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The paper presents initial results in development of dynamic equivalent model of Distributed Network Cell (DNC) comprising various types of loads and distributed energy resources. The method used for development of the model is based on Prony analysis and Nonlinear least square optimization. The dynamic equivalent model is developed in MATLAB based on simulated measurement data of DNC. The model is given in the form of an equivalent second order transfer function that can be used in dynamic stability studies. The model estimation procedure is evaluated on a case study and the initial simulation results show very good performance of the proposed dynamic equivalent model. Index Terms-Dynamic equivalent, Prony analysis, Nonlinear least square optimization. § · § ¸ © ¹ © Z · ¹ (5) The accuracy of the estimation procedure was checked by calculating the Root Mean Square Error (RMSE) values. The RMSE is a frequently used measure of the differences between values predicted by a model or an estimator, and the Samila Mat Zali received her B.Eng (1999) and M.Sc. (2002) degrees from National University of Malaysia (Malaysia), both in Electrical, Electronic and System Engineering,. Currently, she is working towards her Ph.D degree in

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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.