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    An Improved Particle Swarm Optimization Algorithm Using Eagle Strategy for Power Loss Minimization
    (HINDAWI LTD, 2017) Yapici, Hamza; Cetinkaya, Nurettin
    The power loss in electrical power systems is an important issue. Many techniques are used to reduce active power losses in a power system where the controlling of reactive power is one of the methods for decreasing the losses in any power system. In this paper, an improved particle swarm optimization algorithm using eagle strategy (ESPSO) is proposed for solving reactive power optimization problem to minimize the power losses. All simulations and numerical analysis have been performed on IEEE 30-bus power system, IEEE 118-bus power system, and a real power distribution subsystem. Moreover, the proposed method is tested on some benchmark functions. Results obtained in this study are compared with commonly used algorithms: particle swarm optimization (PSO) algorithm, genetic algorithm (GA), artificial bee colony (ABC) algorithm, firefly algorithm (FA), differential evolution (DE), and hybrid genetic algorithm with particle swarm optimization (hGAPSO). Results obtained in all simulations and analysis show that the proposed method is superior and more effective compared to the other methods.
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    Reduction of Power Loss Using Reactive Power Optimization in a Real Distribution System
    (IEEE, 2015) Yapici, Hamza; Cetinkaya, Nurettin
    Electrical power losses are an important factor for the operation of power systems. Reactive power optimization can reduce power loss. This paper describes the solution of reactive power optimization problem using particle swarm optimization and genetic algorithm. The numerical analysis has been carried out in a part of real power system in Turkey that is managed by MEDA. The goal of this study is minimizing the active power loss of the whole distribution network. Due to the absence of any generators in the distribution network, shunt capacitors and bus voltages are taken as control variables. The values of control variables are determined by the both algorithms and the results are compared.
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    Wind Power Estimation Algorithm Using Artificial Neural Networks Case Study: Eregli
    (IEEE, 2014) Cetinkaya, Nurettin; Yapici, Hamza
    By the global warming and decreasing fossil fuel, alternative energy sources are looked for future and protecting environment. In the recent years, many studies are made about wind power whereby deteriorating environment will be regarded. This study prefers artificial neural network (ANN) algorithm to estimate electrical energy output of wind turbines can be constructed. Although many environmental effects such as wind speed, air density or temperature influence wind turbines installation, ANN estimates electrical energy and power output in the minimum cost. The wind turbine parameters of three manufacturers have been chosen so as to train ANN. For the structure of ANN, 1 hidden layer and 26 neurons have been set. Data in this work have been measured at Eregli terrain in Konya, Turkey. This daily data have been taken between January 2013 and February 2014.