Journal Articles
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Item Modeling of photovoltaic system for uniform and non-uniform irradiance: A critical review(Elsevier Ltd, 2015) Jena, D.; Ramana, V.V.A critical review on various modeling approaches of photovoltaic array both under uniform and non-uniform irradiance is presented in this paper. The main approaches that have been deliberated are based on the variation of analytical methods, classical optimization techniques and soft computing techniques. The review has been taken from papers published up to 2015. In this paper a detailed description and classifications of modeling techniques for both uniform and non-uniform irradiance conditions are presented. Modeling of PV systems under uniform irradiance is classified into non-iterative methods, iterative methods, artificial intelligence based methods and dynamic models. Under non-uniform irradiance, they are classified into non-iterative methods, iterative methods and artificial intelligence based methods. It is envisaged that this paper can serve as valuable information for researchers to work on photovoltaic array modeling under partial shaded condition. © 2015 Elsevier Ltd. All rights reserved.Item 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.Item 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 LtdItem Review of preprocessing methods for univariate volatile time-series in power system applications(Elsevier Ltd, 2021) Ranjan, K.G.; Prusty, B.R.; Jena, D.Outlier detection and correction of time-series referred to as preprocessing, play a vital role in forecasting in power systems. Rigorous research on this topic has been made in the past few decades and is still ongoing. In this paper, a detailed survey of different preprocessing methods is made, and the existing preprocessing methods are categorized. Also, the preprocessing capability of each method is highlighted. The well-established methods of each category applicable to univariate data are critically analyzed and compared based on their preprocessing ability. The result analysis includes applying the well-established methods to volatile time-series frequently used in power system applications. PV generation, load power, and ambient temperature time-series (clean and raw) of different time-step collected from various places/weather zones are considered for index-based and graphical-based comparison among the well-established methods. The impact of change in the crucial parameter(s) values and time-resolution of the data on the methods’ performance is also elucidated in this paper. The pros and cons of methods are discussed along with the scope for improvisation. © 2020Item Uncertainty handling techniques in power systems: A critical review(Elsevier Ltd, 2022) Singh, V.; Moger, T.; Jena, D.Integration of renewable generations with electrical power systems has gained considerable attention in recent years due to environmental and economic benefits. However, this integration introduces additional uncertainties into the existing system and requires appropriate uncertainty modeling for power systems. Typically the uncertainties in power systems are modeled using probabilistic or possibilistic approaches. A combined probabilistic-possibilistic approach is necessary when some uncertain variables are probabilistic and others are possibilistic. This paper presents a complete review of uncertainty categorization and several techniques to address the uncertainty in power systems, along with the merits and weaknesses of each technique. The challenges have been highlighted for future research directions. Analytical and approximate methods are reviewed in this paper when wind power generations are integrated into the existing power grid. Considering the uncertainties of wind power generation and system load demands, the basic probabilistic methods such as Monte-Carlo simulation, cumulant, and 2n+1 point estimation methods are implemented. To explore the capability and shortcoming of these basic methods, a 72-bus equivalent system of Indian southern region power grid is taken into consideration. The results obtained using Monte-Carlo simulation method are treated as a benchmark to analyze the performance of the cumulant and 2n+1 point estimation methods. © 2021 Elsevier B.V.Item 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.Item Wind Turbine Emulators—A Review(Multidisciplinary Digital Publishing Institute (MDPI), 2023) RAJENDRAN, S.; Diaz-D, M.; Devi, V.S.K.; Jena, D.; Travieso-Torres, J.C.; Rodríguez, J.Renewable energy sources have become a significant alternative energy source due to the continuing depletion of conventional energy sources and fluctuation in fuel costs. Currently, wind energy is the foremost among all other renewable energy sources. However, modeling and analyzing industrial wind turbines is complex as the wind turbine power ratio and size have steadily increased. Undoubtedly, industrial wind turbines are huge and challenging to keep in research labs; simultaneously, exploring the controller/power converter performance is practically impossible. Therefore, to overcome the above drawbacks, wind turbine emulators have been developed to achieve the static and dynamic characteristics of wind energy conversion systems. This paper aims to present a comprehensive review of the different wind turbine emulators available in the literature. In addition, the implementation of real-time emulators is classified according to the structure and approaches. Furthermore, an extensive analysis of the emulators was presented based on the significant parameters utilized for the real-time wind turbine emulators. Finally, this review analyzes the different emulator topologies according to cost, accuracy, complexity, and hardware implementation. © 2023 by the authors.Item A survey on load frequency control using reinforcement learning-based data-driven controller(Elsevier Ltd, 2024) Muduli, R.; Jena, D.; Moger, T.Load frequency control (LFC) is a significant control problem in the operation of interconnected power systems. It keeps the change in system frequency within specific limits by maintaining the balance between power generation and load demand. In modern interconnected power systems, various control strategies, including conventional control techniques and other data-driven approaches, have been adopted to improve the effectiveness of LFC. The control technique based on reinforcement learning (RL) is one of the contemporary data-driven control strategies for LFC. Recently, the attention of researchers has surged towards RL-based control strategies for LFC. Several survey literature has been published in the field of LFC concerning the various control strategies for the effective operation of the power system. However, these surveys have not considered a complete systematic review of RL-driven LFC. An exhaustive review is essential to demonstrate the current status and identify future advancements in this field. This paper presents a comprehensive review of LFC based on the RL-driven control strategy. This study begins by presenting a mathematical and conceptual understanding of reinforcement learning. Finally, a broad classification of RL algorithms and the algorithm-wise literature survey on LFC are provided extensively. This comprehensive and insightful literature survey may serve as a valuable resource for the researchers, addressing the gaps between recent advances, implementation difficulties, and future developments in LFC using the RL-driven control strategy. © 2024 Elsevier B.V.Item Advances in Composite Power System Reliability Assessment: Trends and Machine Learning Role(Institute of Electrical and Electronics Engineers Inc., 2025) Yarramsetty, C.; Moger, T.; Jena, D.; Rao, V.S.This paper provides a detailed review of reliability assessment methods for composite power systems, focusing on integrating renewable energy and advanced computational approaches. The study classifies existing research into three main areas: investigation studies, planning and optimization studies, and efficient evaluation studies. Findings indicate that machine learning techniques are increasingly important in improving accuracy and computational performance in reliability analysis. A comparative examination of conventional and Machine Learning (ML)-based methods demonstrates that deep learning models, such as Convolutional Neural Networks, offer substantial reductions in computational time while maintaining reliability assessment precision. The review also identifies key research gaps, such as the need for realistic test systems and enhanced hybrid ML strategies. Additionally, recommendations are proposed to address these challenges and strengthen the effectiveness of future reliability evaluation methodologies. The insights presented in this study aim to support researchers and industry professionals in developing more efficient and scalable reliability assessment models for evolving power systems. © 2013 IEEE.Item Condition Monitoring of Submodule Capacitors in Modular Multilevel Converters—A Review(Elsevier Ltd, 2025) Saravanakumar, R.; Sivakumar, N.; Devi, V.S.K.; Shanthini, C.; Jena, D.; Ibaceta, E.; Diaz-D, M.; Rodríguez, J.Modular Multilevel Converters are highly promising power converter technologies used in high-voltage and high-power applications. The applications of modular multilevel converters are being increased in various industrial and renewable energy sectors due to their superior performance and efficiency. The modular multilevel converters contain multiple submodule capacitors, and these capacitors are the fragile components. The operating conditions and performance of these capacitors directly influence the system's reliability and operation. Hence, condition monitoring schemes are essential for submodule capacitors to ensure and enhance the modular multilevel converters operation which consequently reduces unscheduled maintenance. This article provides a detailed review and comprehensive analysis of condition monitoring schemes for submodule capacitors in modular multilevel converters. The review classifies the existing condition monitoring schemes into four major groups and thirteen subgroups and analyzes their methodologies using advantages and limitations of each scheme. Further, a critical analysis is presented with five significant parameters used to evaluate the condition monitoring schemes. The review highlighted the challenges related to condition monitoring accuracy, cost-effectiveness and system architecture that are to be studied in future. © 2025 Elsevier Ltd