Innovative Approach to Enhance Stability: Neural Network Control and Aquila Optimization Integration in Single Machine Infinite Bus Systems


  • Yogesh Kalidas Kirange Department of Electrical and Electronics Engineering, Oriental University, Indore, India
  • Pragya Nema Department of Electrical and Electronics Engineering, Oriental University, Indore, India



Aquila optimization algorithm, electrical power systems, neural network, power system stabilizers, single machine infinite bus


This paper highlights the need to improve the stability of single-machine infinite-bus (SMIB) systems, which is crucial for maintaining the dependability, efficiency, and safety of electrical power systems. The changing energy environment, characterized by a growing use of renewable sources and more intricate power networks, is challenging established stability measures. SMIB systems exhibit dynamic behavior, particularly during faults or unexpected load variations, requiring sophisticated real-time stabilization methods to avert power failures and provide a steady energy supply. This paper suggests a complex approach that combines power system stability analysis with a neural network controller enhanced by the Aquila optimization algorithm (AOA) to address the dynamic issues of SMIB systems. The study shows that the AOA-optimized neural network (AOA-NN) controller outperforms in avoiding disruptions and attaining speedy stabilization by exhaustively examining electrical, mechanical, and rotor dynamics. This method improves power system resilience and operational efficiency as demands and technology expand.

Author Biography

Pragya Nema, Department of Electrical and Electronics Engineering, Oriental University, Indore, India

DEpartment of Electrical and Electronics Engineering, Professor


K. Kalegowda, A. D. I. Srinivasan, and N. Chinnamadha, “Particle Swarm Optimization and Taguchi Algorithm-Based Power System Stabilizer-Effect of Light Loading Condition,” International Journal of Electrical and Computer Engineering, vol. 12, no. 5, pp. 4672-4679, October 2022.

M. Kahl and T. Leibfried, “Decentralized Model Predictive Control of Electrical Power Systems,” 10th International Conference on Power Systems Transients, article no. 1000036565, July 2013.

Q. Yang, T. Ding, H. He, X. Chen, F. Tao, Z. Zhou, et al., “Model Predictive Control of MMC-UPFC Under Unbalanced Grid Conditions,” International Journal of Electrical Power & Energy Systems, vol. 117, article no. 105637, May 2020.

A. Shetgaonkar, L. Liu, A. Lekić, M. Popov, and P. Palensky, “Model Predictive Control and Protection of MMC-Based MTDC Power Systems,” International Journal of Electrical Power & Energy Systems, vol. 146, article no. 108710, March 2023.

P. Karamanakos, E. Liegmann, T. Geyer, and R. Kennel, “Model Predictive Control of Power Electronic Systems: Methods, Results, and Challenges,” IEEE Open Journal of Industry Applications, vol. 1, pp. 95-114, 2020.

L. Peng, Y. Xu, A. H. Abolmasoumi, L. Mili, Z. Zheng, S. Xu, et al., “AC/DC Hybrid Power System Damping Control Based on Estimated Model Predictive Control Considering the Real-Time LCC-HVDC Stability,” IEEE Transactions on Power Systems, vol. 39, no. 1, pp. 506-516, January 2024.

J. P. Therattil, J. Jose, P. R. N. Prasannakumari, A. G. Abo-khalil, A. S. Alghamdi, B. G. Rajalekshmi, et al., “Hybrid Control of a Multi-Area Multi-Machine Power System with FACTS Devices Using Non-Linear Modelling,” IET Generation, Transmission & Distribution, vol. 14, no. 10, pp. 1993-2003, May 2020.

M. A. Kamarposhti, I. Colak, C. Iwendi, S. S. Band, and E. Ibeke, “Optimal Coordination of PSS and SSSC Controllers in Power System Using Ant Colony Optimization Algorithm,” Journal of Circuits, Systems and Computers, vol. 31, no. 04, article no. 2250060, March 2022.

L. Wang, H. Cheung, A. Hamlyn, and R. Cheung, “Model Prediction Adaptive Control of Inter-Area Oscillations in Multi-Generators Power Systems,” IEEE Power & Energy Society General Meeting, pp. 1-7, July 2009.

N. Rosle, N. F. Fadzail, and M. N. K. H. Rohani, “A Study of Artificial Neural Network (ANN) in Power System Dynamic Stability,” 10th International Conference on Robotics, Vision, Signal Processing and Power Applications: Enabling Research and Innovation Towards Sustainability, pp. 11-17, August 2018.

A. Sabo, N. I. A. Wahab, M. L. Othman, M. Z. A. Mohd Jaffar, H. Acikgoz, and H. Beiranvand, “Application of Neuro-Fuzzy Controller to Replace SMIB and Interconnected Multi-Machine Power System Stabilizers,” Sustainability, vol. 12, no. 22, article no. 9591, November 2020.

A. Sabo, N. I. Abdul Wahab, M. L. Othman, M. Z. A. Mohd Jaffar, H. Beiranvand, and H. Acikgoz, “Application of a Neuro-Fuzzy Controller for Single Machine Infinite Bus Power System to Damp Low-Frequency Oscillations,” Transactions of the Institute of Measurement and Control, vol. 43, no. 16, pp. 3633-3646, December 2021.

P. K. Ray, S. R. Das, and A. Mohanty, “Fuzzy-Controller-Designed-PV-Based Custom Power Device for Power Quality Enhancement,” IEEE Transactions on Energy Conversion, vol. 34, no. 1, pp. 405-414, March 2019.

A. Ramshanker and S. Chakraborty, “Maiden Application of Skill Optimization Algorithm on Cascaded Multi-Level Neuro-Fuzzy Based Power System Stabilizers for Damping Oscillations,” International Journal of Renewable Energy Research, vol. 12, no. 4, pp. 2152-2167, December 2022.

B. Srinivasarao, G. Sreenivasan, and S. Sharma, “A Neuro-Fuzzy Controller for Compensation of Voltage Disturbance in SMIB System,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 5, no. 1, pp. 72-80, January 2017.

W. A. Oraibi, M. J. Hameed, and A. K. Abbas, “An Adaptive Neuro-Fuzzy Based on Reference Model Power System Stabilizer,” Journal of Xi’an Shiyou University, vol. 5, no. 3, pp. 17-38, 2019.

B. Saleem, R. Badar, A. Manzoor, M. A. Judge, J. Boudjadar, and S. U. Islam, “Fully Adaptive Recurrent Neuro-Fuzzy Control for Power System Stability Enhancement in Multi Machine System,” IEEE Access, vol. 10, pp. 36464-36476, 2022.

P. Cheng, Z. Xu, R. Li, and C. Shi, “A Hybrid Taguchi Particle Swarm Optimization Algorithm for Reactive Power Optimization of Deep-Water Semi-Submersible Platforms with New Energy Sources,” Energies, vol. 15, no. 13, article no. 4565, July 2022.

L. Abualigah, D. Yousri, M. A. Elaziz, A. A. Ewees, M. A. A. Al-Qaness, and A. H. Gandomi, “Aquila Optimizer: A Novel Meta-Heuristic Optimization Algorithm,” Computers & Industrial Engineering, vol. 157, article no. 107250, July 2021.

J. Zhao, Z. M. Gao, and H. F. Chen, “The Simplified Aquila Optimization Algorithm,” IEEE Access, vol. 10, pp. 22487-22515, 2022.

W. Aribowo, S. Supari, and B. Suprianto, “Optimization of PID Parameters for Controlling DC Motor Based on the Aquila Optimizer Algorithm,” International Journal of Power Electronics and Drive Systems, vol. 13, no. 1, pp. 216-222, March 2022.

Y. C. Wu and J. W. Feng, “Development and Application of Artificial Neural Network,” Wireless Personal Communications, vol. 102, no. 2, pp. 1645-1656, September 2018.

M. Islam, G. Chen, and S. Jin, “An Overview of Neural Network,” American Journal of Neural Networks and Applications, vol. 5, no. 1, pp. 7-11, June 2019.

N. Gupta, “Artificial Neural Network,” Network and Complex Systems, vol. 3, no. 1, pp. 24-28, 2013.




How to Cite

Yogesh Kalidas Kirange and Pragya Nema, “Innovative Approach to Enhance Stability: Neural Network Control and Aquila Optimization Integration in Single Machine Infinite Bus Systems”, Adv. technol. innov., vol. 9, no. 2, pp. 99–115, Apr. 2024.