Mining for similarities in time series data using wavelet-based feature vectors and neural networks
Engineering Applications of Artificial Intelligence
A basis for efficient representation of the S-transform
Digital Signal Processing
Voltage stability evaluation of power system with FACTS devices using fuzzy neural network
Engineering Applications of Artificial Intelligence
A window width optimized S-transform
EURASIP Journal on Advances in Signal Processing
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Review of Power-Quality Disturbance Recognition Using S-transform
CASE '09 Proceedings of the 2009 IITA International Conference on Control, Automation and Systems Engineering (case 2009)
Rule based system for power quality disturbance classification incorporating S-transform features
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Power quality diagnosis using time frequency analysis and rule based techniques
Expert Systems with Applications: An International Journal
Computers and Electrical Engineering
Localization of the complex spectrum: the S transform
IEEE Transactions on Signal Processing
Engineering Applications of Artificial Intelligence
Hi-index | 12.05 |
At present, many analytical methods are used for the analysis, detection and classification of electrical signal perturbations. One of these methods, the S-transform, has proven effective under specific conditions for acquiring information and parameters of interest associated with a signal. However, depending on the nature of the signal and the input parameters, this method offers different results that sometimes negatively impact the quality of information obtained in the time and frequency domains. This paper describes the design of a genetic algorithm that optimises the S-transform for analysis and classification of the perturbations in electrical signals. This algorithm provides the best parameter values for optimising the Gaussian window, maximising the precision obtained with regard to classification and, later, analysis (via other techniques, such as neural networks or rule-based systems). This paper demonstrates the effectiveness of the S-transform (specified herein) with respect to the original S-transform and reports the best values obtained after optimisation via a comparative study that includes both typical cases and perturbations in modern electrical systems.