Samuel, I. A. and Soyemi, Adebola and Awelewa, A. A. and Adekitan, Aderibigbe I. (2021) Artificial Neural Network Based Load Flow Analysis for Power System Networks. International Journal of Computer Science, 48 (4).
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Abstract
Load flow analysis has become increasingly important as power system expansion now involves unbundling, liberalization, and restructuring networks, putting power system operators in a competitive electricity market. On the other hand, advancements in technology, computing, and software have led to new techniques for carrying out load flow analysis. In this paper, the load flow problem is approached using two techniques: the traditional load flow analysis using the Newton-Raphson method and a non-conventional method using an artificial neural network. This paper presents a load flow solution using the developed artificial neural network on the IEEE 14-bus system and the Nigerian 330kV 28-bus national grid. The results show that load flow analysis can be carried out using the developed artificial neural network with negligible errors between the actual values of voltage magnitudes and voltage phase angles and the neural network output, thus validating the proposed approach. Using the proposed approach, an R-value of 0.9884 and a mean square error of 1.6701x10−3 was obtained for the IEEE 14-bus system. For the Nigerian 330kV 28-bus national grid, an R-value of 0.99972 and a mean square error of 3.8624 × 10−3. MATLAB's neural network toolbox was used to design, develop, and train the artificial neural network used in this paper.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | ANN, Load flow analysis, MATLAB, NNG |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science |
| Depositing User: | Mrs Patricia Nwokealisi |
| Date Deposited: | 19 Jan 2022 10:53 |
| Last Modified: | 19 Jan 2022 10:53 |
| URI: | http://eprints.covenantuniversity.edu.ng/id/eprint/15577 |
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