Browsing by Author "Jena, D."

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A Comparative Study of Different Capacitor Voltage Control Design Strategies for Z-Source Inverter
(Taylor and Francis Ltd., 2022) Kumar, K.V.; Reddivari, R.; Jena, D.
The Z-source inverter (ZSI) is a prominent single-stage power conversion topology compared to traditional voltage source inverter (VSI)/ current source inverter (CSI). It adds the additional buck–boost capability to input voltage with improved reliability. However, the non-minimum phase (NMP) behavior is the major disadvantage of ZSI due to the existence of the right half plane (RHP) zero in the converter transfer functions. The existences of RHP zero destabilize the wideband feedback loops, which imply high gain instability and introduce the constraints on controller design. This paper presents different types of controllers and its design to maintain the required capacitor voltage with better transient response for non-minimum phase ZSI. Different tuning algorithms have been considered for both proportional–integral (PI), and integral–proportional (IP) control schemes. Also, the unified control algorithm has been implemented with both simple boost pulse width modulation (SBPWM) and a modified space vector pulse width modulation (MSVPWM) schemes to obtain the required capacitor voltage. The converter performance is comprehensively analyzed for different controllers and observations are tabulated. The complete analysis has been carried out using the MATLAB/Simulink environment for the proposed models. © 2022 IETE.
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. Â© 2012 IEEE.
A Correlative Investigation of Impedance Source Networks: A Comprehensive Review
(Taylor and Francis Ltd., 2022) Reddivari, R.; Jena, D.
In recent times, impedance networks have been developed to overcome the limitations and problems of traditional VSI / CSI and various traditional dc-dc converter networks. From then on, impedance source converters replace the entire range of power electronic converters: dc-dc (converters), dc-ac (inverters), ac-dc (rectifiers), ac-ac frequency regulators (matrix converters). In addition, the impedance source networks are used in a wide range of applications like PV-Grid tied systems, wind energy systems, distributed generations, adjustable speed drives, UPS systems, battery/supercapacitor/flywheel energy storage systems, electric vehicles, electronic loads, and dc circuit breakers, etc. Several topological changes have occurred to improve the performance of conventional ZSIs. This paper provides a concise review of the state-of-the-art impedance source topologies. This paper categorized the impedance topologies based on their functionality, performance improvements, and switching configuration employed. This paper also demonstrates the fundamental structural similarities, advantages, and disadvantages of each topology, which helps the end-users in topology selection. Â© 2022 IETE.
A Correlative Investigation of Impedance Source Networks: A Comprehensive Review
(Taylor and Francis Ltd., 2022) Reddivari, R.; Jena, D.
In recent times, impedance networks have been developed to overcome the limitations and problems of traditional VSI / CSI and various traditional dc-dc converter networks. From then on, impedance source converters replace the entire range of power electronic converters: dc-dc (converters), dc-ac (inverters), ac-dc (rectifiers), ac-ac frequency regulators (matrix converters). In addition, the impedance source networks are used in a wide range of applications like PV-Grid tied systems, wind energy systems, distributed generations, adjustable speed drives, UPS systems, battery/supercapacitor/flywheel energy storage systems, electric vehicles, electronic loads, and dc circuit breakers, etc. Several topological changes have occurred to improve the performance of conventional ZSIs. This paper provides a concise review of the state-of-the-art impedance source topologies. This paper categorized the impedance topologies based on their functionality, performance improvements, and switching configuration employed. This paper also demonstrates the fundamental structural similarities, advantages, and disadvantages of each topology, which helps the end-users in topology selection. © 2022 IETE.
A cost-effective single-phase semi flipped gamma type magnetically coupled impedance source inverters
(John Wiley and Sons Ltd, 2021) Gautham, T.N.; Reddivari, R.; Jena, D.
This paper presents a new two winding coupled inductor architecture for a semi magnetically coupled impedance source (SMCIS) inverter by connecting the coupled inductor windings in flipped gamma fashion. The proposed topology is derived from the conventional MCIS inverters. It can produce sinusoidal output voltage/current without using any shoot-through operation and output LC filter, which improves the system reliability. Further, a doubly grounded feature, no start-up inrush current, reduced component count, low input current ripple, continuous output currents, and small leakage currents are the major advantages of the proposed inverter. However, the proposed semi flipped gamma MCIS inverters still suffer from limited output voltage gain problem. The voltage-boosting feature is added to the proposed inverter by connecting two converter modules in differential boost configuration through the embedded structure. The voltage-boosting ability is the major advantage of this differential boost embedded configuration. It has flexibility in choosing a wide range of duty cycle operation from zero to one (whereas, the duty cycle was limited to 0.666 in case of semi-Z-source inverter [ZSI]). The modes of operation, design procedure, and feature comparisons of proposed inverters are discussed in this paper. Finally, the effectiveness of proposed inverters is validated through simulation and experimental results in terms of component count, voltage gain, and feature comparison. © 2020 John Wiley & Sons, Ltd.
A critical analysis of Z-source converters considering the effects of internal resistances
(Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2018) Reddivari, R.; Jena, D.
Nowadays Z-source networks are the most promising power converter networks that cover almost all electric power conversion (dc–dc, dc–ac, ac–dc and ac–ac) applications. However, the controller design is critical for Z-source converter (ZSC) due to the presence right-half-plane zero (RHPZ) in the control-to-capacitor-voltage transfer function. This RHPZ exhibits non-minimum phase undershoot in the capacitor voltage and also in the dc-link voltage waveforms. A perfect small-signal model is required to predict locations of the RHP zero and its dynamics. This paper contributes towards the small-signal analysis of ZSC under continuous conduction mode considering the parasitic resistance of the inductor, equivalent series resistance of the capacitor, internal resistances of active switch and forward voltage drop of the diode. The maximum allowable value of shoot-through duty ratio (STDR) and voltage gain for different values of the internal resistance and load resistance are discussed in this paper. The accuracy of the developed small-signal average model is compared with detailed circuit model in MATLAB/SIMULINK. Finally, the steady-state simulation results of ZSC are validated with hardware results. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.
A critical review on probabilistic load flow studies in uncertainty constrained power systems with photovoltaic generation and a new approach
(Elsevier Ltd, 2017) Prusty, B.R.; Jena, D.
A power system with large integration of renewable energy based generations is inherently associated with different types of uncertainties. In such cases, probabilistic load flow is a vital tool for delivering comprehensive information for power system planning and operation. Efforts have been made in this paper to perform a critical review on different probabilistic load flow models, uncertainty characterization and uncertainty handling methods, since from its inspection in 1974. An efficient analytical method named multivariate-Gaussian mixture approximation is proposed for precise estimation of probabilistic load flow results. The proposed method considers the uncertainties pertaining to photovoltaic generations and load demands. At the same time, it effectively incorporates multiple input correlations. In order to examine the performance of the proposed method, modified IEEE 118-bus test system is taken into consideration and results are compared with univariate-Gaussian mixture approximation, series expansion based cumulant methods and Monte Carlo simulation. Effect of various correlation cases on distribution of result variables is also studied. The effectiveness of the proposed method is justified in terms of accuracy and execution time. © 2016 Elsevier Ltd
A critical review on probabilistic load flow studies in uncertainty constrained power systems with photovoltaic generation and a new approach
(Elsevier Ltd, 2017) Prusty, B.R.; Jena, D.
A power system with large integration of renewable energy based generations is inherently associated with different types of uncertainties. In such cases, probabilistic load flow is a vital tool for delivering comprehensive information for power system planning and operation. Efforts have been made in this paper to perform a critical review on different probabilistic load flow models, uncertainty characterization and uncertainty handling methods, since from its inspection in 1974. An efficient analytical method named multivariate-Gaussian mixture approximation is proposed for precise estimation of probabilistic load flow results. The proposed method considers the uncertainties pertaining to photovoltaic generations and load demands. At the same time, it effectively incorporates multiple input correlations. In order to examine the performance of the proposed method, modified IEEE 118-bus test system is taken into consideration and results are compared with univariate-Gaussian mixture approximation, series expansion based cumulant methods and Monte Carlo simulation. Effect of various correlation cases on distribution of result variables is also studied. The effectiveness of the proposed method is justified in terms of accuracy and execution time. Â© 2016 Elsevier Ltd
A detailed formulation of sensitivity matrices for probabilistic load flow assessment considering electro-thermal coupling effect
(IEEE Computer Society, 2017) Prusty, B.R.; Jena, D.
In recent times, use of an analytical method (AM) is prevalent in solving probabilistic load flow (PLF) problem for better computational efficiency. AMs are employed to power system models that endure linear relations between the result variables and input random variables via sensitivity matrices. The accuracy of a sensitivity matrix-based PLF model can be improved by considering the effects of environmental conditions on line parameters. Looking out for an opportunity to upgrade existing PLF model to foresee the strength of thermal resistance model, a temperature-augmented model is presented. A detailed mathematical formulation of the aforesaid model is deliberated. The influence of temperature-augmentation on distributions of resistances, temperatures, power flows, and power losses of the temperature dependent branches is studied in detail. Finally, a note on applicability of the proposed model in the assessment of various power system studies is discussed. Â© 2017 IEEE.
A detailed model of Z-source converter considering parasitic parameters
(Institute of Electrical and Electronics Engineers Inc., 2018) Reddivari, R.; Jena, D.
This paper contributes towards the small-signal analysis of Z-Source converter considering all the losses introduced by non-ideal inductors, capacitor and semiconductor switches. The mathematical model is formulated using state-space averaging method under continuous conduction mode (CCM). The system dynamics are analyzed through computer simulation and reported using frequency response plots and pole-zero plots. The optimum values of the ZSC parameters i.e. the value of inductors and capacitors under CCM operating condition are determined. The effects of equivalent series resistance values (ESR) on the efficiency and boost capability of ZSC are analyzed mathematically, validated with MATLAB/SIMULINK and also with help laboratory proto-type model. Â© 2018 IEEE.
A differential evolution based neural network approach to nonlinear system identification
(2011) Subudhi, B.; Jena, D.
This paper addresses the effectiveness of soft computing approaches such as evolutionary computation (EC) and neural network (NN) to system identification of nonlinear systems. In this work, two evolutionary computing approaches namely differential evolution (DE) and opposition based differential evolution (ODE) combined with Levenberg Marquardt algorithm have been considered for training the feed-forward neural network applied for nonlinear system identification. Results obtained envisage that the proposed combined opposition based differential evolution neural network (ODE-NN) approach to identification of nonlinear system exhibits better model identification accuracy compared to differential evolution neural network (DE-NN) approach. The above method is finally tested on a one degree of freedom (1DOF) highly nonlinear twin rotor multi-input-multi-output system (TRMS) to verify the identification performance. © 2010 Elsevier B.V. All rights reserved.
A hybrid model of convolutional neural network and an extreme gradient boosting for reliability evaluation in composite power systems integrated with renewable energy resources
(Springer Science and Business Media Deutschland GmbH, 2025) Yarramsetty, C.; Moger, T.; Jena, D.
This paper introduces an approach that enhances the computational efficiency of reliability assessment for composite power systems by integrating machine learning (ML) techniques with sequential monte carlo simulation (SMCS). Integration of renewable energy resources (RERs) into power systems is increasing at a rapid pace. Evaluating the reliability of composite power systems is helpful in identifying any deficiencies in their operation. As power systems operation becomes more fluctuating and stochastic, it is necessary to update the tools used to analyse reliability. In this paper, SMCS is used as a conventional method, as it provides results by taking chronological nature of RERs. However, SMCS is highly computational. ML models fit for solving complex problems that require computational power. ML techniques, such as convolutional neural network (CNN) and hybrib models of Convolutional and Extreme Gradient Boosting (ConXGB), and Convolutional and Random Forest (ConRF) are proposed to determine the expectation of load curtailment and minimum amount of load curtailments. The proposed technique is applied on test system IEEE RTS-79. Results indicate the ConvXGB method is fast and accurate in computing composite reliability indices. For instance, it achieved a Loss of Load Probability (LOLP) of 0.0025 and an Expected Demand Not Supplied (EDNS) of 0.1850 MW, compared to SMCS’s LOLP of 0.0021 and EDNS of 0.1794 MW while reducing computational time from 12900 to 5414 s. These results confirm the proposed method’s speed and accuracy, making it a robust solution for modern power system reliability evaluation. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
A low voltage harvesting in photovoltaic generation systems using negative embedded Z-source inverter
(John Wiley and Sons Ltd, 2021) Reddivari, R.; Jena, D.
Commercial two-stage grid-connected photovoltaic (PV) inverters suffer from a narrow band maximum power point (MPP) voltage operation. If the voltage falls outside this narrow band, the inverter switches its operation from MPP to power limitation mode. At the same time, these inverters need high start-up voltages to turn them ON again. The evolutionary algorithms are widely used to track the global MPP at wide input (PV) voltage range. However, the global MPP at low PV voltages cannot be boosted to grid voltage level due to the limited duty ratio of conventional DC-DC converters that restricts the inverter MPP voltage range. This paper summarizes the potential challenges of narrow range MPP voltage solar inverters under partial shading scenarios. Also, demonstrates a proposed single-stage negative embedded Z-source single-stage inverter (NEZSI) to extend the MPP voltage range. The proposed topology wakes up the inverter at lower threshold voltages that enables it to extract energy from low PV string voltages. In addition, the proposed inverter tracks the MPP at a faster rate with low input current ripple, inrush current, and device stresses. Further in this paper, qualitative and quantitative comparisons between state-of-the-art topologies are provided to highlight the superiority of the proposed inverter. Simulation and experimental validation of the proposed inverter are demonstrated with traditional perturb and observe (P&O) and variable step P&O algorithms. © 2021 John Wiley & Sons Ltd.
A modified point estimate-based probabilistic load flow approach for improving tail accuracy in wind-integrated power systems
(Elsevier Ltd, 2025) Singh, V.; Moger, T.; Jena, D.
Modern power systems confront risks, including demand variations and forced outages of traditional generators. Moreover, the extensive grid integration of new energy generation has exacerbated the uncertainty because of its intermittent nature. The Hong's three-point estimation method (3PEM) for performing probabilistic load flow (PLF) is commonly used to cope with power system uncertainties; however, it has poor tail accuracy. To overcome this issue, the basic 3PEM is modified by adding a new pair of tail points. This modified 3PEM (MH3PEM) is equivalent to 5PEM but utilize reduced order moments. Also, a hybrid Hong-Harr PEM approach is proposed to efficiently deal with a mixture of independent and correlated input variables. The input variables’ correlation is modeled using the Nataf transformation. The proposed approaches are tested on wind farm-integrated 24-bus and 72-bus equivalent systems, and their findings are compared with the fundamental PEM schemes. Utilizing the Monte-Carlo simulation as a reference, the MH3PEM provides the most accurate results with a low computational burden. © 2025 Elsevier B.V.
A Negative Embedded Differential Mode ?-Source Inverter with Reduced Switching Spikes
(Institute of Electrical and Electronics Engineers Inc., 2020) Reddivari, R.; Jena, D.
Magnetically coupled impedance source networks (MCIS) are capable of producing higher voltage gains at the expense of high switching voltage spikes due to the presence of leakage inductance. These voltage spikes decorate the converter efficiency and life expectancy of switches. Therefore, to reduce the voltage spikes, a negative embedded differential mode gamma source inverter (NEDM ${{\Gamma }}$ ZSI) is presented in this brief. The proposed inverter can achieve higher voltage gains with reduced switching voltage spikes and low capacitor voltage stresses compared to other MCIS networks. Also, the proposed inverter draws continuous input current from the dc mains, having a common ground, and uses the minimum number of component in a circuit. The operating principle of the proposed NEDM ${{\Gamma }}$ ZSI is analyzed in electrical and magnetic domains. The ability of the proposed impedance network, in terms of voltage spike suppression has been verified experimentally using DC-DC converter configuration. Finally, the performance of a NEDM ${{\Gamma }}$ ZSI is validated with simulation and experimental verification using a single-phase inverter configuration. © 2004-2012 IEEE.
A Novel Transformer-Based Approach for Reliability Evaluation of Composite Systems With Renewables and Plug-in Hybrid Electric Vehicles
(Institute of Electrical and Electronics Engineers Inc., 2025) Yarramsetty, C.; Moger, T.; Jena, D.; Rao, V.S.
This paper proposes a novel hybrid framework that integrates machine learning (ML) techniques with Sequential Monte Carlo Simulation (SMCS) to enhance the reliability assessment of modern power systems incorporating renewable energy resources (RER) and plug-in hybrid electric vehicle (PHEVs) integration. While PHEVs can leverage RER to significantly reduce greenhouse gas emissions, the increased energy demand from large PHEVs fleets poses potential challenges to power system reliability. To address these issues, this research presents an advanced mixed-integer linear programming (MILP) based algorithm for optimizing EV charging. The algorithm prioritizes clean energy utilization through intelligent power allocation strategies while considering cost-revenue trade-offs. A probabilistic model is developed to account for factors such as driving distance, charging times, locations, battery state of charge, and charging needs of PHEVs. The proposed approach is tested on the IEEE RTS-79 test system and evaluates multiple ML architectures, including Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Transformer models, often combined with boosting algorithms, across three scenarios: base case, uncontrolled charging, and intelligent charging. Results highlight that ML-based approaches, particularly the Transformer model, achieve computational time reductions of up to 49% compared to traditional SMCS methods while maintaining comparable accuracy. The Transformer model identified 1,788 loss-of-load states compared to 1,510 actual instances, requiring only 176 minutes of computation. Among all models, the BiLSTM with Adaptive Boosting (BiLSTM+AB) achieved the lowest overestimation, exceeding actual instances by just 256 states. Performance metrics such as Loss of Load Probability (LOLP) and Expected Demand Not Supplied (EDNS) validate the effectiveness of the proposed ML approaches in balancing accuracy and computational efficiency. © 2013 IEEE.
A review of estimation of effective wind speed based control of wind turbines
(Elsevier Ltd, 2015) Jena, D.; RAJENDRAN, S.
This paper provides a comprehensive literature review on the estimation of effective wind Speed (EEWS), and EEWS based control techniques applied to wind turbine (WT). Several numbers of good publications have reported the EEWS based control of wind turbine. Wind speed is a driving force for the wind turbine system. In general wind speed measurement is carried out by anemometer which is located at the top of the nacelle. The optimal shaft speed is derived from the exact measurement of wind speed to extract the optimal power output at below rated wind speed. The wind speed provided by the anemometer is measured at a single point of the rotor plane which is not the accurate effective wind speed. At the same time anemometer increases the overall cost, maintenance and reduce the reliability of the overall system. So an accurate EEWS based control technique is required for WT systems to get the optimal power output. In this paper, a detailed description and classification of EEWS and some EEWS based control techniques have been discussed which is based on control strategy and complexity level of WT system. All most all previous work estimates the wind speed using EEWS techniques such as Kalman filter (KF), extended Kalman filter (EKF), neural network (NN) etc., and then different control techniques are applied. In the last section of this paper integral sliding mode control (ISMC) of a WT at below rated speed region is considered. Operating points are determined by proper estimation of effective wind speed, and modified Newton Raphson (MNR) is employed to estimate this. Finally simulation results are presented with a comparison between proposed ISMC, sliding mode control (SMC) and classical controllers such as aerodynamic torque feed forward (ATF) and indirect speed control (ISC). © 2014 Elsevier Ltd. All rights reserved.
A review of estimation of effective wind speed based control of wind turbines
(Elsevier Ltd, 2015) Jena, D.; RAJENDRAN, S.
This paper provides a comprehensive literature review on the estimation of effective wind Speed (EEWS), and EEWS based control techniques applied to wind turbine (WT). Several numbers of good publications have reported the EEWS based control of wind turbine. Wind speed is a driving force for the wind turbine system. In general wind speed measurement is carried out by anemometer which is located at the top of the nacelle. The optimal shaft speed is derived from the exact measurement of wind speed to extract the optimal power output at below rated wind speed. The wind speed provided by the anemometer is measured at a single point of the rotor plane which is not the accurate effective wind speed. At the same time anemometer increases the overall cost, maintenance and reduce the reliability of the overall system. So an accurate EEWS based control technique is required for WT systems to get the optimal power output. In this paper, a detailed description and classification of EEWS and some EEWS based control techniques have been discussed which is based on control strategy and complexity level of WT system. All most all previous work estimates the wind speed using EEWS techniques such as Kalman filter (KF), extended Kalman filter (EKF), neural network (NN) etc., and then different control techniques are applied. In the last section of this paper integral sliding mode control (ISMC) of a WT at below rated speed region is considered. Operating points are determined by proper estimation of effective wind speed, and modified Newton Raphson (MNR) is employed to estimate this. Finally simulation results are presented with a comparison between proposed ISMC, sliding mode control (SMC) and classical controllers such as aerodynamic torque feed forward (ATF) and indirect speed control (ISC). Â© 2014 Elsevier Ltd. All rights reserved.
A sensitivity matrix-based temperature-augmented probabilistic load flow study
(Institute of Electrical and Electronics Engineers Inc., 2017) Prusty, B.R.; Jena, D.
This paper proposes a hybridmethod for probabilistic load flow (PLF) study to analyze the influence of uncertain photovoltaic generations and load demands on transmission system performance. Besides, the paper focuses on accurate approximation of multimodal distributions of result variables in a temperatureaugmented PLF model without using any series expansion methods. The effect of uncertain ambient temperature on result variables is discussed. Multiple correlation cases between the input bus powers are considered. The performance of the proposed method is investigated on modified New England 39-bus power system. The results are compared with four well-established analyticalmethods and Monte Carlo simulation. The effect of multiple input correlations on probability distributions of result variables is analyzed. © 2017 IEEE.
A spatiotemporal probabilistic model-based temperature-augmented probabilistic load flow considering PV generations
(John Wiley and Sons Ltd vgorayska@wiley.com Southern Gate Chichester, West Sussex PO19 8SQ, 2019) Prusty, B.R.; Jena, D.
The probabilistic steady-state forecasting of a PV-integrated power system requires a suitable forecasting model capable of accurately characterizing the uncertainties and correlations among multivariate inputs. The critical and foremost difficulties in the development of such a model include the accurate representation of the characterizing features such as complex nonstationary pattern, non-Gaussianity, and spatial and temporal correlations. This paper aims at developing an improved high-dimensional multivariate spatiotemporal model through enhanced preprocessing, transformation techniques, principal component analysis, and a suitable time series model that is capable of accurately modeling the trend in the variance of uncertain inputs. The proposed model is applied to the probabilistic load flow carried out on the modified Indian utility 62-bus transmission system using temperature-augmented system model for an operational planning study. A detailed discussion of various results has indicated the effectiveness of the proposed model in capturing the aforesaid characterizing features of uncertain inputs. © 2019 John Wiley & Sons, Ltd.