CN108170200B - Improved particle swarm MPPT algorithm based on dynamic inertia weight and multi-threshold restart condition - Google Patents
Improved particle swarm MPPT algorithm based on dynamic inertia weight and multi-threshold restart condition Download PDFInfo
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Abstract
The invention discloses an improved particle swarm MPPT algorithm based on dynamic inertia weight and multi-threshold restarting conditions, and relates to the technical field of photovoltaic power generation. The method dynamically solves the inertia weight value by taking the distance between the particles and the optimal solution and the iteration times as variables, has the advantages of high convergence rate and small oscillation amplitude compared with a fixed inertia weight or weight segmentation value-taking method, and achieves the aim of dynamically adjusting the iteration process according to the search condition; the invention sets a multi-threshold restart condition aiming at the complex and changeable external environment, avoids the problems of unnecessary voltage full-range scanning, frequent system oscillation, power loss increase, tracking delay caused by fixed period and the like caused by the fixed period scanning method compared with the conventional fixed period scanning method, can more accurately identify the external environment change compared with the single condition of judging by using the power change, and avoids the problem that the system cannot be normally restarted due to monitoring failure caused by single judgment condition.
Description
Technical Field
The invention relates to the technical field of photovoltaic power generation, in particular to the technical field of photovoltaic maximum power tracking.
Background
At present, various MPPT algorithms are proposed at home and abroad, including a disturbance observation method, a conductance increment method and the like. However, these algorithms are only suitable for the case where the output of the photovoltaic array exhibits a single peak characteristic under uniform illumination conditions. In practical application, the photovoltaic array is inevitably shielded by local shadows caused by cloud movement, dirt and the like, so that the working conditions of the photovoltaic modules in the array are different, and the output characteristic curve of the photovoltaic array is changed from a single peak value to a multi-peak value. The conventional MPPT algorithm is easy to search local extreme points by mistake, so that the power generation power of the photovoltaic array is reduced, and energy loss is caused. In order to solve the above problems, scholars at home and abroad propose various improved MPPT algorithms, for example, an article [1] proposes that a conductance increment method is used to perform a partition search according to an open-circuit voltage of a photovoltaic module with a voltage difference between adjacent peak points of about 0.8 times, and the method has some defects: the tracking range is selected by experience, tracking can be performed only under a known single environment, and search errors can be caused by external environment changes. The Eberhart and Kennedy, in 1995, proposed particle swarm algorithms, and evolved an algorithm model suitable for nonlinear global optimization by simulating the group behavior of bird group predation. The literature [2] applies the particle swarm algorithm to the global optimization problem of the photovoltaic array, but the particle swarm algorithm belongs to a bionic model, lacks a perfect mathematical theory basis, relies on experience for parameter selection, and adopts a fixed inertia weight in the literature [2], so that the photovoltaic system can oscillate, and the convergence rate and the tracking efficiency of the algorithm are low.
On the other hand, the shading of the photovoltaic array is not constant, and when the shading changes, the MPPT controller needs to follow the change, so that the array is ensured to work at the maximum power point. Researchers have proposed a periodic scanning method, that is, scanning a P-V curve in a full range at intervals, and performing MPPT search again to determine whether the maximum power point of the photovoltaic array changes. The disadvantage of this method is that the scanning frequency is high, the voltage varies from zero to the maximum value, the scanning range is large, the system is frequently oscillated, and the power loss is increased. And because of the periodic scanning, the system cannot respond in time when the shadow changes. Therefore, aiming at a changeable external environment, how to judge the change of the working condition of the photovoltaic array through a simple and easily obtained variable to realize timely and effective restart of the MPPT controller becomes an important problem for carrying out real-time tracking on the maximum power of the photovoltaic.
The invention designs the following technical terms:
MPPT: and Tracking the Maximum Power Point, and Tracking and controlling the output voltage and current of the photovoltaic array to keep the working Point of the array at the position of Maximum Power output.
PSO algorithm: particle Swarm Optimization, evolutionary algorithm that finds a global optimal solution by following the currently searched optimal value.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an improved particle swarm MPPT algorithm based on dynamic inertia weight and multi-threshold restart conditions, which is used for solving the technical problem of the particle swarm algorithm in the MPPT tracking.
In order to achieve the purpose, the invention adopts the technical scheme that:
establishing an inertia weight w dynamic resolving expression: w is aik＝wmax-(wmax-wmin)(1-Lig/Lmax)k/kmaxWherein k represents the number of iterations, LigDenotes the distance between the particle i (i ═ 1, 2.. n, n is the total number of particles) and the optimal particle, wikRepresenting the inertial weight, w, of the particle i at the kth iterationmaxIs the maximum inertial weight, wminIs the minimum inertial weight, LmaxIs a granuleMaximum distance between children, kmaxIs the maximum number of iterations. And introducing dynamic inertia weight into the particle iteration speed to perform weighting calculation based on the inertia weight. In the early stage of search, the iteration times k are smaller, so the inertia weight wikLarger, the particles can have higher speed to carry out global search, and better points can be searched as much as possible; in the later stage of search, the iteration number k is greater, so the inertia weight wikAnd the method is small, so that the particles can be gathered to a global optimal point, and the convergence speed of the algorithm is accelerated. By simultaneous introduction of LigControlling the particle search field, when the particle i is far away from the optimal point, the inertia weight wikThe particle size is correspondingly increased, so that the particle gathering speed to the optimal point is accelerated; when the particle i is located near the optimum point, the inertial weight wikAnd correspondingly reduced to prevent the particle velocity from jumping out of the optimal search area too much.
Aiming at a complex and changeable external environment, a multi-threshold restart condition is set: Δ P ═ Ps-Pmax|/Pmax＞η1And | Δ P/Δ V | > η2，η1And η2Respectively a set power change rate threshold value and a P-V curve slope threshold value, PsPhotovoltaic array output power, P, for real-time monitoringmaxThe maximum power reached when the particle swarm algorithm completes the search. When the monitoring mechanism detects that the system meets the set multi-threshold restarting condition, the controller restarts the particle swarm algorithm to search a new maximum power point again.
Further, the improved particle swarm algorithm is applied to MPPT, and the specific implementation steps are as follows:
step 1: selecting a photovoltaic array output voltage VPVAs particles, photovoltaic array output power PPVAs an adaptive value for representing the quality of the particles, initializing a particle swarm, including a cluster position and an initial iteration speed, and randomly and uniformly distributing the particles on a photovoltaic array P-V characteristic curve;
step 2: obtaining the current corresponding adaptive value of each particle and the optimal position x experienced by the particle individual in the searching processibestComparing the corresponding adaptive values, and updating x if the adaptive value of the current position is betteribestElse xibestKeeping the same; optimal position g experienced by particle group in search processbestComparing the corresponding adaptive values, and updating g if the adaptive value of the current position is betterbestOtherwise gbestKeeping the same;
and step 3: for each particle i, the sum g is calculatedbestDistance L betweenigIs prepared by mixing LigSubstituting k into the self-adaptive adjustment formula of the inertia weight w: w is aik＝wmax-(wmax-wmin)(1-Lig/Lmax)k/kmaxCalculating the inertia weight of the particle i in the k iteration according to an iteration formula vi(k+1)＝wikvi(k)+c1r1(xibest-xi(k))+c2r2(gbest-xi(k) ) and xi(k+1)＝xi(k)+vi(k +1) updating the velocity and position of each particle, where vi(k) And vi(k +1) denotes the velocity of the particle i at the k < th > and k +1 < th > iterations, xi(k) Is the position of the kth iteration, x, of particle ii(k +1) is the position of the (k +1) th iteration of particle i, c1And c2To adjust the coefficient, r1And r2Is [0, 1]]A random number of (c);
and 4, step 4: setting an iteration termination condition that the maximum distance between the particles is smaller than a set threshold epsilon, stopping iteration if the iteration termination condition is met, enabling the particles to be gathered at a global optimal point, enabling the photovoltaic array to work at a global maximum power point, and otherwise, returning to the step 2;
and 5: and setting a monitoring mechanism, detecting the output voltage and current of the photovoltaic array in real time on line, calculating the output power, the power change rate and the P-V curve slope, and when the multi-threshold restart condition is met, carrying out MPPT tracking by a controller restart algorithm to ensure that a system tracks a new global maximum power point in time.
The invention has the beneficial effects that:
1. the MPPT tracking is carried out by applying the particle swarm algorithm, iterative parameters in the algorithm are optimized, the convergence and tracking speed of the algorithm are improved, the self-adaptive adjustment of the searching process according to the searching condition is realized, the MPPT tracking problem of the photovoltaic array under the shielding of local shadows is solved, and the phenomenon that the MPPT tracking falls into a local maximum power point is avoided;
2. the method comprises the steps of setting a multi-threshold restart condition, calculating a power change rate and a P-V change rate by detecting output voltage and current of a photovoltaic array, judging whether the restart condition is met, enabling a system to timely respond to environmental changes if the restart condition is met, avoiding a meaningless algorithm restart process, reducing energy loss and avoiding the problem of restart failure.
Drawings
FIG. 1 is a diagram of an embodiment of the present invention;
fig. 2 is a flow chart of MPPT control based on the modified particle swarm optimization.
Fig. 3 is a photovoltaic array output characteristic.
FIG. 4 is a comparison graph of MPPT tracking results before and after particle swarm optimization.
Fig. 5 is a system restart power tracking simulation diagram.
FIG. 6 is MPPT tracking experimental waveforms before and after particle swarm optimization.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings.
Taking a photovoltaic power generation system based on a Boost circuit as an example, the system structure is shown in fig. 1, an inductor L, a capacitor C, a switching tube S, a diode D and a direct current load R form a Boost main circuit, and a Boost input end is connected with the output of a photovoltaic array.
The working principle of the system for realizing MPPT tracking by applying the improved particle swarm optimization is as follows: sampling photovoltaic array output current IPVAnd an output voltage VPVParticle swarm algorithm is based on IPVAnd VPVPerforming iterative search, and transmitting voltage reference signal V of next iteration to PWM controllerrefThe controller sends a driving signal to a switching tube S of the Boost circuit to control the switching on and off of the switching tube S, and the duty ratio of the switching tube S is changed to enable the working point of the photovoltaic array to change correspondingly. And finally, the system can operate at the global maximum power point through continuous iterative search of a particle swarm algorithm. When the external environment changes to change the output characteristicsAnd monitoring and judging by an excessive threshold restart mechanism and executing system restart to enable the array to work at the maximum power point again.
The algorithm flow is shown in fig. 2, and the specific implementation steps are as follows:
(1) initializing a population of particles comprising setting a number n of particles, a velocity v of each particleiAnd position xi. In MPPT tracking, the particles are photovoltaic array output voltage VPVThus the particle position is VPVThe particle velocity corresponds to the voltage difference of two iterations, and the particles are distributed on the P-V characteristic curve as uniformly as possible during initialization;
(2) the n particles are iterated in a certain order. For a single particle, update x with the position where it has experienced the best adaptation value (power max) on its ownibestFor a population of particles, g is updated with the position at which the population has experienced the best fitness (power maximum)best；
(3) For each particle i (i ═ 1, 2.. n), its corresponding inertial weight w at the k-th iteration is calculated as followsik: inertial weight wik＝wmax-(wmax-wmin)(1-Lig/Lmax)k/kmax；
(4) For each particle i, according to an iterative formula vi(k+1)＝wikvi(k)+c1r1(xibest-xi(k))+c2r2(gbest-xi(k) ) and xi(k+1)＝xi(k)+vi(k +1) updating the iteration speed and position to obtain the adaptive value corresponding to the new position, i.e. by sampling VPVAnd IPVObtaining the output power P corresponding to the current voltage valuePV；
(5) Comparing the current adaptation value (P)PV) And xibestAnd gbestCorresponding adaptation value (P)PV) Is determined according to the relative merits of the adaptive value to xibestAnd gbestUpdating is carried out;
(6) if the iteration termination condition L is satisfiedi-imaxIf the value is less than epsilon, stopping iteration, otherwise, returning to the step 3 to carry out the next iteration；
(7) After the iteration is terminated, the monitoring mechanism passes through a sample IPVAnd VPVAnd (4) carrying out real-time monitoring, when a multi-threshold restart condition is met, triggering the system to restart by a monitoring mechanism, and returning to the step 1 to execute a new round of MPPT tracking.
A photovoltaic system simulation model shown in fig. 1 is established on a Matlab/Simulink simulation platform, a 3 × 3 photovoltaic array is adopted, and internal parameters of each photovoltaic module are as follows: open circuit voltage Voc42.93V, short-circuit current Isc4.37A, maximum power point voltage Vm33.68V, maximum power point current Im3.96A. Standard illumination intensity SrefIs 1000W/m2Temperature TrefIt was 25 ℃. There are three illuminations 1000W/m in shadow mode one2、400W/m2And 100W/m2Two branches in the array are shielded by the shadow; shadow mode two exists two kinds of illumination 1000W/m2And 400W/m2One branch in the array is blocked by a shadow. Fig. 3 shows the output characteristics of the photovoltaic array in these two shading modes. The power tracking result of the improved particle swarm algorithm in the mode 1 is shown in fig. 4, and meanwhile, the tracking result of the particle swarm algorithm which is not improved is given for comparison. Obviously, the improved particle swarm algorithm has the advantages that the tracking speed is improved, the iterative process is optimized, the oscillation in the searching process is reduced, and the energy loss caused by the MPPT tracking process is reduced. When the system stably works at the global maximum power point of the shadow mode 1, the shadow is switched from the mode 1 to the mode 2 after 0.02s, and it can be known from fig. 3 that the global maximum power point changes and meets the multi-threshold restart condition, at this moment, the controller restarts and carries out MPPT search again until a new global maximum power point is searched. Fig. 5 shows simulation results of a fixed-period scanning method and a multi-threshold restart method, which can realize timely restart, avoid system oscillation and energy loss caused by fixed-period full-range search, and reduce the working frequency of the circuit.
A system experiment prototype platform shown in fig. 1 is built, a photovoltaic array is realized by adopting a Chroma company model 62050 photovoltaic simulator, and tracking effects before and after improvement of a particle swarm algorithm are verified. Fig. 6 shows experimental waveforms of the output voltage of the photovoltaic array by using the particle swarm optimization before and after the improvement. The improved particle swarm optimization can find the global maximum power point in a shorter time, and the oscillation of the searching process is reduced.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.
Accessories: (e.g., patents/articles/standards)
Document 1: patel H, agar v.maximum Power Point Tracking Scheme for PVSystems Operating Under partial shaped conditions Control Engineering of china, 2008, 55 (4): 1689-1698.
Document 2: phimmosone V, Endo T, Kondo Y, et al, improvement of the maximum Power Point Tracker for photovoltaic generators with Particle swarm optimization technique by adding a pulse for amplifying agents [ C ]// International Conference on electric Machines and systems, IEEE, 2010: 1-6.
Claims (1)
1. An improved particle swarm MPPT algorithm based on dynamic inertia weight and multi-threshold restart conditions is characterized in that: dynamic inertia weight is introduced into the particle swarm iterative speed to carry out weighting calculation based on the inertia weight, and the resolving expression of the inertia weight is as follows: w is aik＝wmax-(wmax-wmin)(1-Lig/Lmax)k/kmaxWherein k represents the number of iterations, LigDenotes the distance between the particle i (i is 1, 2, … n, n is the total number of particles) and the optimum particle, wikRepresenting the inertial weight, w, of the particle i at the kth iterationmaxAnd wminRespectively, the upper and lower limits of the value of the inertia weight, LmaxIs the maximum distance between particles, kmaxIs the maximum iteration number;
aiming at a complex and changeable external environment, a multi-threshold restart condition is set: Δ P ═ Ps-Pmax|/Pmax＞η1And | Δ P/Δ V | > η2，η1And η2Respectively a set power change rate threshold value and a P-V curve slope threshold value, PsPhotovoltaic array output power, P, for real-time monitoringmaxThe maximum power reached when the particle swarm algorithm completes searching; when the monitoring mechanism detects that the system meets the set multi-threshold restarting condition, the controller restarts the particle swarm algorithm to search a new maximum power point again;
applying an improved particle swarm algorithm based on dynamic inertia weight and multi-threshold restart conditions to MPPT, and specifically realizing the following steps:
step 1: selecting a photovoltaic array output voltage VPVAs particles, photovoltaic array output power PPVAs an adaptive value for representing the quality of the particles, initializing a particle swarm, including a cluster position and an initial iteration speed, and randomly and uniformly distributing the particles on a photovoltaic array P-V characteristic curve;
step 2: obtaining the current corresponding adaptive value of each particle and the optimal position x experienced by the particle individual in the searching processibestComparing the corresponding adaptive values, and updating x if the adaptive value of the current position is betteribestElse xibestKeeping the same; optimal position g experienced by particle group in search processbestComparing the corresponding adaptive values, and updating g if the adaptive value of the current position is betterbestOtherwise gbestKeeping the same;
and step 3: for each particle i, the sum g is calculatedbestDistance L betweenigIs prepared by mixing LigSubstituting k into the self-adaptive adjustment formula of the inertia weight w: w is aik＝wmax-(wmax-wmin)(1-Lig/Lmax)k/kmaxCalculating the inertia weight of the particle i in the k iteration according to an iteration formula vi(k+1)＝wikvi(k)+c1r1(xibest-xi(k))+c2r2(gbest-xi(k) ) and xi(k+1)＝xi(k)+vi(k +1) updating the velocity and bit of each particleIn the formula vi(k) And vi(k +1) denotes the velocity of the particle i at the k < th > and k +1 < th > iterations, xi(k) Is the position of the kth iteration, x, of particle ii(k +1) is the position of the (k +1) th iteration of particle i, c1And c2To adjust the coefficient, r1And r2Is [0, 1]]A random number of (c);
and 4, step 4: setting an iteration termination condition that the maximum distance between the particles is smaller than a set threshold epsilon, stopping iteration if the iteration termination condition is met, enabling the particles to be gathered at a global optimal point, enabling the photovoltaic array to work at a global maximum power point, and otherwise, returning to the step 2;
and 5: and setting a monitoring mechanism, detecting the output voltage and current of the photovoltaic array in real time on line, calculating the output power, the power change rate and the P-V curve slope, and when the multi-threshold restart condition is met, carrying out MPPT tracking by a controller restart algorithm to ensure that a system tracks a new global maximum power point in time.
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| CN111796629A (en) * | 2020-06-11 | 2020-10-20 | 江苏大学 | Composite MPPT tracking method under photovoltaic cell local shadow condition |
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