc ), generator speed ( ω s ), stator current ( I sabc ), input wind speed ( v wind ) and other parameters, the grid-connected system parameters include the equivalent impedance of the box transformer (R Teq +jX Teq ), the equivalent impedance of the transmission line (R Leq +jX Leq )wait.

[0011] Furthermore, the process of constructing the dq-axis model of the wind turbine grid-connected system in step S2-2 is as follows: Generally, the wind turbine grid-connected system builds a phasor model based on the terminal voltage and output power, which is: , Where, and They are the phasors of the wind turbine terminal voltage and the public grid connection point voltage respectively. P WT and Q WT are the active power and reactive power output by the wind turbine respectively. R eq and X eq are the equivalent resistance and reactance between the generator end and the public grid connection point, respectively. However, this model requires the use of a power flow calculation method, and the iterative process in the solution process increases the solution time.

[0012] Therefore, the dq axis model of the wind turbine grid-connected system constructed in the present invention is: , The calculation method of the wind turbine output power in step S1 is: , Where, U WT is the voltage amplitude at the wind turbine terminal; P WT and Q WT are the active power and reactive power output by the wind turbine respectively; U pcc is the voltage at the output of the converter; d WT is the power angle of the wind turbine terminal voltage; R eq and X eq are the equivalent resistance and equivalent reactance respectively; U WTd and U WTq The fan terminal voltage is d Axis and q Axis component; I WTd and I WTq They are the fan output current d Axis and q Axis component.

[0013] Furthermore, in step S2-2, in the dq coordinate system, it is assumed that the phase-locked loop of the wind turbine can always complete the terminal voltage orientation. Therefore, the q-axis component of the terminal voltage is always zero, that is, U WTq =0. Then the relationship between the terminal voltage and output power can be simplified to: .

[0014] Furthermore, the optimization model in step S2-3 is: Goal 1, J 1. Reduce active power loss: , Goal 2, J 2. Increase the terminal voltage: , Constraints: , Where, P Norm It is the active power generated by the fan under normal working conditions; U Norm is the terminal voltage amplitude of the wind turbine under normal operating conditions; U pcc It is the voltage at the common point of the active power transmission bus; K LVRT is the low voltage ride through reactive current coefficient; I max It is the maximum current that the converter can withstand; ΔU WT The terminal voltage and the low voltage crossing threshold voltage ( U LVRT =0.9 pu). I gdrefT and I gqrefT They are the grid-side d-axis and q-axis current reference values ​​output by the current optimization controller respectively.

[0015] Furthermore, the inertia weight coefficient of the improved multi-objective particle swarm optimization algorithm described in step S2-4 ( w i ) uses a dynamic adjustment strategy based on the golden section point. In the early stages of the search, moderate overshooting is allowed to improve global exploration capabilities. In the later stages, the accuracy of feasible solutions is enhanced to accelerate convergence. Taking into account both global search and local fine-tuning, the quality of the optimal solution and the stability of the algorithm are improved. The inertia weight coefficient is calculated as follows: , Where, w ik Represents the optimization goalJ i Inertia weight coefficient at the kth iteration; X i(k-1) To optimize the goal J i The population after the k-1th execution. When k=0, that is, the first optimization execution, w ik Then directly take the value .

[0016] Furthermore, the final output result selection method of the improved multi-objective particle swarm optimization algorithm in step S2-4 is as follows: At the end of the execution of the multi-objective optimization algorithm, a set of Pareto solution sets will be obtained. The objective function values ​​corresponding to each solution in the Pareto solution set are normalized and calculated according to ( J 1, J 2) The order of the optimization objectives is used to generate the corresponding coordinates and calculate the distance from each coordinate to (0,0).

[0017] , Where, d k It represents the distance from the kth group of solutions to the origin after normalization. J 1k Indicates that the optimization goal J 1 is the objective function value of the kth group of solutions. min( J 1) Indicates the objective function in the Pareto solution set J The minimum value of 1. min( J 2) Indicates the objective function in the Pareto solution set J The minimum value of 2. max( J 1) Indicates the objective function in the Pareto solution set J The maximum value of 1. max( J 2) Indicates the objective function in the Pareto solution set J The maximum value of 2.

[0018] Select the one closest to the origin d k The corresponding solution min( d k ), as the final optimization result, is output to the grid-side current inner loop controller.

[0019] An optimization control device for a distributed direct-drive wind turbine generator system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the steps in the above-mentioned improved control method are implemented.

[0020] A computer-readable storage medium stores a computer program thereon, wherein the computer program implements the steps of the above-mentioned improved control method when executed by a processor.

[0021] A computer program product includes a computer program, and when the computer program is executed by a processor, the steps in the improved control method are implemented.

[0022] Compared with the prior art, the present invention has the following beneficial technical effects: the improved control method for low voltage ride-through of distributed direct-drive wind turbines provided by the present invention enables the wind turbines to reduce active power loss and increase terminal voltage while meeting the grid connection guidelines. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flow chart of the improved control method for low voltage ride through of distributed direct-drive wind turbines provided by the present invention.

[0024] Figure 2 The present invention provides a schematic diagram of the control system structure for implementing the improved control method for low voltage ride-through of distributed direct-drive wind turbines.

[0025] Figure 3 It is to verify the simulation model.

[0026] Figure 4 is the optimal solution set for multi-objective optimization.

[0027] Figure 5 This is a comparison chart of the implementation effects of the present invention and the traditional method on PCC point voltage.

[0028] Figure 6 This is a comparison chart of the implementation effects of the present invention and the traditional method on the terminal voltage.

[0029] Figure 7 It is a comparison diagram of the implementation effects of the present invention and the traditional method on system frequency response. DETAILED DESCRIPTION

[0030] The following is a clear and complete description of the technical solution of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0031] In the description of the present invention, it should be understood that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like, indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate description and simplify the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limiting the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance, quantity, or position.

[0032] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0033] The process of the improved low voltage ride-through control method of the distributed direct-drive wind turbine provided by the present invention is as follows: Figure 1 As shown, the system structure is as Figure 2 shown.

[0034] An improved low voltage ride-through control method for distributed direct-drive wind turbines includes the following steps: S1: The wind turbine collects parameters such as terminal voltage and current, DC bus voltage, generator speed, stator current, and input wind speed. It also obtains grid-connected system parameters, including the equivalent impedance of the box transformer and the equivalent impedance of the transmission line. The power module calculates the current output active and reactive power, and the amplitude module calculates the current terminal voltage amplitude.

[0035] S2: When it is detected that the voltage amplitude at the generator terminal is lower than the threshold value of the wind turbine low voltage ride-through, the system enters low voltage ride-through improved control. The specific process is as follows: S2-1: The unloading circuit (Chopper) starts working, detecting the DC bus voltage and using the Bang-Bang controller to stabilize the DC bus voltage.

[0036] S2-2: During a grid voltage sag, a dq-axis model of the wind turbine grid-connected system is constructed based on instantaneous power theory, and an expression for the wind turbine output power is given to quantify the terminal voltage amplitude and its relationship with the output power. S2-3: With the dual objectives of "reducing active power loss" and "increasing terminal voltage", and with grid-connection guidelines, current limit, and power limit as constraints, a multi-objective optimization problem is constructed; S2-4: using an improved multi-objective particle swarm optimization algorithm to solve the multi-objective optimization problem, and obtaining the optimal active current setting value and reactive current setting value during the fault period online and in real time; S2-5: Inputting the optimal active current setting value and the reactive current setting value into the current inner loop of the grid-side converter, so that the converter outputs corresponding control signals; S3: Change the grid-side converter operating state according to the real-time control signal to achieve the control target; S4: Detect that the voltage at the grid connection point has returned to normal, exit the low voltage ride-through improved control, and resume normal control.

[0037] In order to verify the feasibility and effectiveness of the technical solution of the present invention, a simulation model was used to simulate the traditional method and the method provided by the present invention. Figure 3 The simulation model is set at t=10s, and the transmission line is short-circuited to ground, causing the terminal voltage of the wind turbine to drop. The wind turbine parameters are shown in Table 1.

[0038] Table 1 2MW wind turbine parameters

[0039] Traditional methods are used to control low voltage ride-through of distributed direct-drive wind turbines. Distributed wind turbines only focus on changes in terminal voltage. Based on these changes in terminal voltage, they output reactive current proportionally to support the grid voltage. The reactive and active currents are determined as follows: .

[0040] in, I dref and I qref are the d-axis and q-axis current reference values ​​respectively; I Nmax is the maximum current of the converter; Under this method, the reactive current changes from 0 before the fault to 0.75 pu; the active current changes from 0.5 pu before the fault to 0.8 pu. At this time, the system dynamics are as follows Figure 5 and Figure 6 shown.

[0041] When controlling low voltage ride-through (LVRT) for distributed direct-drive wind turbines using the method provided by this invention, reactive and active currents are determined using an optimization approach based on the aforementioned method. By incorporating optimization objectives for frequency deviation and terminal voltage deviation, the optimal reactive and active currents for the wind turbine are determined within the aforementioned constraints.

[0042] Under this method, the improved multi-objective particle swarm optimization algorithm is used, and the optimization result output and the distribution of Pareto solutions are as follows: Figure 4 As shown. According to the method described above, based on the changes in the actual terminal voltage and the public grid connection point voltage, in this implementation case, the distance from the optimization result of the 18th iteration to the origin is the smallest, so this optimization result is output as a control instruction to the current inner loop controller. At this time, the reactive current changes from 0 before the fault to 0.87 pu; the active current changes from 0.5 pu before the fault to 0.67 pu. At this time, the system dynamics are as follows Figure 5 and Figure 6 shown.

[0043] Figure 5 and Figure 6 The comparison of the implementation effects of the present invention and the traditional method in terms of terminal voltage and system frequency response is shown in the following figure. The suffix "-com" indicates the traditional method, and the suffix "-pro" indicates the method of the present invention.

[0044] like Figure 5 and Figure 6 As shown, the improved control method for low voltage ride-through of distributed direct-drive wind turbines provided by the present invention has higher voltage amplitudes than traditional methods in both the machine-end voltage and the PCC point voltage during the fault continuation stage, indicating that the method provided by the present invention has advantages in voltage support.

[0045] like Figure 7 As shown, the improved control method for low voltage ride-through of distributed direct-drive wind turbines provided by the present invention has improvements in the rate of decrease of system frequency, the bottom value of system frequency, and the frequency recovery time compared with the traditional method, indicating that the method provided by the present invention has advantages in system frequency response.

[0046] Although the embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention without departing from the principles and intent of the present invention. The scope of protection of the present invention is defined by the claims and their equivalents.

Claims

1. An improved control method for low voltage ride-through of distributed direct-drive wind turbines, characterized in that: The following steps are involved: S1: Collect wind turbine system information parameters, obtain grid-connected system parameters, calculate the current output active power and reactive power through the power module, and calculate the current terminal voltage amplitude through the amplitude module; S2: When it is detected that the voltage amplitude at the generator terminal is lower than the threshold value of the wind turbine low voltage ride-through, the system enters low voltage ride-through improved control. The specific process is as follows: S2-1: The unloading circuit starts working, detecting the DC bus voltage and using the Bang-Bang controller to stabilize the DC bus voltage. S2-2: During a grid voltage sag, a dq-axis model of the wind turbine grid-connected system is constructed based on instantaneous power theory, and an expression for the wind turbine output power is given to quantify the terminal voltage amplitude and its relationship with the output power. S2-3: With the dual objectives of "reducing active power loss" and "increasing terminal voltage", and with grid-connection guidelines, current limit, and power limit as constraints, a multi-objective optimization problem is constructed; S2-4: using an improved multi-objective particle swarm optimization algorithm to solve the multi-objective optimization problem, and obtaining the optimal active current setting value and reactive current setting value during the fault period online and in real time; S2-5: Inputting the optimal active current setting value and the reactive current setting value into the current inner loop of the grid-side converter, so that the converter outputs corresponding control signals; S3: Change the grid-side converter operating state according to the real-time control signal to achieve the control target; S4: Detect that the voltage at the grid connection point has returned to normal, exit the low voltage ride-through improved control, and resume normal control.

2. The method according to claim 1, characterized in that The wind turbine system information parameters in step S1 include the three-phase terminal voltage and terminal current, DC bus voltage, generator speed, stator current, and input wind speed. The grid-connected system parameters include the equivalent impedance of the box transformer and the equivalent impedance of the transmission line.

3. The method according to claim 1, characterized in that The dq-axis model of the wind turbine grid-connected system constructed in step S2-2 is: ， The calculation method of the wind turbine output power in step S1 is: ， Where, U WT is the voltage amplitude at the wind turbine terminal; P WT and Q WT are the active power and reactive power output by the wind turbine respectively; U pcc is the voltage at the output of the converter; d WT is the power angle of the wind turbine terminal voltage; R eq and X eq are the equivalent resistance and equivalent reactance respectively; U WTd and U WTq The fan terminal voltage is d Axis and q Axis component; I WTd and I WTq They are the fan output current d Axis and q Axis component.

4. The method according to claim 3, characterized in that The relationship between the terminal voltage and the output power in step S2-2 is simplified to: 。 5. The method according to claim 1, wherein The optimization model in step S2-3 is: Goal 1, J 1. Reduce active power loss: ， Goal 2, J 2. Increase the terminal voltage: ， Constraints: ， Where, P Norm It is the active power generated by the fan under normal working conditions; U Norm is the terminal voltage amplitude of the wind turbine under normal operating conditions; U pcc It is the voltage at the common point of the active power transmission bus; K LVRT is the low voltage ride through reactive current coefficient; I max It is the maximum current that the converter can withstand; ΔU WT is the difference between the terminal voltage and the low voltage ride-through threshold voltage; I gdrefT and I gqrefT They are the grid-side d-axis and q-axis current reference values ​​output by the current optimization controller respectively.

6. The method according to claim 1, characterized in that The inertia weight coefficient of the improved multi-objective particle swarm optimization algorithm described in step S2-4 is determined by a dynamic adjustment strategy based on the golden section point. The inertia weight coefficient is calculated as follows: ， Where, w ik Represents the optimization goal J i Inertia weight coefficient at the kth iteration; i takes a value of 1 or 2, indicating the selection of the optimization target J 1 or J 2; X i(k - 1) To optimize the goal J i The population after the k-1th execution; When k=0, the optimization is performed for the first time. w ik Direct value .

7. The improved control method for low voltage ride-through of distributed direct-drive wind turbines according to claim 1, characterized in that: The method for selecting the final output result of the improved multi-objective particle swarm optimization algorithm in step S2-4 is as follows: At the end of the execution of the multi-objective optimization algorithm, a set of Pareto solutions will be obtained; Normalize the objective function values ​​corresponding to each solution in the Pareto solution set, and follow ( J 1, J 2) The order of the optimization objectives, generate the corresponding coordinates, and calculate the distance between each coordinate and (0,0); ， Where, d k represents the distance from the kth group of solutions to the origin after normalization; J 1k Indicates that the optimization goal J 1 is the objective function value of the kth group of solutions; min( J 1) Indicates the objective function in the Pareto solution set J The minimum value of 1; min( J 2) Indicates the objective function in the Pareto solution set J The minimum value of 2; max( J 1) Indicates the objective function in the Pareto solution set J The maximum value of 1; max ( J 2) Indicates the objective function in the Pareto solution set J The maximum value of 2; Select the one closest to the origin d k The corresponding solution min( d k ), as the final optimization result, is output to the grid-side current inner loop controller.

8. An optimization control device for a distributed direct-drive wind turbine generator system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the computer program is executed by a processor, the steps of the improved control method according to any one of claims 1 to 7 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of improving the control method according to any one of claims 1 to 7 are implemented.

10. Computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the improved control method according to any one of claims 1 to 7 are implemented.

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