Faculty Publications
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Publications by NITK Faculty
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Item Evolutionary computing approaches to system identification(IGI Global, 2016) Subudhi, B.; Jena, D.In this chapter, we describe an important class of engineering problem called system identification which is an essential requirement for obtaining models of system of concern that would be necessary for controlling, analyzing the systems. The system identification problem is essentially to pick up the best model out of the several candidate models. Thus, the problem of system identification or modeling building turns out to be an optimization problem. The chapter explain what are different evolutionary computing techniques used in the past and the state- of the art technologies on evolutionary computation. Then, some case studies have been included how the system identification of a number of complex systems effectively achieved by employing these evolutionary computing techniques. © 2016 by IGI Global. All rights reserved.Item Probabilistic Steady-State Analysis of Power Systems Integrated with Renewable Generations(CRC Press, 2022) Singh, V.; Moger, T.; Jena, D.[No abstract available]Item Comparison of solar irradiance forecasting performance with K- means++ clustering combined with hybrid deep learning models(IGI Global, 2025) Chiranjeevi, M.; Ramesh Torun, S.; Ghangale, V.S.; Pundir, A.; Moger, T.; Jena, D.Solar irradiance forecasting plays a crucial role in renewable energy, weather prediction, and climate modeling. Accurate forecasts are essential for optimizing solar power efficiency, grid integration, and energy planning. Traditional forecasting methods, based on physical and statistical models, struggle to capture the complex, nonlinear relationships inherent in solar irradiance. To address these challenges, this chapter presents a comparative analysis of advanced machine learning (ML) and deep learning (DL) models. Techniques like CNNs, RNNs, and hybrid models have demonstrated strong capabilities in extracting temporal and spatial patterns from Solar data. The integration of K- means++ clustering with DL frameworks further enhances model robustness, generalization, and interpretability. This chapter evaluates hybrid models, such as Temporal CNN- LSTM, CNN- GRU using metrics based on Solcast data. Results reveal that the TCNN- GRU model outperforms other state- of- the- art approaches, underscoring the value of clustering- enhanced DL frameworks for accurate solar irradiance forecasting. © 2025, IGI Global Scientific Publishing. All rights reserved.Item A continuous-discrete mode of optimal control of AGC for multi area hydrothermal system using genetic algorithm(2012) Vijay, M.; Jena, D.This paper deals with Automatic generation control (AGC) of interconnected hydrothermal system in continuous-discrete mode using proportional-integral (PI) controller with different tuning approaches. Here the PI controller is initially tuned using local optimization technique such as Fminsearch (Existing MATLAB function) and optimal control strategies were taken as integral square error (ISE)' integral time-absolute error (IATE) and integral time square error (ISTE). Then the same PI controller is tuned by using evolutionary algorithm i.e. genetic algorithm (GA). For the given system appropriate generation rate constraint (GRC) has been considered both for the thermal and hydro plants. System performances is examined considering 1% step load perturbation in both thermal and hydro area with 1 second sampling period. Finally the performance of both the local and global optimization algorithms is compared in terms of the time domain specifications both for frequency deviation in each area' and tie line power. © 2012 IEEE.Item Simple and accurate method of modeling Photovoltaic module: A different approach(IEEE Computer Society help@computer.org, 2013) Jena, D.; Ramana, V.V.This paper proposes a different method of modeling Photovoltaic (PV) System in uniform irradiance conditions. It provides a simple and accurate method of modeling PV system using a single diode model by considering series and shunt resistance. This model computes five parameters and is having better accuracy than the existing models in literature. The accuracy of the proposed modeling technique is validated by comparing the model with the experimental values and datasheet values. The proposed model can be extended for modeling the effect of non-uniform irradiance conditions on PV system and also to track the maximum power from the PV source under non-uniform irradiance conditions. © 2013 IEEE.Item Nonlinear estimation and control of wind turbine(2013) RAJENDRAN, R.; Jena, D.Wind energy is one of the major renewable energy sources which continue to be one of the fastest growing power generation sectors. For variable speed operation of wind energy conversion system, it is required to generate the maximum power at below the rated speed using an authentic and powerful control strategy. Wind speed has the major impact on the dynamics and control of wind turbines. But in practice there is no accurate measurement of effective wind speed available for direct measurement. In this paper a new technique is proposed for optimal power generation of wind turbine below rated speed without estimating the wind speed. An extended Kalman filter (EKF) is used to estimate the rotor speed and a proportional (P) controller is used to track the error between the measured and estimated rotor speed. The output of the P controller is the estimated aerodynamic torque. The estimated aerodynamic torque and the rotor speed act as an input to the aerodynamic torque feed-forward (ATF) controller. The output of the ATF controller is the generated torque. As the aerodynamic torque is highly dependent on the wind speed so it cannot be controlled. So we have to control the generated torque by using ATF for generating optimal power output. Finally the estimated outputs are validated through correlation analysis. © 2013 IEEE.Item Second order ISMC for variable speed wind turbine(2013) RAJENDRAN, R.; Jena, D.In this paper, a nonlinear controller is designed for variable speed wind turbine (WT) where the dynamics of the WT is derived for single mass model. The main aim of the controller is to extract the optimum power capture from the wind and minimize the transient load on low speed shaft. Modified Newton Rapshon (MNR) is used to estimate the effective wind speed and the optimal rotor speed is derived from it. The controller is used to track the optimal rotor speed by adjusting the generator torque which is acting as a control input to the WT. Existing controllers such as Nonlinear static state feedback with estimator (NSSFE) and Nonlinear dynamic state feedback with estimator (NDSFE) are unable to track the WT dynamics and introduces more transient on drive trains. In order to overcome the above drawbacks a nonlinear controller i.e. sliding mode control with integral action (ISMC) is used. In this paper an ISMC with MNR based wind speed estimator is used to control the single mass WT. The result shows the significance improvement in proposed controllers compared with NSSFE and NDSFE. © 2013 IEEE.Item GA based adaptive controller for 2DOF robot manipulator(IFAC Secretariat, 2014) Vijay, M.; Jena, D.In this paper different types of robotic manipulator controllers are developed to acquire dynamic properties and improve the global stability. Here two control schemes for two degrees of freedom (2DOF) robot manipulator are discussed. The controllers are developed using Fminsearch and Genetic Algorithm (GA). The main objectives of these controllers are to provide stability, good disturbance rejection and small tracking error. The Proportional Derivative (PD) and Proportional Integral Derivative (PID) conventional controllers are developed for three different control strategies (IATE, ISE and ISTE) using Fminsearch and GA. The performances of these controllers are compared with various torque disturbances in terms of path tracking and disturbance rejection. © 2014 IFAC.Item ISMC based variable speed wind turbine for maximum power capture(Institute of Electrical and Electronics Engineers Inc., 2014) RAJENDRAN, S.; Jena, D.This paper presents the nonlinear control for variable speed wind turbine (WT) where the dynamics of WT is derived from single mass model. The main objective is to maximize the energy capture from the wind and reduce the drive train oscillations. In order to control the WT the generator torque is considered as the control input. This torque depends on the optimal rotor speed derived from the effective wind speed. The effective wind speed is estimated from aerodynamic torque and rotor speed by using modified Newton Rapshon (MNR). The conventional techniques such as aero dynamic torque feed forward (ATF) & Indirect speed control (ISC) which does not depend on the effective wind speed, are unable to track the dynamic aspect of the WT. The other disadvantages of the above conventional methods are more power loss and not robust with respect to disturbances and uncertainties. To overcome these weaknesses nonlinear controllers are found to be more suitable than the conventional controller. In this paper a sliding mode control with integral action i.e. integral sliding mode controller (ISMC) is applied to the WT and a modified Newton Rapshon is used to estimate the effective wind speed. The result shows the significance improvement in proposed controllers compared with existing controllers. © 2014 IEEE.Item Advantage of Unscented Kalman Filter over Extended Kalman Filter in dynamic state estimation of power system network(Institution of Engineering and Technology, 2015) Rampelli, M.; Jena, D.The Kalman filter is a set of mathematical equations which are used to estimate the state of a system by minimizes the mean of the squared error. In this paper Kalman filtering is used for the estimation of states of IEEE 14 bus power system network. Here we considered dynamic states i.e. rotor angle in radians and speed in rad/sec of all the generators present in the system. We mainly focus on the advantages of Unscented Kalman Filter (UKF) over Extended Kalman Filter (EKF) by comparing both estimation methods. In EKF state distribution is approximated by a Gaussian Random Variable (GRV), which is then propagated analytically through a first order linearization of the non-linear system. This introduces errors in posteriori mean and covariance of the transformed GRV, which may lead to suboptimal performance of the filter. The UKF addresses this problem by using a deterministic sampling approach. In this paper these two algorithms are tested on an IEEE-14 bus,5-generator test system by applying test cases like sudden load change and configuration topology error to show how adaptive these filters during those conditions.