Dynamic model of distribution network cell using artificial intelligence approach

The aim of this project is to develop a dynamic model of distribution network cell (DNC) using artificial intelligence approach. The increasing number of distributed generation (DG) technology has lead to difficulty in modeling the DNC model. The simple load modeling is no longer reliable in presen...

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Main Author:	Noor Fazliana, Fadzail
Format:	Thesis
Language:	English
Subjects:	Distribution Network Cell (DNC) Artificial intelligence Distributed generation (DG) Power systems
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44259/1/P.1-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44259/2/Full%20Text.pdf
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id	my-unimap-44259
record_format	uketd_dc
spelling	my-unimap-442592016-12-01T00:56:44Z Dynamic model of distribution network cell using artificial intelligence approach Noor Fazliana, Fadzail Dr. Samila Mat Zali The aim of this project is to develop a dynamic model of distribution network cell (DNC) using artificial intelligence approach. The increasing number of distributed generation (DG) technology has lead to difficulty in modeling the DNC model. The simple load modeling is no longer reliable in presenting the DNC model. In this project,the equivalent dynamic model of DNC consists of the converter-connected generator and the composite load model. The model was developed in the form of seven order state-space model. This model was adopted from Samila Mat Zali in 2012. The parameter estimation of the model was developed 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 full DNC model was also adopted from Samila Mat Zali in 2012. The performance of the fuzzy model was validated by calculating the value of root means square error (RMSE) and the best fit value. Later, the performance of the fuzzy model was also compared with the system identification model by Samila Mat Zali in 2012. 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. The efficiency was also good based on the low RMSE value and high best fit value. In conclusion, the equivalent dynamic model of DNC based on fuzzy system approach was successfully developed. Universiti Malaysia Perlis (UniMAP) 2013 Thesis en http://dspace.unimap.edu.my:80/xmlui/handle/123456789/44259 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44259/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44259/1/P.1-24.pdf d5a2e9ecd4458783cedb2d104cc91ff5 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44259/2/Full%20Text.pdf 38696fdcca1515019e76d101471d2311 Distribution Network Cell (DNC) Artificial intelligence Distributed generation (DG) Power systems School of Electrical System Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
advisor	Dr. Samila Mat Zali
topic	Distribution Network Cell (DNC) Artificial intelligence Distributed generation (DG) Power systems
spellingShingle	Distribution Network Cell (DNC) Artificial intelligence Distributed generation (DG) Power systems Noor Fazliana, Fadzail Dynamic model of distribution network cell using artificial intelligence approach
description	The aim of this project is to develop a dynamic model of distribution network cell (DNC) using artificial intelligence approach. The increasing number of distributed generation (DG) technology has lead to difficulty in modeling the DNC model. The simple load modeling is no longer reliable in presenting the DNC model. In this project,the equivalent dynamic model of DNC consists of the converter-connected generator and the composite load model. The model was developed in the form of seven order state-space model. This model was adopted from Samila Mat Zali in 2012. The parameter estimation of the model was developed 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 full DNC model was also adopted from Samila Mat Zali in 2012. The performance of the fuzzy model was validated by calculating the value of root means square error (RMSE) and the best fit value. Later, the performance of the fuzzy model was also compared with the system identification model by Samila Mat Zali in 2012. 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. The efficiency was also good based on the low RMSE value and high best fit value. In conclusion, the equivalent dynamic model of DNC based on fuzzy system approach was successfully developed.
format	Thesis
author	Noor Fazliana, Fadzail
author_facet	Noor Fazliana, Fadzail
author_sort	Noor Fazliana, Fadzail
title	Dynamic model of distribution network cell using artificial intelligence approach
title_short	Dynamic model of distribution network cell using artificial intelligence approach
title_full	Dynamic model of distribution network cell using artificial intelligence approach
title_fullStr	Dynamic model of distribution network cell using artificial intelligence approach
title_full_unstemmed	Dynamic model of distribution network cell using artificial intelligence approach
title_sort	dynamic model of distribution network cell using artificial intelligence approach
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Electrical System Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44259/1/P.1-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44259/2/Full%20Text.pdf
_version_	1747836830682710016

Dynamic model of distribution network cell using artificial intelligence approach

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