@article{Saeed_Mehmood_Khan_Shah_Ullah_Ali_2022, title={An improved search ability of particle swarm optimization algorithm for tracking maximum power point under shading conditions}, url={http://eie.khpi.edu.ua/article/view/254744}, DOI={10.20998/2074-272X.2022.2.04}, abstractNote={<p><strong><em>Introduction.</em></strong><em> Extracting maximum possible power from solar energy is a hot topic of the day as other sources have become costly and lead to pollution. <strong>Problem.</strong> Dependency on sunlight for power generation makes it unfeasible to extract maximum power. Environmental conditions like shading, partial shading and weak shading are the major aspect due to which the output of photovoltaic systems is greatly affected. Partial shading is the most known issue. <strong>Goal.</strong> There have been many proposed techniques and algorithms to extract maximum output from solar resources by use of photovoltaic arrays but every technique has had some shortcomings that couldn’t serve the complete purpose. <strong>Methodology.</strong> Nature inspired algorithms have proven to be good to search global maximum in a partially shaded multipeak curve which includes particle swarm optimization, artificial bee colony algorithm, and flower pollination algorithm. <strong>Methods.</strong> Particle swarm optimization algorithm is best among these in finding global peaks with less oscillation around maximum power point, less complexity, and easy to implement nature. Particle swarm optimization algorithm has the disadvantage of having a long computational time and converging speed, particularly under strong shading conditions. <strong>Originality.</strong> In this paper, an improved opposition based particle swarm optimization algorithm is proposed to track the global maximum power point of a solar photovoltaic module. Simulation studies have been carried out in MATLAB/Simulink R2018a. <strong>Practical value.</strong> Simulation studies have proved that opposition based particle swarm optimization algorithm is more efficient, less complex, more robust, and more flexible and has better convergence speed than particle swarm optimization algorithm, perturb and observe algorithm, hill climbing algorithm, and incremental conductance algorithm.</em></p>}, number={2}, journal={Electrical Engineering & Electromechanics}, author={Saeed, H. and Mehmood, T. and Khan, F. A. and Shah, M. S. and Ullah, M. F. and Ali, H.}, year={2022}, month={Apr.}, pages={23–28} }