Estimation of Real Power Transfer Allocation Using Intelligent Systems
This paper presents application artificial intelligent (AI) techniques, namely artificial neural network (ANN), adaptive neuro fuzzy interface system (ANFIS), to estimate the real power transfer between generators and loads. Since these AI techniques adopt supervised learning, it first uses modi...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/9474/1/v78-209%28HS_waset%29.pdf http://eprints.utem.edu.my/id/eprint/9474/ |
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| Institution: | Universiti Teknikal Malaysia Melaka |
| Language: | English |
| Summary: | This paper presents application artificial intelligent
(AI) techniques, namely artificial neural network (ANN), adaptive
neuro fuzzy interface system (ANFIS), to estimate the real power
transfer between generators and loads. Since these AI techniques
adopt supervised learning, it first uses modified nodal equation
method (MNE) to determine real power contribution from each
generator to loads. Then the results of MNE method and load flow
information are utilized to estimate the power transfer using AI
techniques. The 25-bus equivalent system of south Malaysia is
utilized as a test system to illustrate the effectiveness of both AI
methods compared to that of the MNE method. The mean squared
error of the estimate of ANN and ANFIS power transfer allocation
methods are 1.19E-05 and 2.97E-05, respectively. Furthermore,
when compared to MNE method, ANN and ANFIS methods
computes generator contribution to loads within 20.99 and
39.37msec respectively whereas the MNE method took 360msec for
the calculation of same real power transfer allocation. |
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