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

• Published in: Energies (Basel) 2022-09, Vol.15 (17), p.6223
• Main Authors: Al-Majidi, Sadeq D., Kh. AL-Nussairi, Mohammed, Mohammed, Ali Jasim, Dakhil, Adel Manaa, Abbod, Maysam F., Al-Raweshidy, Hamed S.
• Format: Article
• Language: English
• Subjects: Analysis; artificial neural network; Controllers; Design; Designers; Electric power distribution; Electric power systems; Electrical loads; Errors; Frequency dependence; Frequency response; Load distribution; load frequency controller; Neural networks; Optimization; particle swarm optimization; power system network and stability; Turbines
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en15176223

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.