Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
Search Results
Item Application of Reinforcement Learning-Based Adaptive PID Controller for Automatic Generation Control of Multi-Area Power System(Institute of Electrical and Electronics Engineers Inc., 2025) Muduli, R.; Jena, D.; Moger, T.This paper presents an application of an actor-critic reinforcement learning (RL) algorithm-based adaptive proportional-integral-derivative (PID) controller for automatic generation control of multi-area power systems. The proposed approach has several advantages over other deep RL algorithm-driven PID controllers, such as simplicity in structure, elimination of pre-learning requirements, and prior tuning of PID parameters. Online adaption of PID parameters is achieved through actor-critic policy. The proposed method implements a single radial basis function (RBF) based neural network for actor and critic networks. Three different case studies are demonstrated with proper illustration and analysis of the result to present the effectiveness and robustness of the proposed control strategy against various uncertainties. The outcomes of the proposed controller are compared with the conventional PID controller tuned by the Particle Swarm Optimization (PSO) algorithm. The results seem competent enough to maintain the frequency within an acceptable limit under various uncertainties. Note to Practitioners - This paper describes the application of an online adaptive PID controller for automatic generation control of power systems. The controller is designed using a model-free reinforcement learning algorithm, which enables it to control the system without requiring prior knowledge of the system dynamics. Additionally, the controller does not need any global optimization algorithm for tuning the parameters (KP, KI, KD) beforehand. This controller can be implemented for both linear and non-linear systems. © 2004-2012 IEEE.Item Automatic generation control of is-landed micro-grid using integral reinforcement learning-based adaptive optimal control strategy(Springer Science and Business Media Deutschland GmbH, 2025) Muduli, R.; Jena, D.; Moger, T.Abstract: Microgrids serve an essential role in the smart grid infrastructure, facilitating the seamless integration of distributed energy resources and supporting the increased adoption of renewable energy sources to satisfy the growing demand for sustainable energy solutions. This paper presents an application of integral reinforcement learning (IRL) algorithm-based adaptive optimal control strategy for automatic generation control of an is-landed micro-grid. This algorithm is a model-free actor-critic method that learns the critic parameters using the recursive least square method. The actor is straightforward and evaluates the action from the critic directly. The robustness of the proposed control technique is investigated under various uncertainties arising from parameter uncertainty, electric vehicle (EV) aggregator, and renewable energy sources. This study incorporates case studies and comparative analyses to demonstrate the control performance of the proposed control strategy. The effectiveness of the technique is evaluated by comparing it with deep Q-learning (DQN) control techniques and PI controllers. The proposed controller significantly improves performance metrics compared to the DQN and PI controllers. It reduces the peak frequency deviation by 6% and 14%, respectively, compared to the DQN and PI controllers. When subjected to multiple-step load disturbances, the proposed controller reduces the mean square error by 28% and 42%, respectively, while lowering both the integral absolute error and the integral time absolute error by 21% and 35% compared to the DQN and PI controllers. Additionally, when operating with renewable energy sources, the proposed controller decreases the standard deviation in the frequency deviation by 17% compared to the DQN controller and 23% compared to the PI controller. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.