Subsynchronous oscillation suppression method for direct-driven wind turbine generator

Abstract

A subsynchronous oscillation suppression method for a direct-driven wind turbine generator comprises the following steps that a simulation model for connecting the direct-driven wind turbine generator to a weak alternating current system is constructed, and the simulation model is composed of a main circuit of the direct-driven wind turbine generator, a control system and an alternating current power grid; the direct-driven wind turbine generator set comprises a wind turbine, a permanent magnet synchronous generator, a machine-side converter and a grid-side converter. Introducing an additional damping stability controller into a grid-side converter control system of the direct-driven wind turbine generator by using the constructed simulation model of the direct-driven wind turbine generator; aiming at a damping stability controller, optimizing a proportionality coefficient Kp, a lead constant T1 and a lag constant T2 by utilizing a particle swarm algorithm; and for the optimized damping stability controller, calculating a damping coefficient, and performing frequency sweeping in subsynchronous and super-synchronous frequency bands to obtain a curve of the damping coefficient in a response frequency band, and verifying the effectiveness of the suppression method. The subsynchronous oscillation suppression mechanism of the direct-driven wind turbine generator is clearly and quantitatively elaborated, and the problem of subsynchronous oscillation is effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power transmission, and is a method for suppressing subsynchronous oscillation of a direct-drive wind turbine generator set. Background Art

[0002] With the increase in the scale of wind power grid connection, the oscillation phenomenon of wind turbine generator sets occurs frequently. In July 2015, multiple subsynchronous oscillation accidents occurred in direct-drive wind turbine generator sets in the Hami area. The oscillation frequency was about 20 - 80 Hz, and the continuous power oscillation even triggered the shaft torsional vibration protection action tripping of a generator set 300 km away, seriously endangering the safe and stable operation of the system. Therefore, researching measures for suppressing subsynchronous oscillation of direct-drive wind turbine generator sets is of great significance for ensuring the safety and stability of the power grid.

[0003] In terms of suppressing the oscillation of wind turbine generator sets, domestic and foreign research scholars mainly conduct research from two aspects: absorbing oscillation energy and destroying oscillation conditions. The method of absorbing oscillation energy needs to apply additional control to the power electronic devices adjacent to the wind turbine generator set to achieve, or improve the damping characteristics of the direct-drive wind turbine generator set at the oscillation frequency by introducing a synchronous condenser, effectively suppressing the subsynchronous oscillation of the direct-drive wind turbine generator set; the method of destroying oscillation conditions is mainly achieved by optimizing the control parameters of the unit or optimizing the control strategy of the unit.

[0004] In the existing technology, most of the methods for suppressing subsynchronous oscillation of wind turbine generator sets are aimed at the double-fed wind turbine generator set connected to the series compensation system, while the research on the direct-drive wind turbine generator set connected to a weak AC grid is still scarce. Some scholars have proposed to optimize the parameters of the phase-locked loop to improve the operation stability of the direct-drive wind turbine generator set. This research work only focuses on the stability problem of the phase-locked loop mode and lacks the analysis of the stability problem dominated by the inner loop control parameters. Therefore, how to suppress the subsynchronous oscillation problem of the direct-drive wind turbine generator set and optimize the controller parameters still requires further improvement. Summary of the Invention

[0005] In view of the technical problems existing in the prior art, the present invention creatively conceives a method for suppressing subsynchronous oscillation of a direct-drive wind turbine generator set. An additional damping stabilizer is introduced into the control system of the direct-drive wind turbine generator set, and the particle swarm optimization algorithm is used to optimize the parameters of the damping stabilizer. The effectiveness of the suppression method is verified by calculating the damping coefficient. The technical solution is as follows:

[0006] A method for suppressing subsynchronous oscillation of a direct-drive wind turbine generator set, comprising:

[0007] Construct a simulation model of a direct-drive wind turbine generator set connected to a weak AC system, where the simulation model consists of three parts: the main circuit of the direct-drive wind turbine generator set, the control system, and the AC grid. The direct-drive wind turbine generator set includes: a wind turbine, a permanent magnet synchronous generator, a machine-side converter, and a grid-side converter;

[0008] Using the constructed direct-drive wind turbine simulation model, an additional damping stabilizer controller is introduced into the grid-side converter control system of the direct-drive wind turbine;

[0009] For the damping stabilizer controller, the particle swarm optimization algorithm is used to optimize the proportional coefficient Kp, the lead constant T1, and the lag constant T2;

[0010] For the optimized damping stabilizer controller, calculate the damping coefficient and perform frequency sweeping in the sub-synchronous and super-synchronous frequency bands to obtain the curve of the damping coefficient in the response frequency band, and verify the effectiveness of the suppression method.

[0011] Furthermore, the input of the damping stabilizer controller is the d-axis output current i gd of the direct-drive wind turbine, and the output is the output voltage increment u SSDC of the damping stabilization control system. The mathematical relationship expression between the input and the output is as follows:

[0012]

[0013] In the formula, G SSDC (s) is the damping control transfer function; K p is the proportional coefficient; T W is the time constant of the DC-blocking link; T 1 is the lead constant; T 2 is the lag constant.

[0014] The control effect expression of the damping stabilizer controller is as follows:

[0015]

[0016] In the formula, f is the performance function; σ j is the closed-loop modal damping; λ j is the closed-loop pole;

[0017] Appropriate restrictions are imposed on the control parameters of the damping stabilizer controller, and the expression is as follows:

[0018]

[0019] In the formula, G k is the gain coefficient; T k is the time constant; G ub,k is the upper limit of the absolute value of the gain, usually set to 10; T ub,k is the upper limit value of the time constant, usually set to 0.1 s.

[0020] The expression for calculating the damping coefficient is as follows:

[0021]

[0022] Where, ΔP e is the output power increment of the direct-drive wind turbine; ΔU dc is the DC voltage increment of the direct-drive wind turbine.

[0023] Furthermore, the main circuit expression of the machine-side converter is as follows:

[0024]

[0025]

[0026] Where: ω pr is the rotor speed of the permanent magnet synchronous generator; R ps is the stator winding resistance; U psd , U psq are the stator winding voltages of the dq axes respectively; i psd , i psq are the stator currents of the dq axes respectively; ψ psd , ψ psq , ψ pf are the stator flux linkages of the dq axes and the permanent magnet flux linkage respectively; L s is the stator winding inductance.

[0027] The main circuit expression of the grid-side converter is as follows:

[0028]

[0029] Where: L f is the filter inductance of the grid-side converter; i pgd , i pgq are the d-axis and q-axis components of the converter output current respectively; U pgd , U pgq are the modulation voltage d-axis and q-axis components output by the current controller respectively.

[0030] Furthermore, the control objective of the machine-side converter is to achieve maximum power tracking, and the control objective of the grid-side converter is to achieve the stability of the DC bus voltage and adjust the grid-connected active and reactive powers at the same time. The control system expression is as follows:

[0031]

[0032]

[0033] Where: x p1 , x p2 , x p3 , x p4 , x p5 , x p6 are the state variables of the PI controller; K pp1 , K pp2, K pp3 , K pp4 , K pp5 , K pp6 are the proportionality coefficients of the speed loop controller, the inner current loop controller on the machine side, the DC voltage loop controller, and the inner current loop controller on the grid side, respectively; K pi1 , K pi2 , K pi3 , K pi4 , K pi5 , K pi6 are the integral coefficients of the speed loop controller, the inner current loop controller on the machine side, the DC voltage loop controller, and the inner current loop controller on the grid side, respectively; U pgd , U pgq are the d-axis and q-axis components of the modulation voltage output by the grid side current controller.

[0034] Furthermore, the direct-drive wind turbine is connected to the power grid through a transmission line, and the AC power grid expression is as follows:

[0035]

[0036] where: L g is the inductance of the transmission line; U tx , U ty are the grid connection point voltages on the x and y axes; U gx , U gy are the grid voltage increments on the d and q axes; i gx , i gy are the grid currents on the x and y axes; ω is the rated speed.

[0037] Furthermore, the steps of the particle swarm algorithm are as follows:

[0038] Initialization of the particle swarm algorithm, setting the maximum number of iterations gen max = 50, the inertia weight coefficient w max = 0.9, w min = 0.4, the acceleration coefficient c 1 = 2, c 2 = 2; setting the initial inertia weight coefficient w(0), initializing the particle positions and the optimal positions, and randomly initializing the particle swarm;

[0039] Update of the number of iterations: updating the number of iterations t = t + 1, updating the inertia weight coefficient w(t) = αw(t - 1);

[0040] Update of the velocity: updating the velocity of the j-th particle in the k-th dimension and checking whether the velocity exceeds the maximum value V max or the minimum value V min , if it exceeds this range, limit the velocity to this extreme value, and the expression is as follows:

[0041]

[0042] Wherein, r 1 and r 2 are random numbers uniformly distributed in [0, 1]; x j,k (t - 1) and v j,k (t - 1) are respectively the position and velocity of particle j in the k-th dimension at the (t - 1)-th iteration; x * j,k (t - 1) is the position of the optimal point of particle j in the k-th dimension after the (t - 1)-th iteration; x ** j,k (t - 1) is the position of the global optimal point of the entire swarm in the k-th dimension after the (t - 1)-th iteration; v j,k (t) is the velocity of particle j in the k-th dimension at the t-th iteration;

[0043] Based on the updated velocity, each particle updates its position according to the following expression:

[0044] x j,k (t) = v j,k (t - 1) + x j,k (t - 1) (12)

[0045] Wherein, x j,k (t) is the position of particle j in the k-th dimension at the t-th iteration;

[0046] Individual optimal update: Calculate the fitness value f j of the particle after updating its position according to the objective function shown in expression (3), evaluate each particle, if then the individual optimal is updated to: the optimal position X j * (t) = X j (t), the optimal fitness value

[0047] Global optimal update: Search for the minimum value f min among them, if f min < f ** then the global optimal is updated to: the optimal position X ** = X min (t), the optimal fitness value f ** = f min ;

[0048] When the performance of the current population is difficult to be significantly improved or the maximum number of iterations gen max is reached, terminate the algorithm and output the control parameters, otherwise continue to execute the iteration number update step.

[0049] A device for suppressing subsynchronous oscillation of a direct-drive wind turbine, comprising:

[0050] A simulation model construction module for constructing a simulation model of a direct-drive wind turbine connected to a weak AC system, where the simulation model includes the main circuit, control system, and AC power grid of the direct-drive wind turbine, and the direct-drive wind turbine includes: a wind turbine, a permanent magnet synchronous generator, a machine-side converter, and a grid-side converter;

[0051] An introduction module for introducing an additional damping stability controller into the grid-side converter control system of the direct-drive wind turbine by using the constructed direct-drive wind turbine simulation model;

[0052] An optimization module for optimizing the proportional coefficient Kp, lead constant T1, and lag constant T2 of the damping stability controller by using the particle swarm algorithm;

[0053] A calculation and verification module for calculating the damping coefficient of the optimized damping stability controller, performing a frequency sweep in the subsynchronous and supersynchronous frequency bands, obtaining the curve of the damping coefficient in the response frequency band, and verifying the effectiveness of the suppression method.

[0054] Furthermore, the input of the damping stability controller is the d-axis output current i of the direct-drive wind turbine gd , and the output is the output voltage increment u of the damping stability control system SSDC , and the mathematical relationship expression between the input and output is as follows:

[0055]

[0056] In the formula, G SSDC (s) is the damping control transfer function; K p is the proportional coefficient; T W is the time constant of the DC blocking link; T 1 is the lead constant; T 2 is the lag constant;

[0057] The control effect expression of the damping stability controller is as follows:

[0058]

[0059] In the formula, f is the performance function; σ j is the closed-loop modal damping; λ j is the closed-loop pole;

[0060] Appropriate restrictions are imposed on the control parameters of the damping stability controller, and the expression is as follows:

[0061]

[0062] In the formula, G k is the gain coefficient; T kis the time constant; G ub,k is the upper limit of the absolute value of the gain, usually set to 10; T ub,k is the upper limit value of the time constant, usually set to 0.1 s;

[0063] The expression for calculating the damping coefficient is as follows:

[0064]

[0065] In the formula, ΔP e is the output power increment of the direct-drive wind turbine; ΔU dc is the DC voltage increment of the direct-drive wind turbine;

[0066] The main circuit expression of the machine-side converter is as follows:

[0067]

[0068]

[0069] In the formula: ω pr is the rotor speed of the permanent magnet synchronous generator; R ps is the stator winding resistance; U psd 、U psq are the stator winding voltages of the dq axes respectively; i psd 、i psq are the stator currents of the dq axes respectively; ψ psd 、ψ psq 、ψ pf are the stator magnetic fluxes and the permanent magnet magnetic flux of the dq axes respectively; L s is the stator winding inductance;

[0070] The main circuit expression of the grid-side converter is as follows:

[0071]

[0072] In the formula: L f is the filter inductance of the grid-side converter; i pgd 、i pgq are the d-axis and q-axis components of the converter output current respectively; U pgd 、U pgq are the modulation voltage d-axis and q-axis components output by the current controller respectively;

[0073] The control system expression is as follows:

[0074]

[0075]

[0076] In the formula: x p1 、xp2 , x p3 , x p4 , x p5 , x p6 are the state variables of the PI controller; K pp1 , K pp2 , K pp3 , K pp4 , K pp5 , K pp6 are the proportionality coefficients of the speed loop controller, the machine-side current inner loop controller, the DC voltage loop controller, and the grid-side current inner loop controller respectively; K pi1 , K pi2 , K pi3 , K pi4 , K pi5 , K pi6 are the integral coefficients of the speed loop controller, the machine-side current inner loop controller, the DC voltage loop controller, and the grid-side current inner loop controller respectively; U pgd , U pgq are the d-axis and q-axis components of the modulation voltage output by the grid-side current controller;

[0077] The expression of the AC power grid is as follows:

[0078]

[0079] In the formula: L g is the inductance of the transmission line; U tx , U ty are the grid connection point voltages on the x and y axes; U gx , U gy are the grid voltage increments on the d and q axes; i gx , i gy are the grid currents on the x and y axes respectively; ω is the rated speed;

[0080] The steps of the particle swarm algorithm are as follows:

[0081] Initialize the particle swarm algorithm, set the maximum number of iterations gen max = 50, the inertia weight coefficient w max = 0.9, w min = 0.4, the acceleration coefficient c 1 = 2, c 2 = 2; set the initial inertia weight coefficient w(0), initialize the particle positions and the optimal positions, and randomly initialize the particle swarm;

[0082] Update the number of iterations: update the number of iterations t = t + 1, update the inertia weight coefficient w(t) = αw(t - 1);

[0083] Velocity update: Update the velocity of the j-th particle in the k-th dimension and check whether the velocity exceeds the maximum value V max or the minimum value V min , if it exceeds this range, limit the velocity to this extreme value, and the expression is as follows:

[0084]

[0085] where r 1 and r 2 are random numbers uniformly distributed in [0, 1]; x j,k (t - 1) and v j,k (t - 1) are the position and velocity of particle j in the k-th dimension at the (t - 1)-th iteration respectively; x * j,k (t - 1) is the position of the optimal point of particle j in the k-th dimension after the (t - 1)-th iteration; x ** j,k (t - 1) is the position of the global optimal point of the entire swarm in the k-th dimension after the (t - 1)-th iteration; v j,k (t) is the velocity of particle j in the k-th dimension at the t-th iteration;

[0086] Based on the updated velocity, each particle updates according to the updated position, and the expression is as follows:

[0087] x j,k (t) = v j,k (t - 1) + x j,k (t - 1) (12)

[0088] where x j,k (t) is the position of particle j in the k-th dimension at the t-th iteration;

[0089] Individual optimal update: Calculate the fitness value f of the particle after updating the position according to the objective function shown in expression (3) j , evaluate each particle, if f j < f j * , j = 1, 2,..., n, then the individual optimal update is: the optimal position X j * (t) = X j (t), the optimal fitness value f j * = f j ;

[0090] Global optimal update: Search for the minimum value f j * in f min , if f min < f ** , then the global optimal update is: the optimal position X** = X min (t), the optimal fitness value f ** = f min ;

[0091] The performance of the current population is difficult to be significantly improved or reaches the maximum number of iterations gen max , terminate the algorithm and output the control parameters, otherwise continue to execute the iteration number update step.

[0092] A computer device includes a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor. When the processor executes the computer program, the steps of any of the sub-synchronous oscillation suppression methods for a direct-drive wind turbine generator set are implemented.

[0093] A storage medium stores a computer program, and the computer program is executed by the processor to perform the steps of any of the sub-synchronous oscillation suppression methods for a direct-drive wind turbine generator set.

[0094] Compared with the prior art, its beneficial effects are as follows: By introducing an additional damping stability controller into the grid-side converter control system of a direct-drive wind turbine generator set and optimizing the parameters of the damping stability controller based on the particle swarm algorithm, the purpose of suppressing oscillation is achieved; clearly and quantitatively elaborating the mechanism of sub-synchronous oscillation suppression for a direct-drive wind turbine generator set, effectively solving the problem of sub-synchronous oscillation that could not be suppressed by previous research methods, and having the advantages of being scientific, reasonable, good in applicability, and clear in mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] The above and / or additional aspects and advantages of the present invention will become obvious and easy to understand from the description of the embodiments in conjunction with the following drawings, wherein:

[0096] Figure 1 is a schematic diagram of the grid connection topology structure of a direct-drive wind turbine generator set;

[0097] Figure 2 is a control block diagram of the machine-side converter of a direct-drive wind turbine generator set;

[0098] Figure 3 is a control block diagram of the grid-side converter of a direct-drive wind turbine generator set;

[0099] Figure 4 is a schematic diagram of the topology structure of an AC power grid;

[0100] Figure 5 is a control block diagram of the grid-side converter of a direct-drive wind turbine generator set with an additional damping stability controller introduced;

[0101] Figure 6 is a schematic diagram of the damping curve of the sub-synchronous oscillation of the system under different grid strengths after the direct-drive wind turbine generator set is connected to the AC system in the embodiment;

[0102] Figure 7 In the embodiment, it is a schematic diagram of the damping curve of the subsynchronous oscillation of the system after the direct-drive wind turbine adopts the damping control strategy;

[0103] In the figure: 1. Wind turbine, 2. Permanent magnet synchronous generator, 3. Machine-side converter, 4. Grid-side converter, 5. Transmission line, 6. Power grid. Specific implementation manners

[0104] In order to be able to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention will be further described in detail below in conjunction with the drawings and specific implementation manners. It should be noted that, without conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other.

[0105] Many specific details are set forth in the following description in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0106] The following combines the attached Figure 1 to the attached Figure 7 and specific embodiments to further describe the present invention in detail. The specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0107] Embodiment 1

[0108] As Figures 1 to 5 shown, a method for suppressing subsynchronous oscillation of a direct-drive wind turbine includes the following steps:

[0109] S1. Construct a simulation model of a direct-drive wind turbine connected to a weak AC system. The simulation model is composed of three parts: the main circuit of the direct-drive wind turbine, the control system, and the AC power grid.

[0110] S2. Use the constructed simulation model of the direct-drive wind turbine to introduce an additional damping stabilizer in the grid-side converter control system of the direct-drive wind turbine. The input of the damping stabilizer is the d-axis output current i gd of the direct-drive wind turbine, and the output is the output voltage increment u SSDC of the damping stabilization control system. The mathematical relationship expression between the input and the output is as follows:

[0111]

[0112] In the formula, G SSDC (s) is the damping control transfer function; K p is the proportional coefficient; T W is the time constant of the DC-blocking link; T 1 is the lead constant; T2 is the lag constant.

[0113] S3. For the damping stability controller in step S2, use the particle swarm optimization algorithm to optimize the proportional coefficient K p , the lead constant T 1 , the lag constant T 2 . The control effect of the damping stability controller is represented by the following performance function:

[0114]

[0115] In the formula, f is the performance function; σ j is the closed-loop modal damping; λ j is the closed-loop pole.

[0116] Considering that a larger gain can achieve better damping under small disturbances, but it is easy to cause output clipping under large disturbances, reducing the effective gain, appropriately restrict the control parameters, and then standardize the control parameter design problem into a nonlinear constrained optimization problem:

[0117]

[0118] In the formula, G k is the gain coefficient; T k is the time constant; G ub,k is the upper limit of the absolute value of the gain, usually set to 10; T ub,k is the upper limit value of the time constant, usually set to 0.1 s.

[0119] S4. For the optimized damping stability controller in step S3, calculate the damping coefficient using expression (4), and perform frequency sweeping in the sub-synchronous and super-synchronous frequency bands to obtain the curve of the damping coefficient in the response frequency band, and verify the effectiveness of the suppression method.

[0120]

[0121] In the formula, ΔP e is the output power increment of the direct-drive wind turbine; ΔU dc is the DC voltage increment of the direct-drive wind turbine.

[0122] Embodiment 2

[0123] The specific process of step S1 is as follows:

[0124] S1-1. Modeling of the direct-drive wind turbine:

[0125] The direct-drive wind turbine includes: a wind turbine, a permanent magnet synchronous generator, a machine-side converter, and a grid-side converter. The mathematical model of the main circuit of the machine-side converter is shown in expressions (5) and (6):

[0126]

[0127]

[0128] Where: ω pr is the rotor speed of the permanent magnet synchronous generator; R ps is the stator winding resistance; U psd , U psq are the stator winding voltages of the dq axes respectively; i psd , i psq are the stator currents of the dq axes respectively; ψ psd , ψ psq , ψ pf are the stator flux linkages of the dq axes and the permanent magnet flux linkage respectively; L s is the stator winding inductance.

[0129] The main circuit mathematical model of the grid-side converter is shown in Expression (7):

[0130]

[0131] Where: L f is the filter inductance of the grid-side converter; i pgd , i pgq are the d-axis and q-axis components of the converter output current respectively; U pgd , U pgq are the modulation voltage d-axis and q-axis components output by the current controller respectively.

[0132] Modeling of the direct-drive wind turbine control system:

[0133] The control objective of the machine-side converter is to achieve maximum power tracking, and the control objective of the grid-side converter is to achieve the stability of the DC bus voltage and simultaneously regulate the grid-connected active and reactive powers. The mathematical models are shown in Expression (8) and Expression (9):

[0134]

[0135]

[0136] Where: x p1 , x p2 , x p3 , x p4 , x p5 , x p6 are the state variables of the PI controller; K pp1 , K pp2 , K pp3 , K pp4 , K pp5 , K pp6are the proportionality coefficients of the speed loop controller, the machine-side current inner loop controller, the DC voltage loop controller, and the grid-side current inner loop controller, respectively; K pi1 、K pi2 、K pi3 、K pi4 、K pi5 、K pi6 are the integral coefficients of the speed loop controller, the machine-side current inner loop controller, the DC voltage loop controller, and the grid-side current inner loop controller, respectively; U pgd 、U pgq are the d-axis and q-axis components of the modulation voltage output by the grid-side current controller, respectively.

[0137] Grid Modeling: The direct-drive wind turbine is connected to the grid through a transmission line, and the mathematical model is shown in Expression (10):

[0138]

[0139] where: L g is the inductance of the transmission line; U tx 、U ty are the grid connection point voltages on the x and y axes; U gx 、U gy are the grid voltage increments on the d and q axes; i gx 、i gy are the grid currents on the x and y axes respectively; ω is the rated speed.

[0140] Example 3

[0141] The specific process of step S3 is as follows:

[0142] S3-1. Initialize the particle swarm algorithm, set the maximum number of iterations gen max = 50, the inertia weight coefficient w max = 0.9, w min = 0.4, the acceleration coefficient c 1 = 2, c 2 = 2; set the initial inertia weight coefficient w(0), initialize the particle position and the optimal position, and randomly initialize the particle swarm.

[0143] S3-2. Update the number of iterations: Update the number of iterations t = t + 1, and update the inertia weight coefficient w(t) = αw(t - 1).

[0144] S3-3. Update the speed: Update the speed of the j-th particle in the k-th dimension according to Expression (11), and check whether the speed exceeds the maximum value V max or the minimum value V min , if it exceeds this range, limit the speed to this extreme value:

[0145]

[0146] where r 1 and r 2 are random numbers uniformly distributed in [0, 1]; x j,k (t - 1) and v j,k (t - 1) are the position and velocity of particle j in the k-th dimension at the (t - 1)-th iteration, respectively; x * j,k (t - 1) is the position of the optimal point of particle j in the k-th dimension after the (t - 1)-th iteration; x ** j,k (t - 1) is the position of the global optimal point of the entire swarm in the k-th dimension after the (t - 1)-th iteration; v j,k (t) is the velocity of particle j in the k-th dimension at the t-th iteration.

[0147] S3 - 4. Based on the updated velocity, each particle updates its position according to the following formula

[0148] x j,k (t) = v j,k (t - 1) + x j,k (t - 1) (12)

[0149] where x j,k (t) is the position of particle j in the k-th dimension at the t-th iteration.

[0150] S3 - 5. Individual optimal update: Calculate the fitness value f j of the particle after updating its position according to the objective function shown in expression (3), evaluate each particle, and if then the individual optimal update is: the optimal position X j * (t) = X j (t), the optimal fitness value

[0151] S3 - 6. Global optimal update: Search for the minimum value f min among them. If f min < f ** then the global optimal update is: the optimal position X ** = X min (t), the optimal fitness value f ** = f min .

[0152] S3 - 7. When the performance of the current population is difficult to improve significantly or reaches the maximum number of iterations gen max , terminate the algorithm and output the control parameters, otherwise go back to step S3 - 2 and continue.

[0153] Example 4

[0154] As Figure 6 and Figure 7 shown, for the structure of the direct-drive wind turbine connected to the AC system of the present invention, after adopting the subsynchronous oscillation suppression method, a good stabilizing effect can be achieved.

[0155] Embodiment 5

[0156] Based on the schematic diagram of the direct-drive wind turbine connected to the grid as Figure 1 shown, a simulation model is built. The control strategy diagram of the machine-side converter is as Figure 2 shown, and the control strategy diagram of the grid-side converter is as Figure 3 shown. By changing the grid strength, the damping coefficient of the system is calculated: when the short-circuit ratio of the grid strength is 1.8, the dominant frequency of the system is 45 Hz, and the damping of the system is 0.014.

[0157] Embodiment 6

[0158] Based on the schematic diagram of the direct-drive wind turbine connected to the grid as Figure 1 shown, a simulation model is built. The control strategy diagram of the machine-side converter is as Figure 2 shown, and the control strategy diagram of the grid-side converter is as Figure 3 shown. By changing the grid strength, the damping coefficient of the system is calculated: when the short-circuit ratio of the grid strength is 1.5, the dominant frequency of the system is 43 Hz, and the damping of the system is 0.005.

[0159] Embodiment 7

[0160] Based on the schematic diagram of the direct-drive wind turbine connected to the grid as Figure 1 shown, a simulation model is built. The control strategy diagram of the machine-side converter is as Figure 2 shown, and the control strategy diagram of the grid-side converter is as Figure 3 shown. By changing the grid strength, the damping coefficient of the system is calculated: when the short-circuit ratio of the grid strength is 1.2, the dominant frequency of the system is 39 Hz, and the damping of the system is -0.022.

[0161] Embodiment 8

[0162] As Figure 5 shown, for the damping stability controller, its control parameters are optimized based on the particle swarm algorithm. The damping analysis results are as Figure 6 shown. Before the damping stability controller is put into operation, the dominant frequency of the system is 39 Hz, and the damping of the system is -0.003; after the damping stability controller is put into operation, the dominant frequency of the system is 28 Hz, and the damping of the system is 0.0935. From the damping analysis results, it can be seen that after the damping stability controller is put into operation, the system damping is positive, effectively suppressing the subsynchronous oscillation caused by the direct-drive wind turbine connected to the grid.

[0163] Embodiment 9

[0164] The present invention further provides an embodiment, which is a sub-synchronous oscillation suppression device for a direct-drive wind turbine generator set, including:

[0165] A simulation model construction module, configured to construct a simulation model of a direct-drive wind turbine generator set connected to a weak AC system, where the simulation model includes the main circuit, control system, and AC power grid of the direct-drive wind turbine generator set, and the direct-drive wind turbine generator set includes: a wind turbine, a permanent magnet synchronous generator, a machine-side converter, and a grid-side converter;

[0166] An introduction module, configured to introduce an additional damping stability controller into the grid-side converter control system of the direct-drive wind turbine generator set by using the constructed simulation model of the direct-drive wind turbine generator set;

[0167] An optimization module, configured to optimize the proportional coefficient Kp, lead constant T1, and lag constant T2 of the damping stability controller by using the particle swarm algorithm;

[0168] A calculation and verification module, configured to calculate the damping coefficient for the optimized damping stability controller, perform frequency sweeping in the sub-synchronous and super-synchronous frequency bands, obtain the curve of the damping coefficient in the response frequency band, and verify the effectiveness of the suppression method.

[0169] This device is used to implement any one of the sub-synchronous oscillation suppression methods for a direct-drive wind turbine generator set described in Embodiments 1-8.

[0170] Embodiment 10

[0171] Based on the same inventive concept, an embodiment of the present invention further provides a computer device, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor. When the processor executes the computer program, the steps of any one of the sub-synchronous oscillation suppression methods for a direct-drive wind turbine generator set described in Embodiment 1 are implemented.

[0172] Embodiment 11

[0173] Based on the same inventive concept, an embodiment of the present invention further provides a computer storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of any one of the sub-synchronous oscillation suppression methods for a direct-drive wind turbine generator set described in Embodiment 1 are implemented.

[0174] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0175] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 block or multiple blocks.

[0176] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 block or multiple blocks.

[0177] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 block or multiple blocks.

[0178] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent substitutions can still be made to the specific embodiments of the present invention, and any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered by the protection scope of the claims of the present invention.

Claims

1. A method for suppressing subsynchronous oscillation of a direct-drive wind turbine generator system, comprising: Constructing a simulation model of a direct-drive wind turbine connected to a weak AC system, wherein the simulation model includes a main circuit, a control system, and an AC power grid of the direct-drive wind turbine, and the direct-drive wind turbine includes: a wind turbine, a permanent magnet synchronous generator, a machine-side converter, and a grid-side converter; By using the constructed simulation model of direct-drive wind turbine, an additional damping stability controller is introduced into the grid-side converter control system of the direct-drive wind turbine. For the damping stability controller, the particle swarm algorithm is used to optimize the proportional coefficient Kp, leading constant T1, and lagging constant T2; For the optimized damping stability controller, the damping coefficient is calculated, and the frequency is swept in the subsynchronous and supersynchronous frequency bands to obtain the curve of the damping coefficient in the response frequency band to verify the effectiveness of the suppression method.

2. The method for suppressing subsynchronous oscillation of a direct-drive wind turbine according to claim 1, characterized in that: The input of the damping stability controller is the d-axis output current i of the direct-drive wind turbine gd , the output is the output voltage increment u of the damping stability control system SSDC , the mathematical relationship between input and output is as follows: In the formula, G SSDC (s) is the damping control transfer function; K p is the proportionality coefficient; T W is the time constant of the DC isolation link; T1 is the leading constant; T2 is the lagging constant; The control effect expression of the damping stability controller is as follows: Where f is the performance function; σ j is the closed-loop modal damping; j is the closed loop pole; The control parameters of the damping stability controller are appropriately restricted, and the expression is as follows: In the formula, G k is the gain coefficient; T k is the time constant; G ub,k is the upper limit of the absolute value of the gain, usually set to 10; T ub,k The upper limit of the time constant is usually set to 0.1s; The expression for calculating the damping coefficient is as follows: Where ΔP e is the output power increment of the direct-drive wind turbine; ΔU dc is the DC voltage increment of the direct-drive wind turbine.

3. The method for suppressing subsynchronous oscillation of a direct-drive wind turbine according to claim 1, characterized in that: The main circuit expression of the machine-side converter is as follows: Where: pr is the rotor speed of the permanent magnet synchronous generator; R ps is the stator winding resistance; U psd , U psq are the dq axis stator winding voltages respectively; i psd 、i psq are the dq axis stator currents respectively; psd , psq , pf are the dq axis stator flux and permanent magnet flux respectively; L s is the stator winding inductance; The main circuit expression of the grid-side converter is as follows: Where: L f Grid-side converter filter inductor; i pgd 、i pgq are the d-axis and q-axis components of the converter output current respectively; U pgd , U pgq They are the d-axis and q-axis components of the modulation voltage output by the current controller respectively.

4. The method for suppressing subsynchronous oscillation of a direct-drive wind turbine according to claim 1, characterized in that: The control system expression is as follows: Where: x p1 、x p2 、x p3 、x p4 、x p5 、x p6 is the state variable of the PI controller; K pp1 , K pp2 , K pp3 , K pp4 , K pp5 , K pp6 They are the proportional coefficients of the speed loop controller, the machine-side current inner loop controller, the DC voltage loop controller and the grid-side current inner loop controller; K pi1 , K pi2 , K pi3 , K pi4 , K pi5 , K pi6 are the integral coefficients of the speed loop controller, the machine-side current inner loop controller, the DC voltage loop controller, and the grid-side current inner loop controller; U pgd , U pgq They are the d-axis and q-axis components of the modulation voltage output by the grid-side current controller respectively.

5. The method for suppressing subsynchronous oscillation of a direct-drive wind turbine according to claim 1, characterized in that: The AC power grid expression is as follows: Where: L g is the inductance of the transmission line; U tx , U ty is the grid-connected point voltage on the xy axis; U gx , U gy is the dq-axis grid voltage increment; i gx 、i gy Divided into xy-axis grid current; ω is the rated speed.

6. The method for suppressing subsynchronous oscillation of a direct-drive wind turbine according to claim 1, characterized in that: The particle swarm algorithm steps are as follows: Initialize the particle swarm algorithm and set the maximum number of iterations gen max =50, inertia weight coefficient w max =0.9,w min =0.4, acceleration coefficient c1=2, c2=2; set the initial inertia weight coefficient w(0), initialize the particle position and optimal position, and randomly initialize the particle swarm; Iteration number update: update the iteration number t = t + 1, update the inertia weight coefficient w(t) = αw(t-1); Velocity update: The velocity of the jth particle in the kth dimension is updated and checked whether the velocity exceeds the maximum value V max or minimum value V min If it exceeds this range, the speed is limited to this extreme value. The expression is as follows: Where r1 and r2 are random numbers uniformly distributed in [0, 1]; x j,k (t-1) and v j,k (t-1) are the position and velocity of particle j in the kth dimension at the t-1th iteration; is the position of the optimal point of particle j in the kth dimension after the t-1th iteration; is the position of the global optimal point of the kth dimension of the entire group after the t-1th iteration; v j,k (t) is the velocity of particle j in the kth dimension at the tth iteration; Based on the updated velocity, each particle updates its position according to the following expression: x j,k (t)=v j,k (t-1)+x j,k (t-1) (12) In the formula, x j,k (t) is the position of particle j in the kth dimension at the tth iteration; Individual optimal update: Calculate the fitness value f after the particle updates its position according to the objective function shown in expression (3) j , evaluate each particle, if f j <f j * , j = 1, 2, ..., n, then the optimal update of the individual is: the optimal position X j * (t) = X j (t), optimal fitness value f j * =f j ; Global optimal update: f j * Search for the minimum value f min , if f min <f ** , then the global optimal update is: optimal position X ** =X min (t), optimal fitness value f ** =f min ; The current population performance is difficult to improve significantly or reach the maximum number of iterations gen max , terminate the algorithm and output the control parameters, otherwise continue to execute the iteration number update step.

7. A device for suppressing subsynchronous oscillation of a direct-drive wind turbine unit, characterized in that: include: A simulation model building module, used to build a simulation model of a direct-drive wind turbine connected to a weak AC system, wherein the simulation model includes a main circuit, a control system and an AC power grid of the direct-drive wind turbine, and the direct-drive wind turbine includes: a wind turbine, a permanent magnet synchronous generator, a machine-side converter and a grid-side converter; An introduction module is used to introduce an additional damping stability controller into the grid-side converter control system of the direct-drive wind turbine using the constructed simulation model of the direct-drive wind turbine; The optimization module is used to optimize the proportional coefficient Kp, leading constant T1, and lagging constant T2 for the damping stability controller using the particle swarm algorithm; The calculation and verification module is used to calculate the damping coefficient of the optimized damping stability controller, and perform frequency sweep in the subsynchronous and supersynchronous frequency bands to obtain the curve of the damping coefficient in the response frequency band to verify the effectiveness of the suppression method.

8. The device for suppressing subsynchronous oscillation of a direct-drive wind turbine according to claim 7, characterized in that: The input of the damping stability controller is the d-axis output current i of the direct-drive wind turbine gd , the output is the output voltage increment u of the damping stability control system SSDC , the mathematical relationship between input and output is as follows: In the formula, G SSDC (s) is the damping control transfer function; K p is the proportionality coefficient; T W is the time constant of the DC isolation link; T1 is the leading constant; T2 is the lagging constant; The control effect expression of the damping stability controller is as follows: Where f is the performance function; σ j is the closed-loop modal damping; j is the closed loop pole; The control parameters of the damping stability controller are appropriately restricted, and the expression is as follows: In the formula, G k is the gain coefficient; T k is the time constant; G ub,k is the upper limit of the absolute value of the gain, usually set to 10; T ub,k The upper limit of the time constant is usually set to 0.1s; The expression for calculating the damping coefficient is as follows: Where ΔP e is the output power increment of the direct-drive wind turbine; ΔU dc is the DC voltage increment of the direct-drive wind turbine; The main circuit expression of the machine-side converter is as follows: Where: pr is the rotor speed of the permanent magnet synchronous generator; R ps is the stator winding resistance; U psd , U psq are the dq axis stator winding voltages respectively; i psd 、i psq are the dq axis stator currents respectively; psd , psq , pf are the dq axis stator flux and permanent magnet flux respectively; L s is the stator winding inductance; The main circuit expression of the grid-side converter is as follows: Where: L f Grid-side converter filter inductor; i pgd 、i pgq are the d-axis and q-axis components of the converter output current respectively; U pgd , U pgq are the d-axis and q-axis components of the modulation voltage output by the current controller respectively; The control system expression is as follows: Where: x p1 、x p2 、x p3 、x p4 、x p5 、x p6 is the state variable of the PI controller; K pp1 , K pp2 , K pp3 , K pp4 , K pp5 , K pp6 They are the proportional coefficients of the speed loop controller, the machine-side current inner loop controller, the DC voltage loop controller and the grid-side current inner loop controller; K pi1 , K pi2 , K pi3 , K pi4 , K pi5 , K pi6 are the integral coefficients of the speed loop controller, the machine-side current inner loop controller, the DC voltage loop controller, and the grid-side current inner loop controller; U pgd , U pgq They are the d-axis and q-axis components of the modulation voltage output by the grid-side current controller respectively; The AC power grid expression is as follows: Where: L g is the inductance of the transmission line; U tx , U ty is the grid-connected point voltage on the xy axis; U gx , U gy is the dq-axis grid voltage increment; i gx 、i gy Divided into xy-axis grid current; ω is the rated speed; The particle swarm algorithm steps are as follows: Initialize the particle swarm algorithm and set the maximum number of iterations gen max =50, inertia weight coefficient w max =0.9,w min =0.4, acceleration coefficient c1=2, c2=2; set the initial inertia weight coefficient w(0), initialize the particle position and optimal position, and randomly initialize the particle swarm; Iteration number update: update the iteration number t = t + 1, update the inertia weight coefficient w(t) = αw(t-1); Velocity update: The velocity of the jth particle in the kth dimension is updated and checked whether the velocity exceeds the maximum value V max or minimum value V min If it exceeds this range, the speed is limited to this extreme value. The expression is as follows: Where r1 and r2 are random numbers uniformly distributed in [0, 1]; x j,k (t-1) and v j,k (t-1) are the position and velocity of particle j in the kth dimension at the t-1th iteration; is the position of the optimal point of particle j in the kth dimension after the t-1th iteration; is the position of the global optimal point of the kth dimension of the entire group after the t-1th iteration; v j,k (t) is the velocity of particle j in the kth dimension at the tth iteration; Based on the updated velocity, each particle updates its position according to the following expression: x j,k (t)=v j,k (t-1)+x j,k (t-1) (12) In the formula, x j,k (t) is the position of particle j in the kth dimension at the tth iteration; Individual optimal update: Calculate the fitness value f after the particle updates its position according to the objective function shown in expression (3) j , evaluate each particle, if f j <f j * , j = 1, 2, ..., n, then the optimal update of the individual is: the optimal position X j * (t) = X j (t), optimal fitness value f j * =f j ; Global optimal update: f j * Search for the minimum value f min , if f min <f ** , then the global optimal update is: optimal position X ** =X min (t), optimal fitness value f ** =f min ; The current population performance is difficult to improve significantly or reach the maximum number of iterations gen max , terminate the algorithm and output the control parameters, otherwise continue to execute the iteration number update step.

9. A computer device, characterized in that: The method comprises a storage medium, a processor and a computer program stored on the storage medium and executable on the processor, wherein when the processor executes the computer program, the steps of the method for suppressing subsynchronous oscillation of a direct-drive wind turbine set as claimed in any one of claims 1 to 6 are implemented.

10. A storage medium, characterized in that: The storage medium stores a computer program, and the computer program is executed by a processor to perform the steps of the method for suppressing subsynchronous oscillation of a direct-drive wind turbine set as described in any one of claims 1 to 6.

Images