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 present...

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Bibliographic Details
Published in2014 IEEE 8th International Power Engineering and Optimization Conference (PEOCO2014) pp. 172 - 177
Main Authors Fazliana, F., Zali, S. M., Arizadayana, Z.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2014
Subjects
Adaptation models
Computational modeling
Data models
Equations
Load modeling
Mathematical model
Reactive power
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Summary: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. 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 response from the fuzzy model was compared with the response from the full DNC model at various type of disturbance. 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 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 compare with the system identification model. 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.
DOI:10.1109/PEOCO.2014.6814420