CN115543004A - MPPT control method and system based on improved particle swarm optimization algorithm - Google Patents
MPPT control method and system based on improved particle swarm optimization algorithm Download PDFInfo
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Abstract
The invention relates to a MPPT control method and a system based on an improved particle swarm optimization algorithm, wherein the method comprises the following steps: acquiring preset iteration parameters, preset particle parameters and current photovoltaic circuit parameters; determining a fitness value according to the iteration parameters and the photovoltaic circuit parameters, and updating the individual optimal position and the group optimal position; and updating the particle parameters according to the updated individual optimal position, the updated group optimal position, the inertia weight and the Gaussian disturbance item until a preset termination condition is met, wherein the inertia weight is set according to chaotic mapping, the Gaussian disturbance item is set according to Gaussian distribution, and the optimal duty ratio is determined according to the finally updated particle parameters. The invention sets the chaotic inertia weight to realize rapid optimization, sets the Gaussian disturbance term to enable the algorithm to jump out of local optimization, ensures the precision, enables the system to track to the global maximum power point at a higher speed, and greatly reduces the searching oscillation degree of the system.
Description
Technical Field
The invention relates to the technical field of photovoltaic power generation, in particular to an MPPT control method and system based on an improved particle swarm optimization algorithm.
Background
Solar energy is widely used as one of the most available clean energy sources with inexhaustible and inexhaustible resources. For a photovoltaic power generation system, it is important to find an optimal operating state of a photovoltaic cell so that the photovoltaic cell operates at a maximum power point. A technique of achieving the maximum power output of the photovoltaic cell using the control method is called a maximum power point tracking technique (MPPT). Due to the nonlinear output characteristic of the photovoltaic cell, a P-V curve can generate a peak value, the peak value is easily influenced by external environment factors such as irradiance, temperature and the like, and the P-V curve can generate a plurality of peak values under the condition of local shading.
The traditional MPPT method mainly comprises a disturbance observation method, a conductance increment method and the like. Such MPPT algorithms are highly prone to fall into local optima in multimodal situations. Aiming at the problem, numerous scholars provide a plurality of novel intelligent MPPT algorithms, such as a particle swarm algorithm, a genetic algorithm, a wolf algorithm, an immune firefly algorithm and the like. The particle swarm optimization is widely applied due to the advantages of simple algorithm, easiness in implementation and the like, but the traditional particle swarm optimization is prone to premature phenomenon when the parameter selection is unreasonable, falls into local optimal solution, and has the problems of low optimization speed and the like. Therefore, how to improve the optimizing speed of the MPPT algorithm and jump out the local optimal solution is an urgent problem to be solved.
Disclosure of Invention
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 of the prior art that the MPPT algorithm is slow in optimization speed and is easy to fall into a local optimal solution.
In order to solve the technical problem, the invention provides an MPPT control method based on an improved particle swarm optimization algorithm, which comprises the following steps:
acquiring preset iteration parameters, preset particle parameters and current photovoltaic circuit parameters;
determining a fitness value according to the iteration parameters and the photovoltaic circuit parameters, and updating 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 disturbance term and the nonlinear learning factor until a preset termination condition is met, wherein the inertia weight is set according to chaotic mapping, the Gaussian disturbance term is set according to Gaussian distribution, and the nonlinear learning factor is set according to a logarithmic function;
and determining the optimal duty ratio according to the finally updated particle parameters.
Further, the photovoltaic circuit parameters include a current output current value and a current output voltage value of the photovoltaic array, and the determining a fitness value according to the iteration parameter and the photovoltaic circuit parameters and updating the individual optimal position and the group optimal position include:
determining the current power of the system according to the product of the current output current value and the current output voltage value, and taking the current power of the system as a system fitness value;
comparing the particle fitness value of the current iteration particle with the system fitness value, and updating the individual optimal position by using the system fitness value;
comparing the first fitness value corresponding to the updated optimal position of the individual with a second fitness value corresponding to the optimal position of the particle group;
and if the first fitness value corresponding to the updated individual optimal position is larger than the second fitness value corresponding to the optimal position of the particle group, updating the optimal position of the particle group, otherwise, keeping the optimal position unchanged.
Further, the particle parameters include particle positions and particle velocities, and the updating the particle parameters according to the updated individual optimal positions, the updated group optimal positions, the inertial weights, the gaussian perturbation terms and the nonlinear learning factors includes:
determining a chaotic Sine mapping function based on chaotic mapping, and setting the inertia weight according to the iteration parameter and the chaotic Sine mapping function; setting a nonlinear learning factor according to a natural logarithm function; setting the Gaussian disturbance term based on the distribution function form of Gaussian distribution;
updating the particle velocity according to the inertial weight, the nonlinear learning factor, the Gaussian disturbance term, the updated individual optimal position and the updated group optimal position;
and determining the updated particle position according to the sum of the current particle position and the current particle speed.
Further, the inertial weight is represented by the following formula:
w k ＝S(k)*w min +(w max -w min )*(k/k_max)
S(k)＝μ*sin(S(k-1)*π)，S(0)＝rand()
wherein w k Representing the inertia weight in the kth iteration, S (k) representing a chaotic Sine mapping function, S (0) representing an initial chaotic value of the chaotic Sine mapping function, rand () representing a selected random value, k representing the current iteration number, k _ max representing a preset iteration parameter, w max Represents an upper bound, w, of the inertial weight at the kth iteration min And the lower limit of the inertia weight in the k iteration is represented, and mu represents a preset chaotic value.
Further, the learning factors include a first learning factor and a second learning factor, wherein:
the first learning factor is expressed by the following formula:
c 1 ＝c 1_max -(c 1_max -C 1_min )*ln(1+(e-1)*(k/k_max))
the second learning factor is expressed by the following formula:
c 2 ＝c 2_min +(c 2_max -c 2_min )*ln(1+(e-1)*(k/k_max))
wherein, c 1 Represents the first learning factor, c 1_max An upper limit value representing the first learning factor, c 1_min A lower limit value representing the first learning factor, c 2 Represents the second learning factor, c 2_max An upper limit value representing the second learning factor, c 2_max And the lower limit value of the second learning factor is represented, k represents the current iteration number, and k _ max represents a preset iteration parameter.
Further, the learning factors include a first learning factor and a second learning factor, and the updating the particle velocity according to the inertial 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 recognition item according to the first learning factor, the updated individual optimal position, the current particle position and the Gaussian disturbance item;
determining a social cognition item according to the second learning factor, the updated optimal position of the group and the current particle position;
and determining the updated particle velocity according to the sum of the velocity inertia component item, the individual recognition item and the social recognition item.
Further, the updated particle velocity is expressed by the following formula:
wherein the content of the first and second substances,
represents the updated particle velocity, w k The inertial weight is represented by a weight of the inertia,
representing the current particle velocity, c 1 Represents a first learning factor, c 2 Represents a second learning factor, r 1 Representing a preset first random parameter, r 2 A second random parameter is indicated that is preset,
represents the updated individual optimum position of the mobile terminal,
representing the updated population optimal location,
representing the current particle position, r3 a first Gaussian random value, r 4 Representing a second random value of the gaussian,
representing a Gaussian perturbation term, gaussian (mu, sigma) 2 ) Denotes mean μ and variance σ 2 K represents the current iteration number, and i represents the number of particles.
Further, the preset termination condition includes: the updated positions among the particles are smaller than a preset distance value; and if the preset termination condition is not met, 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.
Further, the control method further includes:
determining a first difference value according to the difference between the actual power and the iteration expected power, wherein the actual power is the actually detected output power of the photovoltaic array, and the iteration expected power is a system adaptability value correspondingly calculated when iteration is terminated;
determining a first ratio value according to the ratio of the first difference value and the iteration expected power;
and when the first ratio is larger than a preset restart value, controlling to restart, and returning to the step of acquiring preset iteration parameters, preset particle parameters and current photovoltaic circuit parameters.
The invention also provides a MPPT control system based on the particle swarm optimization algorithm, which comprises a photovoltaic array, a first capacitor, a first inductor, a second capacitor and a first resistor which are sequentially connected with the photovoltaic array in parallel, a first MOS (metal oxide semiconductor) tube arranged between parallel ends on the same side of the first capacitor and the first inductor, a second MOS tube arranged between 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 for executing the MPPT control method based on the particle swarm optimization algorithm, and inputting pulse signals corresponding to the optimal duty ratio to the first MOS tube and the second MOS tube.
Compared with the prior art, the invention has the beneficial effects that: firstly, effectively acquiring iteration parameters, particle parameters and current photovoltaic circuit parameters, and initializing particle swarm parameters; then, determining a fitness value by using parameters of the photovoltaic circuit so as to effectively optimize the individual optimal position and the group optimal position; furthermore, inertia weight, gaussian disturbance terms and nonlinear learning factors are combined, so that the phenomenon that the local optimization is caused is avoided, the oscillation degree is reduced, the optimization capability of the algorithm is improved, and the accuracy of an iteration result is ensured; finally, an optimal duty cycle is determined based on the last iteratively determined particle parameters.
In conclusion, the chaotic inertial weight set by the invention has the capability of fast optimizing, the Gaussian disturbance term is set to enable the algorithm to have the capability of jumping out of a local optimal point, the nonlinear learning factor is set to balance the global capability and the local capability of the algorithm, the convergence speed is higher by integrating the advantages of the three, the system oscillation is smaller, the global development and the local exploration capability of the algorithm are balanced, the system is prevented from falling into a local maximum power point, the system can track the global maximum power point at a higher speed while the accuracy is ensured, and the oscillation searching degree of the system is greatly reduced.
Drawings
Fig. 1 is a schematic flow chart of an embodiment of an MPPT control method based on an improved particle swarm optimization algorithm according to the present invention;
FIG. 2 is a schematic flowchart of one embodiment of the step S102 in FIG. 1 according to the present invention;
FIG. 3 is a flowchart illustrating an embodiment of step S103 in FIG. 1 according to the present invention;
FIG. 4 is a flowchart illustrating an embodiment of step S302 in FIG. 3 according to the present invention;
FIG. 5 is a schematic flow chart of another embodiment of the MPPT control method based on the improved particle swarm optimization algorithm provided by the present invention;
FIG. 6 is a system diagram of an embodiment of an MPPT control system based on an improved particle swarm optimization algorithm according to the present invention;
FIG. 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;
FIG. 8 is a schematic diagram of the distribution of an embodiment of the chaotic Sine mapping provided by the present invention;
FIG. 9 is a weight diagram of an embodiment of the chaotic Sine mapping inertial weights provided by the present invention;
FIG. 10 is a schematic diagram of an embodiment of a particle motion trajectory provided by the present invention;
FIG. 11 is a power waveform diagram of an embodiment of the MPPT controlled output power of the Sin-GPSO algorithm provided by the present invention;
FIG. 12 is a graph comparing power waveforms of an embodiment of the Sin-GPSO algorithm provided by the present invention with other algorithms;
FIG. 13 is a graph comparing power waveforms of another embodiment of the Sin-GPSO algorithm provided by the present invention with other algorithms;
fig. 14 is a schematic structural diagram of an embodiment of an MPPT control apparatus based on an improved particle swarm optimization algorithm according to the present invention;
fig. 15 is a schematic structural diagram of an embodiment of an electronic device provided in the present invention.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.
In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. Further, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Reference throughout this specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present invention. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the described embodiments can be combined with other embodiments.
In the description of the present invention, the execution sequence of the steps in the process is not limited to the sequence presented herein, and the corresponding sequence may be adjusted in sequence or presented in parallel.
The invention provides an MPPT control method and system based on an improved particle swarm optimization algorithm, wherein inertia weight is set based on chaotic mapping, gaussian disturbance terms are set based on Gaussian distribution, and meanwhile, nonlinear learning factors are set based on a logarithmic function, so that a new thought is provided for further improving the optimization speed and accuracy of the MPPT control method.
Before the description of the embodiments, the related words are paraphrased:
MPPT control technology: the MPPT (Maximum Power Point Tracking, MPPT for short) system is an electric system which can make a photovoltaic panel output more electric energy by adjusting the working state of an electric module, and can effectively store the direct current generated by a solar panel in a storage battery, thereby effectively solving the problems of domestic and industrial electricity consumption in remote areas and tourist areas which can not be covered by the conventional Power grid and no environmental pollution. The solar photovoltaic power generation system can detect the power generation voltage of the solar panel in real time, and track the highest voltage current Value (VI), so that the system charges the storage battery at the maximum power output. The solar photovoltaic system is applied to a solar photovoltaic system, coordinates the work of a solar cell panel, a storage battery and a load, and is the brain of the photovoltaic system.
Particle swarm optimization: particle Swarm Optimization (PSO) is in turn translated into a Particle Swarm algorithm, or a Particle Swarm optimization algorithm. The method is a random search algorithm based on group cooperation and developed by simulating foraging behavior of a bird group. It is generally considered as one of cluster intelligence (SI), which is a classic intelligent optimization algorithm, and its essence is to continuously iterate to update positions to solve the optimal value.
Based on the description of the technical nouns, the particle swarm optimization at the present stage is easy to generate premature phenomenon when the parameter selection is unreasonable, falls into the local optimal solution, and has the problems of low optimization speed and the like. Aiming at the problems, the invention aims to provide a MPPT control method and a MPPT control system based on a particle swarm optimization algorithm.
Specific examples are described in detail below, respectively:
an embodiment of the present invention provides an MPPT control method based on an improved particle swarm optimization algorithm, and referring to fig. 1, fig. 1 is a schematic flow chart of an embodiment of the MPPT control method based on the improved particle swarm optimization algorithm provided by the present invention, and includes steps S101 to S104, where:
in step S101, obtaining preset iteration parameters and particle parameters, and current parameters of the photovoltaic circuit;
in step S102, determining a fitness value according to the iteration parameter and the photovoltaic circuit parameter, and updating an individual optimal position and a group optimal position;
in step S103, updating the particle parameters according to the updated individual optimal position, the updated group optimal position, the inertial weight, the gaussian disturbance term, and the nonlinear learning factor until a preset termination condition is satisfied, wherein the inertial weight is set according to the iteration parameter based on chaotic mapping, the gaussian disturbance term is set according to gaussian distribution, and the nonlinear learning factor is set according to the logarithmic function;
in step S104, an optimal duty ratio is determined according to the finally updated particle parameters.
In the embodiment of the invention, firstly, iteration parameters, particle parameters and current photovoltaic circuit parameters are effectively obtained, and particle swarm parameters are initialized; then, determining a fitness value by utilizing the photovoltaic circuit parameters so as to effectively optimize the individual optimal position and the group optimal position; furthermore, the inertia weight, the Gaussian disturbance term and the nonlinear learning factor are combined, so that the local optimization is avoided, the local optimization has higher convergence rate, the oscillation degree is reduced, the optimization capability of the algorithm is improved, and the accuracy of the iteration result is ensured; and finally, determining the optimal duty ratio based on the particle parameters determined by the last iteration.
The particle parameter includes at least one of a particle position and a particle velocity. Wherein the particle position corresponds to the duty cycle.
It should be noted that the photovoltaic circuit parameter includes at least one of a current output current value and a current output voltage value of the photovoltaic array. It can be understood that the current output current value and the current output voltage value can be obtained by monitoring and collecting.
As a preferred embodiment, referring to fig. 2, fig. 2 is a schematic flowchart of an embodiment of step S102 in fig. 1 provided by the present invention, and includes steps S201 to S204, where:
in step S201, determining a current power of a system according to a product of the current output current value and the current output voltage value, and taking the current power of the system as a system fitness value;
in step S202, comparing the particle fitness value of the current iteration particle with the system fitness value, and updating the individual optimal position by using the system fitness value;
in step S203, comparing the first fitness value corresponding to the updated optimal position of the individual with the second fitness value corresponding to the optimal position of the population of particles;
in step S204, if the first fitness value corresponding to the updated optimal position of the individual 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, the first fitness value remains unchanged.
In the embodiment of the invention, the product of the current output current value and the current output voltage value, namely the power output by the photovoltaic array is used for determining the corresponding fitness value, and the individual particles and the whole particles are updated.
As a preferred embodiment, referring to fig. 3, fig. 3 is a schematic flowchart of an embodiment of step S103 in fig. 1 provided by the present invention, and includes steps S301 to S303, where:
in step S301, based on chaotic mapping, determining a chaotic Sine mapping function, and setting the inertial weight according to the iterative parameter and the chaotic Sine mapping function; setting a nonlinear learning factor according to a natural logarithm function; setting the Gaussian disturbance term based on the distribution function form of Gaussian distribution;
in step S302, updating the particle velocity according to the inertial weight, the nonlinear learning factor, the gaussian disturbance term, an updated individual optimal position, and an updated group optimal position;
in step S303, the updated particle position is determined from the sum of the current particle position and the current particle velocity.
In the embodiment of the invention, the position and the speed of the particle are effectively updated by combining the inertia weight, the Gaussian disturbance and the nonlinear learning factor.
As a preferred embodiment, the inertial weight is represented by the following formula:
w k ＝S(k)*w min +(w max -w min )*(k/k_max)
S(k)＝μ*sin(S(k-1)*π)，S(0)＝rand()
wherein, w k Representing the inertia weight in the kth iteration, S (k) representing a chaotic Sine mapping function, S (0) representing an initial chaotic value of the chaotic Sine mapping function, rand () representing a selected random value, k representing the current iteration number, k _ max representing a preset iteration parameter, w max Represents an upper bound, w, of the inertial weight at the kth iteration min And the lower limit of the inertia weight in the k iteration is represented, and mu represents a preset chaotic value.
In the embodiment of the invention, the chaotic Sine mapping is adopted to set the inertia weight, and the random nonlinear incremental inertia weight is constructed, so that the iteration speed of the particle swarm is randomly and nonlinearly changed, the global optimization capability of the particles is improved, and the particles have higher convergence speed.
It should be noted that chaos occurs when μ e (0.87,0.93) & (0.95,1), and a preferred value of μ in the embodiment of the present invention is 0.99.
In a preferred embodiment, the learning factors include a first learning factor and a second learning factor, wherein:
the first learning factor is expressed by the following formula:
c 1 ＝c 1_max -(c 1_max -c 1_min )*ln(1+(e-1)*(k/k_max))；
the second learning factor is expressed by the following formula:
c 2 ＝c 2_min +(c 2_max -c 2_min )*ln(1+(e-1)*(k/k_max))；
wherein, c 1 Represents the first learning factor, C 1_max An upper limit value representing the first learning factor, c 1_min A lower limit value representing the first learning factor, c 2 Represents the second learning factor, c 2_max An upper limit value representing the second learning factor, c 2_max And the lower limit value of the second learning factor is represented, k represents the current iteration number, and k _ max represents a preset iteration parameter.
In the embodiment of the invention, the learning factor is set by adopting a natural logarithm function, and the nonlinear learning factor is constructed, so that the local searching capability of the particle is improved in the early stage of searching, the global optimizing capability of the particle is improved in the later stage of searching, even if the algorithm focuses on the self-learning capability in the early stage, and the social learning capability in the later stage, the global development and the local exploration capability of the algorithm are balanced.
As a preferred embodiment, referring to fig. 4, fig. 4 is a schematic flowchart of an embodiment of step S302 in fig. 3 provided by the present invention, and includes steps S401 to S405, where:
in step S401, a velocity inertia component term is determined according to the product of the inertia weight and the current particle velocity;
in step S402, determining the gaussian disturbance term according to a first gaussian random value, a second gaussian random value and a gaussian normal distribution;
in step S403, determining an individual recognition term according to the first learning factor, the updated individual optimal position, the current particle position, and the gaussian disturbance term;
in step S404, determining a social cognitive term according to the second learning factor, the updated group optimal position, and the current particle position;
in step S405, an updated particle velocity is determined from the sum of the velocity inertia component term, the individual recognition term, and the social recognition term.
In the embodiment of the invention, a velocity inertia component item is determined according to the inertia weight, an individual recognition item is determined according to a Gaussian disturbance item, and the particle velocity is effectively updated by combining the items.
As a preferred embodiment, the updated particle velocity is expressed by the following formula:
wherein, the first and the second end of the pipe are connected with each other,
represents the updated particle velocity, w k The inertial weight is represented by a weight of the said inertia,
representing the current particle velocity, c 1 Denotes a first learning factor, c 2 Represents a second learning factor, r 1 Representing a preset first random parameter, r 2 A second random parameter that is preset is indicated,
represents the updated individual optimum position of the mobile terminal,
representing the updated population-optimal location of the mobile device,
indicates the current particle position, r 3 Representing a first Gaussian random value, r 4 Representing a second random value of the gaussian,
representing a Gaussian perturbation term, gaussian (mu, sigma) 2 ) Denotes the mean value μ and the variance σ 2 K represents the current iteration number, and i represents the number of particles.
In addition, r is 1 、r 2 、r 3 、r 4 Is a random number between 0 and 1,
representing the gaussian perturbation produced by particle i at the kth iteration.
In the embodiment of the invention, the Gaussian disturbance is adopted to reset the speed of the particles approaching to the optimal position of the individual, which substantially increases the Gaussian disturbance to the learning speed of the particles to the self optimal experience, thereby enhancing the ability of the particles to jump out of the local optimal position.
As a more specific example, the formula for updating the particle position is represented by the following formula:
in the embodiment of the invention, the current particle position is effectively updated by using the current particle speed.
As a preferred embodiment, the preset termination condition includes: the positions among the updated particles are smaller than a preset distance value; and if the preset termination condition is not met, 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.
In the embodiment of the invention, a proper preset termination condition is set, so that the optimization is effectively carried out, and a corresponding iterative process is skipped.
The above-mentioned steps of determining a fitness value according to the iteration parameter and the photovoltaic circuit parameter, and updating the individual optimal position and the group optimal position are returned to, that is, the step S102 is returned to.
As a preferred embodiment, referring to fig. 5, fig. 5 is a schematic flowchart of another embodiment of the MPPT control method based on the particle swarm optimization algorithm provided by the present invention, and includes steps S501 to S503, where:
in step S501, a first difference is determined according to a difference between an actual power and an iteration expected power, where the actual power is an actually detected output power of the photovoltaic array, and the iteration expected power is a system fitness value correspondingly calculated when iteration is terminated;
in step S502, determining a first ratio according to a ratio between the first difference and the iteration expected power;
in step S503, when the first ratio is greater than a preset restart value, the control is restarted, and the step of obtaining the preset iteration parameter, the preset particle parameter, and the current photovoltaic circuit parameter is returned.
In the embodiment of the invention, the maximum power point is effectively determined by comparing the first ratio with the preset restart value, namely, the actual power is promoted to reach the iteration expected power.
And returning to the step of acquiring the preset iteration parameters and the preset particle parameters and the current photovoltaic circuit parameters, namely returning to the step S101.
In one embodiment of the present invention, when the predetermined restart condition is satisfied
And then, restarting the algorithm, and returning to the step 1 to start a new iteration. Wherein p is m Beta is a set threshold of 0.05 for the maximum power of the system to be tracked.
The embodiment of the present invention further provides an MPPT control system based on a particle swarm optimization algorithm, and as shown in fig. 6, fig. 6 is a system schematic diagram of an embodiment of the MPPT control system based on the particle swarm optimization algorithm provided by the present invention, and the system schematic diagram 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 disposed between parallel ends on the same side of the first capacitor and the first inductor, a second MOS transistor disposed between parallel ends on the same side of the first inductor and the second capacitor, and a controller, wherein a processor of the controller is configured to execute the above MPPT control method based on the particle swarm optimization algorithm, and input pulse signals corresponding to an optimal duty ratio to the first MOS transistor and the second MOS transistor.
In the embodiment of the invention, the MPPT control method based on the particle swarm optimization algorithm is applied to a photovoltaic array MPPT control system, so that the photovoltaic array MPPT intelligent control is realized. Specifically, the particle position corresponds to the duty ratio of the working pulse of the power electronic device, and the output power of the photovoltaic array is used as the value of the particle fitness function. The controller collects the current output current value I of the photovoltaic array pv Sum voltage value V pv The method comprises the steps of obtaining a duty ratio corresponding to a maximum power point by adopting a particle swarm optimization algorithm (Sin-GPSO algorithm) based on fusion chaotic mapping and Gaussian disturbance, outputting a fixed-frequency pulse signal through a PWM (pulse-width modulation) controller, driving a power device of a Buck-boost circuit, enabling a photovoltaic array to work at the maximum power point obtained through iteration, and achieving efficient and accurate MPPT control of the photovoltaic array.
Specifically, the MPPT control method based on the particle swarm optimization algorithm is applied to a photovoltaic power generation system, the MPPT control method based on the particle swarm optimization algorithm is embedded in a corresponding controller and is in control connection with a photovoltaic array in the photovoltaic power generation system, so that the MPPT control method based on the particle swarm optimization algorithm is realized, the optimal photovoltaic array output is realized, and the best power generation effect is achieved.
In a specific embodiment of the present invention, with reference to fig. 7 to 13, fig. 7 is a schematic distribution diagram of an embodiment of gaussian distribution with a mean value of 0 and a variance of 1 provided by the present invention, fig. 8 is a schematic distribution diagram of an embodiment of chaotic Sine mapping provided by the present invention, fig. 9 is a schematic weight diagram of an embodiment of inertial weight of chaotic Sine mapping provided by the present invention, fig. 10 is a schematic trajectory diagram of an embodiment of a particle motion trajectory provided by the present invention, fig. 11 is a power waveform diagram of an embodiment of output power controlled by MPPT algorithm provided by the present invention, fig. 12 is a comparison diagram of power waveforms of an embodiment of Sin-GPSO algorithm provided by the present invention and other algorithms, and fig. 13 is a comparison diagram of power waveforms of another embodiment of Sin-GPSO algorithm provided by the present invention and other algorithms;
the method is characterized in that a Sin-GPSO algorithm (the abbreviation of the MPPT control method based on the improved particle swarm optimization algorithm) is applied to the MPPT control of the photovoltaic power generation system. A photovoltaic power generation system (see fig. 6) is composed of a photovoltaic array, a Buck-boost circuit and a load (e.g., a battery). The method comprises the steps of acquiring voltage and current values output by a photovoltaic array, obtaining a duty ratio corresponding to a maximum power point by adopting a particle swarm optimization algorithm based on chaotic mapping and Gaussian disturbance fusion, outputting a fixed-frequency pulse signal through a PWM (pulse-width modulation) controller, driving a power device of a Buck-boost circuit, enabling the photovoltaic array to work at the maximum power point, and realizing MPPT (maximum power point tracking) control of a photovoltaic power generation system. The concrete implementation steps are as follows:
And step 3: comparing the fitness value of the particle in the current iteration with the fitness value of the current system, using
Updating individual optimal positions P ibest (ii) a Comparison of p ibest Corresponding fitness value and optimal position g of particle population best If the current position is better, the corresponding fitness value is updated to g best Otherwise, keep g best And is not changed.
And 4, step 4: setting inertial weight by using chaotic Sine mapping, wherein the specific formula is described above and is not described herein again;
and 5: setting a learning factor by using a natural logarithm function, wherein the specific formula is as described above, and the detailed description is omitted;
step 6: adding a Gaussian disturbance term to an individual cognitive part of the speed updating formula, wherein the specific formula is described above and is not described herein again;
and 7: updating the particle position by using a formula, wherein the specific formula is described above and is not described herein again;
and step 8: when the distance (difference of duty ratios) between the particles is less than a threshold value of 0.01, satisfying the termination condition, stopping iteration, otherwise returning to the step 3 to continue iteration;
and step 9: when the preset restart condition is met
And then, restarting the algorithm, and returning to the step 1 to start a new iteration. Wherein, P m Beta is a set threshold value of 0.05 for the maximum power of the tracked system;
step 10: and obtaining the duty ratio corresponding to the maximum power point, outputting a fixed-frequency pulse signal through the PWM controller, and driving a power device of the Buck-boost circuit to enable the photovoltaic array to work at the maximum power point, thereby realizing the MPPT control of the photovoltaic system.
Wherein, the comparison simulation result shows that: the particle swarm optimization MPPT control method based on the fusion of chaotic mapping and Gaussian disturbance has the advantages of higher convergence speed, smaller system oscillation and better optimization effect compared with other algorithms on the premise of ensuring the precision. The simulation comparison results are shown in fig. 7 to 13.
An embodiment of the present invention further provides a MPPT control device based on a particle swarm optimization algorithm, and with reference to fig. 14, fig. 14 is a schematic structural diagram of an embodiment of the MPPT control device based on the particle swarm optimization algorithm according to the present invention, where the MPPT control device 1400 based on the particle swarm optimization algorithm includes:
an obtaining unit 1401, configured to obtain preset iteration parameters and particle parameters, and current parameters of the photovoltaic circuit;
a processing unit 1402, configured to determine a fitness value according to the iteration parameter and the photovoltaic circuit parameter, and update an individual optimal position and a group optimal position; updating the particle parameters according to the updated individual optimal position, the updated group optimal position, the inertia weight and the Gaussian disturbance term until a preset termination condition is met, wherein the inertia weight is set according to the iteration parameters based on chaotic mapping, and the Gaussian disturbance term is set according to Gaussian distribution;
and a control unit 1403, configured to determine an optimal duty ratio according to the finally updated particle parameter.
For a more specific implementation manner of each unit of the MPPT control device based on the particle swarm optimization algorithm, reference may be made to the description of the MPPT control method based on the particle swarm optimization algorithm, and similar beneficial effects are obtained, and details are not repeated herein.
Embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the MPPT control method based on the particle swarm optimization algorithm as described above.
Generally, computer instructions for carrying out the methods of the present invention may be carried using any combination of one or more computer-readable storage media. Non-transitory computer readable storage media may include any computer readable medium except for the signal itself, which is temporarily propagating.
A 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 of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages, and in particular may employ Python languages suitable for neural network computing and TensorFlow, pyTorch-based platform frameworks. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may 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 the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
Fig. 15 is a schematic structural diagram of an embodiment of the electronic device provided by the present invention, and an electronic device 1500 includes a processor 1501, a memory 1502, and a computer program stored in the memory 1502 and operable on the processor 1501, where when the processor 1501 executes the program, the MPPT control method based on the particle swarm optimization algorithm and/or the MPPT control method based on the particle swarm optimization algorithm are implemented as described above.
As a preferred embodiment, the electronic device 1500 further comprises a display 1503 for displaying that the processor 1501 executes the MPPT control method based on the particle swarm optimization algorithm as described above.
Illustratively, the computer program may be divided into one or more modules/units, 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 certain functions, the instruction segments being used to describe the execution of computer programs in the electronic device 1500. For example, the computer program may be divided into the obtaining unit 1401, the processing unit 1402, and the control unit 1403 in the above embodiments, and specific functions of each unit are as described above, which are not described herein again.
The electronic device 1500 may be a desktop computer, a notebook, a palm top computer, or a smart phone with an adjustable camera module.
The processor 1501 may be an integrated circuit chip, which has signal processing capability. The Processor 1501 may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field-Programmable Gate arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
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 Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 1502 is used for storing a program, and the processor 1501 executes the program after receiving an execution instruction, and the method defined by the flow disclosed in any of the foregoing embodiments of the present invention can be applied to the processor 1501, or implemented by the processor 1501.
The display 1503 may be an LCD display or an LED display. Such as a display screen on a cell phone.
It is understood that the configuration shown in fig. 15 is only a schematic configuration of the electronic device 1500, and the electronic device 1500 may further include more or less components than those shown in fig. 15. The components shown in fig. 15 may be implemented in hardware, software, or a combination thereof.
According to the computer-readable storage medium and the electronic device provided by the above embodiments of the present invention, the MPPT control method based on the particle swarm optimization algorithm described above can be implemented by referring to the content specifically described in the implementation of the MPPT control method based on the particle swarm optimization algorithm according to the present invention, and the beneficial effects similar to the MPPT control method based on the particle swarm optimization algorithm described above are obtained, and are not described herein again.
The invention discloses a particle swarm optimization algorithm-based MPPT control method and a particle swarm optimization algorithm-based MPPT control system, wherein iteration parameters, particle parameters and current photovoltaic circuit parameters are effectively acquired, and particle swarm parameters are initialized; then, determining a fitness value by utilizing the photovoltaic circuit parameters so as to effectively optimize the individual optimal position and the group optimal position; furthermore, the inertia weight and the Gaussian disturbance term are combined, so that the fast falling into local optimization is avoided, the oscillation degree is reduced, the optimization capability of the algorithm is improved, and the accuracy of the iteration result is ensured; finally, an optimal duty cycle is determined based on the last iteratively determined particle parameters.
According to the technical scheme, the set chaotic inertial weight has the capability of fast optimizing, the Gaussian disturbance term is set to enable the algorithm to have the capability of jumping out of a local optimal point, the nonlinear learning factor is set to balance the global capability and the local capability of the algorithm, the convergence speed is higher by integrating the advantages of the three, the system oscillation is smaller, the global development and the local exploration capability of the algorithm are balanced, the system is prevented from falling into a local maximum power point, the system can track the global maximum power point at a higher speed while the accuracy is ensured, and the system searching oscillation degree is greatly reduced.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (10)
1. An MPPT control method based on an improved particle swarm optimization algorithm is characterized by comprising the following steps:
acquiring preset iteration parameters, preset particle parameters and current photovoltaic circuit parameters;
determining a fitness value according to the iteration parameters and the photovoltaic circuit parameters, and updating 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 and the Gaussian disturbance term until a preset termination condition is met, wherein the inertia weight is set according to chaotic mapping, and the Gaussian disturbance term is set according to Gaussian distribution;
and determining the optimal duty ratio according to the finally updated particle parameters.
2. The MPPT control method based on the improved particle swarm optimization algorithm of claim 1, 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 according to the iteration parameter and the photovoltaic circuit parameter, and updating an individual optimal position and a group optimal position, wherein the steps comprise:
determining the current power of the system according to the product of the current output current value and the current output voltage value, and taking the current power of the system as a system fitness value;
comparing the particle fitness value of the current iteration particle with the system fitness value, and updating the optimal position of the individual by using the system fitness value;
comparing the first fitness value corresponding to the updated optimal position of the individual with a second fitness value corresponding to the optimal position of the particle group;
and if the first fitness value corresponding to the updated individual optimal position is larger than the second fitness value corresponding to the optimal position of the particle group, updating the optimal position of the particle group, otherwise, keeping the optimal position unchanged.
3. The MPPT control method based on improved particle swarm optimization algorithm according to claim 1, wherein the particle parameters include particle position and particle velocity, and the updating the particle parameters according to the updated individual optimal position, the updated group optimal position, the inertial weight, the Gaussian disturbance term and the nonlinear learning factor comprises:
determining a chaotic Sine mapping function based on chaotic mapping, and setting the inertia weight according to the iteration parameter and the chaotic Sine mapping function; setting a nonlinear learning factor according to a natural logarithm function; setting the Gaussian disturbance term based on the distribution function form of Gaussian distribution;
updating the particle velocity according to the inertial weight, the nonlinear learning factor, the Gaussian disturbance term, the updated individual optimal position and the updated group optimal position;
and determining the updated particle position according to the sum of the current particle position and the current particle speed.
4. The MPPT control method based on improved particle swarm optimization algorithm according to claim 3, characterized in that the inertia weight is expressed by the following formula:
W k ＝S(k)*W min +(w max -W min )*(k/k_max)
S(k)＝μ*sin(S(k-1)*π)，S(0)＝rand()
wherein, w k Representing the inertial weight at the kth iteration, S (k) representing chaotic Sine mappingThe method comprises the following steps that S (0) represents an initial chaotic value of a chaotic Sine mapping function, rand () represents a selected random numerical value, k represents the current iteration times, k _ max represents a preset iteration parameter, and w max Represents an upper bound, w, of the inertial weight at the k-th iteration min And the lower limit of the inertia weight in the k iteration is represented, and mu represents a preset chaotic value.
5. The MPPT control method based on the improved particle swarm optimization algorithm of claim 3, characterized in that the learning factors include a first learning factor and a second learning factor, wherein:
the first learning factor is expressed by the following formula:
c 1 ＝c 1_max -(c 1_max -c 1_min )*ln(1+(e-1)*(k/k_max))
the second learning factor is expressed by the following formula:
c 2 ＝c 2_min +(c 2_max -c 2_min )*ln(1+(e-1)*k/k_max))
wherein, c 1 Represents the first learning factor, c 1_max An upper limit value representing the first learning factor, c 1_min A lower limit value representing the first learning factor, c 2 Represents the second learning factor, c 2_max An upper limit value representing the second learning factor, c 2_max And representing a lower limit value of the second learning factor, k representing the current iteration number, and k _ max representing a preset iteration parameter.
6. The MPPT control method based on the improved particle swarm optimization algorithm according to claim 5, wherein the updating the particle speed according to the inertia weight, the nonlinear learning factor, the Gaussian disturbance term, the updated individual optimal position and the updated group optimal position comprises:
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 recognition item according to the first learning factor, the updated individual optimal position, the current particle position and the Gaussian disturbance item;
determining a social cognition item according to the second learning factor, the updated optimal position of the group and the current particle position;
and determining the updated particle velocity according to the sum of the velocity inertia component item, the individual recognition item and the social recognition item.
7. The MPPT control method based on improved particle swarm optimization algorithm according to claim 3, characterized in that the updated particle speed is expressed by the following formula:
wherein, the first and the second end of the pipe are connected with each other,
represents the updated particle velocity, w k The inertial weight is represented by a weight of the inertia,
represents the current particle velocity, c 1 Denotes a first learning factor, c 2 Represents a second learning factor, r 1 Representing a preset first random parameter, r 2 A second random parameter is indicated that is preset,
represents the updated individual optimal position of the mobile device,
representing the updated population optimal location,
indicates the current particle position, r 3 Represents a first Gaussian random value, r 4 Representing a random value of a second gaussian,
representing a Gaussian perturbation term, gaussian (mu, sigma) 2 ) Denotes mean μ and variance σ 2 K represents the current iteration number, and i represents the number of particles.
8. The MPPT control method based on the improved particle swarm optimization algorithm according to claim 1, characterized in that the preset termination condition comprises: the positions among the updated particles are smaller than a preset distance value; if the preset termination condition is not met, 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.
9. The MPPT control method based on improved particle swarm optimization algorithm according to claim 1, characterized in that the control method further comprises:
determining a first difference value according to the difference between the actual power and the iteration expected power, wherein the actual power is the actually detected output power of the photovoltaic array, and the iteration expected power is a system fitness value correspondingly calculated when iteration is terminated;
determining a first ratio value according to the ratio of the first difference value and the iteration expected power;
and when the first ratio is larger than a preset restart value, controlling to restart, and returning to the step of acquiring preset iteration parameters, preset particle parameters and current photovoltaic circuit parameters.
10. An MPPT control system based on an improved particle swarm optimization algorithm is characterized by comprising: the MPPT control method based on the particle swarm optimization algorithm comprises a photovoltaic array, a first capacitor, a first inductor, a second capacitor and a first resistor which are sequentially connected with the photovoltaic array in parallel, a first MOS (metal oxide semiconductor) tube arranged between parallel ends on the same side of the first capacitor and the first inductor, a second MOS tube arranged between 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 for executing the MPPT control method based on the particle swarm optimization algorithm according to any one of claims 1 to 9, and pulse signals corresponding to the optimal duty ratio are input to the first MOS tube and the second MOS tube.
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| CN116205377B (en) * | 2023-04-28 | 2023-08-18 | 江西恒能电力工程有限公司 | Distributed photovoltaic power station output prediction method, system, computer and storage medium |
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