Ahmed M. Othman

Realworld maximum power point tracking simulation of PV system based on Fuzzy Logic control

Abstract In the recent years, the solar energy becomes one of the most important alternative sources of electric energy, so it is important to improve the efficiency and reliability of the photovoltaic (PV) systems. Maximum power point tracking (MPPT) plays an important role in photovoltaic power systems because it maximize the power output from a PV system for a given set of conditions, and therefore maximize their array efficiency. This paper presents a maximum power point tracker (MPPT) using Fuzzy Logic theory for a PV system. The work is focused on the well known Perturb and Observe (P&O) algorithm and is compared to a designed fuzzy logic controller (FLC). The simulation work dealing with MPPT controller; a DC/DC Ćuk converter feeding a load is achieved. The results showed that the proposed Fuzzy Logic MPPT in the PV system is valid.

A new technique based on Artificial Bee Colony Algorithm for optimal sizing of stand-alone photovoltaic system

One of the most recent optimization techniques applied to the optimal design of photovoltaic system to supply an isolated load demand is the Artificial Bee Colony Algorithm (ABC). The proposed methodology is applied to optimize the cost of the PV system including photovoltaic, a battery bank, a battery charger controller, and inverter. Two objective functions are proposed: the first one is the PV module output power which is to be maximized and the second one is the life cycle cost (LCC) which is to be minimized. The analysis is performed based on measured solar radiation and ambient temperature measured at Helwan city, Egypt. A comparison between ABC algorithm and Genetic Algorithm (GA) optimal results is done. Another location is selected which is Zagazig city to check the validity of ABC algorithm in any location. The ABC is more optimal than GA. The results encouraged the use of the PV systems to electrify the rural sites of Egypt.

A New Optimization Approach for Maximizing the Photovoltaic Panel Power Based on Genetic Algorithm and Lagrange Multiplier Algorithm

In recent years, the solar energy has become one of the most important alternative sources of electric energy, so it is important to operate photovoltaic (PV) panel at the optimal point to obtain the possible maximum efficiency. This paper presents a new optimization approach to maximize the electrical power of a PV panel. The technique which is based on objective function represents the output power of the PV panel and constraints, equality and inequality. First the dummy variables that have effect on the output power are classified into two categories: dependent and independent. The proposed approach is a multistage one as the genetic algorithm, GA, is used to obtain the best initial population at optimal solution and this initial population is fed to Lagrange multiplier algorithm (LM), then a comparison between the two algorithms, GA and LM, is performed. The proposed technique is applied to solar radiation measured at Helwan city at latitude 29.87°, Egypt. The results showed that the proposed technique is applicable.

Enhanced Backtracking Search Algorithm for Optimal Coordination of Directional Over-current Relays Including Distributed Generation

Abstract In this article, the enhanced backtracking search algorithm is employed to achieve optimal coordination of directional over-current relays. A novel objective function is formulated to minimize the total operating times while maintaining the validity of the coordination time interval. The proposed technique is applied to optimize the influential variables of the coordination problem, which are plug tap setting, time dial setting, and type of inverse relay characteristics. Both old electromechanical and digital relays are considered in the study. The enhanced backtracking search algorithm is a recent heuristic-based optimization, and its performance in solving relay coordination problem is compared with other well-established algorithms to demonstrate its viability and effectiveness.

Flywheel-Based Fast Charging Station – FFCS for Electric Vehicles and Public Transportation

Flywheel-Based Fast Charging Station – FFCS for Electric Vehicles and Public Transportation Hossam A. Gabbar *, Ahmed M. Othman b Faculty of Energy Systems and Nuclear Science, University of Ontario Institute of Technology, 2000 Simcoe Street North, Oshawa L1H7K4 ON, Canada. Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, 2000 Simcoe Street North, Oshawa L1H7K4 ON, Canada.

Optimal Reconfiguration Comprising Voltage Stability Aspect Using Enhanced Binary Particle Swarm Optimization Algorithm

Abstract This study addresses the application of an enhanced binary particle swarm optimization algorithm to generate optimal switching topology along radial distribution networks. The objective function is established with a weighting factor to offer flexibility consistent with the user decision. The active power loss minimization, voltage profile improvement, and enhancements of fast voltage stability indices are approached. Various S- and V-shaped transfer functions are attempted and analyzed to guarantee good performance of the proposed approach. The proposed method is applied to two well-known systems: the 33- and the 118-node radial distribution networks, to validate its significance and applicability. The realized results are compared to those reported for other recent heuristic competing techniques in the literature. The comparisons and subsequent discussions prove that the proposed methodology is able to generate high-quality solutions to the optimal switching schemes.