Anulekha Saha
@nitsikkim.ac.in
Department of Electrical and Electronics Engineering
National Institute of Technology Sikkim
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Prasenjit Dey, Shwe Myint, Phumin Kirawanich, Anulekha Saha, and Chaiyut Sumpavakup
Institute of Electrical and Electronics Engineers (IEEE)
This article presents a meticulous exploration of on-board traction converters deployed in Electric Multiple Units (EMUs). The study involves the development of a comprehensive traction converter and control system, encompassing essential elements such as transformers, front-end rectifiers, and DC link capacitors. The precise control of the front-end rectifier’s switching states is crucial for achieving high-quality power. A new application of the advanced Hybrid Particle Swarm Optimization (Hybrid PSOS) technique for the optimization of controller parameters is presented. This parameter tuning process aims to minimize the integral time absolute error (ITAE), a critical metric governing the regulation of DC-link capacitor voltage. Simulation results showcase the impressive attributes of on-board traction converters, including low harmonic content, a high-power factor, and stable DC voltage. Additionally, a rigorous comparative analysis is conducted between Hybrid PSOS and other established algorithms like Symbiotic Organisms Search (SOS) and Particle Swarm Optimization (PSO). Hybrid PSOS traction unit outperforms SOS and PSO, with a minimal overshoot of 1.3401%, faster settling time of 0.2413 seconds, compared to SOS (0.3884 seconds) and PSO (0.5531 seconds). Total Harmonic Distortion (THD) for secondary line currents, the values are 12.48% for PSO, 2.17% for SOS, and 1.08% for Hybrid PSOS. Hybrid PSOS consistently demonstrates its superiority, significantly enhancing system performance and stability. This research underscores the substantial potential of on-board traction converters, emphasizing their role in facilitating efficient and stable electric multiple unit (EMU) operations.
Prasenjit Dey, Anulekha Saha, Aniruddha Bhattacharya, Priyanath Das, Boonruang Marungsri, Phumin Kirawanich, and Chaiyut Sumpavakup
Elsevier
Bishwajit Dey, Raj Jadav, Anulekha Saha, and Prasenjit Dey
IEEE
When fossil fuel-powered generators produce electricity, toxic substances are emitted into the atmosphere. Power engineers have a duty to establish a compromise that would reduce dangerous gas emissions when electricity is produced profitably in addition to promoting the usage of Renewable Energy Sources (RES). Among the different combined economic emission dispatch (CEED) strategies available to solve this problem, a numerical approach termed as fractional programming (FP) method was used and compared with price penalty factor (PPF) method. The dispatchable loads are modelled using a technique known as demand side management (DSM). To reduce the cost of generation without ever reducing demand, it restructures the load demand profile. The Slime Mould Algorithm (SMA) was hybridized with JAYA and it is used in this research to achieve a balance between least generation cost and pollutants both with and without the involvement of Demand Side Management (DSM). Generation cost was minimized to ${\\$}$78032 using JAYA algorithm. This value was further reduced to ${\\$}$76757 using JAYA-SMA algorithm. Numerical data also suggest that the combined use of DSM and JAYA-SMA is superior to several commonly used algorithms in tackling dynamic economic emission dispatch problems.
Prasenjit Dey, Anulekha Saha, Poluri Srimannarayana, Aniruddha Bhattacharya, and Boonruang Marungsri
Springer Science and Business Media LLC
Prasenjit Dey, Anulekha Saha, Sourav Mitra, Bishwajit Dey, Aniruddha Bhattacharya, and Boonruang Marungsri
Springer Singapore
Prasenjit Dey, Anulekha Saha, Aniruddha Bhattacharya, and Boonruang Marungsri
Springer Science and Business Media LLC
Power system often suffers from low frequency oscillations (LFOs) which might result in instability in the long run, if allowed to sustain in the system for a long time. In order to mitigate these oscillations, power system stabilizers (PSS) are used through excitation control. Three recently developed meta-heuristic algorithms namely: Collective Decision Optimization (CDO), Grasshopper Optimization Algorithm (GOA) and Salp Swarm Algorithm (SSA) have been applied for the optimal tuning of PSS parameters for small signal stability analysis of a renewable integrated power network. This was done by designing a conventional speed-based lead-lag PSS in a multi-machine interconnected power system, whose parameters have been tuned using CDO, GOA and SSA in a way to shift all the eigenvalues associated to electromechanical modes to the left half of S plane. Comparison of the results obtained by the algorithms demonstrates the superiority of SSA over GOA and CDO to boost the overall system stability over a wide range of operating conditions. The PSS controller designed using SSA is observed to be more robust and efficient in damping out oscillations under different operating conditions.
Anulekha Saha, Aniruddha Bhattacharya, Priyanath Das, and Ajoy Kumar Chakraborty
Springer Science and Business Media LLC
This article presents a new algorithm aimed toward effective handling of the transient-stability-constrained optimal power flow (TSC_OPF) problem. The algorithm is a hybridized version of the existing differential evolution (DE) and symbiotic organism search (SOS) algorithms. It combines exploration and exploitation ability of both algorithms which results in its better performance as compared to DE and SOS acting alone. It was tested on IEEE 30 bus test system and the New England 39 bus test system. The results obtained by the proposed approach were compared with conventional TSC_OPF and also with other algorithms available in the literature. Results obtained using the proposed approach demonstrates superiority in comparison with other available algorithms in the literature.
Anulekha Saha, Aniruddha Bhattacharya, Priyanath Das, and Ajoy Kumar Chakraborty
Wiley
Anulekha Saha, A.K. Chakraborty, and Priyanath Das
SciTech Solutions
This paper offers a novel variant to the existing symbiotic organisms search (SOS) algorithm, to address optimal power flow (OPF) problems considering effects of valve-point loading (VE) and prohibited zones (POZ). Problem formulation includes minimization of cost, loss, voltage stability index (VSI) and voltage deviation (VD) and simultaneous minimization of their combinations. Quadratic cost function, effects of VE and effects of both VE and POZ have been considered. OPF formulation considering effects of both VE and POZ are not yet available in the literature. Efficacy of SOS in resolving OPF is recognized in the literature. An opposition based learning technique named quasi-reflection, is merged into existing SOS to enhance its prospects of getting nearer to superior quality solution. The proposed algorithm, named quasi-reflected symbiotic organisms search (QRSOS), is assessed for IEEE 30 and IEEE 118 bus test systems. It shows promising results in reducing the objective function values of both the systems by large margins (78.98 % in case of VD when compared to SOS and NSGA-II and 46.06 % in case of loss as compared to QOTLBO in IEEE 30 and IEEE 118 bus respectively). QRSOS also outperformed its predecessors, in terms of convergence speed and global search ability.
Anulekha Saha, , Aniruddha Bhattacharya, Ajoy Kumar Chakraborty, Priyanath Das, , , and
School of Electrical Engineering and Informatics (STEI) ITB
This piece of work deals with implementing a new meta-heuristic algorithm symbiotic organisms search to address multi-objective optimal power flow (OPF) problems in power systems considering several operational constraints. The algorithm has been implemented on IEEE 30 and IEEE 118 bus test systems for various single objective and bi-objective functions to assess its efficacy in solving the OPF problem and its ability to handle large systems. A comparative study of the results, predominantly considering those obtained using quasi oppositional teaching learning optimization(QOTLBO), teaching learning optimization (TLBO), multiobjective harmony search algorithm (MOHS), nondominated sorting genetic algorithm II (NSGA-II) from the literature are detailed in this paper. Investigation of the results reveal that the algorithm is successful in producing superior results for both the systems and its performance is also encouraging in solving conflicting objectives.
Anulekha Saha, Aniruddha Bhattacharya, Priyanath Das, and Ajoy Kumar Chakraborty
IEEE
This paper presents a new metaheuristic optimization tool for solving the transient stability constrained optimal power flow problem (TSC_OPF), by limiting the relative rotor angles of generators with respect to the center of inertia to a particular value. The algorithm is based on collective decision making approach of human beings to find solution to a particular problem. The algorithm is tested on IEEE 30 bus test system and results obtained show that the proposed technique helped in attaining stability within a very short span of time as well as handle the increased fault duration when compared to a normal OPF problem.
Anulekha Saha, Priyanath Das, and Ajoy Kumar Chakraborty
Elsevier BV
Abstract A relatively new technique to solve the optimal power flow (OPF) problem inspired by the evaporation (vaporization) of small quantity water particles from dense surfaces is presented in this paper. IEEE 30 bus and IEEE 118 bus test systems are assessed for various objectives to determine water evaporation algorithm’s (WEA) efficiency in handling the OPF problem after satisfying constraints. Comparative study with other established techniques demonstrate competitiveness of WEA in treating varied objectives. It achieved superior results for all the objectives considered. The algorithm is found to minimize its objective values by great margins even in case of large test system. Statistical analysis of all the cases using Wilcoxon’s signed rank test resulted in p-values much lower than the required value of 0.05, thereby establishing the robustness of the applied technique. Best performance of the algorithm are obtained for voltage deviation minimization and voltage stability index minimization objectives in case of IEEE 30 and IEEE 118 bus test systems respectively.
Anulekha Saha, Aniruddha Bhattacharya, Priyanath Das, and Ajoy Kumar Chakraborty
IEEE
A new evolutionary optimization technique, crow search algorithm (CSA) is proposed in this paper for solving the optimal power flow (OPF) problem. The algorithm has been implemented on IEEE 30 bus test system to check its effectiveness in solving the OPF problem after satisfying various operational constraints. A comparative study of the results is presented in this paper. Analysis of the results reveals that the algorithm is effective in producing superior results for the system.