CN111399584A - Composite MPPT control algorithm of local shadow photovoltaic system - Google Patents
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Abstract
The invention discloses a composite MPPT control algorithm of a local shadow photovoltaic system; the method comprises the following steps: s1, introducing a photovoltaic cell engineering mathematical model, calculating the influence of light intensity and temperature change, and improving the model; s2, analyzing the reason of the multimodal phenomenon of the photovoltaic array under the local shadow and outputting a multimodal oscillogram; s3, according to the analysis of the step S2, an improved 0.8-time open-circuit voltage method is introduced to carry out primary tracking of a multi-peak extreme value; and S4, improving the inertia weight coefficient, the self-learning factor and the social learning factor change of the particle swarm algorithm on the basis of S3, and accurately tracking the maximum power point by utilizing the improved particle swarm algorithm. Compared with the prior art, the method improves the tracking speed, replaces a static value with a dynamic learning factor and an inertia weight coefficient, and improves the steady-state precision.
Description
Technical Field
The invention relates to the technical field of photovoltaic power generation, in particular to a composite MPPT control algorithm of a local shadow photovoltaic system.
Background
Solar energy is clean and pollution-free, and solar photovoltaic power generation is increasingly important in energy systems. The manufacturing cost of the photovoltaic cell is high, and the MPPT technology becomes a key technology for improving the photovoltaic power generation efficiency. However, the output power of a single photovoltaic cell is small, and in practice, a plurality of photovoltaic cells are usually connected in series and in parallel to form a photovoltaic array. In actual operation, the photovoltaic array generates local shadows under the influence of weather, cloud cover, dust and the like, so that the power characteristic curve is multimodal. The traditional MPPT algorithm, such as a constant voltage method, a disturbance observation method, a conductance increment method and the like, can be trapped in a local extreme value at the moment, and a global maximum power point cannot be tracked, so that the research of the MPPT technology under the condition of multiple peaks of a photovoltaic array is very important.
In 2016 (power electronics technology), a cat swarm algorithm is provided in photovoltaic array multimodal MPPT control strategy based on a cat swarm algorithm, so that the problem of multimodal optimization of maximum power is solved, the control is simple, the parameters are few, the overall search capability is good, and the MPPT tracking effect under dynamic shadow is not discussed. A fuzzy logic double-loop control-based photovoltaic power generation system maximum power tracking algorithm is provided in ' power system protection and control ' in ' 38 th year 2010, the algorithm is simple in structure and easy to implement, and parameter setting depends on experience of designers. In 'MPPT for photovoltaic power generation combining particle swarm and conductance increment method' in 'Proc for simulation of System' at 28 th year 2016, a hybrid algorithm combining the conductance increment method and the particle swarm algorithm is proposed, the algorithm can effectively track the maximum power point, but the parameters of the particle swarm algorithm are constant values, so that the convergence speed and the optimization precision are poor. A method combining global search and conductance increment is proposed in improved INC algorithm-based maximum power tracking of a photovoltaic power generation system in No. 41 Power technology of 2017, and a maximum power point can be quickly and accurately found through reasonable selection of a threshold; however, the selection of the parameters requires a large amount of experimental data and has errors.
The particle swarm optimization is widely applied to multi-peak optimization, but learning factors and inertia weight of the traditional particle swarm optimization are constants and need to be iteratively calculated for many times, so that the tracking speed and the steady-state accuracy are poor.
Disclosure of Invention
The invention aims at the problems that the global maximum power point can not be tracked under the local shadow, and the maximum power point is tracked too slowly and the precision is not high in the prior art. The composite MPPT control algorithm of the local shadow photovoltaic system is provided, and the problems of low tracking speed and low accuracy of the tracked maximum power point are effectively solved.
In order to achieve the purpose, the invention adopts the following technical scheme:
a composite MPPT control algorithm of a partial shadow photovoltaic system comprises the following steps:
s1, introducing a photovoltaic cell engineering mathematical model, calculating the influence of light intensity and temperature change, and improving the model; the photovoltaic cell engineering mathematical model is as follows:
the model only requires knowing the short circuit current Isc, the open circuit voltage Uoc and the voltage Um at MPP, the current Im.
S2, analyzing the reason of the multimodal phenomenon of the photovoltaic array under the local shadow and outputting a multimodal oscillogram;
s3, according to the analysis of the step S2, an improved 0.8-time open-circuit voltage method is introduced to carry out primary tracking of a multi-peak extreme value; the extreme point of the photovoltaic P-U characteristic curve is near 0.8 time of UOC, the extreme point under the condition of multiple peaks is integral multiple of 0.8UOC, and the tracking range is set in two ranges of 0.7UOC-0.9UOC and 1.5UOC-1.7 UOC.
And S4, improving the inertia weight coefficient, the self-learning factor and the social learning factor change in the conventional particle swarm algorithm on the basis of S3, and accurately tracking the maximum power point by utilizing the improved particle swarm algorithm.
Further, the model modified in step S1 is
The correction coefficient a is 0.0025, b is 0.5, c is 0.0028, Δ t is t-tref, Δ S is S/Sref-1, Sref is 1000W/m2, tref is 25 ℃.
Further, the conventional particle swarm algorithm in step S4 is
Wherein vik is the position change, herein the current change, of the ith particle at the kth iteration; xik is the position of the kth iteration of the ith particle, i.e., the current value; r1 and r2 are random numbers between 0 and 1; pbesi is the individual optimum position of the ith particle, i.e., the current at the local extremum; gbest is the global optimum position, i.e. the current at the global maximum power point; f () is a fitness function, i.e. a power value; omega is an inertia weight coefficient, c1 is a self-learning factor, and c2 is a social learning factor.
Further, the improved inertia weight coefficient, the self-learning factor and the social learning factor in step S4 are
Wherein, c1start，c2start，ωstartRespectively a self-learning factor, a social learning factor, an initial value of an inertia weight coefficient, c1end，c2end，ωendThe final values of the three coefficients.
Under the control strategy, the self-adaptive virtual impedance value can be changed in a self-adaptive manner according to the output reactive power of the inverter and an optimal value is obtained, so that the reactive power sharing error of the system is kept in a small range, and meanwhile, the reactive power sharing precision is improved. The problem that a conventional virtual impedance value cannot be selected and the problem that the output power sharing precision of an inverter is poor are solved, and the low-medium voltage microgrid can be flexibly and effectively controlled.
Drawings
FIG. 1 is a graph showing P-U, I-U characteristics of a single photovoltaic cell;
FIG. 2 is a schematic diagram of a characteristic curve of a photovoltaic array I-U in a shaded condition;
FIG. 3 is a P-U characteristic curve of a photovoltaic array in a shaded condition;
FIG. 4 is a schematic view of a series partial shading P-U, I-U characteristic curve;
FIG. 5 is a graph showing a variation curve of a particle swarm algorithm parameter;
FIG. 6 is a flow chart of a partial shadow photovoltaic system recombination algorithm.
Detailed Description
The present invention will be further described with reference to the following embodiments.
A composite MPPT control algorithm of a local shadow photovoltaic system; the method comprises the following steps:
s1, introducing a photovoltaic cell engineering mathematical model, calculating the influence of light intensity and temperature change, and improving the model;
the common engineering mathematical model of the photovoltaic cell is as follows:
the model only requires short circuit current Isc, open circuit voltage Uoc and voltage Um at MPP, current Im, and is data obtained under standard temperature (25 ℃), standard illumination (1000W/m 2). When the light intensity and the temperature change, a new calculation formula (2) is introduced to obtain parameters under different conditions, wherein the calculation formula is as follows:
in formula (2), correction coefficient a is 0.0025, b is 0.5, c is 0.0028, Δ t is t-tref, Δ S is S/Sref-1, Sref is 1000W/m2, tref is 25 ℃.
S2, analyzing the reason of the multimodal phenomenon of the photovoltaic array under the local shadow and outputting a multimodal oscillogram;
the output P-U is shown in fig. 1, and the curve of I-U is shown in fig. 1, where S is 1000W/m2, t is 25 ℃, and the parameters of the photovoltaic cell unit are Uoc 23.36V, Isc 3A, Um 18.47V, and Im 2.8A.
The modeling and characteristic curve of the single photovoltaic cell is characterized in that the photovoltaic array is formed by connecting a plurality of photovoltaic cells in series and in parallel, and each photovoltaic cell is connected with a diode in parallel, so that the hot spot phenomenon is prevented. Since the local shadow of the parallel connection of the photovoltaic cells does not generate a multi-peak phenomenon, the output characteristics of the photovoltaic cells are observed by connecting two photovoltaic cells in series. Assuming that the photovoltaic cell temperatures are all 25 ℃, the output P-U, I-U characteristic curves of the photovoltaic array under the shadow condition are shown in figures 2 and 3.
(1) Shadow case 1: the illumination is uniform, namely S1-S2-1000W/m 2
(2) Shadow case 2: S1-1000W/m 2 and S2-800W/m 2.
(3) Shadow case 3: s1 is 1000W/m2, S2 is 600W/m 2.
As can be seen from fig. 2 and 3, when the battery is uniformly illuminated, the output characteristic curve has only one peak point and is equal to the maximum power point current when the battery is singly output; the local shadow enables an I-U characteristic curve of the photovoltaic cell system to be in a multi-knee point form, a P-U characteristic curve is in a multi-peak form, and the knee points of the I-U characteristic and the peak points of the P-U characteristic curve are in one-to-one correspondence.
S3, according to the analysis of the step S2, an improved 0.8-time open-circuit voltage method is introduced to carry out primary tracking of a multi-peak extreme value;
the simulation is carried out by the condition that 2 photovoltaic cells connected in series are partially shaded, namely two peaks, and when S1 is 1000W/m2 and S2 is 800W/m2, the output characteristic curve is shown in FIG. 4.
As can be seen from the analysis of fig. 4, if Iph1 and Iph2 are the photo-generated currents of the two photovoltaic cells, the photovoltaic cells have two operation modes.
(1) When Iph2< I < Iph1, the bypass diode of the photovoltaic cell 2 is turned on, the photovoltaic cell 2 is short-circuited, power is output from the photovoltaic cell 1 alone to the outside at this time, and the maximum output power is P1 at this time, depending on the output characteristics of the photovoltaic cell 1. The corresponding maximum power point voltage and current are the same as those in independent operation.
(2) The external equivalent load is increased, and the output current I is gradually reduced. When the I < Iph2 is 0, the two diodes are in a blocking state, the two batteries are normally connected in series and output power outwards together, and the maximum output power of the system is P2 at the moment, which mainly depends on the output characteristics of the photovoltaic battery 2.
The voltage corresponding to P1 is 18.28V and about 0.8 time Uoc, the voltage corresponding to P2 is 38.87V and about 1.6 times Uoc, and the difference between the two is about 0.8 times Uoc, so that if the voltage corresponding to the third local extreme point is about 2.4Uoc in the case of three peaks. This is 0.8 times the voltage method. In order to prevent missing the maximum power point, the search ranges of the two local extrema are respectively set to be 0.7-0.9 Uoc and 1.5-1.7 Uoc, the current range to be searched is obtained by calculating the voltage range, the search range of the particle swarm algorithm is reduced, and the tracking speed is improved.
And S4, improving the inertia weight coefficient, the self-learning factor and the social learning factor change in the conventional particle swarm algorithm on the basis of S3, and accurately tracking the maximum power point by utilizing the improved particle swarm algorithm.
The particle swarm algorithm is a bionic algorithm. The basic idea is to initialize a group of particles in the feasible space, and the velocity, position, etc. of the particles are continuously updated to be close to the optimal value. The updating formula is shown as formula (3).
In the formula vi kIs the change in position, herein the change in current, of the ith particle for the kth iteration; x is the number ofi kThe position of the kth iteration of the ith particle, namely the current value; r1 and r2 are random numbers between 0 and 1; pbesiIs the individual optimal position of the ith particle, i.e. the current at the local extremum; gbestThe current is the global optimal position, namely the current at the global maximum power point; f () is a fitness function, i.e. a power value; omega is an inertia weight coefficient, c1 is a self-learning factor, and c2 is a social learning factor. The parameters ω, c1, c2 have a great influence on the iterative process of the algorithm. The magnitude of omega is related to the overall searching capability of the algorithm, the global optimizing capability of the omega-increasing algorithm is enhanced, and the local optimizing capability of the omega-decreasing algorithm is enhancedThe capability is enhanced; the self-learning factor c1 influences the approaching speed to the optimal position of the individual, and the larger the c1 is, the faster the approaching speed is; the social learning factor c2 influences the approaching speed to the global optimal position, and the larger c2 is, the faster the approaching speed is. Omega, c1 and c2 of the conventional particle swarm algorithm are constant values, so that local search and global optimization can not be considered simultaneously, and phenomena such as prematurity and oscillation are easily caused.
In order to solve the above problems, the particle swarm optimization is improved, and unlike the conventional particle swarm optimization, the inertia weight coefficient ω, the self-learning factor c1 and the social learning factor c2 of the particle swarm optimization are updated as the number of iterations increases. The algorithm has larger global optimization capability at the initial stage, so that larger omega, c1 and smaller c2 are set, the process of searching the individual optimal extreme value by the particles is accelerated, and the situation that the particles fall into a local optimal solution is avoided; and the later coefficient is updated along with the iteration times, and has higher local optimization capability, so that the values of omega and c1 are smaller, the value of c2 is larger, the process of searching the global optimal extreme value by the algorithm is accelerated, and the time for tracking to the maximum power point is reduced. The parameter adjustment rule along with the iteration number is shown as a formula (4).
In the formula (4) c1start，c2start，ωstartRespectively a self-learning factor, a social learning factor, an initial value of an inertia weight coefficient, c1end，c2end，ωendThe final values of the three coefficients. The parameter variation is shown in fig. 5.
Aiming at the problem of multiple peaks of the photovoltaic cell under the local shadow, a composite control algorithm combining a 0.8-time voltage method and an improved particle swarm algorithm is provided. The algorithm firstly uses a 0.8-time voltage method to reduce the range of secondary optimization, improve the tracking speed, and then uses an improved particle swarm algorithm to carry out secondary optimization on the range to find an accurate maximum power point. The particle swarm algorithm is improved to end the condition of reaching the iteration times or
Wherein P isrealFor the measured power, Pm is the maximum power obtained by theoretical calculation or characteristic curve, and the algorithm flow is shown in fig. 6.
The above-mentioned embodiments are merely preferred embodiments for fully illustrating the present invention, and the scope of the present invention is not limited thereto. The equivalent substitution or change made by the technical personnel in the technical field on the basis of the invention is all within the protection scope of the invention.
Claims (4)
1. A composite MPPT control algorithm of a partial shadow photovoltaic system is characterized by comprising the following steps:
s1, introducing a photovoltaic cell engineering mathematical model, calculating the influence of light intensity and temperature change, and improving the model; the photovoltaic cell engineering mathematical model is as follows:
the model only requires knowing the short circuit current Isc, the open circuit voltage Uoc and the voltage Um at MPP, the current Im.
S2, analyzing the reason of the multimodal phenomenon of the photovoltaic array under the local shadow and outputting a multimodal oscillogram;
s3, according to the analysis of the step S2, an improved 0.8-time open-circuit voltage method is introduced to carry out primary tracking of a multi-peak extreme value; the extreme point of the photovoltaic P-U characteristic curve is near 0.8 time of UOC, the extreme point under the condition of multiple peaks is integral multiple of 0.8UOC, and the tracking range is set in two ranges of 0.7UOC-0.9UOC and 1.5UOC-1.7 UOC.
And S4, improving the inertia weight coefficient, the self-learning factor and the social learning factor change in the conventional particle swarm algorithm on the basis of S3, and accurately tracking the maximum power point by utilizing the improved particle swarm algorithm.
2. The composite MPPT control algorithm of partial shadow photovoltaic system as claimed in claim 1, wherein the modified model in step S1 is
The correction coefficient a is 0.0025, b is 0.5, c is 0.0028, Δ t is t-tref, Δ S is S/Sref-1, Sref is 1000W/m2, tref is 25 ℃.
3. The composite MPPT control algorithm of partial shadow photovoltaic system as claimed in claim 1, wherein the conventional particle swarm algorithm in step S4 is
Wherein vik is the position change, herein the current change, of the ith particle at the kth iteration; xik is the position of the kth iteration of the ith particle, i.e., the current value; r1 and r2 are random numbers between 0 and 1; pbesi is the individual optimum position of the ith particle, i.e., the current at the local extremum; gbest is the global optimum position, i.e. the current at the global maximum power point; f () is a fitness function, i.e. a power value; omega is an inertia weight coefficient, c1 is a self-learning factor, and c2 is a social learning factor.
4. The MPPT control algorithm for local shadow PV system as claimed in claim 1, wherein the modified inertial weight coefficients, self learning factor and social learning factor in step S4 are
Wherein, c1start，c2start，ωstartRespectively a self-learning factor, a social learning factor, an initial value of an inertia weight coefficient, c1end，c2end，ωendThe final values of the three coefficients.
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