TY - JOUR

T1 - Model-based and fuzzy logic approaches to condition monitoring of operational wind turbines

AU - Cross, Philip

AU - Ma, Xiandong

PY - 2015/2

Y1 - 2015/2

N2 - It is common for wind turbines to be installed in remote locations on land or offshore,leading to difficulties in routine inspection and maintenance.Further,wind turbines in these locations are often subject to harsh operating conditions. These challenges mean there is a requirement for a high degree of maintenance.The data generated by monitoring systems can be used to obtain models of wind turbines operating under different conditions,and hence predict output signals based on known inputs.A model-based condition monitoring system can be implemented by comparing output data obtained from operational turbines with those predicted by the models,detecting changes that could be due to the presence of faults.This paper discusses several techniques for model-based condition monitoring systems:linear models,artificial neural networks,and state dependent parameter‘pseudo’transfer functions.The models are identified using SCADA(Supervisory Control and Data Acquisition)data acquired from an operational wind firm.It is found that the multiple-input,single-output state dependent parameter method outperforms both multivariate linear and artificial neural network-based approaches. Subsequently,state dependent parameter models are used to develop adaptive thresholds for critical output signals.In order to provide an early warning of a developing fault,it is necessary to interpret the amount the threshold is exceeded together with the period of time over which this occurs;in this regard,a fuzzy logic-based inference system is proposed and demonstrated to be practically feasible.

AB - It is common for wind turbines to be installed in remote locations on land or offshore,leading to difficulties in routine inspection and maintenance.Further,wind turbines in these locations are often subject to harsh operating conditions. These challenges mean there is a requirement for a high degree of maintenance.The data generated by monitoring systems can be used to obtain models of wind turbines operating under different conditions,and hence predict output signals based on known inputs.A model-based condition monitoring system can be implemented by comparing output data obtained from operational turbines with those predicted by the models,detecting changes that could be due to the presence of faults.This paper discusses several techniques for model-based condition monitoring systems:linear models,artificial neural networks,and state dependent parameter‘pseudo’transfer functions.The models are identified using SCADA(Supervisory Control and Data Acquisition)data acquired from an operational wind firm.It is found that the multiple-input,single-output state dependent parameter method outperforms both multivariate linear and artificial neural network-based approaches. Subsequently,state dependent parameter models are used to develop adaptive thresholds for critical output signals.In order to provide an early warning of a developing fault,it is necessary to interpret the amount the threshold is exceeded together with the period of time over which this occurs;in this regard,a fuzzy logic-based inference system is proposed and demonstrated to be practically feasible.

KW - Condition monitoring

KW - Wind turbines

KW - Artificial neural network

KW - State dependent parameter model

KW - Fuzzy logic

U2 - 10.1007/s11633-014-0863-9

DO - 10.1007/s11633-014-0863-9

M3 - Journal article

VL - 12

SP - 25

EP - 34

JO - International Journal of Automation and Computing

JF - International Journal of Automation and Computing

SN - 1476-8186

IS - 1

ER -