CN108549456B - Photovoltaic array MPPT control method based on moth fire suppression algorithm - Google Patents

Abstract

The invention relates to a photovoltaic array MPPT control method based on a moth fire suppression algorithm, which comprises the following steps: at duty cycle [0,1]Selecting k points as initial space position X of moth_i(0) (ii) a Measuring the output current I of a photovoltaic array_PVAnd an output voltage V_PVAnd calculateCorresponding power P_i(n); to P_i(n) sorting; evaluating the fitness value corresponding to each moth, sequencing the spatial positions of the moths in the order of descending fitness value, and assigning to flames as the spatial positions of the flames; adaptively reducing the number of flames in an iterative process, and updating the self position of the moth corresponding to the reduced flames in the sequence in each generation according to the flame with the worst current fitness value; and entering the next generation until the iteration times meet the convergence condition, and stabilizing the duty ratio at the global optimal position.

Description

Photovoltaic array MPPT control method based on moth fire suppression algorithm

Technical Field

The invention relates to the technical field of photovoltaic power generation engineering, in particular to a maximum power point tracking method.

Background

Increasingly tense energy supply and demand contradiction, environmental pressure and the like require that renewable clean energy should undertake main energy supply tasks in a future energy system. The european union, the united states and china have successively proposed the goal of realizing the renewable energy source accounting for 100%, 80% and 60% -70% of the energy supply in 2050, respectively. Among various renewable energy sources, solar energy has a very wide development prospect as a renewable clean energy source with unlimited reserves. In order to efficiently use solar energy for photovoltaic power generation, Maximum Power Point Tracking (MPPT) technology is indispensable.

Under the condition of local shielding, the output characteristic curve of the photovoltaic cell has multiple peaks, and the traditional MPPT algorithm such as a disturbance observation method, a conductance increment method and the like has a simple structure but can fall into a local extreme value. While the intelligent algorithm (such as particle swarm algorithm, firefly algorithm, cuckoo algorithm, wolf optimization algorithm, etc.) can realize maximum power point tracking under multimodal conditions, certain energy loss is caused due to overlong tracking time.

The essence of MPPT control is how to find the global maximum power point quickly and accurately, which can be realized by an evolutionary algorithm. The moth fire suppression algorithm is a novel intelligent algorithm proposed in 2016, and due to the special structure of the moth fire suppression algorithm, the moth fire suppression algorithm can avoid trapping in a local extreme value, and due to the fact that the number of flames is adaptively adjusted in the iteration process, the moth fire suppression algorithm can focus on global search in the early stage and also focus on local search in the later stage. Therefore, the moth fire-fighting algorithm has great advantages in the aspect of nonlinear curve optimization, and the optimal solution can be found simply and effectively. And has demonstrated satisfactory performance in many fields of parameter selection, training of multilayer perceptrons, power flow calculations, etc. of solar cell three-diode models.

Disclosure of Invention

The invention aims to provide a photovoltaic array MPPT control method based on a moth fire suppression algorithm, and the tracking method can achieve both tracking speed and tracking efficiency. The specific technical scheme is as follows:

a photovoltaic array MPPT control method based on a moth fire suppression algorithm comprises the following steps:

1) at duty cycle [0,1]Selecting k points as initial space position X of moth_i(0)；

2) After the photovoltaic system is stabilized, measuring the output current I of the photovoltaic array_PVAnd an output voltage V_PVAnd calculating the corresponding power P_i(n), n represents the current number of iterations.

3) For the P_i(n) sorting;

4) evaluating the fitness value corresponding to each moth, sequencing the spatial positions of the moths in the order of descending fitness value, and assigning to flames as the spatial positions of the flames;

5) adaptively reducing the number of flames in an iterative process, the adaptively reducing the number of flames having the following formula: parameter number ═ round (N-N (N-1)/T) in the formula: n is the maximum number of flames; t represents the maximum iteration number, and meanwhile, due to the reduction of flames, the moth corresponding to the reduced flames in the sequence in each generation updates the position of the moth according to the flame with the worst current fitness value;

6) according to the helical equation M_i＝D_i·e^bt·cos(2πt)+F_jDetermining spatial position X of moth iterated for n +1 times_i(n +1) wherein M_iRepresenting the spatial position of the ith moth; f_jDenotes the jth flame, D_i＝|F_j-M_i|，D_iIndicating the distance between the ith moth and the jth flame; b is a defined helical shape constant; the path coefficient t is [ r,1 ]]Wherein the variable r will linearly decrease from-1 to-2 during the optimization iteration;

7) entering the next generation until the iteration times meet the convergence condition, and stabilizing the duty ratio at the global optimal position;

8) and detecting whether the external environment is mutated or not, if the external environment is mutated, carrying out MPPT again, and if not, continuously stabilizing the MPPT on the global optimal duty ratio.

Drawings

Fig. 1 shows a maximum power point tracking system of a Boost circuit to which the present invention is applied.

FIG. 2 is a control flow diagram of the present invention.

Detailed Description

In order to track the global maximum power point under a complex condition, the invention provides a control method based on a moth-fire suppression algorithm (MFO). As a novel heuristic intelligent optimization algorithm, a moth fire suppression algorithm is introduced into the MPPT technology for the first time. Due to the special algorithm structure, the MFO algorithm not only has high diversification and can avoid falling into local extreme values, but also can focus on local search in the later period while focusing on global search in the earlier period due to self-adaptive adjustment of the flame number in the iterative process. By adopting the method and the system, the maximum power point in the photovoltaic system can be found more quickly and accurately.

In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below. The present invention uses the maximum power of the Boost circuit as shown in fig. 1The rate point tracking system adopts a 4X 1 photovoltaic array, the switching frequency f is 50kHz, C₁＝100μF,L＝0.5mH,C₂＝100μF,R_load40 Ω. The algorithm flow chart is shown in fig. 2, and the specific scheme is as follows:

the invention adopts a direct duty ratio control mode, and the duty ratio is [0,1 ]]Selecting k points as initial space position X of moth_i(0)；

After the photovoltaic system is stabilized, measuring the output current I of the photovoltaic array_PVAnd an output voltage V_PVAnd calculating the corresponding power P_i(n), n represents the current iteration number;

for the P_i(n) sorting;

evaluating the fitness value corresponding to each moth, sequencing the spatial positions of the moths in the order of descending fitness value, and assigning to flames as the spatial positions of the flames;

if each spatial position update of k moths is based on k different spatial positions in the search space, the local development capability of the algorithm is reduced. To solve this problem, an adaptive mechanism is proposed for the number of flames, so that the number of flames can be adaptively reduced in an iterative process, thereby balancing the global search capability and the local development capability of the algorithm in the search space, and the formula for adaptively reducing the number of flames is as follows: parameter number (N-N (N-1)/T), wherein: n is the maximum number of flames; t denotes the maximum number of iterations. Meanwhile, due to the reduction of the flames, the moth corresponding to the reduced flames in the sequence in each generation updates the position of the moth according to the flame with the worst current fitness value.

According to the helical equation M_i＝D_i·e^bt·cos(2πt)+F_jDetermining spatial position X of moth iterated for n +1 times_i(n +1) wherein M_iRepresents the ith moth; d_i＝|F_j-M_i|，F_jDenotes the jth flame, D_iIndicating the distance between the ith moth and the jth flame; b is a defined helical shape constant; the path coefficient t is [ r,1 ]]Random number in which the variable r is in the optimization iterationThe process will decrease linearly from-1 to-2. The coefficient t in the spiral equation represents the distance that the next location of the moth is close to the flame (t-2 represents the closest location to the flame, and t-1 represents the farthest location). The spiral equation enables the moth to fly around the flame and not only in the space between the flame and the flame, thereby ensuring the global search capability and the local development capability of the algorithm;

and entering the next generation until the iteration times meet the convergence condition, and stabilizing the duty ratio at the global optimal position.

And detecting whether the external environment is mutated or not, if the external environment is mutated, carrying out MPPT again, and if not, continuously stabilizing the MPPT on the global optimal duty ratio.

Claims (1)

1. A photovoltaic array MPPT control method based on a moth fire suppression algorithm comprises the following steps:

1) at duty cycle [0,1]Selecting k points as initial space position X of moth_i(0)；

2) After the photovoltaic system is stabilized, measuring the output current IPV and the output voltage VPV of the photovoltaic array, and calculating the corresponding power P_i(n), n represents the current iteration number;

3) for the P_i(n) sorting;

4) evaluating the fitness value corresponding to each moth, sequencing the spatial positions of the moths in the order of descending fitness value, and assigning to flames as the spatial positions of the flames;

5) adaptively reducing the number of flames in an iterative process, the adaptively reducing the number of flames having the following formula: (iv) flamenumber ═ round (N-N (N-1)/T) formula (iv): n is the maximum number of flames; t represents the maximum iteration number, and meanwhile, due to the reduction of flames, the moth corresponding to the reduced flames in the sequence in each generation updates the position of the moth according to the flame with the worst current fitness value;

6) according to the helical equation M_i＝D_i·e^bt·cos(2πt)+F_jDetermining spatial position X of moth iterated for n +1 times_i(n +1) wherein M_iRepresents the spatial position of the ith moth, wherein i is 1,2……k；F_jDenotes the jth flame, D_i＝|F_j-M_i|，D_iIndicating the distance between the ith moth and the jth flame; b is a defined helical shape constant; the path coefficient t is [ r,1 ]]Wherein the variable r will linearly decrease from-1 to-2 during the optimization iteration;

7) entering the next generation until the iteration times meet the convergence condition, and stabilizing the duty ratio at the global optimal position;

8) and detecting whether the external environment is mutated or not, if the external environment is mutated, carrying out MPPT again, and if not, continuously stabilizing the MPPT on the global optimal duty ratio.

Images