Using Wavelet Hybrid Self-Organizing Feature Map Network for V-I Based Multiple Harmonic Sources Classification

Cong Hui Huang; Chia Hung Lin; Chung Chi Huang; Chia Hung Wang

doi:10.4028/www.scientific.net/AMM.764-765.545

Paper Titles

Optimization of Holding Voltage for 5V Multi-Finger NMOS Using Voltage Stepping Simulation
p.526

Simulation of P-Type Doping Profile Prediction Using Different Ion Implantation and Diffusion Model
p.530

Unambiguous Composite BOC Signal Tracking Based on Partitioned Sub-Carriers
p.535

Using GaN-Si FET Cascode Switches for Improving the Light-Load Efficiency of LLC Converters
p.540

Using Wavelet Hybrid Self-Organizing Feature Map Network for V-I Based Multiple Harmonic Sources Classification
p.545

Localization Experiments Based on Inertial Sensors for Navigation Applications
p.550

Mitigation of Multipath Errors Using the Iterative Least Absolute Deviation Approach
p.555

Neural Network Assisted Vector Tracking Loop for Bridging GPS Signal Outages
p.560

Multipath Parameter Estimation Using the Unscented Particle Filter Based Code Tracking Loop for Non-Gaussian Noises
p.565

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 764-765Using Wavelet Hybrid Self-Organizing Feature Map...

Using Wavelet Hybrid Self-Organizing Feature Map Network for V-I Based Multiple Harmonic Sources Classification

Abstract:

This paper proposes a method using non-linear voltage-current characteristics for multiple harmonic sources classification using wavelet hybrid neural network (WHNN). Typical voltage-current characteristics of harmonic sources are non-linear closed curves in the time-domain, referring to the converters, reactors, and non-linear loads. The hybrid neural network is a two-subnetwork architecture, consisting of wavelet layer and a self-organizing feature map (SOFM) network connected in cascade. The effectiveness of the proposed method is demonstrated by numerical tests. The results of multiple harmonic sources show the computational efficiency and accurate classification.

You might also be interested in these eBooks

Modern Design Technologies and Experiment for Advanced Manufacture and Industry

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 764-765)

Pages:

545-549

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.764-765.545

Citation:

Cite this paper

Online since:

May 2015

Authors:

Cong Hui Huang*, Chia Hung Lin, Chung Chi Huang, Chia Hung Wang

Keywords:

Harmonic, Neural Network (NN), Self-Organizing Feature Map, Wavelet Hybrid Neural Network

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] IEEE Recommended Practices for Monitoring Electric Power Quality ANSI/IEEE Std. 1159- (1995).

Google Scholar

[2] L. Angrisani, P. Daponte, and M. D' Apuzzo: Wavelet Network-Based Detection and Classification of Transients, IEEE Trans. on Instrumentation and Measurement, 50 (2001) 1425-1435.

DOI: 10.1109/19.963220

Google Scholar

[3] Y. Y. Hong and Y. C. Chen: Application of Algorithms and Artificial-intelligence Approach for Locating Multiple Harmonics in Distribution System, IEE Proc. -Gener. Transm. Distrib., 146 (1999) 325-329.

DOI: 10.1049/ip-gtd:19990164

Google Scholar

[4] W. M. Lin, C. H. Lin, K. P. Tu, and C. H. Wu: Multiple Harmonic Source Detection and Equipment Identification with Cascade Correlation Network, IEEE Trans. on Power Delivery, 20 (2005) 2166- 2173.

DOI: 10.1109/tpwrd.2004.843462

Google Scholar

[5] C. H. Lin, M. S. Kang, C. H. Huang, and C. L. Kuo: Fractal Patterns for Power Quality Detection Using Color Relational Analysis Based Classifier, International Conference on Computer, Electrical, System Science, and Engineering, Tokyo, Japan, 2010, pp.75-81.

Google Scholar

[6] C. H. Wu, and C. R. Chen: Apply Artificial Neural Network to Power System in Detecting the Positions of the Harmonic Sources, Proceedings of the 21th Symposium on Electrical Power Engineering, Taiwan, 2000, pp.329-333.

Google Scholar

[7] C. H. Lin and C. H. Wang: Adaptive Wavelet Networks for Power Quality Detection and Discrimination in a Power System, IEEE Trans. on Power Delivery, 21 (2006) 1106- 1113.

DOI: 10.1109/tpwrd.2006.874105

Google Scholar

[8] Y. Y. Hsu, and C. C. Yang: A Hybrid Artificial Neural Network Dynamic Programming Approach for Feeder Capacitor Scheduling, IEEE Trans. on Power Systems, 9 (1994) pp.1069-1075.

DOI: 10.1109/59.317624

Google Scholar

[9] IEEE Recommended Practices and Requirements for Harmonic Control in Electrical power Systems ANSI/IEEE Std 519-(1992).

Google Scholar