CN113363963A - Optimized three-phase SAPF direct current side control method by improving sparrow search algorithm - Google Patents
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Abstract
The invention discloses an improved sparrow search algorithm optimized three-phase SAPF direct current side control method, which comprises the following steps of 1, carrying out nonlinear load current ILRemoving the positive sequence active component i of the current fundamental wave through a reference current detection modulepabcThen, the harmonic current i is obtainedh(ii) a Step 2, calculating an algorithm optimized objective function ITAE by using a difference value of a voltage reference value of the direct current side of the SAPF and an actual voltage value; step 3, parameter setting is carried out by ISSA algorithm, and K is found outp、KiIs given to the PI controller and converts the voltage deviation into a compensation current increment delta i for stabilizing the DC side voltagep(ii) a Step 4, converting delta ipAnd ipabcMultiplication by ihMaking a difference to obtain a reference current iref(ii) a Step 5, reference current irefMinus the compensation current IcThe difference value of (a) is controlled by PR, and a signal g is output; step 6, the output signal g controls the PWM converter to enable the SAPF to output reasonable compensation current Ic. The invention adopts ISSA algorithm to adjust the control parameter, the optimizing precision is higher, the convergence speed is faster, and the DC side voltage stabilizing effect is better.
Description
Technical Field
The invention relates to the technical field of electric power, in particular to a method for optimizing three-phase SAPF direct current side control by improving a sparrow search algorithm.
Background
With the development of power electronic technology, a large number of nonlinear loads in a micro-grid are put into use, so that harmonic pollution of grid current is more and more serious. The three-phase parallel active power filter (SAPF) can effectively compensate harmonic waves. In practice, SAPF is a double closed loop system consisting of a current inner loop and a voltage outer loop. When the power grid is connected with a nonlinear load, a plurality of harmonic waves and reactive current are injected into the power grid, the harmonic wave content exceeding a certain standard can seriously threaten the safe and stable operation of electrical equipment, and therefore, in order to improve the compensated power grid current waveform and suppress the 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 plays an important role in perfecting the compensation current, the control parameters of the capacitor voltage control link play a decisive role in the control performance, and the reasonable selection of the control parameters is very important.
The control of the direct current side capacitor voltage adopts a traditional PI controller, and in the aspect of selecting controller parameters, the parameter setting method mainly comprises a traditional parameter setting method and an intelligent parameter optimization method. The traditional parameter setting method comprises the following steps: the method comprises a Z-N method, a critical proportion method, an attenuation curve method and the like, wherein approximate setting is carried out according to an engineering empirical formula, final adjustment and perfection are needed in actual operation, setting precision is not high, an accurate object model is 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, fuzzy control, genetic algorithms, particle swarm optimization algorithms, and the like. The setting parameter effect of the neural network is greatly influenced by the initial value; the fuzzy control needs a setter to have rich prior knowledge to compile fuzzy rules; 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 performing parameter setting on a capacitor voltage controller on the SAPF direct current side by adopting an Improved Sparrow Search Algorithm (ISSA), and compared with the optimization result of the traditional particle swarm algorithm, the defects that the particle swarm algorithm is easy to fall into local optimization and the convergence speed is low are overcome, and the control performance of a voltage side PI controller is improved.
The invention adopts the following technical scheme:
an improved sparrow search algorithm optimized three-phase SAPF direct current side control method comprises the following steps:
Step 3, carrying out parameter setting by using an improved sparrow search algorithm ISSA (isogenic Standard) to find out Kp、KiIs given to a PI controller in a simulation model, and converts the voltage deviation into a compensation current increment Delta i for stabilizing the DC side voltagep。
Step 5, reference current irefMinus the compensation current IcThe difference value of (d) is controlled by PR, which is a current tracking control, to output a signal g.
Further, the objective function optimized by the algorithm in step 2 is calculated by the following method:
reference value of voltage Ud *With the actual voltage value UdIs defined as e (t), then
Further, in the step 3, the improved sparrow search algorithm ISSA includes the following steps:
(1) initializing a sparrow population RP, setting the total number of sparrows to be N, setting the maximum iteration number to be Max _ iter, randomly initializing the positions of the sparrow population, forming a reverse population OP by reverse individuals of each sparrow individual in the initial population, combining the populations RP and OP, performing ascending sorting on 2N sparrow individuals according to fitness values, and selecting N sparrows before the fitness values as the initial population.
(2) The fitness values, fitness, of all sparrows in the initial population are calculated and the positions are ranked.
(3) During each iteration, the location update of the finder is described as follows:
where t represents the current iteration number, and j ═ 1, 2, 3. itermaxDenotes the maximum number of iterations, Xi,jIndicating the position information of the ith sparrow in the jth dimension, R2And ST represents the early warning value and the safety value, respectively.
When R is2<ST, which means that there are no predators around the foraging environment at this time, the finder may perform an extensive search operation. If R is2Gtst, which means that some sparrows in the population have found predators and raised an alarm to other sparrows in the population, when all sparrows need to fly quickly to other safe locations for foraging.
(4) And updating the position of the joiner, wherein the position updating formula is as follows:
wherein, XpIs the optimum position occupied by the finder at present, XworstThen the current global worst position is indicated. A represents a 1 × d matrix in which each element is randomly assigned a value of 1 or-1, and A+＝AT(AAT)-1. When i is>At n/2, this indicates that the i-th user with the lower fitness value does not obtain food, is in a state of full hunger, and needs to fly elsewhere to feed for more energy.
(5) In the experiments, it was assumed that these danger-aware sparrows account for 10% to 20% of the total number. The initial position of these sparrows was randomly generated in the population. The update formula is as follows:
wherein, XbestIs the current global optimum position. β is a random number that follows a normal distribution with a mean value of 0 and a variance of 1 as a step size control parameter. K ∈ [ -1,1]Is a random number, fiIt is the fitness value of the current sparrow individual. f. ofgAnd fwRespectively the current global best and worst fitness value. ε is the smallest constant to avoid zero at the denominator.
For simplicity, when fi>fgThis indicates that the sparrow is now at the border of the population and is extremely vulnerable to predators. XbestIt is also quite safe that sparrows representing this location are the best location in the population. f. ofi＝fgThis indicates that sparrows in the middle of the population are perceived as dangerous and need to be close to other sparrows to minimize their risk of being prey. K denotes the direction of movement of the sparrows and is also a step size control parameter.
(6) Calculate fitness value and update the position of sparrows.
(7) Judging whether a termination condition is met, if so, outputting an optimal solution, and ending the program; otherwise, repeating the improved sparrow search algorithm flow and continuing the optimization iteration processing.
(8) Parameter K obtained by optimizing improved sparrow search algorithmp、KiAnd assigning to a PI controller in a Simulink simulation model.
The invention has the beneficial effects that:
compared with direct-current side voltage PI control or PSO algorithm parameter setting, the method provided by the invention adopts ISSA algorithm to perform parameter setting, the SAPF direct-current side voltage stabilization effect is better, and 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. Has great significance for effectively controlling harmonic waves and improving the quality of electric energy.
Drawings
FIG. 1 is a schematic diagram of an ISSA algorithm optimized SAPF DC side PI controller of the present invention;
FIG. 2 is a PSO algorithm optimization iteration curve;
FIG. 3 is a diagram of SAPF DC side voltage results after PSO algorithm optimization;
FIG. 4 is an ISSA algorithm optimization iteration curve of the present invention;
FIG. 5 is a diagram of SAPF DC side voltage results after ISSA algorithm optimization according to the present invention.
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. 1-5, an embodiment of the present invention discloses a method for optimizing parameters of a SAPF dc-side voltage controller by improving a sparrow search algorithm, comprising the following steps:
Step 3, changePerforming parameter setting by entering sparrow search algorithm ISSA to find out Kp、KiIs given to a PI controller in a simulation model, and converts the voltage deviation into a compensation current increment Delta i for stabilizing the DC side voltagep。
Step 5, reference current irefMinus the compensation current IcThe difference value of (d) is controlled by PR, which is a current tracking control, to output a signal g.
Further, the objective function optimized by the algorithm in step 2 is calculated by the following method:
reference value of voltage Ud *With the actual voltage value UdIs defined as e (t), then
Further, in the step 3, the improved sparrow search algorithm ISSA includes the following steps:
step 3.1, initializing a sparrow population RP, setting the total number of sparrows to be N, setting the maximum iteration number to be Max _ iter, randomly initializing the positions of the sparrow population, forming a reverse population OP by the reverse individuals of each sparrow individual in the initial population, combining the populations RP and OP, performing ascending sorting on 2N sparrow individuals according to fitness values, and selecting N sparrows before the fitness values as the initial population.
And 3.2, calculating the fitness values fitness of all sparrows in the initial population and sequencing the positions.
Step 3.3, in the process of each iteration, the location update of the finder is described as follows:
where t represents the current iteration number, and j ═ 1, 2, 3. itermaxDenotes the maximum number of iterations, Xi,jIndicating the position information of the ith sparrow in the jth dimension, R2And ST represents the early warning value and the safety value, respectively.
When R is2<ST, which means that there are no predators around the foraging environment at this time, the finder may perform an extensive search operation. If R is2Gtst, which means that some sparrows in the population have found predators and raised an alarm to other sparrows in the population, when all sparrows need to fly quickly to other safe locations for foraging.
And 3.4, updating the position of the adder, wherein the position updating formula is as follows:
wherein, XpIs the optimum position occupied by the finder at present, XworstThen the current global worst position is indicated. A represents a 1 × d matrix in which each element is randomly assigned a value of 1 or-1, and A+＝AT(AAT)-1. When i is>n/2, this indicates that the ith participant with the lower fitness value does not obtain food, is in a state of full hunger, and needs to fly to other places to feed to obtain more energy.
Step 3.5, in the experiment, it was assumed that these danger-aware sparrows account for 10% to 20% of the total number. The initial position of these sparrows was randomly generated in the population. The update formula is as follows:
wherein, XbestIs the current global optimum position. Beta workThe step size control parameter is a random number that follows a normal distribution with a mean value of 0 and a variance of 1. K ∈ [ -1,1]Is a random number, fiIt is the fitness value of the current sparrow individual. f. ofgAnd fwRespectively the current global best and worst fitness value. ε is the smallest constant to avoid zero at the denominator.
For simplicity, when fi>fgThis indicates that the sparrow is now at the border of the population and is extremely vulnerable to predators. XbestIt is also quite safe that sparrows representing this location are the best location in the population. f. ofi＝fgThis indicates that sparrows in the middle of the population are perceived as dangerous and need to be close to other sparrows to minimize their risk of being prey. K denotes the direction of movement of the sparrows and is also a step size control parameter.
And 3.6, calculating the fitness value and updating the position of the sparrow.
Step 3.7, judging whether a termination condition is met, if so, outputting an optimal solution, and ending the program; otherwise, repeating the improved sparrow search algorithm flow and continuing the optimization iteration processing.
Step 3.8, optimizing the improved sparrow searching algorithm to obtain a parameter Kp、KiAnd assigning to a PI controller.
The algorithm parameters in the embodiment take the following values:
PSO algorithm: the particle swarm size is Swarmsize ═ 20, the dimension Dim ═ 2, and the maximum number of iterations MaxIter ═ 20.
ISSA algorithm: the size of the sparrow group is N equal to 20, the dimension Dim is 2, the maximum iteration time Max _ iter is 20, the proportion PD of the predators is 20%, the proportion SD of the dangerous sparrows is 20%, and the early warning value ST is 0.8.
The Improved Sparrow Search Algorithm (ISSA) provided by the invention is adopted to optimize the SAPF direct current side control, and compared with the Particle Swarm Optimization (PSO) results, and the results of figures 2 to 5 show that compared with the traditional PSO algorithm optimization, the improved sparrow search algorithm provided by the invention has the advantages of higher convergence rate, higher optimization precision, better voltage stabilization effect of the SAPF direct current side and better harmonic compensation effect.
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 those 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 (3)
1. An improved sparrow search algorithm optimized three-phase SAPF direct current side control method is characterized by comprising the following steps:
step 1, nonlinear load current ILRemoving a current fundamental wave positive sequence active component i through a reference current detection module based on an FBD methodpabcThen, the harmonic current i is obtainedh；
Step 2, the voltage reference value U of the direct current side of the SAPFd *With the actual voltage value UdThe integral of the absolute value multiplied by time is taken as an objective function ITAE optimized by the algorithm;
step 3, carrying out parameter setting by using an improved sparrow search algorithm ISSA (isogenic Standard) to find out Kp、KiIs given to a PI controller in a simulation model, and converts the voltage deviation into a compensation current increment Delta i for stabilizing the DC side voltagep；
Step 4, increasing the compensating current by delta ipAnd the current fundamental positive sequence component ipabcMultiplication by harmonic current ihMaking a difference to obtain a reference current iref；
Step 5, reference current irefMinus the compensation current IcThe difference value of (a) is controlled by current tracking control (PR), and a signal g is output;
step 6, the output signal g controls the PWM converter to enable the SAPF to output reasonable compensation current IcThe harmonic and reactive currents to be compensated in the compensation current and the load current are offset, so that the network current I is compensatedSApproaching a sine wave.
2. The optimized three-phase SAPF direct current side control method based on the improved sparrow search algorithm of claim 1, wherein: the objective function optimized by the algorithm in the step 2 is calculated by the following method:
reference value of voltage Ud *With the actual voltage value UdIs defined as e (t), then
3. The optimized three-phase SAPF direct current side control method based on the improved sparrow search algorithm of claim 1, wherein: in the step 3, the improved sparrow search algorithm ISSA includes the following steps:
step 3.1, initializing a sparrow population RP, setting the total number of sparrows to be N, setting the maximum iteration number to be Max _ iter, randomly initializing the positions of the sparrow population, forming a reverse population OP by reverse individuals of each sparrow individual in the initial population, combining the populations RP and OP, performing ascending sorting on 2N sparrow individuals according to fitness values, and selecting N sparrows before the fitness values as the initial population;
step 3.2, calculating the fitness values fitness of all sparrows in the initial population, and sequencing the positions;
step 3.3, in the process of each iteration, the location update of the finder is described as follows:
wherein, t represents the current iteration number, and j is 1, 2, 3.. d; itermaxDenotes the maximum number of iterations, Xi,jIndicating the position information of the ith sparrow in the jth dimension, R2And ST represents an early warning value and a safety value respectively;
when R is2<ST, which means that there are no predators around the foraging environment at this time, the finder can perform extensive search operations; if R is2gtoreq.ST, which means that some sparrows in the population have developedThe predators are available, and other sparrows in the population are alarmed, and all sparrows need to quickly fly to other safe places to forage for food at the moment;
and 3.4, updating the position of the adder, wherein the position updating formula is as follows:
wherein, XpIs the optimum position occupied by the finder at present, XworstThen the current global worst position is indicated; a represents a 1 × d matrix in which each element is randomly assigned a value of 1 or-1, and A+＝AT(AAT)-1(ii) a When i is>n/2, this shows that the ith subscriber with lower fitness value does not obtain food, is in a state of full hunger, and needs to fly to other places to find food at this time to obtain more energy;
step 3.5, assuming that these danger-aware sparrows account for 10% to 20% of the total number, the initial positions of these sparrows are randomly generated in the population, the update formula is as follows:
wherein, XbestThe current global optimal position is adopted, beta is taken as a step length control parameter and is a random number which follows normal distribution with the mean value of 0 and the variance of 1; k ∈ [ -1,1]Is a random number, fiThe fitness value of the current sparrow individual is obtained; f. ofgAnd fwThe current global best and worst fitness values, respectively; ε is the smallest constant to avoid zero denominator;
when f isi>fgThe sparrows are at the edge of the population at the moment and are extremely easy to be attacked by predators; xbestThe sparrow representing this location is also very safe as the best location in the population; f. ofi＝fgThis indicates a sparrow in the middle of the populationRecognizing the danger, it is necessary to approach other sparrows to minimize their risk of being prey, K representing the direction in which the sparrow is moving and also being the step size control parameter;
step 3.6, calculating a fitness value and updating the position of the sparrow;
step 3.7, judging whether a termination condition is met, if so, outputting an optimal solution, and ending the program; otherwise, repeating the improved sparrow search algorithm flow to continue optimizing iterative processing;
step 3.8, optimizing the improved sparrow searching algorithm to obtain a parameter Kp、KiAnd assigning to a PI controller.
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