Yazar "Cinar, Mehmet" seçeneğine göre listele

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    Artificial Immunity Based Wound Healing Algorithm for Power Loss Optimization in Smart Grids
    (Univ Suceava, Fac Electrical Eng, 2020) Cinar, Mehmet; Kaygusuz, Asim
    In this study, a human immune system based wound healing algorithm is mentioned to optimize power losses in the smart grids. The smart grids are a concept that uses communication and control techniques to increase the efficiency of today's electrical systems, provide bidirectional communication and allow instant monitoring of the grid. The wound healing algorithm is computationally simulated in the event of a possible injury to the human body and there are very few publications on the proposed algorithm when the literature review is performed. Therefore, the proposed algorithm is capable of removing this gap in the literature. The codes are written in the Matlab GUI environment and applied to the IEEE 30-busbar system and power losses are tried to be optimized Simulation results show that the actual power loss is significantly reduced. The obtained results were compared with the results of other algorithms that are available in the literature. The proposed wound healing algorithm has given more optimum and superior solutions than the other algorithms compared in terms of calculation time and optimum power loss values and it was emphasized that it was a more effective method in providing the solution.
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    Optimization algorithms used for self-healing of smart grids
    (Ieee, 2018) Cinar, Mehmet; Kaygusuz, Asim
    The electrical networks used today are based on Tesla's design principles in 1883. Over time, owing to the fast progress of technology, it has become unable to meet today's needs. The concept of smart grid has been introduced especially since the year 2000 to remove this problem. The smart grid is the network system that is achieved by integrating today's technologies into the electric power networks. Smart grids that can provide real-time bi-directional information-exchange at all stages from generation to consumption of electricity; enabling energy to be used in a sustainable, safe and highly efficient energy network. The main objective of smart grid is; By providing a smooth integration of renewable energy sources into the system, the system is able to self-heal itself in the shortest possible time by using energy efficiently and reducing the downtime and the areas that can be affected at the time of the interruption to a minimum. In particular, there are basic optimization algoritms used in the literature for self healing of smart grids. In this study; the algoritms used for self-healing of smart grids will be mentioned.
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    Optimum Fuel Cost in Load Flow Analysis of Smart Grid by Using Artificial Bee Colony Algorithm
    (Ieee, 2019) Cinar, Mehmet; Kaygusuz, Asim
    Smart grids have a structure that can feel overloads with real-time communication infrastructure, regulate energy flow directions, optimize the use of renewable energy sources and reduce user costs. The smart grid vision aims to develop the power system towards a network that is well integrated with advanced measurement technologies, wide area communication, and automatic controls. The benefits include fast decision making, high controllability, and system reliability. Various optimization methods are used to reduce the optimum fuel cost in the load flow analysis in smart grids. The classical optimization methods that have been used before have now been replaced by intuitive and herd based algorithms. One of the herd based algorithms is the artificial bee colony algorithm. The use of an artificial bee colony algorithm is often preferred in smart grids since it is flexible and the number of parameters is less than other algorithms and the online run time is low. In this study, an artificial bee colony algorithm is mentioned from herd based algorithms to obtain optimum fuel cost in the smart grid. The program written in the MATLAB environment was applied to the IEEE30 test busbar system and the results were compared to other heuristic algorithms.