CN112596575B - Maximum power point tracking method based on NPSO algorithm and hierarchical automatic restart - Google Patents

Maximum power point tracking method based on NPSO algorithm and hierarchical automatic restart Download PDF

Info

Publication number
CN112596575B
CN112596575B CN202011521558.6A CN202011521558A CN112596575B CN 112596575 B CN112596575 B CN 112596575B CN 202011521558 A CN202011521558 A CN 202011521558A CN 112596575 B CN112596575 B CN 112596575B
Authority
CN
China
Prior art keywords
particle
particles
free
convergence
algorithm
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202011521558.6A
Other languages
Chinese (zh)
Other versions
CN112596575A (en
Inventor
迟耀丹
陈兵
赵阳
王琰妮
杨小天
吴博琦
王超
赵春雷
高晓红
杨佳
闫兴振
杨帆
慕雨松
王艳杰
曹煜
袁旭
王旭
刘秀琦
李腾
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Jilin Jianzhu University
Original Assignee
Jilin Jianzhu University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Jilin Jianzhu University filed Critical Jilin Jianzhu University
Priority to CN202011521558.6A priority Critical patent/CN112596575B/en
Publication of CN112596575A publication Critical patent/CN112596575A/en
Application granted granted Critical
Publication of CN112596575B publication Critical patent/CN112596575B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05F—SYSTEMS FOR REGULATING ELECTRIC OR MAGNETIC VARIABLES
    • G05F1/00—Automatic systems in which deviations of an electric quantity from one or more predetermined values are detected at the output of the system and fed back to a device within the system to restore the detected quantity to its predetermined value or values, i.e. retroactive systems
    • G05F1/66—Regulating electric power
    • G05F1/67—Regulating electric power to the maximum power available from a generator, e.g. from solar cell

Landscapes

  • Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Sustainable Development (AREA)
  • Sustainable Energy (AREA)
  • Power Engineering (AREA)
  • Physics & Mathematics (AREA)
  • Electromagnetism (AREA)
  • General Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Automation & Control Theory (AREA)
  • Control Of Electrical Variables (AREA)
  • Photovoltaic Devices (AREA)

Abstract

The invention discloses a maximum power point tracking method based on an NPSO algorithm and a hierarchical automatic restart, which is characterized in that a hierarchical automatic restart method is added before a maximum power point tracking method based on the NPSO algorithm, the maximum power point tracking method based on the NPSO algorithm is characterized in that on the basis of a PSO algorithm, the duty ratio D of a PWM signal is used as the position of a population particle, the output power is used as the adaptive value of the population particle, the population particle is divided into a convergence particle and a free particle, the convergence particle has the same property with the particle in the PSO algorithm, the free particle has no memory, the convergence particle and the free particle are updated at the same time, and the individual optimal position of the convergence particle and the global optimal position of the population are updated through the positions of the convergence particle and the free particle after each update; when the convergence particle falls into the local optimum solution, i.e. the local optimum position, the convergence particle is pulled out of the local optimum solution by using the free particle. The power generation efficiency and the stability are improved.

Description

Maximum power point tracking method based on NPSO algorithm and hierarchical automatic restart
Technical Field
The invention belongs to the technical field of new energy photovoltaic power generation, and relates to a maximum power point tracking method based on an NPSO (novel Particle Swarm optimization) algorithm and hierarchical automatic restart.
Background
With the great investment of various production equipment and household appliances, the global demand for electric energy is increasing. In the face of limited fossil fuels and infinitely increased electric energy demand, the world has been paid attention to renewable energy sources, and solar energy is globally approved by virtue of the advantages of low pollution, low noise, inexhaustible energy and the like. According to data of an international renewable energy agency (IRENA), the global photovoltaic accumulated installed capacity maintains a stable rising trend in 2010-2019, the installed capacity is 578533MW in 2019 and increases by 20.3% compared with 2018, and the increase trend is expected to be maintained for a period of time in the future. Under the complex environment such as local shadow, the P-V output curve of the photovoltaic array is a multi-peak nonlinear curve with a plurality of local maxima and one maximum. At present, as the photovoltaic power generation system has the automatic and unattended capability, once the photovoltaic power generation system falls into the local power maximum, the photovoltaic power generation system can operate at the local power maximum for a long time, so that the efficiency is reduced, and the energy is wasted. In order to ensure high power generation efficiency of a photovoltaic power station, a photovoltaic power generation system must be operated at a maximum power point in real time.
The P-V output characteristic curve of the photovoltaic array is a nonlinear curve with one or more peak values, the output power can be adjusted by adjusting the output voltage of the photovoltaic array, and when the power corresponding to the output voltage of the photovoltaic array is the maximum power which can be output by the photovoltaic array, the maximum power point tracking is realized. The traditional MPPT (Maximum Power Point Tracking) technology has the defect of easily falling into local Power Maximum, and the MPPT technology is always a popular problem that people pay attention to, and scholars at home and abroad put forward a plurality of effective methods for the MPPT technology, wherein the traditional methods include a disturbance observation method, a conductance increment method and the like, and the methods are mature in technology, convenient to implement and widely applied to a photovoltaic Power generation system. However, the conventional MPPT method has the disadvantages of slow convergence speed, large oscillation amplitude, easy falling into local power maximum, and the like, and is not suitable for being applied to a photovoltaic power generation system in a complex environment. In recent years, people apply an intelligent control algorithm to the MPPT technology, and certain breakthrough is made. An MPPT technology based on a PSO (particle Swarm optimization) algorithm is provided in Liuyangli, Zhouyang, Chengzui, a photovoltaic system MPPT control method [ J ] based on particle Swarm optimization, computer engineering, 2010,36(15):265-267, so that the speed and the precision of maximum power point tracking are effectively improved, the possibility that the photovoltaic power generation system falls into a local power maximum value is reduced, but the problem that the photovoltaic power generation system easily falls into the local power maximum value is not completely solved by the MPPT technology because the PSO algorithm has the defect that the photovoltaic power generation system easily falls into a local optimal solution.
On the other hand, the external environment is constantly changing, for example, clouds move, the shielding area of dust in a photovoltaic array, the shielding area of shadows such as trees and the like, when the external environment changes, the maximum power point of the photovoltaic power generation system also moves correspondingly, so that the MPPT algorithm needs to be restarted to search the maximum power point again, but how the photovoltaic power generation system senses the change of the external environment and how to restart the system after sensing the change of the external environment is another problem to solve the problem. In addition, the fixed period scanning method is restarted in the maximum search area [0,1] of the PWM duty ratio in the restarting process, even if the external environment changes slightly, the restarting method is restarted in the maximum search area [0,1], so that the tracking time can be prolonged, the disturbance of the power generation system can be increased, the larger disturbance generated in the restarting process can not only impact the power generation system and a power grid and reduce the quality of electric energy, but also can cause certain damage to electric equipment, can reduce the power generation efficiency of the photovoltaic power generation system and waste energy, and the waste energy can be dispersed into the atmosphere in a heat mode to accelerate global warming. Further exploration of MPPT technology is therefore needed to address the deficiencies of this technology.
Disclosure of Invention
The embodiment of the invention aims to provide a maximum power point tracking method based on an NPSO algorithm, so as to solve the problem that the conventional maximum power point tracking algorithm is easy to fall into a local power maximum value when being shielded by a local shadow, so that the power generation efficiency is low.
Another object of the embodiments of the present invention is to provide a hierarchical automatic restart method for a maximum power point tracking algorithm, so as to solve the problems that the restart algorithm of the conventional maximum power point tracking algorithm cannot track the maximum power point in time after the external environment changes, or the maximum power point tracking algorithm is restarted without the external environment changing, which causes low power generation efficiency and increases power fluctuation.
Another objective of the embodiments of the present invention is to provide a maximum power point tracking method based on NPSO algorithm and hierarchical automatic restart.
To solve the above technical problems, the present invention has been madeThe embodiment provides a maximum power point tracking method based on an NPSO algorithm, which is based on a PSO algorithm, takes the duty ratio D of a PWM control signal of a photovoltaic power generation system as the position of a population particle, and takes the output power P of a photovoltaic array of the photovoltaic power generation systempvAs an adaptive value of a population particle, the population particle is divided into two types of a convergence particle and a free particle, the convergence particle is consistent with the particle property in the PSO algorithm, the free particle has no memory, the convergence particle and the free particle are updated at the same time, the speed and the position of the convergence particle are updated according to the speed and the position updating method of the particle in the PSO algorithm, the position of each free particle is updated randomly in a given search interval, the search interval of the free particle is that the global search interval of the convergence particle is equally divided according to the number of the free particles, after each update, the position of the convergence particle is adopted to update the individual optimal position of the convergence particle according to the adaptive value of the convergence particle and the free particle, and the global optimal position of the population is updated according to the positions of the convergence particle and the free particle; when the convergence particles are trapped in the local optimal solution, namely the local optimal position, all the free particles continue to search in the global search interval of the convergence particles, and when the position of a certain free particle is superior to the current global optimal solution, the position of the free particle is adopted to update the global optimal position, namely the global optimal solution, the convergence particles are pulled out of the local optimal solution, and therefore the convergence particles continue to search the global optimal solution.
In order to solve the above technical problem, an embodiment of the present invention further provides a hierarchical automatic restart method for a maximum power point tracking algorithm, where the maximum power point tracking algorithm is the maximum power point tracking method based on the NPSO algorithm, and the method includes the following specific implementation steps:
firstly, acquiring the output voltage U of the photovoltaic array in real time through a voltage sensorsThen, the rate of change Δ U of the photovoltaic array output voltage is calculated according to equation (9):
ΔU＝|Us-Umax|/Umax； (9)
wherein, UmaxOutput at the maximum power point collected after last execution of the maximum power point tracking algorithm for the photovoltaic arrayOutputting voltage;
and secondly, intelligently sensing the change degree of the external environment through a grading method according to the change rate delta U of the output voltage of the photovoltaic array, resetting a global search interval according to different change degrees of the external environment, and finally restarting a maximum power point tracking algorithm by utilizing the reset global search interval to re-track the maximum power point of the photovoltaic power generation system.
In order to solve the above technical problem, an embodiment of the present invention further provides a maximum power point tracking method based on an NPSO algorithm and a hierarchical automatic restart, which is a hierarchical automatic restart method that adds the above maximum power point tracking algorithm before the above maximum power point tracking method based on the NPSO algorithm, and includes the following specific steps:
step S1, the duty ratio D of the PWM control signal of the photovoltaic power generation system is used as the position of the population particles, and the photovoltaic array output power P of the photovoltaic power generation system is usedpvAs an adaptive value of the population particle, the population particle is divided into a plurality of convergent particles and two free particles, and a global search range of positions of the convergent particles is [0,1]]The search interval of free particle 1 in the two free particles is [0,0.5 ]]The search interval of the free particle 2 is [0.5, 1]]And initializing a particle population;
step S2, obtaining the real-time output voltage V of the photovoltaic arraypvAnd real-time output current IpvAnd according to the real-time output voltage V of the photovoltaic arraypvAnd real-time output current IpvCalculating the adaptive value P of the ith convergence particle after the k generation updatingi kAnd the adapted value of the ith' free particle after the k generation update
Figure BDA0002849545460000041
Wherein, i is more than 0 and less than Np，i′＝1,2，NpIs the total number of convergence particles;
step S3, updating the adaptive value P according to the ith convergent particle in the kth generationi kAnd the adapted value of the ith' free particle after the k generation update
Figure BDA0002849545460000042
Updating the individual optimal position Dpbest of the ith convergence particleiAnd the overall optimal position Dgbest of the population, if Pi k＞PpbestiLet Ppbesti＝Pi k、
Figure BDA0002849545460000043
Otherwise PpbestiAnd DpbestiThe change is not changed; if Pi kIf Pgbest is greater than Pgbest, let Pgbest be Pi k、
Figure BDA0002849545460000044
Otherwise, Pgbest and Dgbest are unchanged; if it is
Figure BDA0002849545460000045
Then order
Figure BDA0002849545460000046
Otherwise, Pgbest and Dgbest are not changed, wherein,
Figure BDA0002849545460000047
for the position of the ith converging particle after the k generation update,
Figure BDA0002849545460000048
ppbest, the position of the ith' free particle after the k-th generation updateiThe optimal adaptive value of the ith convergence particle is an individual optimal adaptive value, and Pgbest is the overall optimal adaptive value of the population;
step S4, updating the speed and the position of the convergence particle and the positions of the two free particles;
step S5, judging whether the iteration number, i.e. the updated algebra k, satisfies k > genmaxNamely, whether the iteration number reaches the maximum iteration number genmaxIf yes, ending iteration, executing step S6, otherwise, adding 1 to the iteration number k and returning to step S2 to continue iteration;
step S6, calculating the change rate delta U of the output voltage of the photovoltaic array;
step S7, intelligently sensing the change degree of the external environment through a grading method according to the change rate delta U of the output voltage of the photovoltaic array, judging whether the external environment needs to return to the step S1 for restarting according to different change degrees of the external environment, resetting a global search interval for the change degrees of the external environment with different grades when the external environment needs to return to the step S1 for restarting, then returning to the step S1 for restarting, and updating the global search interval of the convergence particles and the free particles in the original step S1 by using the reset global search interval.
The embodiment of the invention has the beneficial effects that: through secondary development of a PSO algorithm, an NPSO algorithm is provided, the NPSO algorithm divides population particles into two types of convergence particles and free particles, because the free particles do not have convergence, the free particles can be searched in a search interval all the time, when the convergence particles reach convergence, namely, when the convergence particles sink into a local power maximum value, the free particles are still randomly searched in a global space, when the free particles search for higher power, the free particles share the positions of the free particles with the convergence particles, namely, the global optimal solution is updated to the solution searched by the free particles, so that the convergence particles are pulled out of the local optimal solution, the global search capability of the PSO algorithm is enhanced, the defect that the PSO algorithm loses the global search capability after the population particles converge is overcome, the NPSO algorithm is applied to the MPPT technology, and the maximum power point tracking method based on the NPSO algorithm is formed, the problem that the existing maximum power point tracking algorithm is easy to fall into a local power maximum value under complex environments such as the situation that a photovoltaic array is located in local shadow shielding and the like, so that the generating efficiency is low is solved, and the generating efficiency of a photovoltaic power generation system is improved.
The method comprises the steps that the change of an external environment can cause the change of the output voltage of a photovoltaic array, the output voltage of the photovoltaic array is collected through a voltage sensor, and whether the maximum power point tracking algorithm is restarted or not is judged according to the change rate of the voltage, so that the purpose of real-time tracking is achieved, whether the external environment changes or not can be automatically detected, and the function of real-time tracking of the maximum power point is achieved; in the restarting process of the maximum power point tracking algorithm, the change degree of the external environment is graded by a grading method, the change degree of the external environment can be intelligently sensed, a global search interval is reasonably formulated, particles are searched near the original maximum power point, useless search is avoided, the tracking time and power fluctuation in the searching process are reduced, so that the power fluctuation in the restarting of the maximum power point tracking algorithm is reduced, and the problems that the maximum power point cannot be timely tracked after the external environment is changed or the maximum power point tracking algorithm is restarted without the change of the external environment in the restarting process of the conventional maximum power point tracking algorithm, the power generation efficiency is low and the power fluctuation is increased are effectively solved. The photovoltaic power generation system is more stable, and the power generation efficiency is higher.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a graph of photovoltaic array P-V output characteristics.
Fig. 2 is a flowchart of a maximum power point tracking method based on an NPSO algorithm and hierarchical automatic restart according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a Boost voltage Boost circuit of a photovoltaic system.
Fig. 4 is a simulation model diagram of a photovoltaic power generation system based on a Boost voltage-boosting circuit.
Fig. 5(a) is a diagram of a simulation result of the maximum power point tracking method based on the PSO algorithm in the illumination mode 3 in which the local shadow is blocked unevenly.
Fig. 5(b) is a primary simulation result diagram of the maximum power point tracking method based on the NPSO algorithm in the illumination mode 3 with uneven local shadow shielding according to the embodiment of the present invention, where the illumination mode with uneven local shadow shielding is the same as that in fig. 5 (a).
Fig. 6(a) is a graph of a simulation result of the maximum power point tracking method based on the fixed period scanning method when the external illumination environment changes.
Fig. 6(b) is a diagram of a simulation result of the hierarchical automatic restart method of the maximum power point tracking algorithm according to the embodiment of the present invention when the external lighting environment changes, where the external lighting environment changes in accordance with fig. 6 (a).
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Taking SIEMENS SP75 solar panels as an example to illustrate the situation that the P-V output curve of the photovoltaic array is a multi-peak value, a photovoltaic array simulation model formed by connecting 3 photovoltaic panels in series is built under Simulink, wherein the parameters of each photovoltaic panel are shown in table 1, the simulated illumination conditions and the simulation results are shown in table 2, and the P-V output curves of the photovoltaic panels under three types of illumination are shown in fig. 1.
TABLE 1 SIEMENS SP75 photovoltaic Panel parameters
Parameter(s) Value taking
Maximum power (W) 75
Maximum power point voltage (V) 17.0
Maximum power point current (A) 4.4
Short-circuit current (A) 4.8
Open circuit voltage (V) 21.7
TABLE 2 photovoltaic array output characteristic data
Figure BDA0002849545460000061
In the P-V output characteristic curves of the photovoltaic array in the three illumination modes shown in fig. 1, the mode 1 is uniform illumination, and the modes 2 and 3 are respectively subjected to uneven illumination shielded by local shadows. It can be seen from fig. 1 that mode 1 has one peak, mode 2 has two peaks, and mode 3 has 3 peaks. In modes 2 and 3, there is a risk that a local maximum value is tracked instead of the maximum value when maximum power point tracking is performed due to the presence of a plurality of peaks. The existing technical scheme is mostly researched under the condition of ideal uniform illumination and no shadow shielding, namely, the P-V output characteristic curve of the photovoltaic array is a single peak value, and the problem that the photovoltaic array is easy to fall into a local power maximum when being shielded by the non-uniform illumination or the local shadow is not met with reality and the conditions of non-uniform illumination or local shadow shielding and the like are not considered. For example, in the MPPT technique based on the PSO algorithm, population particles are converged gradually in an iteration process, that is, a searchable interval is smaller and smaller, when the particles all converge to a point, the PSO algorithm completely loses the global search capability, and if the power of the searched point is not the maximum power, the point falls into the maximum local power and cannot jump out.
The PSO algorithm is created by inspiring the behavior of birds to search for food, and is applied to various optimization questionsThe composition shows high performance and has wide application range. At the beginning of the PSO algorithm, a particle group with N particles is first defined, each particle having a velocity as the subject of the algorithm execution
Figure BDA0002849545460000071
And position
Figure BDA0002849545460000072
Two parameters. Then, each particle randomly acquires its initial position in the global search space and the predefined speed range
Figure BDA0002849545460000073
And initial velocity
Figure BDA0002849545460000074
I.e. the position and velocity of the first generation particles. The position of each particle can be calculated according to the objective function
Figure BDA0002849545460000075
Corresponding adapted values, wherein the objective function is dependent on the problem to be solved. Then, in each iteration, updating the speed and position of each particle in the particle group through the formula (1) and the formula (2), and taking the current optimal position searched by each particle as the individual optimal solution xpbest of the particleiAnd individual optimal solutions xpbest of all particles in the population of particlesiAnd selecting the optimal solution as the global optimal solution xgbest of the whole particle swarm.
Figure BDA0002849545460000076
Figure BDA0002849545460000077
Where k is the algebra of particle updates,
Figure BDA0002849545460000078
indicating the updated position of the ith particle in the population at the kth generation,
Figure BDA0002849545460000079
indicating the updated position of the ith particle in the population at the k-1 generation,
Figure BDA00028495454600000710
representing the velocity of the ith particle in the population after the update of the kth generation,
Figure BDA00028495454600000711
represents the updated speed of the ith particle in the population after the k-1 generation, i is 1,2,3.. N, N is the total number of particles in the population; ω is an inertial weight factor, rand () is a random number between 0 and 1, c1And c2Is a learning factor.
Further applying the PSO algorithm to the photovoltaic MPPT method, the positions of the particles in the particle swarm represent the duty ratio D of the PWM control signal of the photovoltaic power generation system, so that the PSO algorithm can be used
Figure BDA00028495454600000712
Indicating the position, velocity, etc. of the particle
Figure BDA00028495454600000713
It is shown that,
Figure BDA00028495454600000714
indicating the position of the ith particle after the update of the kth generation,
Figure BDA0002849545460000081
represents the velocity of the ith particle after the k update, 0<i<And N is the total number of particles in the population. At this time, the global search space of the particle, that is, the position range of the particle is [0,1]]The upper limit of the velocity of the particles is vmaxLower limit of vminWhen calculated by the formula (4)
Figure BDA0002849545460000082
Less than vminWhen the temperature of the water is higher than the set temperature,
Figure BDA0002849545460000083
when calculated by equation (4)
Figure BDA0002849545460000084
Greater than vmaxWhen the temperature of the water is higher than the set temperature,
Figure BDA0002849545460000085
vmaxand vminThe specific value is debugged according to the actual application scene. The movement speed of the particles is limited, so that the situation that the position of the particles moves too much each time can be avoided, and the particles possibly miss the optimal solution due to the fact that the movement of the position of the particles is too big, and the convergence of the particles is not facilitated; in addition, the position of the particles is moved too much, so that the power change is too large, and the power grid is not favorable.
The objective function of the particles is shown in formula (3).
Ppv＝Vpv×Ipv (3)
Wherein P ispvThe adaptive value of the population particles is represented by the output power of the photovoltaic array of the photovoltaic power generation system, Vpv represents the voltage value output by the photovoltaic array in real time, and Ipv represents the current value output by the photovoltaic array in real time.
The adaptation value P of the primary particles is carried out in each generationi kComparison of (P)i kThe adaptive value of the ith particle of the kth generation is represented, the adaptive value is a standard for evaluating the position of the particle, and the maximum adaptive value of each particle in all generations at present is recorded as an individual optimal adaptive value PpbestiAnd define PpbestiThe position of the corresponding particle is the individual optimal solution Dpbest of the particlei. And at the present time, the individual optimal adaptive value Ppbest of all the particlesiAnd selecting an optimal adaptive value as a global optimal adaptive value Pgbest of the particle swarm, and defining the position of a particle corresponding to the Pgbest as a global optimal solution Dgbest of the particle swarm. In the photovoltaic MPPT method, the above formulas (1) and (2) may be rewritten as formulas (4) and (5).
Figure BDA0002849545460000088
Figure BDA0002849545460000089
Wherein,
Figure BDA00028495454600000810
representing the velocity of the ith convergent particle in the population after the update of the kth generation,
Figure BDA00028495454600000811
represents the velocity, v, of the ith convergent particle in the population after the update of the k-1 generationmaxUpper limit of velocity for converging particles, vminFor converging the lower speed limit of the particle, when calculated by equation (4)
Figure BDA00028495454600000812
Less than vminWhen the temperature of the water is higher than the set temperature,
Figure BDA00028495454600000813
when calculated by equation (4)
Figure BDA00028495454600000814
Greater than vmaxWhen the temperature of the water is higher than the set temperature,
Figure BDA00028495454600000815
Figure BDA00028495454600000816
indicating the updated position of the ith convergent particle in the population at the kth generation,
Figure BDA00028495454600000817
represents the position of the ith convergent particle in the population after the k-1 generation update, i is 1,2,3<k<genmax，genmaxIs the maximum iteration number; omega is the inertial weight factor, and rand () is [0,1]]Interval(s)Random number of inner, c1And c2Is a learning factor.
The MPPT (maximum power point tracking) method based on the PSO algorithm comprises the following steps:
step one, taking the duty ratio D of a PWM control signal of a photovoltaic power generation system as the position of a population particle
Figure BDA0002849545460000091
Position of population particle
Figure BDA0002849545460000092
Has a global interval of [0,1]]To the output power P of the photovoltaic array of the photovoltaic power generation systempvAs adaptation value P of the particlei kAnd defining an upper limit v for the velocity of the population of particlesmaxLower limit vminAnd maximum number of iterations genmaxA value of (d); initialising the population of particles, i.e. initialising the positions of all particles in the population at the first generation
Figure BDA0002849545460000093
Speed in the first generation
Figure BDA0002849545460000094
Total number of particles N, learning factor c1And c2And an inertial weight factor omega.
Step two, calculating the adaptive value P of the particles according to the formula (3)i k。
Step three, if Pi k＞PpbestiLet Ppbesti＝Pi k、
Figure BDA0002849545460000095
Otherwise PpbestiAnd DpbestiThe change is not changed; if Pi kIf Pgbest is greater than Pgbest, let Pgbest be Pi k、
Figure BDA0002849545460000096
Otherwise, Pgbest and Dgbest are unchanged;
step four, updating the speed and the position of the population particles according to iterative formulas (4) and (5);
step five, if the termination condition k > gen of the particle swarm algorithm is metmaxIf the maximum iteration times are reached, ending the iteration, executing the step six, and otherwise, returning to the step two;
and step six, if the current environment is not changed, outputting a global optimal solution Dgbest, taking the Dgbest as the duty ratio of a PWM control signal of the photovoltaic power generation system, and otherwise, returning to the step one.
Example 1
The embodiment of the invention provides the NPSO algorithm by carrying out secondary development on the PSO algorithm, the algorithm divides the population particles into two types of convergence particles and free particles, enhances the global search capability of the PSO algorithm, makes up the defect that the PSO algorithm is easy to fall into the local optimal solution, and applies the NPSO algorithm to the MPPT technology.
The NPSO algorithm classifies the population particles into two types, a convergent particle and a free particle, wherein the number of the free particles is two, and the two types are divided into a free particle 1 and a free particle 2. The converging particle is consistent with the particle property in the PSO algorithm and has the position of
Figure BDA0002849545460000097
At a speed of
Figure BDA0002849545460000098
The convergence particle and the free particle are updated in the same generation, the speed and the position of the convergence particle are updated according to the formula (4) and the formula (5) at each iteration, and the objective function is shown as the formula (3), namely the adaptive value P of the convergence particle and the free particle is obtained according to the formula (3)i k(ii) a The free particles have no memory, and take values randomly in a given search interval during each updating, and the adaptive value of the free particles is used
Figure BDA0002849545460000099
Indicating that if the solution found by the free particle is found, the position of the free particle
Figure BDA00028495454600000910
When the global optimal solution is better than the current global optimal solution Dgbest, the global optimal solution is updated to the solution searched by the free particles at the moment, namely the order is ordered
Figure BDA00028495454600000911
After the convergence particles in the NPSO algorithm are trapped in the local optimal solution, the convergence particles lose the global search capability, and because the free particles are still searched in the global search space, after the free particles update the global optimal solution, the convergence particles can move towards the updated global optimal solution according to the formula (3) and the formula (4), so that the free particles pull out the local optimal solution from the convergence particles, and the global optimal solution is found.
The free particle searching process is divided into two stages through a threshold value gen of a free particle searching stage, the first stage is that the iteration times of the free particle and the convergent particle are within gen generation, the free particle is searched in a global searching space, and the global searching space is [0,1]]And defining the search interval of the free particles is to divide the global search interval of the convergence particles equally according to the number of the free particles. The function of the free particle at this stage is to search in the global search interval, and help the convergent particle to jump out of the local optimal solution. The second stage is that after the iteration times of the population particles are in the gen generation, the free particles do not perform global search any more, but perform radius r by taking the global optimal solution Dgbest as the center1Random search of the micro area. The effect of the free particles at this stage is to enable the current population particles to search the global optimal solution more carefully, improve the search precision, and accelerate the convergence speed of the convergence particles.
The maximum power point tracking method based on the NPSO algorithm comprises the following specific steps:
step 1, taking the duty ratio D of a PWM control signal of a photovoltaic power generation system as the position of a population particle, and taking the photovoltaic array output power P of the photovoltaic power generation systempvAs an adaptive value of the population particle, the population particle is divided into a plurality of convergent particles and two free particles, and the global search interval of the positions of the convergent particles is [0,1]]The search interval of free particle 1 in the two free particles is [0,0.5 ]]Free particles2 has a search interval of [0.5, 1]]The search interval of the free particles is to divide the global search interval of the convergence particles equally according to the number of the free particles; then initializing the particle population, i.e. initializing the position of the converging particles in the first generation
Figure BDA0002849545460000101
Velocity of converging particles in first generation
Figure BDA0002849545460000102
Upper limit of velocity v of converging particlesmaxLower limit of velocity vminFree particles in the first generation position
Figure BDA0002849545460000103
Total number of converging particles NpLearning factor c1And c2Inertia weight factor ω, maximum number of iterations genmaxAnd a search algebra threshold gen of free particles, where 0 < i < Np，i′＝1,2；
Step 2, acquiring real-time output voltage V of the photovoltaic arraypvAnd real-time output current IpvAnd outputs the voltage V according to the real-timepvAnd real-time output current IpvCalculating and calculating an adaptive value P of the ith convergence particle after the k generation updating through the formula (3)i kAnd the adapted value of the ith' free particle after the k generation update
Figure BDA0002849545460000104
Step 3, updating the adaptive value P according to the ith convergent particle in the kth generationi kAnd the adapted value of the ith' free particle after the k generation update
Figure BDA0002849545460000105
Updating the individual optimal position Dpbest of the ith convergence particleiAnd the overall optimal position Dgbest of the population, if Pi k＞PpbestiLet Ppbesti＝Pi k、
Figure BDA0002849545460000106
Otherwise PpbestiAnd DpbestiThe change is not changed; if Pi kIf Pgbest is greater than Pgbest, let Pgbest be Pi k、
Figure BDA0002849545460000107
Otherwise, Pgbest and Dgbest are unchanged; if it is
Figure BDA0002849545460000108
Then order
Figure BDA0002849545460000109
Otherwise, Pgbest and Dgbest are unchanged;
and 4, updating the speed and the position of the convergence particle according to the formulas (4) and (5), and updating the positions of two free particles according to the formulas (6) to (8):
Figure BDA0002849545460000111
Figure BDA0002849545460000112
wherein,
Figure BDA0002849545460000113
the position of the free particle 1 after the k-th generation update,
Figure BDA0002849545460000114
is the updated position of the free particle 2 in the k generation; dminLower bound of global search region for convergent particles, dmaxThe upper limit of the global search region for the converging particle, i.e., the global search region for the converging particle, is [ dmin,dmax](ii) a rand () is the interval [0,1]]Inner random number, k is the iterative algebra of the particle, the search radius r of the random search of the free particle1Determined by equation (8):
Figure BDA0002849545460000115
wherein,
Figure BDA0002849545460000116
represents the position of the 1 st convergent particle after the kth update, i.e. iteration, d1 k-1Represents the position of the 1 st convergent particle after the (k-1) th iteration;
Figure BDA0002849545460000118
indicating the position of the 2 nd converging particle after the kth iteration,
Figure BDA0002849545460000119
represents the position of the 2 nd convergent particle after the (k-1) th iteration;
Figure BDA00028495454600001110
indicating the position of the ith converging particle after the kth iteration,
Figure BDA00028495454600001111
represents the position of the ith convergent particle after the (k-1) th iteration;
Figure BDA00028495454600001112
denotes the NthpThe position of the convergent particle after the kth iteration,
Figure BDA00028495454600001113
denotes the NthpThe position of each convergent particle after the (k-1) th iteration.
Step 5, judging whether the iteration times, namely the updating algebra k, meet the condition that k is larger than genmaxJudging whether the maximum iteration number is reached, if so, ending the iteration, executing the step 6, otherwise, adding 1 to the iteration number k and returning to the step 2 to continue the iteration;
and 6, judging whether the current external environment changes, if the current external environment does not change, outputting a global optimal solution Dgbest, taking the Dgbest as the duty ratio of a PWM control signal of the photovoltaic power generation system, and if not, returning to the step 1.
Example 2
Aiming at the problems of poor real-time performance, large disturbance amount during restarting and the like of the existing restarting system, the hierarchical automatic restarting method of the maximum power point tracking algorithm is provided, and the specific implementation steps are as follows:
firstly, acquiring the output voltage U of the photovoltaic array in real time through a voltage sensorsThen, the rate of change Δ U of the photovoltaic array output voltage is calculated according to equation (9):
ΔU＝|Us-Umax|/Umax； (9)
wherein, UmaxAnd the output voltage at the maximum power point collected after the maximum power point tracking algorithm is executed for the photovoltaic array last time. When the external environment changes, the output voltage of the photovoltaic array changes inevitably, so that if the output voltage of the photovoltaic array changes, the external environment changes, otherwise, the external environment does not change.
And secondly, intelligently sensing the change degree of the external environment through a grading method according to the change rate delta U of the output voltage of the photovoltaic array, resetting an MPPT algorithm, namely a global search interval of restarting a maximum power point tracking algorithm according to different change degrees of the external environment, and finally re-tracking the maximum power point of the photovoltaic power generation system.
When the change of the external environment is detected, the MPPT technology is restarted, the change rate delta U of the output voltage of the photovoltaic array is obtained and analyzed when the MPPT technology is restarted, the degree of the change of the external environment can be extracted from the change rate delta U data of the output voltage of the photovoltaic array, a global search interval is reasonably worked out according to the change degree of the external environment, and finally the maximum power point of the photovoltaic power generation system is retraced according to the MPPT technology based on the NPSO algorithm.
In the process of intelligently sensing the change degree of the external environment by a grading method, three voltage change rate threshold values eta are firstly set1、η2And η3Using a voltage rate of change threshold eta1、η2And η3And the change rate delta U of the output voltage of the photovoltaic array divides the change degree of the external environment into three grades: delta U is more than or equal to 0 and less than eta1When the external environment changes, the degree of change of the corresponding external environment is a first level; eta1≤ΔU＜η2When the external environment changes, the corresponding degree of change of the external environment is the second level; eta2≤ΔU＜η3The degree of change of the corresponding external environment is a third level. The first grade represents that the external environment only slightly changes, the output power of the photovoltaic power generation system also slightly changes at the moment, and the maximum power point of the photovoltaic power generation system does not move or moves to a very small degree, so that the first grade MPPT algorithm does not need to be restarted; the third level represents that the external environment is changed greatly, and the maximum power point of the photovoltaic power generation system at the third level moves greatly, so that the global search needs to be defined as the maximum search interval when the MPPT algorithm is restarted, namely [0,1]](ii) a The second level represents that the external environment has a small change, and the maximum power point of the photovoltaic power generation system of the second level also has a small movement correspondingly, so that when the MPPT algorithm is restarted, the search interval needs to be redefined reasonably according to the change rate delta U of the output voltage of the photovoltaic array. The specific method is that the upper limit and the lower limit of the search interval are respectively DmaxAnd DminI.e. the search interval is [ D ]min,Dmax]Wherein D isminNot less than 0 and Dmax≤1；DmaxAnd DminThe value of (d) is determined by equations (10) and (11):
Dmax＝Dgbest+r2； (10)
Dmin＝Dgbest-r2； (11)
wherein Dgbest is the global optimal solution found by the MPPT algorithm executed last time, and r2Search radius for searching centered on Dgbest, r2The value of (c) is determined according to the magnitude of the change rate Δ U of the output voltage of the photovoltaic array, and is specifically shown in formula (12):
Figure BDA0002849545460000131
wherein eta is21And η22Are all located at the voltage change rate threshold eta1And η2Two voltage rate of change sub-thresholds in between, and η1＜η21＜η22＜η2。η21And η22The specific value of (a) is determined according to a specific photovoltaic power generation system and can be obtained through field debugging. Here is at η1≤ΔU≤η2When Δ U is small, r2Taken as 0.1, when Δ U is large, r2Taken as 0.2, when Δ U is larger, r2Take 0.3.
The maximum power point tracking algorithm, i.e., the MPPT algorithm, of this embodiment may use an existing maximum power point tracking method, such as a maximum power point tracking method based on a PSO algorithm, or may use the maximum power point tracking method based on an NPSO algorithm, which is provided in embodiment 2.
Example 3
A two-stage system structure is mostly adopted in a photovoltaic power generation system, and a one-stage DC/DC conversion link is added before a DC/AC (direct current/alternating current) conversion link. The addition of the DC/DC conversion link can separately control the grid-connected technology and the maximum power point tracking algorithm, so that the control is more convenient and flexible. In the embodiment of the invention, a Boost voltage booster circuit is adopted in the DC/DC link, the implementation of the maximum power point tracking algorithm is also carried out in the Boost voltage booster circuit, and the Boost voltage booster circuit of the photovoltaic system is shown in figure 3. The embodiment provides a maximum power point tracking method based on an NPSO algorithm and hierarchical automatic restart, which is an algorithm for automatically resetting a global search area in one stage at the previous stage of the original maximum power point tracking method based on the NPSO algorithm. As shown in fig. 2, the method comprises the following steps:
step S1, the duty ratio D of the PWM control signal of the photovoltaic power generation system is used as the position of the population particles, and the photovoltaic array output power P of the photovoltaic power generation system is usedpvAs an adaptive value of the population particle, the population particle is divided into a plurality of convergent particles and two free particles, and a global search range of positions of the convergent particles is[0,1]I.e. the lower limit d of the global search space of the position of the converging particle min0, upper limit of global search interval d of the position of the convergent particle max1, the search space of free particle 1 of the two free particles is [0,0.5 ]]The search interval of the free particle 2 is [0.5, 1]]And initializing the particle population, i.e. initializing the position of the converging particles in the first generation
Figure BDA0002849545460000132
Velocity of converging particles in first generation
Figure BDA0002849545460000133
Upper limit of velocity v of converging particlesmaxLower limit of velocity vminFree particles in the first generation position
Figure BDA0002849545460000141
Total number of converging particles NpLearning factor c1And c2Inertia weight factor ω, maximum number of iterations genmaxSearch algebra threshold gen of free particle, voltage rate of change threshold η of graded restart1、η2And η3And voltage rate of change sub-threshold η21And η22Wherein, i is more than 0 and less than Np，i′＝1,2；
Step S2, obtaining the real-time output voltage V of the photovoltaic arraypvAnd real-time output current IpvAnd according to the real-time output voltage V of the photovoltaic arraypvAnd real-time output current IpvCalculating the adaptive value P of the ith convergence particle after the k generation updating by adopting the formula (3)i kAnd the adapted value of the ith' free particle after the k generation update
Figure BDA0002849545460000142
Step S3, updating the adaptive value P according to the ith convergent particle in the kth generationi kAnd the adapted value of the ith' free particle after the k generation update
Figure BDA0002849545460000143
Updating the individual optimal position Dpbest of the ith convergence particleiAnd the overall optimal position Dgbest of the population, if Pi k＞PpbestiLet Ppbesti＝Pi k、
Figure BDA0002849545460000144
Otherwise PpbestiAnd DpbestiThe change is not changed; if Pi kIf Pgbest is greater than Pgbest, let Pgbest be Pi k、
Figure BDA0002849545460000145
Otherwise, Pgbest and Dgbest are unchanged; if it is
Figure BDA0002849545460000146
Then order
Figure BDA0002849545460000147
Otherwise, Pgbest and Dgbest are unchanged;
step S4, updating the speed and the position of the convergence particle according to the formulas (4) and (5), and updating the positions of the two free particles according to the formulas (6) to (8);
step S5, judging whether the iteration number, i.e. the updated algebra k, satisfies k > genmaxIf yes, ending iteration, and executing step S6, otherwise adding 1 to the iteration number k and returning to step S2 to continue iteration;
step S6, calculating the change rate delta U of the output voltage of the photovoltaic array through a formula (9);
step S7, intelligently sensing the change degree of the external environment through a grading method according to the change rate delta U of the output voltage of the photovoltaic array, judging whether the restart needs to be returned to the step S1 according to different change degrees of the external environment, resetting the global search interval for the change degrees of the external environment with different grades when the restart needs to be returned to the step S1, then returning to the step S1 to restart, and replacing the global search interval of the convergent particles in the step S1 by using the reset global search interval. Specifically, if 0 is more than or equal to delta U and less than eta1Then there is no need to return to stepStep S1 is restarted, the global optimal position Dgbest is directly output, the Dgbest is used as the duty ratio of the PWM control signal of the photovoltaic power generation system, and the step S6 is returned to continue to calculate the change rate of the output voltage of the photovoltaic array; if eta1≤ΔU＜η2Returning to step S1 to restart and reset the global search area to [ Dmin,Dmax]I.e. the global search interval [0,1] of the convergent particle in step S1]Is updated to [ Dmin,Dmax]I.e. the lower limit d of the global search space of the particle to be convergedminIs updated to DminAnd limiting the global search interval of the convergent particles by an upper limit dmaxIs replaced by Dmax. Search interval [0,0.5 ] of free particle 1]Is replaced by
Figure BDA0002849545460000151
Search interval [0.5, 1] of free particle 2]Is replaced by
Figure BDA0002849545460000152
If eta2≤ΔU＜η3The process returns to step S1 to restart the engine.
Example 4
In order to verify the maximum power point tracking method based on the NPSO algorithm, a simulation model of the photovoltaic power generation system based on the Boost booster circuit shown in fig. 3 is built under the Simulink environment, as shown in fig. 4. The PV array is a photovoltaic array formed by serially connecting three photovoltaic cells, wherein parameters of one photovoltaic cell are shown in table 1, C1 ═ 10 μ F, L ═ 1.5mH, C2 ═ 50 μ F, and R1 ═ 53 Ω.
In the three illumination modes in table 2, simulation tests are respectively performed on the maximum power point tracking method based on the PSO algorithm and the NPSO algorithm. The particle number of the PSO algorithm and the particle number of the NPSO algorithm are both 6, the maximum iteration time is 20 times, and the parameter gen value of the NPSO algorithm is 12.
Fig. 5(a) and 5(b) are graphs of a simulation result of the maximum power point tracking method based on the PSO algorithm and the NPSO algorithm in the illumination mode 3. It can be seen from fig. 5(a) and 5(b) that the maximum power point tracking method based on the PSO algorithm before the improvement falls into the local power maximum, and under the same particle swarm initial condition, the improved maximum power point tracking method based on the NPSO algorithm jumps out of the local power maximum due to the global search function of the free particles at 0.33s, and all the particles converge to the power maximum 96.8W at 1.43 s. The global search capability of the particle swarm optimization is enhanced, the success rate of tracking the maximum power point of the photovoltaic power generation system is effectively improved under the condition that the photovoltaic array is shielded by local shadows, the problem of falling into local power maximum is solved, the power generation efficiency and the economy of a photovoltaic power station are improved, and the waste of energy is reduced.
Fig. 6(a) and 6(b) are simulation diagrams of a hierarchical auto-restart method using a fixed-period scanning method and an embodiment of the present invention. The experimental conditions were set to change from illumination mode 3 to illumination mode 2 at 1.3s, and the periodic scanning method performed every 1.5 s. From the simulation result, it can be seen that the MPPT algorithm cannot be restarted by immediately responding when the external environment changes by the fixed-period scanning method, but the MPPT algorithm must be restarted when the scanning time comes. On the other hand, the external environment does not change at 3s, but the scanning time of the fixed-period scanning method comes, so that the MPPT algorithm is restarted, unnecessary scanning is performed, and the power generation efficiency is reduced. When the external environment changes within 1.3s by the hierarchical automatic restart method, the system can automatically detect the change of the external environment and immediately restart the MPPT algorithm. In addition, in fig. 6(a), no ranking method is added in the restart of the MPPT algorithm, and in fig. 6(b), a ranking method is added in the restart of the MPPT algorithm, and it can be seen by comparison that the power fluctuation range in the restart of the MPPT algorithm in fig. 6(b) is approximately 110 to 150W, while the power fluctuation range in the restart of the MPPT algorithm in fig. 6(a) is approximately 0 to 150W, and it is obvious that the power fluctuation is reduced in the restart of the MPPT algorithm after the ranking method is added, so that the photovoltaic power generation system is more stable, the photovoltaic power generation efficiency is increased, and the waste of energy is reduced.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.

Claims (9)

1. A maximum power point tracking method based on an NPSO algorithm is characterized in that on the basis of a PSO algorithm, the duty ratio D of a PWM control signal of a photovoltaic power generation system is used as the position of a population particle, and the photovoltaic array output power P of the photovoltaic power generation system is usedpvAs an adaptive value of a population particle, the population particle is divided into two types of a convergence particle and a free particle, the convergence particle is consistent with the particle property in the PSO algorithm, the free particle has no memory, the convergence particle and the free particle are updated at the same time, the speed and the position of the convergence particle are updated according to the speed and the position updating method of the particle in the PSO algorithm, the position of each free particle is updated randomly in a given search interval, the search interval of the free particle is that the global search interval of the convergence particle is equally divided according to the number of the free particles, after each update, the position of the convergence particle is adopted to update the individual optimal position of the convergence particle according to the adaptive value of the convergence particle and the free particle, and the global optimal position of the population is updated according to the positions of the convergence particle and the free particle; when the convergence particles are trapped in a local optimal solution, namely a local optimal position, all the free particles continue to search in a global search interval of the convergence particles, and when the position of a certain free particle is superior to the current global optimal solution, the position of the free particle is adopted to update the global optimal position, namely the global optimal solution, the convergence particles are pulled out of the local optimal solution, so that the convergence particles continue to search the global optimal solution;
the method comprises the following specific steps:
step 1, taking the duty ratio D of a PWM control signal of a photovoltaic power generation system as the position of a population particle, and taking the photovoltaic array output power P of the photovoltaic power generation systempvAs an adaptive value of the population particle, the population particle is divided into a plurality of convergent particles and two free particles, and a global search range of positions of the convergent particles is [0,1]]The search interval of free particle 1 in the two free particles is [0,0.5 ]]The search interval of the free particle 2 is [0.5, 1]]And initializing a particle population;
step 2, obtaining photovoltaicReal-time output voltage V of arraypvAnd real-time output current IpvAnd outputs the voltage V according to the real-timepvAnd real-time output current IpvCalculating the adaptive value P of the ith convergent particle in the kth generationi kAnd the adaptation value of the ith' free particle in the kth generation
Figure FDA0003394618760000011
Wherein, i is more than 0 and less than Np，i′＝1,2，NpIs the total number of convergence particles;
step 3, updating the adaptive value P according to the ith convergent particle in the kth generationi kAnd the adapted value of the ith' free particle after the k generation update
Figure FDA0003394618760000012
Updating the individual optimal position Dpbest of the ith convergence particleiAnd the overall optimal position of the population Dgbest:
if Pi k＞PpbestiLet Ppbesti＝Pi k、
Figure FDA0003394618760000013
Otherwise PpbestiAnd DpbestiThe change is not changed;
if Pi kIf Pgbest is greater than Pgbest, let Pgbest be Pi k、
Figure FDA0003394618760000014
Otherwise, Pgbest and Dgbest are unchanged;
if it is
Figure FDA0003394618760000015
Then order
Figure FDA0003394618760000016
Otherwise, Pgbest and Dgbest are unchanged;
wherein,
Figure FDA0003394618760000017
for the position of the ith converging particle after the k generation update,
Figure FDA0003394618760000018
ppbest, the position of the ith' free particle after the k-th generation updateiThe optimal adaptive value of the ith convergence particle is an individual optimal adaptive value, and Pgbest is the overall optimal adaptive value of the population;
step 4, updating the speed and the position of the convergence particle and the positions of the two free particles;
step 5, judging whether the iteration times, namely the updating algebra k, meet the condition that k is larger than genmaxNamely, whether the iteration number reaches the maximum iteration number genmaxIf yes, ending iteration and executing step 6, otherwise adding 1 to the iteration number k and returning to the step 2 to continue the iteration;
and 6, judging whether the current external environment changes, if the current external environment does not change, outputting the global optimal solution Dgbest, otherwise, returning to the step 1.
2. The maximum power point tracking method based on the NPSO algorithm according to claim 1, wherein the step 4 updates the speed and the position of the convergent particles according to the following equations (4) - (5):
Figure FDA0003394618760000021
Figure FDA0003394618760000022
wherein,
Figure FDA0003394618760000023
representing the velocity of the ith convergent particle in the population after the update of the kth generation,
Figure FDA0003394618760000024
represents the velocity, v, of the ith convergent particle in the population after the update of the k-1 generationmaxUpper limit of velocity for converging particles, vminFor converging the lower speed limit of the particle, when calculated by equation (4)
Figure FDA0003394618760000025
Less than vminWhen the temperature of the water is higher than the set temperature,
Figure FDA0003394618760000026
when calculated by equation (4)
Figure FDA0003394618760000027
Greater than vmaxWhen the temperature of the water is higher than the set temperature,
Figure FDA0003394618760000028
Figure FDA0003394618760000029
represents the updated position of the ith convergent particle in the population at the k-1 generation, 1<k<genmax(ii) a Omega is the inertial weight factor, and rand () is the interval [0,1]]Random number of inner, c1And c2Is a learning factor.
3. The maximum power point tracking method based on the NPSO algorithm according to claim 1, wherein the step 4 updates the positions of two free particles according to the following equations (6) to (7):
Figure FDA00033946187600000210
Figure FDA00033946187600000211
wherein,
Figure FDA00033946187600000212
the position of the free particle 1 after the k-th generation update,
Figure FDA00033946187600000213
is the updated position of the free particle 2 in the k generation; dminGlobal search space lower bound for convergent particles, dmaxA global search interval upper limit for a converging particle; rand () is the interval [0,1]]The random number in the table, gen is the search algebra threshold of the free particle, after the iteration number is after gen generation, the free particle does not perform global search, but performs radius r with the global optimal solution Dgbest as the center1Random search of micro-area, search radius r of free particle random search1Determined by equation (8):
Figure FDA0003394618760000031
wherein,
Figure FDA0003394618760000032
represents the position of the 1 st convergent particle after the kth update, i.e. iteration, d1 k-1Represents the position of the 1 st convergent particle after the (k-1) th iteration;
Figure FDA0003394618760000033
indicating the position of the 2 nd converging particle after the kth iteration,
Figure FDA0003394618760000034
represents the position of the 2 nd convergent particle after the (k-1) th iteration;
Figure FDA0003394618760000035
indicating the position of the ith converging particle after the kth iteration,
Figure FDA0003394618760000036
represents the position of the ith convergent particle after the (k-1) th iteration;
Figure FDA0003394618760000037
denotes the NthpThe position of the convergent particle after the kth iteration,
Figure FDA0003394618760000038
denotes the NthpThe position of each convergent particle after the (k-1) th iteration.
4. The maximum power point tracking method based on the NPSO algorithm as claimed in claim 1, wherein the initialization particle population is the position of the initialization convergence particles at the first generation
Figure FDA0003394618760000039
Velocity of converging particles in first generation
Figure FDA00033946187600000310
Upper limit of velocity v of converging particlesmaxLower limit of velocity vminFree particles in the first generation position
Figure FDA00033946187600000311
Total number of converging particles NpLearning factor c1And c2Inertia weight factor ω, maximum number of iterations genmaxAnd a search algebra threshold gen for free particles.
5. A grading automatic restarting method of a maximum power point tracking algorithm is characterized in that the maximum power point tracking algorithm is the maximum power point tracking method based on the NPSO algorithm according to any one of claims 1-4, and the grading automatic restarting method specifically comprises the following implementation steps:
firstly, acquiring the output voltage U of the photovoltaic array in real time through a voltage sensorsThen, the rate of change Δ U of the photovoltaic array output voltage is calculated according to equation (9):
ΔU＝|Us-Umax|/Umax； (9)
wherein, UmaxAcquiring the output voltage at the maximum power point after the maximum power point tracking algorithm is executed for the photovoltaic array last time;
and secondly, intelligently sensing the change degree of the external environment through a grading method according to the change rate delta U of the output voltage of the photovoltaic array, resetting a global search interval according to different change degrees of the external environment, and finally restarting a maximum power point tracking algorithm by utilizing the reset global search interval to re-track the maximum power point of the photovoltaic power generation system.
6. The method as claimed in claim 5, wherein the method comprises the steps of intelligently sensing the change degree of the external environment according to the change rate Δ U of the output voltage of the photovoltaic array by a hierarchical method, and resetting the global search interval according to the different change degrees of the external environment, wherein different voltage change rate thresholds are set, the change degree of the external environment is divided into a plurality of levels according to the different voltage change rate thresholds and the change rate Δ U of the output voltage of the photovoltaic array, and then whether the maximum power point tracking algorithm needs to be restarted is determined for the change degrees of the external environments of the different levels, and when the maximum power point tracking algorithm needs to be restarted, the different global search intervals are set for the change degrees of the external environments of the different levels.
7. The method as claimed in claim 5, wherein the variation degree of the external environment is sensed intelligently by a hierarchical method according to the variation rate Δ U of the output voltage of the photovoltaic array, the global search interval is reset according to the variation degree of the external environment, and finally η is first set when the maximum power point tracking algorithm is restarted by using the reset global search interval1、η2And η3These three voltage rate of change thresholds are then used1、η2And η3And the change rate delta U of the output voltage of the photovoltaic array divides the change degree of the external environment into three grades: delta U is more than or equal to 0 and less than eta1In the process, the change degree of the corresponding external environment is the first grade, and the maximum power point tracking algorithm of the first grade does not need to be restarted; eta1≤ΔU＜η2Then, the degree of change of the corresponding external environment is the second level, and at this time, the global search interval is reset to [ D ]min,Dmax]Wherein D isminNot less than 0 and Dmax≤1；η2≤ΔU＜η3Then, the degree of change of the corresponding external environment is the third level, and at this time, the global search interval is set to be the maximum search interval, namely [0,1]]；
Global search Interval [ Dmin,Dmax]In DmaxAnd DminThe value of (b) is determined by the following equations (10) and (11):
Dmax＝Dgbest+r2； (10)
Dmin＝Dgbest-r2； (11)
wherein Dgbest is the global optimal solution found by the last execution of the maximum power point tracking algorithm, r2Search radius for searching centered on Dgbest, r2The value of (c) is determined according to the magnitude of the change rate Δ U of the output voltage of the photovoltaic array, and is specifically shown in formula (12):
Figure FDA0003394618760000041
wherein eta is21And η22Are all located at the voltage change rate threshold eta1And η2Two voltage rate of change sub-thresholds in between, and η1＜η21＜η22＜η2。
8. A maximum power point tracking method based on an NPSO algorithm and hierarchical automatic restart is characterized in that the hierarchical automatic restart method of the maximum power point tracking algorithm of claim 5 is added after the maximum power point tracking method based on the NPSO algorithm of any one of claims 1 to 4, and the specific steps are as follows:
step S1, the duty ratio D of the PWM control signal of the photovoltaic power generation system is used as the position of the population particles, and the photovoltaic array output power P of the photovoltaic power generation system is usedpvAs an adaptive value of the population particle, the population particle is divided into a plurality of convergent particles and two free particles, and a global search range of positions of the convergent particles is [0,1]]The search interval of free particle 1 in the two free particles is [0,0.5 ]]The search interval of the free particle 2 is [0.5, 1]]And initializing a particle population;
step S2, obtaining the real-time output voltage V of the photovoltaic arraypvAnd real-time output current IpvAnd according to the real-time output voltage V of the photovoltaic arraypvAnd real-time output current IpvCalculating the adaptive value P of the ith convergence particle after the k generation updatingi kAnd the adapted value of the ith' free particle after the k generation update
Figure FDA0003394618760000051
Wherein, i is more than 0 and less than Np，i′＝1,2，NpIs the total number of convergence particles;
step S3, updating the adaptive value P according to the ith convergent particle in the kth generationi kAnd the adapted value of the ith' free particle after the k generation update
Figure FDA0003394618760000052
Updating the individual optimal position Dpbest of the ith convergence particleiAnd the overall optimal position Dgbest of the population, if Pi k＞PpbestiLet Ppbesti＝Pi k、
Figure FDA0003394618760000053
Otherwise PpbestiAnd DpbestiThe change is not changed; if Pi kIf Pgbest is greater than Pgbest, let Pgbest be Pi k、
Figure FDA0003394618760000054
Otherwise, Pgbest and Dgbest are unchanged; if it is
Figure FDA0003394618760000055
Then order
Figure FDA0003394618760000056
Otherwise, Pgbest and Dgbest are not changed, wherein,
Figure FDA0003394618760000057
for the position of the ith converging particle after the k generation update,
Figure FDA0003394618760000058
ppbest, the position of the ith' free particle after the k-th generation updateiThe optimal adaptive value of the ith convergence particle is an individual optimal adaptive value, and Pgbest is the overall optimal adaptive value of the population;
step S4, updating the speed and the position of the convergence particle and the positions of the two free particles;
step S5, judging whether the iteration number, i.e. the updated algebra k, satisfies k > genmaxNamely, whether the iteration number reaches the maximum iteration number genmaxIf yes, ending iteration, executing step S6, otherwise, adding 1 to the iteration number k and returning to step S2 to continue iteration;
step S6, calculating the change rate delta U of the output voltage of the photovoltaic array;
step S7, intelligently sensing the change degree of the external environment through a grading method according to the change rate delta U of the output voltage of the photovoltaic array, judging whether the restart needs to be returned to the step S1 according to different change degrees of the external environment, resetting the global search interval for the change degrees of the external environment with different grades when the restart needs to be returned to the step S1, then returning to the step S1 to restart, and replacing the global search interval of the convergent particles in the step S1 by using the reset global search interval.
9. A substrate according to claim 8The maximum power point tracking method based on NPSO algorithm and hierarchical auto-restart is characterized in that the particle population is initialized in step S1, i.e. the position of the convergence particle at the first generation is initialized
Figure FDA0003394618760000061
Velocity of converging particles in first generation
Figure FDA0003394618760000062
Upper limit of velocity v of converging particlesmaxLower limit of velocity vminFree particles in the first generation position
Figure FDA0003394618760000063
Total number of converging particles NpLearning factor c1And c2Inertia weight factor ω, maximum number of iterations genmaxSearch algebra threshold gen of free particle, voltage rate of change threshold η of graded restart1、η2And η3And voltage rate of change sub-threshold η21And η22；
In the step S7, if Δ U is greater than or equal to 0 and less than η1If so, directly outputting the global optimal position Dgbest without returning to the step S1 for restarting, and returning to the step S6 to continue calculating the change rate of the output voltage of the photovoltaic array; if eta1≤ΔU＜η2Returning to step S1 to restart and reset the global search area to [ Dmin,Dmax]I.e. the global search interval [0,1] of the convergent particle in step S1]Is replaced by [ D ]min,Dmax]Search interval [0,0.5 ] of free particle 1]Is replaced by
Figure FDA0003394618760000064
Search interval [0.5, 1] of free particle 2]Is replaced by
Figure FDA0003394618760000065
If eta2≤ΔU＜η3The process returns to step S1 to restart the engine.
CN202011521558.6A 2020-12-21 2020-12-21 Maximum power point tracking method based on NPSO algorithm and hierarchical automatic restart Active CN112596575B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011521558.6A CN112596575B (en) 2020-12-21 2020-12-21 Maximum power point tracking method based on NPSO algorithm and hierarchical automatic restart

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011521558.6A CN112596575B (en) 2020-12-21 2020-12-21 Maximum power point tracking method based on NPSO algorithm and hierarchical automatic restart

Publications (2)

Publication Number Publication Date
CN112596575A CN112596575A (en) 2021-04-02
CN112596575B true CN112596575B (en) 2022-01-07

Family

ID=75199899

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011521558.6A Active CN112596575B (en) 2020-12-21 2020-12-21 Maximum power point tracking method based on NPSO algorithm and hierarchical automatic restart

Country Status (1)

Country Link
CN (1) CN112596575B (en)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114167937A (en) * 2022-02-12 2022-03-11 武汉理工大学 Improved thermoelectric maximum power tracking method and system based on particle swarm optimization
CN117950444B (en) * 2023-12-06 2024-10-18 长江三峡水务(宜昌)有限公司 Photovoltaic voltage tracking MPPT control method based on optimization algorithm

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104238625A (en) * 2014-10-15 2014-12-24 珠海格力电器股份有限公司 Maximum power tracking control method and device
CN108170200A (en) * 2018-01-03 2018-06-15 南京航空航天大学 The improvement population MPPT algorithm of condition is restarted based on dynamic inertia weight and multi-threshold
CN109361237A (en) * 2018-11-30 2019-02-19 国家电网公司西南分部 Based on the micro-capacitance sensor capacity configuration optimizing method for improving Hybrid Particle Swarm
CN109755967A (en) * 2019-03-26 2019-05-14 安徽工程大学 The Optimal Configuration Method of light-preserved system in a kind of power distribution network
CN110928357A (en) * 2019-12-16 2020-03-27 徐州工业职业技术学院 Maximum power point tracking method of photovoltaic array under time-varying shadow condition

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104238625A (en) * 2014-10-15 2014-12-24 珠海格力电器股份有限公司 Maximum power tracking control method and device
CN108170200A (en) * 2018-01-03 2018-06-15 南京航空航天大学 The improvement population MPPT algorithm of condition is restarted based on dynamic inertia weight and multi-threshold
CN109361237A (en) * 2018-11-30 2019-02-19 国家电网公司西南分部 Based on the micro-capacitance sensor capacity configuration optimizing method for improving Hybrid Particle Swarm
CN109755967A (en) * 2019-03-26 2019-05-14 安徽工程大学 The Optimal Configuration Method of light-preserved system in a kind of power distribution network
CN110928357A (en) * 2019-12-16 2020-03-27 徐州工业职业技术学院 Maximum power point tracking method of photovoltaic array under time-varying shadow condition

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
"基于改进BA算法的光伏发电系统MPPT优化控制";刘杨 等;《水电能源科学》;20171231;全文 *

Also Published As

Publication number Publication date
CN112596575A (en) 2021-04-02

Similar Documents

Publication Publication Date Title
Lasheen et al. Maximum power point tracking using Hill Climbing and ANFIS techniques for PV applications: A review and a novel hybrid approach
CN112596575B (en) Maximum power point tracking method based on NPSO algorithm and hierarchical automatic restart
CN108334152A (en) A kind of photovoltaic array under local shadow maximum power point prediction optimization control method
Fu et al. A complex-method-based PSO algorithm for the maximum power point tracking in photovoltaic system
da Rocha et al. Comparative analysis of ABC, Bat, GWO and PSO algorithms for MPPT in PV systems
Verma et al. Asymmetrical fuzzy logic control-based MPPT algorithm for stand-alone photovoltaic systems under partially shaded conditions
Zhao et al. MPPT for photovoltaic system using multi-objective improved particle swarm optimization algorithm
Al-Majidi et al. Maximum power point tracking technique based on a neural-fuzzy approach for stand-alone photovoltaic system
Naseem et al. Particle swarm optimization based maximum power point tracking technique for solar PV system under partially shaded conditions
Lodhi et al. Rapid and efficient MPPT technique with competency of high accurate power tracking for PV system
Kacimi et al. A new combined method for tracking the global maximum power point of photovoltaic systems
Abdelkhalek et al. An improved MPPT tactic for PV system under temperature variation
Chen et al. MPPT control strategy of photovoltaic based on improved particle swarm optimization
Ge et al. An Improved Distributed Maximum Power Point Tracking Technique in Photovoltaic Systems Based on Reinforcement Learning Algorithm
Alshareef et al. A modified particle swarm optimization based maximum power point tracking for PV systems
Zabia et al. A novel equilibrium optimizer for tracking the maximum power of a photovoltaic system under non-uniform conditions
ur Rehman et al. Artificial Neural Network Based Efficient Maximum Power Point Tracking for Photovoltaic Systems
Atig et al. A novel optimization of the particle swarm based maximum power point tracking for photovoltaic systems under partially shaded conditions
ABENA MALOBE et al. Meta-heuristic Optimization of the Neuro-Fuzzy MPPT Controller for PV Systems Under Partial Shading Conditions
Abbas et al. A comparative study between the most used MPPT methods and particle swarm optimization method for a standalone PV system under fast change in irradiance level
CN112631365A (en) Photovoltaic power generation multi-peak MPPT control method based on SCASL
Huang et al. Development of intelligent solar panel cleaning system with fuzzy logic theorem
Zhou et al. Research on photovoltaic MPPT Control strategy Based on Improved Manta Ray Foraging Optimization Algorithm
Hu et al. Maximum Power Point Tracking Algorithm Based on Adaptive Particle Swarm Optimization Under Partial Shading Conditions
Yılmaz et al. PSO Training Neural Network MPPT with CUK Converter Topology for Stand-Alone PV Systems Under Varying Load and Climatic Conditions

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant