Application of a Continuous Particle Swarm Optimization (CPSO) for the Optimal Coordination of Overcurrent Relays Considering a Penalty Method

In an electrical power system, the coordination of the overcurrent relays plays an important role in protecting the electrical system by providing primary as well as backup protection. To reduce power outages, the coordination between these relays should be kept at the optimum value to minimize the total operating time and ensure that the least damage occurs under fault conditions. It is also imperative to ensure that the relay setting does not create an unintentional operation and consecutive sympathy trips. In a power system protection coordination problem, the objective function to be optimized is the sum of the total operating time of all main relays. In this paper, the coordination of overcurrent relays in a ring fed distribution system is formulated as an optimization problem. Coordination is performed using proposed continuous particle swarm optimization. In order to enhance and improve the quality of this solution a local search algorithm (LSA) is implanted into the original particle swarm algorithm (PSO) and, in addition to the constraints, these are amalgamated into the fitness function via the penalty method. The results achieved from the continuous particle swarm optimization algorithm (CPSO) are compared with other evolutionary optimization algorithms (EA) and this comparison showed that the proposed scheme is competent in dealing with the relevant problems. From further analyzing the obtained results, it was found that the continuous particle swarm approach provides the most globally optimum solution

Artificial neural network modeling and optimization of the Solid Oxide Fuel Cell parameters using grey wolf optimizer

Using Green and carbon-free energy sources is a new concept in the energy conversion, power generation, and energy management framework. Since there is a relatively small number of neural network applications in the field of fuel cells, especially in the case of solid oxide fuel cells, this work adopts the Artificial Neural Network model for modeling aims according to the empirical datasets. Besides, a new optimization method is applied to optimize the solid oxide fuel cell efficiency. The grey wolf optimizer with fast, robust, and simple features is applied to obtain the optimal operational variables of solid oxide fuel cells. The key operational parameters used for the optimization comprise the thickness of the anode support layer, the porosity of the anode layer, the thickness of the electrolyte layer, and the thickness of the cathode layer. The modeling results compared to the laboratory that confirms the ability of the artificial neural network model and optimization method in parameter identification. Two case study optimization procedure was assessed. Firstly, the variables optimized under the operational temperature of 800 °C and the values of 19 μm, 0.52 mm, 62.16 μm, and 75% are obtained for the electrolyte layer thickness, anode support layer thickness, cathode thickness, and anode support layer porosity, respectively. For the second case study, the power density based on the suggested method maximized up to 28% compared to the experimental results

A Study on an Improved Three-Winding Coupled Inductor Based DC/DC Boost Converter with Continuous Input Current

This paper proposes a novel high voltage conversion gain DC/DC boost converter for renewable energy applications and systems. The proposed converter utilizes a three-winding coupled inductor. The presented converter benefits from a unique advantage, as the actual turn ratio of the coupled inductor is decreased in the charging state of the coupled inductor. However, while the inductor is discharging, the actual turn ratio is increased. This feature leads to a very high voltage conversion gain. Furthermore, a passive clamp circuit is employed to recover the leakage current of the coupled inductor. The voltage stresses on the semiconductors are also reduced. In addition, the average current of the primary side of the coupled inductor is zero. This will reduce the total energy stored in the passive elements of the converter. The paper analyzes the Continuous Conduction Mode (CCM) and the operation principles of the presented converter are thoroughly derived. A 250 W laboratory hardware prototype is prepared to verify the proper operation of the presented converter. The obtained experimental results validate the feasibility of the presented converter

Investigation and Optimization of Grounding Grid Based on Lightning Response by Using ATP-EMTP and Genetic Algorithm

A large number of electromagnetic transient studies have been analyzed for finding the overvoltage behavior of power system. A grounding grid of power system is so important for reducing the effect of overvoltage phenomena during a short-circuit event. Two sections are important in grounding system behavior: soil ionization and inductive behavior; this paper focuses on the inductive manner of grounding grid. The grounding grid is considered as a conductor segment; each conductor segment acts as a grounding unit. In this paper, the transient methodology is introduced to investigate the lightning effect on grounding body at each point of grounding grid in normal and optimized conditions. Genetic algorithm is applied for regular and irregular grounding grid to obtain best values of mesh size with the lower ground potential rise (GPR) as compared with the normal condition for more safety. The grounding grid is a combination of inductance, resistance, and capacitance. This model is suitable for practical applications related to fault diagnosis. Several voltages on different positions of grounding grid are described in this paper using ATP-EMTP and genetic algorithm. The computer simulation shows that the proposed scheme is highly feasible and technically attractive