ISSN 1 746-7233, England, UK World Journal of Modelling and Simulation Vol. 11 (2015) No. 2, pp. 83-93 Energy comparative analysis of MPPT techniques for PV system using interleaved soft-switching boost converter M. Muthuramalingam 1 , P. S. Manoharan 2* 1 P.T.R. College of Engineering and Technology, Madurai, Tamilnadu, India 2 Thiagarajar College of Engineering, Madurai, Tamilnadu, India (Received September 1 2014, Accepted December 29 2014) Abstract. The availability of solar energy varies widely with ambient temperature and different atmospheric conditions and hence the maximum power point (MPP) of photovoltaic system (PV) system is not stable. Therefore, a maximum power point tracking (MPPT) controllers are needed to operate the PV at its MPP. This work presents an evaluation of four different MPPT techniques for PV system such as perturb and observation (P&O), incremental conductance (INC), fuzzy logic controller (FLC), and genetic algorithm (GA). These MPPT techniques are implemented in an interleaved soft switching boosts converter (ISSBC) and performance comparison is made for these four MPPT algorithms in terms of parameters like global peak, tracking speed and power extraction. These algorithms are methodically investigated by means of simulation and a projected efficiency estimate method of experimentation. The simulation and hardware results show that GA algorithm is outperforming than the other algorithms. Keywords: maximum power point tracking (MPPT), fuzzy logic controller (FLC), genetic algorithm (GA), microcontroller, interleaved soft switching boost inverter (ISSBC) 1 Introduction Photovoltaic energy has increased interest in electrical power applications, since it is considered as a basically limitless and generally on hand energy resource. However, the output power induced in the photo- voltaic modules depends on solar irradiance and temperature of the solar cells. This makes the extraction of maximum power a complex task. The efficiency of the PV generation depends on maximum power extraction of PV system. Therefore, to maximize the efficiency of the renewable energy system, it is necessary to track the maximum power point of the PV array [19] . The PV array has a single in service point that can supply maximum power to the load. This point is called the maximum power point (MPP). The locus of this point has a nonlinear distinction with solar irradiance and the cell temperature. Thus, in order to operate the PV array at its MPP, the PV system must contain a maximum power point tracking (MPPT) controller. Many MPPT techniques have been reported in the literature. The P&O method is an iterative algorithm to track the MPP by measuring the current and voltage of the PV module. This algorithm is easy to implement but the problem of oscillation of operating point around MPP is unavoidable as discussed in [9, 10]. INC method presented in [7, 11] is most widely used method. It tracks the MPP by comparing instantaneous conductance to the incre- mental conductance. The INC method requires complex computations to acquire good accuracy under rapidly changing weather conditions and the response time to reach MPP is also relatively long as discussed in [2, 13]. The Perturbation and Observation (P&O) and Incremental conductance (INC) algorithms, which works sat- isfactorily when the irradiance varies very slowly but fails to track global MPP when irradiance changed. The above mentioned algorithms work satisfactorily only under uniform irradiance conditions in which PV curve has a unique MPP. Recently artificial intelligence methods which include Fuzzy and Neural Network * Corresponding author. E-mail address: psmeee@tce.edu. Published by World Academic Press, World Academic Union