A PMSG control parameter optimization processing method and device

Abstract

The present invention provides a PMSG control parameter optimization processing method and device, which relates to the field of optimization technology. The method includes: determining the optimization feasible domain of the control parameters to be optimized according to the low voltage ride-through performance index and the preset model; randomly generating the control parameter combination value including the first proportional coefficient, the first integral coefficient, the second proportional coefficient and the second integral coefficient in the optimization feasible domain, and respectively determining the transient voltage increase index value and the transient voltage drop index value corresponding to each group of control parameter combination values; determining the comprehensive index value reflecting the low voltage ride-through performance according to the transient voltage increase index value and the transient voltage drop index value, taking the minimum comprehensive index value as the optimization target, and solving a set of target control parameter combination values ​​when the optimization target is achieved. The device executes the above method. The method and device provided by the embodiment of the present invention can greatly improve the low voltage ride-through capability.

Description

Technical Field

[0001] The present invention relates to the field of optimization technology, and in particular to a PMSG control parameter optimization processing method and device. Background Art

[0002] Building a new power system has become the development goal of the power industry. New energy sources, mainly wind power, have great development potential and high technical maturity, and the use of wind energy has become a trend. When the power system experiences a voltage drop and requires low voltage ride through (LVRT) of the wind turbine, the grid regulations require the wind turbine to remain connected to the grid and inject a certain amount of reactive power into the system. However, when wind turbines are connected to a weak grid system, they are prone to problems such as low voltage ride through failure and transient overvoltage caused by instantaneous reactive surplus after a fault, which can cause large-scale disconnection of wind turbines. In order to meet the requirements of the regulations and ensure the stable operation of the power system during LVRT, the low voltage ride through performance optimization of permanent magnet synchronous generators (PMSG) for wind power connected to a weak grid system has important theoretical value and practical significance.

[0003] The existing technology improves the low voltage ride-through capability of the unit by improving the wind turbine control. In order to solve the transient overvoltage problem caused by the low voltage ride-through of the new energy unit, the influence of PMSG control strategy and parameters on the power characteristics of the unit and the transient overvoltage characteristics of the machine end is analyzed, and a solution and idea are provided for the transient overvoltage problem of the wind turbine. However, the existing methods often only focus on the low voltage ride-through characteristics of the wind turbine or the transient overvoltage characteristics of the machine end when optimizing the low voltage ride-through performance of the wind turbine, resulting in limited improvement in the low voltage ride-through capability. Summary of the invention

[0004] In view of the problems in the prior art, an embodiment of the present invention provides a PMSG control parameter optimization processing method and device, which can at least partially solve the problems in the prior art.

[0005] On the one hand, the present invention provides a PMSG control parameter optimization processing method, comprising:

[0006] Determine the optimization feasible domain of the control parameters to be optimized according to the low voltage ride-through performance index and the preset model;

[0007] Among them, the low voltage ride-through performance index includes a transient voltage rise index and a transient voltage drop index; the preset model is a PMSG transient reactive voltage response analytical model considering the dynamic characteristics of the phase-locked loop under a weak power grid; the control parameters to be optimized include a first proportional coefficient and a first integral coefficient corresponding to the dq axis current loop controller, and a second proportional coefficient and a second integral coefficient corresponding to the phase-locked loop controller;

[0008] Randomly generate control parameter combination values ​​including the first proportional coefficient, the first integral coefficient, the second proportional coefficient and the second integral coefficient in the optimization feasible domain, and respectively determine transient voltage increase index values ​​and transient voltage drop index values ​​corresponding to each group of control parameter combination values;

[0009] A comprehensive index value reflecting low voltage ride-through performance is determined according to the transient voltage rise index value and the transient voltage drop index value, and the minimum comprehensive index value is taken as the optimization target to solve a set of target control parameter combination values ​​when the optimization target is achieved.

[0010] The step of determining the optimization feasible region of the control parameters to be optimized according to the low voltage ride-through performance index and the preset model includes:

[0011] Keeping the first proportional coefficient and the first integral coefficient unchanged, determining a second control parameter combination according to the second proportional coefficient and the second integral coefficient determined by engineering data, substituting the second control parameter combination into the preset model, and obtaining a second PMSG grid connection point voltage and reactive power response waveform;

[0012] Determine the second machine-end transient low voltage amplitude, the second fault-occurring steady-state voltage, the second machine-end transient overvoltage peak value, and the second fault-recovery steady-state voltage according to the second PMSG grid-connected point voltage and reactive power response waveform;

[0013] A second transient voltage drop index value is calculated based on a preset reference voltage, a transient low voltage amplitude at the second machine end, and a steady-state voltage after the second fault occurs; a second transient voltage increase index value is calculated based on a preset reference voltage, a transient overvoltage peak value at the second machine end, and a steady-state voltage after the second fault is recovered;

[0014] Taking the second transient voltage drop index value and the second transient voltage increase index value as optimization index functions and the second control parameter combination as independent variables, a second change surface diagram is obtained in which the second transient voltage drop index value and the second transient voltage increase index value respectively change with the second control parameter combination, and in response to an operation selected in the second change surface diagram that makes both the second transient voltage drop index value and the second transient voltage increase index value smaller than corresponding preset values, a second control parameter optimization feasible domain is obtained;

[0015] In the second control parameter optimization feasible domain, multiple groups of values ​​are respectively taken for the second transient voltage drop index value and the second transient voltage increase index value, and the second control parameter optimization feasible domain corresponding to each group of values ​​is intersected to obtain a second optimization feasible domain corresponding to the second control parameter combination;

[0016] Keeping the second proportional coefficient and the second integral coefficient unchanged, determining a first control parameter combination according to the first proportional coefficient and the first integral coefficient determined by engineering data, substituting the first control parameter combination into the preset model, and obtaining a first PMSG grid connection point voltage and reactive power response waveform;

[0017] Determine the first machine-end transient low voltage amplitude, the steady-state voltage after the first fault occurs, the first machine-end transient overvoltage peak value, and the steady-state voltage after the first fault is recovered according to the first PMSG grid connection point voltage and reactive power response waveform;

[0018] A first transient voltage drop index value is calculated based on a preset reference voltage, a transient low voltage amplitude at the first machine end, and a steady-state voltage after the first fault occurs; a first transient voltage increase index value is calculated based on a preset reference voltage, a transient overvoltage peak value at the first machine end, and a steady-state voltage after the first fault is recovered;

[0019] Taking the first transient voltage drop index value and the first transient voltage increase index value as optimization index functions and the first control parameter combination as independent variables, a first change surface diagram is obtained in which the first transient voltage drop index value and the first transient voltage increase index value respectively change with the first control parameter combination, and in response to an operation selected in the first change surface diagram that makes both the first transient voltage drop index value and the first transient voltage increase index value smaller than corresponding preset values, a first control parameter optimization feasible domain is obtained;

[0020] In the first control parameter optimization feasible domain, multiple groups of values ​​are respectively taken for the first transient voltage drop index value and the first transient voltage increase index value, and the first control parameter optimization feasible domain corresponding to each group of values ​​is intersected to obtain a first optimization feasible domain corresponding to the first control parameter combination;

[0021] The combination of the first optimization feasible domain and the second optimization feasible domain is used as the optimization feasible domain.

[0022] Wherein, the second control parameter combination is determined according to the second proportional coefficient and the second integral coefficient determined according to the engineering data, including:

[0023] Points are evenly taken according to respective corresponding preset step sizes within a second proportional coefficient optimization interval and a second integral coefficient optimization interval respectively corresponding to the second proportional coefficient and the second integral coefficient determined according to engineering data to obtain a second control parameter combination.

[0024] Wherein, the first control parameter combination is determined based on the first proportional coefficient and the first integral coefficient determined according to the engineering data, including:

[0025] In a first proportional coefficient optimization interval and a first integral coefficient optimization interval respectively corresponding to the first proportional coefficient and the first integral coefficient determined according to engineering data, points are evenly taken according to respective corresponding preset step sizes to obtain a first control parameter combination.

[0026] Wherein, the determining of the comprehensive index value reflecting the low voltage ride-through performance according to the transient voltage rise index value and the transient voltage drop index value includes:

[0027] The comprehensive index value is calculated based on the transient voltage increase index value, the transient voltage decrease index value, and the weights corresponding thereto.

[0028] Wherein, the PMSG control parameter optimization processing method further includes:

[0029] The entropy weight method is used to determine the weights corresponding to the transient voltage increase index value and the transient voltage decrease index value respectively.

[0030] Wherein, the PMSG control parameter optimization processing method further includes:

[0031] The Monte Carlo optimization algorithm is used to realize the optimization goal of minimizing the value of the comprehensive index, and to solve a set of target control parameter combination values ​​when the optimization goal is achieved.

[0032] On the one hand, the present invention provides a PMSG control parameter optimization processing device, comprising:

[0033] A determination unit, used to determine an optimization feasible domain of a control parameter to be optimized according to a low voltage ride-through performance index and a preset model;

[0034] Among them, the low voltage ride-through performance index includes a transient voltage rise index and a transient voltage drop index; the preset model is a PMSG transient reactive voltage response analytical model considering the dynamic characteristics of the phase-locked loop under a weak power grid; the control parameters to be optimized include a first proportional coefficient and a first integral coefficient corresponding to the dq axis current loop controller, and a second proportional coefficient and a second integral coefficient corresponding to the phase-locked loop controller;

[0035] a generating unit, configured to randomly generate control parameter combination values ​​including the first proportional coefficient, the first integral coefficient, the second proportional coefficient and the second integral coefficient within the optimization feasible domain, and respectively determine transient voltage increase index values ​​and transient voltage drop index values ​​corresponding to each group of control parameter combination values;

[0036] The optimization unit is used to determine a comprehensive index value reflecting low voltage ride-through performance according to the transient voltage rise index value and the transient voltage drop index value, take the minimum comprehensive index value as the optimization target, and solve a set of target control parameter combination values ​​when the optimization target is achieved.

[0037] In another aspect, an embodiment of the present invention provides an electronic device, including: a processor, a memory, and a bus, wherein:

[0038] The processor and the memory communicate with each other via the bus;

[0039] The memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the following method:

[0040] Determine the optimization feasible domain of the control parameters to be optimized according to the low voltage ride-through performance index and the preset model;

[0041] Among them, the low voltage ride-through performance index includes a transient voltage rise index and a transient voltage drop index; the preset model is a PMSG transient reactive voltage response analytical model considering the dynamic characteristics of the phase-locked loop under a weak power grid; the control parameters to be optimized include a first proportional coefficient and a first integral coefficient corresponding to the dq axis current loop controller, and a second proportional coefficient and a second integral coefficient corresponding to the phase-locked loop controller;

[0042] Randomly generate control parameter combination values ​​including the first proportional coefficient, the first integral coefficient, the second proportional coefficient and the second integral coefficient in the optimization feasible domain, and respectively determine transient voltage increase index values ​​and transient voltage drop index values ​​corresponding to each group of control parameter combination values;

[0043] A comprehensive index value reflecting low voltage ride-through performance is determined according to the transient voltage rise index value and the transient voltage drop index value, and the minimum comprehensive index value is taken as the optimization target to solve a set of target control parameter combination values ​​when the optimization target is achieved.

[0044] An embodiment of the present invention provides a non-transitory computer-readable storage medium, including:

[0045] The non-transitory computer-readable storage medium stores computer instructions, which cause the computer to execute the following method:

[0046] Determine the optimization feasible domain of the control parameters to be optimized according to the low voltage ride-through performance index and the preset model;

[0047] Among them, the low voltage ride-through performance index includes a transient voltage rise index and a transient voltage drop index; the preset model is a PMSG transient reactive voltage response analytical model considering the dynamic characteristics of the phase-locked loop under a weak power grid; the control parameters to be optimized include a first proportional coefficient and a first integral coefficient corresponding to the dq axis current loop controller, and a second proportional coefficient and a second integral coefficient corresponding to the phase-locked loop controller;

[0048] Randomly generate control parameter combination values ​​including the first proportional coefficient, the first integral coefficient, the second proportional coefficient and the second integral coefficient in the optimization feasible domain, and respectively determine transient voltage increase index values ​​and transient voltage drop index values ​​corresponding to each group of control parameter combination values;

[0049] A comprehensive index value reflecting low voltage ride-through performance is determined according to the transient voltage rise index value and the transient voltage drop index value, and the minimum comprehensive index value is taken as the optimization target to solve a set of target control parameter combination values ​​when the optimization target is achieved.

[0050] The PMSG control parameter optimization processing method and device provided by the embodiment of the present invention determine the optimization feasible domain of the control parameters to be optimized according to the low voltage ride-through performance index and the preset model; wherein the low voltage ride-through performance index includes a transient voltage rise index and a transient voltage drop index; the preset model is a PMSG transient reactive voltage response analytical model considering the dynamic characteristics of the phase-locked loop under a weak power grid; the control parameters to be optimized include a first proportional coefficient and a first integral coefficient corresponding to the dq axis current loop controller, and a second proportional coefficient and a second integral coefficient corresponding to the phase-locked loop controller; a control parameter combination value including the first proportional coefficient, the first integral coefficient, the second proportional coefficient and the second integral coefficient is randomly generated in the optimization feasible domain, and the transient voltage rise index value and the transient voltage drop index value corresponding to each group of control parameter combination values ​​are respectively determined; a comprehensive index value reflecting the low voltage ride-through performance is determined according to the transient voltage rise index value and the transient voltage drop index value, and the minimum comprehensive index value is taken as the optimization target, and a set of target control parameter combination values ​​when the optimization target is achieved is solved, and the low voltage ride-through capability can be greatly improved by obtaining the optimized PMSG control parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0052] Figure 1 It is a flow chart of a PMSG control parameter optimization processing method provided in one embodiment of the present invention.

[0053] FIG. 2( a ) is a diagram of an embodiment of the present invention. U lv Indicators K p_pll and K i_pll 3D surface plot of control parameters variation.

[0054] FIG. 2( b ) is a diagram of an embodiment of the present invention. U hv Indicators K p_pll and K i_pll 3D surface plot of control parameters variation.

[0055] FIG. 3( a ) is a diagram of an embodiment of the present invention. Ulv Indicators K p and K i 3D surface plot of control parameters variation.

[0056] FIG. 3( b ) is a diagram of an embodiment of the present invention. U hv Indicators K p and K i 3D surface plot of control parameters variation.

[0057] Figure 4 It is a flow chart of a PMSG control parameter optimization processing method provided by another embodiment of the present invention.

[0058] FIG5( a ) is a comparison diagram of PMSG transient reactive voltage response before and after optimization of a 30% voltage step drop operating condition provided by an embodiment of the present invention.

[0059] FIG5( b ) is a comparison diagram of the transient reactive voltage response of the PMSG before and after the optimization of the voltage step drop of 50% according to an embodiment of the present invention.

[0060] FIG5( c ) is a comparison diagram of the transient reactive voltage response of the PMSG before and after the optimization of the voltage step drop of 70% according to an embodiment of the present invention.

[0061] Figure 6 It is a structural diagram of a PMSG control parameter optimization processing device provided in one embodiment of the present invention.

[0062] Figure 7 A schematic diagram of the physical structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention. It should be noted that, in the absence of conflict, the embodiments in this application and the features in the embodiments can be combined with each other arbitrarily.

[0064] Figure 1 FIG. 1 is a flow chart of a PMSG control parameter optimization processing method provided by an embodiment of the present invention. Figure 1 As shown, the PMSG control parameter optimization processing method provided by the embodiment of the present invention includes:

[0065] Step S1: determining an optimization feasible region of the control parameters to be optimized according to the low voltage ride-through performance index and a preset model;

[0066] Among them, the low voltage ride-through performance indicators include a transient voltage rise indicator and a transient voltage drop indicator; the preset model is a PMSG transient reactive voltage response analytical model that considers the dynamic characteristics of the phase-locked loop under a weak power grid; the control parameters to be optimized include a first proportional coefficient and a first integral coefficient corresponding to the dq-axis current loop controller, and a second proportional coefficient and a second integral coefficient corresponding to the phase-locked loop controller.

[0067] Step S2: randomly generate control parameter combination values ​​including the first proportional coefficient, the first integral coefficient, the second proportional coefficient and the second integral coefficient within the optimization feasible domain, and respectively determine the transient voltage rise index value and the transient voltage drop index value corresponding to each set of control parameter combination values.

[0068] Step S3: Determine a comprehensive index value reflecting low voltage ride-through performance according to the transient voltage rise index value and the transient voltage drop index value, take the minimum comprehensive index value as the optimization target, and solve a set of target control parameter combination values ​​when the optimization target is achieved.

[0069] In the above step S1, the device determines the optimization feasible domain of the control parameters to be optimized according to the low voltage ride-through performance index and the preset model;

[0070] Among them, the low voltage ride-through performance index includes a transient voltage rise index and a transient voltage drop index; the preset model is a PMSG transient reactive voltage response analytical model considering the dynamic characteristics of the phase-locked loop under a weak power grid; the control parameters to be optimized include a first proportional coefficient and a first integral coefficient corresponding to the dq axis current loop controller, and a second proportional coefficient and a second integral coefficient corresponding to the phase-locked loop controller. The device can be a computer device that executes the method. It should be noted that the acquisition and analysis of data involved in the embodiments of the present invention are authorized by the user.

[0071] In the above step S2, the device randomly generates control parameter combination values ​​including the first proportional coefficient, the first integral coefficient, the second proportional coefficient and the second integral coefficient within the optimization feasible domain, and respectively determines the transient voltage rise index value and the transient voltage drop index value corresponding to each set of control parameter combination values.

[0072] In the above step S3, the device determines a comprehensive index value reflecting the low voltage ride-through performance according to the transient voltage rise index value and the transient voltage drop index value, takes the minimum comprehensive index value as the optimization target, and solves a set of target control parameter combination values ​​when the optimization target is achieved.

[0073] The PMSG control parameter optimization processing method provided by the embodiment of the present invention may include steps 1 to 4, which are respectively described as follows:

[0074] Step 1:

[0075] An analytical model of PMSG transient reactive voltage response considering the dynamic characteristics of the phase-locked loop under weak power grid is established.

[0076] PMSG consists of impeller, permanent magnet synchronous generator, machine-side converter, grid-side converter and filter. The traditional control strategy of PMSG is that the machine-side converter uses a control strategy based on rotor flux orientation to control electromagnetic power and achieve maximum wind power tracking; the grid-side converter uses a control strategy based on grid voltage orientation to control the DC bus voltage and achieve decoupling control of output active power and reactive power. According to the equivalent circuit topology and control block diagram of the existing PMSG grid-side converter, its transient reactive voltage response analytical model can be constructed.

[0077] According to the equivalent circuit topology, the voltage and current output by the grid-side converter and the grid voltage can be calculated, which can be expressed in the dq rotating coordinate system as follows:

[0078] (1)

[0079] In the formula, u d , u q , i d , i q The terminal voltage and current are dq Quantity; u gd , u gq , i gd , i gq The grid voltage and current are dq Quantity; u Ld , u Lq For filter inductance L f Pressure Drop dq Quantity; i RCd , i RCq is the RC filter branch current dq Quantity; R f is the filter resistor; uCd , u Cq For filter capacitor C f Voltage dq Quantity.

[0080] The mathematical model of the filter of the grid-side converter can be expressed as:

[0081] (2)

[0082] In the formula, C f , L f is the filter capacitor inductor; is the angular velocity of rotation corresponding to the grid voltage frequency.

[0083] The output voltage of the grid-side converter is jointly affected by the parameters of the current loop proportional-integral (PI) regulator. According to the control principle of the grid-side converter, the PMSG terminal voltage can be expressed as:

[0084] (3)

[0085] In the formula, i dref , i qref is the machine-end current reference value; K p , K i for dq Proportional and integral coefficients of the axis current loop controller.

[0086] When a short circuit occurs, the actual power grid can be equivalent to a voltage source E s Equivalent inductance to the power grid L g The grid voltage is dq The axis components can be expressed as:

[0087] (4)

[0088] In the formula, e d ' , e q ' is the grid voltage after the fault occurs dq Quantity.

[0089] After the fault occurs, V g_abc Voltage phase generation Jump, convert it to the complex frequency domain The phase-locked deviation between the phase of the phase-locked loop output and the actual phase in the complex frequency domain is:

[0090] (5)

[0091] Where: E m is the grid voltage amplitude before the fault, K p_pll , K i_pll are the proportional and integral coefficients of the phase-locked loop controller.

[0092] right By performing an inverse Laplace transform, the phase deviation of the phase-locked loop output corresponding to the response in the time domain can be obtained as:

[0093] (6)

[0094] Therefore, when a short circuit occurs on the AC side of the system, the grid voltage amplitude drops instantly to E m ' , grid voltage after fault e dq ' It can be expressed as:

[0095] (7)

[0096] Where: is the grid voltage phase before the fault; is the phase of the phase-locked loop output before the fault.

[0097] Because the phase-locking deviation is small enough before the fault occurs, it can be approximated as , after the failure occurs dq Grid voltage component on the axis e dq ' The expression is:

[0098] (8)

[0099] By converting equations (1) to (4) and equation (8) into the complex frequency domain and solving them simultaneously, we can obtain the complex frequency domain solutions of the filter capacitor voltage, current and grid current under the dq axis:

[0100] (9)

[0101] Where: u Cd0 , uCq0 for dq Initial value of the on-axis filter capacitor voltage; i d0 , i q0 for dq Initial value of the on-axis filter inductor current; i gd0 , i gq0 for dq The initial value of the equivalent inductance current on the axis; i dref (s), i qref (s) is the command value in the complex frequency domain after the grid voltage changes.

[0102] Performing an inverse Laplace transform on equation (9), we can obtain the voltage and current of the filter capacitor and the grid current. dq Substituting it into equation (1), we can get the time domain expression of the voltage and current on the grid side of the direct-drive wind turbine: u gd (t) u gq (t) i gd (t) i gq (t).

[0103] Therefore, the grid connection point voltage of the direct-drive wind power system and its transient reactive power output to the grid can be expressed as:

[0104] (10)

[0105] Step 2: Use the low voltage ride-through performance index to calculate the optimal feasible region of the control parameters to be optimized.

[0106] In order to avoid the problem of low calculation efficiency of the optimization algorithm due to the large range of the parameters to be optimized, before optimizing the control parameters, it is necessary to first determine the constraints of the optimization problem, that is, the feasible domain of the control parameters to be optimized. To determine the feasible domain of optimization, it is necessary to introduce the transient voltage rise U hv Indicators and transient voltage drop U lv The indicators are taken as optimization objectives, and the established PMSG transient voltage reactive response analytical model is used to obtain an accurate and intuitive visualization parameter feasible domain.

[0107] First, the optimization range of the PMSG grid-side phase-locked loop control parameters to be optimized is given according to the actual project, without the need for precise calculation; then, the initial control parameters of the grid-side current inner loop are maintained. K p andK i The control parameters of the phase-locked loop are uniformly selected in a certain step size within this optimization interval to obtain the combination of each control parameter; secondly, based on the established PMSG transient reactive voltage response analytical model, a U lv , U hv Indicators K p_pll and K i_pll The three-dimensional surface diagrams of the control parameter changes are shown in Figure 2 (a) and Figure 2 (b). The construction process is as follows:

[0108] 1) Substitute each control parameter combination into the established PMSG transient reactive voltage response analytical model for calculation to obtain the PMSG grid connection point voltage corresponding to each combination U g and reactive power Q g Response waveform.

[0109] 2) According to formula (11) and formula (12), the low voltage ride-through performance index corresponding to each combination is calculated: U lv and U hv The value of .

[0110] (11)

[0111] Where: U min is the transient low voltage amplitude at the machine end, U l0 is the steady-state voltage after the fault occurs, U base is the reference voltage, set to 560V.

[0112] (12)

[0113] Where: U max is the transient overvoltage peak value at the machine end, U h0 is the steady-state voltage after fault recovery.

[0114] 3) Respectively U lv , U hv To optimize the indicator function, K p_pll and K i_pll As the independent variable, we get Ulv , U hv The changes when the two control parameters change.

[0115] Finally, select the surface plot so that the index U lv and U hv The phase-locked loop control parameter range with a smaller value is used as the feasible domain for control parameter optimization to extract different optimization indicators. U lv and U hv The feasible domain of control parameter optimization under , and the intersection can be taken to obtain the final feasible domain of optimization of PMSG grid-side phase-locked loop control parameters.

[0116] In the same way, we can obtain U lv , U hv Indicators K p and K i The three-dimensional surface diagrams of the control parameter changes are shown in Figure 3 (a) and Figure 3 (b), thereby determining the final optimization feasible domain of the PMSG grid-side current inner loop control parameters.

[0117] Step 3: Determine the objective weight of each performance indicator based on the entropy weight method to obtain a comprehensive indicator that can simultaneously reflect the low voltage ride-through characteristics and transient overvoltage characteristics of PMSG.

[0118] First, according to the control parameter combination to be optimized, m The transient voltage rise is calculated by analyzing the combination of the candidate control parameters. U hv and transient voltage drop U lv As an optimization index of low voltage ride-through performance of alternative control parameter combination; this is a combination of two optimization indexes, m The multi-objective optimization problem of candidate control parameter combinations is constructed according to formula (13):

[0119] (13)

[0120] Where: For the i The optimization index is j The jth solution is the jth alternative control parameter combination solution.

[0121] Secondly, the entropy of each optimization index is calculated according to formula (14) and formula (15) H 1 andH 2 and the corresponding entropy weight w 1 and w 2, which is the objective weight of the two optimization indicators.

[0122]

[0123] (14)

[0124]

[0125] (15)

[0126] Where: and .

[0127] Finally, the objective weights of the two optimization indicators are obtained w 1 and w Substituting 2 into equation (16), the comprehensive index of PMSG low voltage ride-through performance of each alternative control parameter combination can be obtained: F j .

[0128] (16)

[0129] The entropy weight method can be used to quantitatively calculate the comprehensive index of PMSG low voltage ride-through performance. F Combining equations (11) to (16), we can see that F The smaller the PMSG is, the better the low voltage ride-through performance will be. F As the objective function of PMSG control parameter optimization, the optimization goal is to find F Get the minimum value F min combination of control parameters.

[0130] Step 4: Use Monte Carlo optimization algorithm to optimize the key control parameters of PMSG.

[0131] Use Figure 4 The Monte Carlo algorithm optimization process shown in the figure optimizes the key control parameters of PMSG. The specific steps are as follows:

[0132] 1) Initialization. Among them, n is the number of iterations in the optimization process, so n The initial value is set to 0, and the maximum number of iterations n max Set to 10000; temp is used to compare comprehensive indicators during the optimization process F The middle value, here the initial value is set to 100;

[0133] 2) Control parameter assignment. Based on the Monte Carlo algorithm, a set of random numbers is generated in the optimization feasible domain as the control parameters to be optimized. K p_pll , K i_pll , K p , K i The value of

[0134] 3) Analytical calculation. Substitute the generated new control parameter combination into the PMSG transient reactive voltage response analytical model for analytical calculation to obtain the comprehensive index corresponding to the combination. F The value of F n ;

[0135] 4) Determine the calculation results. F n Is it less than temp? If so, F n Assign the value to temp and assign the new control parameter combination to the optimized control parameter combination ; Otherwise, go to step 5;

[0136] 5) Determine the number of iterations. n Is it greater than or equal to 1000 (i.e., whether the optimization process is over), if so, output the optimized control parameter combination As an approximate solution to the optimization problem, the optimized intermediate quantity temp is output as the minimum value of the comprehensive index in the optimization process. F min ; Otherwise, proceed to step 2.

[0137] By using the above optimization process based on the Monte Carlo algorithm to optimize the control parameters of the PMSG, the final optimization result can be obtained.

[0138] A PMSG wind farm grid-connected system model was constructed, and a three-phase short circuit occurred at the wind turbine grid connection point, and the fault was cleared at 1.7s. The optimized control parameters were tested under different operating conditions, and the comparison results before and after optimization are shown in Figure 5 (a), Figure 5 (b), and Figure 5 (c).

[0139] The PMSG control parameter optimization processing method provided by the embodiment of the present invention determines the optimization feasible domain of the control parameters to be optimized according to the low voltage ride-through performance index and the preset model; wherein the low voltage ride-through performance index includes a transient voltage increase index and a transient voltage drop index; the preset model is a PMSG transient reactive voltage response analytical model considering the dynamic characteristics of the phase-locked loop under a weak power grid; the control parameters to be optimized include a first proportional coefficient and a first integral coefficient corresponding to the dq axis current loop controller, and a second proportional coefficient and a second integral coefficient corresponding to the phase-locked loop controller; a control parameter combination value including the first proportional coefficient, the first integral coefficient, the second proportional coefficient and the second integral coefficient is randomly generated in the optimization feasible domain, and the transient voltage increase index value and the transient voltage drop index value corresponding to each group of control parameter combination values ​​are respectively determined; a comprehensive index value reflecting the low voltage ride-through performance is determined according to the transient voltage increase index value and the transient voltage drop index value, and the minimum comprehensive index value is taken as the optimization target, and a set of target control parameter combination values ​​when the optimization target is achieved is solved, and the low voltage ride-through capability can be greatly improved by obtaining the optimized PMSG control parameters.

[0140] Furthermore, the step of determining the optimization feasible region of the control parameters to be optimized according to the low voltage ride-through performance index and the preset model includes:

[0141] Keep the first proportional coefficient and the first integral coefficient unchanged, determine the second control parameter combination according to the second proportional coefficient and the second integral coefficient determined by the engineering data, substitute the second control parameter combination into the preset model, and obtain the second PMSG grid-connected point voltage and reactive power response waveform; refer to the above-mentioned embodiment for description and no further details will be given.

[0142] The transient low voltage amplitude at the second machine end, the steady-state voltage after the second fault occurs, the transient overvoltage peak value at the second machine end and the steady-state voltage after the second fault is recovered are determined according to the second PMSG grid connection point voltage and reactive power response waveform; the above-mentioned embodiments can be referred to for description and will not be repeated here.

[0143] The second transient voltage drop index value is calculated based on the preset reference voltage, the transient low voltage amplitude of the second machine end and the steady-state voltage after the second fault occurs. The second transient voltage increase index value is calculated based on the preset reference voltage, the transient overvoltage peak value of the second machine end and the steady-state voltage after the second fault is recovered. Please refer to the above-mentioned embodiment for description and will not be repeated here.

[0144] Taking the second transient voltage drop index value and the second transient voltage increase index value as the optimization index function and the second control parameter combination as the independent variable, a second change surface diagram is obtained in which the second transient voltage drop index value and the second transient voltage increase index value respectively change with the second control parameter combination; in response to the operation action selected in the second change surface diagram so that the second transient voltage drop index value and the second transient voltage increase index value are both less than the corresponding preset values, a feasible domain for optimizing the second control parameter is obtained; the above-mentioned embodiment can be referred to for description and will not be repeated here.

[0145] In the feasible domain for optimizing the second control parameter, multiple groups of values ​​are taken for the second transient voltage drop index value and the second transient voltage increase index value, and the intersection of the feasible domains for optimizing the second control parameter corresponding to each group of values ​​is taken to obtain the second optimized feasible domain corresponding to the second control parameter combination; the above-mentioned embodiments can be referred to for description and will not be repeated here.

[0146] Keep the second proportional coefficient and the second integral coefficient unchanged, determine the first control parameter combination according to the first proportional coefficient and the first integral coefficient determined by the engineering data, substitute the first control parameter combination into the preset model, and obtain the first PMSG grid-connected point voltage and reactive power response waveform; refer to the above-mentioned embodiment for description and no further details will be given.

[0147] According to the first PMSG grid connection point voltage and reactive power response waveform, the first machine end transient low voltage amplitude, the steady-state voltage after the first fault occurs, the first machine end transient overvoltage peak value and the steady-state voltage after the first fault is recovered are determined; the above-mentioned embodiments can be referred to for description and will not be repeated here.

[0148] According to the preset reference voltage, the transient low voltage amplitude of the first machine end and the steady-state voltage after the first fault occurs, the first transient voltage drop index value is calculated; according to the preset reference voltage, the transient overvoltage peak value of the first machine end and the steady-state voltage after the first fault is recovered, the first transient voltage increase index value is calculated; the above-mentioned embodiment can be referred to for description and will not be repeated here.

[0149] Taking the first transient voltage drop index value and the first transient voltage increase index value as optimization index functions and the first control parameter combination as independent variables, a first change surface diagram is obtained in which the first transient voltage drop index value and the first transient voltage increase index value respectively change with the first control parameter combination; in response to an operation action selected in the first change surface diagram that makes both the first transient voltage drop index value and the first transient voltage increase index value smaller than corresponding preset values, a first control parameter optimization feasible domain is obtained; the above-mentioned embodiment can be referred to for description and will not be repeated here.

[0150] In the first control parameter optimization feasible domain, multiple groups of values ​​are taken for the first transient voltage drop index value and the first transient voltage increase index value, and the intersection of the first control parameter optimization feasible domain corresponding to each group of values ​​is taken to obtain the first optimization feasible domain corresponding to the first control parameter combination; the above-mentioned embodiment can be referred to for description and will not be repeated here.

[0151] The combination of the first optimization feasible region and the second optimization feasible region is used as the optimization feasible region. The above description can be referred to in the above embodiment and will not be repeated here.

[0152] Further, the second control parameter combination is determined according to the second proportional coefficient and the second integral coefficient determined according to the engineering data, including:

[0153] The second control parameter combination is obtained by evenly selecting points in the second proportional coefficient optimization interval and the second integral coefficient optimization interval corresponding to the second proportional coefficient and the second integral coefficient respectively determined according to the engineering data according to the respective corresponding preset step sizes.

[0154] Further, the first control parameter combination is determined according to the first proportional coefficient and the first integral coefficient determined according to the engineering data, including:

[0155] In the first proportional coefficient optimization interval and the first integral coefficient optimization interval respectively corresponding to the first proportional coefficient and the first integral coefficient determined according to the engineering data, points are uniformly selected according to the respective corresponding preset step sizes to obtain the first control parameter combination.

[0156] Further, the determining of a comprehensive index value reflecting low voltage ride-through performance according to the transient voltage rise index value and the transient voltage drop index value includes:

[0157] The comprehensive index value is obtained by calculating the transient voltage increase index value, the transient voltage drop index value, and the weights corresponding thereto. The above description can be referred to in detail in the above embodiment, which will not be described in detail.

[0158] Furthermore, the PMSG control parameter optimization processing method further includes:

[0159] The entropy weight method is used to determine the weights corresponding to the transient voltage increase index value and the transient voltage decrease index value. The above embodiments can be referred to for explanation and will not be described in detail.

[0160] Furthermore, the PMSG control parameter optimization processing method further includes:

[0161] The Monte Carlo optimization algorithm is used to realize the optimization goal of minimizing the value of the comprehensive index and to solve a set of target control parameter combination values ​​when the optimization goal is achieved. The above-mentioned embodiments can be referred to for explanation and will not be described in detail.

[0162] Figure 6 FIG. 1 is a schematic diagram of the structure of a PMSG control parameter optimization processing device provided by an embodiment of the present invention. Figure 6 As shown, the PMSG control parameter optimization processing device provided by the embodiment of the present invention includes a determination unit 601, a generation unit 602 and an optimization unit 603, wherein:

[0163] The determination unit 601 is used to determine the optimization feasible domain of the control parameters to be optimized according to the low voltage ride-through performance index and the preset model; wherein the low voltage ride-through performance index includes a transient voltage rise index and a transient voltage drop index; the preset model is a PMSG transient reactive voltage response analytical model considering the dynamic characteristics of the phase-locked loop under a weak power grid; the control parameters to be optimized include a first proportional coefficient and a first integral coefficient corresponding to the dq axis current loop controller, and a second proportional coefficient and a second integral coefficient corresponding to the phase-locked loop controller; the generation unit 602 is used to randomly generate a control parameter combination value including the first proportional coefficient, the first integral coefficient, the second proportional coefficient and the second integral coefficient in the optimization feasible domain, and respectively determine the transient voltage rise index value and the transient voltage drop index value corresponding to each group of control parameter combination values; the optimization unit 603 is used to determine the comprehensive index value reflecting the low voltage ride-through performance according to the transient voltage rise index value and the transient voltage drop index value, take the minimum comprehensive index value as the optimization target, and solve a set of target control parameter combination values ​​when the optimization target is achieved.

[0164] Specifically, the determination unit 601 in the device is used to determine the optimization feasible domain of the control parameters to be optimized according to the low voltage ride-through performance index and the preset model; wherein the low voltage ride-through performance index includes a transient voltage rise index and a transient voltage drop index; the preset model is a PMSG transient reactive voltage response analytical model considering the dynamic characteristics of the phase-locked loop under a weak power grid; the control parameters to be optimized include a first proportional coefficient and a first integral coefficient corresponding to the dq axis current loop controller, and a second proportional coefficient and a second integral coefficient corresponding to the phase-locked loop controller; the generation unit 602 is used to randomly generate a control parameter combination value including the first proportional coefficient, the first integral coefficient, the second proportional coefficient and the second integral coefficient in the optimization feasible domain, and respectively determine the transient voltage rise index value and the transient voltage drop index value corresponding to each group of control parameter combination values; the optimization unit 603 is used to determine the comprehensive index value reflecting the low voltage ride-through performance according to the transient voltage rise index value and the transient voltage drop index value, take the minimum comprehensive index value as the optimization target, and solve a set of target control parameter combination values ​​when the optimization target is achieved.

[0165] The PMSG control parameter optimization processing device provided by the embodiment of the present invention determines the optimization feasible domain of the control parameters to be optimized according to the low voltage ride-through performance index and the preset model; wherein the low voltage ride-through performance index includes a transient voltage increase index and a transient voltage drop index; the preset model is a PMSG transient reactive voltage response analytical model considering the dynamic characteristics of the phase-locked loop under a weak power grid; the control parameters to be optimized include a first proportional coefficient and a first integral coefficient corresponding to the dq axis current loop controller, and a second proportional coefficient and a second integral coefficient corresponding to the phase-locked loop controller; a control parameter combination value including the first proportional coefficient, the first integral coefficient, the second proportional coefficient and the second integral coefficient is randomly generated in the optimization feasible domain, and the transient voltage increase index value and the transient voltage drop index value corresponding to each group of control parameter combination values ​​are respectively determined; a comprehensive index value reflecting the low voltage ride-through performance is determined according to the transient voltage increase index value and the transient voltage drop index value, and the minimum comprehensive index value is taken as the optimization target, and a set of target control parameter combination values ​​when the optimization target is achieved is solved, and the low voltage ride-through capability can be greatly improved by obtaining the optimized PMSG control parameters.

[0166] The embodiment of the present invention provides an embodiment of a PMSG control parameter optimization processing device which can be specifically used to execute the processing flow of the above-mentioned method embodiments. Its functions are not described in detail here, and reference can be made to the detailed description of the above-mentioned method embodiments.

[0167] Figure 7 A schematic diagram of the physical structure of an electronic device provided in an embodiment of the present invention, such as Figure 7 As shown, the electronic device includes: a processor (processor) 701, a memory (memory) 702 and a bus 703;

[0168] The processor 701 and the memory 702 communicate with each other via a bus 703;

[0169] The processor 701 is used to call the program instructions in the memory 702 to execute the methods provided by the above method embodiments, for example, including:

[0170] Determine the optimization feasible domain of the control parameters to be optimized according to the low voltage ride-through performance index and the preset model;

[0171] Among them, the low voltage ride-through performance index includes a transient voltage rise index and a transient voltage drop index; the preset model is a PMSG transient reactive voltage response analytical model considering the dynamic characteristics of the phase-locked loop under a weak power grid; the control parameters to be optimized include a first proportional coefficient and a first integral coefficient corresponding to the dq axis current loop controller, and a second proportional coefficient and a second integral coefficient corresponding to the phase-locked loop controller;

[0172] Randomly generate control parameter combination values ​​including the first proportional coefficient, the first integral coefficient, the second proportional coefficient and the second integral coefficient in the optimization feasible domain, and respectively determine transient voltage increase index values ​​and transient voltage drop index values ​​corresponding to each group of control parameter combination values;

[0173] A comprehensive index value reflecting low voltage ride-through performance is determined according to the transient voltage rise index value and the transient voltage drop index value, and the minimum comprehensive index value is taken as the optimization target to solve a set of target control parameter combination values ​​when the optimization target is achieved.

[0174] This embodiment discloses a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can perform the methods provided by the above method embodiments, for example, including:

[0175] Determine the optimization feasible domain of the control parameters to be optimized according to the low voltage ride-through performance index and the preset model;

[0176] Among them, the low voltage ride-through performance index includes a transient voltage rise index and a transient voltage drop index; the preset model is a PMSG transient reactive voltage response analytical model considering the dynamic characteristics of the phase-locked loop under a weak power grid; the control parameters to be optimized include a first proportional coefficient and a first integral coefficient corresponding to the dq axis current loop controller, and a second proportional coefficient and a second integral coefficient corresponding to the phase-locked loop controller;

[0177] Randomly generate control parameter combination values ​​including the first proportional coefficient, the first integral coefficient, the second proportional coefficient and the second integral coefficient in the optimization feasible domain, and respectively determine transient voltage increase index values ​​and transient voltage drop index values ​​corresponding to each group of control parameter combination values;

[0178] A comprehensive index value reflecting low voltage ride-through performance is determined according to the transient voltage rise index value and the transient voltage drop index value, and the minimum comprehensive index value is taken as the optimization target to solve a set of target control parameter combination values ​​when the optimization target is achieved.

[0179] This embodiment provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein the computer program enables the computer to execute the methods provided by the above method embodiments, for example, including:

[0180] Determine the optimization feasible domain of the control parameters to be optimized according to the low voltage ride-through performance index and the preset model;

[0181] Among them, the low voltage ride-through performance index includes a transient voltage rise index and a transient voltage drop index; the preset model is a PMSG transient reactive voltage response analytical model considering the dynamic characteristics of the phase-locked loop under a weak power grid; the control parameters to be optimized include a first proportional coefficient and a first integral coefficient corresponding to the dq axis current loop controller, and a second proportional coefficient and a second integral coefficient corresponding to the phase-locked loop controller;

[0182] Randomly generate control parameter combination values ​​including the first proportional coefficient, the first integral coefficient, the second proportional coefficient and the second integral coefficient in the optimization feasible domain, and respectively determine transient voltage increase index values ​​and transient voltage drop index values ​​corresponding to each group of control parameter combination values;

[0183] A comprehensive index value reflecting low voltage ride-through performance is determined according to the transient voltage rise index value and the transient voltage drop index value, and the minimum comprehensive index value is taken as the optimization target to solve a set of target control parameter combination values ​​when the optimization target is achieved.

[0184] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take 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.

[0185] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0186] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0187] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0188] In the description of this specification, the description with reference to the terms "one embodiment", "a specific embodiment", "some embodiments", "for example", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0189] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A PMSG control parameter optimization processing method, characterized in that: include: Determine the optimization feasible domain of the control parameters to be optimized according to the low voltage ride-through performance index and the preset model; Among them, the low voltage ride-through performance index includes a transient voltage rise index and a transient voltage drop index; the preset model is a PMSG transient reactive voltage response analytical model considering the dynamic characteristics of the phase-locked loop under a weak power grid; the control parameters to be optimized include a first proportional coefficient and a first integral coefficient corresponding to the dq axis current loop controller, and a second proportional coefficient and a second integral coefficient corresponding to the phase-locked loop controller; Randomly generate control parameter combination values ​​including the first proportional coefficient, the first integral coefficient, the second proportional coefficient and the second integral coefficient in the optimization feasible domain, and respectively determine transient voltage increase index values ​​and transient voltage drop index values ​​corresponding to each group of control parameter combination values; Determine a comprehensive index value reflecting low voltage ride-through performance according to the transient voltage rise index value and the transient voltage drop index value, take the minimum comprehensive index value as the optimization target, and solve a set of target control parameter combination values ​​when the optimization target is achieved; The step of determining a comprehensive index value reflecting low voltage ride-through performance according to the transient voltage rise index value and the transient voltage drop index value includes: The comprehensive index value is obtained by calculating the transient voltage increase index value, the transient voltage decrease index value, and the weights corresponding thereto respectively; The PMSG control parameter optimization processing method further includes: Determine the weights corresponding to the transient voltage increase index value and the transient voltage decrease index value respectively by using the entropy weight method; The PMSG control parameter optimization processing method further includes: The Monte Carlo optimization algorithm is used to realize the optimization goal of minimizing the value of the comprehensive index, and to solve a set of target control parameter combination values ​​when the optimization goal is achieved.

2. The PMSG control parameter optimization processing method according to claim 1 is characterized in that: The step of determining the optimization feasible domain of the control parameters to be optimized according to the low voltage ride-through performance index and the preset model includes: Keeping the first proportional coefficient and the first integral coefficient unchanged, determining a second control parameter combination according to the second proportional coefficient and the second integral coefficient determined by engineering data, substituting the second control parameter combination into the preset model, and obtaining a second PMSG grid connection point voltage and reactive power response waveform; Determine the second machine-end transient low voltage amplitude, the second fault-occurring steady-state voltage, the second machine-end transient overvoltage peak value, and the second fault-recovery steady-state voltage according to the second PMSG grid-connected point voltage and reactive power response waveform; A second transient voltage drop index value is calculated based on a preset reference voltage, a transient low voltage amplitude at the second machine end, and a steady-state voltage after the second fault occurs; a second transient voltage increase index value is calculated based on a preset reference voltage, a transient overvoltage peak value at the second machine end, and a steady-state voltage after the second fault is recovered; Taking the second transient voltage drop index value and the second transient voltage increase index value as optimization index functions and the second control parameter combination as independent variables, a second change surface diagram is obtained in which the second transient voltage drop index value and the second transient voltage increase index value respectively change with the second control parameter combination, and in response to an operation selected in the second change surface diagram that makes both the second transient voltage drop index value and the second transient voltage increase index value smaller than corresponding preset values, a second control parameter optimization feasible domain is obtained; In the second control parameter optimization feasible domain, multiple groups of values ​​are respectively taken for the second transient voltage drop index value and the second transient voltage increase index value, and the second control parameter optimization feasible domain corresponding to each group of values ​​is intersected to obtain a second optimization feasible domain corresponding to the second control parameter combination; Keeping the second proportional coefficient and the second integral coefficient unchanged, determining a first control parameter combination according to the first proportional coefficient and the first integral coefficient determined by engineering data, substituting the first control parameter combination into the preset model, and obtaining a first PMSG grid connection point voltage and reactive power response waveform; Determine the first machine-end transient low voltage amplitude, the steady-state voltage after the first fault occurs, the first machine-end transient overvoltage peak value, and the steady-state voltage after the first fault is recovered according to the first PMSG grid connection point voltage and reactive power response waveform; A first transient voltage drop index value is calculated based on a preset reference voltage, a transient low voltage amplitude at the first machine end, and a steady-state voltage after the first fault occurs; a first transient voltage increase index value is calculated based on a preset reference voltage, a transient overvoltage peak value at the first machine end, and a steady-state voltage after the first fault is recovered; Taking the first transient voltage drop index value and the first transient voltage increase index value as optimization index functions and the first control parameter combination as independent variables, a first change surface diagram is obtained in which the first transient voltage drop index value and the first transient voltage increase index value respectively change with the first control parameter combination, and in response to an operation selected in the first change surface diagram that makes both the first transient voltage drop index value and the first transient voltage increase index value smaller than corresponding preset values, a first control parameter optimization feasible domain is obtained; In the first control parameter optimization feasible domain, multiple groups of values ​​are respectively taken for the first transient voltage drop index value and the first transient voltage increase index value, and the first control parameter optimization feasible domain corresponding to each group of values ​​is intersected to obtain a first optimization feasible domain corresponding to the first control parameter combination; The combination of the first optimization feasible domain and the second optimization feasible domain is used as the optimization feasible domain.

3. The PMSG control parameter optimization processing method according to claim 2 is characterized in that: The determining of the second control parameter combination according to the second proportional coefficient and the second integral coefficient determined according to the engineering data comprises: Points are evenly taken according to respective corresponding preset step sizes within a second proportional coefficient optimization interval and a second integral coefficient optimization interval respectively corresponding to the second proportional coefficient and the second integral coefficient determined according to engineering data to obtain a second control parameter combination.

4. The PMSG control parameter optimization processing method according to claim 2 is characterized in that: The determining of the first control parameter combination based on the first proportional coefficient and the first integral coefficient determined according to the engineering data includes: In a first proportional coefficient optimization interval and a first integral coefficient optimization interval respectively corresponding to the first proportional coefficient and the first integral coefficient determined according to engineering data, points are evenly taken according to respective corresponding preset step sizes to obtain a first control parameter combination.

5. A PMSG control parameter optimization processing device, characterized in that: include: A determination unit, used to determine an optimization feasible domain of a control parameter to be optimized according to a low voltage ride-through performance index and a preset model; Among them, the low voltage ride-through performance index includes a transient voltage rise index and a transient voltage drop index; the preset model is a PMSG transient reactive voltage response analytical model considering the dynamic characteristics of the phase-locked loop under a weak power grid; the control parameters to be optimized include a first proportional coefficient and a first integral coefficient corresponding to the dq axis current loop controller, and a second proportional coefficient and a second integral coefficient corresponding to the phase-locked loop controller; a generating unit, configured to randomly generate control parameter combination values ​​including the first proportional coefficient, the first integral coefficient, the second proportional coefficient and the second integral coefficient within the optimization feasible domain, and respectively determine transient voltage increase index values ​​and transient voltage drop index values ​​corresponding to each group of control parameter combination values; an optimization unit, configured to determine a comprehensive index value reflecting low voltage ride-through performance according to the transient voltage rise index value and the transient voltage drop index value, take the minimum comprehensive index value as an optimization target, and solve a set of target control parameter combination values ​​when the optimization target is achieved; The optimization unit is specifically used for: The comprehensive index value is obtained by calculating the transient voltage increase index value, the transient voltage decrease index value, and the weights corresponding thereto respectively; The PMSG control parameter optimization processing device is also used for: Determine the weights corresponding to the transient voltage increase index value and the transient voltage decrease index value respectively by using the entropy weight method; The PMSG control parameter optimization processing device is also used for: The Monte Carlo optimization algorithm is used to realize the optimization goal of minimizing the value of the comprehensive index, and to solve a set of target control parameter combination values ​​when the optimization goal is achieved.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

7. 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 the method according to any one of claims 1 to 4 are implemented.

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