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Various butterfly patterns at Bus13. (a) Butterfly patterns for harmonics, (b) butterfly patterns for normal voltage and voltage interruption, (c) and (d) butterfly patterns for sag events, and (e) and (f) butterfly patterns for swell events.
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This document proposes multiple chaos synchronization (CS) systems for power quality (PQ) disturbances classification in a power system. Chen-Lee based CS systems use multiple detectors to track the dynamic errors between the normal signal and the disturbance signal, including power harmonics, voltage fluctuation phenomena, and voltage interruption...
Citations
... Different neuronal models have been broadly investigated to understand the phenomenon of synchronization of coupled neurons by many researchers [13][14][15][16][17]. In the past few years, neural networks and the study of their dynamical properties including synchronization, chaos, and bifurcation have gained tremendous interest and are greatly explored for their possible applications in many research fields [18][19][20][21][22]. Among these, the synchronization of neurons is contributory to the insight into the functionality of neurobiological networks and the core processing of information transfer/ handling in the brain [23,24]. ...
In the biological neural bursting and firing synchronization plays a vital role in all neuronal activities that are utilized for making decisions, executing commands, and sending information by neurons and their complex networks in the biological complexed brain. Understanding how the biological brain functionality comes out from different patterns of neuronal transmission between the large group of neural networks stands as one of the enduring challenges of modern neuroscience. This study investigated a methodology for synchronization of multiple single/dual state gap junctions FitzHugh-Nagumo (FHN) drive and slave networks under the condition of external noise. The theory of control was utilized to propose simple and diverse controllers to examine the synchronization problem of the different single and dual state gap junctions coupled nonnoisy and noisy FHN neurobiological drive and slave networks. Control laws are designed to stabilize the error dynamics without direct cancelation and synchronize all the states of both FHN neurobiological drive and slave networks. Sufficient conditions for achieving synchronization in the multiple single/dual state gap junction FHN noisy and nonnoisy neurobiological drive and slave networks were derived analytically using the theory of Lyapunov stability. Furthermore, the proposed controllers have been verified by using five noisy/nonnoisy FHN neurobiological drive and slave networks through numerical simulations.
... Ray et al. (2014) have used hyperbolic-based transform for feature extraction along with classifier constructed using decision tree and support vector machine to solve disturbance classification problem of the power system. Huang and Lin (2014) have used chaos theory to define a novel classifier system for identifying disturbances. The work of Biscaro et al. (2016) has implemented a fuzzy ARTMAP for identifying the classified location of the fault as well as analysis of power quality. ...
... Kumar et al. [15] have presented a technique where the symmetrical components residing in the time domain is used for identifying disturbances in power system using phase locked loop. Huang and Lin [16] have used chaos theory in order to define a novel classifier system for identifying disturbances. Rodriuguez et al. [17] have used neural network for performing the classification process. ...
At present, there are various sources and unsolved reasons causing potential disturbances that are significantly detrimental towards efficiency of various individual component operations in power system. We reviewed existing approaches toward detection mechanism of disturbances to find that there is potential tradeoff in the techniques that leads to non-uniform growth in higher detection accuracy and lower computational efficiency. Therefore, the present manuscript introduces a simple framework that retains a balance between higher accuracy in detection of disturbances as well as also maintains an effective computational performance for large number of the power signals. The proposed technique uses orthogonal transforms for feature extraction and uses feed forward algorithm for optimizing the search towards elite result of convergence. The study outcome shows proposed system to excel better with respect to accuracy in identification in comparison to existing approaches.
... The study outcome was found to posses 98.19% of accuracy in the detection. Huang and Lin [42] have used maximum likelihood method for developing a classification technique. Most recently Goes et al. [43] have used data mining process to perform classification of the power quality disturbances. ...
The growing demands of global consumer market in green energy system have opened the doors for many technologies as well as various sophisticated electrical devices for both commercial and domestic usage. However, with the increasing demands of energy and better quality of services, there is a significant increase in non-linearity in load distribution causing potential effect on the Power Quality (PQ). The harmful effects on PQ are various events e.g. sag, swell, harmonics etc that causes significant amount of system degradation. Therefore, this paper discusses various significant research techniques pertaining to the PQ disturbance classification system introduced by the authors in the past and analyzes its effectiveness scale in terms of research gap. The paper discusses some of the frequently exercised PQ classification techniques from the most relevant literatures in order to have more insights of the techniques. Copyright © 2016 Institute of Advanced Engineering and Science. All rights reserved.
... The study outcome was found to posses 98.19% of accuracy in the detection. Huang and Lin [42] have used maximum likelihood method for developing a classification technique. Most recently Goes et al. [43] have used data mining process to perform classification of the power quality disturbances. ...
p>The growing demands of global consumer market in green energy system have opened the doors for many technologies as well as various sophisticated electrical devices for both commercial and domestic usage. However, with the increasing demands of energy and better quality of services, there is a significant increase in non-linearity in load distribution causing potential effect on the Power Quality (PQ). The harmful effects on PQ are various events e.g. sag, swell, harmonics etc that causes significant amount of system degradation. Therefore, this paper discusses various significant research techniques pertaining to the PQ disturbance classification system introduced by the authors in the past and analyzes its effectiveness scale in terms of research gap. The paper discusses some of the frequently exercised PQ classification techniques from the most relevant literatures in order to have more insights of the techniques.</p
