CN114489228A - MPPT device and method based on improved PSO algorithm - Google Patents
MPPT device and method based on improved PSO algorithm Download PDFInfo
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Abstract
The invention discloses an MPPT device and method based on an improved PSO algorithm, which comprises constructing an MPPT processing unit, a voltage control unit, a data acquisition unit, an upper computer, a thermal power generation unit and a load; the data acquisition unit is respectively connected with the MPPT processing unit and the upper computer to finish the acquisition of voltage and current data and transmit the voltage and current data to the MPPT processing unit, and the MPPT processing unit carries out iterative processing on input data by utilizing an improved POS algorithm to obtain an optimal solution; the voltage control unit is used for receiving the optimal solution and carrying out control and regulation on the output voltage; according to the invention, the maximum power point tracking is carried out in the thermoelectric power generation through the particle swarm algorithm, so that the power generation device can continuously and efficiently keep the maximum power output in a long time period through a control strategy, the voltage control unit is adjusted, the real-time power working point of the thermal power generation is changed, the global optimum condition can be converged in a short time under the condition of ensuring the tracking time, the tracking precision and the response speed, and the power loss is reduced.
Description
Technical Field
The invention belongs to the technical field of thermal power generation, and particularly relates to an MPPT device and an MPPT method based on an improved PSO algorithm.
Background
Thermoelectric power generation technology, also known as thermoelectric heating technology, is a technology for converting heat energy into electric energy based on the seebeck effect, i.e. the thermoelectric effect of thermoelectric semiconductor materials. Because the thermoelectric power generation device has the advantages of no moving parts, low use and maintenance requirements, high power ratio compared with solar power generation, chemical batteries, fuel cells and the like, and the thermoelectric power generation device is frequently used for energy conversion and supply in space and deep sea. Meanwhile, the technology utilizes the characteristic of temperature difference power generation, so that the temperature difference power generation is widely applied to reducing energy loss and reusing energy. However, in application, due to the problems of low thermoelectric generation efficiency of automobile exhaust and the like, the thermoelectric generation technology cannot be utilized in a large area so far, and the technology starts late in China, mainly focuses on theoretical research and lacks specific practical application, so how to maximize the utilization of waste heat becomes a hotspot of research in the field of thermal power generation.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an MPPT method and device based on an improved PSO algorithm, which are used for solving the problem of current thermoelectric generation efficiency improvement and optimizing thermoelectric generation based on the MPPT algorithm.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that:
in one aspect, an MPPT device based on an improved PSO algorithm includes:
the method comprises the following steps: the system comprises an MPPT processing unit, a voltage control unit, a data acquisition unit, an upper computer, a thermal power generation unit and a load;
the data acquisition unit is respectively connected with the thermal power generation unit, the upper computer and the MPPT processing unit, and is used for acquiring temperature data of the thermal power generation unit, detecting voltage and current of the thermal power generation unit in real time and respectively transmitting the acquired data to the upper computer and the MPPT processing unit;
the thermal power generation unit is connected with the voltage control unit and is used for providing voltage for the voltage control unit;
the upper computer is used for displaying the results of the data acquisition unit and the MPPT processing unit;
the MPPT processing unit is connected with the voltage control unit and used for processing the data transmitted by the data acquisition unit and transmitting the processed data to the voltage control unit;
the voltage control unit is connected with the load and used for receiving the data processed by the MPPT processing unit, adjusting the output voltage of the load and changing the power working point.
Preferably, the MPPT processing unit includes an analog signal input subunit, a PWM signal converter, and a PSO comparator;
the analog signal input subunit is respectively connected with the data acquisition unit and the PSO comparator, and is used for receiving and processing the data transmitted by the data acquisition unit and transmitting the processed analog quantity to the PSO comparator;
the PSO comparator is connected with the PWM signal converter and used for receiving the processed analog quantity, searching and iterating to obtain the optimal quantity and transmitting the optimal quantity to the PWM signal converter;
and the PWM signal converter is connected with the voltage control unit and used for receiving the optimal quantity, converting the optimal quantity into a duty ratio and transmitting the duty ratio to the voltage control unit.
Preferably, the voltage control unit comprises a BOOST based DC/DC circuit;
the DC/DC circuit based on the BOOST is connected with the PWM signal converter and used for receiving the duty ratio transmitted by the PWM signal converter and correspondingly adjusting the output voltage according to the duty ratio.
In another aspect, an MPPT method based on an improved PSO algorithm includes the steps of:
s1, collecting temperature information and voltage and current information of the thermal power generation unit by using the data collection unit;
s2, constructing an improved PSO algorithm model, and acquiring an optimal quantity in the MPPT processing unit according to temperature information and voltage and current information;
and S3, regulating the load voltage by combining the optimal quantity through the voltage control unit.
Preferably, step S2 specifically includes the following sub-steps:
s21, initializing particle positions and corresponding parameters;
s22, calculating thermoelectric power among the particles according to the voltage and current information, selecting the maximum power among the particles as an individual optimal solution, and initializing iteration times;
s23, traversing each particle, judging whether the latest interparticle thermoelectric power is larger than the historical individual optimal solution, if so, taking the latest interparticle thermoelectric power as the individual optimal solution, updating the individual optimal solution, and entering the step S24; otherwise, maintaining the historical individual optimal solution as the individual optimal solution, and entering the step S24;
s24, comparing the sizes of the individual optimal solutions of the particles to obtain the global optimal solution of the round, judging whether the global optimal solution of the round is larger than the historical global optimal solution, if so, taking the global optimal solution of the round as the global optimal solution, updating the global optimal solution, and entering the step S25; otherwise, the historical global optimal solution is maintained as the global optimal solution, and the step S25 is entered;
s25, judging whether the current global optimal solution exceeds the preset maximum power, if so, returning to the step S23; otherwise, go to step S26;
s26, updating the particle velocity according to the global optimal solution;
s27, updating the particle position according to the particle velocity, expressed as:
pos*＝pos+vel
wherein pos is the position of the particle before updating, vel is the velocity of the particle after updating, and pos is the position of the particle before updating;
and S28, judging whether the iteration times meet the preset iteration times, if so, outputting the current particle position as the optimal quantity, and otherwise, returning to the step S23.
Preferably, step S21 is specifically:
and taking the duty ratio in the DC/DC circuit based on the BOOST in the voltage control unit as a particle position, and initializing the particle position and relevant parameters corresponding to the learning factors.
Preferably, the calculation formula for updating the particle velocity in step S26 is represented as:
vel＝w×vel+c1×r1×(pbest-pos)+c2×r2×(gbest-pos)
wherein vel is the updated particle velocity, w is the inertial weight, r1、r2Are respectively provided withIs a random number, c1、c2The first learning factor and the second learning factor are respectively, pos is the particle position before updating, pbest is the individual optimal solution, and gbest is the global optimal solution.
Preferably, the inertia weight value is updated nonlinearly with the number of iterations;
the updating expression of the inertia weight value is as follows:
wherein w is an inertial weight value, wmaxIs the maximum value of the inertial weight w, wminAnd the minimum value of the inertia weight w, T is a preset iteration number, and T is the current iteration number.
Preferably, the learning factor is updated non-linearly as the number of iterations increases, with the update being represented as:
wherein, c1For the updated first learning factor, c2E is a constant for the updated second learning factor.
The invention has the following beneficial effects:
constructing an MPPT processing unit, a voltage control unit, a data acquisition unit, an upper computer, a thermal power generation unit and a load; the data acquisition unit is respectively connected with the MPPT processing unit and the upper computer to finish the acquisition of voltage and current data and transmit the voltage and current data to the MPPT processing unit, and the MPPT processing unit carries out iterative processing on input data by utilizing an improved POS algorithm to obtain an optimal solution; the voltage control unit is used for receiving the optimal solution and carrying out control and regulation on the output voltage; according to the invention, Maximum Power Point Tracking (MPPT) is carried out in thermoelectric power generation through a Particle Swarm Optimization (PSO) algorithm, so that the power generation device can continuously and efficiently keep maximum power output in a long time period through a control strategy, and a voltage control unit is adjusted, so that the voltage output by MPPT processing through the PSO algorithm is adjusted, the real-time power working point of thermal power generation is changed, the tracking time, the tracking precision and the response speed are ensured, the global optimum condition can be converged in a short time, and the power loss is reduced.
Drawings
Fig. 1 is a system structure diagram of an MPPT apparatus based on an improved PSO algorithm according to the present invention;
FIG. 2 is a flow chart of the MPPT device based on the improved PSO algorithm provided by the present invention;
FIG. 3 is a flow chart illustrating steps of an MPPT method based on an improved PSO algorithm according to the present invention;
FIG. 4 is a flowchart illustrating the substeps of step S2;
FIG. 5 is a simulation circuit diagram of Simulink according to an embodiment of the present invention;
FIG. 6 is a diagram of simulation results for Simulink in an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
As shown in fig. 1 and fig. 2, an embodiment of the present invention provides an MPPT device based on an improved PSO algorithm, including: the system comprises an MPPT processing unit, a voltage control unit, a data acquisition unit, an upper computer, a thermal power generation unit and a load;
the data acquisition unit is respectively connected with the thermal power generation unit, the upper computer and the MPPT processing unit, and is used for acquiring temperature data of the thermal power generation unit, detecting voltage and current of the thermal power generation unit in real time and respectively transmitting the acquired data to the upper computer and the MPPT processing unit;
the thermal power generation unit is connected with the voltage control unit and is used for providing voltage for the voltage control unit;
the upper computer is used for displaying the results of the data acquisition unit and the MPPT processing unit;
the MPPT processing unit is connected with the voltage control unit and used for processing the data transmitted by the data acquisition unit and transmitting the processed data to the voltage control unit;
preferably, the MPPT processing unit includes an analog signal input subunit, a PWM signal converter, and a PSO comparator;
the analog signal input subunit is respectively connected with the data acquisition unit and the PSO comparator and is used for receiving and processing the data transmitted by the data acquisition unit and transmitting the processed analog quantity to the PSO comparator;
the PSO comparator is connected with the PWM signal converter and used for receiving the processed analog quantity, searching and iterating to obtain the optimal quantity and transmitting the optimal quantity to the PWM signal converter;
the PWM signal converter is connected with the voltage control unit and used for receiving the optimal quantity, converting the optimal quantity into a duty ratio and transmitting the duty ratio to the voltage control unit.
The voltage control unit is connected with the load and used for receiving the data processed by the MPPT processing unit, adjusting the input voltage of the load and changing the power working point.
Preferably, the voltage control unit comprises a BOOST based DC/DC circuit;
and the DC/DC circuit based on the BOOST is connected with the PWM signal converter and is used for receiving the duty ratio transmitted by the PWM signal converter and correspondingly adjusting the output voltage according to the duty ratio.
Optionally, the data acquisition unit provided in the embodiment of the present invention may continuously detect a temperature difference between the cold end and the hot end of the thermal power generation unit, detect a voltage and a current at the power generation end and a voltage and a current at the load end in real time, send the acquired data to the upper computer and the MPPT processing unit, then the MPPT processing unit may perform input analog quantity conversion on the acquired data, search for an optimal quantity through the PSO comparator in an iterative manner, convert the optimal quantity into a duty ratio through the PWM signal converter, and apply the output duty ratio to the DC/DC circuit of the Boost to change the output voltage, so that the power operating point in the thermal power generation circuit is changed.
As shown in fig. 3, the present invention provides an MPPT method based on an improved PSO algorithm, comprising the following steps:
s1, collecting temperature information and voltage and current information of the thermal power generation unit by using the data collection unit;
optionally, in the embodiment of the present invention, a temperature difference is provided to two ends of a thermoelectric sheet set of the thermal power generation unit, so that output voltage and current can be generated at two ends of the thermoelectric sheet set according to the seebeck effect, and voltage and current information of the thermoelectric sheet set can be acquired by the data acquisition unit.
S2, constructing an improved PSO algorithm model, and acquiring an optimal quantity in the MPPT processing unit according to temperature information and voltage and current information;
as shown in fig. 4, step S2 specifically includes the following sub-steps:
s21, initializing particle positions and corresponding parameters;
preferably, step S21 is specifically:
and taking the duty ratio in the DC/DC circuit based on the BOOST in the voltage control unit as a particle position, and initializing the particle position and relevant parameters corresponding to the learning factors.
Optionally, the initialized parameters include an inertia weight, a learning factor, a particle velocity, and a particle position, the initial inertia weight is generated by a random number, and the initial position of the particle is generated by a uniform distribution of the learning factor.
S22, calculating thermoelectric power among the particles according to the voltage and current information, selecting the maximum power among the particles as an individual optimal solution, and initializing iteration times;
optionally, the thermoelectric power between particles is calculated according to the voltage and current information, and the calculation formula is represented as: pi＝i×IiWherein P isiIs the i-th inter-particle thermoelectric power, ViIs the ith particle current, ViIs the ith particle voltage and defines the maximum power of the individual as the individualAnd the optimal solution takes the global maximum power as the global optimal solution.
S23, traversing each particle, judging whether the latest interparticle thermoelectric power is larger than the historical individual optimal solution, if so, taking the latest interparticle thermoelectric power as the individual optimal solution, updating the individual optimal solution, and entering the step S24; otherwise, maintaining the historical individual optimal solution as the individual optimal solution, and entering the step S24;
optionally, traversing each particle, comparing the latest electrothermal power of each particle obtained by calculation with the historical optimal solution obtained by the individual particle, updating the individual optimal solution, and obtaining the current round of global optimal solution, namely: the value corresponding to the maximum value corresponding to the optimal solution among the particles in the round.
S24, comparing the sizes of the individual optimal solutions of the particles to obtain the global optimal solution of the round, judging whether the global optimal solution of the round is larger than the historical global optimal solution, if so, taking the global optimal solution of the round as the global optimal solution, updating the global optimal solution, and entering the step S25; otherwise, the historical global optimal solution is maintained as the global optimal solution, and the step S25 is entered;
s25, judging whether the current global optimal solution exceeds the preset maximum power, if so, returning to the step S23; otherwise, go to step S26;
optionally, when the preset maximum power point is tracked, if the power change exceeds the preset threshold, the PSO may search again at this time, that is, perform a new round of maximum power point tracking again, so as to adapt to the change of the external condition.
S26, updating the particle velocity according to the global optimal solution;
preferably, the calculation formula for updating the particle velocity in step S26 is represented as:
vel＝w×vel+c1×r1×(pbest-pos)+c2×r2×(gbest-pos)
wherein vel is the updated particle velocity, w is the inertial weight, r1、r2Respectively random number, having a value of 0-1, c1、c2Respectively, a first learning factor, a second learning factor, pos isAnd updating the positions of the particles before updating, wherein pbest is an individual optimal solution, and gbest is a global optimal solution.
Preferably, the inertia weight value is updated nonlinearly with the number of iterations;
the updating expression of the inertia weight value is as follows:
wherein w is an inertial weight value, wmaxIs the maximum value of the inertial weight w, wminAnd the minimum value of the inertia weight w, T is a preset iteration number, and T is the current iteration number.
Optionally, the maximum range w of the inertia weight w is set in the embodiment of the present inventionmaxSatisfy wmaxMinimum range w of sexual weight w ═ 0.9minSatisfy wmin＝0.4。
Optionally, the inertia weight is reduced with the increase of the number of iterations, and the larger the inertia weight is, the smaller the speed and the particle position are affected by the global optimal solution and the individual optimal solution, and the speed and the particle position may possibly fall into the local optimal solution.
Preferably, the learning factor is updated non-linearly as the number of iterations increases, with the update being represented as:
wherein, c1Is a first learning factor, c2Is the second learning factor, e is a constant.
Optionally, in the embodiment of the inventionA learning factor c1And a second learning factor c2The moving speed of the particles to the individually optimal position and the globally optimal position is respectively controlled, the particles are prevented from falling into the locally optimal solution in the early iteration stage, the diversification of the particle swarm is kept, so that a larger self learning factor and a smaller social learning factor are selected, the algorithm is ensured to be accurate in the later iteration stage, the algorithm is rapidly converged to the globally optimal position, and a smaller c is selected1And a larger c2。
S27, updating the particle position according to the particle velocity, expressed as:
pos*＝pos+vel
wherein pos is the position of the particle before updating, vel is the velocity of the particle after updating, and pos is the position of the particle before updating;
and S28, judging whether the iteration times meet the preset iteration times, if so, outputting the current particle position as the optimal quantity, and otherwise, returning to the step S23.
Optionally, in the embodiment of the present invention, in consideration of time cost, the preset number of iterations is set to 300.
Optionally, in the thermoelectric power generation system, the MPPT processing unit is mainly configured to sample and collect output voltages and currents at two ends of the thermoelectric sheet set in real time, and search for an optimal amount by improving a PSO algorithm, thereby optimizing the duty ratio of the Boost circuit.
And S3, regulating the load voltage by combining the optimal quantity through the voltage control unit.
As shown in fig. 5, the embodiment of the present invention provides a method for verifying the effect of the invention by constructing a thermoelectric generation simulation platform through Simulink, detecting current and voltage within a certain time period, using the detected current and voltage as the input of a PSO module, using the duty ratio of PWM to represent the position of a particle and using the position as the output of the PSO module, and monitoring the voltage of the tracked particle, and using the current and the power as the output characteristic curve of a simulation system at a time sequence; the experimental result is shown in fig. 6, it can be known that the improved PSO algorithm can stably find the position of the maximum power point within the acceptable time range, the transition process of the global search and the local search, which affects the program along with the operation, better meets the actual requirement in the search, and more short maximum power point movements occur under the condition that the temperature difference frequently changes;
compared with other MPPT methods, the MPPT method based on the improved PSO algorithm has the characteristics of stability, rapidness and the like, and can be better used in an actual thermoelectric system.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.
Claims (9)
1. An MPPT device based on an improved PSO algorithm, comprising: the system comprises an MPPT processing unit, a voltage control unit, a data acquisition unit, an upper computer, a thermal power generation unit and a load;
the data acquisition unit is respectively connected with the thermal power generation unit, the upper computer and the MPPT processing unit, and is used for acquiring temperature data of the thermal power generation unit, detecting voltage and current of the thermal power generation unit in real time and respectively transmitting the acquired data to the upper computer and the MPPT processing unit;
the thermal power generation unit is connected with the voltage control unit and is used for providing voltage for the voltage control unit;
the upper computer is used for displaying the results of the data acquisition unit and the MPPT processing unit;
the MPPT processing unit is connected with the voltage control unit and used for processing the data transmitted by the data acquisition unit and transmitting the processed data to the voltage control unit;
the voltage control unit is connected with the load and used for receiving the data processed by the MPPT processing unit, adjusting the output voltage of the load and changing the power working point.
2. The MPPT device based on the improved PSO algorithm of claim 1, wherein the MPPT processing unit includes an analog signal input sub-unit, a PWM signal converter, a PSO comparator;
the analog signal input subunit is respectively connected with the data acquisition unit and the PSO comparator, and is used for receiving and processing the data transmitted by the data acquisition unit and transmitting the processed analog quantity to the PSO comparator;
the PSO comparator is connected with the PWM signal converter and used for receiving the processed analog quantity, searching and iterating to obtain the optimal quantity and transmitting the optimal quantity to the PWM signal converter;
and the PWM signal converter is connected with the voltage control unit and used for receiving the optimal quantity, converting the optimal quantity into a duty ratio and transmitting the duty ratio to the voltage control unit.
3. The MPPT device based on the improved PSO algorithm of claim 2, wherein the voltage control unit includes a BOOST based DC/DC circuit;
the DC/DC circuit based on the BOOST is connected with the PWM signal converter and used for receiving the duty ratio transmitted by the PWM signal converter and correspondingly adjusting the output voltage according to the duty ratio.
4. An MPPT method based on an improved PSO algorithm is characterized by comprising the following steps:
s1, collecting temperature information and voltage and current information of the thermal power generation unit by using the data collection unit;
s2, constructing an improved PSO algorithm model, and acquiring an optimal quantity in the MPPT processing unit according to temperature information and voltage and current information;
and S3, regulating the load voltage by combining the optimal quantity through the voltage control unit.
5. The MPPT method based on the improved PSO algorithm as claimed in claim 4, wherein the step S2 specifically includes the following sub-steps:
s21, initializing particle positions and corresponding parameters;
s22, calculating thermoelectric power among the particles according to the voltage and current information, selecting the maximum power among the particles as an individual optimal solution, and initializing iteration times;
s23, traversing each particle, judging whether the thermoelectric power among the latest particles is larger than the size among historical individual optimal solutions, if so, taking the thermoelectric power among the latest particles as the individual optimal solution, updating the individual optimal solution, and entering the step S24; otherwise, maintaining the historical individual optimal solution as the individual optimal solution, and entering the step S24;
s24, comparing the sizes of the individual optimal solutions of the particles to obtain the global optimal solution of the round, judging whether the global optimal solution of the round is larger than the historical global optimal solution, if so, taking the global optimal solution of the round as the global optimal solution, updating the global optimal solution, and entering the step S25; otherwise, the historical global optimal solution is maintained as the global optimal solution, and the step S25 is entered;
s25, judging whether the current global optimal solution exceeds the preset maximum power, if so, returning to the step S23; otherwise, go to step S26;
s26, updating the particle velocity according to the current global optimal solution;
s27, updating the particle position according to the particle velocity, expressed as:
pos*＝pos+vel
wherein pos is the position of the particle before updating, vel is the velocity of the particle after updating, and pos is the position of the particle before updating;
and S28, judging whether the iteration times meet the preset iteration times, if so, outputting the current particle position as the optimal quantity, and otherwise, returning to the step S23.
6. The MPPT method based on the improved PSO algorithm of claim 5, wherein the step S21 is specifically as follows:
and taking the duty ratio in the DC/DC circuit based on the BOOST in the voltage control unit as a particle position, and initializing the particle position and relevant parameters corresponding to the learning factors.
7. The MPPT method based on the improved PSO algorithm of claim 5, wherein the calculation formula for updating the particle velocity in step S26 is expressed as:
vel＝w×vel+c1×r1×(pbest-pos)+c2×r2×(gbest-pos)
wherein vel is the updated particle velocity, w is the inertial weight, r1、r2Are respectively random numbers, c1、c2The first learning factor and the second learning factor are respectively, pos is the particle position before updating, pbest is the individual optimal solution, and gbest is the global optimal solution.
8. The MPPT method based on the improved PSO algorithm of claim 7, wherein the inertial weight values are non-linearly updated with the number of iterations;
the updating expression of the inertia weight value is as follows:
wherein w is an inertial weight value, wmaxIs the maximum value of the inertial weight w, wminAnd the minimum value of the inertia weight w, T is a preset iteration number, and T is the current iteration number.
9. The MPPT method based on the improved PSO algorithm as claimed in claim 7, wherein the learning factor is updated nonlinearly as the number of iterations increases, the updating being expressed as:
wherein, c1For the updated first learning factor, c2E is a constant for the updated second learning factor.
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