A New Frequency Control Method for Isolated Networks with Neural Network Controller Trained by PSO algorithm
Publish place: 28th International Power System Conference
Publish Year: 1392
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
PSC28_126
تاریخ نمایه سازی: 25 اردیبهشت 1393
Abstract:
This paper proposes a multilayer perceptron neural network (MLPNN) trained by particle swarm optimization (PSO) algorithm for frequency control of a new hybrid wind turbine (WT), photovoltaic (PV), fuel cell (FC) and ultra-capacitor (UC) for stand-alone applications. WT and PV are the primary power sources of the system, and an FC is expected to provide long-term energy balance, whereas the UC is employed as buffer storage for the short-term compensation. Changes in wind, solar light, and load consumption in isolated networks, prevent droop controllers and improved droop controllers to provide a proper performance over a wide range of operating conditions. To overcome this challenge, this paper proposes a new intelligent method by using a combination of MLPNN controller and particle swarm optimization (PSO) techniques for frequency controllers in isolated networks. To demonstrate the effectiveness of the proposed MLPNN controller, comparison with droop and improved droop controllers are performed using MATLAB/Simulink by integrating the detailed mathematical and electrical models of the hybrid isolated network
Keywords:
Frequency control , dynamic modeling , Isolated Network , Multilayer Perceptron Neural Network (MLPNN) , Improved droop control , Particle Swarm ptimization (PSO)
Authors
Mahdi Taghizadeh
Department of Electrical Engineering Faculty of Engineering, Shiraz university of Shiraz Shiraz, Iran
Ali Ramezani
Golestan Electrical Distribution Co. Ped-Golestan Gorgan-Iran
Hamid Khamoshi
Bisetoon Power Generation ManagementZagross Power PlantKermanshah, Iran