Browsing by Author "Rakesh Kumar Misra"

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A Nested-Iterative Newton-Raphson based Power Flow Formulation for Droop-based Islanded Microgrids
(Elsevier Ltd, 2020) Abhishek Kumar; Bablesh Kumar Jha; Dharmendra Kumar Dheer; Rakesh Kumar Misra; Devender Singh
In this paper, an iterative novel power flow technique is proposed to obtain the operating point of Droop Based Islanded Microgrid (DBIMG). The proposed technique considers system frequency as an additional variable to obtain the steady-state operating point of the system. To generalize the proposed technique, four operating modes of Distributed Generations (DGs) including droop control, isochronous, PV and PQ mode are considered. In this study, the formulated power flow problem consists of a set of non linear and linear power flow equations. To solve these set of equations, a Nested-Iterative Newton-Raphson algorithm is proposed. The proposed algorithm is implemented on several test systems including 6-bus, 22-bus, 38-bus, 69-bus, and 160-bus. To examine the robustness and effectiveness of the proposed algorithm, the power flow solutions obtained by implementing the proposed algorithm are compared with the power flow solutions obtained by implementing the existing Newton-Raphson algorithms including Modified Newton-Raphson (MNR), Newton Trust-Region (NTR) and time-domain simulator: PSCAD/EMTDC. The results show better efficiency and superior convergence of the proposed algorithm in comparison to the existing algorithms. © 2019 Elsevier B.V.
A Novel False Data Injection Attack Formulation Based on CUR Low-Rank Decomposition Method
(Institute of Electrical and Electronics Engineers Inc., 2022) Debottam Mukherjee; Sandip Ghosh; Rakesh Kumar Misra
State estimation algorithms deployed in modern grids are vulnerable towards false data injection attacks. This work demonstrates a novel attack vector (AV) formulation scheme by exploiting the low-rank subspace of the topology matrix. With constraints over the state deviation vector, this work showcases that the developed AV can effectively bypass the residue test for a demonstrated test bench. © 2010-2012 IEEE.
Coordinated effect of PHEVs with DGs on distribution network
(John Wiley and Sons Ltd, 2019) Bablesh Kumar Jha; Abhishek Kumar; Devender Singh; Rakesh Kumar Misra
In this paper, a base case of distribution system without PHEVs and DGs has been studied to evaluate system performance characteristics. Further, effect of introduction of PHEVs has been investigated in terms of system operating cost, losses, voltage profile, and load flattening. To improve these characteristics, introduction of DGs has been investigated by simulating different penetration level of PHEVs along with different demand response (DR) levels. It has been demonstrated that with the DG scheduling considering appropriate DR levels, the system operating cost, losses, voltage profile, and load flattening can be improved. The 24-hour DG scheduling is carried out to optimize the system cost, which is function of charging/discharging cost, losses, and cost of DGs power. A differential evolution (DE) search algorithm is used to optimize single objective weighted fitness function. IEEE 38-Bus test system is used for demonstration of the investigations. In case of lower-penetration levels, operating cost is significantly affected by the DR characteristics of a system. The method suggested can help local distribution companies (LDC) in optimally scheduling the DGs, in presence of PHEV, to achieve the minimum system cost, improved voltage profile, minimum losses, and improved load fattening. © 2018 John Wiley & Sons, Ltd.
Intelligent fuzzy rough set based feature selection using swarm algorithms with improved initialization
(IOS Press, 2019) Tarun Maini; Abhishek Kumar; Rakesh Kumar Misra; Devender Singh
This paper focuses on Fuzzy rough set, which is the fusion of fuzzy sets and rough sets theory for doing feature selection. For selecting the appropriate feature subset, swarm algorithms are used. The fitness function used here is Fuzzy Rough Dependency Measure. This paper demonstrates that by optimizing the fitness function, swarm algorithms are capable to select the best subset of features. Further, in this paper, an attempt has been made to improve the capability of the swarm based algorithms such as Intelligent Dynamic Swarm (IDS) and Particle Swarm Optimization (PSO) through modified initialization of solutions, for picking the appropriate features for the feature selection task. Improvement in the size of reducts and classification accuracy of these reducts are observed when initialization is done using the proposed method. Statistical t-tests have also been performed for the validation of the results. © 2019 - IOS Press and the authors. All rights reserved.
Phase unbalance and PAR constrained optimal active and reactive power scheduling of Virtual Power Plants (VPPs)
(Elsevier Ltd, 2021) Bablesh Kumar Jha; Amit Singh; Abhishek Kumar; Rakesh Kumar Misra; Devender Singh
In near future, the integration of unknown and unpredictable quantity of Electric Vehicles (EVs) can violate reliable and quality power service of distribution system. In spite of that, ability of simultaneously regulate active and reactive power by Plug-in-Hybrid Electric Vehicles (PHEVs) in quick response time, without affecting the batteries can help to ensure reliable and quality power service in distribution system. This paper investigates the optimal active and reactive power scheduling of PHEVs and Distributed Generations (DGs) in Virtual Power Plants (VPPs), considering unbalance and Peak-to-Average Ratio (PAR) constraints. To compute stochastic model of PHEVs, we considers dynamic nature of driving pattern based on NHTS 2017 data. The proposed approach is implemented on IEEE-25 bus unbalanced distribution system. The developed planning and operational investigation of VPPs also presents dependencies of cost and losses in terms of Unbalance factor and PAR. © 2020 Elsevier Ltd
Smart home energy management system under false data injection attack
(John Wiley and Sons Ltd, 2020) Basant K. Sethi; Debottam Mukherjee; Devender Singh; Rakesh Kumar Misra; S.R. Mohanty
Modern smart home energy management system (SHEMS) is naturally prone to cyber attack, hence it demands cyber attack resilient scheduling schemes. Current scenario of SHEMS may result in increased charging and discharging cycles deteriorating the battery life. Therefore, demand scheduling formulations also need to cater the effect of battery degradation cost along with user comfort. The present work attempts to formulate a comprehensive scheduling problem in terms of energy cost minimization considering the battery degradation cost. Further, a cyber attack resilient scheduling model is proposed in this study. This article investigates the effect of demand scheduling on the life span of battery as well as the energy cost. Further, false data injection attack (FDIA) has been modeled using machine learning techniques, and its effects on the scheduling has also been incorporated in the objective function. Scenario tree based stochastic bill generation has been also formulated to develop an FDIA resilient scheduling. Optimisation results of the study have established that the resulting formulation is robust against FDI attacks. © 2020 John Wiley & Sons Ltd