Multi-objective optimization method, device and equipment for filtering parameters of intermediate-frequency converter and medium

Abstract

The invention relates to the technical field of wind turbine generator grid connection, in particular to a multi-objective optimization method, device and equipment for filtering parameters of an intermediate-frequency converter and a medium, and the method comprises the steps: determining an intermediate-frequency grid-connected wind turbine generator model, and calculating the inductance and capacitance ranges of a filter; establishing a multi-objective optimization function by taking the average value and the mean square error of the direct-current side voltage of the intermediate-frequency converter and the electric energy quality of the alternating-current side current as optimization objectives; building a multi-objective optimization model by taking a parameter combination of inductance and capacitance of the filter as a population parameter and taking a multi-objective optimization function as a degree function; and solving the optimization model by adopting a particle swarm algorithm to obtain an optimal solution of the inductance and capacitance parameter combination of the filter. According to the method, the parameter combination of capacitance and inductance in the filter circuit serves as the population parameter, the total objective function in the multi-objective optimization model serves as the adaptability function, the optimization model is solved, the filter parameters of the intermediate-frequency converter can be more accurate, and therefore a better filter effect is obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of wind turbine grid connection, and in particular to a method, device, equipment and medium for multi-objective optimization of filter parameters of a medium frequency converter. Background Art

[0002] As offshore wind power develops towards the far seas and large capacity, the use of new flexible direct current transmission technology to achieve offshore wind power transmission has become an inevitable choice. However, the existing centralized commutation method has problems such as large converter size, high platform construction difficulty, and high cost. If the operating frequency of the offshore wind farm system is increased to the medium frequency range, according to the principle of electromagnetic induction, increasing the operating frequency can reduce the size and weight of the transformer and reactor, and can also achieve the miniaturization and lightweight of the offshore converter station platform, thereby realizing low-cost direct current transmission of offshore wind power.

[0003] When the system frequency is increased to the intermediate frequency, if the system parameters at the power frequency are still used, the system will not be able to operate, so a new set of system parameters needs to be redesigned. In the process of designing the entire set of system parameters, the design of the filter parameters is crucial, which can control the signal frequency components in the circuit, that is, allowing signals in a specific frequency range to pass through while suppressing signals of other frequencies.

[0004] Since the calculation formulas for different filter parameters are different, the commonly used method for filter parameter design is to first calculate the value range of the filter parameters through the formula, and then manually optimize within this range. However, for some topological structures containing a large number of capacitor and inductor filters, manual adjustment takes a lot of time and it is difficult to accurately optimize the filter parameters.

[0005] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0006] The present invention provides a method, device, equipment and medium for multi-objective optimization of filter parameters of a medium frequency converter, thereby effectively solving the problems in the background technology.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is: a multi-objective optimization method for filter parameters of a medium frequency converter, comprising the following steps:

[0008] Determine the medium frequency grid-connected wind turbine model and calculate the range of filter inductance and capacitance;

[0009] A multi-objective optimization function is established with the average value and mean square error of the DC side voltage of the medium frequency converter and the power quality THD of the AC side current as optimization targets.

[0010] A multi-objective optimization model is constructed using the parameter combination of the filter inductance and capacitance as a population parameter and the multi-objective optimization function as a cost function;

[0011] The multi-objective optimization model is solved by using a particle swarm algorithm to obtain an optimal solution for the combination of inductance and capacitance parameters of the filter.

[0012] Furthermore, the determining of the medium frequency grid-connected wind turbine model includes:

[0013] The wind turbine is connected to the motor to form a wind turbine set, which is then connected to a set of converters and then connected to the grid after being connected to the filter;

[0014] The mathematical model of the wind turbine is:

[0015]

[0016] Where: S is the blade swept area, unit is m 2 ; ρ is the air density, unit is kg / m 3 ; V w is the wind speed in m / s; θ is the pitch angle of the blade; λ is the tip speed ratio; ω w is the wind turbine rotor speed, in rad / s; R is the wind turbine rotor radius, in m; C p is the power coefficient related to the pitch angle θ and the tip speed ratio λ; P m To capture the mechanical power converted from wind energy; T m is the input mechanical torque;

[0017] The motor is a permanent magnet synchronous motor. According to the rotor magnetic field orientation, the stator voltage equation of the permanent magnet synchronous generator in the synchronous rotating coordinate system is:

[0018]

[0019] Where: e is the electrical angular frequency; f is the flux linkage of the permanent magnet; L d , L q are the d-axis and q-axis inductances of the generator respectively; i sd .i sq are the d-axis and q-axis components of the stator output current of the permanent magnet synchronous generator respectively; u sd 、u sq are the d-axis and q-axis components of the stator output voltage of the permanent magnet synchronous generator respectively; T e is the mechanical torque of the permanent magnet synchronous generator; R a is the stator resistance; L is the stator inductance; n p is the number of motor pole pairs;

[0020] The converter is a back-to-back converter, and the mathematical model of the grid-side converter under the dq synchronous rotating axis is:

[0021]

[0022] Where: U pd , U pq are the d-axis and q-axis control voltage components on the medium frequency power supply side respectively; i pd 、i pq are the d-axis and q-axis current components on the medium frequency power supply side respectively; ω p is the synchronous electrical angular velocity of the medium frequency power supply; R p , L p are the resistance and inductance of the reactor respectively; u pd is the d-axis component of the grid voltage.

[0023] Furthermore, the grid-side converter adopts double closed-loop control, the outer loop is a voltage loop, and the inner loop is a current loop, and the output active power P p and reactive power Q p The expression is as follows:

[0024]

[0025] When the intermediate frequency power supply voltage integrated vector is oriented on the d-axis, the grid voltage is projected to 0 on the q-axis, i.e. u pq =0, the converter output power changes as follows:

[0026]

[0027] By adjusting the current components i on the d and q axes pd with i pq , control the active power and reactive power changes of the converter to achieve decoupling of active and reactive power;

[0028] The specific control strategy of the grid-side converter is:

[0029] Through the external loop voltage reference value U dc_ref With power reference value P dc_ref Respectively with the voltage value U dc , power value P dc The deviation is generated and the PI control is used to obtain the d and q control current I pd* ,I pq* , and then the corresponding control voltage U is obtained through the current inner loop PI control pd* , U pq* , and add the cross-coupling voltage compensation term ΔU pd and ΔU pq , we can get the final d and q axis control voltage components U pd and Upq , and then the driving signal required by the grid-side converter is obtained through PWM control technology to realize power transmission.

[0030] Furthermore, the establishing of the multi-objective optimization function comprises:

[0031] Construct the average voltage U of the DC side of the medium frequency converter dc-av The objective function of

[0032] Construct the mean square error U of the DC side voltage of the medium frequency converter dc-MSE The objective function of

[0033] Construct the objective function of power quality THD of AC side current of medium frequency converter;

[0034] The average value of the DC side voltage of the medium frequency converter is U dc-av , mean square error U dc-MSE Based on the objective function of the power quality THD of the AC side current, the overall objective function is constructed.

[0035] Furthermore, the average value U of the DC side voltage dc-av It is expressed as:

[0036]

[0037] Where U dcn is the DC side voltage value of n medium frequency converters;

[0038] The mean square error U of the DC side voltage dc-MSE It is expressed as:

[0039]

[0040] Where U dc-ref is the DC side voltage reference value of the medium frequency converter;

[0041] The AC side current power quality THD is:

[0042]

[0043] Where: H1 is the effective value of the fundamental wave; H2, H3, H4, ..., Hn are the effective values ​​of the corresponding harmonic components respectively;

[0044] The overall objective function is:

[0045] Z=λ1U dc-av +λ2U dc-MSE +λ3THD;

[0046] Where: λ1, λ2, λ3 are the weight coefficients of the corresponding objective function.

[0047] Furthermore, the adopting of particle swarm algorithm to solve the multi-objective optimization model comprises the following steps:

[0048] Initialize the number, position and speed of particle swarm;

[0049] Substituting the parameter combination of the filter inductance and capacitance into the multi-objective optimization model as a population parameter, solving the multi-objective optimization model to obtain the average value and mean square error of the DC side voltage of the medium frequency converter and the weighted objective function value Z of the power quality THD of the AC side current;

[0050] Compare the obtained objective function value with the historical optimal value of the objective function. If the obtained objective function value is better than the historical optimal value of the objective function, store the obtained objective function value in the optimal value of the objective function, store the obtained parameter combination in the optimal parameter, and then execute the next step; if the obtained objective function value is not better than the historical optimal value of the objective function, directly proceed to the next step;

[0051] Determine whether the maximum number of iterations has been reached. If so, output the current optimal parameters. If not, update the population position speed and repeat the process of substituting parameters and solving the objective function value Z.

[0052] Further, the updating population position speed includes:

[0053] The MOPSO algorithm is used to update the population position speed. The speed and position of the particles in the MOPSO algorithm are shown in the following formula:

[0054]

[0055] Where: ω is the inertia weight; c1 is the individual learning factor; c2 is the social learning factor; r1 and r2 are random values ​​generated between 0 and 1; V i (t) is the speed of the tth generation; X i (t) is the t-th generation position; P best is the individual optimal particle value; G best is the optimal particle value for all particles.

[0056] Furthermore, in the updating of the population position velocity by the MOPSO algorithm, the inertia weight ω is optimized by the linear weight reduction method, wherein the value of ω at the kth iteration is:

[0057]

[0058] Where: G is the maximum number of iterations; ω1 is the maximum weight value; ω2 is the minimum weight value.

[0059] The present invention also includes a device for multi-objective optimization of filter parameters of a medium frequency converter, using the above method, the device comprising:

[0060] Range unit, used to determine the medium frequency grid-connected wind turbine model and calculate the range of filter inductance and capacitance;

[0061] The target unit is used to establish a multi-objective optimization function with the average value and mean square error of the DC side voltage of the medium frequency converter and the power quality THD of the AC side current as optimization targets;

[0062] A modeling unit, used to build a multi-objective optimization model using a parameter combination of the filter inductance and capacitance as a population parameter and using the multi-objective optimization function as a cost function;

[0063] A solving unit is used to solve the multi-objective optimization model using a particle swarm algorithm to obtain an optimal solution for the combination of filter inductance and capacitance parameters.

[0064] The present invention also includes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described above is implemented.

[0065] The present invention also includes a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0066] The beneficial effects of the present invention are as follows: the present invention takes the average value, mean square error of the DC side voltage of the medium frequency converter and the power quality of the AC side current as optimization targets, and establishes a multi-objective optimization model; takes the parameter combination of capacitors and inductors in the filter circuit as the population parameter, takes the total objective function in the multi-objective optimization model as the fitness function, and adopts the particle swarm algorithm to solve the multi-objective optimization model, thereby obtaining the optimal solution of the capacitor and inductor parameter combination, which can make the filter parameters of the medium frequency converter more accurate, thereby obtaining a better filtering effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0068] Figure 1 is a flow chart of the method in Example 1;

[0069] Figure 2 It is a structural schematic diagram of the device in Example 1;

[0070] Figure 3 The wind turbine network side control strategy diagram in Example 2;

[0071] Figure 4 It is the algorithm flow chart in Example 2;

[0072] Figure 5 This is the voltage diagram of the DC side after stabilization before optimization in Example 2;

[0073] Figure 6 This is the current diagram after the AC side stabilizes before optimization in Example 2;

[0074] Figure 7 This is the voltage diagram of the DC side after stabilization after optimization in Example 2;

[0075] Figure 8 This is the current diagram after the optimized AC measurement stabilizes in Example 2;

[0076] Fig. 9 A schematic diagram of the structure of a computer device. DETAILED DESCRIPTION

[0077] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0078] Embodiment 1:

[0079] like Figure 1 As shown: A multi-objective optimization method for medium frequency converter filter parameters includes the following steps:

[0080] Determine the medium frequency grid-connected wind turbine model and calculate the range of filter inductance and capacitance;

[0081] A multi-objective optimization function is established with the average value and mean square error of the DC side voltage of the medium frequency converter and the power quality THD of the AC side current as optimization targets.

[0082] A multi-objective optimization model is constructed using the parameter combination of the filter inductor and capacitor as the population parameter and the multi-objective optimization function as the cost function;

[0083] The particle swarm algorithm is used to solve the multi-objective optimization model and obtain the optimal solution for the combination of filter inductance and capacitance parameters.

[0084] A multi-objective optimization model is established by taking the average value, mean square error of the DC side voltage of the medium frequency converter and the power quality of the AC side current as the optimization objectives; the parameter combination of capacitors and inductors in the filter circuit is used as the population parameter, and the total objective function in the multi-objective optimization model is used as the fitness function. The multi-objective optimization model is solved using a particle swarm algorithm to obtain the optimal solution of the capacitor and inductor parameter combination, which can make the filter parameters of the medium frequency converter more accurate, thereby obtaining a better filtering effect.

[0085] In this embodiment, determining the medium frequency grid-connected wind turbine model includes:

[0086] The wind turbine is connected to the motor to form a wind turbine set, which is then connected to a set of converters and then connected to the grid after being connected to the filter;

[0087] The mathematical model of wind turbine is:

[0088] Where: S is the blade swept area, unit is m 2 ; ρ is the air density, unit is kg / m 3 ; V w is the wind speed in m / s; θ is the pitch angle of the blade; λ is the tip speed ratio; ω w is the wind turbine rotor speed, in rad / s; R is the wind turbine rotor radius, in m; C p is the power coefficient related to the pitch angle θ and the tip speed ratio λ; P m To capture the mechanical power converted from wind energy; T m is the input mechanical torque;

[0089] The motor is a permanent magnet synchronous motor. According to the rotor magnetic field orientation, the stator voltage equation of the permanent magnet synchronous generator in the synchronous rotating coordinate system is:

[0090]

[0091] Where: e is the electrical angular frequency; f is the flux linkage of the permanent magnet; L d , L q are the d-axis and q-axis inductances of the generator respectively; i sd .i sq are the d-axis and q-axis components of the stator output current of the permanent magnet synchronous generator respectively; u sd 、u sq are the d-axis and q-axis components of the stator output voltage of the permanent magnet synchronous generator respectively; T e is the mechanical torque of the permanent magnet synchronous generator; R a is the stator resistance; L is the stator inductance; n p is the number of motor pole pairs;

[0092] The converter is a back-to-back converter, and the mathematical model of the grid-side converter under the dq synchronous rotating axis is:

[0093]

[0094] Where: U pd , U pq are the d-axis and q-axis control voltage components on the medium frequency power supply side respectively; i pd 、i pq are the d-axis and q-axis current components on the medium frequency power supply side respectively; ω p is the synchronous electrical angular velocity of the medium frequency power supply; R p , L p are the resistance and inductance of the reactor respectively; u pd is the d-axis component of the grid voltage.

[0095] The grid-side converter adopts double closed-loop control, with the outer loop being the voltage loop and the inner loop being the current loop. p and reactive power Q p The expression is as follows:

[0096]

[0097] When the intermediate frequency power supply voltage integrated vector is oriented on the d-axis, the grid voltage is projected to 0 on the q-axis, i.e. u pq =0, the converter output power changes as follows:

[0098]

[0099] By adjusting the current components i on the d and q axes pd with i pq , control the active power and reactive power changes of the converter to achieve decoupling of active and reactive power;

[0100] The specific control strategy of the grid-side converter is:

[0101] Through the external loop voltage reference value U dc_ref With power reference value P dc_ref Respectively with the voltage value U dc , power value P dc The deviation is generated and the PI control is used to obtain the d and q control current I pd* ,I pq* , and then the corresponding control voltage U is obtained through the current inner loop PI control pd* , U pq* , and add the cross-coupling voltage compensation term ΔU pd and ΔU pq , we can get the final d and q axis control voltage components U pd and U pq, and then the driving signal required by the grid-side converter is obtained through PWM control technology to realize power transmission.

[0102] Among them, establishing a multi-objective optimization function includes:

[0103] Construct the average voltage U of the DC side of the medium frequency converter dc-av The objective function of

[0104] Construct the mean square error U of the DC side voltage of the medium frequency converter dc-MSE The objective function of

[0105] Construct the objective function of power quality THD of AC side current of medium frequency converter;

[0106] The average value of the DC side voltage of the medium frequency converter is U dc-av , mean square error U dc-MSE Based on the objective function of the power quality THD of the AC side current, the overall objective function is constructed.

[0107] The average value of the DC voltage U dc-av It is expressed as:

[0108]

[0109] Where U dcn is the DC side voltage value of n medium frequency converters;

[0110] The mean square error of DC voltage U dc-MSE It is expressed as:

[0111]

[0112] Where U dc-ref is the DC side voltage reference value of the medium frequency converter;

[0113] The power quality THD of the AC side current is:

[0114]

[0115] Where: H1 is the effective value of the fundamental wave; H2, H3, H4, ..., Hn are the effective values ​​of the corresponding harmonic components respectively;

[0116] The overall objective function is:

[0117] Z=λ1U dc-av +λ2U dc-MSE +λ3THD;

[0118] Where: λ1, λ2, λ3 are the weight coefficients of the corresponding objective function.

[0119] As a preferred embodiment of the above, a particle swarm algorithm is used to solve the multi-objective optimization model, including the following steps:

[0120] Initialize the number, position and speed of particle swarm;

[0121] Substitute the parameter combination of the filter inductor and capacitor as the population parameter into the multi-objective optimization model, solve the multi-objective optimization model to obtain the average value and mean square error of the DC side voltage of the medium frequency converter, and the weighted objective function value Z of the power quality THD of the AC side current;

[0122] Compare the obtained objective function value with the historical optimal value of the objective function. If the obtained objective function value is better than the historical optimal value of the objective function, store the obtained objective function value in the optimal value of the objective function, store the obtained parameter combination in the optimal parameter, and then execute the next step; if the obtained objective function value is not better than the historical optimal value of the objective function, directly proceed to the next step;

[0123] Determine whether the maximum number of iterations has been reached. If so, output the current optimal parameters. If not, update the population position speed and repeat the process of substituting parameters and solving the objective function value Z.

[0124] Among them, the speed of updating population position includes:

[0125] The MOPSO algorithm is used to update the population position and velocity. The velocity and position of the particles in the MOPSO algorithm are shown as follows:

[0126]

[0127] Where: ω is the inertia weight; c1 is the individual learning factor; c2 is the social learning factor; r1 and r2 are random values ​​generated between 0 and 1; V i (t) is the speed of the tth generation; X i (t) is the t-th generation position; P best is the individual optimal particle value; G best is the optimal particle value for all particles.

[0128] When the MOPSO algorithm is used to update the population position and velocity, the linear weight reduction method is used to optimize the inertia weight ω, where the value of ω at the kth iteration is:

[0129]

[0130] Where: G is the maximum number of iterations; ω1 is the maximum weight value; ω2 is the minimum weight value.

[0131] like Figure 2As shown, this embodiment also includes a medium frequency converter filter parameter multi-objective optimization device, using the above method, the device includes:

[0132] Range unit, used to determine the medium frequency grid-connected wind turbine model and calculate the range of filter inductance and capacitance;

[0133] The target unit is used to establish a multi-objective optimization function with the average value and mean square error of the DC side voltage of the medium frequency converter and the power quality THD of the AC side current as optimization targets;

[0134] A modeling unit, used to build a multi-objective optimization model using a parameter combination of the filter inductor and capacitor as a population parameter and a multi-objective optimization function as a cost function;

[0135] The solving unit is used to solve the multi-objective optimization model by using a particle swarm algorithm to obtain an optimal solution for the combination of filter inductance and capacitance parameters.

[0136] Embodiment 2:

[0137] This embodiment includes a multi-objective optimization method for medium frequency converter filter parameters based on particle swarm algorithm, which uses the voltage and current on the DC and AC sides of the converter as a link and adjusts the capacitor and inductor parameters in the filter to achieve better filtering effect. The specific steps are as follows:

[0138] A. First determine the model of the medium-frequency grid-connected wind turbine, then calculate the range of the filter inductance L and capacitance C, and use the average value of the DC side voltage of the medium-frequency converter U dc-av , mean square error U dc-MSE The power quality THD of the AC side current is taken as the optimization target, and a multi-objective optimization function is established;

[0139] B. Use the parameter combination of capacitors and inductors in the filter circuit as the population parameter, and use the total objective function in the multi-objective optimization model as the fitness function to build a multi-objective optimization model. Use the particle swarm algorithm to solve the multi-objective optimization model to obtain the optimal solution for the combination of capacitor and inductor parameters.

[0140] The steps for solving the multi-objective optimization model in step A are:

[0141] A1. Determine the simulation model and calculate the range of the filter parameter LC;

[0142] A2. Construct the average voltage U of the DC side of the medium frequency converter dc-av The objective function of

[0143] A3. Construct the mean square error U of the DC side voltage of the medium frequency converter dc-MSE The objective function of

[0144] A4. Construct the objective function of power quality THD of the AC side current of the medium frequency converter;

[0145] A5. Take the average value of the DC side voltage of the medium frequency converter U dc-av , mean square error U dc-MSE Based on the objective function of the power quality THD of the AC side current, the overall objective function is constructed.

[0146] According to step A1, the model structure of the medium frequency grid-connected wind turbine model is that the wind turbine is connected to the motor to form a wind turbine, the wind turbine is connected to a set of converters, and then connected to the grid after the filter is connected;

[0147] A wind turbine uses a wind rotor to receive wind energy, convert it into mechanical energy and transmit it through the wind rotor shaft. When the pitch angle is constant, there is an optimal tip speed ratio under different wind speeds and wind turbine speeds to maximize the power factor. Its mathematical model is:

[0148] Where: S: blade swept area, unit is m 2 ;

[0149] Ρ: air density, unit is kg / m 3 ;

[0150] V w : wind speed, in m / s;

[0151] θ: pitch angle of the blade;

[0152] λ: tip speed ratio;

[0153] ω w : Wind turbine rotor speed, in rad / s;

[0154] R: wind turbine rotor radius, in m;

[0155] C p : Power coefficient related to pitch angle and tip speed ratio;

[0156] P m : Capture the mechanical power converted from wind energy;

[0157] T m : Input mechanical torque;

[0158] The motor adopts a permanent magnet synchronous motor. According to the rotor magnetic field orientation, the stator voltage equation of the permanent magnet synchronous generator in the synchronous rotating coordinate system is:

[0159]

[0160] Where:

[0161] ωe : electrical angular frequency;

[0162] Ψ f : magnetic flux linkage of permanent magnet;

[0163] L d , L q : d-axis and q-axis inductance of the generator;

[0164] i sd 、i sq : d-axis and q-axis components of the stator output current of the permanent magnet synchronous generator;

[0165] u sd 、u sq : d-axis and q-axis components of the stator output current and voltage of the permanent magnet synchronous generator;

[0166] T e : Mechanical torque of permanent magnet synchronous generator;

[0167] R a : stator resistance;

[0168] L: stator inductance;

[0169] n p : Number of motor pole pairs;

[0170] The converter adopts a back-to-back converter, which consists of a full-bridge rectifier, a full-bridge inverter and an intermediate DC link. The rectifier converts the input AC power into intermediate DC power, and the inverter converts the intermediate DC power into output AC power.

[0171] The converter adopts a back-to-back converter, and the mathematical model of the grid-side converter under the dq synchronous rotating axis is:

[0172]

[0173] Where:

[0174] U pd , U pq : d and q axis control voltage components on the medium frequency power supply side;

[0175] i pd 、i pq : d and q axis current components on the medium frequency power supply side;

[0176] ω p : Medium frequency power supply synchronous electrical angular velocity;

[0177] R p , L p : Reactor resistance and inductance;

[0178] u pd : d-axis component of grid voltage.

[0179] The grid-side converter control adopts double closed-loop control, which can realize the operation of the generator at unity power factor and the stability of the DC bus voltage. The voltage outer loop is a voltage loop, which can dynamically track the power changes of the medium-frequency power supply in real time, and indirectly determines the stability of the DC bus voltage. The inner and outer loops are current loops, which can realize the regulation function of the reactive power on the AC side and achieve the goal of the grid-side inverter working at unity power factor.

[0180] Active power P output by the grid-side converter p and reactive power Q p The expression is as follows:

[0181]

[0182] When the intermediate frequency power supply voltage integrated vector is oriented on the d-axis, the grid voltage is projected to 0 on the q-axis, i.e. u pq =0, the converter output power changes as follows:

[0183]

[0184] By adjusting the current components i on the d and q axes pd with i pq , the active power and reactive power changes of the converter can be controlled, thereby achieving the decoupling of active and reactive power.

[0185] The specific control strategies are:

[0186] Through the outer ring U dc_ref With P dc_ref Respectively with U dc , P dc The deviation is generated and the PI control is used to obtain the d and q control current I pd* ,I pq* , and then the corresponding control voltage U is obtained through the current inner loop PI control pd* , U pq* , and add the cross-coupling voltage compensation term ΔU pd and ΔU pq , the final d and q axis control voltage components U can be obtained pd and U pq , and then the driving signal required by the grid-side converter can be obtained through PWM control technology to realize power transmission.

[0187] The filter on the grid-connected side uses a three-phase LC filter, which connects an inductor and a capacitor in parallel in the circuit. The approximate parameters of the filter can be determined by calculation, and the given parameters can be adjusted according to needs to achieve the filtering purpose.

[0188] The grid-connected side uses a medium-frequency power supply, which is regarded as a medium-frequency power grid for grid connection. The medium-frequency grid connection is achieved by collecting the signal of the medium-frequency power supply and controlling the operation of the inverter.

[0189] The energy flow of the entire system is as follows: the wind turbine converts wind energy into electrical energy through a generator and outputs it in the form of AC power; the back-to-back converter rectifies and boosts the AC power output by the wind turbine into DC power, and then converts the DC power into AC power through an inverter; the generated AC power is filtered through a filter circuit and then connected to the grid.

[0190] According to step A2, the average value of the DC voltage U dc-av It can be expressed as:

[0191]

[0192] According to step A3, the mean square error of the DC side voltage U dc-MSE It can be expressed as:

[0193]

[0194] According to step A4, the power quality of the AC side current is:

[0195]

[0196] Where:

[0197] H1: effective value of fundamental wave;

[0198] H2, H3, H4,…, Hn: effective values ​​of corresponding harmonic components.

[0199] According to step A5, construct the overall objective function:

[0200] Z=λ1U dc-av +λ2U dc-MSE +λ3THD;

[0201] Where:

[0202] λ1, λ2, λ3: objective function weight coefficients.

[0203] The solution steps of the multi-objective optimization model in step B are:

[0204] B1. Construct a multi-objective optimization model based on the objective function;

[0205] B2, initialization of particle swarm quantity, position and velocity;

[0206] B3. Substitute the filter parameter combination into the model and solve the model to obtain U dc-av , U dc-MSE, THD weighted objective function value Z;

[0207] B4. Compare the obtained objective function value with the historical optimal value of the objective function. If the obtained objective function value is better than the historical optimal value of the objective function, store the obtained objective function value in the optimal value of the objective function, store the obtained parameter combination in the optimal parameter, and finally execute the next step; if the obtained objective function value is not better than the historical optimal value of the objective function, proceed directly to the next step.

[0208] B5. Determine whether the maximum number of iterations has been reached. If so, output the current optimal parameters. If not, update the population position speed and return to step B3.

[0209] According to step B5, the population position speed is updated using the MOPSO model. The speed and position of the particles in the MOPSO algorithm are shown as follows:

[0210]

[0211] Where:

[0212] ω: inertia weight;

[0213] c1: individual learning factor;

[0214] c2: social learning factor;

[0215] r1, r2: random values ​​generated between 0 and 1;

[0216] V i (t): speed of the tth generation;

[0217] X i (t): position of the tth generation;

[0218] P best : Individual optimal particle value;

[0219] G best : The optimal particle value of the whole;

[0220] In order to prevent the MOPSO algorithm from falling into the local optimum, a linear weight reduction method is used to optimize the inertia weight ω. By improving the setting of the inertia weight ω, the convergence speed of the MOPSO algorithm is improved. The value of ω at the kth iteration is:

[0221]

[0222] Where:

[0223] G: maximum number of iterations;

[0224] ω1: maximum weight value;

[0225] ω2: minimum weight value.

[0226] The contents involved in the above embodiment are described below in conjunction with a preferred embodiment.

[0227] A medium-frequency wind turbine grid-connected model is built in Simulink. The wind turbine uses a permanent magnet synchronous generator, the converter uses a back-to-back converter, the filter uses an LC filter, and the grid-connected power supply is a medium-frequency power supply. The system parameters are shown in Table 1.

[0228] Table 1 System parameters

[0229]

[0230] The model converter adopts a back-to-back converter, and the mathematical model of its grid-side converter under the dq synchronous rotating axis is:

[0231]

[0232] Where:

[0233] U pd , U pq : d and q axis control voltage components on the medium frequency power supply side;

[0234] i pd 、i pq : d-axis and q-axis current components on the medium frequency power supply side;

[0235] ω p : Medium frequency power supply synchronous electrical angular velocity;

[0236] R p , L p : Reactor resistance and inductance;

[0237] u pd : is the d-axis component of the grid voltage.

[0238] The grid-side converter control adopts double closed-loop control, which can realize the operation of the generator at unity power factor and the stability of the DC bus voltage. The voltage outer loop is a voltage loop, which can dynamically track the power changes of the medium-frequency power supply in real time, and indirectly determines the stability of the DC bus voltage. The inner and outer loops are current loops, which can realize the regulation function of the reactive power on the AC side and achieve the goal of the grid-side inverter working at unity power factor.

[0239] Active power P output by the grid-side converter p and reactive power Q p The expression is as follows:

[0240]

[0241] When the intermediate frequency power supply voltage integrated vector is oriented on the d-axis, the grid voltage is projected to 0 on the q-axis, i.e. u pq =0, the converter output power changes as follows:

[0242]

[0243] By adjusting the current components i on the d and q axes pd with i pq , the active power and reactive power changes of the converter can be controlled, thereby achieving the decoupling of active and reactive power.

[0244] The specific control strategies are:

[0245] Its control strategy is as follows Figure 3 As shown, through the outer ring U dc_ref With P dc_ref Respectively with U dc , P dc The deviation is generated and the PI control is used to obtain the d and q control current I pd* ,I pq* , and then the corresponding control voltage U is obtained through the current inner loop PI control pd* , U pq* , and add the cross-coupling voltage compensation term ΔU pd and ΔU pq , the final d and q axis control voltage components U can be obtained pd and U pq , and then the driving signal required by the grid-side converter can be obtained through PWM control technology to realize power transmission.

[0246] The model uses an LC filter. First, the range of capacitance and inductance is calculated. The capacitance range is 2.32e-4F≤C≤8.17e-4F, and the inductance range is 1.11e-3H≤L≤1.53e-3H.

[0247] Set the initial value of the capacitor to 5e-4F and the initial value of the inductor to 1.3e-3H; set the algorithm model, take λ1=0.1, λ1=0.2, λ1=0.7; c1=0.5, c2=0.5; the number of particles n=10; the number of iterations k=100. Initialize the particle position and velocity.

[0248] Run the model. The model works as follows Figure 4 The process shown is run, and the optimal capacitance is 0.000297F and the optimal inductance is 0.001416H.

[0249] Example analysis:

[0250] Substituting the set initial capacitor value of 5e-4F and the inductor value of 1.3e-3H into the grid-connected model of the medium-frequency wind turbine, the DC side voltage before optimization and after stabilization can be obtained as follows: Figure 5 As shown, the AC side current before optimization and after stabilization is as follows Figure 7 shown.

[0251] Substituting the optimized capacitor and inductor into the grid-connected model of the medium-frequency wind turbine, the DC side voltage after optimization and stability can be obtained as follows: Figure 6 As shown, after optimization and stabilization, the AC side current is as follows Figure 8 shown.

[0252] The data before and after optimization are shown in Table 2:

[0253] C(F) L(H) Objective function value Z AC side current THD Before optimization 5e-4 1.3e-3 5.894565 4.16％ After optimization 2.97e-4 1.416e-3 4.870302 2.89％

[0254] By comparison Figure 5 and Figure 7 It can be clearly seen that the optimized DC side voltage waveform is closer to the set value of 1800V; by comparing Figure 6 and Figure 8 Combined with the AC side current THD in Table 2, it can be found that the current quality after optimization is better than the power quality before optimization, which is improved by 1.27%.

[0255] See also Fig. 9 A computer device 400 provided in an embodiment of the present application includes: a processor 410 and a memory 420, wherein the memory 420 stores a computer program executable by the processor 410, and when the computer program is executed by the processor 410, the above method is executed.

[0256] The embodiment of the present application further provides a storage medium 430 on which a computer program is stored. When the computer program is run by the processor 410, the above method is executed.

[0257] Among them, the storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0258] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. "Multiple" means two or more, unless otherwise clearly and specifically defined.

[0259] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0260] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. 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 representations of the above terms do 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. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0261] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0262] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0263] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0264] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0265] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A multi-objective optimization method for medium frequency converter filter parameters, characterized in that: The steps include: Determine the medium frequency grid-connected wind turbine model and calculate the range of filter inductance and capacitance; A multi-objective optimization function is established with the average value and mean square error of the DC side voltage of the medium frequency converter and the power quality THD of the AC side current as optimization targets. A multi-objective optimization model is constructed using the parameter combination of the filter inductance and capacitance as a population parameter and the multi-objective optimization function as a cost function; The multi-objective optimization model is solved by using a particle swarm algorithm to obtain an optimal solution for the combination of inductance and capacitance parameters of the filter.

2. The multi-objective optimization method for medium frequency converter filter parameters according to claim 1, characterized in that: The step of determining the medium frequency grid-connected wind turbine model comprises: The wind turbine is connected to the motor to form a wind turbine set, which is then connected to a set of converters and then connected to the grid after being connected to the filter; The mathematical model of the wind turbine is: Where: S is the blade swept area, unit is m 2 ; ρ is the air density, unit is kg / m 3 ; V w is the wind speed in m / s; θ is the pitch angle of the blade; λ is the tip speed ratio; ω w is the wind turbine rotor speed, in rad / s; R is the wind turbine rotor radius, in m; C p is the power coefficient related to the ratio of pitch angle θ and blade tip speed λ; P m To capture the mechanical power converted from wind energy; T m is the input mechanical torque; The motor is a permanent magnet synchronous motor. According to the rotor magnetic field orientation, the stator voltage equation of the permanent magnet synchronous generator in the synchronous rotating coordinate system is: Where: e is the electrical angular frequency; f is the flux linkage of the permanent magnet; L d , L q are the d-axis and q-axis inductances of the generator respectively; i sd .i sq are the d-axis and q-axis components of the stator output current of the permanent magnet synchronous generator respectively; u sd 、u sq are the d-axis and q-axis components of the stator output voltage of the permanent magnet synchronous generator respectively; T e is the mechanical torque of the permanent magnet synchronous generator; R a is the stator resistance; L is the stator inductance; n p is the number of motor pole pairs; The converter is a back-to-back converter, and the mathematical model of the grid-side converter under the dq synchronous rotating axis is: Where: U pd , U pq are the d-axis and q-axis control voltage components on the medium frequency power supply side respectively; i pd 、i pq are the d-axis and q-axis current components on the medium frequency power supply side respectively; ω p is the synchronous electrical angular velocity of the medium frequency power supply; R p , L p are the resistance and inductance of the reactor respectively; u pd is the d-axis component of the grid voltage.

3. The multi-objective optimization method for medium frequency converter filter parameters according to claim 2 is characterized in that: The grid-side converter adopts double closed-loop control, the outer loop is the voltage loop, the inner loop is the current loop, and the output active power P p and reactive power Q p The expression is as follows: When the intermediate frequency power supply voltage integrated vector is oriented on the d-axis, the grid voltage is projected to 0 on the q-axis, i.e. u pq =0, the converter output power changes as follows: By adjusting the current components i on the d and q axes pd with i pq , control the active power and reactive power changes of the converter to achieve decoupling of active and reactive power; The specific control strategy of the grid-side converter is: Through the external loop voltage reference value U dc_ref With power reference value P dc_ref Respectively with the voltage value U dc , power value P dc The deviation is generated and the PI control is used to obtain the d and q control current I pd* ,I pq* , and then the corresponding control voltage U is obtained through the current inner loop PI control pd* , U pq* , and add the cross-coupling voltage compensation term ΔU pd and ΔU pq , we can get the final d and q axis control voltage components U pd and U pq , and then the driving signal required by the grid-side converter is obtained through PWM control technology to realize power transmission.

4. The multi-objective optimization method for filter parameters of a medium frequency converter according to claim 1, characterized in that: The establishment of a multi-objective optimization function comprises: Construct the average voltage U of the DC side of the medium frequency converter dc-av The objective function of Construct the mean square error U of the DC side voltage of the medium frequency converter dc-MSE The objective function of Construct the objective function of power quality THD of the AC side current of the medium frequency converter; The average value of the DC side voltage of the medium frequency converter is U dc-av , mean square error U dc-MSE Based on the objective function of the power quality THD of the AC side current, the overall objective function is constructed.

5. The multi-objective optimization method for medium frequency converter filter parameters according to claim 4 is characterized in that: The average value of the DC voltage U dc-av It is expressed as: Where U dcn is the DC side voltage value of n medium frequency converters; The mean square error U of the DC side voltage dc-MSE It is expressed as: Where U dc-ref is the DC side voltage reference value of the medium frequency converter; The AC side current power quality THD is: Where: H1 is the effective value of the fundamental wave; H2, H3, H4, ..., Hn are the effective values ​​of the corresponding harmonic components respectively; The overall objective function Z is: Z=λ1U dc-av +λ2U dc-MSE +λ3THD; Where: λ1, λ2, λ3 are the weight coefficients of the corresponding objective function.

6. The multi-objective optimization method for medium frequency converter filter parameters according to claim 1, characterized in that: The method of using a particle swarm algorithm to solve the multi-objective optimization model comprises the following steps: Initialize the number, position and speed of particle swarm; Substituting the parameter combination of the filter inductance and capacitance into the multi-objective optimization model as a population parameter, solving the multi-objective optimization model to obtain the average value and mean square error of the DC side voltage of the medium frequency converter and the weighted objective function value Z of the power quality THD of the AC side current; Compare the obtained objective function value with the historical optimal value of the objective function. If the obtained objective function value is better than the historical optimal value of the objective function, store the obtained objective function value in the optimal value of the objective function, store the obtained parameter combination in the optimal parameter, and then execute the next step; if the obtained objective function value is not better than the historical optimal value of the objective function, directly proceed to the next step; Determine whether the maximum number of iterations has been reached. If so, output the current optimal parameters. If not, update the population position speed and repeat the process of substituting parameters and solving the objective function value Z.

7. The multi-objective optimization method for medium frequency converter filter parameters according to claim 6, characterized in that: The updating population position speed includes: The MOPSO algorithm is used to update the population position speed. The speed and position of the particles in the MOPSO algorithm are shown in the following formula: Where: ω is the inertia weight; c1 is the individual learning factor; c2 is the social learning factor; r1 and r2 are random values ​​generated between 0 and 1; V i (t) is the speed of the tth generation; X i (t) is the t-th generation position; P best is the individual optimal particle value; G best is the optimal particle value for all particles.

8. The multi-objective optimization method for medium frequency converter filter parameters according to claim 7, characterized in that: In the MOPSO algorithm for updating the population position and velocity, the linear weight reduction method is used to optimize the inertia weight ω, where the value of ω at the kth iteration is ω (k) for: Where: G is the maximum number of iterations; ω1 is the maximum weight value; ω2 is the minimum weight value.

9. A multi-objective optimization device for medium frequency converter filter parameters, characterized in that: Using the method according to any one of claims 1 to 8, the device comprises: Range unit, used to determine the medium frequency grid-connected wind turbine model and calculate the range of filter inductance and capacitance; The target unit is used to establish a multi-objective optimization function with the average value and mean square error of the DC side voltage of the medium frequency converter and the power quality THD of the AC side current as optimization targets; A modeling unit, used to build a multi-objective optimization model using a parameter combination of the filter inductance and capacitance as a population parameter and using the multi-objective optimization function as a cost function; A solving unit is used to solve the multi-objective optimization model using a particle swarm algorithm to obtain an optimal solution for the combination of filter inductance and capacitance parameters.

10. A computer 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 method according to any one of claims 1 to 8 is implemented.

11. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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