An MPPT control method and system based on improved particle swarm optimization algorithm

Abstract

The present invention relates to an MPPT control method and system based on an improved particle swarm optimization algorithm. The method comprises: obtaining preset iteration parameters and particle parameters, as well as current photovoltaic circuit parameters; determining a fitness value based on the iteration parameters and photovoltaic circuit parameters, and updating an individual optimal position and a group optimal position; updating the particle parameters based on the updated individual optimal position, the updated group optimal position, an inertia weight, and a Gaussian perturbation term until a preset termination condition is met, wherein the inertia weight is set according to a chaotic map, the Gaussian perturbation term is set according to a Gaussian distribution, and the optimal duty cycle is determined based on the particle parameters finally updated. The present invention sets a chaotic inertia weight to achieve rapid optimization, and sets a Gaussian perturbation term to enable the algorithm to escape from a local optimum. While ensuring accuracy, the system can track the global maximum power point at a relatively fast speed and significantly reduce the degree of search oscillation of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation, and in particular to an MPPT control method and system based on an improved particle swarm optimization algorithm. Background Art

[0002] Solar energy, as one of the most readily available and inexhaustible clean energy sources, is widely used. For photovoltaic power generation systems, it is particularly important to find the optimal operating state of photovoltaic cells, ensuring that they operate at their maximum power point. The technology that uses control methods to achieve maximum power output from photovoltaic cells is called maximum power point tracking (MPPT). Due to the nonlinear output characteristics of photovoltaic cells, the PV curve exhibits peaks, which are easily affected by external environmental factors such as irradiance and temperature. Under partial shading conditions, the PV curve may exhibit multiple peaks.

[0003] Traditional MPPT methods include the perturbation-observation method and the conductance increment method. These MPPT algorithms are prone to getting stuck in local optima in multi-peak situations. To address this issue, numerous researchers have proposed a number of new intelligent MPPT algorithms, such as the particle swarm optimization (PSO), genetic algorithm, gray wolf algorithm, and immune firefly algorithm. The PSO is widely used due to its simplicity and ease of implementation. However, traditional PSOs are prone to premature convergence and getting stuck in local optima when parameters are poorly selected. They also suffer from slow optimization speed. Therefore, improving the optimization speed of MPPT algorithms and avoiding local optima are pressing issues. Summary of the Invention

[0004] In view of this, it is necessary to provide an MPPT control method and system based on an improved particle swarm optimization algorithm to overcome the problems in the prior art of slow optimization speed and easy falling into local optimal solutions of the MPPT algorithm.

[0005] In order to solve the above technical problems, the present invention provides an MPPT control method based on an improved particle swarm optimization algorithm, comprising:

[0006] Obtain preset iteration parameters and particle parameters, as well as current photovoltaic circuit parameters;

[0007] Determine the fitness value according to the iteration parameter and the photovoltaic circuit parameter, and update the individual optimal position and the group optimal position;

[0008] updating the particle parameters according to the updated individual optimal position, the updated group optimal position, the inertia weight, the Gaussian perturbation term, and the nonlinear learning factor until a preset termination condition is satisfied, wherein the inertia weight is set according to a chaotic map, the Gaussian perturbation term is set according to a Gaussian distribution, and the nonlinear learning factor is set according to a logarithmic function;

[0009] The optimal duty cycle is determined based on the final updated particle parameters.

[0010] Furthermore, the photovoltaic circuit parameters include a current output current value and a current output voltage value of the photovoltaic array. The determining of the fitness value based on the iteration parameters and the photovoltaic circuit parameters, and updating of the individual optimal position and the group optimal position include:

[0011] Determine the current power of the system according to the product of the current output current value and the current output voltage value, and use the current power of the system as the system fitness value;

[0012] Comparing the particle fitness value of the current iteration particle with the system fitness value, and using the system fitness value to update the individual optimal position;

[0013] Compare the first fitness value corresponding to the updated individual optimal position with the second fitness value corresponding to the optimal position of the particle group;

[0014] If the first fitness value corresponding to the updated individual optimal position is greater than the second fitness value corresponding to the optimal position of the particle group, the optimal position of the particle group is updated, otherwise it remains unchanged.

[0015] Furthermore, the particle parameters include particle position and particle velocity, and updating the particle parameters according to the updated individual optimal position, the updated group optimal position, the inertia weight, the Gaussian perturbation term, and the nonlinear learning factor includes:

[0016] Based on the chaotic mapping, a chaotic Sine mapping function is determined, and the inertia weight is set according to the iteration parameter and the chaotic Sine mapping function; a nonlinear learning factor is set according to the natural logarithm function; and the Gaussian disturbance term is set based on the distribution function form of the Gaussian distribution;

[0017] updating the particle velocity according to the inertia weight, the nonlinear learning factor, the Gaussian disturbance term, the updated individual optimal position, and the updated group optimal position;

[0018] The updated particle position is determined based on the sum of the current particle position and the current particle velocity.

[0019] Furthermore, the inertia weight is expressed by the following formula:

[0020] w k =S(k)*w min +(w max -w min )*(k / k_max)

[0021] S(k)=μ*sin(S(k-1)*π), S(0)=rand()

[0022] Among them, w k represents the inertia weight at the kth iteration, S(k) represents the chaotic Sine mapping function, S(0) represents the initial chaotic value of the chaotic Sine mapping function, rand() represents the selection of a random value, k represents the current number of iterations, k_max represents the preset iteration parameter, w max represents the upper limit of the inertia weight at the kth iteration, w min represents the lower limit of the inertia weight at the kth iteration, and μ represents the preset chaos value.

[0023] Furthermore, the learning factor includes a first learning factor and a second learning factor, wherein:

[0024] The first learning factor is expressed by the following formula:

[0025] c1=c 1_max -(c 1_max -C 1_min )*ln(1+(e-1)*(k / k_max))

[0026] The second learning factor is expressed by the following formula:

[0027] c2=c 2_min +(c 2_max -c 2_min )*ln(1+(e-1)*(k / k_max))

[0028] Wherein, c1 represents the first learning factor, c 1_max represents the upper limit of the first learning factor, c 1_min represents the lower limit of the first learning factor, c2 represents the second learning factor, c 2_max represents the upper limit of the second learning factor, c 2_max represents the lower limit of the second learning factor, k represents the current number of iterations, and k_max represents the preset iteration parameter.

[0029] Furthermore, the learning factor includes a first learning factor and a second learning factor, and updating the particle velocity according to the inertia weight, the nonlinear learning factor, the Gaussian disturbance term, the updated individual optimal position, and the updated group optimal position includes:

[0030] Determining a velocity inertia component term according to the product of the inertia weight and the current particle velocity;

[0031] Determining the Gaussian disturbance term according to the first Gaussian random value, the second Gaussian random value and the Gaussian normal distribution;

[0032] determining an individual cognitive term according to the first learning factor, the updated individual optimal position, the current particle position, and the Gaussian disturbance term;

[0033] determining a social cognition item based on the second learning factor, the updated optimal position of the group, and the current particle position;

[0034] An updated particle velocity is determined according to the sum of the velocity inertia component term, the individual cognition term, and the social cognition term.

[0035] Furthermore, the updated particle velocity is expressed by the following formula:

[0036]

[0037]

[0038] in, represents the updated particle velocity, w k represents the inertia weight, represents the current particle speed, c1 represents the first learning factor, c2 represents the second learning factor, r1 represents the preset first random parameter, r2 represents the preset second random parameter, represents the updated individual optimal position, represents the updated swarm optimal position, Indicates the current particle position, r3 indicates the first Gaussian random value, r4 indicates the second Gaussian random value, Represents the Gaussian perturbation term, Gaussian(μ, σ 2 ) means the mean is μ and the variance is σ 2 Gaussian distribution, k represents the current iteration number, and i represents the number of particles.

[0039] Furthermore, the preset termination condition includes: the position between the updated particles is less than a preset distance value; wherein, if the preset termination condition is not met, returning to the step of determining the fitness value based on the iteration parameters and the photovoltaic circuit parameters, and updating the individual optimal position and the group optimal position.

[0040] Furthermore, the control method further includes:

[0041] Determining a first difference value based on a difference between actual power and iterative expected power, wherein the actual power is the output power of the photovoltaic array actually detected, and the iterative expected power is the system fitness value calculated correspondingly when the iteration is terminated;

[0042] determining a first ratio according to a ratio of the first difference to the iterative expected power;

[0043] When the first ratio is greater than a preset restart value, the control is restarted and returns to the step of obtaining the preset iteration parameters and particle parameters, as well as the current photovoltaic circuit parameters.

[0044] The present invention also provides an MPPT control system based on a particle swarm optimization algorithm, comprising a photovoltaic array, a first capacitor, a first inductor, a second capacitor and a first resistor connected in parallel with the photovoltaic array in sequence, a first MOS transistor arranged between the parallel ends on the same side of the first capacitor and the first inductor, a second MOS transistor arranged between the parallel ends on the same side of the first inductor and the second capacitor, and a controller, wherein the processor of the controller is used to execute the MPPT control method based on the particle swarm optimization algorithm as described above, and input a pulse signal corresponding to the optimal duty cycle into the first MOS transistor and the second MOS transistor.

[0045] Compared with the prior art, the beneficial effects of the present invention include: first, the iteration parameters, particle parameters, and current photovoltaic circuit parameters are effectively acquired to initialize the particle swarm parameters; then, the fitness value is determined using the photovoltaic circuit parameters to effectively optimize the individual optimal position and the group optimal position; further, the inertia weight, Gaussian perturbation term and nonlinear learning factor are combined to avoid falling into the local optimum, reduce the degree of oscillation, improve the algorithm's optimization ability, and ensure the accuracy of the iterative results; finally, the optimal duty cycle is determined based on the particle parameters determined in the last iteration.

[0046] In summary, the chaotic inertia weight set in the present invention has the ability to quickly find the best solution, and setting the Gaussian perturbation term will enable the algorithm to have the ability to jump out of the local optimal point. Setting the nonlinear learning factor can balance the global and local capabilities of the algorithm. The combined advantages of the three make the convergence speed faster and the system oscillation smaller, balance the global development and local exploration capabilities of the algorithm, avoid the system from falling into the local maximum power point, and enable the system to track the global maximum power point at a faster speed while ensuring accuracy, and greatly reduce the degree of search oscillation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A flow chart of an embodiment of an MPPT control method based on an improved particle swarm optimization algorithm provided by the present invention;

[0048] Figure 2 The present invention provides Figure 1 A flow chart of an embodiment of step S102;

[0049] Figure 3 The present invention provides Figure 1 A flow chart of an embodiment of step S103;

[0050] Figure 4 The present invention provides Figure 3 Flow chart of step S302 in an embodiment;

[0051] Figure 5 A flow chart of another embodiment of the MPPT control method based on the improved particle swarm optimization algorithm provided by the present invention;

[0052] Figure 6 A system diagram of an embodiment of an MPPT control system based on an improved particle swarm optimization algorithm provided by the present invention;

[0053] Figure 7 2 is a distribution diagram of an embodiment of a Gaussian distribution with a mean of 0 and a variance of 1 provided by the present invention;

[0054] Figure 8 This is a distribution diagram of an embodiment of a chaotic Sine map provided by the present invention;

[0055] Figure 9 1 is a weight diagram of an embodiment of the inertia weight of the chaotic Sine map provided by the present invention;

[0056] Figure 10 This is a trajectory diagram of an embodiment of a particle motion trajectory provided by the present invention;

[0057] Figure 11 This is a power waveform diagram of an embodiment of output power controlled by the Sin-GPSO algorithm MPPT provided by the present invention;

[0058] Figure 12 This is a power waveform comparison diagram of the Sin-GPSO algorithm provided by the present invention and another algorithm embodiment;

[0059] Figure 13 This is a power waveform comparison diagram of another embodiment of the Sin-GPSO algorithm provided by the present invention and other algorithms;

[0060] Figure 14 A schematic structural diagram of an embodiment of an MPPT control device based on an improved particle swarm optimization algorithm provided by the present invention;

[0061] Figure 15 This is a structural diagram of an embodiment of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0062] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, and are not used to limit the scope of the present invention.

[0063] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include at least one of such features. Furthermore, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0064] In the description of the present invention, reference to an "embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the described embodiment may be combined with other embodiments.

[0065] In the description of the present invention, the execution order of the steps between processes is not limited to the order in which they appear in the text, and the corresponding order can be adjusted in sequence or appear in parallel.

[0066] The present invention provides an MPPT control method and system based on an improved particle swarm optimization algorithm, sets the inertia weight based on a chaotic map, sets the Gaussian disturbance term based on a Gaussian distribution, and sets the nonlinear learning factor based on a logarithmic function, providing a new idea for further improving the optimization speed and accuracy of the MPPT control method.

[0067] Before describing the embodiments, the following definitions are given for the relevant terms:

[0068] MPPT control technology: A Maximum Power Point Tracking (MPPT) system adjusts the operating state of electrical modules to maximize the power output of photovoltaic panels. This system effectively stores the DC power generated by solar panels in batteries, effectively addressing household and industrial electricity needs in remote and tourist areas not covered by conventional power grids without causing environmental pollution. It detects the voltage generated by solar panels in real time and tracks the peak voltage and current (V1) value, allowing the system to charge the battery at maximum power output. Applied to solar photovoltaic systems, it coordinates the operation of solar panels, batteries, and loads, serving as the brains of the photovoltaic system.

[0069] Particle Swarm Optimization (PSO), also known as particle swarm optimization (PSO), is a collaborative random search algorithm developed by simulating the foraging behavior of bird flocks. It is generally considered a form of swarm intelligence (SI). As a classic intelligent optimization algorithm, its essence is to iteratively update positions to find the optimal value.

[0070] Based on the description of the technical terms above, the current particle swarm optimization algorithm is prone to premature maturation when the parameters are not properly selected, falling into local optimal solutions. It also has problems such as slow optimization speed. To address the above problems, the present invention aims to propose an MPPT control method and system based on the particle swarm optimization algorithm.

[0071] The specific embodiments are described in detail below:

[0072] The embodiment of the present invention provides an MPPT control method based on an improved particle swarm optimization algorithm, combined with Figure 1 Come and see, Figure 1 The flowchart of an embodiment of the MPPT control method based on the improved particle swarm optimization algorithm provided by the present invention includes steps S101 to S104, wherein:

[0073] In step S101, the preset iteration parameters and particle parameters, as well as the current photovoltaic circuit parameters are obtained;

[0074] In step S102, a fitness value is determined according to the iteration parameter and the photovoltaic circuit parameter, and the individual optimal position and the group optimal position are updated;

[0075] In step S103, the particle parameters are updated according to the updated individual optimal position, the updated group optimal position, the inertia weight, the Gaussian perturbation term, and the nonlinear learning factor until a preset termination condition is met, wherein the inertia weight is set according to the iterative parameter based on the chaotic mapping, the Gaussian perturbation term is set according to the Gaussian distribution, and the nonlinear learning factor is set according to the logarithmic function;

[0076] In step S104, the optimal duty cycle is determined according to the finally updated particle parameters.

[0077] In an embodiment of the present invention, first, the iteration parameters, particle parameters, and current photovoltaic circuit parameters are effectively acquired to initialize the particle swarm parameters; then, the photovoltaic circuit parameters are used to determine the fitness value, thereby effectively optimizing the individual optimal position and the group optimal position; further, the inertia weight, Gaussian perturbation term, and nonlinear learning factor are combined to avoid falling into the local optimum, so that the algorithm has a faster convergence speed, reduces the degree of oscillation, improves the optimization ability of the algorithm, and ensures the accuracy of the iterative results; finally, based on the particle parameters determined in the last iteration, the optimal duty cycle is determined.

[0078] It should be noted that the above-mentioned particle parameters include at least one of particle position and particle velocity, wherein the particle position corresponds to the duty cycle.

[0079] It should be noted that the photovoltaic circuit parameters include at least one of the current output current value and the current output voltage value of the photovoltaic array. It is understandable that the current output current value and the current output voltage value can be obtained through monitoring and collection.

[0080] As a preferred embodiment, Figure 2 Come and see, Figure 2 The present invention provides Figure 1 The flowchart of an embodiment of step S102 includes steps S201 to S204, wherein:

[0081] In step S201, the current power of the system is determined according to the product of the current output current value and the current output voltage value, and the current power of the system is used as the system fitness value;

[0082] In step S202, the particle fitness value of the current iteration particle is compared with the system fitness value, and the individual optimal position is updated using the system fitness value;

[0083] In step S203, the first fitness value corresponding to the updated individual optimal position is compared with the second fitness value corresponding to the optimal position of the particle group;

[0084] In step S204 , if the first fitness value corresponding to the updated individual optimal position is greater than the second fitness value corresponding to the optimal position of the particle swarm, the optimal position of the particle swarm is updated, otherwise it remains unchanged.

[0085] In the embodiment of the present invention, the product of the current output current value and the current output voltage value, that is, the power output by the photovoltaic array, is used to determine the corresponding fitness value, and the individual particles and the overall particles are updated.

[0086] As a preferred embodiment, Figure 3 Come and see, Figure 3The present invention provides Figure 1 The flowchart of an embodiment of step S103 includes steps S301 to S303, wherein:

[0087] In step S301, based on the chaotic mapping, a chaotic Sine mapping function is determined, and the inertia weight is set according to the iteration parameter and the chaotic Sine mapping function; a nonlinear learning factor is set according to the natural logarithm function; and the Gaussian disturbance term is set based on the distribution function form of the Gaussian distribution;

[0088] In step S302, the particle velocity is updated according to the inertia weight, the nonlinear learning factor, the Gaussian disturbance term, the updated individual optimal position, and the updated group optimal position;

[0089] In step S303, the updated particle position is determined according to the sum of the current particle position and the current particle velocity.

[0090] In an embodiment of the present invention, the particle position and particle velocity are effectively updated by combining inertia weight, Gaussian perturbation and nonlinear learning factor.

[0091] As a preferred embodiment, the inertia weight is expressed by the following formula:

[0092] w k =S(k)*w min +(w max -w min )*(k / k_max)

[0093] S(k)=μ*sin(S(k-1)*π), S(0)=rand()

[0094] Among them, w k represents the inertia weight at the kth iteration, S(k) represents the chaotic Sine mapping function, S(0) represents the initial chaotic value of the chaotic Sine mapping function, rand() represents the selection of a random value, k represents the current number of iterations, k_max represents the preset iteration parameter, w max represents the upper limit of the inertia weight at the kth iteration, w min represents the lower limit of the inertia weight at the kth iteration, and μ represents the preset chaos value.

[0095] In an embodiment of the present invention, a chaotic Sine map is used to set the inertia weight, and a random nonlinear increasing inertia weight is constructed, thereby randomly and nonlinearly changing the iteration speed of the particle swarm, improving the global optimization ability of the particles, and making it converge faster.

[0096] It should be noted that when μ∈(0.87, 0.93) & (0.95, 1), chaos occurs. In the embodiment of the present invention, the optimal value of μ is 0.99.

[0097] As a preferred embodiment, the learning factor includes a first learning factor and a second learning factor, wherein:

[0098] The first learning factor is expressed by the following formula:

[0099] c1=c 1_max -(c 1_max -c 1_min )*ln(1+(e-1)*(k / k_max));

[0100] The second learning factor is expressed by the following formula:

[0101] c2=c 2_min +(c 2_max -c 2_min )*ln(1+(e-1)*(k / k_max));

[0102] Where c1 represents the first learning factor, C 1_max represents the upper limit of the first learning factor, c 1_min represents the lower limit of the first learning factor, c2 represents the second learning factor, c 2_max represents the upper limit of the second learning factor, c 2_max represents the lower limit of the second learning factor, k represents the current number of iterations, and k_max represents the preset iteration parameter.

[0103] In an embodiment of the present invention, a natural logarithm function is used to set a learning factor and construct a nonlinear learning factor, so that the local search ability of particles is improved in the early stage of the search, and the global optimization ability of particles is improved in the later stage of the search. Even if the algorithm focuses on self-learning ability in the early stage and social learning ability in the later stage, the global development and local exploration capabilities of the algorithm are balanced.

[0104] As a preferred embodiment, Figure 4 Come and see, Figure 4 The present invention provides Figure 3 The flowchart of an embodiment of step S302 includes steps S401 to S405, wherein:

[0105] In step S401, the velocity inertia component term is determined according to the product of the inertia weight and the current particle velocity;

[0106] In step S402, the Gaussian disturbance term is determined according to the first Gaussian random value, the second Gaussian random value and the Gaussian normal distribution;

[0107] In step S403, an individual cognitive term is determined according to the first learning factor, the updated individual optimal position, the current particle position, and the Gaussian disturbance term;

[0108] In step S404, a social cognition item is determined based on the second learning factor, the updated group optimal position, and the current particle position;

[0109] In step S405 , the updated particle velocity is determined according to the sum of the velocity inertia component term, the individual cognition term, and the social cognition term.

[0110] In an embodiment of the present invention, the velocity inertia component term is determined based on the inertia weight, and the individual cognition term is determined based on the Gaussian disturbance term. The above terms are combined to effectively update the particle velocity.

[0111] As a preferred embodiment, the updated particle velocity is expressed by the following formula:

[0112]

[0113]

[0114] in, represents the updated particle velocity, w k represents the inertia weight, represents the current particle speed, c1 represents the first learning factor, c2 represents the second learning factor, r1 represents the preset first random parameter, r2 represents the preset second random parameter, represents the updated individual optimal position, represents the updated swarm optimal position, Indicates the current particle position, r3 indicates the first Gaussian random value, r4 indicates the second Gaussian random value, Represents the Gaussian perturbation term, Gaussian(μ, σ 2 ) means the mean is μ and the variance is σ 2 Gaussian distribution, k represents the current iteration number, and i represents the number of particles.

[0115] It should be noted that r1, r2, r3, and r4 are random numbers between 0 and 1. represents the Gaussian perturbation generated by particle i at the kth iteration.

[0116] In the embodiment of the present invention, Gaussian perturbation is used to reset the speed at which particles approach their individual optimal positions. In essence, this is to increase the Gaussian perturbation in the learning speed of particles based on their own optimal experience, thereby enhancing the ability of particles to escape from local optimality.

[0117] As a more specific embodiment, updating the particle position using a formula is expressed by the following formula:

[0118]

[0119] In the embodiment of the present invention, the current particle position is effectively updated using the current particle velocity.

[0120] As a preferred embodiment, the preset termination condition includes: the position between the updated particles is less than a preset distance value; wherein, if the preset termination condition is not met, returning to the step of determining the fitness value based on the iteration parameters and the photovoltaic circuit parameters, and updating the individual optimal position and the group optimal position.

[0121] In the embodiment of the present invention, a suitable preset termination condition is set to effectively find the optimal solution and exit the corresponding iterative process.

[0122] The above step of returning to the step of determining the fitness value according to the iteration parameter and the photovoltaic circuit parameter, and updating the individual optimal position and the group optimal position, namely, returning to step S102.

[0123] As a preferred embodiment, Figure 5 Come and see, Figure 5 This is a flow chart of another embodiment of the MPPT control method based on the particle swarm optimization algorithm provided by the present invention, including steps S501 to S503, wherein:

[0124] In step S501, a first difference value is determined based on the difference between the actual power and the iterative expected power, wherein the actual power is the output power of the photovoltaic array actually detected, and the iterative expected power is the system fitness value calculated correspondingly when the iteration is terminated;

[0125] In step S502, a first ratio is determined according to the ratio of the first difference to the iterative expected power;

[0126] In step S503, when the first ratio is greater than a preset restart value, the control is restarted and returns to the step of obtaining the preset iteration parameters and particle parameters, and the current photovoltaic circuit parameters.

[0127] In the embodiment of the present invention, the maximum power point is effectively determined by comparing the first ratio with the preset restart value, that is, the actual power is forced to reach the iterative expected power.

[0128] Here, returning to the step of obtaining the preset iteration parameters and particle parameters, and the current photovoltaic circuit parameters, that is, returning to step S101.

[0129] In a specific embodiment of the present invention, when the preset restart condition is met After that, the algorithm restarts and returns to step 1 to start a new round of iteration. m is the maximum power of the system tracked, and β is the set threshold value of 0.05.

[0130] The embodiment of the present invention also provides an MPPT control system based on particle swarm optimization algorithm, combined with Figure 6 Come and see, Figure 6 A system schematic diagram of an embodiment of an MPPT control system based on a particle swarm optimization algorithm provided by the present invention includes a photovoltaic array, a first capacitor, a first inductor, a second capacitor and a first resistor connected in parallel with the photovoltaic array in sequence, a first MOS transistor arranged between the parallel ends on the same side of the first capacitor and the first inductor, a second MOS transistor arranged between the parallel ends on the same side of the first inductor and the second capacitor, and a controller, wherein the processor of the controller is used to execute the MPPT control method based on the particle swarm optimization algorithm as described above, and input a pulse signal corresponding to the optimal duty cycle into the first MOS transistor and the second MOS transistor.

[0131] In an embodiment of the present invention, the MPPT control method based on the particle swarm optimization algorithm is applied to the photovoltaic array MPPT control system to realize the photovoltaic array MPPT intelligent control. Specifically, the particle position corresponds to the duty cycle of the power electronic device working pulse, and the photovoltaic array output power is used as the particle fitness function value. The controller collects the current output current value I of the photovoltaic array. pv and voltage value V pv , a particle swarm optimization algorithm (Sin-GPSO algorithm) based on the fusion of chaotic mapping and Gaussian perturbation is used to obtain the duty cycle corresponding to the maximum power point, and a fixed-frequency pulse signal is output through the PWM controller to drive the power devices of the Buck-boost circuit, so that the photovoltaic array works at the maximum power point obtained by iteration, realizing efficient and accurate MPPT control of the photovoltaic array.

[0132] Specifically, the MPPT control method based on the particle swarm optimization algorithm is applied to the photovoltaic power generation system, the MPPT control method based on the particle swarm optimization algorithm is built into the corresponding controller, and the control connection is performed with the photovoltaic array in the photovoltaic power generation system to implement the MPPT control method based on the particle swarm optimization algorithm, achieve the optimal photovoltaic array output, and achieve the best power generation effect.

[0133] In a specific embodiment of the present invention, Figures 7 to 13 Come and see, Figure 7 : is a distribution diagram of an embodiment of a Gaussian distribution with a mean of 0 and a variance of 1 provided by the present invention, Figure 8 : is a distribution diagram of an embodiment of the chaotic Sine map provided by the present invention, Figure 9 : is a weight diagram of an embodiment of the chaotic Sine mapping inertia weight provided by the present invention, Figure 10 1 is a schematic diagram of a particle motion trajectory according to an embodiment of the present invention. Figure 11 This is a power waveform diagram of an embodiment of the output power controlled by the Sin-GPSO algorithm MPPT provided by the present invention. Figure 12 This is a power waveform comparison diagram of the Sin-GPSO algorithm provided by the present invention and another algorithm embodiment. Figure 13 This is a power waveform comparison diagram of another embodiment of the Sin-GPSO algorithm provided by the present invention and other algorithms;

[0134] Among them, the Sin-GPSO algorithm (the abbreviation of the MPPT control method based on the improved particle swarm optimization algorithm proposed in this invention) is applied to the MPPT control of the photovoltaic power generation system. Photovoltaic power generation system (see Figure 6 ) consists of a photovoltaic array, a buck-boost circuit, and a load (such as a battery). By collecting the voltage and current values ​​output by the photovoltaic array, a particle swarm optimization algorithm based on the fusion of chaotic mapping and Gaussian perturbation is used to obtain the duty cycle corresponding to the maximum power point. A fixed-frequency pulse signal is then output through the PWM controller to drive the power devices of the buck-boost circuit, making the photovoltaic array operate at the maximum power point, thus achieving MPPT control of the photovoltaic power generation system. The specific implementation steps are as follows:

[0135] Step 1: Initialize the particle swarm parameters, including the number of iterations, the total number of particles, the particle velocity, and the particle position, and make the particles randomly and evenly distributed in the duty cycle range of [0, 1]. The particle position corresponds to the duty cycle of the working pulse of the power electronic device, and the output power of the photovoltaic array is used as the particle fitness function value.

[0136] Step 2: The controller collects the current output current value I of the photovoltaic array pv and voltage value V pv , and calculate the current power of the system k is the current iteration number. All particles are regarded as a group and the particle group is iterated.

[0137] Step 3: Compare the fitness value of the particle in the current iteration with the fitness value of the current system, and use Update individual optimal position P ibest ; compare p ibest The corresponding fitness value and the optimal position g of the particle group best The corresponding fitness value, if the current position is better, then update g best , otherwise keep g best constant.

[0138] Step 4: Use the chaotic Sine map to set the inertia weight. The specific formula is as mentioned above and will not be repeated here.

[0139] Step 5: Use the natural logarithm function to set the learning factor. The specific formula is as mentioned above and will not be repeated here.

[0140] Step 6: Add a Gaussian perturbation term to the "individual cognition part" of the velocity update formula. The specific formula is as described above and will not be repeated here.

[0141] Step 7: Use the formula to update the particle position. The specific formula is as mentioned above and will not be repeated here;

[0142] Step 8: When the distance between particles (difference in duty cycle) is less than a threshold of 0.01, the termination condition is met and the iteration stops. Otherwise, return to step 3 to continue the iteration.

[0143] Step 9: When the preset restart conditions are met After that, the algorithm restarts and returns to step 1 to start a new round of iteration. m is the maximum power of the system tracked, β is the set threshold value of 0.05;

[0144] Step 10: Obtain the duty cycle corresponding to the maximum power point from the above, and output a fixed-frequency pulse signal through the PWM controller to drive the power devices of the Buck-boost circuit, so that the photovoltaic array operates at the maximum power point, realizing MPPT control of the photovoltaic system.

[0145] Among them, the comparative simulation results show that: the MPPT control method based on the particle swarm optimization algorithm fused with chaotic mapping and Gaussian perturbation has a faster convergence speed than other algorithms, smaller system oscillation, and better optimization effect under the premise of ensuring accuracy. Figures 7 to 13 shown.

[0146] The embodiment of the present invention also provides an MPPT control device based on particle swarm optimization algorithm, combined with Figure 14 Come and see, Figure 14 This is a schematic structural diagram of an embodiment of an MPPT control device based on a particle swarm optimization algorithm provided by the present invention. The MPPT control device 1400 based on a particle swarm optimization algorithm includes:

[0147] An acquisition unit 1401 is used to acquire preset iteration parameters and particle parameters, as well as current photovoltaic circuit parameters;

[0148] Processing unit 1402 is configured to determine a fitness value based on the iteration parameter and the photovoltaic circuit parameter, and update the individual optimal position and the group optimal position; further update the particle parameters based on the updated individual optimal position, the updated group optimal position, the inertia weight, and the Gaussian perturbation term until a preset termination condition is met, wherein the inertia weight is set according to the iteration parameter based on a chaotic map, and the Gaussian perturbation term is set according to a Gaussian distribution;

[0149] The control unit 1403 is configured to determine an optimal duty cycle according to the finally updated particle parameters.

[0150] The more specific implementation of each unit of the MPPT control device based on the particle swarm optimization algorithm can be found in the description of the above-mentioned MPPT control method based on the particle swarm optimization algorithm, and has similar beneficial effects, which will not be repeated here.

[0151] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the MPPT control method based on the particle swarm optimization algorithm as described above is implemented.

[0152] Generally speaking, computer instructions for implementing the method of the present invention may be carried by any combination of one or more computer-readable storage media. Non-transitory computer-readable storage media may include any computer-readable media except for signals that are temporarily propagating.

[0153] The computer-readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more conductors, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the context of the present invention, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0154] Computer program code for performing the operations of the present invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages, in particular, Python suitable for neural network computing and platform frameworks based on TensorFlow, PyTorch, etc. can be used. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, through the Internet using an Internet service provider).

[0155] The embodiment of the present invention further provides an electronic device, Figure 15 Come and see, Figure 15 This is a structural diagram of an embodiment of an electronic device provided by the present invention. The electronic device 1500 includes a processor 1501, a memory 1502, and a computer program stored in the memory 1502 and executable on the processor 1501. When the processor 1501 executes the program, the MPPT control method based on the particle swarm optimization algorithm as described above and / or the MPPT control method based on the particle swarm optimization algorithm as described above are implemented.

[0156] As a preferred embodiment, the electronic device 1500 further includes a display 1503 for displaying that the processor 1501 executes the MPPT control method based on the particle swarm optimization algorithm as described above.

[0157] Exemplarily, the computer program may be divided into one or more modules / units, one or more of which are stored in the memory 1502 and executed by the processor 1501 to implement the present invention. One or more modules / units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program in the electronic device 1500. For example, the computer program may be divided into the acquisition unit 1401, processing unit 1402, and control unit 1403 in the above-mentioned embodiment. The specific functions of each unit are as described above and are not further described here.

[0158] The electronic device 1500 may be a desktop computer, notebook, PDA, or smart phone with an adjustable camera module.

[0159] Among them, the processor 1501 may be an integrated circuit chip with signal processing capabilities. The above-mentioned processor 1501 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0160] The memory 1502 may be, but is not limited to, a random access memory (RAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), etc. The memory 1502 is used to store programs, and the processor 1501 executes the programs after receiving an execution instruction. The process definition method disclosed in any of the aforementioned embodiments of the present invention may be applied to the processor 1501 or implemented by the processor 1501.

[0161] The display 1503 may be an LCD display or an LED display, for example, a display on a mobile phone.

[0162] It is understandable that Figure 15 The structure shown is only a schematic diagram of the structure of the electronic device 1500. The electronic device 1500 may also include Figure 15 More or fewer components as shown. Figure 15 Each component shown in the figure can be implemented by hardware, software or a combination thereof.

[0163] The computer-readable storage medium and electronic device provided according to the above embodiments of the present invention can be implemented with reference to the specific description of the MPPT control method based on the particle swarm optimization algorithm as described above according to the present invention, and have similar beneficial effects to the MPPT control method based on the particle swarm optimization algorithm as described above, which will not be repeated here.

[0164] The present invention discloses an MPPT control method and system based on a particle swarm optimization algorithm. First, iteration parameters, particle parameters, and current photovoltaic circuit parameters are effectively acquired to initialize the particle swarm parameters. Then, the photovoltaic circuit parameters are used to determine the fitness value, thereby effectively optimizing the individual optimal position and the group optimal position. Furthermore, the inertia weight and Gaussian disturbance term are combined to avoid the algorithm from quickly falling into the local optimum, reduce the degree of oscillation, improve the optimization ability of the algorithm, and ensure the accuracy of the iterative results. Finally, based on the particle parameters determined by the last iteration, the optimal duty cycle is determined.

[0165] The technical solution of the present invention has the ability to quickly find the best by setting the chaotic inertia weight, and the ability to jump out of the local optimum by setting the Gaussian perturbation term. The nonlinear learning factor can balance the global and local capabilities of the algorithm. The advantages of the three are combined to make the convergence faster and the system oscillation smaller, balance the global development and local exploration capabilities of the algorithm, avoid the system from falling into the local maximum power point, and enable the system to track the global maximum power point at a faster speed while ensuring accuracy, and greatly reduce the degree of search oscillation of the system.

[0166] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention should be covered by the scope of protection of the present invention.

Claims

1. An MPPT control method based on an improved particle swarm optimization algorithm, characterized in that: include: Obtain preset iteration parameters and particle parameters, as well as current photovoltaic circuit parameters; Determine the fitness value according to the iteration parameter and the photovoltaic circuit parameter, and update the individual optimal position and the group optimal position; updating the particle parameters according to the updated individual optimal position, the updated group optimal position, the inertia weight, the Gaussian perturbation term, and the nonlinear learning factor until a preset termination condition is satisfied, wherein the inertia weight is set according to a chaotic map, and the Gaussian perturbation term is set according to a Gaussian distribution; Determine the optimal duty cycle based on the final updated particle parameters; The particle parameters include particle position and particle velocity. The updating of the particle parameters according to the updated individual optimal position, the updated group optimal position, the inertia weight, the Gaussian perturbation term and the nonlinear learning factor includes: Based on the chaotic mapping, a chaotic Sine mapping function is determined, and the inertia weight is set according to the iteration parameter and the chaotic Sine mapping function; a nonlinear learning factor is set according to the natural logarithm function; and the Gaussian disturbance term is set based on the distribution function form of the Gaussian distribution; updating the particle velocity according to the inertia weight, the nonlinear learning factor, the Gaussian disturbance term, the updated individual optimal position, and the updated group optimal position; Determine the updated particle position based on the sum of the current particle position and the current particle velocity; The inertia weight is expressed by the following formula: in, Indicates the The inertia weight at the iteration, represents the chaotic Sine mapping function, represents the initial chaotic value of the chaotic Sine mapping function, Indicates the selection of a random value. Indicates the current iteration number, Represents the preset iteration parameters, Indicates the The upper limit of the inertia weight at the iteration, Indicates the The lower limit of the inertia weight at the iteration, Indicates the preset chaos value.

2. The MPPT control method based on the improved particle swarm optimization algorithm according to claim 1 is characterized in that: The photovoltaic circuit parameters include a current output current value and a current output voltage value of the photovoltaic array; determining a fitness value based on the iteration parameters and the photovoltaic circuit parameters, and updating an individual optimal position and a group optimal position, including: Determine the current power of the system according to the product of the current output current value and the current output voltage value, and use the current power of the system as the system fitness value; Comparing the particle fitness value of the current iteration particle with the system fitness value, and using the system fitness value to update the individual optimal position; Compare the first fitness value corresponding to the updated individual optimal position with the second fitness value corresponding to the optimal position of the particle group; If the first fitness value corresponding to the updated individual optimal position is greater than the second fitness value corresponding to the optimal position of the particle group, the optimal position of the particle group is updated, otherwise it remains unchanged.

3. The MPPT control method based on the improved particle swarm optimization algorithm according to claim 1 is characterized in that: The learning factor includes a first learning factor and a second learning factor, wherein: The first learning factor is expressed by the following formula: The second learning factor is expressed by the following formula: in, represents the first learning factor, represents the upper limit of the first learning factor, represents the lower limit of the first learning factor, represents the second learning factor, represents the upper limit of the second learning factor, represents the lower limit of the second learning factor, Indicates the current iteration number, Represents the preset iteration parameters.

4. The MPPT control method based on the improved particle swarm optimization algorithm according to claim 3 is characterized in that: The updating of the particle velocity according to the inertia weight, the nonlinear learning factor, the Gaussian disturbance term, the updated individual optimal position, and the updated group optimal position includes: Determining a velocity inertia component term according to the product of the inertia weight and the current particle velocity; Determining the Gaussian disturbance term according to the first Gaussian random value, the second Gaussian random value and the Gaussian normal distribution; determining an individual cognitive term according to the first learning factor, the updated individual optimal position, the current particle position, and the Gaussian disturbance term; determining a social cognition item based on the second learning factor, the updated optimal position of the group, and the current particle position; An updated particle velocity is determined according to the sum of the velocity inertia component term, the individual cognition term, and the social cognition term.

5. The MPPT control method based on the improved particle swarm optimization algorithm according to claim 1 is characterized in that: The updated particle velocity is expressed by the following formula: in, represents the updated particle velocity, represents the inertia weight, Indicates the current particle speed, represents the first learning factor, represents the second learning factor, represents the preset first random parameter, represents the preset second random parameter, represents the updated individual optimal position, represents the updated swarm optimal position, Indicates the current particle position, represents the first Gaussian random value, represents the second Gaussian random value, represents the Gaussian disturbance term, Indicates the mean , the variance is Gaussian distribution, Represents the current iteration number, and i represents the number of particles.

6. The MPPT control method based on the improved particle swarm optimization algorithm according to claim 1 is characterized in that: The preset termination condition includes: the position between the updated particles is less than a preset distance value; wherein, if the preset termination condition is not met, returning to the step of determining the fitness value based on the iteration parameter and the photovoltaic circuit parameter, and updating the individual optimal position and the group optimal position.

7. The MPPT control method based on the improved particle swarm optimization algorithm according to claim 1, characterized in that: The control method further includes: Determining a first difference value based on a difference between actual power and iterative expected power, wherein the actual power is the output power of the photovoltaic array actually detected, and the iterative expected power is the system fitness value calculated correspondingly when the iteration is terminated; determining a first ratio according to a ratio of the first difference to the iterative expected power; When the first ratio is greater than a preset restart value, the control is restarted and returns to the step of obtaining the preset iteration parameters and particle parameters, as well as the current photovoltaic circuit parameters.

8. An MPPT control system based on an improved particle swarm optimization algorithm, characterized in that: include: A photovoltaic array, a first capacitor, a first inductor, a second capacitor, and a first resistor sequentially connected in parallel with the photovoltaic array, a first MOS transistor arranged between the parallel ends on the same side of the first capacitor and the first inductor, a second MOS transistor arranged between the parallel ends on the same side of the first inductor and the second capacitor, and a controller, wherein a processor of the controller is used to execute the MPPT control method based on the improved particle swarm optimization algorithm according to any one of claims 1 to 7, and input a pulse signal corresponding to an optimal duty cycle into the first MOS transistor and the second MOS transistor.

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