Debashisha Jena - Profile on Academia.edu (original) (raw)

Papers by Debashisha Jena

Semi-Г Type Single Phase Differential Boost Inverter With High Voltage Gain

IEEE Transactions on Circuits and Systems II: Express Briefs

Machine learning based condition monitoring of a DC-link capacitor in a Back-to-Back converter

2022 IEEE International Conference on Automation/XXV Congress of the Chilean Association of Automatic Control (ICA-ACCA)

Comparative analysis of different machine learning techniques for condition monitoring of capacitors in a SEPIC converter

2022 IEEE Global Conference on Computing, Power and Communication Technologies (GlobConPT)

Improved Gamma type Y-source inverter for rooftop PV based V-G applications

International Journal of Electrical Power & Energy Systems

Estimation of optimal number of components in Gaussian mixture model-based probabilistic load flow study

2016 IEEE Annual India Conference (INDICON), 2016

Gaussian mixture approximation (GMA)-based probabilistic load flow (PLF) is an efficacious approa... more Gaussian mixture approximation (GMA)-based probabilistic load flow (PLF) is an efficacious approach for quantifying the uncertainties associated with non-Gaussian and discrete input random variables (RVs). GMA approximates these input RVs by an equivalent weighted finite sum of Gaussian components. Expectation maximization (EM) algorithm is a well-established approach to estimate the parameters of the mixture components. The critical aspect is to know a priori the optimal number of components approximating the non-Gaussian distributions. The estimation of optimal number of parameters is essential because the parameters with inappropriate components may not evaluate the mixture model accurately. This paper adopts a cluster distortion function-based approach to determine the optimal number of mixture components. The k-means clustering result pertaining to that optimal number is then used for EM initialization. PLF using multivariate-GMA is performed on two IEEE test systems, considering various types of input RVs and their multiple correlations.

A low voltage harvesting in photovoltaic generation systems using negative embedded Z‐source inverter

International Transactions on Electrical Energy Systems, 2021

Intelligent adaptive observer-based optimal control of overhead transmission line de-icing robot manipulator

Advanced Robotics, 2016

Graphical Abstract In cold season, wet snow ice accretion on overhead transmission lines increase... more Graphical Abstract In cold season, wet snow ice accretion on overhead transmission lines increases wind load effects which in turn increases line tension. This increased line tension causes undesirable effects in power systems. This paper discusses the design of an observer-based boundary sliding mode control (BSMC) for 3 DOF overhead transmission line de-icing robot manipulator (OTDIRM). A robust radial basis functional neural network (RBFNN) observer-based neural network (NN) controller is developed for the motion control of OTDIRM, which is a combination of BSMC, NN approximation and adaptation law. The RBFNN-based adaptive observer is designed to estimate the positions and velocities. The weights of both NN observer and NN approximator are tuned off-line using particle swarm optimization. Using Lyapunov analysis the closed loop tracking error was verified for a 3 DOF OTDIRM. Finally, the robustness of the proposed neural network-based adaptive observer boundary sliding mode control (NNAOBSMC) was verified against the input disturbances and uncertainties.

Power System Analysis Operation and Control

Maximum power point tracking of PV array under non-uniform irradiance using artificial neural network

2015 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES), 2015

This paper presents a maximum power point tracking (MPPT) method for tracking the global peak (GP... more This paper presents a maximum power point tracking (MPPT) method for tracking the global peak (GP) of photovoltaic (PV) array under non-uniform irradiance using artificial neural network (ANN). A feed forward multilayer perceptron model with Levenberg - Marquardt back propagation algorithm is used for tracking the global peak. The MPPT algorithm takes irradiance of PV modules as input and gives duty ratio of boost converter as output. The MPPT presented using ANN is compared with conventional hill climbing (HC) method and the actual values obtained from the P-V characteristics. Root Mean Square Error (RMSE) of PV array output power is calculated for both hill climbing method and proposed ANN method. Finally, a qualitative comparison is made between hill climbing method and ANN method.

Optimal GA based SMC with adaptive PID sliding surface for robot manipulator

2014 9th International Conference on Industrial and Information Systems (ICIIS), 2014

Different types of robotic manipulator controllers are developed to acquire dynamic properties an... more Different types of robotic manipulator controllers are developed to acquire dynamic properties and improve the global stability. In this paper a control strategy for robotic manipulator based on the coupling of the Artificial Neuro Fuzzy Inference System (ANFIS) with sliding mode control (SMC) approach has been presented. Initially, the Proportional Integral Derivative (PID) controller has developed for three different control strategies (IATE, ISE and ISTE) using GA. SMC has developed for best optimal criterion by using GA. The main objectives of these controller are to provide stability, good disturbance rejection and small tracking error. Finally, we have trained an ANFIS network, which can generate the adaptive PID control signal to the SMC of robot manipulator. The stability of the system is guaranteed by the checking of the Lyapunov stability theorem. Numerical simulations using the dynamic model of 2 DOF planner rigid robot manipulator with input torque disturbance shows the effectiveness in trajectory tracking problem and disturbance rejection. The simulation results of these controllers are compared with various torque disturbances in terms of path tracking and disturbance rejection. The proposed ANFIS adaptive SMC controller can achieve favorable tracking performance and it is robust with regard to disturbances in input torque.

A combined differential evolution and neural network approach to nonlinear system identification

TENCON 2008 - 2008 IEEE Region 10 Conference, 2008

This paper addresses the effectiveness of soft computing approaches such as Evolutionary Computat... more This paper addresses the effectiveness of soft computing approaches such as Evolutionary Computation (EC) and Artificial Neural Network (ANN) to system identification of nonlinear systems. In this work, three approaches namely a neuro-fuzzy, differential evolution (DE) and a combined DE-ANN have been applied for nonlinear system identification problem. Results obtained envisage that the proposed combined differential evolution-ANN approach to identification of nonlinear system exhibits better model identification accuracy and less computation time compared to the existing neural network approach and neuro-fuzzy technique (NFT).

Backstepping Sliding Mode Control for variable speed wind turbine

2014 Annual IEEE India Conference (INDICON), 2014

This paper presents the nonlinear control for variable speed wind turbine (VSWT). The dynamics of... more This paper presents the nonlinear control for variable speed wind turbine (VSWT). The dynamics of the wind turbine (WT) are derived from the single mass model. The control objective is to maximize the energy capture from the wind with reduced oscillation on the drive train. The generator torque is considered as the control input and it depends on the optimal rotor speed which is derived from the effective wind speed. The effective wind speed is estimated from the aerodynamic torque and rotor speed by using the modified Newton Rapshon (MNR). Initially the conventional sliding mode controller (SMC) is considered. The control performance of SMC was compared with Backstepping Sliding Mode Control (BSMC) for different level of disturbance. The conventional SMC and proposed BSMC are tested with mathematical model and validated through the different mean wind speed. The result shows the better performance of BSMC and robustness to disturbances.

ISMC based variable speed wind turbine for maximum power capture

Proceedings of The 2014 International Conference on Control, Instrumentation, Energy and Communication (CIEC), 2014

This paper presents the nonlinear control for variable speed wind turbine (WT) where the dynamics... more 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.

Second order ISMC for variable speed wind turbine

2013 IEEE 8th International Conference on Industrial and Information Systems, 2013

ABSTRACT In this paper, a nonlinear controller is designed for variable speed wind turbine (WT) w... more ABSTRACT 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.

Nonlinear estimation and control of wind turbine

2013 IEEE International Conference on Electronics, Computing and Communication Technologies, 2013

ABSTRACT Wind energy is one of the major renewable energy sources which continue to be one of the... more ABSTRACT 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.

Neurocomputing, 2011

Several gradient-based approaches such as back propagation (BP) and Levenberg Marquardt (LM) meth... more Several gradient-based approaches such as back propagation (BP) and Levenberg Marquardt (LM) methods have been developed for training the neural network (NN) based systems. But, for multimodal cost functions these procedures may lead to local minima, therefore, the evolutionary algorithms (EAs) based procedures are considered as promising alternatives. In this paper we focus on a memetic algorithm based approach for training the multilayer perceptron NN applied to nonlinear system identification. The proposed memetic algorithm is an alternative to gradient search methods, such as back-propagation and back-propagation with momentum which has inherent limitations of many local optima. Here we have proposed the identification of a nonlinear system using memetic differential evolution (DE) algorithm and compared the results with other six algorithms such as Back-propagation (BP), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Genetic Algorithm Back-propagation (GABP), Particle Swarm Optimization combined with Back-propagation (PSOBP). In the proposed system identification scheme, we have exploited DE to be hybridized with the back propagation algorithm, i.e. differential evolution back-propagation (DEBP) where the local search BP algorithm is used as an operator to DE. These algorithms have been tested on a standard benchmark problem for nonlinear system identification to prove their efficacy. First examples shows the comparison of different algorithms which proves that the proposed DEBP is having better identification capability in comparison to other. In example 2 good behavior of the identification method is tested on an one degree of freedom (1DOF) experimental aerodynamic test rig, a twin rotor multi-input-multi-output system (TRMS), finally it is applied to Box and Jenkins Gas furnace benchmark identification problem and its efficacy has been tested through correlation analysis.

International Journal of Renewable Energy Research

Efficient modeling and simulation of photovoltaic (PV) systems has become more important due to t... more Efficient modeling and simulation of photovoltaic (PV) systems has become more important due to the wide integration of solar energy in modern power systems. The equations describing the PV systems are transcendental nonlinear in natu re, this results a slow and inefficient simulations for long-term analysis. This paper proposes a modified approach of modeling photovoltaic array for uniform and non-uniform irradiance condition. Initially for uniform irradiance condition single diode mod el is used as equivalent circuit. A method based on adaptively varying the value of series resistance is proposed to find the equivalent circuit parameters. The proposed model is simulated using MATLAB and results are validated using the experimental results obtained from the datasheet values and other models in the literature. The model is extended for non-uniform irradiance and the results are validated. The proposed methodology found to have advantage over the other conventional methods in terms of accur acy and less simulation time.

TENCON 2017 - 2017 IEEE Region 10 Conference, 2017

A new dual-input and dual-output switched capacitor (SC) converter is designed to operate with tw... more A new dual-input and dual-output switched capacitor (SC) converter is designed to operate with two independent voltage sources that provides two different output voltages. The only converter generates 32 voltage conversion ratios (VCRs). The converter is portable to operate with one or two input sources alternatively and having the ability to vary 32 voltage ratios. An efficient low power SC converter is designed for input voltage of 1.5 V to 5 V that gives dual output voltages of 1 V to 10 V. The designed converter can operate in both buck and boost modes. This SC converter has high drive capability of load current from 10 µA to 25 mA that is adjusted by operating frequency. Modeling, analysis are performed to verify the dual output converter mathematically and also verified using PSIM simulations. The mathematical results and simulation results show excellent proof of newly designed converter.

Differential mode gamma source inverter with reduced switching stresses

2017 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), 2017

Impedance source inverters are covered with entire spectrum of power conversion process (dc-dc, d... more Impedance source inverters are covered with entire spectrum of power conversion process (dc-dc, dc-ac, ac-dc, and ac-ac). The traditional impedance source inverter suffers from high switching stresses and poor efficiency during high boost requirements. Compared to traditional impedance source inverters, the transformer based impedance source inverters are able to boost the output voltage gain and modulation index simultaneously with reduced passive components. The applications of transformer based gamma source impedance inverters are limited due to the difficulty in maintaining tight coupling, high instantaneous currents, increasing turn's ratio, high cost and large size. However, the gamma source inverter increases voltage gain by reducing turns ratio. This paper presents the operational modes of gamma source inverter in electric and magnetic domains, which helps the researchers to understand effects of transformer coupling in converter performance. In addition, the differential mode gamma source inverter is proposed that reduces the switching stresses. The operating principles of the proposed converter have been analyzed mathematically. Finally the theoretical analysis of proposed impedance inverter is validated by using MATLAB/SIMULINK.

2016 National Power Systems Conference (NPSC), 2016

This paper proposes an efficient hybrid technique for probabilistic load flow study. A mixture of... more This paper proposes an efficient hybrid technique for probabilistic load flow study. A mixture of correlated Gaussian and non-Gaussian as well as discrete distributions is considered for input random variables. Distributions of desired random variables pertaining to the input random variables are found to be multimodal. Analysis using Gaussian mixture approximation is promising in this context, but computational burden increases significantly with the increase in number of discrete random variables. In contrast, the proposed method precisely obtains distribution of desired random variables in considerably less time without compromising accuracy. Multiple input correlations are effectively incorporated. Accuracy of the proposed method is examined in IEEE 14-bus and 57-bus test systems. Results are compared with combined cumulant-Gaussian mixture approximation method and Monte-Carlo simulation.