Maximum Power Point Tracking of Photovoltaic Systems with Modified Particle Swarm Optimization Technique Under Partial-Shading Conditions

• Main Authors: Po-Hsien Tu, 杜柏憲
• Other Authors: Jung-Shan Lin
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/38051014257635357920

碩士 === 國立暨南國際大學 === 電機工程學系 === 104 === The major target of this thesis is to develop the maximum power tracker of photovoltaic (PV) systems under the partial-shading conditions. Since the weather is unpredictable, there might exist local and global maximum power points (MPP) in the systems. Therefore, we must be able to track the global MPP under the partial-shading conditions in order to make our PV systems offer effective maximum power output for obtaining optimal system performance. First of all, the mathematical model is established for a PV array system to investigate and analyze the voltage and power output under partial-shade and non-partial-shade conditioning. However, the output power of PV systems could have various MPP under partial-shading conditions, so we have to determine an appropriate technology for the tracking control of global MPP. A novel concept is presented to modify the traditional particle swarm optimization method for strengthening algorithm capability and improving the system performance. In addition to using linear decreasing inertia weight, we apply nonlinear adapting learning factors for enhancing the tracking ability. It can avoid falling into local maximum solutions and provide the system to have more accurate convergence. As a result, the simulation results show that the modified particle swarm optimization has the potentials to track the global MPP with accurate rate of convergence under partial-shading conditions.