IEEE TRANSACTIONS ON POWER ELECTRONICS, VOL. 27, NO. 8, AUGUST 2012 3627 An Improved Particle Swarm Optimization (PSO)–Based MPPT for PV With Reduced Steady-State Oscillation Kashif Ishaque, Zainal Salam, Member, IEEE, Muhammad Amjad, and Saad Mekhilef, Member, IEEE Abstract—This paper proposes an improved maximum power point tracking (MPPT) method for the photovoltaic (PV) system using a modified particle swarm optimization (PSO) algorithm. The main advantage of the method is the reduction of the steady- state oscillation (to practically zero) once the maximum power point (MPP) is located. Furthermore, the proposed method has the ability to track the MPP for the extreme environmental condition, e.g., large fluctuations of insolation and partial shading condition. The algorithm is simple and can be computed very rapidly; thus, its implementation using a low-cost microcontroller is possible. To evaluate the effectiveness of the proposed method, MATLAB sim- ulations are carried out under very challenging conditions, namely step changes in irradiance, step changes in load, and partial shad- ing of the PV array. Its performance is compared with the con- ventional Hill Climbing (HC) method. Finally, an experimental rig that comprises of a buck–boost converter fed by a custom-designed solar array simulator is set up to emulate the simulation. The soft- ware development is carried out in the Dspace 1104 environment using a TMS320F240 digital signal processor. The superiority of the proposed method over the HC in terms of tracking speed and steady-state oscillations is highlighted by simulation and experi- mental results. Index Terms—Buck–boost converter, Hill Climbing (HC), max- imum power point tracking (MPPT), partial shading, particle swarm optimization (PSO), photovoltaic (PV) system. I. INTRODUCTION S OLAR photovoltaic (PV) is envisaged to be a popular source of renewable energy due to several advantages, no- tably low operational cost, almost maintenance free and envi- ronmentally friendly. Despite the high cost of solar modules, PV power generation systems, in particular the grid-connected type, have been commercialized in many countries because of its potential long-term benefits [1]–[6]. Furthermore, generous Manuscript received September 14, 2011; revised September 21, 2011; accepted January 8, 2012. Date of current version April 20, 2012. Recom- mended for publication by Associate Editor M. Liserre. K. Ishaque was with the Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia. He is now with the Department of Electronics Engineer- ing, Karachi Institute of Economics and Technology, Karachi 75190, Pakistan (e-mail: kashif.ishaque@pafkiet.edu.pk). Z. Salam (corresponding author) and M. Amjad are with the Uni- versiti Teknologi Malaysia, Johor Bahru 81310, Malaysia (e-mail: zainals@fke.utm.my; Muhammad.Amjad@iub.edu.pk). S. Mekhilef is with the University of Malaya, Kuala Lumpur 50603, Malaysia (e-mail: saad@um.edu.my). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TPEL.2012.2185713 financial schemes, for example, the feed-in tariff [7] and sub- sidized policies [8], have been introduced by various countries, resulting in rapid growth of the industry. To optimize the uti- lization of large arrays of PV modules, maximum power point tracker (MPPT) is normally employed in conjunction with the power converter (dc–dc converter and/or inverter). The objec- tive of MPPT is to ensure that the system can always harvest the maximum power generated by the PV arrays. However, due to the varying environmental condition, namely temperature and solar insolation, the P –V characteristic curve exhibits a max- imum power point (MPP) that varies nonlinearly with these conditions—thus posing a challenge for the tracking algorithm. To date, various MPP tracking methods have been proposed [9]. These techniques vary in complexity, accuracy, and speed. Each method can be categorized based on the type of the control variable it uses: 1) voltage, 2) current, or 3) duty cycle. For the voltage- and current-based techniques, two approaches are used. The first one is the observation of MPP voltage V MP or current I MP with respect to the open circuit voltage V OC [10] and short circuit current I SC , respectively [11]. Since this method ap- proximates a constant ratio, its accuracy cannot be guaranteed. Consequently, the tracked power would most likely be below the real MPP, resulting in significant power loss [12]. The second approach is to obtain the information on the actual operating point of the PV array (i.e., voltage and current) and these points are updated according to the variation in environmental condi- tions. The most popular technique is the perturb and observe (P&O) method. It is based on the perturbation of voltage (or current) using the present P and previous P old operating power, respectively. If P is improved, the direction of perturbation is retained; otherwise, the direction is reversed accordingly. Despite the simplicity of the algorithm, the performance of P&O method is heavily dependent on the tradeoff between the tracking speed and the oscillations that occurs around the MPP [13]. A small perturbation reduces the oscillations but at the expense of tracking speed, or vice versa. Another major drawback of P&O is that during rapid fluctuations of insolation, the algorithm is very likely to lose its direction while tracking the true MPP. Several improvements are proposed to address this issue—mainly by considering adaptive perturbation. However, these techniques are not fully adaptive and hence are not very effective [14]. Moreover, under special condition such as partial shading and modules irregularities, these methods often fail to track the true MPP because the PV curves are characterized by multiple peaks (several local and one global). Since the P&O algorithm could not distinguish the correct peak, its usefulness 0885-8993/$31.00 © 2012 IEEE