Photovoltaic MPPT control method based on beluga improved particle swarm optimization algorithm

Abstract

The application discloses a photovoltaic MPPT control method based on a beluga whale improved particle swarm algorithm, which comprises the following steps: obtaining the output voltage and current of a photovoltaic array and inputting the beluga whale improved particle swarm algorithm module, replacing the fixed inertia factor and learning factor in the particle swarm algorithm with a linear function; introducing the beluga whale algorithm, combining the beluga whale algorithm with the improved particle swarm algorithm through a common population pool method, initializing parameters, corresponding the beluga whale individual position and the target function to the output voltage and power of the photovoltaic system, and outputting the duty cycle of a Boost voltage converter; inputting the duty cycle into a PWM pulse signal generation module to generate a PWM pulse signal, and realizing the maximum power tracking of the photovoltaic system through a Boost impedance conversion circuit. The MPPT control method based on the beluga whale improved particle swarm algorithm can effectively improve the tracking precision and search speed of the MPPT algorithm under local shadow, and reduce the power fluctuation of the photovoltaic system during tracking.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power generation technology control strategies, and in particular to a photovoltaic MPPT control method based on the improved particle swarm optimization algorithm of beluga whales. Background Technology

[0002] In recent years, the photovoltaic (PV) power generation industry has experienced rapid development globally. However, the output of PV power generation is easily affected by external environmental factors, such as temperature, light intensity, and localized shading. Due to the presence of shadows, the output power of PV cells, as nonlinear components, fluctuates with changes in light intensity and temperature, making it difficult to maintain operation at maximum power output. Therefore, improving the performance of the maximum power point tracking (MPPT) control algorithm for PV systems is a key technology for the efficient utilization of PV power generation.

[0003] Currently, methods for directly optimizing the maximum power point (MPP) of photovoltaic (PV) systems using traditional MPPT algorithms mainly include the perturbation-observation method, the constant voltage method, and the incremental conductance method. However, under local shading conditions, the PU characteristic curve of the PV array exhibits a multi-peak state. These traditional algorithms, due to their fixed step size and overly simplistic tracking principle, cannot actually track the system's maximum power point, leading to getting trapped in a local maximum power point (LMPP) and causing misjudgments. To address the multi-peak problem, researchers have proposed several MPPT strategies based on intelligent algorithm control, such as ant colony optimization, neural networks, gray wolf algorithms, and particle swarm optimization. Among these, particle swarm optimization (PSO) has advantages such as simple concept, fast search, and wide search range, thus showing significant advantages in studying multi-peak maximum power tracking problems. To address the issue that traditional PSO algorithms may get stuck in local optima and cause power loss, the inertial weight ω and learning factors c1 and c2 in the particle velocity update formula are improved. The improved Particle Swarm Optimization (IPSO) algorithm can basically meet the requirements for global maximum power point tracking in multi-peak conditions, but the algorithm has a slow search speed and large power fluctuations in the early search stages. Summary of the Invention

[0004] The purpose of this invention is to provide a photovoltaic MPPT control method based on the improved beluga particle swarm algorithm, which solves the problems of slow tracking speed, low accuracy and large power fluctuation of the IPSO algorithm, thereby improving the MPPT control efficiency of photovoltaic power generation systems under local shading.

[0005] The technical solution to achieve the purpose of this invention is as follows: First, a photovoltaic MPPT control method based on the beluga whale improved particle swarm algorithm, comprising the following steps:

[0006] Step 1, the output voltage and current of the photovoltaic array are acquired and input into the beluga whale improved particle swarm algorithm module, and the fixed inertia factor omega and learning factors C1 and C2 in the particle swarm algorithm are replaced with linear functions;

[0007] Step 2, the beluga whale algorithm is introduced, the beluga whale algorithm is combined with the improved particle swarm algorithm through the method of sharing the population pool, parameters are initialized, the position of the beluga whale individual and the target function are corresponded to the output voltage and power of the photovoltaic system, and the duty ratio of the Boost step-up converter is output;

[0008] Step 3, the duty ratio D is input into the PWM pulse signal generation module to generate a PWM pulse signal, and the photovoltaic system maximum power tracking is realized through the Boost impedance conversion circuit.

[0009] In the second aspect, the present application provides an electronic device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method according to the first aspect when executing the program.

[0010] In the third aspect, the present application provides a computer readable storage medium, which stores a computer program, wherein the program is executable on a processor to implement the steps of the method according to the first aspect.

[0011] Compared with the prior art, the present application has the following advantages: the photovoltaic MPPT control method of the beluga whale improved particle swarm algorithm is designed to solve the problems of slow tracking speed, low precision and large power fluctuation of the IPSO algorithm, so as to improve the MPPT control efficiency of the photovoltaic power generation system under local shadow.

[0012] The present application will be further described in combination with the specific embodiments and the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0013] Figure 1 Fig. 1 is a structural schematic diagram of a photovoltaic MPPT control system in an embodiment of the present application.

[0014] Figure 2 Fig. 2 is a photovoltaic array simulation model in an embodiment of the present application.

[0015] Figure 3 Fig. 3 is a flow chart of the beluga whale improved particle swarm algorithm in an embodiment of the present application.

[0016] Fig. 4(a) is an I-U output characteristic curve diagram of a photovoltaic array under different shadow conditions in an embodiment of the present application.

[0017] Fig. 4(b) is a P-U output characteristic curve diagram of a photovoltaic array under different shadow conditions in an embodiment of the present application.

[0018] Figure 5is a power waveform graph of different MPPT algorithms in the embodiment of the application under dynamic shading of a photovoltaic system.

[0019] Fig. 6(a) is a partial enlarged view of a power waveform of different MPPT algorithms in the embodiment of the application under dynamic shading of a photovoltaic system.

[0020] Fig. 6(b) is a partial enlarged view of a power waveform of different MPPT algorithms in the embodiment of the application under dynamic shading of a photovoltaic system.

[0021] Fig. 6(c) is a partial enlarged view of a power waveform of different MPPT algorithms in the embodiment of the application under dynamic shading of a photovoltaic system. DETAILED DESCRIPTION

[0022] A photovoltaic MPPT control system structure diagram is shown in Figure 1 .

[0023] The application provides a photovoltaic MPPT control method based on a beluga whale improved particle swarm algorithm (BWO-IPSO), comprising the following steps:

[0024] Step 1, obtaining the output voltage and current of a photovoltaic array and inputting a beluga whale improved particle swarm algorithm module, replacing the fixed inertia factor ω and learning factors C1 and C2 in the particle swarm algorithm with a linear function;

[0025] Step 2, introducing a beluga whale algorithm, combining the beluga whale algorithm with the improved particle swarm algorithm through a common population pool method, initializing parameters, corresponding the beluga individual position and the target function to the output voltage and power of the photovoltaic system, and outputting the duty ratio of a Boost impedance converter;

[0026] Step 3, inputting the duty ratio D into a PWM pulse signal generation module to generate a PWM pulse signal, and realizing photovoltaic system maximum power tracking through a Boost impedance conversion circuit.

[0027] Further, the fixed inertia factor ω and learning factors C1 and C2 in the particle swarm algorithm are replaced with a linear function in step 1, and the expression is:

[0028]

[0029] In the formula, w min and w max are the lower bound and upper bound of the inertia weight w, respectively, and the values are w min = 0.2; w max = 0.9. k is the current iteration number; K is the maximum iteration number.

[0030] The improvement of the learning factors C1 and C2 is as follows:

[0031]

[0032]

[0033] where c 1min , c 1max and c 2min , c 2max are the lower and upper bounds of learning factors c1 and c2, respectively; c 1min = c 2min = 0; c 1max = c 2max = 2; k is the current iteration number; K is the maximum iteration number.

[0034] Further, the process of outputting the duty cycle for controlling the Boost voltage converter in step 2 is as follows:

[0035] (1) Initialize population parameters: set the maximum iteration number T, population size N, and algorithm parameters, etc.

[0036] (2) Take the position X i of the white whale individual as the current voltage U i for the next iteration, calculate the objective function of BWO-IPSO by collecting the voltage and current of the photovoltaic output, and obtain the corresponding photovoltaic output power. The best position and power value of the population individual are recorded in the process;

[0037] (3) When the random number RAND>0.5, the BWO algorithm is used for tracking iteration, and the individual position is updated by using the balance factor B f and the probability of whale falling W f ; when RAND<0.5, the IPSO algorithm is used for tracking iteration. The balance factor B f is expressed as:

[0038]

[0039] where t is the current iteration, T is the maximum iteration number, the random number B0 changes randomly between (0, 1) at each iteration; the exploration stage occurs when the balance factor B f >0.5, while the development stage occurs when B f ≤0.5. As the iteration T increases, the fluctuation range of B f decreases from (0, 1) to (0, 0.5), indicating that the probabilities of the development and exploration stages change significantly, and the probability of the development stage increases with the increasing iteration T.

[0040] When the balance factor B f >0.5, the individual position update formula is:

[0041]

[0042] where t is the current iteration number, is the new position of the i-th white whale in the j-th dimension in the next iteration. r1 and r2 are random numbers in the range of [0, 1] to enhance the random operator in the exploration phase. sin(2πr2) and cos(2πr2) are used to average the random numbers between the fins, and the updated position reflects the synchronization or mirror behavior of the white whale when swimming or diving according to the selected dimensions of odd and even numbers.

[0043] When the balance factor B f <0.5, the individual position update formula is:

[0044]

[0045] where t is the current iteration number, and is the current position of the i-th white whale and a random white whale, is the new position of the i-th white whale, is the best position in the whale, r3 and r4 are random numbers in the range of (0, 1), and C1 = 2r4(1-t / T) is used to measure the random jump strength of the Levy flight.

[0046] L F is the Levy flight function, which is calculated as follows:

[0047]

[0048] where u and v are normally distributed random numbers with B is the default constant, equal to 1.5. The variance of both is:

[0049]

[0050] By adding the Levy flight strategy to the algorithm, the global search ability of the white whale algorithm itself can be effectively compensated without the need to satisfy the search speed and improve the population size, so as to better perform the full space optimization.

[0051] (4) When the balance factor B f <whale falling probability W f , the individual position update formula is:

[0052]

[0053] where r5, r6 and r7 are random numbers between 0 and 1, X stepis the step size of the whale falling, determined as:

[0054]

[0055] where C2 is a step size factor related to the falling probability of the whale and the population size (C2 = 2W f , u b and l b are the upper and lower bounds of the variables, respectively. It can be seen that the step size is affected by the bounds of the design variables, the iteration number and the maximum number of iterations.

[0056] (5) When the system determines that the maximum number of iterations is reached, the current optimal individual, i.e. the optimal voltage value corresponding to it, is output, and when the restart condition is met, the algorithm is restarted, so that the control algorithm always tracks the global maximum power point.

[0057] Further, the restart condition of the algorithm also includes:

[0058] When the light intensity changes, i.e. the power values before and after are detected to change, the algorithm is restarted, and the restart condition is as follows:

[0059] (P1-P0) / P1>λ (11)

[0060] where P0 and P1 are the output powers of the photovoltaic system at the previous time and the current time, respectively, and λ is the maximum power change, which is taken as 0.05 through simulation experiments.

[0061] Further, it also includes: verifying the maximum power tracking performance of the beluga improved particle swarm algorithm under local shadow through a photovoltaic system model.

[0062] Further, before verifying the maximum power tracking performance of the beluga improved particle swarm algorithm under local shadow through the photovoltaic system model, it also includes:

[0063] Establishing a photovoltaic power generation system simulation model, setting different light conditions, obtaining the P-U and I-U output characteristic curves of the photovoltaic array under different light, and preparing for the MPPT control algorithm of the photovoltaic system.

[0064] Further, the photovoltaic power generation system simulation model includes: a photovoltaic array, a Boost circuit, a load, and an MPPT algorithm module; the MPPT controller adjusts the output duty cycle according to the collected working voltage and power of the photovoltaic system, controls the working voltage of the photovoltaic power generation system, and thus realizes maximum power point tracking.

[0065] Embodiment

[0066] Considering that the output power characteristics of a photovoltaic array in a local shading environment are relatively complex, in order to further summarize the multi-peak law of the output power of the photovoltaic array under local shading, a simulation model of the photovoltaic array under local shading is first built through Matlab / Simulink, Figure 2 The simulation model of the photovoltaic array in the embodiment of the present application is shown in Table 1, and Table 1 shows the parameters of different light intensities set in the embodiment of the present application:

[0067] Table 1 shows the light intensity parameter settings under four shading conditions

[0068]

[0069] Figure 3 is a flowchart of the improved particle swarm optimization algorithm of beluga whales in the embodiment of the present application, and specifically includes the following steps:

[0070] (1) initializing population parameters: setting the maximum number of iterations T, the population size N and algorithm parameters, etc.;

[0071] (2) updating the position X i of the beluga whale individual and the corresponding velocity V i of the beluga whale individual according to the following formula:

[0072] (3) when the random number RAND>0.5, the BWO algorithm is used for tracking iteration, and the individual position is updated by using the balance factor B f and the probability W f of the whale falling in the BWO algorithm; when RAND<0.5, the IPSO algorithm is used for tracking iteration. The balance factor B f is expressed as:

[0073]

[0074] In the formula, t is the current iteration, T is the maximum number of iterations, and the random number B0 varies randomly between (0, 1) at each iteration. The exploration stage occurs when the balance factor B f >0.5, while the development stage occurs when B f ≤0.5. As the iteration T increases, the fluctuation range of B f decreases from (0, 1) to (0, 0.5), indicating that the probabilities of the development and exploration stages have changed significantly, and the probability of the development stage increases as the iteration T increases.

[0075] When the balance factor B f >0.5, the individual position update formula is:

[0076]

[0077] where t is the current iteration number, is the new position of the i-th white whale in the j-th dimension in the next iteration. r1 and r2 are random numbers in the range of [0, 1] to enhance the random operator in the exploration phase. sin(2πr2) and cos(2πr2) are used to average the random numbers between the fins, and the updated position reflects the synchronization or mirror behavior of the white whale when swimming or diving according to the selected dimensions of odd and even numbers.

[0078] When the balance factor B f <0.5, the individual position update formula is:

[0079]

[0080] where t is the current iteration number, and is the current position of the i-th white whale and a random white whale, is the new position of the i-th white whale, is the best position in the whale, r3 and r4 are random numbers in the range of (0, 1), and C1 = 2r4(1-t / T) is used to measure the random jump strength of Levy flight strength.

[0081] L F is the Levy flight function, which is calculated as follows:

[0082]

[0083] where u and v are normally distributed random numbers with B is the default constant, equal to 1.5. The variance of both is:

[0084]

[0085] By adding the Levy flight strategy to the algorithm, the global search ability of the white whale algorithm itself can be effectively compensated without the need to satisfy the search speed and improve the population size, so as to better perform the full space optimization.

[0086] (4) When the balance factor B f <Whale falling probability W f , the individual position update formula is:

[0087]

[0088] where r5, r6 and r7 are random numbers between 0 and 1, X step is the step size of the whale falling, determined as:

[0089]

[0090] where C2 is a step size factor related to the probability of whale descent and population size (C2 = 2W f ×n), u b and l b are the upper and lower bounds of the variable, respectively. It can be seen that the step size is affected by the bounds of the design variable, the number of iterations, and the maximum number of iterations.

[0091] (5) When the system determines that the maximum number of iterations is reached, the current optimal individual, i.e., the optimal voltage value corresponding to the optimal individual, is outputted, and when the restart condition is met, the algorithm is restarted, so that the control algorithm always tracks the global maximum power point.

[0092] Fig. 4(a) and Fig. 4(b) are P-U and I-U output characteristic curve diagrams of a photovoltaic array in different shading conditions in the embodiment of the present application. It can be seen that in working condition 1, the light intensity is uniform, and the I-U and P-U curve diagrams of the photovoltaic array both present a single peak value. In working conditions 2, 3, and 4, the P-U curve diagrams of the photovoltaic arrays in different shading conditions present 2, 3, and 4 peak points, respectively, and each condition has only one maximum power point, and the rest are local extreme points. Through the above analysis, it can be known that for a photovoltaic array of {m x n}, due to different light received by the photovoltaic cells in series branches, the photovoltaic array output power presents at most n peak points, which lays a foundation for subsequent research on the MPPT algorithm of the photovoltaic array under local shading.

[0093] Figure 5 is a power waveform diagram of different MPPT algorithms in the embodiment of the present application under dynamic shading of a photovoltaic system. Figures 6(a) to 6(c) is a local enlarged diagram of the power waveform of different MPPT algorithms in the embodiment of the present application under dynamic shading of a photovoltaic system. The results show that compared with the IPSO algorithm, the tracking speed of the BWO algorithm is similar, the tracking accuracy of the BWO algorithm is slightly higher than that of the IPSO algorithm, the advantage of the BWO-IPSO algorithm in tracking the maximum power of the photovoltaic system under local shading is more obvious, the tracking speed is the fastest, and the power fluctuation is smaller, which effectively improves the power generation efficiency of the photovoltaic power generation system.

[0094] The above discussion is only one embodiment of the present application, and any equivalent transformation made on the basis of the present application is included in the patent protection scope of the present application.

Claims

1. A photovoltaic MPPT control method based on beluga improved particle swarm algorithm, characterized in that, Comprising the following steps: Step 1, get the output voltage and current of photovoltaic array and input into beluga improved particle swarm algorithm module, replace the fixed inertia factor C1 and learning factor C2 in particle swarm algorithm with linear function and learning factor C1, C2 with linear function Step 2, introduce beluga algorithm, combine beluga algorithm with improved particle swarm algorithm by the method of common population pool, initialize parameters, correspond the position of beluga individual and target function to the output voltage and power of photovoltaic system, output the duty ratio of Boost converter, including: (1) Initialize population parameters: set the maximum number of iterations T, population size N and algorithm parameters; (2) the position of the individual white whale as the current voltage The next iteration is performed by collecting the voltage and current of the photovoltaic output to calculate the objective function of BWO-IPSO, and the corresponding photovoltaic output power is obtained; and the best position and power value of the population individual are recorded in the process; (3) When the random number RAND>0.5, the BWO algorithm is used for tracking iteration, and the balance factor B f and the probability W of whale falling f The individual position is updated; when RAND<0.5, the IPSO algorithm is used for tracking iteration; (4) When the balance factor B f < Whale falling probability W f The update formula of the individual position is: （9） where r5, r6, and r7are random numbers between (0, 1), and is the current position of the ith beluga and a random beluga, X step is the step size of the whale fall, determined as: （10） where C2is a step factor related to the whale drop probability and population size, u b and l b are the upper and lower bounds of the variable, respectively, and t is the current iteration number. (5) When the system determines that the maximum number of iterations is reached, the current optimal individual is output, that is, the optimal voltage value corresponding to it, and when the restart condition is met, the algorithm is restarted, so that the control algorithm always tracks the global maximum power point; Step 3, input the duty ratio D into the PWM pulse signal generation module to generate PWM pulse signal, and realize the maximum power tracking of photovoltaic system through Boost impedance conversion circuit.

2. The photovoltaic MPPT control method based on the improved white whale particle swarm algorithm according to claim 1, characterized in that, The fixed inertia factor in the particle swarm algorithm in the step 1 and the learning factors C1, C2 are replaced by linear functions, the expression being: （1） wherein and are the lower and upper bounds of the inertia weight respectively, taking values = 0.2, = 0.9; is the current iteration number; is the maximum iteration number; The improvement of learning factors C1 and C2 is expressed as: （2） （3） wherein: , and , are lower and upper bounds of learning factors and , respectively; take values = = 0; = = 2; is the current iteration number; is the maximum iteration number.

3. The photovoltaic MPPT control method based on the improved particle swarm algorithm of beluga whale according to claim 1, characterized in that, Balancing factor B f The expression is: （4） where t is the current iteration, T is the maximum number of iterations, and the random number B0 varies randomly between (0, 1) at each iteration; the exploration phase occurs when the balance factor B f > 0.5, while the exploitation phase occurs when B f ≤ 0.

5. When the balance factor B f > 0.5, the individual position update formula is: （5） where t is the current iteration number, is the new position of the ith beluga in the jth dimension in the next iteration; r1 and r2 are random numbers ranging in [0,1] used to enhance the random operator in the exploration phase; and is the random number used to average the fins, the updated position reflects the synchronized or mirrored behavior of the belugas when swimming or diving, depending on the dimension chosen as odd or even. When the balance factor B f < 0.5, the individual position update formula is: （6） where t is the current iteration number, and is the current position of the ith white whale and a random white whale, is the new position of the ith white whale, is the best position in the whale population, r3and r4are random numbers ranging in (0, 1), and C1= 2r4(1−t / T) is used to measure the random jump strength of Levy flight. L F Levy flight function, which is calculated as follows: （7） where u and v are random numbers of normal distribution with mean 0 and variance 1 ; ; is a default constant equal to 1.5; and the variance is: （8）。 4. The photovoltaic MPPT control method based on the improved white whale particle swarm algorithm according to claim 2, characterized in that, It also includes the restart condition of the algorithm: When the light intensity changes, that is, the power values before and after are detected to change, the algorithm is restarted, and the restart condition is as follows: （11） wherein , are the output power of the photovoltaic system corresponding to the previous time and the current time, respectively, is the maximum amount of power change.

5. The photovoltaic MPPT control method based on the improved white whale particle swarm algorithm according to claim 1, characterized in that, It also includes: The performance of the beluga improved particle swarm algorithm in maximum power tracking under local shadow is verified through the photovoltaic system model.

6. The photovoltaic MPPT control method based on the improved white whale particle swarm algorithm according to claim 5, characterized in that, Before the performance of the beluga improved particle swarm algorithm in maximum power tracking under local shadow is verified through the photovoltaic system model, it further includes: Establish a photovoltaic power generation system simulation model, set different light conditions, and obtain the P-U and I-U output characteristic curves of photovoltaic array under different light conditions.

7. The photovoltaic MPPT control method based on the improved white whale particle swarm algorithm according to claim 6, characterized in that, The photovoltaic power generation system simulation model includes: photovoltaic array, Boost circuit, load, MPPT algorithm module; the MPPT controller adjusts the output duty ratio according to the collected photovoltaic system operating voltage and power, controls the photovoltaic power generation system operating voltage, so as to realize maximum power point tracking.

8. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to realize the steps of the method of any one of claims 1-7.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to realize the steps of the method of any one of claims 1-7.

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