Method for optimizing photovoltaic MPPT (Maximum Power Point Tracking) through grey wolf optimization algorithm improved based on chaos theory

Abstract

The invention discloses a method for optimizing photovoltaic MPPT (Maximum Power Point Tracking) based on a grey wolf optimization algorithm improved based on a chaos theory, and belongs to the field of intelligent optimization algorithms. The method comprises the following steps: constructing an improved grey wolf optimization algorithm based on a chaos sequence; the improved mode is that random weight guide items of a first fluctuation item and a second fluctuation item of a grey wolf optimization algorithm are generated through a chaos sequence; based on an improved grey wolf optimization algorithm, the optimal duty ratio of stable operation of the photovoltaic system is calculated; and based on the optimal duty ratio, verifying the effect of the improved grey wolf optimization algorithm in the photovoltaic system, and completing the optimization of the photovoltaic maximum power point tracking system. According to the method, premature convergence of the algorithm is avoided through ergodicity of the chaos sequence, and the global optimal solution search probability is improved; fluctuation parameters A and C are driven through a chaos sequence, and exploration and development requirements of the algorithm in different stages are balanced; and through collaborative updating of the three leader wolves, the convergence speed is increased, and invalid search is reduced.

Description

Technical Field

[0001] The present invention relates to the field of intelligent optimization algorithms, and in particular to a method for optimizing photovoltaic MPPT using a gray wolf optimization algorithm improved based on chaos theory. Background Art With the increasing depletion of traditional fossil energy, the development and use of renewable energy has become an inevitable trend. Solar energy, with its clean, pollution-free, and unlimited reserves, is the most ideal renewable energy source. Reducing costs and improving efficiency are key issues in solar power generation technology. Because the output characteristics of solar cells are affected by external conditions such as light and temperature, implementing maximum power point tracking (MPPT) is a key technology for improving overall system efficiency.

[0002] In practical applications, photovoltaic arrays can exhibit multiple peaks in their output power (PU) characteristic curves due to partial shading. The traditional Gray Wolf Optimization (GWO) algorithm achieves global optimization by simulating the hunting behavior of gray wolf packs. However, its parameter randomness can lead to low search efficiency and a tendency to get stuck in local optima. In photovoltaic systems, traditional MPPT methods (such as particle swarm optimization (PSO) and cuckoo optimization (CSA)) suffer from slow dynamic response and insufficient accuracy. A method that is less prone to low search efficiency and local optima is urgently needed. Summary of the Invention

[0003] In order to solve the above technical problems, the present invention provides a photovoltaic MPPT optimization method based on an improved gray wolf optimization algorithm based on chaos theory.

[0004] To implement the above technology, the specific steps are as follows: S1. Based on chaotic sequence, construct an improved gray wolf optimization algorithm; The improvement method is to generate random weight guidance terms for the first and second fluctuation terms of the gray wolf optimization algorithm through chaotic sequences, thereby enhancing the nonlinear perturbation capability of the parameters and avoiding premature convergence of the algorithm. The improved steps include: S1.1. Use the nonlinear regression analysis model (Logistic) to map and generate chaotic order. The expression is as follows: Where, Indicates the The chaotic order value generated by the iteration; Indicates the The chaotic order value generated by the iteration; Indicates the chaos parameter. In this embodiment ; , generate N chaotic values ​​through chaotic order to initialize parameters and population positions; S1.2. Generate the first and second fluctuation terms of the Grey Wolf Optimization Algorithm based on the chaotic order; The expressions of the random weight guidance terms of the first and second fluctuation terms of the traditional grey wolf algorithm are as follows: Where, represents the first fluctuation term; represents the second fluctuation term; represents the first random weight guide term; represents the second random weight guide term; Represents the convergence factor, the initial value is 2, it is negatively correlated with the number of iterations, and the minimum value is 0; The expressions for generating the first and second fluctuation terms of the Grey Wolf optimization algorithm are as follows: Where, Indicates the first chaos value of chaotic order generation; Indicates the second chaos value generated by chaotic order; S2. Calculate the optimal duty cycle for stable operation of the photovoltaic system based on the improved grey wolf optimization algorithm; The calculation method is as follows: by initializing the gray wolf position and chaotic order in the improved gray wolf optimization algorithm, and by updating the first fluctuation term and the second fluctuation term of the improved gray wolf optimization algorithm, the gray wolf position is guided to update. In the present invention, the gray wolf position represents the duty cycle of the photovoltaic system; The calculation steps include: S2.1, control the photovoltaic system to operate according to the current gray wolf position (duty cycle) and measure the output power; S2.2. Take output power as fitness, and the power and fitness are linearly proportional; select the top three leading wolves with the highest fitness Wolf, Wolf and Wolf (top three solutions for optimal duty cycle), and record the top three gray wolves Wolf, Wolf and The wolf's position 、 and ; S2.3. Calculate the distance between other wolves (other duty cycles) and the top three leaders using the following expression: wherein, represents the second fluctuation term; represents the current position of other wolves (duty cycle in the current iteration); represents the distance between the current wolf and the first candidate solution of the current duty cycle; represents the distance between the current wolf and the second candidate solution of the current duty cycle; represents the distance between the current wolf and the third candidate solution of the current duty cycle; S2.4, updating the position of the current wolf (current duty cycle) based on the distances between other wolves and the top three leader wolves to obtain a new position; The expressions for updating the positions of the top three leader wolves are as follows: wherein, represents the first fluctuation term; represents the first candidate solution of the current duty cycle; represents the second candidate solution of the current duty cycle; represents the third candidate solution of the current duty cycle; The way to obtain the new position is to take the middle value of the three candidate solutions, and the expression is as follows: wherein, represents the new position, i.e., the new duty cycle, if the fitness (power) of the new wolf exceeds the wolf, then the wolf is replaced; S2.5, updating the convergence factor to shrink the search range of the grey wolf population and control the algorithm to transition from global search to local optimization; The expression for updating the convergence factor is as follows: wherein, represents the current iteration number, and the interval is [0, ]; represents the maximum iteration number; the convergence factor is in the interval [0, 2]; S2.6, judging whether the maximum iteration number is reached, if yes, stopping the iteration and stabilizing the photovoltaic system at the duty cycle corresponding to the wolf; otherwise, returning to step S2.1 and continuing the iteration.

[0005] S3, based on the optimal duty cycle, verify the effect of the improved grey wolf optimization algorithm in the photovoltaic system, and complete the optimization of the maximum power point tracking (MPPT) system.

[0006] Advantages of the present application The present application aims at the problems of low accuracy and local optimum of the traditional grey wolf optimization algorithm, avoids premature convergence of the algorithm through the ergodicity of the chaotic sequence, improves the global optimal solution search probability, and thus improves the global search ability; through the chaotic sequence driving fluctuation parameters A and C, the exploration and development needs of different stages of the algorithm are balanced, and thus the parameter dynamic adaptability is increased; through the cooperative update of three leader wolves, the convergence speed is accelerated, and because of the constraint of the dynamic boundary, the invalid search is reduced, and thus the convergence speed is optimized. BRIEF DESCRIPTION OF DRAWINGS

[0007] Figure 1 The step flow chart of the present application is shown in the figure; Figure 2 The LGWO algorithm flow chart of the present application is shown in the figure; Figure 3 The photovoltaic system diagram of the present application embodiment based on MPP tracker and DC-DC boost converter is shown in the figure; Figure 4 The P-U characteristic curve diagram of the present application embodiment is shown in the figure; Figure 5 The comparison of LGWO, PSO and CSA algorithms for global maximum power point (GMPP) tracking under uniform illumination conditions is shown in the figure; Figure 6 The comparison of LGWO, PSO and CSA algorithms for GMPP tracking under local shading (4 / 16 S) conditions is shown in the figure. DETAILED DESCRIPTION

[0008] The present application will be further described in detail below in combination with specific embodiments.

[0009] In this embodiment, the photovoltaic system is composed of 4×3 photovoltaic cells in series.

[0010] The photovoltaic maximum power point tracking system (MPPT) adjusts the output duty cycle according to the collected photovoltaic array output power, controls the working voltage of the photovoltaic array, and thus realizes maximum power point tracking.

[0011] The photovoltaic maximum power point tracking system (MPPT) has a memory and a processor, the memory stores a computer program executable by the processor, and the computer program implements the steps of the improved grey wolf optimization algorithm based on chaos theory and the photovoltaic MPPT method when executed.

[0012] Embodiment 1 As shown in Figure 1 and Figure 2 , an improved grey wolf optimization algorithm based on chaos theory optimizes a photovoltaic MPPT method, including the following steps: S1, based on the chaotic sequence, construct the improved grey wolf optimization algorithm; The improved way is: through the chaotic sequence to generate the random weight guide item of the first fluctuation item and the second fluctuation item of the grey wolf optimization algorithm, enhance the nonlinear disturbance ability of the parameter, avoid the premature convergence of the algorithm; The improved steps include: S1.1, use the nonlinear regression analysis model (Logistic) mapping to generate chaotic order, the expression is as follows: In the formula, Chaotic order value generated in the m-th iteration; Chaotic order value generated in the m-th iteration; Chaotic parameter, in the embodiment ; , generate N chaotic values for initializing parameters and population positions through chaotic order; S1.2, generate the first fluctuation item and the second fluctuation item of the grey wolf optimization algorithm based on the chaotic order; The expression of the random weight guide item of the first fluctuation item and the second fluctuation item of the traditional grey wolf algorithm is as follows: In the formula, The first fluctuation item; The second fluctuation item; The first random weight guide item; The second random weight guide item; Convergence factor, the initial value is 2, and it is negatively related to the number of iterations, and the minimum value is 0; The expression of the first fluctuation item and the second fluctuation item of the grey wolf optimization algorithm is as follows: ​​Where, Indicates the first chaos value of chaotic order generation; Indicates the second chaos value generated by chaotic order; S2. Calculate the optimal duty cycle for stable operation of the photovoltaic system based on the improved grey wolf optimization algorithm; The calculation method is as follows: by initializing the gray wolf position and chaotic order in the improved gray wolf optimization algorithm, and by updating the first fluctuation term and the second fluctuation term of the improved gray wolf optimization algorithm, the gray wolf position is guided to update. In the present invention, the gray wolf position represents the duty cycle of the photovoltaic system; The calculation steps include: S2.1, control the photovoltaic system to operate according to the current gray wolf position (duty cycle) and measure the output power; S2.2. Take output power as fitness, and the power and fitness are linearly proportional; select the top three leading wolves with the highest fitness Wolf, Wolf and Wolf (top three solutions for optimal duty cycle), and record the top three gray wolves Wolf, Wolf and The wolf's position 、 and ; S2.3. Calculate the distance between other wolves (other duty cycles) and the top three leaders using the following expression: Where, represents the second fluctuation term; represents the current position of other wolves (duty cycle in the current iteration); Indicates the current wolf and the distance of the wolf; Indicates the current wolf and the distance of the wolf; Indicates the current wolf and the distance of the wolf; S2.4. Update the position of the current wolf (current duty cycle) based on the distances between the other wolves and the top three leaders to obtain a new position; The expression for updating the positions of the top three leading wolves is as follows: Where, represents the first fluctuation term; Indicates the first candidate solution for the current duty cycle; Indicates the second candidate solution for the current duty cycle; represents the third candidate solution for the current duty cycle; The new position is obtained by taking the middle value of the three candidate solutions, as shown below: Where, Represents the new position, that is, the new duty cycle, if The fitness (power) exceeds Wolf, then replace Wolf; S2.5, update the convergence factor, shrink the search range of the gray wolf population, and control the algorithm to transition from global search to local optimization; The expression for updating the convergence factor is as follows: Where, Indicates the current number of iterations, the interval is [0, ]; Indicates the maximum number of iterations; convergence factor is in the interval [0, 2]; S2.6, determine whether the maximum number of iterations has been reached, if so, stop the iteration and stabilize the photovoltaic system at the obtained Otherwise, return to step S2.1 and continue iterating.

[0013] S3. Based on the optimal duty cycle, verify the effect of the improved gray wolf optimization algorithm in the photovoltaic system and complete the optimization of the photovoltaic maximum power point tracking system (MPPT).

[0014] like Figure 3 As shown, in order to verify the effect of the present invention in the photovoltaic maximum power point tracking (MPPT) system, this embodiment established a 4×3 photovoltaic system MPPT model using the present invention (LGWO), CSA, and PSO in Matlab / Simulink. The simulation was performed under uniform illumination conditions. like Figure 4 As shown in the PU characteristic curve, under uniform illumination conditions, the irradiance of the 12 photovoltaic cells is 1000W / m 2 , so there is only one global maximum power point (GMPP), the maximum output power of the PV array is 6657 W, and the ambient temperature is 25℃.

[0015] like Figure 5As shown in the figure, under uniform illumination conditions, the convergence time of the LGWO algorithm is 0.06 s, and the maximum output power is 6655 W. The maximum power point tracking of the CSA algorithm and the PSO algorithm has a convergence time of 1 s and 1.04 s, respectively, and a maximum output power of 6640 W and 6654 W, respectively.

[0016] The layout finally obtained in this embodiment, the tracking time of the present invention (LGWO) is improved by 0.94 s and 0.98 s compared with CSA and PSO, respectively, and the efficiency is improved by 0.23% and 0.02%, respectively. The results are shown in Table 1; Table 1: Comparison results under uniform lighting conditions As can be seen from Table 1, it is verified that the present invention (LGWO) is a fast, accurate and efficient MPPT method.

[0017] Example 2 Example 2 uses the same method as Example 1, except that: S3. Based on the optimal duty cycle, verify the effect of the improved gray wolf optimization algorithm in the photovoltaic system and complete the optimization of the photovoltaic maximum power point tracking system (MPPT).

[0018] like Figure 3 As shown, in order to verify the effect of the present invention in the photovoltaic maximum power point tracking (MPPT) system, this embodiment established a 4×3 photovoltaic system MPPT model using the present invention (LGWO), CSA, and PSO in Matlab / Simulink. The simulation was carried out under partial shading (4 / 16 S) conditions. like Figure 4 As shown in the PU characteristic curve, under partial shading conditions, because the photovoltaic array has two sub-arrays, the first row has 3 photovoltaic panels, and the irradiance is 1000 W / m 2 There are 9 photovoltaic panels in the 2nd, 3rd and 4th rows, and the irradiance is 800 W / m 2 , so there are two peaks, but only one global maximum power point GMPP, the maximum output power of the PV array is 5448 W, and the ambient temperature is 25℃.

[0019] like Figure 6As shown in the figure, under partial shading conditions, the convergence time of the present invention (LGWO) is 0.10 s, and the maximum output power is 5444 W. The maximum power point tracking of the CSA algorithm and the PSO algorithm has a convergence time of 0.85 s and 1.20 s, respectively, and a maximum output power of 5433 W and 5438 W, respectively.

[0020] The layout finally obtained in this embodiment shows that the tracking time of the present invention (LGWO) is improved by 0.75 s and 1.10 s compared with CSA and PSO, respectively, and the efficiency is improved by 0.21% and 0.12%, respectively, as shown in Table 2; Table 2: Comparison results under partial lighting conditions As can be seen from Table 2, it is verified that the present invention (LGWO) is a fast, accurate and efficient MPPT method.

[0021] The above description is merely an embodiment of the present invention, and further details the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is merely a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A photovoltaic MPPT optimization method based on the improved gray wolf optimization algorithm based on chaos theory, characterized in that: The following steps are involved: S1. Based on chaotic sequence, construct an improved gray wolf optimization algorithm; The improved method is: generating random weight guide items of the first and second fluctuation items of the gray wolf optimization algorithm through a chaotic sequence; S2. Calculate the optimal duty cycle for stable operation of the photovoltaic system based on the improved grey wolf optimization algorithm; The calculation method is as follows: by initializing the gray wolf position and chaotic order in the improved gray wolf optimization algorithm, and by updating the first fluctuation term and the second fluctuation term of the improved gray wolf optimization algorithm, the gray wolf position is guided to update. In the present invention, the gray wolf position represents the duty cycle of the photovoltaic system; S3. Based on the optimal duty cycle, verify the effect of the improved gray wolf optimization algorithm in the photovoltaic system and complete the optimization of the photovoltaic maximum power point tracking system.

2. The photovoltaic MPPT optimization method based on the improved gray wolf optimization algorithm based on chaos theory according to claim 1 is characterized in that: The steps of constructing the improved gray wolf optimization algorithm based on chaotic sequence are as follows: S1.

1. Use nonlinear regression analysis model to map and generate chaotic order. The expression is as follows: Where, Indicates the The chaotic order value generated by the iteration; Indicates the The chaotic order value generated by the iteration; represents the chaos parameter; S1.

2. Generate the first and second fluctuation terms of the Grey Wolf Optimization Algorithm based on the chaotic order; The expressions for generating the first and second fluctuation terms of the Grey Wolf optimization algorithm are as follows: Where, represents the first fluctuation term; represents the second fluctuation term; represents the convergence factor; Indicates the first chaos value of chaotic order generation; Indicates the second chaos value of chaotic order generation.

3. The photovoltaic MPPT optimization method based on the improved gray wolf optimization algorithm based on chaos theory according to claim 1 is characterized in that: The steps for calculating the optimal duty cycle for stable operation of a photovoltaic system based on the improved grey wolf optimization algorithm are as follows: S2.

1. Control the photovoltaic system to operate according to the current gray wolf position and measure the output power; S2.

2. Take output power as fitness, and the power and fitness are linearly proportional; select the top three leading wolves with the highest fitness Wolf, Wolf and Wolf, and record the first three gray wolves Wolf, Wolf and The wolf's position, 、 and ; S2.

3. Calculate the distance between the other wolves and the top three leaders using the following expression: Where, represents the second fluctuation term; Indicates the current location of other wolves; Indicates the current wolf and the distance of the wolf; Indicates the current wolf and the distance of the wolf; Indicates the current wolf and the distance of the wolf; S2.

4. Update the current wolf's position based on the distances between the other wolves and the top three leaders to obtain a new position. The expression for updating the positions of the top three leading wolves is as follows: Where, represents the first fluctuation term; Indicates the first candidate solution for the current duty cycle; Indicates the second candidate solution for the current duty cycle; represents the third candidate solution for the current duty cycle; The new position is obtained by taking the middle value of the three candidate solutions, as shown below: Where, Represents the new position, that is, the new duty cycle, if The fitness exceeds Wolf, then replace Wolf; S2.5, update the convergence factor, shrink the search range of the gray wolf population, and control the algorithm to transition from global search to local optimization; The expression for updating the convergence factor is as follows: Where, Indicates the current number of iterations, the interval is [0, ]; Indicates the maximum number of iterations; convergence factor is in the interval [0, 2]; S2.6, determine whether the maximum number of iterations has been reached, if so, stop the iteration and stabilize the photovoltaic system at the obtained On the duty cycle corresponding to the wolf; Otherwise, return to step S2.1 and continue iterating.

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