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# Development of maximum power point tracking algorithm: A Review

**Development of maximum power point tracking algorithm: A Review**

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**Abstract**: This paper information the improvement of a maximum power-point tracking method for photovoltaic systems using optimization techniques. Intelligent method of maximum power point tracking using fuzzy logic control for separate photovoltaic system has been presented. The proposed firefly algorithm is simple computational steps, faster convergence. Photovoltaic generation systems have become an attractive option among renewable energy sources because they are clean, maintenance-free and environmental friendly. Maximum power point tracking techniques are working in photovoltaic systems to make full consumption of PV array output power which depends on solar irradiation and ambient temperature. The objective is to improve the efficiency of a standalone Solar energy system consisting of a

Photovoltaic panel.

**Keywords**: Maximum power point traction (MPPT), fuzzy logic (FL), firefly algorithm,(FA) Photovoltaic (PV), artificial neural network(ANN), particle swarm optimization (PSO).

**1. INTRODUCTION**

Renewable sources of energy acquire growing importance due to massive consumption and exhaustion of fossil fuel. Studies of photovoltaic (PV) generation systems are actively being promoted in order to cope with environment issues such as the green house effect and air pollution. [1] PV generation systems have two big problems that the efficiency of electric-power generation is very low, especially under low irradiation states, and the amount of the electric power generated by solar arrays is always changing with weather conditions, i.e. the intensity of the solar radiation. A maximum power point tracking method, which has quick response characteristics and is able to make good use of the electric power generated in any weather, is needed to cope with the former problem.[2] The photovoltaic power generation has seen a rapid growth in the last few years leading to extensive use of solar energy; a PV system has the advantages of low maintenance cost, absence of moving or rotating parts and freedom from environmental pollution. Many countries provide generous financial schemes such as feed-in tariff, subsidized policies leading to rapid growth of PV power generation systems. Due to high initial cost of PV power generation systems and its low energy conversion efficiency, a PV system is generally operated to extract maximum power from the PV source. In order to optimize the utilization of PV systems, maximum power-point tracking is generally employed, which requires power electronic interfaces such as dc–dc converter and/or inverter. The objective of MPPT is to extract maximum power generated by the PV systems under varying condition of temperature and solar insulation. [3]

When climatic conditions vary, the MPP of the PV system also changes its position and several methods have been presented for tracking the MPP. These methods include perturb and observe, incremental conductance , short circuit current , open circuit voltage , load current/load voltage maximization technique , fuzzy control , neural network- based schemes .PV modules receive different solar insulation due to shadow of building, moving clouds, and other neighbouring objects.[4]

In general there is only one maximum power point curve on the P-V curve, it operates with maximum power efficiency, produces its maximum output power. In partial shading conditions, MPPT is an important concept for PV systems.[5] The performance of MPPT techniques is compared on the basis of desirable features like difficulty, speed, hardware accomplishment, sensors required, cost, range of value and efficiency of the system The location of MPP is not identified, but can be calculated either through calculation models or by search algorithms.

**2.** **METAHEURISTICS & OPTIMIZATION**

2.**1 Firefly algorithm**

2.1.1**. Behaviour of Fireflies**:

The sky filled with the light of fireflies is a spectacular sight in the summer in the moderately temperature regions. There are near to two thousand firefly species, and most of them produce short and rhythmic flashes. The pattern observed for these flashes is unique for most of the times for a specific species. The rhythm of the flashes, rate of flashing and the amount of time for which the flashes are observed are together forming a kind of a pattern that attracts both the males and females to each other. Females of a species respond to individual pattern of the male of the same species. We know that the intensity of light at a certain distance r from the light source conforms to the inverse square law. That is the intensity of the light I goes on decreasing as the distance r will increase in terms of I α 1/r2. Additionally, the air keeps absorbing the light which becomes weaker with the increase in the distance. These two factors when combined make most fireflies visible at a limited distance, normally to a few hundred meters at night, which is quite enough for fireflies to communicate with each other.[6]

**2.1.2 Structure of firefly algorithm:**

In firefly algorithm, there are two important variables, which is the light intensity and attractiveness. Firefly is attracted toward the other firefly that has brighter flash than itself. The attractiveness is depended with the light intensity [7].

**1.6 FIREFLY BASED MPPT**:

Firefly algorithm is a new meta heuristic algorithm inspired by a flashing of fireflies, for optimization problems. It was introduced in the year 2009 at Cambridge University by Yang. In this algorithm, randomly generated solutions will be considered as fireflies. Brightness is assigned depending on their performance on the objective function. One important rule of this algorithm is all fireflies are unisex. It means that regardless of sex, any firefly can be attracted to any other brighter one. Second rule is that flashing light is determined from the objective function. Light intensity at a particular distance ‘r’ from light source obeys inverse square law. Attractiveness is directly proportional to brightness and it decreases with distance[8][9].

**3. FUZZY LOGIC:**

The proposed MPPT controller builds upon the simplicity of the P&O technique but eliminates the resulting steady state oscillations by adaptively modifying the reference voltage perturbation step-size C using a fuzzy logic controller. The proposed control scheme takes the absolute power slope Sa of the PV panel curve and the old voltage perturbation step Cold as its inputs and calculates the change in the new P&O step size C. The two inputs will be fuzzified by using normalized fuzzy sets with three triangular membership functions (MFs): Small, Medium, and Large as shown in Fig. 6. The output variable consists of a normalized fuzzy set with triangular MF: Negative Big (NB), Negative Small (NS), Zero(ZO), Positive Small (PS), and Positive Big (PB).After the fuzzification of the crisp inputs, the resulting fuzzy sets have to be compared to the rule-base. The rule base is a set of "If premise Then consequent" rules constructed according to the designer system knowledge and experience. Depending on the value of the absolute power slope, the PV panel curve (Fig. 2) can be divided into three regions. Given the old reference voltage and perturbation step Cold, the controller will determine the change to the new step in order to reach the MPP.

**3.1***FLC based MPPT:*

Fuzzy logic based MPPT does not require the knowledge of the PV panel. It has two inputs and one output. Mamdanis method is used for fuzzy inference and centre of gravity method for defuzzification and the duty ratio is computed.[10]

**4. Artificial neural network based MPPT:**

The three layer RBFN NN is adopted for implementing the MPPT. The number of input units in the input layer is three while the hidden layer has nine input units and the output layer has one unit. To control the duty cycle of the switch, PWM pulses are generated using PV module. Enhancement of weight of links and adjustment of parameters used for learning will enhance the performance of the system.ANN based methods is suitable for the systems that can get sufficient training data.[11]

FIG 1. ANN BASED MPPT

**5.** **PSO BASED MPPT:**

To demonstrate the application of PSO for MPPT, a solution vector of duty cycles with Np particles is to be determined. The algorithm transmits three duty cycles di (i=1,2,3,4,….Np) to the power converter. The value of duty cycle is approximately a constant after subsequent iteration and hence the operating point will be maintained. PSO method is efficient for non-uniform irradiance conditions but its convergence depends on the initial place of the agents.[12]

CONCLUSION:

The intensive and massive use of energy from the solar cell is essential for providing solutions to environmental problems. Implementing the MPPT algorithm through digital controllers is easier if it is possible to minimize error functions. The differences between the various MPPT techniques are very slight and they can be evaluated according to the situation. This paper has presented a new MPPT algorithm based on a colony of fireflies for quickly tracking GMPP in partially shaded PV array. The FFA not only includes the self improving process with the current space, but it also includes the improvement among its own space from the previous stages. Also Firefly is better than PSO in terms of the time taken for the optimum or near optimum value to be generated provided certain high level of noise where the difference in time taken becomes more evident with the increase in the level of noise. Firefly algorithm also suitable is used for the high dimensional and nonlinear problems.[13] [14]

REFRENCES :

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

[2] H. Patel and V. Agarwal, “Maximum power point tracking scheme for PV systems operating under partially 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 observe maximum 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 tracking: An algorithm for rapidly changing atmospheric conditions,” in *Proc. 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 converter module 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 comparison 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 using Cuk converter. 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 engineers and Computer Scientists, 2011, Volume II.

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

Mathematics, Article ID 467631, 2012, 12 pages

[12]A. Mathew and A. I. Selvakumar, “New MPPT for PV arrays using fuzzy controller in close cooperation with fuzzy cognitive network,” 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” PrzeglÄ…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