CN112182943A - Parameter optimization method for improving short circuit between poles of DC/DC direct-current transformer - Google Patents

Parameter optimization method for improving short circuit between poles of DC/DC direct-current transformer Download PDF

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CN112182943A
CN112182943A CN202010874608.2A CN202010874608A CN112182943A CN 112182943 A CN112182943 A CN 112182943A CN 202010874608 A CN202010874608 A CN 202010874608A CN 112182943 A CN112182943 A CN 112182943A
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史林军
史江峰
朱昊卿
吴峰
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Abstract

The invention discloses a parameter optimization method for improving interelectrode short circuit of a DC/DC direct current transformer, which comprises the following steps: analyzing a pre-constructed mathematical model of the combined DC/DC direct-current transformer, and determining an expression of short-circuit current between poles of the DC/DC direct-current transformer; determining an optimization objective function with minimum inter-electrode short-circuit current as a target according to the expression of the inter-electrode short-circuit current of the DC/DC direct-current transformer, and calculating a parameter variable constraint condition of the optimization objective function; and calculating the optimal value of the parameter variable by using an improved particle swarm optimization. The advantages are that: the factors of current and voltage electric energy quality are considered, the current size of the DC/DC direct-current transformer during short circuit between poles is effectively reduced by combining the topological structure of the DC/DC direct-current transformer and key parameters, and the damage of the DC/DC direct-current transformer caused by overlarge short-circuit current during short circuit is prevented; and the complementation of the global search capability and the local search capability is realized by adopting an improved particle swarm algorithm.

Description

Parameter optimization method for improving short circuit between poles of DC/DC direct-current transformer
Technical Field
The invention relates to a parameter optimization method for short circuit of a DC/DC (direct current/direct current) transformer, in particular to a parameter optimization method for short circuit between poles of the DC/DC transformer.
Background
At present, due to the development of new energy represented by distributed photovoltaic and the appearance of a large number of direct current loads, a direct current power distribution network becomes the mainstream of urban power distribution network construction in the future, and a DC/DC direct current transformer is a key device in the direct current power distribution network. The interelectrode short circuit is used as the most serious fault in the direct-current power distribution network, has the characteristics of large short-circuit current, high current rising speed and the like, and is easy to damage power electronic devices such as a DC/DC direct-current transformer and the like, so a parameter optimization method of the interelectrode short circuit is introduced, and aims to limit the size of the short-circuit current during short circuit.
Disclosure of Invention
The technical problem to be solved by the invention is to overcome the defects of the prior art and provide a parameter optimization method for improving the short circuit between poles of a DC/DC direct-current transformer.
In order to solve the above technical problem, the present invention provides a parameter optimization method for improving an inter-electrode short circuit of a DC/DC direct current transformer, comprising:
analyzing a pre-constructed mathematical model of the combined DC/DC direct-current transformer, and determining an expression of short-circuit current between poles of the DC/DC direct-current transformer;
determining an optimization objective function with minimum inter-electrode short-circuit current as a target according to the expression of the inter-electrode short-circuit current of the DC/DC direct-current transformer, and calculating a parameter variable constraint condition of the optimization objective function;
and calculating the optimal value of the parameter variable by using an improved particle swarm optimization.
Further, the mathematical model of the combined DC/DC direct current transformer is:
the topological structure of the combined DC/DC direct-current transformer is formed by combining N DC/DC direct-current transformers according to an IPOS structure.
Further, the expression of the short-circuit current between the poles of the DC/DC direct-current transformer is:
Figure BDA0002652244760000011
in the formula: i isdcmaxThe maximum value of the short-circuit current is shown, N is the number of the DC/DC direct-current transformers of the submodules of the combined DC/DC direct-current transformer, CfRepresents the equivalent capacitance L of the secondary side output side of the combined DC/DC direct current transformerfThe equivalent inductance of the secondary side output side of the combined DC/DC direct current transformer is shown.
Further, the optimization objective function is:
J＝minf(x)＝Idcmax
wherein, min represents taking the minimum value,
Figure BDA0002652244760000021
further, the parameter variable N, Cf、LfThe constraint conditions of (a) are respectively:
Figure BDA0002652244760000022
in the formula, LfmaxIs the maximum value of the equivalent inductance on the output side, LfminIs the minimum value of the equivalent inductance of the output side, CfmaxIs the maximum value of the output-side equivalent capacitance, CfminIs the minimum value of the equivalent capacitance at the output side.
Further, the process of calculating the optimal value of the parameter variable by using the improved particle swarm optimization comprises the following steps:
setting maximum iteration times, population scale and particle speed interval;
the fitness function is set to be F,
F＝J
solving an individual extreme value and a global optimal solution according to an improved particle swarm algorithm;
the particle swarm updates the position and the speed of the particle swarm by the following two formulas:
Figure BDA0002652244760000023
in the above formula: omega is the inertial weight; d1, 2, ·, D; 1,2, n; the superscript k is the current iteration number, VidIs the velocity of the particle, PidFor local optimum, XidBeing the position of the particle itself, PgdFor a global optimum, XgdThe position of the particle is corresponding to the global optimal value; c. C1,c2Is a non-negative constant, called acceleration factor; r is1,r2Is distributed in [0,1 ]]A random number in between;
the inertia weight factor of the improved particle swarm algorithm adopts linear degressive, and the learning factor adopts asynchronous method to update the speed and position:
Figure BDA0002652244760000031
Figure BDA0002652244760000032
where ω is the inertial weight, T is the current iteration number, T is the maximum iteration number, c1,start＞c1,end，c2,start＜c2,end，ωmaxRepresenting the maximum value of the inertial weight factor, ωminRepresenting the minimum value of the inertial weight factor, ωmaxRepresenting the maximum value of the inertial weight factor, c1,start，c1,end，c2,start，c2,endRepresenting the initial and terminal values of the two acceleration factors.
Therefore, the improved particle swarm algorithm adopts the following formula to update the position and the speed of the improved particle swarm algorithm:
Figure BDA0002652244760000033
judging the capacitance C according to the iteration total fitness function FfInductor LfAnd whether the number N of the sub-modules meets the requirement of the objective function or reaches the maximum iteration number, and if the number N of the sub-modules meets the requirement or reaches the maximum iteration number, outputtingThe value at this time is taken as the optimum value.
The invention achieves the following beneficial effects:
the factors of current and voltage electric energy quality are considered, the current size of the DC/DC direct-current transformer during short circuit between poles is effectively reduced by combining the topological structure of the DC/DC direct-current transformer and key parameters, and the damage of the DC/DC direct-current transformer caused by overlarge short-circuit current during short circuit is prevented; the global search capability and the local search capability of the algorithm can be complemented by adopting the improved particle swarm algorithm, the initial learning factor local search capability is strong, and the situation that the optimal solution is missed due to the fact that the global search capability is too strong in the initial iteration stage because of the large inertia weight is prevented; in the later iteration stage, the learning factor has strong global search capability, so that the situation that the local search capability is strong and the local optimal solution is trapped due to small inertia weight can be avoided, and the iteration times for obtaining the optimal solution are reduced.
Drawings
FIG. 1 is a topology structure diagram of a grid-connected side of a DC/DC direct current transformer;
FIG. 2 is a simplified DC/DC transformer output side circuit diagram;
FIG. 3 is a flow chart of an improved PSO algorithm;
FIG. 4 is a graph of two algorithms solving for an optimum comparison;
FIG. 5 is a graph of DC distribution network side voltage waveforms under different conditions;
FIG. 6 is a graph of output current waveforms before and after optimization;
FIG. 7 is a graph of current FFT analysis under different parameters;
FIG. 8 is a DC/DC transformer short circuit current;
FIG. 9 is a table of pre-optimization parameters for a DC/DC converter.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
Example 1: the parameter optimization method for the short circuit of the DC/DC direct current transformer comprises the following steps:
s1: because the main research objects of the invention are the capacitance and inductance parameters of the output side of the DC/DC direct current transformer, the primary circuit of the DC/DC direct current transformer can be ignored. A combined DC/DC direct-current transformer formed by combining N DC/DC direct-current transformers according to an IPOS structure is shown in fig. 1, where fig. 1 shows the combined DC/DC direct-current transformer omitting the primary side, and a secondary output side series structure is adopted. The individual DC/DC converter parameters are shown in fig. 9. The proposed mathematical model is simplified according to the series-parallel law, and a simplified circuit diagram is shown in fig. 2. Analyzing the short-circuit condition between poles of the DC/DC transformer to obtain an expression of short-circuit current:
Figure BDA0002652244760000041
in the formula: i isdcmaxRepresenting the maximum value of the short-circuit current, N representing the number of submodules, CfDenotes the output side equivalent capacitance, LfRepresenting the output side equivalent inductance.
S2: taking the minimum interelectrode short-circuit current as an optimization objective function J, the following are provided:
J＝minf(x)＝Idcmax
s3 finding parameter variables N, Cf、LfThe constraint of (2).
1) For the number N of the DC/DC direct-current transformer submodules, the minimum value cannot be lower than 1, and when N is larger than 25, the transmission loss of the DC/DC direct-current transformer is large, the economy is reduced, and the control is complex, so that the maximum value of N is 25, and N is an integer. Specifically, it can be expressed as:
1≤N≤25
2) in practical engineering, the value of the designed inductor is set according to the current pulsating quantity, and if the inductor current is to be kept continuous, the maximum pulsating quantity of the current cannot be higher than 20% of the current on the output side, namely the output current is 10% of the rated current. Considering that the direct current transformer adopts an IPOS structure with a staggered control strategy, the current fluctuation and the harmonic content are effectively reduced, so that the allowable fluctuation amplitude of the current is set to be smaller, the fluctuation amplitude of the direct current side current is not more than 1%, and the inductance value meets the following inequality constraint:
Figure BDA0002652244760000051
in the above formula: k is the transformation ratio of a single DC/DC direct-current transformer; viRepresenting the primary side input voltage of the DC/DC direct current transformer; voRepresenting the direct current distribution network side voltage; vin_maxThe maximum voltage of the input side of the DC/DC direct-current transformer allows the photovoltaic output voltage to exceed 1.05 times of the specified voltage in engineering, namely Vin_max＝1.05Vi；fLfRepresenting the inductive frequency, which is twice the switching frequency.
When N is 1, the maximum value of the inductance when the constraint condition is just met is 0.019H. Meanwhile, the maximum value L of the inductance is considered to be a 20% margin generally left in engineeringfmaxIt was 0.0229H. When the maximum value of the equivalent inductance of the cascade DC/DC direct current transformer containing N sub-modules cannot be higher than Lfmax：
N·Lf≤Lfmax
When N is 25, the inductance L is obtainedfMinimum 0.76 mH. The direct-current power distribution network contains a large number of power electronic devices, and the inductance L is obtained by considering the influence of current harmonicsfMinimum value of Lfmin0.8 mH. I.e. the inductance also satisfies the constraint:
Lfmin≤Lf≤Lfmax
3) in engineering design, the amplitude of the output side DC voltage fluctuation is generally required not to exceed 1% of the total output voltage amplitude in a steady state, namely Vo_PP≤0.01V0. Meanwhile, the voltage fluctuation of the grid-connected side is effectively reduced by considering that the direct-current transformer adopts an IPOS structure with a staggered control strategy, so that the allowable voltage fluctuation amplitude is set to be smaller, and the voltage fluctuation amplitude of the direct-current side is not more than 0.1%. Capacitor CfThe design satisfies the following formula:
Figure BDA0002652244760000052
in the above formula: f. ofcfRepresenting the capacitance frequency, which is twice the switching frequency.
The minimum value of the capacitance cannot be lower than the equivalent capacitance value of the DC/DC direct current transformer, in the invention, the number of the sub-modules of the DC/DC direct current transformer is 4, the capacitance of each sub-module is 10uF, and the voltage ripple of the direct current distribution network side meets the requirement of 1%, so that C exists at the momentfmin. When the minimum value of the equivalent capacitance of the cascade type DC/DC direct current transformer containing N sub-modules cannot be lower than CfminThat is, the following equation is satisfied:
Figure BDA0002652244760000061
when inductance LfWhen taking the minimum value, the capacitance CfThe value of the capacitance when the constraint condition is just met is obtained to be 0.000783F, and 20% margin is left in consideration of the capacitance value, so that the maximum value C of the capacitancefmaxIt was 0.00094F. Namely, the capacitance satisfies:
Cfmin≤Cf≤Cfmax
and S4, solving the optimal solution of the parameters of the output side of the DC/DC direct current transformer by adopting an improved particle swarm optimization on MATLAB according to the flow chart shown in the figure 3.
1) Initialization: and setting the maximum iteration times, the population scale and the particle speed interval.
2) Calculating the fitness: herein, the minimum value of the objective function is found within the parameter optimization range. When the smaller the objective function value is, the closer the corresponding particle search result is to the optimal solution, the fitness function is set to be F, which can be specifically expressed as:
F＝J
3) and solving the individual extreme value and the global optimal solution.
4) The inertia weight factor adopts linear degression and the learning factor adopts asynchronous method to update:
the traditional particle swarm algorithm is used for solving the optimal value by simulating bird predation behaviors, and the particle swarm updates the position and the speed of the particle swarm by the following two formulas:
Figure BDA0002652244760000062
in the above formula: omega is the inertial weight; d1, 2, ·, D; 1,2, n; k is the current iteration number; vidIs the velocity of the particle; c. C1,c2Is a non-negative constant, called acceleration factor; r is1,r2Is distributed in [0,1 ]]A random number in between.
Wherein the learning factors and weights of the particles determine the number of iterations of the algorithm and the result of the optimal solution. Learning factor c1Determines the local search capability, c2The global searching capability is determined, the larger the inertial weight is, the stronger the global searching capability is, the smaller the inertial weight is, the stronger the local searching capability is. However, if the global search capability is too strong, the optimal solution is easy to jump out; if the local search capability is too strong, the method is easy to fall into a local optimal solution, so the method adopts the variable inertia weight and the learning factor asynchronous method to solve the optimal value.
The inertia weight factor of the improved particle swarm algorithm is updated by adopting a linear degressive method and the learning factor is updated by adopting an asynchronous method
Body speed and position:
Figure BDA0002652244760000071
Figure BDA0002652244760000072
where T is the current iteration number, T is the maximum iteration number, c1,start＞c1,end，c2,start＜c2,end，ωmaxRepresenting the maximum value of the inertial weight factor, ωminWhat represents the minimum value of the inertial weight factor, c1,start，c1,end，c2,start，c2,endRepresenting the initial and terminal values of the two acceleration factors.
5) Update speed and position formula:
Figure BDA0002652244760000073
6) termination conditions were as follows: and judging whether the number of the capacitors, the inductors and the submodules meets the requirement of the target function or not, or whether the maximum iteration number is reached. And if the requirement is met or the maximum iteration number is reached, terminating the program and outputting an optimal value. The decision condition chosen here is whether or not the maximum number of iterations is reached.
Example 2: a parameter optimization method based on improved particle swarm DC/DC direct current transformer short circuit is characterized in that the results obtained by solving the optimal value through the method and solving the optimal value through a traditional particle swarm algorithm are compared, and the results are shown in figure 4. It can be seen that before the improvement: when N is 25, LfTaking 0.91mH, CfWhen 66.3uF is taken, the optimal solution is 0.216; after the improvement: when N is 8, LfTaking 2.86mH, CfWhen 20.8uF is taken, the optimal solution is 0.213. As can be seen from the above figure, the value of the learning factor c1 of the improved PSO algorithm at the initial iteration is much greater than c2, and the local search capability of the improved particle swarm optimization is greater than that of the conventional PSO, so the initial value of the improved PSO fitness at the initial iteration is smaller than that of the conventional PSO, that is, the improved PSO algorithm is closer to the optimal solution at the initial iteration, and the degradation rate of the improved PSO algorithm is lower than that of the conventional PSO because the improved PSO algorithm has strong local search capability at the initial iteration. With the increase of the iteration times, the global search capability of the improved PSO algorithm is greater than the local search capability, so that the optimal solution can be obtained, and the optimal solution obtained by the traditional PSO algorithm is higher than the optimal solution obtained by the improved PSO algorithm, namely the traditional PSO algorithm obtains a local optimum. The traditional PSO algorithm obtains local optimal solution through 180 iterations, and the improved PSO algorithm obtains the optimal solution 65 times.
And substituting the optimal value obtained by the method into a PSCAD program for simulation verification, and comparing the obtained result with the result before optimization.
1) As shown in fig. 5, the maximum amplitude of the voltage fluctuation after optimization is 0.03%, and the fluctuation period is 0.0033 seconds; the maximum amplitude of the voltage fluctuation before optimization is 0.2%, and the voltage fluctuation period is 0.00026 second. Therefore, the voltage fluctuation amplitude is reduced after optimization, the requirement that the voltage fluctuation is less than 0.1% in engineering is met, in the same time, the voltage fluctuation times are reduced after optimization, the direct current distribution network side is more stable, and the voltage and electric energy quality of the distribution network is effectively improved.
2) As shown in fig. 6 and 7, the current fluctuation amplitude after parameter optimization is 0.82%, and the current ripple coefficient before parameter optimization is 7.41%. After optimization, the current fluctuation is extremely small, and the requirement of less than 1 percent I in engineering is metoThe requirements of (a). And the switching frequency after parameter optimization is reduced to zero by harmonic, the frequency doubling subharmonic of the switch 2 is greatly reduced, the harmonic mainly comprises an integer subharmonic taking power frequency as fundamental wave, and the total harmonic distortion rate is far smaller than that before parameter optimization. Therefore, the current and power quality is also obviously improved after the parameters are optimized.
3) As shown in fig. 8, when an inter-electrode short circuit occurs in the DC distribution network, the maximum short-circuit current value is consistent with the theoretical value, thereby effectively reducing the damage of the power electronic devices of the DC/DC transformer caused by an excessive fault current during the short circuit. And the duration time of the second-stage inductance freewheeling stage in the short-circuit process after the parameter optimization is obviously longer than that before the parameter optimization, so that the requirement on the working performance of the direct-current circuit breaker is reduced. The DC/DC direct-current transformer with optimized parameters not only improves the safe and stable operation capacity of the direct-current power distribution network, but also ensures that the DC/DC direct-current breaker equipment is not damaged by a short-circuit, and simultaneously reduces the difficulty of the direct-current breaker in relay protection of the direct-current power distribution network.
C is obtained according to the optimized capacitance, inductance and the number parameter value of the sub-modulesf20.8uF、Lf2.86mH and N are 4, the current and voltage electric energy quality factors are considered at the same time, the optimized short-circuit current is calculated to be 0.213kA, and the fact that the current size during short circuit between poles of the DC/DC direct current transformer is effectively reduced by combining the self topological structure and key parameters of the DC/DC direct current transformer can be seen, so that the damage caused by overlarge short-circuit current during short circuit of the DC/DC direct current transformer can be prevented; the global search capability and the local search capability of the algorithm can be complemented by adopting the improved particle swarm algorithm, as shown in fig. 4, the local search capability of the initial learning factor is strong, and the initial iteration stage is prevented from being caused by inertiaThe weight is large, so that the global search capability is too strong and the optimal solution is missed; in the later iteration stage, the learning factor has strong global search capability, so that the situation that the local search capability is strong and the optimal solution falls into due to small inertia weight can be avoided, the iteration times for obtaining the optimal solution are reduced, the local optimal solution is obtained by the traditional PSO algorithm through 180 iterations, and the optimal solution is obtained by the improved PSO algorithm in 65 iterations.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.

Claims (6)

1.一种改善DC/DC直流变压器极间短路的参数优化方法，其特征在于，包括：1. a parameter optimization method for improving the short-circuit between poles of DC/DC DC transformer, is characterized in that, comprises: 对预先构建的组合型DC/DC直流变压器数学模型进行分析，确定DC/DC直流变压器极间短路电流的表达式；Analyze the mathematical model of the pre-built combined DC/DC transformer to determine the expression of the short-circuit current between the poles of the DC/DC transformer; 根据所述DC/DC直流变压器极间短路电流的表达式确定以极间短路电流最小为目标的优化目标函数，计算优化目标函数的参数变量约束条件；According to the expression of the short-circuit current between the poles of the DC/DC transformer, determine the optimization objective function with the minimum short-circuit current between poles as the goal, and calculate the parameter variable constraint conditions of the optimization objective function; 利用改进粒子群算法计算参数变量的最优值。The optimal value of the parameter variable is calculated by the improved particle swarm algorithm. 2.根据权利要求1所述的改善DC/DC直流变压器极间短路的参数优化方法，其特征在于，所述组合型DC/DC直流变压器数学模型为：2. the parameter optimization method that improves DC/DC direct current transformer interpole short circuit according to claim 1, is characterized in that, described combined DC/DC direct current transformer mathematical model is: 采用了N个DC/DC直流变压器按照IPOS结构组合而成的组合型DC/DC直流变压器的拓扑结构。The topology of the combined DC/DC DC transformer is adopted, which is composed of N DC/DC DC transformers according to the IPOS structure. 3.根据权利要求1所述的改善DC/DC直流变压器极间短路的参数优化方法，其特征在于，所述DC/DC直流变压器极间短路电流的表达式为：3. the parameter optimization method that improves the short-circuit between poles of DC/DC transformer according to claim 1, it is characterized in that, the expression of the short-circuit current between poles of described DC/DC transformer is:
Figure FDA0002652244750000011
Figure FDA0002652244750000011
 式中：Idcmax表示短路电流的最大值，N表示组合型DC/DC直流变压器子模块DC/DC直流变压器个数，Cf表示组合型DC/DC直流变压器副边输出侧等效电容，Lf表示组合型DC/DC直流变压器副边输出侧等效电感。In the formula: I dcmax represents the maximum value of short-circuit current, N represents the number of combined DC/DC transformer sub-module DC/DC transformers, C f represents the equivalent capacitance on the secondary output side of combined DC/DC transformer, L f represents the equivalent inductance at the output side of the secondary side of the combined DC/DC DC transformer. 4.根据权利要求3所述的改善DC/DC直流变压器极间短路的参数优化方法，其特征在于，所述优化目标函数为：4. the parameter optimization method that improves DC/DC direct current transformer interpole short circuit according to claim 3, is characterized in that, described optimization objective function is: J＝minf(x)＝Idcmax J=minf(x)=I dcmax 其中，min表示取最小值，
Figure FDA0002652244750000012
Among them, min means to take the minimum value,
Figure FDA0002652244750000012
 5.根据权利要求3所述的改善DC/DC直流变压器极间短路的参数优化方法，其特征在于，所述参数变量N、Cf、Lf的约束条件分别为：5. The parameter optimization method for improving the short-circuit between poles of DC/DC transformer according to claim 3, is characterized in that, the constraints of described parameter variables N, C f , L f are respectively:
Figure FDA0002652244750000021
Figure FDA0002652244750000021
 式中，Lfmax为输出侧等效电感最大值，Lfmin为输出侧等效电感最小值，Cfmax为输出侧等效电容最大值，Cfmin为输出侧等效电容最小值。In the formula, L fmax is the maximum value of the equivalent inductance of the output side, L fmin is the minimum value of the equivalent inductance of the output side, C fmax is the maximum value of the equivalent capacitance of the output side, and C fmin is the minimum value of the equivalent capacitance of the output side. 6.根据权利要求4所述的改善DC/DC直流变压器极间短路的参数优化方法，其特征在于，所述利用改进粒子群算法计算参数变量的最优值的过程包括：6. The parameter optimization method for improving the short-circuit between poles of DC/DC DC transformer according to claim 4, is characterized in that, the described process utilizing improved particle swarm algorithm to calculate the optimal value of parameter variable comprises: 设置最大迭代次数、种群规模以及粒子速度区间；Set the maximum number of iterations, population size and particle velocity interval; 设置适应度函数为F，Set the fitness function to F, F＝JF=J 根据改进粒子群算法求出个体极值与全局最优解；According to the improved particle swarm algorithm, the individual extreme value and the global optimal solution are obtained; 粒子群通过下列两式更新自身位置和速度：The particle swarm updates its position and velocity by the following two equations:
Figure FDA0002652244750000022
Figure FDA0002652244750000022
 上式中：ω为惯性权重；d＝1,2,...,D；i＝1,2,...,n；上标k为当前迭代次数，Vid为粒子的速度，Pid为局部最优值，Xid为粒子自身的位置，Pgd为全局最优值，Xgd为全局最优值对应粒子的位置；c1,c2为非负常数，称为加速因子；r1,r2是分布于[0,1]之间的随机数；In the above formula: ω is the inertia weight; d=1,2,...,D; i=1,2,...,n; the superscript k is the current iteration number, V id is the speed of the particle, P id is the local optimum value, X id is the position of the particle itself, P gd is the global optimum value, X gd is the position of the particle corresponding to the global optimum value; c 1 , c 2 are non-negative constants, called acceleration factors; r 1 ,r 2 is a random number distributed between [0,1]; 改进粒子群算法惯性权重因子采用线性递减、学习因子采用异步法来更新自身速度和位置：The inertia weight factor of the improved particle swarm optimization adopts linear decrease, and the learning factor adopts the asynchronous method to update its own speed and position:
Figure FDA0002652244750000023
Figure FDA0002652244750000023
Figure FDA0002652244750000031
Figure FDA0002652244750000031
 式中，ω为惯性权重，t为当前迭代次数，T为最大迭代次数，c1,start＞c1,end，c2,start＜c2,end，ωmax表示惯性权重因子的最大值，ωmin表示惯性权重因子最小值，ωmax表示惯性权重因子最大值,c1,start，c1,end，c2,start，c2,end表示两个加速因子的初始值和终止值。In the formula, ω is the inertia weight, t is the current iteration number, T is the maximum iteration number, c 1,start >c 1,end , c 2,start <c 2,end , ω max represents the maximum value of the inertia weight factor, ω min represents the minimum value of the inertia weighting factor, ω max represents the maximum value of the inertia weighting factor, c 1,start , c 1,end , c 2,start , c 2,end represent the initial value and the end value of the two acceleration factors. 所以，改进粒子群算法采用下式更新自身位置和速度更：Therefore, the improved particle swarm algorithm uses the following formula to update its position and speed:
Figure FDA0002652244750000032
Figure FDA0002652244750000032
 根据迭代总适应度函数F，判断电容Cf、电感Lf以及子模块个数N是否满足目标函数需求，或者是否达到最大迭代次数，若满足要求或者达到最大迭代次数，则输出此时的值作为最优值。According to the iterative total fitness function F, determine whether the capacitance C f , the inductance L f and the number of sub-modules N meet the requirements of the objective function, or whether the maximum number of iterations is reached. If the requirements are met or the maximum number of iterations is reached, the current value is output. as the optimal value.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114580306A (en) * 2022-04-24 2022-06-03 锦浪科技股份有限公司 Flyback transformer design method based on improved PSO algorithm
CN116451544A (en) * 2023-05-31 2023-07-18 南通大学 Intelligent optimization design method for high-power high-frequency transformer

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108521114A (en) * 2018-05-10 2018-09-11 四川大学 An Optimal Configuration Method of Transformer Neutral Point Capacitive DC Blocking Device
CN110021940A (en) * 2019-04-25 2019-07-16 国网重庆市电力公司璧山供电分公司 A kind of capacitor placement optimization method based on improvement particle swarm algorithm
CN111222286A (en) * 2020-01-07 2020-06-02 东北电力大学 A Parameter Optimization Method Based on Transmission Line State Estimation

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108521114A (en) * 2018-05-10 2018-09-11 四川大学 An Optimal Configuration Method of Transformer Neutral Point Capacitive DC Blocking Device
CN110021940A (en) * 2019-04-25 2019-07-16 国网重庆市电力公司璧山供电分公司 A kind of capacitor placement optimization method based on improvement particle swarm algorithm
CN111222286A (en) * 2020-01-07 2020-06-02 东北电力大学 A Parameter Optimization Method Based on Transmission Line State Estimation

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114580306A (en) * 2022-04-24 2022-06-03 锦浪科技股份有限公司 Flyback transformer design method based on improved PSO algorithm
CN114580306B (en) * 2022-04-24 2022-07-29 锦浪科技股份有限公司 Flyback transformer design method based on improved PSO algorithm
CN116451544A (en) * 2023-05-31 2023-07-18 南通大学 Intelligent optimization design method for high-power high-frequency transformer
CN116451544B (en) * 2023-05-31 2023-11-07 南通大学 Intelligent optimization design method for high-power high-frequency transformer

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