Artificial Neural Network Based Fault Diagnostic System for Wind Turbines

O. Yılmaz, Tolga Yüksel
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Abstract

Increasing global energy demand and decreasing energy resources have led to an increase in the use of renewable energy resources. In terms of continuity and accessibility of energy, wind energy has the largest share among these resources. The size of the energy demand also increases the turbine dimensions. Due to the growing turbine sizes and increasing electrical power, safety and efficiency factors, the system requires a detection structure against failures. In this study, fault detection was carried out in a three-bladed, horizontal axis, pitch-controlled, 4.8MW turbine. Various data gathered from the system are processed by a decision structure and it makes a decision about the system status. Input data, measured or obtained by various calculations, are used in fault diagnosis with artificial neural network(ANN).
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基于人工神经网络的风力发电机故障诊断系统
全球能源需求的增加和能源资源的减少导致可再生能源的使用增加。就能源的连续性和可及性而言,风能在这些资源中所占的份额最大。能源需求的大小也增加了涡轮机的尺寸。由于涡轮机尺寸越来越大,电力、安全和效率因素也越来越多,系统需要一个故障检测结构。在本研究中,对一台三叶片、水平轴、节距控制的4.8MW涡轮机进行了故障检测。决策结构处理从系统收集的各种数据,并对系统状态做出决策。输入数据,通过各种计算测量或获得,用于人工神经网络(ANN)的故障诊断。
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