Hybrid signal processing and machine intelligence techniques for detection, quantification and classification of power quality disturbances

Integrating the S-PQDA software tool in the utility power quality management system

“…The superiority of the ST against the wavelet transform (WT) in the detection of isolated PQ disturbances was proven based on the previous studies [8,9,10,11]. The ST was also deemed superior in the detection of sags, swells and momentary interruption compared to the WT [12] and the ST is more superior that the WT in detecting all PQ disturbances under noisy condition [13]. In this paper, the ST will be used to extract features of disturbances for use in the PQ disturbance classification and diagnosis.…”

Wavelet transform and multi-class relevance vector machines based recognition and classification of power quality disturbances

“…Signal processing techniques are widely used in analyzing PQ events to extract important information of disturbances. Some examples are fast Fourier transform method [3], fractal-based method [4], S-transform method [5], time-frequency ambiguity plane method [6], short time power and correlation transform method [7], wavelet transform method [8], Hilbert transform method [9], and Chirp-Z transform (CZT) method [10]. Neural networks (NNs) [11,12] with different structures are traditionally used as classifier but recently probabilistic neural network (PNN) [2,13] and support vector machines (SVMs) [14][15][16] are introduced as new learning machines which are more effective.…”

A Review of Classification Problems and Algorithms in Renewable Energy Applications

“…Problem Specific Methodology Used [3] 2015 Sea wave Ordinal classification SVM, ANN, LR [4] 2015 Solar Classification SVM [5] 2009 Power disturbance Classification SVM, wavelets [10] 2015 Wind Optimization Bio-inspired, meta-heuristics [14] 2015 Wind Classification Fuzzy SVM [15] 2011 Wind Classification DT, SOM [16] 2015 Wind Classification SVM, k-NN, fuzzy, ANN [17] 2010 Solar Classification Semi-supervised SVM [20] 2013 Wind Ordinal classification SVM, DT, LR, HMM [30] 2014 Wind Classification SVM, LR, RF, rotation forest [31] 2011 Wind Classification ANN, LR, DT, RF [32] 2013 Wind Classification k-NN, RBF, DT [33] 2011 Wind Classification, regression BN [34] 2014 Wind Classification, regression Heuristic methodology: WPPT [35] 2011 Wind Classification Bagging, ripper, rotation forest, RF, k-NN [36] 2013 Wind Classification ANFIS, ANN [37] 2012 Wind Classification SVM [38] 2015 Wind Classification ANN, SVM [39] 2015 Wind Classification PNN [40] 2015 Wind Classification DT, BN, RF [41] 2015 Wind Classification, clustering AuDyC [42] 2016 Wind Classification, clustering AuDyC [43] 2010 Power disturbance Classification HMM, SVM, ANN [44] 2015 Power disturbance Classification SVM, NN, fuzzy, neuro-fuzzy, wavelets, GA [45] 2015 Power disturbance Classification SVM, k-NN, ANN, fuzzy, wavelets [46] 2002 Power disturbance Classification Rule-based classifiers, wavelets, HMM [47] 2004 Power disturbance Classification PNN [48] 2006 Power disturbance Classification ANN, RBF, SVM [49] 2007 Power disturbance Classification ANN, wavelets [50] 2012 Power disturbance Classification PNN [51] 2014 Power disturbance Classification ANN Table 3. Summary of the main references analyzed, grouped by application field, problem type and methodologies considered (II)...…”

“…In this case, the predictive variables are obtained from the subtraction of the estimated fundamental component from the acquired signal. In [5], an SVM is applied to predictive variables obtained using the S-transform and the wavelet transform. The results reported indicate that in the case of using the S-transform, features based on magnitude, frequency and phase of the disturbance signal are enough to classify the steady-state power signal disturbance events with good accuracy.…”

“…In this regard, Machine Learning (ML) techniques have been demonstrated to be excellent tools to cope with difficult problems arising from new RE sources [3][4][5]. More specifically, ML addresses the question of how to build computer-based systems that automatically improve through experience.…”