484 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 25, NO. 2, JUNE 2010 A Biological Swarm Chasing Algorithm for Tracking the PV Maximum Power Point Liang-Rui Chen, Member, IEEE, Chih-Hui Tsai, Yuan-Li Lin, and Yen-Shin Lai, Senior Member, IEEE Abstract—In this paper, a novel photovoltaic (PV) maximum power point tracking (MPPT) based on biological swarm chasing behavior is proposed to increase the MPPT performance for a module-integrated PV power system. Each PV module is viewed as a particle, and as a result, the maximum power point is viewed as the moving target. Thus, every PV module can chase the maximum power point (MPP) automatically. A 525 W prototype constructed by three parallel-connected 175 W PV modules is implemented to assess the MPPT performance. Comparing with a typical perturb and observe (P&O) MPPT method, the MPPT efficiency η MPPT is improved about 12.19% in transient state by the proposed MPPT as theoretical prediction. Index Terms—Maximum power point tracking (MPPT), particle swarm optimization, photovoltaic (PV), swarm intelligence. I. INTRODUCTION A S GLOBAL environmental concern on the decrease of fossil fuel sources increases day-by-day, renewable en- ergy sources such as photovoltaic (PV), wind, geothermal en- ergy, and sea tide are attracting more and more attention as alternative energy sources. Among them, PV power is an es- tablished technology and has rapid growth in recent years. It is also the most potential candidate of green energy. Since the PV panel performs a nonlinear voltage–current curve, its max- imum power point (MPP) varies with irradiation and tempera- ture. To solve this problem, many MPP tracking (MPPT) meth- ods were proposed such as, hill climbing method [1], [2], per- turb and observe (P&O) method [3]–[5], incremental conduc- tion method [6]–[8], constant voltage method [9], [10], short- circuit current method [11], and as well, intelligent computing method [12]–[16]. These presented MPPT methods can control the PV panel’s voltage or current to track and maintain the MPP of the PV panel to increase the PV power efficiency. Recently, the module-integrated PV architecture was presented to improve PV power system performance, in comparison with the tradi- tional centralized PV architecture. In the module-integrated PV architecture, each module consists of a PV panel and an MPPT converter and operates in parallel. Manuscript received March 5, 2009; revised July 21, 2009; accepted November 11, 2009. Date of publication February 2, 2010; date of current version May 21, 2010. Paper no. TEC-00061-2009. L.-R. Chen and Y.-L. Lin are with the Department of Electrical Engineering, National Changhua University of Education, Changhua 500, Taiwan (e-mail: lrchen@cc.ncue.edu.tw). C.-H. Tsai is with the Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan (e-mail: chtsai@cc.hwh. edu.tw). Y.-S. Lai is with the Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan (e-mail: yslai@ntut.edu.tw). 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/TEC.2009.2038067 The module-integrated PV architecture shows many advan- tages like high PV power utilization, cost reduction of the MPPT converter, as well as high flexibility in the expansion of the power level [17]–[19]. In practice applications, conventional MPPT methods [20] can be directly used in each module in a module-integrated PV architecture. However, these conven- tional MPPT methods can only obtain one measured data (i.e., voltage, current, and power) of PV in one sampling time. Thus, two or more sampling times are needed to obtain sufficient in- formation so as to decide the MPP tracking direction. If the irradiation or the temperature varies within two sampling time, the decided MPP tracking direction will be wrong in some times. Therefore, a conventional module-integrated PV power system does not have a more excellent MPPT performance than the traditional centralized PV architecture. In this paper, a novel MPPT method based on biological swarm tracking behavior is proposed to make all modules cooperatively to obtain suf- ficient information in one sampling time to decide accurately the MPP tracking direction. In the proposed biological swarm chasing-based MPPT (Bio-MPPT) algorithm, each PV module is emulated as a particle and the MPP is viewed as the moving target. Thus, every PV module can automatically chase the MPP by the proposed Bio-MPPT algorithm. A 525 W prototype con- structed by three 175 W PV module is implemented to assess the MPPT performance. Comparing with a typical P&O algo- rithm, the MPPT efficiency η MPPT is improved about 12.19% in transient state by the proposed Bio-MPPT algorithm. II. BIOLOGICAL SWARM CHASING ALGORITHM Swarm intelligence is an artificial intelligence technique in- volving the study of collective behavior in decentralized sys- tems. One of the most popular swarm intelligence paradigms is the particle swarm optimization (PSO), which is basically devel- oped through the simulation of social behavior of bird flocking and fish schooling [21], [22]. PSO is a global optimization algo- rithm for dealing with problems on which a point or surface in an n-dimensional space represents a best solution. Potential solu- tions are plotted in this space and seeded with an initial velocity. Particles move through the problem space, then, a certain fitness criteria evaluates them. As time goes by, particles accelerate to- ward those with better fitness. Several areas have adopted the idea that swarms can solve complex problems. The term swarm refers to a large group of simple components that work together to achieve a goal and to produce significant results [21]–[26]. Unfortunately, a typical PSO is used to solve these problems that the targets (i.e., optimal solutions) are time invariable. On the other hand, the target (i.e., MPP) of a PV MPPT problem is time variable. In order to overcome this problem, a modified 0885-8969/$26.00 © 2010 IEEE