J. Kamala

Bio: J. Kamala is an academic researcher from Anna University. The author has contributed to research in topics: Maximum power principle & Solar power. The author has an hindex of 1, co-authored 2 publications receiving 4 citations.

Evaluation of particle swarm optimization algorithm in photovoltaic applications

Abstract: Solar energy is the prime source of consumption for the world. It is the potential candidate for meeting the growing energy demand and solving environmental issues. To derive the maximum Power (MP) from the system, Maximum Power Point Tracking (MPPT) methods are implemented. It is highly essential to derive MP from the available solar energy. Over the years, numerous MPPT methods have been developed and presented in the literature. This paper discusses the evaluation of Particle Swarm Optimization (PSO) algorithm in MPPT based solar power generation systems. It describes the different methodologies adopted to extract the maximum power from the solar array in photovoltaic (PV) power systems.

Implementation of Enhanced Global Search Particle Swarm Optimization Algorithm for Photovoltaic Systems

Abstract: Solar Power is the potential alternative to other sources of energy due to its pure and readily available nature. Solar power wattage is not always wonted or constant. It is due to its unaccustomed multi-peak power voltage characteristics exhibited by a variable swing in environmental conditions and shading effects. Swarm Intelligence has been playing a vital role in optimization problem owing to its intelligibility and directness. This article presents an Inertia Modified Particle Swarm Optimization (IM-PSO) algorithm for photovoltaic power extraction. The proposed method helps to keep track of the maximum power point in lesser effort thereby ensuring proper maximum power. The algorithm is modeled and simulated in MATLAB/SIMULINK environment. The results prove it especial to other traditional techniques.

An Advanced Particle Swarm Optimization Algorithm for MPPTs in PV Systems

Abstract: Maximum power point trackers are in charge of absorbing the maximum potential power from the photovoltaic panels. Thus, this makes the maximum power point trackers the fundamental parts of the photovoltaic panel systems. The conventional maximum power point tracker algorithms are working well under balanced insolation conditions, however when the partial shade condition occurs, those algorithms are trapped at the local maxima. Hence, under partial shade conditions, the need for a global maximum power point tracking algorithm arises. Particle swarm optimization is a preferential algorithm of maximum power point trackers in literature, especially in partial shade conditions. This paper is focused on improving the existing particle swarm optimization algorithm for maximum power point trackers. The proposed advanced particle swarm optimization algorithm aims to catch the global maximum power point much faster, accurately and to reduce the chatter in the power curve. The proposed method accelerates the global maximum tracking time with gridding the initial search area. The effectiveness of the proposed method is demonstrated with simulation results and these results are compared with a conventional particle swarm optimization method under step changes in irradiance and partial shade conditions of an array of photovoltaic panels.

Particle Swarm Optimization with Reducing Boundaries (PSO-RB) for Maximum Power Point Tracking of Partially Shaded PV Arrays

Abstract: Multiple Local Maximums (LM) and one Global Maximum (GM) can be present on the Power-Voltage (P-V) output curve during partial shading conditions of a Photovoltaic system. Particle Swarm Optimization (PSO) can consistently find the GM 100% of the time when the swarm size is large enough but the time to find the GM also increases as the swarm size increases - thus reducing the speed of tracking. In this work, we present the performance results of a modified PSO algorithm with dynamically reduced boundary (PSO-RB) for the search space of the particles. Our results indicate that the global maxima search time can be significantly improved while maintaining the accuracy by implementing the PSO-RB algorithm. A direct comparison between the original PSO algorithm and the new PSO-RB algorithm showed that PSO-RB can find the GM point 54.3% (over 2 times) faster than the conventional PSO.

Analysis and simulation of photovoltaic systems incorporating battery energy storage

Abstract: OF THESIS ANALYSIS AND SIMULATION OF PHOTOVOLTAIC SYSTEMS INCORPORATING BATTERY ENERGY STORAGE Solar energy is an abundant renewable source, which is expected to play an increasing role in the grid’s future infrastructure for distributed generation. The research described in the thesis focuses on the analysis of integrating multi-megawatt photovoltaics systems with battery energy storage into the existing grid and on the theory supporting the electrical operation of components and systems. The PV system is divided into several sections, each having its own DC-DC converter for maximum power point tracking and a two-level grid connected inverter with different control strategies. The functions of the battery are explored by connecting it to the system in order to prevent possible voltage fluctuations and as a buffer storage in order to eliminate the power mismatch between PV array generation and load demand. Computer models of the system are developed and implemented using the PSCAD/EMTDC software.

Optimization based optimal control of solar PV system

Abstract: in recent trend, there has been rapidly increase of demand in energy. For conserving energy, innovative solutions are being proposed for reducing it. An eco-friendly system should be created in order to save investment on electricity and then maximize the return cost on investment for investing in solar modules. The photovoltaic industry happens to be more efficient and less expensive systems so that it can be more competitive in nature when it is being compared with other conventional sources. For PV modules, irradiation and panel temperature faces more challenges because these parameters are unstable. Therefore, the electricity generations from PV panel are not stable in nature. So, Maximum Power Point Tracking (MPPT) technique is used for extracting maximum power from PV modules. A DC-to-DC converter has been used for matching the impedance between PV module or array of modules so as to extract maximum power from PV modules or strings of PV modules. In this paper, perturb & observe (P&O), Particle swarm 0ptimization (PSO) and Grey wolf optimization (GWO) methods have been applied. The performance of these algorithms has been verified for MPPT in MATLAB /Simulink environment.