• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.78-83
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• 2014

Recently, a rapid growth of wind power system as a leading renewable energy source has compelled a number of companies to develop intelligent monitoring and diagnostic system. Such systems can detect early mechanical faults, which prevents from costly repairs. Generally, fault diagnostic system for wind turbines is based on vibration and process signal analysis. In this work, different type of mechanical faults such as mass unbalance and shaft misalignment which can always happen in wind turbine system is considered. The proposed intelligent fault diagnostic algorithm utilizes artificial neural network and Wavelet transform. In order to verify the feasibility of the proposed algorithm, mechanical fault generation experimental system manufactured by Gaon corporation is utilized.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.143-144
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• 2011

Due to enhanced demands on quality, security and reliability of the electric power energy system, a microgrid has become a subject of special interest. In this paper, output characteristics of energy storage system (ESS) with an electric double layer capacitor (EDLC) and battery energy storage system (BESS) of a renewable energy based microgrid were analyzed under grid-connected and islanded operation modes. The microgrid which consists of photovoltaic and wind power turbine generators, diesel generator, ESS with an EDLC, BESS and loads was modeled using real time digital simulator. The results present the effective control patterns of the microgrid system.