Faculty Publications
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Publications by NITK Faculty
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Item Probabilistic Load Flow for Wind Integrated Power System Considering Node Power Uncertainties and Random Branch Outages(Institute of Electrical and Electronics Engineers Inc., 2023) Singh, V.; Moger, T.; Jena, D.This paper proposes an analytical probabilistic load flow (PLF) approach that considers conventional generator outages, load variability, and random branch outages. The branch outages are modeled as 0-1 distributions of fictitious power injections at the appropriate nodes. The distribution of state variables and line power flows is then obtained using a combined Cumulant and Gram-Charlier series expansion approach. The proposed PLF performs contingency sequencing with fuzzy logic to eliminate random line checking and avoid masking mistakes faced by performance index-based algorithms. The Jacobian inverse calculation in the traditional Cumulant method is eliminated to conserve storage space and speed up the computation using the Gauss-Jordan method. The correlations among loads and wind power generations has been modeled using the Nataf transformation process. Results of 24-bus and 259-bus equivalent systems of the Indian southern and western power grids are analyzed and validated with those obtained using the Monte Carlo simulation method. The suggested method's efficacy is justified by its accuracy and low computational burden. © 2010-2012 IEEE.Item Probabilistic Load Flow Approach Combining Cumulant Method and K-Means Clustering to Handle Large Fluctuations of Stochastic Variables(Institute of Electrical and Electronics Engineers Inc., 2023) Singh, V.; Moger, T.; Jena, D.The modern electrical power system faces various uncertainties, including load fluctuations, forced outages of conventional generators, network branches. Furthermore, the rising penetration of wind power generation introduces additional uncertainty, causing difficulties in power system planning, operation. This paper uses an analytical probabilistic load flow approach to account for all such uncertainties. The random branch outages are simulated using the fictional powers injections into the relevant nodes. A fuzzy method is used to perform contingency sequencing to avoid masking mistakes that might occur when utilizing performance index-based sequencing methods. The sparse Jacobian inverse is eliminated to preserve storage space, accelerate the computation. A modified Cumulant method is used in conjunction with the K-means clustering process to deal with the substantial fluctuations of the input variables. In the proposed approach, the correlated samples are generated using inverse Nataf transformation. These correlated samples are clustered using K-means clustering. The Cumulant method is applied within each cluster, total probability law is used to integrate each cluster's findings. The proposed PLF is tested on 24-bus, 259-bus wind integrated equivalent systems. Compared with the Monte-Carlo simulation, the proposed PLF yields computationally efficient, more accurate findings. © 1972-2012 IEEE.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.