Design of a Load Frequency Controller Based on an Optimal Neural Network

Sadeq D. Al-Majidi (), Mohammed Kh. AL-Nussairi, Ali Jasim Mohammed, Adel Manaa Dakhil, Maysam F. Abbod () and Hamed S. Al-Raweshidy
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Sadeq D. Al-Majidi: Department of Electrical Engineering, College of Engineering, University of Misan, Amarah 62001, Iraq
Mohammed Kh. AL-Nussairi: Department of Electrical Engineering, College of Engineering, University of Misan, Amarah 62001, Iraq
Ali Jasim Mohammed: Directorate General of Education in Amarah, Ministry of Education, Amarah 62001, Iraq
Adel Manaa Dakhil: Department of Electrical Engineering, College of Engineering, University of Misan, Amarah 62001, Iraq
Maysam F. Abbod: Department of Electronic and Computer Engineering, College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UK
Hamed S. Al-Raweshidy: Department of Electronic and Computer Engineering, College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UK

Energies, 2022, vol. 15, issue 17, 1-28

Abstract: A load frequency controller (LFC) is a crucial part in the distribution of a power system network (PSN) to restore its frequency response when the load demand is changed rapidly. In this paper, an artificial neural network (ANN) technique is utilised to design the optimal LFC. However, the training of the optimal ANN model for a multi-area PSN is a major challenge due to its variations in the load demand. To address this challenge, a particle swarm optimization is used to distribute the nodes of a hidden layer and to optimise the initial neurons of the ANN model, resulting in obtaining the lower mean square error of the ANN model. Hence, the mean square error and the number of epochs of the ANN model are minimised to about 9.3886 × 10 −8 and 25, respectively. To assess this proposal, a MATLAB/Simulink model of the PSN is developed for the single-area PSN and multi-area PSN. The results show that the LFC based on the optimal ANN is more effective for adjusting the frequency level and improves the power delivery of the multi-area PSN comparison with the single-area PSN. Moreover, it is the most reliable for avoiding the fault condition whilst achieving the lowest time multiplied absolute error about 3.45 s when compared with the conventional ANN and PID methods.

Keywords: load frequency controller; artificial neural network; particle swarm optimization; power system network and stability (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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