Pohang University of Science and Technology · エネルギー
Professor J. Prasanth Ram's research lab specializes in renewable energy systems, with a primary focus on optimizing photovoltaic (PV) power generation under challenging environmental conditions such as partial shading and variable irradiance. The lab develops advanced metaheuristic optimization algorithms—such as Flower Pollination Algorithm (FPA), Enhanced Leader Particle Swarm Optimization (ELPSO), and hybrid techniques—to enhance Maximum Power Point Tracking (MPPT) performance, ensuring global maximum power extraction. Research also extends to innovative PV system configurations, array reconfiguration strategies, and accurate modeling of organic photovoltaic cells for improved efficiency and real-time applicability. The lab emphasizes nature-inspired algorithms, system-level optimization, and practical implementation for sustainable energy solutions.
Figures are computed from collected data and may differ slightly.
To maximize solar photovoltaic (PV) output under dynamic weather conditions, maximum power point tracking (MPPT) controllers are incorporated in solar PV systems. However, the occurrence of multiple peaks due to partial shading adds complexity to the tracking process. Even though conventional and soft computing techniques are widely used to solve MPPT problem, conventional methods exhibit limited performance due to fixed step size, whereas soft computing techniques are restricted by insufficient
Nonhomogeneous irradiation conditions due to environmental changes introduce multiple peaks in nonlinear P -V characteristics. Hence, to operate photovoltaic at the global power point, numerous algorithms have been proposed in the literature. However, due to the insufficient exploitation of control variables, all the maximum power point tracking (MPPT) methods presented in the literature fail to guarantee global maximum power point (GMPP) operation. In this paper, a new detection technology to i
Photovoltaic Electrical Power Generation System (PV-EPGS) is gaining more importance due to its benefits like no fuel cost, eco-friendliness and less maintenance. However, harnessing the maximum power from large PV-EPGS has become difficult due to the occurrence of a number of peaks in P-V characteristics under partial shaded conditions. This paper presents simulation study of MPPT under partially shaded conditions using the modified version of Particle Swarm Optimization method known as Enhance
Due to the regular availability, pollution free and eco-friendly nature, power generation from solar energy has gained considerable attention. Although, power generation from solar is attractive; the environmental factors such as irradiation and temperature makes it challenging. In particular, it is a demanding task to researchers to extract maximum power different shading conditions. However, to extract maximum power various MPPT techniques have been proposed in conventional method and Evolutio
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