Use of Reinforcement Learning Algorithms to Optimize Control Strategies for Single Machine Systems

R. Merina Queentin*
Department of Electrical and Electronics Engineering, CSI Institute of Technology, Thovalai, Tamil Nadu, India.
Periodicity:July - December'2022


The stability of power systems is critical to ensuring reliable and efficient operation of electrical grids. In recent years, there has been a growing interest in the use of artificial intelligence techniques, such as reinforcement learning, to improve the stability of single machine systems. Reinforcement learning is a machine learning approach that enables agents to learn optimal control policies through trial and error. In this paper, we explore the use of reinforcement learning algorithms to optimize control strategies for single machine systems. We demonstrate how these algorithms can be used to identify the best control actions to take in real-time to prevent system instability. The challenges and limitations of using reinforcement learning in power system applications are discussed and recommendations are provided for future research in this area. Our results show that reinforcement learning has great potential for improving the stability of single machine systems and can be a valuable tool for power system operators and engineers.


Reinforcement Learning, Control Strategies, Single Machine Systems, System Stability, Optimization, Real-Time Control, Artificial Intelligence.

How to Cite this Article?

Queentin, R. M. (2022). Use of Reinforcement Learning Algorithms to Optimize Control Strategies for Single Machine Systems. i-manager’s Journal on Instrumentation & Control Engineering, 10(2), 36-45.


[1]. Berkenkamp, F., Turchetta, M., Schoellig, A., & Krause, A. (2017). Safe model-based reinforcement learning with stability guarantees. Advances in Neural Information Processing Systems, 30, 1-11.
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