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.
Highlights
The term power quality is commonly defined as “the degree of user satisfaction with the quality of the power supplied by power utilities” and is generally quantified in terms of the reliability, compatibility or fundamental characteristics of the power supply signal [1]
In the chaotic synchronization (CS)-based system proposed in the present study, any changes in the power system signal caused by waveform disturbances or noise result in a significant change in the characteristic features of the chaotic dynamic error trajectory
This study has proposed a hybrid scheme comprising a chaos synchronization (CS)-based detection system and an Extension Neural Network (ENN) clustering algorithm for analyzing four power quality disturbance events, namely voltage sag, voltage swell, interruption and harmonics
Summary
The term power quality is commonly defined as “the degree of user satisfaction with the quality of the power supplied by power utilities” and is generally quantified in terms of the reliability, compatibility or fundamental characteristics of the power supply signal [1]. In [8], a method was proposed for extracting the unique features associated with particular power quality disturbance events from the frequency characteristics of the voltage and/or current waveform of the power supply system by means of Fourier and wavelet transforms. Many researchers have used the wavelet transform method to extract the fundamental characteristics of the power signal for subsequent power disturbance event classification by means of artificial intelligence (AI) algorithms such as Neural Networks (NNs) [11], Fuzzy Theoretic (FT). The CS method is used to identify noise within the power signal and to extract a small number of characteristic features from the power supply waveform. The ENN has both a shorter learning time and a more rapid detection time than existing neural network (NN)-based approaches
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
