CN107957743B - A kind of photovoltaic maximum power point method for tracing - Google Patents
A kind of photovoltaic maximum power point method for tracing Download PDFInfo
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- CN107957743B CN107957743B CN201711114897.0A CN201711114897A CN107957743B CN 107957743 B CN107957743 B CN 107957743B CN 201711114897 A CN201711114897 A CN 201711114897A CN 107957743 B CN107957743 B CN 107957743B
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- 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
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
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Abstract
It include: to choose grey wolf initial position the present invention relates to a kind of photovoltaic maximum power point method for tracing;After photovoltaic system is stablized, the output electric current and output voltage of the corresponding photovoltaic array of each Xi (n) are measured, and calculate corresponding power P i (n);Corresponding two grey wolves of maximum two Pi (n) are set as wolf king, and record the position of the wolf king;Iteration n+1 times grey wolf position Xi (n+1) is determined by grey wolf algorithm;When two wolf kings obtain the approximate location of prey, it is believed that be the approximate location for having found prey, be directly entered the local search executed by golden ratio algorithm at this time, otherwise enter back into local search until reaching maximum number of iterations;Whether detection external environment mutates.
Description
Technical field
The present invention relates to photovoltaic power generation field of engineering technology, more particularly to a kind of maximum power point tracking method.
Background technique
To alleviate international energy crisis, reduce environmental pollution, renewable energy is all being greatly developed in countries in the world.Various
In renewable energy, solar energy is considered as a kind of inexhaustible, nexhaustible important eco-friendly power source.In order to efficiently utilize
The sun can be carried out photovoltaic power generation, and maximum power point tracking (maximum power point tracking, MPPT) technology can not
Or it lacks.The main problem that MPPT technique faces can be attributed to two o'clock: first, output characteristic curve is by light intensity, temperature, load etc.
Factor influences, and output characteristic curve is in non-linear.Second, for avoid hot spot effect must in each photovoltaic panel inverse parallel one
Bypass diode, but which results in output characteristic curves, and multimodal state to be presented when locally covering.
The structures such as traditional MPPT algorithm such as perturbation observation method and conductance increment method are simple but may fall into local extremum,
It faces that structure is complicated if intelligent algorithm such as particle swarm algorithm, glowworm swarm algorithm, wolf pack searching algorithm, cuckoo algorithm etc. and calculates
The problems such as time is longer, the MPPT method based on model are then needed by the cumbersome derivation of equation.
Summary of the invention
The object of the present invention is to provide a kind of photovoltaic maximum power point method for tracing, which can have both and chase after
Track speed and tracking efficiency, and can solve tradition and restart method of discrimination and face the problem of restarting unsuccessfully under specific circumstances.
Specific technical solution is as follows:
A kind of photovoltaic maximum power point method for tracing, includes the following steps:
1) N number of point is chosen in duty ratio [0,1], as grey wolf initial position;
2) current iteration is set as nth iteration, and the position of grey wolf is Xi(n),i∈[1,2,……N];
3) after photovoltaic system is stablized, the output electric current and output voltage of the corresponding photovoltaic array of each Xi (n) are measured, and
Calculate corresponding power Pi(n);
4) to the Pi(n) it is ranked up;
5) maximum two Pi(n) corresponding two grey wolves are set as wolf king, and record the position of the wolf king;
6) iteration n+1 times grey wolf position X is determined by improving grey wolf algorithmi(n+1), improvements are as follows: two wolf kings'
Decision weights coefficient is adjusted with the process dynamics of hunting, i.e.,WithWherein W1And W2The decision weights coefficient of respectively described two wolf kings, n is the number of iterations,
nmaxFor maximum number of iterations;
7) when tracking, grey wolf can be jumped on the position that prey is likely to occur, and when two wolf kings obtain the big of prey
When causing position, it is believed that it is the approximate location for having found prey, is directly entered the local search executed by golden ratio algorithm at this time,
Otherwise local search is entered back into until reaching maximum number of iterations;
8) whether detection external environment mutates: default mutation threshold epsilon0If P0And P1It is before illuminance abrupt variation and prominent respectively
Power after change, U0And U1It is the voltage before illuminance abrupt variation and after mutation respectively, μ is set as 0.001 to prevent zero female, judgement |
(P1-P0)/(U1-U0+ μ) | > ε0, if satisfied, then thinking to mutate, restarting algorithm tracks maximum power point, is otherwise
System is stablized in global optimum's duty ratio.
Detailed description of the invention
Fig. 1 is the maximum power point tracking system using Boost circuit
Fig. 2 hybrid algorithm flow chart
Fig. 3 expands initial ranging interval diagram
Photovoltaic array P-D curve when Fig. 4 particular light is mutated
Specific embodiment
In order to quickly and accurately track global maximum power point, the present invention proposes a kind of based on improvement grey wolf-gold ratio
Example hybrid algorithm (modified grey wolf optimization and golden-section optimization,
MGWO-GSO mixing control method).Improved grey wolf algorithm (modified grey wolf is used first
Optimization, MGWO) global search is carried out to determine optimal partial;Then, using golden ratio partitioning algorithm
(golden-section optimization, GSO) carries out local search.Different Strategies are used in different phase, realize control
The optimization of effect.The decision weights of wolf king remain unchanged in original grey wolf algorithm, this, which may cause, determines between wolf king in the later period
It is sluggish that decision is generated when plan.For the decision weights of wolf king with the propulsion dynamic change of hunting, this has grey wolf more in the present invention
Add specific hunting target.Also, the invention proposes a kind of based on the class P-U slope of curve novel restarts method of discrimination to increase
Reliability when strong MPPT system reply illuminance abrupt variation.
In order to make the foregoing objectives, features and advantages of the present invention clearer and more comprehensible, with reference to the accompanying drawing to the present invention
Specific embodiment be described in detail.The present invention uses the maximum power point tracking system of Boost circuit as shown in Figure 1
System, photovoltaic array use 3 × 1 photovoltaic arrays, switching frequency f=50kHz, C1=100 μ F, L=0.5mH, C2=100 μ F, Rload
=40 Ω.The hybrid algorithm flow chart is as shown in Figure 2, and concrete scheme is as follows:
The present invention uses direct duty ratio control mode, N number of point is chosen in duty ratio [0,1] first, as at the beginning of grey wolf
Beginning position Xi(n), i ∈ [1,2 ... N], n indicate the number of iterations.By global maximum power in maximum power point tracking algorithm
Point is considered as prey, and the quality of prey is replaced with watt level;
After photovoltaic system is stablized, each X is measuredi(n) the output electric current I of corresponding photovoltaic arrayPVWith output voltage VPV,
And calculate corresponding power Pi(n);
To the Pi(n) it is ranked up, and i ∈ [1,2 ... N], n indicates the number of iterations;
Maximum two Pi(n) corresponding two grey wolves are set as wolf king, and record the position of the wolf kingWith
Iteration n+1 times grey wolf position X is determined by improving grey wolf algorithmi(n+1), two improved in grey wolf algorithm
The decision weights coefficient of wolf king is adjusted with the process dynamics of hunting, i.e.,WithWherein W1And W2The decision weights coefficient of respectively described two wolf kings, n is the number of iterations, nmax
For maximum number of iterations;
When tracking, grey wolf can be jumped on the position that prey is likely to occur.And when two wolf kings obtain prey substantially
When position, it is believed that be the approximate location for having found prey, i.e. d1-2≤ 1/80N, in formula: d1-2It is the distance between two head wolves, N
It is series board subnumber.It is directly entered the local search executed by golden ratio algorithm at this time, otherwise until reaching greatest iteration time
Number enters back into local search.And the position of two head wolves is set to the initial ranging section of golden ratio partitioning algorithm, it is possible to
Global optimum can be encountered not the initial ranging section the case where.So initial ranging siding-to-siding block length is expanded desiTo avoid upper
The occurrence of stating.desiSize be desi=1/40N, direction are as shown in Figure 3;
Whether detection external environment mutates, and after external condition changes, global power maximum value will become
Change, algorithm will be restarted at this moment to track new global power maximum value.Restarting for front and back power variation rate is mutated based on detection
Differentiation mechanism is most common to restart one of method of discrimination: | (P1-P0)/P0| > τ0, in formula: P0And P1It is that mutation is preceding and prominent respectively
Power after change, τ0To be mutated threshold value.But this method there are problems that restarting unsuccessfully, such as under the specific condition of similar Fig. 4
Changed power very little before and after illuminance abrupt variation is so that described restart method of discrimination and can not detect, i.e. τ < τ0.By conductance increment
The inspiration of method, the present invention are restarted method of discrimination and are enhanced the reliability of MPPT, the novel heavy using the novel of the P-U slope of curve
Opening method of discrimination may be expressed as: | (P1-P0)/(U1-U0+ μ) | > ε0.In formula: U0And U1It is the preceding electricity with after mutation of mutation respectively
Pressure, μ are set as 0.001 to prevent zero mother, ε0It is threshold value, ε in the present invention0It is selected as 2.Novel method of discrimination meter is restarted through described
It can be obtained after calculation, ε is 3.83 in Fig. 4, has been enough to detect that acute variation has occurred in external condition.
Claims (1)
1. a kind of photovoltaic maximum power point method for tracing, includes the following steps:
1) N number of point is chosen in duty ratio [0,1], as grey wolf initial position;
2) current iteration is set as nth iteration, and the position of grey wolf is Xi(n),i∈[1,2,……N];
3) after photovoltaic system is stablized, the output electric current and output voltage of the corresponding photovoltaic array of each Xi (n) are measured, and calculate
Corresponding power Pi(n);
4) to the Pi(n) it is ranked up;
5) maximum two Pi(n) corresponding two grey wolves are set as wolf king, and record the position of the wolf king;
6) iteration n+1 times grey wolf position X is determined by improving grey wolf algorithmi(n+1), improvements are as follows: the decision-making power of two wolf kings
Weight coefficient is adjusted with the process dynamics of hunting, i.e.,WithIts
Middle W1And W2The decision weights coefficient of respectively described two wolf kings, n is the number of iterations, nmaxFor maximum number of iterations;
7) when tracking, grey wolf can be jumped on the position that prey is likely to occur, and when two wolf kings obtain the substantially position of prey
When setting, it is believed that be the approximate location for having found prey, be directly entered the local search executed by golden ratio algorithm at this time, otherwise
Local search is entered back into until reaching maximum number of iterations;
8) whether detection external environment mutates: default mutation threshold epsilon0If P0And P1It is before illuminance abrupt variation and after mutation respectively
Power, U0And U1It is the voltage before illuminance abrupt variation and after mutation respectively, μ is set as 0.001 to prevent zero female, judgement | (P1-
P0)/(U1-U0+ μ) | > ε0, if satisfied, then thinking to mutate, restarting algorithm tracks maximum power point, and otherwise system is steady
It is scheduled in global optimum's duty ratio.
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CN108549456B (en) * | 2018-05-10 | 2020-12-29 | 天津大学 | Photovoltaic array MPPT control method based on moth fire suppression algorithm |
CN108646849B (en) * | 2018-07-11 | 2019-10-18 | 东北大学 | Based on the maximum power point of photovoltaic power generation system tracking for improving wolf pack algorithm |
CN113342124B (en) * | 2021-06-11 | 2022-08-09 | 中国电建集团华东勘测设计研究院有限公司 | Photovoltaic MPPT method based on improved wolf optimization algorithm |
CN114442725B (en) * | 2022-02-16 | 2023-09-05 | 东南大学 | Photovoltaic maximum power point tracking method, storage medium and tracking device |
CN115509294B (en) * | 2022-09-16 | 2023-08-01 | 哈尔滨工程大学 | Photovoltaic array maximum power tracking method and system |
CN116301183B (en) * | 2023-03-06 | 2023-09-08 | 哈尔滨工业大学 | Maximum power point tracking method of space power generation system |
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