Automatic pattern recognition of single and multiple power quality disturbances. Issue 1 (2nd January 2016)

• Record Type: Journal Article
• Authors: Khokhar, S.; Mohd Zin, A.A.; Mokhtar, A.S.; Zareen, N.
• Is Part Of: Australian journal of electrical & electronics engineering. Volume 13:Issue 1(2016)
• Page Start: 43
• Page End: 53
• Publication Date: 2016-01-02
• Subjects: Power quality disturbances -- wavelet norm entropy -- multiresolution analysis -- feature extraction -- artificial neural network; Electrical engineering -- Periodicals; Electronics -- Periodicals; Periodicals; 621.305
• DOI: 10.1080/1448837X.2015.1092932
• Languages: English
• ISSNs: 1448-837X

Abstract

Abstract: The automatic pattern recognition of single and multiple power quality (PQ) disturbances is a very important task for the detection and monitoring of multiple faults and events in electrical power system. This paper presents an automatic classification algorithm for PQ disturbances based on wavelet norm entropy features and probabilistic neural network (PNN) as an effective pattern classifier. The proposed method employs the discrete wavelet transform based on multi-resolution analysis technique to extract the most important and constructive features of PQ disturbances at various resolution levels. The distinctive norm entropy features of the PQ disturbances have been extracted and were employed as inputs to the PNN. Various other architectures of artificial neural network such as multilayer perceptron and radial basis function neural network have also been employed for comparison. The PNN is found the most suitable pattern recognition tool for the classification of the PQ disturbances. Various PQ disturbances used for analysis were generated by simulating a typical 11-kV distribution system. The simulation results obtained show that the proposed approach can detect and classify the PQ disturbances effectively and can be implemented successfully in real-time electrical power distribution networks.