Bacterial Foraging Algorithm for a Neural Network Learning Improvement in an Automatic Generation Controller

Sadeq D. Al-Majidi (), Hisham Dawood Salman Altai, Mohammed H. Lazim, Mohammed Kh. Al-Nussairi, 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
Hisham Dawood Salman Altai: Department of Electrical Engineering, College of Engineering, University of Misan, Amarah 62001, Iraq
Mohammed H. Lazim: 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
Maysam F. Abbod: Department of Electronic and Electrical Engineering, College of Engineering, Brunel University London, London UB8 3PH, UK
Hamed S. Al-Raweshidy: Department of Electronic and Electrical Engineering, College of Engineering, Brunel University London, London UB8 3PH, UK

Energies, 2023, vol. 16, issue 6, 1-19

Abstract: The frequency diversion in hybrid power systems is a major challenge due to the unpredictable power generation of renewable energies. An automatic generation controller (AGC) system is utilised in a hybrid power system to correct the frequency when the power generation of renewable energies and consumers’ load demand are changing rapidly. While a neural network (NN) model based on a back-propagation (BP) training algorithm is commonly used to design AGCs, it requires a complicated training methodology and a longer processing time. In this paper, a bacterial foraging algorithm (BF) was employed to enhance the learning of the NN model for AGCs based on adequately identifying the initial weights of the model. Hence, the training error of the NN model was addressed quickly when it was compared with the traditional NN model, resulting in an accurate signal prediction. To assess the proposed AGC, a power system with a photovoltaic (PV) generation test model was designed using MATLAB/Simulink. The outcomes of this research demonstrate that the AGC of the BF-NN-based model was effective in correcting the frequency of the hybrid power system and minimising its overshoot under various conditions. The BP-NN was compared to a PID, showing that the former achieved the lowest standard transit time of 5.20 s under the mismatching power conditions of load disturbance and PV power generation fluctuation.

Keywords: automatic generation controller; neural network model; bacterial foraging algorithm; hybrid power system; photovoltaic power generation (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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