Parameter Estimation for Dynamic Model of Distribution Network Cell (DNC) Using Fuzzy System

Noor Fazliana Fadzail; Samila Mat Zali; Arizadayana Zahalan; Mohd Alif Ismail

doi:10.4028/www.scientific.net/AMM.793.489

Paper Titles

Reviews on MATLAB DSTATCOM Simulation Models in Mitigating Power Quality Problems
p.467

Modified Swarm Firefly Algorithm for Optimal Settings of Directional Overcurrent Relays
p.473

Analysis on Load Model for Cost Optimization Using Embedded Meta EP-Firefly Algorithm for DG Installation
p.478

Risk of Voltage Collapse Using Multi-Layer Feed-Forward Neural Network and Generalized Regression Neural Network
p.483

Parameter Estimation for Dynamic Model of Distribution Network Cell (DNC) Using Fuzzy System
p.489

Load Flow Analysis Using Improved Newton-Raphson Method
p.494

An Application of Cuckoo Search Algorithm for Solving Optimal Chiller Loading Problem for Energy Conservation
p.500

Intelligent Controller of High Voltage Power Station Based Artificial Neural Network
p.505

ANOVA Based Feature Analysis and Selection in Power Quality Disturbances Identification
p.510

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 793Parameter Estimation for Dynamic Model of...

Parameter Estimation for Dynamic Model of Distribution Network Cell (DNC) Using Fuzzy System

Abstract:

The aim of this project is to develop parameter estimation for dynamic model of distribution network cell (DNC) using fuzzy system. The parameter value was updated through adaptive neuro-fuzzy inference system (ANFIS). The active and reactive power responses from the fuzzy model were compared with the response from the full DNC model at various types of disturbances. The response of full DNC model was obtained from the UK 11 kV distribution network model. The model was built in DigSILENT PowerFactory software. The results obtained shown that the fuzzy model was more simple as only a few parameters involved in developing the equivalent model. This simplicity was reflected in the low computational time. In conclusion, the parameter estimation using fuzzy system was successfully developed.

You might also be interested in these eBooks

Electrical Power Engineering and Sustainable Development of Industry

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 793)

Pages:

489-493

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.793.489

Citation:

Cite this paper

Online since:

September 2015

Authors:

Noor Fazliana Fadzail, Samila Mat Zali, Arizadayana Zahalan, Mohd Alif Ismail

Keywords:

ANFIS, Distribution Network Cell, Fuzzy System, Parameter Estimation

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Donghua, P., and Fengxiang, W. Modeling and simulation of fuzzy control system for dual stator winding induction generator. Electrical Machines and Systems, 2007. ICEMS. International Conference.

DOI: 10.1109/icems12746.2007.4411984

Google Scholar

[2] M. Tim Jones. AI Application Programming., Charles River Media, Inc, United States of America, (2003).

Google Scholar

[3] Jang, J.S.R., ANFIS: adaptive-network-based fuzzy inference system. Systems, Man and Cybernetics, IEEE Transactions on, 1993. 23(3): pp.665-685.

DOI: 10.1109/21.256541

Google Scholar

[4] Zali, S. M., Equivalent Dynamic Model of Distribution Network With Distributed Generation. A thesis for degree of Doctor of Philosophy in the Faculty of Engineering and Physical Science, School of Electrical and Electronic Engineering, University of Manchester, (2012).

Google Scholar

[5] Ali, M.S., et al. Identification of critical parameters for distribution networks with DFIG and dynamic loads. in Universities Power Engineering Conference (AUPEC), 2011 21st Australasian.

Google Scholar