Cooperative Optimization Control Method for Doubly Fed Wind Power Multi-Machine System under Symmetrical Short-Circuit Fault in Weak Power Grid

Abstract

The present invention discloses a cooperative optimization control method for a doubly-fed wind power multi-machine system under a weak grid symmetrical short-circuit fault. 1) An adaptive damping controller is designed for each DFIG single-machine system. The adaptive damping controller feeds the deviation Δ between the output angular velocity of the phase-locked loop and the system target angular velocity back to the output of the d-axis current loop of the grid-side converter through a PI controller. Once deviates from during the fault, the DFIG will automatically adjust its d-axis current component according to Δ. 2) Combining the actual fault scenario, parameters are configured for the adaptive damping controller in each DFIG. Without adding hardware devices, the present invention redesigns the control strategy of the doubly-fed wind power multi-machine system, enhances the small-signal stability of the system during the weak grid symmetrical short-circuit fault, and improves the success rate of crossing the weak grid symmetrical short-circuit fault. ω pll and the system target angular velocity ω g between the deviation Δ ω pll negative feedback to the q axis current loop output of the grid-side converter. Once ω pll deviates from during the fault ω g , the DFIG will be based on Δ ω pll automatically adjust its q axis current component. 2) Combining the actual fault scenario, configure parameters for the adaptive damping controller in each DFIG. Without adding hardware devices, the present invention redesigns the control strategy of the doubly-fed wind power multi-machine system, enhances the small-signal stability of the system during the weak grid symmetrical short-circuit fault, and improves the success rate of crossing the weak grid symmetrical short-circuit fault.

Description

Technical Field

[0001] The present invention relates to a control method for a doubly-fed wind power multi-machine system during a symmetrical short-circuit fault in a weak power grid, so as to enhance the small-signal stability of the system during a symmetrical short-circuit fault in the weak power grid and improve the success rate of crossing during a symmetrical short-circuit fault in the weak power grid, belonging to the technical field of new energy power generation. Background Technique

[0002] In actual new energy power generation bases, a multi-machine grid-connected form is adopted. During the steady state of a symmetrical short-circuit fault in the power grid, there is a complex dynamic coupling effect among various new energy power generation devices in the multi-machine grid-connected system. Moreover, the dynamic behaviors of each device inside the system are different, and there is a dynamic interaction among the controllers with multiple time scales in each device, thus bringing a new risk of oscillation instability to the multi-machine system during the fault. The doubly-fed wind power grid-connected system is a typical new energy grid-connected system widely used in the market. The stator side of a doubly fed induction generator (DFIG) is directly connected to the power grid, which makes the DFIG more sensitive to power grid faults. At present, the industrial community has studied the small-signal stability improvement strategy of the DFIG single-machine system during a weak power grid fault, but the small-signal stability improvement strategy of the DFIG wind power multi-machine system during a weak power grid fault is still a research blank.

[0003] The influence brought by the dynamic coupling among multiple DFIGs to the multi-machine system under the fault steady state can be divided into two aspects: on the one hand, the changes in the power grid structure and control state caused by the fault lead to the change of the dynamic coupling among multiple devices; on the other hand, during a weak power grid fault, the dynamic coupling among DFIGs will in turn affect the stability of the system under the fault steady state. It can be seen that the traditional small-signal stability improvement strategy of the DFIG single-machine system during a weak power grid fault may fail for the DFIG wind power multi-machine system.

[0004] At present, no research has been carried out on the stability enhancement control technology for a symmetrical short-circuit fault in a weak power grid applicable to the DFIG wind power multi-machine system at home and abroad. The currently designed control strategies mostly focus on the stability improvement technology of the DFIG single-machine system during a weak power grid fault. Such as the following publicly disclosed documents:

[0005] (1) Xie Zhen, Cui Jian, Li Zhe, Zhang Xing. Fault ride-through strategy for voltage-controlled doubly-fed wind turbines based on improved active disturbance rejection control [J]. Automation of Electric Power Systems, 2022, 46(21): 160-169.

[0006] (2) J. Hu, B. Wang, W. Wang, H. Tang, Y. Chi and Q. Hu, “Small Signal Dynamics of DFIG-Based Wind Turbines During Riding Through Symmetrical Faults in Weak AC grid,” IEEE Transactions on Energy Conversion, vol. 32, no. 2, pp. 720 - 730, Jun. 2017.

[0007] Reference (1) proposed a rotor overcurrent suppression strategy based on improved active disturbance rejection control (ADRC) for DFIG under grid symmetrical faults. On the one hand, it further improved the disturbance estimation speed and accuracy, enhancing the rotor overcurrent suppression ability of DFIG under faults. On the other hand, it suppressed the disturbance caused by the change of transient components, reducing the dependence on the accuracy of transient magnetic flux observation and having good robustness. However, how to improve the small-signal stability of DFIG wind power multi-machine systems during weak grid faults has not been studied. And during weak grid faults, the dynamic coupling effect between multiple DFIGs will bring new instability risks to the system, making the system more prone to oscillatory instability. How to improve the small-signal stability of DFIG wind power multi-machine systems during symmetrical short-circuit faults in weak grids needs to be studied.

[0008] Reference (2) studied the small-signal stability of doubly-fed wind power systems during symmetrical short-circuit faults in weak grids, but it also did not study the impact of the dynamic coupling effect between multiple DFIGs on the small-signal stability of the system during faults, and did not design a control strategy to improve the small-signal stability for DFIG wind power multi-machine systems. Therefore, how to improve the small-signal stability of DFIG wind power multi-machine systems during symmetrical short-circuit faults in weak grids has not been studied yet. Summary of the Invention

[0009] Aiming at the above deficiencies of the existing technology, the purpose of the present invention is to propose a cooperative optimization control method for a doubly-fed wind power multi-machine system under symmetrical short-circuit faults in a weak grid. The present invention can enhance the small-signal stability of the system during symmetrical short-circuit faults in a weak grid and improve the success rate of riding through symmetrical short-circuit faults in a weak grid.

[0010] The technical solution of the present invention is realized as follows:

[0011] A cooperative optimization control method for a doubly-fed wind power multi-machine system under symmetrical short-circuit faults in a weak grid, including multiple DFIG single-machine systems; the specific steps are as follows:

[0012] 1) Configure an adaptive damping controller for each DFIG single - machine system;

[0013] 2) Establish a time - domain simulation model of the DFIG multi - machine grid - connected system; Based on different fault scenarios, configure the coefficients of the adaptive damping controller added in each DFIG single - machine system, so as to obtain the coefficients of all adaptive damping controllers corresponding to different fault scenarios, which are used as the control data for the corresponding fault scenarios;

[0014] 3) When an actual fault occurs, find the fault scenario corresponding to the actual fault from the time - domain simulation model, and send the control data corresponding to this fault scenario to the corresponding DFIG single - machine system, so that each DFIG single - machine system operates under the control of its own adaptive damping controller, thereby changing the damping of each single - machine grid - connected system, that is, improving the damping of the entire system and enhancing the stability of the multi - machine system during the fault steady - state period.

[0015] In step 1), the adaptive damping controller uses a PI controller to negatively feedback the deviation Δω pll between the output angular velocity ω of the phase - locked loop and the system target angular velocity ω g to the q - axis current loop output of the grid - side converter; Once ω pll deviates from ω pll during the fault, the DFIG will automatically adjust its q - axis current component according to Δω g pll .

[0016] Preferably, in step 2), the method for configuring the parameters of the adaptive damping controller added in each DFIG single - machine system is as follows,

[0017] ① Combine the actual system parameters to establish the small - signal state - space equation of the overall DFIG multi - machine grid - connected system; Deduce the fault distance and the range of fault severity of the symmetrical short - circuit fault scenario that will cause oscillatory instability;

[0018] ② Establish a time - domain simulation model of the DFIG multi - machine grid - connected system, and simulate the typical symmetrical short - circuit fault in combination with the actual situation;

[0019] ③ Predetermine a fault scenario, calculate the position of the dominant pole of the phase - locked loop in the system, and calculate the phase - locked loop modal damping ratio ξ * of each DFIG;

[0020] ④ Judge whether the phase - locked loop modal damping ratio ξ * of each DFIG is greater than 0; If the phase - locked loop modal damping ratio ξ * of all DFIGs is greater than 0, then go to step ⑥; Otherwise, go to step ⑤;

[0021] ⑤ Determine the phase - locked loop modal damping ratio ξ *For the sequence of DFIG less than 0, adjust the proportional coefficient and integral coefficient of the adaptive damping controller applied therein according to the damping ratio calculation formula; subsequently, update the time-domain simulation model parameters in step ②, keep the previous fault scenario unchanged, and repeat steps ③ - ④;

[0022] ⑥ Output the optimal solution, that is, the proportional coefficient and integral coefficient of each adaptive damping controller, and use it as the control data under this fault scenario;

[0023] ⑦ Store the fault data and control data in the lumped control system;

[0024] ⑧ Change the fault scenario, recalculate the new control data until all fault scenarios are calculated; thus, obtain the proportional coefficient and integral coefficient of all adaptive damping controllers under different fault scenarios as the control data corresponding to the fault scenarios.

[0025] After using the adaptive damping controller, the PLL modal damping ratio ξ of each DFIG single-machine system * is calculated by the following formula:

[0026]

[0027] In the formula, ξ * is the PLL modal damping ratio of the DFIG single-machine system after applying the adaptive damping controller; K p , K i are the proportional coefficient and integral coefficient of the PI controller in the PLL; K cp , K ci are the proportional coefficient and integral coefficient of the PI controller in the adaptive damping controller; when calculating the PLL modal damping ratio ξ * of the DFIG for the first time in step ③, the proportional coefficient and integral coefficient of the PI controller in each adaptive damping controller are both 0; U td0 is the stable value of the grid-connected d-axis voltage; R G is the grid resistance; X G is the grid reactance; I gd0 , I gq0 are the stable values of the system d-axis and q-axis output currents.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] The present invention configures the coefficients of the adaptive damping controller added in each DFIG by the state of the entire system during the fault steady state. Without adding hardware devices, the control strategy of the doubly-fed wind power multi-machine system is redesigned, thereby improving the damping of the entire system and enhancing the stability of the multi-machine system during the fault steady state. The present invention can enhance the small-signal stability of the system during weak grid symmetrical short-circuit faults and improve the success rate of crossing weak grid symmetrical short-circuit faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 The structural block diagram after adding an adaptive damping controller to each DFIG.

[0031] Figure 2 The flowchart for configuring parameters for the adaptive damping controller in each DFIG.

[0032] Figure 3 The schematic diagram of the eigenvalue trajectory of the doubly-fed wind power multi-machine system after applying the cooperative optimization control strategy. (a) is the eigenvalue trajectory when the fault location becomes farther; (b) is the eigenvalue trajectory when the fault severity becomes deeper.

[0033] Figure 4 The simulation result diagram of the DFIG 3-machine system when the fault point voltage drops to 0.1 p.u. without using the cooperative optimization control strategy. (a) is the three-phase voltage at the fault point, (b) is the three-phase grid-connected current, and (c1)-(c3) and (d1)-(d3) are the dq components of the output voltage and output current of the first, second, and third DFIGs.

[0034] Figure 5 The simulation result diagram of the DFIG 3-machine system when the fault point voltage drops to 0.1 p.u. after using the cooperative optimization control strategy. (a) is the three-phase voltage at the fault point, (b) is the three-phase grid-connected current, and (c1)-(c3) and (d1)-(d3) are the dq components of the output voltage and output current of the first, second, and third DFIGs. DETAILED DESCRIPTION OF THE INVENTION

[0035] The following describes the specific implementation of the present invention in detail with reference to the accompanying drawings.

[0036] Figure 1 The control structural block diagram of the adaptive damping controller designed for each DFIG single-machine grid-connected system. The adaptive damping controller feeds the deviation between ω pll and ω g back to the q-axis current loop output of the grid-side converter through a PI controller. Where k cp and k ci are the proportional coefficient and integral coefficient of the adaptive damping controller respectively. Once ωpll Deviation from ω during a fault g The DFIG will automatically adjust its q-axis current component according to Δω pll where I cq is the actual q-axis current value of the DFIG grid-side converter, and I cqref is the q-axis current command value of the DFIG grid-side converter. U cq is the voltage at the output terminal of the DFIG grid-side converter.

[0037] Figure 2 is a flowchart of the configuration parameters of the adaptive damping controller in each DFIG. An adaptive damping controller in Figure 1 will be added to each DFIG, and the system will configure the coefficients of the adaptive damping controller in each DFIG single-machine grid-connected system through the state of the entire system during the steady state of the fault, so as to change the damping of each single-machine grid-connected system and ensure that the entire system remains small-signal stable during the steady state of the fault.

[0038] The specific implementation steps of the present invention are as follows, and also refer to Figure 2 :

[0039] 1) Design an adaptive damping controller for each DFIG single-machine system, and its control structure is as Figure 1 shown. The adaptive damping controller feeds back the deviation Δω pll between the output angular velocity ω g of the phase-locked loop and the system target angular velocity ω pll to the output of the q-axis current loop of the grid-side converter through a PI controller. Once ω pll deviates from ω g during a fault, the DFIG will automatically adjust its q-axis current component according to Δω pll .

[0040] After the system uses the adaptive damping controller, the damping ratio of the phase-locked loop mode of the independent DFIG single-machine grid-connected system changes from Equation (1) to Equation (2): The damping ratio of the phase-locked loop mode of the independent DFIG single-machine grid-connected system during the steady state of the fault changes with the adjustment of the control coefficient of the adaptive damping controller.

[0041]

[0042]

[0043] where ξ is the original damping ratio of the phase-locked loop mode of the independent DFIG single-machine grid-connected system before adding the adaptive damping controller, and ξ * is the damping ratio of the phase-locked loop mode of the DFIG system after applying the adaptive damping controller. K p , K iare the proportional coefficient and integral coefficient of the PI controller in the phase-locked loop. K cp , K ci are the proportional coefficient and integral coefficient of the PI controller in the adaptive damping controller. U td0 is the stable value of the grid-connected point d-axis voltage. R G is the grid resistance, X G is the grid reactance. I gd0 , I gq0 are the stable values of the system d-axis and q-axis output currents.

[0044] 2) Establish a time-domain simulation model of the DFIG multi-machine grid-connected system; based on different fault scenarios, configure the coefficients of the adaptive damping controllers added in each DFIG single-machine system, so as to obtain the coefficients of all adaptive damping controllers corresponding to different fault scenarios, which are used as the control data for the corresponding fault scenarios; the specific implementation process of this step is as follows,

[0045] ① Combine the actual system parameters to establish the small-signal state-space equation of the overall DFIG multi-machine grid-connected system. Use a computer to calculate the fault distance and fault severity range of the symmetrical short-circuit fault scenario that will cause oscillation instability.

[0046] ② Establish a time-domain simulation model of the DFIG multi-machine grid-connected system, and simulate typical symmetrical short-circuit faults within the calculation range (including different fault positions and different fault severities) in combination with the actual situation.

[0047] ③ Predetermine a fault scenario for simulation, calculate the position of the dominant poles of the phase-locked loop in the system, and calculate the damping ratio ξ of each DFIG according to Equation (2) * . When calculating the phase-locked loop modal damping ratio ξ * of the DFIG for the first time, the proportional coefficient and integral coefficient of the PI controller in each adaptive damping controller are both 0;

[0048] ④ Judge whether the phase-locked loop modal damping ratio ξ * of each DFIG is greater than 0; if the phase-locked loop modal damping ratio ξ * of all DFIGs is greater than 0, then go to step ⑥; otherwise, go to step ⑤.

[0049] ⑤ If the condition in step ④ is not satisfied, determine the sequence of DFIGs with the phase-locked loop modal damping ratio ξ * less than 0, and adjust the parameters of the adaptive damping controllers applied therein according to Equation (2). Subsequently, update the parameters of the time-domain simulation model in step ②, keep the previous fault scenario unchanged, and repeat steps ③ - ④;

[0050] ⑥Output the optimal solution, that is, the proportional coefficient and integral coefficient of each adaptive damping controller, and use them as the control data under this fault scenario; enter the simulation model for verification.

[0051] ⑦Store the fault data and control data in the lumped control system.

[0052] ⑧Change the fault scenario and recalculate the new control data until all fault scenarios are calculated. Thus, obtain the proportional coefficients and integral coefficients of all adaptive damping controllers under different fault scenarios, and use them as the control data for the corresponding fault scenarios.

[0053] 3) When a fault occurs in the actual DFIG multi - machine grid - connected system, the fault information will be obtained by the lumped control system. Find the fault scenario corresponding to the actual fault (including the same and similar) from the time - domain simulation model, and send the control data corresponding to this fault scenario to the corresponding DFIG single - machine system, so that each DFIG single - machine system operates under the control of its respective adaptive damping controller, thereby changing the damping of each single - machine grid - connected system, that is, improving the damping of the entire system and enhancing the stability of the multi - machine system during the fault steady state.

[0054] Description of the effects of the present invention:

[0055] Figure 3 The trajectory of the dominant pole of the phase - locked loop is given for the doubly - fed wind power multi - machine system after applying the cooperative optimization control strategy proposed by the present invention with the change of the fault distance or fault severity. It can be seen from Figure 3 that after applying the cooperative optimization control strategy, regardless of whether the fault location becomes farther or the fault severity becomes deeper, each DFIG can ensure the small - signal stability during the fault steady state.

[0056] Figure 4 The simulation results of the DFIG 3 - machine system when the fault point voltage drops to 0.1 p.u. without using the cooperative optimization control strategy are given. It can be seen that the first DFIG experiences oscillatory instability during the fault steady state of the symmetrical short - circuit fault. Due to the dynamic coupling effect between DFIGs, during the fault steady state, the outputs of other DFIGs will also be affected by the disturbance. The harmonic contents of the a - phase voltage at the output terminals of the first, second, and third DFIGs are 29.15%, 17.67%, and 9.82% respectively.

[0057] Figure 5 The simulation results of the DFIG 3 - machine system when the fault point voltage drops to 0.1 p.u. after using the cooperative optimization control strategy are given. Comparing Figure 4 and Figure 5After applying the cooperative optimization control strategy, the small-signal stability of the entire DFIG wind power multi-machine system during the fault steady state of the symmetrical short-circuit fault is improved, and the dynamic stability is guaranteed. The cooperative optimization control strategy can improve the small-signal stability of the DFIG wind power multi-machine system during the fault steady state of the symmetrical short-circuit fault.

[0058] In summary, a cooperative optimization control strategy proposed by the present invention can enhance the small-signal stability of the DFIG wind power multi-machine system during the symmetrical short-circuit fault in a weak grid, which can enhance the small-signal stability of the DFIG wind power multi-machine system during the symmetrical short-circuit fault in a weak grid, avoid the small-signal instability phenomenon that may occur during the fault, and improve the success rate of fault ride-through.

[0059] Finally, it should be noted that the above examples of the present invention are only examples for explaining the present invention, and are not intended to limit the implementation manners of the present invention. Although the applicant has described the present invention in detail with reference to the preferred embodiments, for those of ordinary skill in the art, other different forms of changes and modifications can be made based on the above description. It is impossible to list all the implementation manners here. Any obvious changes or modifications derived from the technical solutions of the present invention are still within the protection scope of the present invention.

Claims

1. A cooperative optimization control method for a doubly-fed wind power multi-machine system under a weak grid symmetrical short-circuit fault, including multiple DFIG single-machine systems; characterized in that: The specific steps are as follows: 1) Configure an adaptive damping controller for each DFIG single-machine system; 2) Establish a time-domain simulation model of the DFIG multi-machine grid-connected system; based on different fault scenarios, configure the coefficients of the adaptive damping controllers added in each DFIG single-machine system, so as to obtain the coefficients of all adaptive damping controllers corresponding to different fault scenarios, which are used as the control data for the corresponding fault scenarios; 3) When an actual fault occurs, find the fault scenario corresponding to the actual fault from the time-domain simulation model, and send the control data corresponding to this fault scenario to the corresponding DFIG single-machine system, so that each DFIG single-machine system operates under the control of its own adaptive damping controller, thereby changing the damping of each single-machine grid-connected system, that is, improving the damping of the entire system and enhancing the stability of the multi-machine system during the fault steady state; In step 2), when configuring the parameters of the adaptive damping controller added to each DFIG single-machine system, calculate the phase-locked loop modal damping ratio ξ of each DFIG single-machine system according to the following formula * ; where ξ * is the PLL modal damping ratio of the DFIG single - machine system after applying the adaptive damping controller; K p , K i are the proportional coefficient and integral coefficient of the PI controller in the PLL; K cp , K ci are the proportional coefficient and integral coefficient of the PI controller in the adaptive damping controller; when initially calculating the PLL modal damping ratio ξ * of the DFIG, the proportional coefficient and integral coefficient of the PI controller in each adaptive damping controller are both 0; U td0 is the stable value of the grid connection point d-axis voltage; R G is the grid resistance; X G is the grid reactance; I gd0 、I gq0 are the stable values of the system d-axis and q-axis output currents; Judge the PLL modal damping ratio ξ of each DFIG * whether it is greater than 0; if the PLL modal damping ratio ξ of all DFIGs * are all greater than 0, output the optimal solution, that is, the proportional coefficient and integral coefficient of each adaptive damping controller, and use them as the control data under this fault scenario; otherwise, determine the sequence of DFIGs with the PLL modal damping ratio ξ * less than 0, and adjust the proportional coefficient and integral coefficient of the adaptive damping controller applied therein according to the damping ratio calculation formula; subsequently, update the parameters of the time-domain simulation model and recalculate the PLL modal damping ratio ξ of each DFIG * .

2. The collaborative optimization control method of the doubly-fed wind power multi-machine system under weak grid symmetrical short-circuit faults according to claim 1, wherein: In step 1), the adaptive damping controller feeds back the deviation Δω between the output angular velocity ω of the phase-locked loop and the system target angular velocity ω to the q-axis current loop output of the grid-side converter through a PI controller; once ω deviates from ω during a fault, the DFIG will automatically adjust its q-axis current component according to Δω. pll and the system target angular velocity ω g between the deviation Δω pll negative feedback to the q-axis current loop output of the grid-side converter; once ω pll during the fault deviates from ω g , the DFIG will be based on Δω pll automatically adjust its q-axis current component.

3. The collaborative optimization control method of the doubly-fed wind power multi-machine system under weak grid symmetrical short-circuit faults according to claim 1, wherein: In step 2), the method for configuring the parameters of the adaptive damping controllers added in each DFIG single-machine system is as follows: ① Combine the actual system parameters to establish the small-signal state-space equation of the overall DFIG multi-machine grid-connected system; deduce the fault distance and the range of fault severity of the symmetrical short-circuit fault scenario that will cause oscillation instability; ② Establish a time-domain simulation model of the DFIG multi-machine grid-connected system and simulate typical symmetrical short-circuit faults in combination with the actual situation; ③Pre-determine a fault scenario, calculate the position of the dominant pole of the phase-locked loop in the system, and calculate the phase-locked loop modal damping ratio ξ of each DFIG * ; ④ Determine the PLL modal damping ratio ξ of each DFIG * whether it is greater than 0; if the PLL modal damping ratio ξ of all DFIGs * is greater than 0, then go to step ⑥; otherwise, go to step ⑤; ⑤Determine the modal damping ratio ξ of the phase-locked loop * For the DFIG sequence less than 0, adjust the proportional coefficient and integral coefficient of the adaptive damping controller applied therein according to the damping ratio calculation formula; subsequently, update the time-domain simulation model parameters in step ②, keep the previous fault scenario unchanged, and repeat steps ③ - ④; ⑥ Output the optimal solution, that is, the proportional coefficient and integral coefficient of each adaptive damping controller, which are used as the control data for this fault scenario; ⑦ Store the fault data and control data in the lumped control system; ⑧ Change the fault scenario and recalculate the new control data until all fault scenarios are calculated; thus, obtain the proportional coefficients and integral coefficients of all adaptive damping controllers under different fault scenarios, which are used as the control data for the corresponding fault scenarios.

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