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Classification of Power Signals using ACO based K-Means Algorithm and Fuzzy C-Means Algorithm

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IJCA Proceedings on International Conference on Emergent Trends in Computing and Communication
© 2014 by IJCA Journal
ETCC - Number 1
Year of Publication: 2014
Authors:
Varsha Bal
Satyasis Mishra

Varsha Bal and Satyasis Mishra. Article: Classification of Power Signals using ACO based K-Means Algorithm and Fuzzy C-Means Algorithm. IJCA Proceedings on International Conference on Emergent Trends in Computing and Communication ETCC(1):6-10, September 2014. Full text available. BibTeX

@article{key:article,
	author = {Varsha Bal and Satyasis Mishra},
	title = {Article: Classification of Power Signals using ACO based K-Means Algorithm and Fuzzy C-Means Algorithm},
	journal = {IJCA Proceedings on International Conference on Emergent Trends in Computing and Communication},
	year = {2014},
	volume = {ETCC},
	number = {1},
	pages = {6-10},
	month = {September},
	note = {Full text available}
}

Abstract

This paper presents pattern classification of power signal disturbances using modified form of S-transform, which is obtained by taking the Inverse Fourier transform of S-Transform is known as time-time transform (TT-transform). The TT-Transform based used for power signals to extract features, visual localization, detection. TT-Transform has good ability in gathering frequency; it gathers the high frequency signals in diagonal position of the spectrum and suppressing the low frequency signals. Only the diagonal of TT-Transform has been used for signal characterization. The diagonal of TT-Transform represent a simple frequency filtered version of the original signal. The extracted features are fed as input to a fuzzy C-means clustering algorithm (FCA) to generate a decision tree. To improve the pattern classification of the fuzzy C-means decision tree, the cluster centers are updated using ant colony optimized technique (ACO). Further K-Means algorithm is used for updation of cluster centers using ant colony optimization technique (ACO) for classification accuracy and the results of both the algorithm are compared.

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