Wind turbine is one of the present renewable energy sources that has become the most popular. The operational and maintenance cost is continuously increasing, especially for wind generator. Early fault detection is very important to optimise the operational and maintenance cost. The goal of this project is to study fault detection and classification for a wind turbine (WT) by using artificial neural network (ANN). In this project, a single phase fault was placed at 9 MW doubly-fed induction generator (DFIG) WT in MATLAB Simulink. The WT was tested under different conditions, i.e., normal condition, fault at Phase A, Phase B and Phase C. The simulation results were used as inputs in the ANN model for training. Then, a new set of data was taken under different conditions as inputs for ANN fault classifier. The target outputs of ANN fault classifier were set as ‘0’ or ‘1’, based on the fault condition. Results obtained showed that the ANN fault classifier outputs had followed the target outputs. In conclusion, the WT fault detection and classification method by using ANN were successfully developed.

This paper presents a stator winding faults detection in induction generator based wind turbines by using artificial neural network (ANN). Stator winding faults of induction generators are the most common fault found in wind turbines. This fault may lead to wind turbine failure. Therefore, fault detection in induction generator based wind turbines is vital to increase the reliability of wind turbines. In this project, the mathematical model of induction generator based wind turbine was developed in MATLAB Simulink. The value of impedance in the induction generators was changed to simulate the inter-turn short circuit and open circuit faults. The simulated responses of the induction generators were used as inputs in the ANN model for fault detection procedures. A set of data was taken under different conditions, i.e. normal condition, inter-turn short circuit and open circuit faults as inputs for the ANN model. The target outputs of the ANN model were set as ‘0’ or ‘1’, based on the fault conditions. Results obtained showed that the ANN model can detect different types of faults based on the output values of the ANN model. In conclusion, the stator winding faults detection procedure for induction generator based wind turbines by using ANN was successfully developed.

Series compensation consists of capacitors in series is used in the transmission lines as a tool to improve the performance after disturbed by a fault. Transmission line needs a protection scheme to protect the lines from faults due to natural disturbances, short circuit and open circuit faults. The fault can happen in any location of transmission line and it is important to know which location has been affected. So that, the fault can be eliminated and can maintain the optimum performance. Therefore, in this paper Artificial Neural Network (ANN) is used to detect and classified the fault happen in single line to ground fault and three phase to ground fault. Two different tests of each types of fault have been tested in order to prove the effectiveness of ANN to detect the fault location by using different length and fault resistance. The simulation has been accomplished in MATLAB with ANN fitting tool which build and train the network before evaluated its performance using regression analysis. The analysis shows that the ANN can accurately detect the different types of faults and classified it into the respective category even the random vectors are put on the system are used.

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Fault detection has drew much attention nowadays, as it can save time and operational maintenance costs, especially in the wind turbine (WT) that is becoming familiar with renewable energy. Machine learning became widespread use in fault detection methods. However, most available machine learning needs more data and much time to train. Therefore, there is a need to detect faults using a few data during the training process. This paper aims to apply Automated Machine Learning (AutoML) method for fault detection in WT systems. The fault detection in the WT system focuses on the internal stator fault in the generator as it is the main part of the WT. The AutoML model was developed using a neural network (NN) algorithm in python based on the Auto-Keras model. The model was developed using four inputs, i.e. stator and rotor currents in the d-q axis (Iqs, Ids, Iqr and Idr ) while the outputs are impedance values, i.e. stator resistance, Rs , and stator inductance, Ls . The WT system used in this research is the doubly-fed induction generator (DFIG) in MATLAB/Simulink. In the Auto-Keras model, the impedance values (Rs and L s) indicated the condition of the DFIG, either normal or fault conditions. Two fault types were applied to the WT system, i.e. inter-turn short circuit and open circuit fault. The Auto-Keras model was trained and tested with the various values of data. The accuracy and the root means square error (RMSE) value of the model were calculated. The result shows that the accuracy is high as it is more than 93% in most conditions, and the RMSE value is low, close to the zero value. Applying the AutoML method in fault detection of the WT system shows its capability to identify faults accurately.