Development of maximum power point tracking algorithm: A Review Essay

Development of maximal power point tracking algorithm: A Reappraisal

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Abstraction: This paper information the betterment of a maximal power-point trailing method for photovoltaic systems utilizing optimisation techniques. Intelligent method of maximal power point tracking utilizing fuzzed logic control for separate photovoltaic system has been presented. The proposed fire beetle algorithm is simple computational stairss, faster convergence. Photovoltaic coevals systems have become an attractive option among renewable energy beginnings because they are clean, maintenance-free and environmental friendly. Maximal power point tracking techniques are working in photovoltaic systems to do full ingestion of PV array end product power which depends on solar irradiation and ambient temperature. The aim is to better the efficiency of a standalone Solar energy system dwelling of a

Photovoltaic panel.

Keywords: Maximal power point grip ( MPPT ) , fuzzed logic ( FL ) , firefly algorithm, ( FA ) Photovoltaic ( PV ) , unreal nervous web ( ANN ) , atom drove optimisation ( PSO ) .

1. Introduction

Renewable beginnings of energy get turning importance due to monolithic ingestion and exhaustion of fossil fuel. Surveies of photovoltaic ( PV ) coevals systems are actively being promoted in order to get by with environment issues such as the green house consequence and air pollution. [ 1 ] PV coevals systems have two large jobs that the efficiency of electric-power coevals is really low, particularly under low irradiation provinces, and the sum of the electric power generated by solar arrays is ever altering with upwind conditions, i.e. the strength of the solar radiation. A maximal power point tracking method, which has speedy response features and is able to do good usage of the electric power generated in any conditions, is needed to get by with the former job. [ 2 ] The photovoltaic power coevals has seen a rapid growing in the last few old ages taking to extended usage of solar energy ; a PV system has the advantages of low care cost, absence of traveling or revolving parts and freedom from environmental pollution. Many states provide generous fiscal strategies such as feed-in duty, subsidised policies taking to rapid growing of PV power coevals systems. Due to high initial cost of PV power coevals systems and its low energy transition efficiency, a PV system is by and large operated to pull out maximal power from the PV beginning. In order to optimise the use of PV systems, maximal power-point trailing is by and large employed, which requires power electronic interfaces such as dc–dc convertor and/or inverter. The aim of MPPT is to pull out maximal power generated by the PV systems under changing status of temperature and solar insularity. [ 3 ]

When climatic conditions vary, the MPP of the PV system besides changes its place and several methods have been presented for tracking the MPP. These methods include perturb and observe, incremental conductance, short circuit current, unfastened circuit electromotive force, burden current/load electromotive force maximization technique, fuzzed control, nervous network- based strategies.PV faculties receive different solar insularity due to shadow of edifice, traveling clouds, and other neighbouring objects. [ 4 ]

In general there is merely one maximal power point curve on the P-V curve, it operates with maximal power efficiency, produces its upper limit end product power. In partial shading conditions, MPPT is an of import construct for PV systems. [ 5 ] The public presentation of MPPT techniques is compared on the footing of desirable characteristics like trouble, velocity, hardware achievement, detectors required, cost, scope of value and efficiency of the system The location of MPP is non identified, but can be calculated either through computation theoretical accounts or by hunt algorithms.


2.1 Firefly algorithm

2.1.1. Behaviour of Fireflies:

The sky filled with the visible radiation of fire beetles is a dramatic sight in the summer in the moderately temperature parts. There are near to two 1000s firefly species, and most of them produce short and rhythmic flashes. The form observed for these flashes is alone for most of the times for a specific species. The beat of the flashes, rate of flashing and the sum of clip for which the flashes are observed are together organizing a sort of a form that attracts both the males and females to each other. Females of a species respond to single form of the male of the same species. We know that the strength of visible radiation at a certain distance R from the light beginning conforms to the opposite square jurisprudence. That is the strength of the visible radiation I goes on diminishing as the distance R will increase in footings of I ? 1/r2. Additionally, the air keeps absorbing the visible radiation which becomes weaker with the addition in the distance. These two factors when combined make most fire beetles visible at a limited distance, usually to a few 100 metres at dark, which is rather adequate for fire beetles to pass on with each other. [ 6 ]

2.1.2 Structure of firefly algorithm:

In fire beetle algorithm, there are two of import variables, which is the light strength and attraction. Firefly is attracted toward the other fire beetle that has brighter flash than itself. The attraction is depended with the light strength [ 7 ] .


Firefly algorithm is a new meta heuristic algorithm inspired by a flashing of fire beetles, for optimisation jobs. It was introduced in the twelvemonth 2009 at Cambridge University by Yang. In this algorithm, indiscriminately generated solutions will be considered as fire beetles. Brightness is assigned depending on their public presentation on the nonsubjective map. One of import regulation of this algorithm is all fire beetles are unisex. It means that regardless of sex, any fire beetle can be attracted to any other brighter one. Second regulation is that blinking visible radiation is determined from the nonsubjective map. Light strength at a peculiar distance ‘r’ from light beginning obeys inverse square jurisprudence. Attractiveness is straight relative to brightness and it decreases with distance [ 8 ] [ 9 ] .


The proposed MPPT accountant builds upon the simpleness of the P & A ; O technique but eliminates the ensuing steady province oscillations by adaptively modifying the mention electromotive force disturbance step-size C utilizing a fuzzed logic accountant. The proposed control strategy takes the absolute power incline Sa of the PV panel curve and the old electromotive force disturbance measure Cold as its inputs and calculates the alteration in the new P & A ; O measure size C. The two inputs will be fuzzified by utilizing normalized fuzzed sets with three triangular rank maps ( MFs ) : Small, Medium, and Large as shown in Fig. 6. The end product variable consists of a normalized fuzzy set with triangular Medium frequencies: Negative Big ( NB ) , Negative Small ( NS ) , Zero ( ZO ) , Positive Small ( PS ) , and Positive Big ( PB ) .After the fuzzification of the chip inputs, the ensuing fuzzed sets have to be compared to the rule-base. The regulation base is a set of “ If premiss Then consequent ” regulations constructed harmonizing to the interior decorator system cognition and experience. Depending on the value of the absolute power incline, the PV panel curve ( Fig. 2 ) can be divided into three parts. Given the old mention electromotive force and disturbance measure Cold, the accountant will find the alteration to the new measure in order to make the MPP.

3.1FLC based MPPT:

Fuzzy logic based MPPT does non necessitate the cognition of the PV panel. It has two inputs and one end product. Mamdanis method is used for fuzzed illation and Centre of gravitation method for defuzzification and the responsibility ratio is computed. [ 10 ]

4. Artificial nervous web based MPPT:

The three bed RBFN NN is adopted for implementing the MPPT. The figure of input units in the input bed is three while the concealed bed has nine input units and the end product bed has one unit. To command the responsibility rhythm of the switch, PWM pulsations are generated utilizing PV faculty. Enhancement of weight of links and accommodation of parametric quantities used for larning will heighten the public presentation of the system.ANN based methods is suited for the systems that can acquire sufficient preparation informations. [ 11 ]



To show the application of PSO for MPPT, a solution vector of responsibility rhythms with Np atoms is to be determined. The algorithm transmits three responsibility rhythms di ( i=1,2,3,4, ….Np ) to the power convertor. The value of responsibility rhythm is about a invariable after subsequent loop and therefore the operating point will be maintained. PSO method is efficient for non-uniform irradiance conditions but its convergence depends on the initial topographic point of the agents. [ 12 ]


The intensive and monolithic usage of energy from the solar cell is indispensable for supplying solutions to environmental jobs. Implementing the MPPT algorithm through digital accountants is easier if it is possible to minimise mistake maps. The differences between the assorted MPPT techniques are really little and they can be evaluated harmonizing to the state of affairs. This paper has presented a new MPPT algorithm based on a settlement of fire beetles for rapidly tracking GMPP in partly shaded PV array. The FFA non merely includes the ego bettering procedure with the current infinite, but it besides includes the betterment among its ain infinite from the old phases. Besides Firefly is better than PSO in footings of the clip taken for the optimum or near optimal value to be generated provided certain high degree of noise where the difference in clip taken becomes more apparent with the addition in the degree of noise. Firefly algorithm besides suited is used for the high dimensional and nonlinear jobs. [ 13 ] [ 14 ]


[ 1 ] M. Z. S. EL-Dein, M. Kazerani, and M. M. A. Salama, “Optimal photovoltaic array reconfiguration to cut down partial shading losingss, ”IEEE Trans. Sustainable Energy, vol. 4, no. 1, pp. 145–153, Jan. 2013.

[ 2 ] H. Patel and V. Agarwal, “Maximum power point tracking strategy for PV systems runing under partly shaded conditions, ”IEEE Trans. Ind.Electron., vol. 55, no. 4, pp. 1689–1698, Apr. 2008

[ 3 ] N. Fernia, G. Petrone, G. Spagnuolo, and M. Vitelli, “Optimization of perturb and detect maximal power point tracking method, ”IEEE Trans.Power Electron., vol. 20, no. 4, pp. 963–973, Jul. 2005.

[ 4 ] K. H. Hussein and I. Muta, “Maximum photovoltaic power trailing: An algorithm for quickly altering atmospheric conditions, ” inProc. Inst.Electr. Eng. Generation, Transmiss. Distrib., Jan. 1995, vol. 142, no. 1, pp. 59–6

[ 5 ] T. Noguchi, S. Togashi, and R. Nakamoto, “Short-current pulse-based maximum-power-point tracking method for multiple photovoltaic and convertor faculty system, ”IEEE Trans. Ind. Electron., vol. 49, no. 1, pp. 217–223, Feb. 2002

[ 6 ] Nnadi D.B.N/ 2012. Environmental Climatic Effect on Stand- Alone Solar Energy Supply Performance for Sustainable Energy/ NIJOTECH/ 31

[ 7 ] Roberto Faranda, Sonia Leva. 2008. Energy comparing of MPPT techniques for PV Systems. ISSN, 3: 1790-5060.

[ 8 ] Azadeh Safari and Saad Mekhilef. 2011. Simulation and Hardware Implementation of Incremental Conductance MPPT with Direct Control method utilizing Cuk convertor. IEEE Transactions on Industrial Electronics. 58 ( 4 )

[ 9 ] Wankhede R.B and Vaidya. U.B. 2014. Energy Comparison of MPPT Techniques Using Cuk Converter. IJIRAE. 1 ( 6 ) .

[ 10 ] N. Chai-ead, P. Aungkulanon, and P. Luangpaiboon, Member, IAENG, “Bees and Firefly Algorithms for Noisy Non-Linear Optimization Problems” , Proceedings of the 26th International multi Conference of applied scientists and Computer Scientists, 2011, Volume II.

[ 11 ] Surafel Luleseged Tilahun and Hong Choon Ong “Modified Firefly Algorithm” Hindawi Publishing Corporation, Journal of Applied

Mathematicss, Article ID 467631, 2012, 12 pages

[ 12 ] A. Mathew and A. I. Selvakumar, “New MPPT for PV arrays utilizing fuzzed accountant in close cooperation with fuzzed cognitive web, ” IEEE Trans. Energy Conv. , 2006, Vol. 21, No. 3, pp. 793–803.-

[ 13 ] Mahmoud A. Younis, Tamer Khatib, Mushtaq Najeeb, A Mohd Ariffin, “An Improved Maximum Power Point Tracking Controller for PV Systems Using Artificial Neural Network” PrzeglA…d Elektrotechniczny, 2012, R. 88 NR 3b

[ 14 ] Whei-Min Lin, Member, IEEE, Chih-Ming Hong, and Chiung-Hsing Chen, “Neural-Network-Based MPPT Control of a Stand-alone Hybrid Power Generation System” IEEE Transactions on Power Electronics, 2011, Vol. 26, No. 12


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