文章基本信息

标题：Artificial Neural Network Based Load Flow Analysis for Power System Networks
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作者：Isaac Samuel ; Adebola Soyemi ; Ayokunle Awelewa 等
期刊名称：IAENG International Journal of Computer Science
印刷版ISSN：1819-656X
电子版ISSN：1819-9224
出版年度：2021
卷号：48
期号：4
语种：English
出版社：IAENG - International Association of Engineers
摘要：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.
关键词：ANN;Load flow analysis;MATLAB;NNG