Comparison of modified teaching–learning-based optimization and extreme learning machine for classification of multiple power signal disturbances. Issue 7 (October 2016)

• Record Type: Journal Article
• Authors: Nayak, P.; Mishra, S.; Dash, P.; Bisoi, Ranjeeta
• Is Part Of: Neural computing & applications. Volume 27:Issue 7(2016)
• Page Start: 2107
• Page End: 2122
• Publication Date: 2016-10
• Subjects: LLRBFNN -- Power disturbance signals -- Feature extraction -- Cumulative sum average filter -- Pattern recognition -- Simultaneous power quality events -- Teaching–learning-based optimization -- Extreme learning machine; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-2010-0
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract This paper presents a modified TLBO (teaching–learning-based optimization) approach for the local linear radial basis function neural network (LLRBFNN) model to classify multiple power signal disturbances. Cumulative sum average filter has been designed for localization and feature extraction of multiple power signal disturbances. The extracted features are fed as inputs to the modified TLBO-based LLRBFNN for classification. The performance of the proposed modified TLBO-based LLRBFNN model is compared with the conventional model in terms of convergence speed and classification accuracy. Also, an extreme learning machine (ELM) approach is used to optimize the performance of the proposed LLRBFNN and is compared with the TLBO method in classifying the multiple power signal disturbances. The classification results reveal that although the TLBO approach produces slightly better accuracy in comparison with the ELM approach, the latter is much faster in implementation, thus making it suitable for processing large quantum of power signal disturbance data.