Next Article in Journal
Development of Natural Gas Fired Combined Cycle Plant for Tri-Generation of Power, Cooling and Clean Water Using Waste Heat Recovery: Techno-Economic Analysis
Next Article in Special Issue
Using the Direct Search Method for Optimal Dispatch of Distributed Generation in a Medium-Voltage Microgrid
Previous Article in Journal
Powering-up Wireless Sensor Nodes Utilizing Rechargeable Batteries and an Electromagnetic Vibration Energy Harvesting System
Previous Article in Special Issue
Optimization Models and Methods for Demand-Side Management of Residential Users: A Survey
Article Menu

Export Article

Open AccessArticle
Energies 2014, 7(10), 6340-6357; doi:10.3390/en7106340

New Power Quality Analysis Method Based on Chaos Synchronization and Extension Neural Network

Department of Electrical Engineering, National Chin-Yi University of Technology, No. 57, Sec. 2, Zhongshan Rd., Taiping Dist., Taichung 41170, Taiwan
*
Author to whom correspondence should be addressed.
Received: 25 August 2014 / Revised: 18 September 2014 / Accepted: 23 September 2014 / Published: 8 October 2014
(This article belongs to the Special Issue Microgrids)
View Full-Text   |   Download PDF [1440 KB, uploaded 17 March 2015]   |  

Abstract

A hybrid method comprising a chaos synchronization (CS)-based detection scheme and an Extension Neural Network (ENN) classification algorithm is proposed for power quality monitoring and analysis. The new method can detect minor changes in signals of the power systems. Likewise, prominent characteristics of system signal disturbance can be extracted by this technique. In the proposed approach, the CS-based detection method is used to extract three fundamental characteristics of the power system signal and an ENN-based clustering scheme is then applied to detect the state of the signal, i.e., normal, voltage sag, voltage swell, interruption or harmonics. The validity of the proposed method is demonstrated by means of simulations given the use of three different chaotic systems, namely Lorenz, New Lorenz and Sprott. The simulation results show that the proposed method achieves a high detection accuracy irrespective of the chaotic system used or the presence of noise. The proposed method not only achieves higher detection accuracy than existing methods, but also has low computational cost, an improved robustness toward noise, and improved scalability. As a result, it provides an ideal solution for the future development of hand-held power quality analyzers and real-time detection devices. View Full-Text
Keywords: power quality; chaos synchronization detection; extension theory power quality; chaos synchronization detection; extension theory
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Wang, M.-H.; Yau, H.-T. New Power Quality Analysis Method Based on Chaos Synchronization and Extension Neural Network. Energies 2014, 7, 6340-6357.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top