CN115549095A - Improved badger algorithm optimized SAPF direct current side voltage control method - Google Patents
Improved badger algorithm optimized SAPF direct current side voltage control method Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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Abstract
The invention discloses an improved badger algorithm optimized SAPF direct current side voltage control method, and relates to the field of power quality harmonic treatment. The control of the direct-current side voltage of the SAPF affects the compensation performance and power loss of the harmonic current, and thus the stability of the direct-current side voltage is one of the main factors affecting the compensation effect of the active power filter (SAPF). At present, the voltage control of the capacitor at the direct current side is mainly PI control, and the invention uses PI λ The controller is applied to control of the voltage on the direct current side of the SAPF. PI controller and PI for optimizing SAPF direct-current side voltage in traditional Particle Swarm Optimization (PSO) λ On the basis of the controller, a novel group intelligent algorithm is used: namely improving the algorithm (IHBA) of the badger on PI λ And setting the control parameters of the controller. PI controller and PI optimized with PSO algorithm λ Compared with the result of the controller, the PI is optimized by the IHBA algorithm λ The DC side voltage stabilization effect of the controller control parameter is better, and the SAPF has higher performance.
Description
Technical Field
The invention relates to the field of electric energy quality harmonic wave treatment, in particular to an improved algorithm optimization SAPF direct-current side voltage control method for badgers.
Background
In recent years, along with the acceleration of the industrialized process of China, nonlinear loads and impact loads are applied in a large quantity, and harmonic waves and reactive power increasingly cause serious pollution to a power grid. The SAPF has high-performance harmonic suppression capability, and the control of the DC side capacitor voltage influences the compensation performance and power loss of harmonic current. When the grid is connected to a non-linear load, many harmonics and reactive currents are injected into it. In order to improve the compensated grid current waveform and suppress harmonic waves, the voltage value of the direct current side is required to be stabilized near a given reference value. The capacitor voltage control link has important influence on the effect of SAPF compensating harmonic current, and the reasonable selection of control parameters is of great importance to the control performance.
The control of the DC side capacitor voltage adopts PI λ Compared with the traditional PI controller, the controller introduces an integration order on the basis of the PI controller. Integral link K in traditional PI controller i s -1 The steady state error is eliminated by delaying the phase angle by 90 degrees, the stability performance of the system is improved, and the dynamic performance of the system is reduced. Fractional order PI λ Integral element K in controller i s -λ The lambda in the system can be any value, and the phase angle lag of the integral link can be adjusted at will between 0 and 180 degrees, so that the control effect is improved. Research shows that the system overshoot can be reduced, the steady-state precision can be improved, and the adjusting time can be reduced by adjusting the lambda value in a proper range.
Fractional order PI λ Compared with an integer-order PI controller, the controller has better control precision and robustness and is more flexible in design. The current main parameter setting methods comprise a traditional parameter setting method, an intelligent optimization algorithm and the like. The traditional parameter setting method comprises the following steps: Z-N method, critical ratio method, attenuation curve method, etc. The method is approximate setting according to an engineering empirical formula, setting precision is low, accurate object models are needed, and models of a plurality of actual objects are not easy to establish in industrial control. The intelligent algorithm optimization method mainly comprises the following steps: neural networks, genetic algorithms, particle swarm optimization algorithms, and the like. Neural netThe setting parameter effect of the complex is greatly influenced by the initial value; cross mutation operations in the genetic algorithm may degrade better; the particle swarm optimization algorithm is the most classical method in the swarm intelligence algorithm, but has the defects of easy falling into a local optimal area and slow convergence.
Disclosure of Invention
In order to solve the problems, the invention provides a method for applying an improved badger algorithm (IHBA) to the capacitance voltage PI at the direct current side of the SAPF λ The controller carries out parameter setting and optimizes the PI controller and the PI with the traditional particle swarm optimization λ Comparing the controller results, and optimizing PI by IHBA algorithm λ The voltage stabilizing effect of the SAPF direct current side after the control parameter compensation of the controller is better.
The invention adopts the following technical scheme:
And 3, optimizing control parameters: PI (proportion integration) by using improved meliger algorithm IHBA λ Optimizing control parameters to find K p 、K i The optimum value of λ is given to PI λ And a controller.
And 5, current tracking control: using PR control strategy, i.e. using reference current i ref Minus the compensation current I c The difference is controlled by PR, and a signal g is output.
Further, the improved FBD method proposed in step 1 is different from the existing detection method in that: (1) the SOGI is adopted to carry out phase shift and filtering processing on each phase voltage respectively, and then the voltage fundamental wave positive sequence component is obtained according to a symmetrical component method, thereby replacing a voltage filter in the traditional method.
(2) The reference voltage is changed into a voltage fundamental wave positive sequence component from a voltage fundamental wave component in the traditional method, so that the problem that a reasonable reference current signal cannot be detected by the traditional method when the voltage is unbalanced can be effectively solved.
Further, the fitness function ITAE optimized by the algorithm in the step 2 is calculated by the following method:
reference value of voltage U ref With the actual voltage value U dc Is defined as e (t), then
Further, in the step 3, improving the badger algorithm IHBA includes the following steps:
step 3.1, initializing the population of the badgers, setting the total number of the badgers to be N, and setting the maximum iteration times to be t max The upper limit and the lower limit of the search space are ub and lb respectively, and the positions of the badger groups are initialized randomly within a set boundary range as follows:
x i ＝lb i +r 1 *(ub i -lb i )
wherein r is 1 Is [0,1]Is a random constant.
Step 3.2, calculating the fitness values fitness of all the badgers in the population, and solving the current optimal position X prey 。
Step 3.3, during each iteration, the improved density factor α update is described as follows:
where t represents the current number of iterations, t mmax Representing the maximum number of iterations, C is a constant of 2,a =0.001.
Step 3.4, in the process of each iteration, the bee attraction degree I i The update is described as follows:
S＝(x i -x i+1 ) 2
d i ＝x prey -x i
where S is the source intensity, d i Is the distance, r, between the prey and the ith badger 2 Is [0,1]A random constant of (2), x prey Is the current optimum position, x i Is the location of the ith badger.
Step 3.5, updating the positions of the badgers, determining whether the positions are updated in a mining stage or a honey stage according to the value of the control direction parameter F, wherein an updating formula is as follows:
x new ＝x prey +F*β*I*x prey +F*r 3 *α*d i *|cos(2πr 4 )*[1-cos(2πr 5 )]|
x new ＝x prey +F*r 7 *α*d i
wherein r is 3 、r 4 、r 5 、r 6 、r 7 Is [0,1]When r is a random constant 6 Less than or equal to 0.5, the stage of digging is selected for the badgers, otherwise, the stage of honey is selected for the badgers. Beta represents the capacity of the badger to take food, here taken as a constant value of 6.
Step 3.6, judging whether a termination condition is met, if so, outputting an optimal solution, and ending the program; otherwise, repeating the improved algorithm process of the badger and continuing to carry out optimization iteration treatment.
Step 3.7, optimizing the improved algorithm of the badger to obtain a parameter K p 、K i Lambda to pI λ And a controller.
The invention has the beneficial effects that:
compared with PSO algorithm parameter setting or direct current side voltage PI control, the method adopts the IHBA algorithm to carry out PI λ And the voltage stabilization effect of the direct current side of the SAPF is better by parameter setting. The harmonic distortion rate THD value of the compensated power grid current is smaller. The effectiveness and the real-time performance of harmonic compensation are greatly improved, and the steady-state error is reduced. The method has great significance for improving the quality of electric energy and effectively controlling harmonic waves.
Drawings
FIG. 1 is an IHBA algorithm optimized SAPF direct current side PI of the present invention λ A schematic diagram of a controller;
FIG. 2 is a PSO algorithm optimized PI control parameter iteration curve;
FIG. 3 is a diagram of the control result of the voltage PI at the direct current side of the SAPF after the optimization of the PSO algorithm;
FIG. 4 is a PSO algorithm optimized PI λ Controlling a parameter iteration curve;
FIG. 5 shows SAPF DC side voltage PI after PSO algorithm optimization λ A control result graph;
FIG. 6 is an IHBA algorithm optimized PI of the present invention λ Controlling a parameter iteration curve;
FIG. 7 shows SAPF DC side voltage PI optimized by IHBA algorithm according to the present invention λ A control result graph;
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
As shown in fig. 1, an embodiment of the invention discloses a method for optimizing parameters of a voltage controller on a direct current side of a salf by improving a badger algorithm, which comprises the following steps:
And 2, selecting a fitness function: using the SAPF DC side voltage reference U ref With the actual voltage value U dc And (4) performing absolute value operation, multiplying by time, and finally integrating, wherein the result is used as an algorithm fitness function.
And 3, optimizing control parameters: PI (proportion integration) by using improved meliger algorithm IHBA λ Optimizing control parameters to find K p 、K i The optimum value of λ is given to PI λ And a controller.
And 5, current tracking control: using PR control strategy, i.e. using reference current i ref Minus the compensation current I c The difference is controlled by PR, and a signal g is output.
Further, the improved FBD method proposed in step 1 is different from the existing detection method in that: (1) the SOGI is adopted to carry out phase shift and filtering processing on each phase voltage respectively, and then the voltage fundamental wave positive sequence component is obtained according to a symmetrical component method, so that a voltage filter in the traditional method is replaced.
(2) The reference voltage is changed into a voltage fundamental wave positive sequence component from a voltage fundamental wave component in the traditional method, so that the problem that the traditional method cannot detect and obtain a reasonable reference current signal when the voltage is unbalanced can be effectively solved.
Further, the fitness function ITAE optimized by the algorithm in the step 2 is calculated by the following method:
reference value of voltage U ref With the actual voltage value U dc Is defined as e (t), then
Further, in the step 3, improving the badger algorithm IHBA includes the following steps:
step 3.1, initializing the population of the badgers, setting the total number of the badgers to be N, and setting the maximum iteration times to be t max The upper limit and the lower limit of the search space are ub and lb respectively, and the positions of the badger groups are initialized randomly within a set boundary range as follows:
x i ＝lb i +r 1 *(ub i -lb i )
wherein r is 1 Is [0,1]Is determined as a random constant.
Step 3.2, calculating the fitness values fitness of all the badgers in the population, and solving the current optimal position X prey 。
Step 3.3, during each iteration, the improved density factor α update is described as follows:
where t represents the current number of iterations, t max Representing the maximum number of iterations, C is a constant of 2,a =0.001.
Step 3.4, in the process of each iteration, the bee attraction degree I i The update is described as follows:
S＝(x i -x i+1 ) 2
d i ＝x prey -x i
where S is the source intensity, d i Is the distance, r, between the prey and the ith badger 2 Is [0,1]A random constant of (2), x prey Is the current optimum position, x i Is the position of the ith badger.
Step 3.5, updating the positions of the badgers, determining whether the position updating adopts a digging stage or a honey stage according to the control direction parameter F, wherein an updating formula is as follows:
x new ＝x prey +F*β*I*x prey +F*r 3 *α*d i *|cos(2πr 4 )*[1-cos(2πr 5 )]|
x new ＝x prey +F*r 7 *α*d i
wherein r is 3 、r 4 、r 5 、r 6 、r 7 Is [0,1]When r is a random constant 6 Less than or equal to 0.5, the stage of digging is selected for the badgers, otherwise, the stage of honey is selected for the badgers. Beta represents the capacity of the badger to take food, and the value is constant 6.
Step 3.6, judging whether a termination condition is met, if so, outputting an optimal solution, and ending the program; otherwise, repeating the improved algorithm process of the badger and continuing optimization iteration processing.
Step 3.7, optimizing the improved algorithm of the badger to obtain a parameter K p 、K i λ to pi λ And a controller.
The algorithm parameters in this embodiment take the following values:
the PSO algorithm optimizes PI controller parameters: the population size of the particle swarm is SwarmSize =20, dimension Dim =2, and maximum number of iterations MaxIter =20.
PSO algorithm optimized pI λ The controller parameters are as follows: the population size of the particle swarm is SwarmSize =20, dimension Dim =3, and maximum number of iterations MaxIter =20.
IHBA algorithm optimization pI λ The controller parameters are as follows: the size of the badger group is N =20, the dimension Dim =3,maximum number of iterations t max And =20, the upper and lower limits of the search space are ub =100 and lb = -100, respectively.
The improved badger algorithm (IHBA) provided by the invention is adopted to optimize the voltage control of the direct current side of the SAPF and is compared with the optimization result of the Particle Swarm Optimization (PSO). From the results shown in fig. 2 to fig. 7, it can be seen that PI is performed by IHBA algorithm in the present invention, compared with PSO algorithm parameter tuning, or PI control using dc side voltage λ And by parameter setting, the voltage stabilization effect of the direct current side of the SAPF is better, and the harmonic compensation effect is better.
Finally, only specific embodiments of the present invention have been described in detail above. The invention is not limited to the specific embodiments described above. Equivalent modifications and substitutions by one skilled in the art are also within the scope of the present invention. Accordingly, equivalent alterations and modifications are intended to be included within the scope of the invention, without departing from the spirit and scope of the invention.
Claims (4)
1. An improved badger algorithm optimized SAPF direct current side voltage control method is characterized in that: the method comprises the following steps:
step 1, harmonic current detection: solving three-phase fundamental current positive sequence active component i by using improved FBD method fabc (ii) a Nonlinear load current I based on current detection module based on improved FBD method L Removing positive-sequence active component i of three-phase fundamental current fabc Then, the harmonic current i can be detected h ；
Step 2, selecting a fitness function: using the voltage reference U of the SAPF DC side ref With the actual voltage value U dc Performing absolute value operation, multiplying by time, and finally integrating, wherein the result is used as an algorithm fitness function;
and 3, optimizing control parameters: PI (proportion integration) by using improved meliger algorithm IHBA λ Optimizing control parameters to find K p 、K i And the optimum value of λ is given to PI λ A controller;
step 4, calculating reference current: passing through PI λ Controller derived compensation current delta i p Then, the harmonic current i h Making a difference to obtain a reference current i ref ；
And 5, current tracking control: using PR control strategy, i.e. using reference current i ref Minus the compensation current I c Controlling the difference value through PR, and outputting a signal g;
step 6, harmonic current compensation: the output signal g controls the PWM converter to enable the SAPF to output a compensation current I c The harmonic and reactive currents to be compensated in the compensation current and the load current are offset, so that the current I of the power grid is compensated s Approaching a sine wave.
2. The improved badger algorithm optimized SAPF direct current side voltage control method according to claim 1, wherein the improved badger algorithm optimized SAPF direct current side voltage control method comprises the following steps: the improved FBD method proposed in the step 1 is different from the existing detection method in that: (1) the SOGI is adopted to carry out phase shift and filtering processing on each phase voltage respectively, and then the voltage fundamental wave positive sequence component is obtained according to a symmetrical component method, so that a voltage filter in the traditional method is replaced;
(2) the reference voltage is changed from a voltage fundamental component in the conventional method to a voltage fundamental positive sequence component.
3. The improved badger algorithm optimized SAPF direct current side voltage control method according to claim 1, wherein the improved badger algorithm optimized SAPF direct current side voltage control method comprises the following steps: the fitness function ITAE optimized by the algorithm in the step 2 is calculated by the following method:
reference value of voltage U ref With the actual voltage value U dc Is defined as e (t), then
4. The improved badger algorithm optimized SAPF direct current side voltage control method according to claim 1, wherein the improved badger algorithm optimized SAPF direct current side voltage control method comprises the following steps: in the step 3, improving the badger algorithm IHBA includes the following steps:
step 3.1, initializing the population of the badgers, and setting the total number of the badgers to be N, mostLarge number of iterations t max The upper limit and the lower limit of the search space are ub and lb respectively, and the positions of the badger groups are initialized randomly within a set boundary range as follows:
x i ＝lb i +r 1 *(ub i -lb i )
wherein r is 1 Is [0,1]A random constant of (2);
step 3.2, calculating the fitness values fitness of all the badgers in the population, and solving the current optimal position X prey ；
Step 3.3, during each iteration, the improved density factor α update is described as follows:
where t represents the current number of iterations, t max Represents the maximum number of iterations, C is a constant of 2, a =0.001;
step 3.4, in the process of each iteration, the bee attraction degree I i The update is described as follows:
S＝(x i -x i+1 ) 2
d i ＝x prey -x i
where S is the source intensity, d i Is the distance between the prey and the ith badger 2 Is [0,1]A random constant of (2), x prey Is the current optimum position, x i Is the location of the ith badger;
step 3.5, updating the positions of the badgers, determining whether the position updating adopts a mining stage or a honey stage according to the value of the control direction parameter F, wherein the updating formula is as follows:
x new ＝x prey +F*β*I*x prey +F*r 3 *α*d i *|cos(2πr 4 )*[1-cos(2πr 5 )]|
x new ＝x prey +F*r 7 *α*d i
wherein r is 3 、r 4 、r 5 、r 6 、r 7 Is [0,1]When r is a random constant 6 Less than or equal to 0.5, selecting the digging stage for the badgers, and otherwise, selecting the honey stage for the badgers; beta represents the food acquisition capacity of the badger, and is taken as a constant value of 6;
step 3.6, judging whether a termination condition is met, if so, outputting an optimal solution, and ending the program; otherwise, repeating the improved algorithm process of the badger and continuing optimizing iteration treatment;
step 3.7, optimizing the improved algorithm of the badger to obtain a parameter K p 、K i Lambda is assigned to PI λ And a controller.
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| CN116609668B (en) * | 2023-04-26 | 2024-03-26 | 淮阴工学院 | Lithium ion battery health state and residual life prediction method |
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