CN105140972A - Method for rapidly searching frequency of high transmission efficiency wireless electric energy transmitting system - Google Patents

Abstract

The invention discloses a fast frequency search method for a high-transmission-efficiency wireless energy transmission system. The particle swarm scales in the general algorithm are set separately, which are respectively the largest particle swarm size <i>Nmax</i>=30 and the smallest particle swarm size Scale <i>Nmin</i>=2, the size of the particle swarm decreases gradually with the increase of the number of iterations, and the overall reduction of the size of the particle swarm is similar to an exponential curve. In the early stage of the search, the size of the particle swarm changes slowly, which is beneficial to the global search. In the later stage of the search, the size of the particle swarm becomes the smallest, subtracting redundant particles, simplifying the algorithm, and speeding up the convergence speed in the later stage of the algorithm. The algorithm of the invention not only makes particle size selection well-founded, but also has greater self-learning ability and social learning ability in the early stage of search, and accelerates the convergence speed and reduces the algorithm search time in the later stage of search.

Description

Fast Frequency Search Method for High Transmission Efficiency Wireless Power Transmission System

technical field

The invention belongs to the technical field of magnetic coupling wireless power transmission, in particular to the field of finding methods for system transmission efficiency in magnetic coupling wireless power transmission systems, and specifically relates to a fast frequency search method for high transmission efficiency wireless power transmission systems.

Background technique

Wireless power supply technology uses electromagnetic fields or electromagnetic waves to realize non-contact energy supply from the transmitter to the receiver. At present, there are many researches at home and abroad, and the more mature non-contact energy transmission technology uses the principle of electromagnetic induction. Although it has certain advantages, its transmission efficiency is low and the transmission distance is short. These shortcomings make the development of this technology have great limitations. In 2007, MIT successfully adjusted the resonant frequency of the receiving and receiving end coils to achieve the electromagnetic resonance between the transmission coils, and successfully achieved a breakthrough in the way of power transmission. This technology The principle of electromagnetic coupling resonance is adopted. This technology has aroused great interest in the industry and has become a research hotspot at home and abroad.

For a system, how to determine the maximum value of the transmission efficiency and find the excitation frequency of the system at the maximum transmission efficiency of the system is an urgent problem to be solved at present. The particle swarm optimization algorithm has advantages in solving general function optimization problems, but For the magnetic coupling wireless energy output system, the efficiency and frequency function curve will have a single and two extreme points. When the system has an extreme point, the general particle swarm optimization algorithm will appear short-term stagnation in the later stage of the search. ; and for the algorithm itself, if the particle size is set too high, the algorithm will perform redundant calculations, while if the particle size is too small, the particles will directly miss the global optimal value, and even the extreme point cannot be found. Generally, the size of the particle swarm is set at 20 Between -40, but the precise selection of its particle size has always been based on the individual's continuous attempts and experiments when solving problems, which is very blind. In view of the above situation, it is urgent to find an optimization algorithm for the characteristics of the magnetic coupling wireless power transfer system itself, so as to solve the problem of finding the system efficiency. Therefore, how to design an algorithm for the magnetic coupling wireless power supply system so that the algorithm can quickly find the maximum efficiency of the system and the corresponding frequency point is necessary. The present invention aims to provide an algorithm that can quickly and accurately find the optimal value of system transmission efficiency and its corresponding frequency.

Contents of the invention

The technical problem solved by the present invention is to provide a fast frequency search method for a wireless power transmission system with high transmission efficiency. The algorithm itself is concerned with the selection of the number of particles, and the algorithm can quickly find the optimal value of the system efficiency.

In order to solve the above technical problems, the present invention adopts the following technical scheme, the frequency fast search method of the high-transmission-efficiency wireless power transmission system, which is characterized in that: the particle swarm scale in the general particle swarm algorithm is set separately, respectively the maximum particle swarm scale Nmax =30 and the minimum particle swarm size Nmin=2, the particle swarm size gradually decreases along the exponential curve as the number of iterations increases, and its specific implementation steps are:

(1), the initialization algorithm, including setting the particle population dimension D, the maximum number of iterations MaxNum, while limiting the particle maximum velocity v _max , and initializing the inertial weight w;

(2), directly set the maximum size Nmax of the particle swarm to 30 and the minimum size Nmin of the particle swarm to 2, randomly initialize the velocity v of the particle and the position of the particle, set the initial particle swarm size to the maximum size Nmax=30, and initialize the number of iterations t=1;

(3), using the fitness function Calculate the fitness function value f _i of each particle in the current population, fi represents the fitness function value of the _i -th particle, where ω=2πf _r , f _r is the current excitation frequency, ω is the angular frequency of the excitation power supply, M is the mutual inductance between the transmitting and receiving coils, L ₁ and L ₂ are the inductances of the transmitting and receiving coils, C ₁ and C ₂ are Capacitance, R _s is the internal resistance of the power supply, R _L is the load resistance, R ₁ and R ₂ are the resistance in the loop;

(4) Use f _i-best to represent the optimal fitness function value searched by the i-th particle until the t-th iteration, and use f _i -gbest to represent the optimal fitness function value searched by all particles until the t-th iteration The optimal fitness function value, before the particle swarm optimization algorithm starts to iterate, set f _i-best = 0, f _i-gbest = 0, the particle fitness function value f _i obtained in step (3) and the individual extremum Compared with f _i-best and the global extremum f _i-gbest , if f _i ≤ f _i-best , then f _i-best = f _i , p _i = x _i , p _i means that the fitness function value is f _i- The _best particle position, x _i is the position of the corresponding fitness function value of f _i particle, if f _i ≤ f _i-gbest , then f _i-gbest = f _i , p _{g =} x _i , p _g is the particle population The particle position where the global optimal value is f _i-gbest ;

(5), according to the formula Update the size of the particle swarm, where Npresent is the current size of the particle swarm, Nmax is the maximum size of the particle swarm, Nmin is the minimum size of the particle swarm, MaxNum is the maximum number of iterations, t is the current number of iterations, and n is the power index that controls the change law of the size of the particle swarm , through the parameter n to adjust the speed of particle swarm scale change, according to the formula $v_i^{t+1}=w*v_i^t+c_1*ra\mathrm{no}d*(p_i-x_i^t)+c_2*ra\mathrm{no}d*(p_g-x_i^t)$ and the formula Update the velocity and position of each particle, then set the number of iterations t=t+1, turn to step (6), where v _i ^t+1 represents the velocity of the i-th particle in the t+1 iteration, v _i ^t represents the current t-th particle The velocity of the i-th particle in the iteration, c ₁ and c ₂ represent learning factors, rand represents a random number between [01], p _i represents the particle position with fitness function value fi _-best , p _g is the particle population In the global optimal value is the particle position of f _i-gbest , x _i ^t+1 represents the position of the i-th particle in the t+1 iteration, x _i ^t represents the current position of the i-th particle in the t-th iteration, and w represents the inertia weight ;

(6), according to the formula Calculate the sum of the variance of particle fitness function values, f _avg is the average value of all particle fitness function values, if (f _i -f _avg )>1, then a=max(f _i -f _avg ), otherwise , a=1, judge whether the variance is equal to 0 or whether the algorithm reaches the maximum number of iterations, if not, then turn to step (3), if so then turn to step (7);

(7), output the searched global optimal value p _g , p _g is the particle position of the global optimal value f _i-gbest in the particle population, that is, the frequency value corresponding to the searched optimal value;

(8) Use a current sensor to detect the peak value of the load current i ₂ , set Δ as the set maximum current peak fluctuation range, i _2max is the detected load current peak value, and i _2max (k) is the kth current cycle of the load Current peak value, i _2max (k+1) is the current peak value of the k+1th current cycle of the load, judge whether |i _2max (k+1)|-|i _2max (k)|>Δ is true, if the judgment result If yes, turn to step (1) and restart the algorithm; if the judgment result is no, turn to step (7).

The present invention inversely deduces the mutual inductance M between the transmitting coil and the receiving coil according to the current excitation frequency f _r , and firstly determines the mutual inductance between the transmitting coil and the receiving coil, so that the fitness function becomes a function only related to the excitation frequency. The invention makes the size of the particles decrease with the increase of the number of iterations in a manner similar to an exponential curve. In the early stage of the algorithm search, the size of the particle swarm is large and the speed of reduction is slow, which can make the algorithm fully search the function globally. In the later stage, the particles The faster the reduction of the group size, the faster the particle convergence speed and reduce the search time of the particle swarm optimization algorithm. This algorithm mainly solves the phenomenon that the particle swarm optimization algorithm in the magnetically coupled wireless power transmission system will temporarily stagnate during the optimization process and the problem of the number of particles selected by the algorithm itself. The algorithm can quickly find the optimal value of the system efficiency. The restart condition set by this algorithm can keep the output of the maximum efficiency of the system at all times when the distance of the transceiver coil is changed.

Description of drawings

Fig. 1 is the particle swarm optimization algorithm flowchart of the present invention;

Fig. 2 is the simulation diagram of general particle swarm optimization algorithm optimization result;

Fig. 3 is the simulation diagram of the optimization result of the particle swarm optimization algorithm of the present invention;

Figure 4 is a graph showing the particle swarm size decreases with the increase of the number of iterations.

Specific implementation method

The specific content of the present invention will be described in detail in conjunction with the accompanying drawings. The invention is mainly aimed at the magnetic coupling wireless power transmission system, and uses the improved particle swarm algorithm to reduce the size of the particles, and the algorithm can quickly find the maximum efficiency point and its corresponding frequency. The following is illustrated by a specific concrete example and simulated with Matlab. The flow of the particle swarm optimization algorithm is shown in Figure 1, a fast frequency search method for a wireless power transmission system with high transmission efficiency, which includes the following steps:

(1), initialization algorithm, including setting particle population dimension D=1, maximum number of iterations MaxNum=200, limiting particle maximum velocity v _max at the same time, initializing inertial weight w;

(2) Directly set the maximum size Nmax of the particle swarm to 30 and the minimum size Nmin of the particle swarm to 2, and initialize the velocity v and the position of the particle randomly. Set the initial particle swarm size to the maximum size Nmax=30, and the number of initialization iterations t=1. At present, there is no uniform rule for setting the particle swarm size, and it is usually set according to the optimization object and personal experience. This algorithm only needs to directly set the maximum size of the particle swarm as Nmax=30, which can solve various situations of optimizing the efficiency of the resonant power transmission device. The minimum scale is set in the algorithm so that the size of the particle swarm gradually decreases from the maximum scale Nmax to the minimum scale Nmin with the increase of the number of iterations. In this algorithm, Nmin=2;

(3), using the fitness function Calculate the fitness function value f _i of each particle in the current population, fi represents the fitness function value of the _i -th particle, where ω=2πf _r , f _r the current excitation frequency, ω is the angular frequency of the excitation power supply, M is the mutual inductance between the transmitting and receiving coils, L ₁ and L ₂ are the inductances of the transmitting and receiving coils, C ₁ and C ₂ are the capacitances , R _s is the internal resistance of the power supply, _RL is the load resistance, R ₁ and R ₂ are the resistance in the loop. This algorithm first consists of the current excitation frequency f _r and the equations $\left\{\begin{array}{c}{\stackrel{\&CenterDot;}{u}}_{L_1}={\mathrm{j\&omega;L}}_1{\stackrel{\&CenterDot;}{I}}_1\\ {\stackrel{\&Center\; Dot;}{u}}_{L_1}=j\mathrm{\&omega;}m({\stackrel{\&Center\; Dot;}{I}}_1-{\stackrel{\&Center\; Dot;}{I}}_2)\\ j\mathrm{\&omega;}m{\stackrel{\&Center\; Dot;}{I}}_1={\stackrel{\&Center\; Dot;}{I}}_2{Z}_2\\ Z_2=R_2+R_L+j({\mathrm{\&omega;L}}_2-\frac{1}{{\mathrm{\&omega;\; C}}_2})\\ \mathrm{\&omega;}=2{\mathrm{\&pi;f}}_r\end{array}\right.$ Deriving the mutual inductance M between the transmitting and receiving coils, $m=\frac{({\mathrm{\&omega;L}}_2-\frac{1}{{\mathrm{\&omega;\; C}}_2})\&lsqb;{(R_2+R_L)}^2+{({\mathrm{\&omega;L}}_2-\frac{1}{{\mathrm{\&omega;\; C}}_2})}^2\&rsqb;}{2\mathrm{\&omega;}(R_2+R_L)},$ Then deduce the fitness function, the fitness function used in this algorithm is the function of efficiency and mutual inductance M. The equations shown can be derived by analyzing the whole system according to the basic circuit theorem. in represents the voltage _of coil L1, is the input current, Load current, the fitness function used in this algorithm is a function of efficiency and mutual inductance M, so when the distance between the two coils changes, M will also change, and the fitness function will also change. At this time, according to the current voltage excitation frequency And the equations can find M, and further determine the fitness function under the current distance of the system;

(4) Use f _i-best to represent the optimal fitness function value searched by the i-th particle until the t-th iteration, and use f _i -gbest to represent the optimal fitness function value searched by all particles until the t-th iteration Optimal fitness function value. Before the algorithm starts to iterate, set f _i-best =0, f _i-gbest =0, the particle fitness function value f _i obtained in step (3), the individual extremum f _i-best and the global extremum f Compared with _i -gbest, if f _i ≤ f _i-best , then f _i-best = f _i , p _i = x _i ; p _i represents the particle position whose fitness function value is f _i-best , and _xi is all For the position where the fitness function value is f _i particle, if f _i ≤ f _i-gbest , then f _i-gbest = f _i , p _{g =} x _i ; p _g is the global optimal value in the particle population is f _i- The particle position of _gbest ;

(5) According to the formula Update the size of the particle swarm, where Npresent is the current size of the particle swarm, Nmax is the maximum size of the particle swarm, MaxNum is the maximum number of iterations, t is the current number of iterations, n is the power exponent that controls the change of the size of the particle swarm, and the particles can be adjusted by parameter n How quickly the group size changes. by formula $v_i^{t+1}=w*v_i^t+c_1*ra\mathrm{no}d*(p_i-x_i^t)+c_2*ra\mathrm{no}d*(p_g-x_i^t)$ and the formula $x_i^{t+1}=x_i^t+v_i^{t+1}$ Update the velocity and position of each particle, then set the number of iterations t=t+1, turn to step (6), where v _i ^t+1 represents the velocity of the i-th particle in the t+1 iteration, v _i ^t represents the current t-th particle The velocity of the i-th particle in the iteration, c ₁ and c ₂ represent learning factors, this time set c ₁ =2, c ₂ =2, and rand represents a random number between [01]. p _i represents the particle position whose fitness function value is f _i-best , p _g is the particle position whose global optimal value is f _i-gbest in the particle population, x _i ^t+1 represents the ith particle of t+1 iteration position, x _i ^t represents the current position of the i-th particle in the t-th iteration, and w represents the inertia weight. Initially setting the size of the particle swarm to 30 can solve the problem that the algorithm misses the global optimal value due to insufficient selection of the size of the particle swarm in the existing technology. With the operation of the algorithm, the setting of the particle size has more and more influence on the convergence of the algorithm Large, using the method that the particle size gradually decreases with the increase of the number of iterations, the size of the particle swarm is simplified, and redundant particles are subtracted, so that the particle swarm algorithm can accelerate the convergence in the later stage of the search and improve the running speed of the algorithm;

(6), according to the formula Calculate the sum of the variance of particle fitness function values, f _avg is the average value of all particle fitness function values, if (f _i -f _avg )>1, then a=max(f _i -f _avg ), otherwise , a=1. Judging whether the variance is equal to 0 or whether the algorithm has reached the maximum number of iterations, if not, then turn to step (3), if yes, turn to step (7);

(7) Output the searched global optimal value p _g , p _g is the particle position in the particle population whose global optimal value is fi _-gbest , that is, the frequency value corresponding to the searched optimal value.

(8) Use a current sensor to detect the peak value of the load current i ₂ , set Δ as the set maximum current peak fluctuation range, i _2max is the detected load current peak value, and i _2max (k) is the kth current cycle of the load Current peak value, i _2max (k+1) is the current peak value of the k+1th current cycle of the load, judge whether |i _2max (k+1)|-|i _2max (k)|>Δ is true, if the judgment result If yes, turn to step (1) and restart the algorithm; if the judgment result is no, turn to step (7).

In order to clearly understand the advantages of this algorithm, the simulation diagrams of the general particle swarm optimization algorithm and the optimization results of this algorithm are given in Figure 2 and Figure 3, respectively, and Figure 4 is a graph showing that the particle swarm size decreases with the increase in the number of iterations.

Figure 2 is the general particle swarm optimization optimization result diagram. The curve in the figure is the function image of the efficiency and frequency of the magnetically coupled wireless power transfer system. The five-pointed star in the figure is the optimal value found. The frequency finally found is 13544961.6816Hz.

Fig. 3 is a diagram of the optimization result of the algorithm of the present invention. Under the condition of the same optimization accuracy, the time consumed by the algorithm is reduced by 56% compared with the general algorithm.

Figure 4 shows the process that the particle swarm size of this algorithm gradually decreases with the increase of the number of iterations.

The above embodiments have described the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What are described in the above embodiments and description are only to illustrate the principles of the present invention. Without departing from the scope of the principle of the present invention, there will be various changes and improvements in the present invention, and these changes and improvements all fall within the protection scope of the present invention.

Claims (1)

1. The frequency fast search method of the wireless energy transmitting system with high transmission efficiency is characterized in that: the particle swarm scale in the general particle swarm algorithm is set separately, being respectively maximum particle swarm scale Nmax=30 and minimum particle swarm scale Nmin=2 , the particle swarm size gradually decreases along the exponential curve as the number of iterations increases. The specific implementation steps are: (1), the initialization algorithm, including setting the particle population dimension D, the maximum number of iterations MaxNum, while limiting the particle maximum velocity v _max , and initializing the inertial weight w; (2), directly set the maximum size Nmax of the particle swarm to 30 and the minimum size Nmin of the particle swarm to 2, randomly initialize the velocity v of the particle and the position of the particle, set the initial particle swarm size to the maximum size Nmax=30, and initialize the number of iterations t=1; (3), using the fitness function Calculate the fitness function value f _i of each particle in the current population, fi represents the fitness function value of the _i -th particle, where ω=2πf _r , f _r is the current excitation frequency, ω is the angular frequency of the excitation power supply, M is the mutual inductance between the transmitting and receiving coils, L ₁ and L ₂ are the inductances of the transmitting and receiving coils, C ₁ and C ₂ are Capacitance, R _s is the internal resistance of the power supply, R _L is the load resistance, R ₁ and R ₂ are the resistance in the loop; (4) Use f _i-best to represent the optimal fitness function value searched by the i-th particle until the t-th iteration, and use f _i -gbest to represent the optimal fitness function value searched by all particles until the t-th iteration The optimal fitness function value, before the particle swarm optimization algorithm starts to iterate, set f _i-best = 0, f _i-gbest = 0, the particle fitness function value f _i obtained in step (3) and the individual extremum Compared with f _i-best and the global extremum f _i-gbest , if f _i ≤ f _i-best , then f _i-best = f _i , p _i = x _i , p _i means that the fitness function value is f _i- The _best particle position, x _i is the position of the corresponding fitness function value of f _i particle, if f _i ≤ f _i-gbest , then f _i-gbest = f _i , p _g = x _i , p _g is the particle population The particle position where the global optimal value is f _i-gbest ; (5), according to the formula Update the size of the particle swarm, where Npresent is the current size of the particle swarm, Nmax is the maximum size of the particle swarm, Nmin is the minimum size of the particle swarm, MaxNum is the maximum number of iterations, t is the current number of iterations, and n is the power index that controls the change law of the size of the particle swarm , through the parameter n to adjust the speed of particle swarm scale change, according to the formula $v_i^{t+1}=w*v_i^t+c_1*ra\mathrm{no}d*(p_i-x_i^t)+c_2*ra\mathrm{no}d*(p_g-x_i^t)$ and the formula Update the speed and position of each particle, then make the number of iterations t=t+1, turn to step (6), where Represents the velocity of the i-th particle in the t+1 iteration, Represents the velocity of the i-th particle in the current t-th iteration, c ₁ and c ₂ represent learning factors, rand represents a random number between [01], p _i represents the particle position whose fitness function value is fi _-best , p _g is the particle position of the global optimal value of f _i-gbest in the particle population, Represents the i-th particle position of the t+1 iteration, Represents the current position of the i-th particle in the t-th iteration, and w represents the inertia weight; (6), according to the formula Calculate the sum of the variance of particle fitness function values, f _avg is the average value of all particle fitness function values, if (f _i -f _avg )>1, then a=max(f _i -f _avg ), otherwise , a=1, judge whether the variance is equal to 0 or whether the algorithm reaches the maximum number of iterations, if not, then turn to step (3), if so then turn to step (7); (7), output the searched global optimal value p _g , p _g is the particle position of the global optimal value f _i-gbest in the particle population, that is, the frequency value corresponding to the searched optimal value; (8) Use a current sensor to detect the peak value of the load current i ₂ , set Δ as the set maximum current peak fluctuation range, i _2max is the detected load current peak value, and i _2max (k) is the kth current cycle of the load Current peak value, i _2max (k+1) is the current peak value of the k+1th current cycle of the load, judge whether |i _2max (k+1)|-|i _2max (k)|>Δ is true, if the judgment result If yes, turn to step (1) and restart the algorithm; if the judgment result is no, turn to step (7).