Condition Monitoring of Wind Turbines Using Intelligent Machine Learning Techniques

Wind Turbine condition monitoring can detect anomalies in turbine performance which have the potential to result in unexpected failure and financial loss. This study examines common Supervisory Control And Data Acquisition (SCADA) data over a period of 20 months for 21 pitch regulated 2.3 MW turbines and is presented in three manuscripts. First, power curve monitoring is targeted applying various types of Artificial Neural Networks to increase modeling accuracy. It is shown how the proposed method can significantly improve network reliability compared with existing models. Then, an advance technique is utilized to create a smoother dataset for network training followed by establishing dynamic ANFIS network. At this stage, designed network aims to predict power generation in future hours. Finally, a recursive principal component analysis is performed to extract significant features to be used as input parameters of the network. A novel fusion technique is then employed to build an advanced model to make predictions of turbines performance with favorably low errors

Data-driven methods for prediction of smallto- medium wind turbines performance

The growth in the wind energy is rapidly increasing. Accurate modelling of wind turbines performance as targeted by ongoing research studies can escalate wind energy production capabilities, reliability, and expand its potential to replace fossil fuels. In addition, optimisation of turbines will considerably expand the profit margins and garner the attraction of investors

Improved power curve monitoring of wind turbines

Wind turbine power output monitoring can detect anomalies in turbine performance which have the potential to result in unexpected failure. This study examines common Supervisory Control And Data Acquisition data over a period of 20 months. It is common to have more than 150 signals acquired by Supervisory Control And Data Acquisition systems, and applying all is neither practical nor useful. Thus, to address the issue, correlation coefficients analysis has been applied in this work to reveal the most influential parameters on wind turbine active power. Then, radial basis function and multilayer perception artificial neural networks are set up, and their performance is compared in two static and dynamic states. The proposed combination of the feature selection method and the dynamic multilayer perception neural network structure has performed well with favorable prediction error levels compared to other methods. Thus, the combination may be a valuable tool for turbine power curve monitoring

Application of imputation techniques and Adaptive Neuro-Fuzzy Inference System to predict wind turbine power production

Wind Turbine power output prediction can prevent unexpected failure and financial loss, through the detection of anomalies in turbine performance in advance so operators can proactively address potential problems. This study examines common Supervisory Control And Data Acquisition (SCADA) data over a period of 20 months for 21 pitch regulated 2.3 MW turbines. To identify the most influential parameters on power production among more than 150 signals in the SCADA data, correlation coefficient analysis has been applied. Further, an algorithm is proposed to impute values that are missing, out-of-range, or outliers. It is shown that appropriate combinations of decision tree and mean value for imputation can improve the data analysis and prediction performance. A dynamic ANFIS network is established to predict the future performance of wind turbines. These predictions are made on a scale of 1 h intervals for a total of 5 h into the future. The proposed combination of feature extraction, imputation algorithm, and the dynamic ANFIS network structure has performed well with favourable prediction error levels in comparison with existing models. Thus, the combination may be a valuable tool for turbine power production prediction

Power production prediction of wind turbines using a fusion of MLP and ANFIS networks

Access to accurate power production prediction of a wind turbine in future hours enables operators to detect possible underperformance and anomalies in advance. This may enable more proactive and strategic operations optimisation. This study examines common Supervisory Control And Data Acquisition (SCADA) data over a period of 20 months for 21 pitch regulated 2.3 MW turbines. In this study, an algorithm is proposed to impute values of data that are missing, out-of-range, or outliers. It is shown that an appropriate combination of a decision tree and mean value for imputation can improve the data analysis and prediction performance by the creation of a smoother dataset. In addition, principal component analysis is employed to extract parameters with power production influence based on all available signals in the SCADA data. Then, a new data fusion technique is applied, combining dynamic multilayer perceptron (MLP) and adaptive neuro-fuzzy inference system (ANFIS) networks to predict future performance of wind turbines. This prediction is made on a scale of one-hour intervals. This novel combination of feature extraction, imputation, and MLP/ANFIS fusion performs well with favourably low prediction error levels. Thus, such an approach may be a valuable tool for turbine power production prediction