Ten lectures on wavelets
Wavelet transform and adaptive neuro-fuzzy inference system for color texture classification
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Localization of the complex spectrum: the S transform
IEEE Transactions on Signal Processing
Mechanic signal analysis based on the Haar-type orthogonal matrix
Expert Systems with Applications: An International Journal
Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data
Expert Systems with Applications: An International Journal
Stockwell transform based palm-print recognition
Applied Soft Computing
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
In this paper, an S-transform-based neural network structure is presented for automatic classification of power quality disturbances. The S-transform (ST) technique is integrated with neural network (NN) model with multi-layer perceptron to construct the classifier. Firstly, the performance of ST is shown for detecting and localizing the disturbances by visual inspection. Then, ST technique is used to extract the significant features of distorted signal. In addition, an optimum combination of the most useful features is identified for increasing the accuracy of classification. Features extracted by using the S-transform are applied as input to NN for automatic classification of the power quality (PQ) disturbances that solves a relatively complex problem. Six single disturbances and two complex disturbances as well pure sine (normal) selected as reference are considered for the classification. Sensitivity of proposed expert system under different noise conditions is investigated. The analysis and results show that the classifier can effectively classify different PQ disturbances.