Browsing by Author "Kalita, Karuna"

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Analysis of Pulsating Magnetic Fields in a Parallel Stator Winding Eccentric Rotor Induction Motor
(2024-02) Kumar, Gaurav; Kalita, Karuna; Tammi, Kari; Kumar, Praveen; Pippuri-Mäkeläinen, Jenni; Banerjee, Atanu
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
A parallel stator winding is considered effective in terms of unbalanced magnetic pull suppression. The present investigation concentrates on bridge configured winding which is a special type of parallel winding connection. When the rotor of the motor is eccentric, an additional pulsating force is generated in parallel stator winding motor compared to a series stator winding motor. This work investigates the behavior of the magnetic field to find out the reason behind the pulsating nature of the eccentric forces in parallel winding. It is demonstrated that the six-pole pulsating magnetic field is the reason behind the generation of pulsating forces. This has also been verified through experimental measurements.
Charging Station Placement for Electric Vehicles : A Case Study of Guwahati City, India
(2019) Deb, Sanchari; Tammi, Kari; Kalita, Karuna; Mahanta, Pinakeswar
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
The ever-increasing population of India accompanied by the recent concerns regarding fossil fuel depletion and environmental pollution has made it indispensable to develop alternate mode of transportation. Electric vehicle (EV) market in India is expanding. For acceptance of EVs among the masses, development of charging infrastructure is of paramount importance. This paper formulates and solves the charging infrastructure-planning problem for Guwahati, India, that will develop as a smart city soon. The allocation of charging station problem was framed in a multi-objective framework considering the economic factors, power grid characteristics, such as voltage stability, reliability, power loss, as well as EV user's convenience, and random road traffic. The placement problem was solved by using a Pareto dominance-based hybrid algorithm amalgamating chicken swarm optimization (CSO) and the teaching learning-based optimization (TLBO) algorithm. Finally, the Pareto optimal solutions were compared by fuzzy decision-making.
Design methodology for a special single winding based bearingless switched reluctance motor
(2017-06-01) Choudhury, Madhurjya Dev; Ahmed, Firdausa; Kumar, Gaurav; Kalita, Karuna; Tammi, Kari
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Bearingless switched reluctance motors (BSRMs) have both magnetic bearing as well as conventional motor characteristics which make them suitable for diverse industrial applications. This study proposes a design methodology for a BSRM in order to calculate the appropriate geometrical dimensions essential for realising a minimum levitation force at every orientation of rotor. It is based on the stator–rotor overlap angle and helps in reducing the complexities associated with the self-bearing operation of a switched reluctance motor (SRM). Different from a conventional SRM, the motor under study deploys a special single set parallel winding scheme for simultaneous production of torque as well as radial force. An analytical model incorporating this single set winding is developed for calculating the torque and the radial force. The proposed bearingless design is verified by developing a two-dimensional finite-element model of a 12/8 SRM in ANSYS Maxwell.
A Hybrid Multi-Objective Chicken Swarm Optimization and Teaching Learning Based Algorithm for Charging Station Placement Problem
(2020-05-29) Deb, Sanchari; Tammi, Kari; Gao, Xiao Zhi; Kalita, Karuna; Mahanta, Pinakeswar
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
A new hybrid multi-objective evolutionary algorithm is developed and deployed in the present work for the optimal allocation of Electric Vehicle (EV) charging stations. The charging stations must be positioned on the road in such a way that they are easily accessible to the EV drivers and the electric power grid is not overloaded. The optimization framework aims at simultaneously reducing the cost, guaranteeing sufficient grid stability and feasible charging station accessibility. The grid stability is measured by a composite index consisting of Voltage stability, Reliability, and Power loss (VRP index). A Pareto dominance based hybrid Chicken Swarm Optimization and Teaching Learning Based Optimization (CSO TLBO) algorithm is utilized to obtain the Pareto optimal solution. It amalgamates swarm intelligence with teaching-learning process and inherits the strengths of CSO and TLBO. The two level algorithm has been validated on the multi-objective benchmark problems as well as EV charging station placement. The performance of the Pareto dominance based CSO TLBO is compared with that of other state-of-the-art algorithms. Furthermore, a fuzzy decision making is used to extract the best solution from the non dominated set of solutions. The combination of CSO and TLBO can yield promising results, which is found to be efficient in dealing with the practical charging station placement problem.
Impact of Electric Vehicle Charging Station Load on Distribution Network
(2018-01-15) Deb, Sanchari; Tammi, Kari; Kalita, Karuna; Mahanta, Pinakeshwar
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Recent concerns about environmental pollution and escalating energy consumption accompanied by the advancements in battery technology have initiated the electrification of the transportation sector. With the universal resurgence of Electric Vehicles (EVs) the adverse impact of the EV charging loads on the operating parameters of the power system has been noticed. The detrimental impact of EV charging station loads on the electricity distribution network cannot be neglected. The high charging loads of the fast charging stations results in increased peak load demand, reduced reserve margins, voltage instability, and reliability problems. Further, the penalty paid by the utility for the degrading performance of the power system cannot be neglected. This work aims to investigate the impact of the EV charging station loads on the voltage stability, power losses, reliability indices, as well as economic losses of the distribution network. The entire analysis is performed on the IEEE 33 bus test system representing a standard radial distribution network for six different cases of EV charging station placement. It is observed that the system can withstand placement of fast charging stations at the strong buses up to a certain level, but the placement of fast charging stations at the weak buses of the system hampers the smooth operation of the power system. Further, a strategy for the placement of the EV charging stations on the distribution network is proposed based on a novel Voltage stability, Reliability, and Power loss (VRP) index. The results obtained indicate the efficacy of the VRP index.
A New Teaching–Learning-based Chicken Swarm Optimization Algorithm
(2019) Deb, Sanchari; Gao, Xiao Zhi; Tammi, Kari; Kalita, Karuna; Mahanta, Pinakeswar
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Chicken Swarm Optimization (CSO) is a novel swarm intelligence-based algorithm known for its good performance on many benchmark functions as well as real-world optimization problems. However, it is observed that CSO sometimes gets trapped in local optima. This work proposes an improved version of the CSO algorithm with modified update equation of the roosters and a novel constraint-handling mechanism. Further, the work also proposes synergy of the improved version of CSO with Teaching–Learning-based Optimization (TLBO) algorithm. The proposed ICSOTLBO algorithm possesses the strengths of both CSO and TLBO. The efficacy of the proposed algorithm is tested on eight basic benchmark functions, fifteen computationally expensive benchmark functions as well as two real-world problems. Further, the performance of ICSOTLBO is also compared with a number of state-of-the-art algorithms. It is observed that the proposed algorithm performs better than or as good as many of the existing algorithms.
A Novel Chicken Swarm and Teaching Learning based Algorithm for Electric Vehicle Charging Station Placement Problem
(2021-04-01) Deb, Sanchari; Gao, Xiaozhi; Tammi, Kari; Kalita, Karuna; Mahanta, Pinakeswar
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
The current concern about the ever-escalating demand for energy, exhaustive nature of fossil fuels, global warming accompanied by climate change has necessitated the development of an alternate pollution-free mode of commute. Electric Vehicles (EV) are an environmentally friendly alternative to reduce the reliance on fossil fuel and pollution. For public acceptance of EVs, functionality and accessibility of charging stations is of paramount importance. Improper planning of EV charging stations, however, is a threat to the power grid stability. EV charging stations must be placed in the transport network in such a way that the safe limit of distribution network parameters is not violated. Thus, charging station placement problem is an intricate problem involving convolution of transport and distribution networks. A novel and simple approach of formulating the charging station placement problem is presented in this work. This approach takes into account integrated cost of charging station placement as well as penalties for violating grid constraints. For obtaining an optimal solution of this placement problem, two efficient evolutionary algorithms, such as Chicken Swarm Optimization (CSO) and Teaching Learning Based Optimization algorithm (TLBO) are amalgamated together thereby extracting the best features of the both algorithms. The efficacy of the proposed algorithm is tested by solving selected standard benchmark problems as well as charging station placement problem. The result of this hybrid algorithm is further compared with other algorithms used for this purpose.
Recent Studies on Chicken Swarm Optimization algorithm: a review (2014–2018)
(2020-03-01) Deb, Sanchari; Gao, Xiao Zhi; Tammi, Kari; Kalita, Karuna; Mahanta, Pinakeswar
A2 Katsausartikkeli tieteellisessä aikakauslehdessä
Solving a complex optimization problem in a limited timeframe is a tedious task. Conventional gradient-based optimization algorithms have their limitations in solving complex problems such as unit commitment, microgrid planning, vehicle routing, feature selection, and community detection in social networks. In recent years population-based bio-inspired algorithms have demonstrated competitive performance on a wide range of optimization problems. Chicken Swarm Optimization Algorithm (CSO) is one of such bio-inspired meta-heuristic algorithms mimicking the behaviour of chicken swarm. It is reported in many literature that CSO outperforms a number of well-known meta-heuristics in a wide range of benchmark problems. This paper presents a review of various issues related to CSO like general biology, fundamentals, variants of CSO, performance of CSO, and applications of CSO.
Review of recent trends in charging infrastructure planning for electric vehicles
(2019-11) Deb, Sanchari; Tammi, Kari; Kalita, Karuna; Mahanta, Pinakeswar
A2 Katsausartikkeli tieteellisessä aikakauslehdessä
The exhaustive nature of fossil fuels and environmental concerns associated with greenhouse gases are the major causes of the paradigm shift from conventional vehicles to electric vehicles (EVs). The electrification of the transportation sector and the increasing popularity of the EVs have driven scientists and researchers to delve into charging stations. Underdeveloped charging infrastructure, optimal placement of charging stations, and charge scheduling in the charging stations are the major concerns for the large-scale deployment of EVs. Even few questions related to EVs, such as the vehicle price and driving range, can be partially solved with a well-developed charging infrastructure. Existing old infrastructure may bring severe limitations in the realization of the optimal placement of charging stations as EVs have not existed when the road and grid infrastructure have been developed. For the past few years, the studies associated with the optimal placement and sizing of EV charging stations have drawn the consideration of the researchers. The researchers have used different approaches, objective functions, and applied different optimization algorithms while dealing with the problem of charging station placement. The complex and dynamic nature of the problem has led researchers to apply different optimization algorithms for the solution of the problem. In this particular review study, the research works bearing on the charging infrastructure planning for the EVs are considered. Thus, this review work will endow the research community with the latest developments and research findings in the paradigm of charging infrastructure planning for EVs. This article is categorized under: Energy Infrastructure > Systems and Infrastructure Energy and Urban Design > Systems and Infrastructure Energy and Transport > Science and Materials Energy Systems Analysis > Science and Materials
A review on linear switched reluctance motor
(2017) Chowdhury, Juan; Kumar, Gaurav; Kalita, Karuna; Tammi, Kari; Kakoty, Sashindra K
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Switched reluctance motors have been extensively studied by researchers for their unparalleled advantages in many applications. The linear versions have been developed around the world in the last couple of decades because of attributes similar to that of rotary switched reluctance motor(RSRM). Owing to their frugal design, robust built and high force density, the linear switched reluctance motor (LSRM) has had significant stages of development and optimization. The flexibility in design and operation makes LSRM a prime contender for any linear motor-actuator application. This paper provides a bird’s eye view across its developmental stages and its various aspects in design, analysis and control. The following content discusses the salient points of research and the contribution by researchers in this field.
A Robust Two-Stage Planning Model for the Charging Station Placement Problem Considering Road Traffic Uncertainty
(2022-07) Deb, Sanchari; Tammi, Kari; Gao, Xiao Zhi; Kalita, Karuna; Mahanta, Pinakeswar; Cross, Sam
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
The current critical global concerns regarding fossil fuel exhaustion and environmental pollution have been driving advancements in transportation electrification and related battery technologies. In turn, the resultant growing popularity of electric vehicles (EVs) calls for the development of a well-designed charging infrastructure. However, an inappropriate placement of charging stations might hamper smooth operation of the power grid and be inconvenient to EV drivers. Thus, the present work proposes a novel two-stage planning model for charging station placement. The candidate locations for the placement of charging stations are first determined by fuzzy inference considering distance, road traffic, and grid stability. The randomness in road traffic is modelled by applying a Bayesian network (BN). Then, the charging station placement problem is represented in a multi-objective framework with cost, voltage stability reliability power loss (VRP) index, accessibility index, and waiting time as objective functions. A hybrid algorithm combining chicken swarm optimization and the teaching-learning-based optimization (CSO TLBO) algorithm is used to obtain the Pareto front. Further, fuzzy decision making is used to compare the Pareto optimal solutions. The proposed planning model is validated on a superimposed IEEE 33-bus and 25-node test network and on a practical network in Tianjin, China. Simulation results validate the efficacy of the proposed model.