International audienceA novel hybrid feature-reduction methodology is proposed as a contribution to the induction motor fault classification, to improve the classification rate of the current waveform events related to varieties of induction machine faults. This methodology relies on the combination of a feature-extraction technique based on the smoothed ambiguity plane designed for maximizing the separability between classes using Fisher's discriminant ratio, with the feature-selection technique, based on the proposed error-probability model to select an optimal number of the extracted features. This model depends on two parameters, namely, the smoothing kernel used to derive the features and the distance measurement. The proposed methodology is validated experimentally on a 5.5-kW induction motor test bench, and their performances are compared with the classification algorithm based on neural networks with sigmoid and wavelets in hidden neurons, known as a flexible tool for learning and recognizing system faults. The results obtained show an accurate classification independent from the load level

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International audienceThis paper presents a new method for the classification of induction machine faults. The method is composed of two steps: feature extraction and classification. Feature extraction is based on the time-frequency representation (TFR), which is deliberately designed for maximizing the separability between classes. A distinct TFR is designed for each class. The classifier is designed with an artificial neural network. The flexibility of this method allows an accurate classification independent from the level of load. This method is validated on a 5.5-kW induction motor test bench

It is probable that future power transmission systems will contain more HVDC-VSC links (High Voltage Direct Current-Voltage Source Converter) leading to a growing complexity in the study of its problem and so do the transient stability problems which are yet to be determined. In this context, this paper presents an efficient method to resolve this problem. Its main objective consists of improving transient stability of the AC/DC (Alternating Current/ Direct Current) power system network using FACTS (Flexible Alternating Current Transmission Systems). The overall performance of the FACTS was evaluated in an IEEE 14 bus test system by nonlinear simulations carried out using Matlab environment to check the performance of FACTS (TCSC, Thyristor Controlled Series Capacitor). The obtained results showed the effectiveness and robustness of FACTS in improving the transient stability of the systemt.

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