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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| 格式: | Thesis |
| 語言: | English |
| 主題: | |
| 在線閱讀: | 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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| 總結: | 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. |
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