Kartik S. Pandya
Parul University, Charotar University of Science & Technology, The M.S. University of Baroda, L. D. College of Engineering, Gujarat Electricity Board, L.D. College of Engineering
0000-0002-0155-000X
About
Kartik S. Pandya obtained his Ph.D from the M. S. University of Baroda, Gujarat, INDIA. His area of interest are: Smart Grid Optimization, Nature-inspired Computational Intelligence methods, Integration of Renewable Energy Sources, Restructured Power System, Power system operation and control and Power system protection optimization.
His team's proposed computational intelligence optimization algorithms entitled, “Hybrid Levy Particle Swarm Variable Neighborhood Search Optimization (HL_PS_VNSO)” and “Gauss Mapped Variable Neighborhood Particle Swarm Optimization (GM_VNPSO)” secured 2nd and 3rd ranks respectively in the international competitions at 2019 IEEE Congress On Evolutionary Computation (CEC) and The Genetic and Evolutionary Computation Conference (GECCO) 2019 at New Zealand and Czech Republic from June 10-13, 2019 and July 13-17, 2019 respectively.
His team's proposed computational intelligence optimization algorithm entitled, “Entropy Enhanced Covariance Matrix Adaptation Evolution Strategy” had secured a 2nd rank in IEEE Power & Energy Society (PES) worldwide Competition 2018 entitled “Emerging heuristic optimization algorithms for operational planning of sustainable electrical power systems” at IEEE PES general meeting at Portland, Oregon, USA from 5-9 August, 2018.
His team's proposed two computational intelligence optimization algorithms entitled, “Enhanced Velocity Differential Evolutionary Particle Swarm Optimization (EVDEPSO)” and “Improved_ Chaotic_Differential Evolutionary Particle Swarm Optimization (IC_DEEPSO)” had secured 2nd rank and 4th ranks respectively in the IEEE World Congress on Computational Intelligence 2018 (WCCI 2018) Competition entitled “Evolutionary Computation in Uncertain Environments: A Smart Grid Application” at WCCI conference at Rio de Janeiro, Brazil from 8-13 July, 2018.
His team's proposed computational intelligence optimization algorithm entitled “Levy Differential Evolutionary Particle Swarm Optimization (Levy DEEPSO)” had secured a 3rd rank in IEEE PES Worldwide Competition 2017 entitled “Evaluating the Performance of Modern Heuristic Optimizers on Smart Grid Operation Problems” at IEEE PES general meeting 2017 at Chicago-USA, from 16-20 July, 2017.
His team had successfully executed a unique consultancy project of ABB entitled “Prototype Development of Switch-Sync Simulator Demo Kit”.
Employment
-
Parul University Associate Professor2023 - Present
-
Charotar University of Science & Technology Professor2014 - 2022
-
Gujarat Electricity Board Apprentice2001 - 2001
Education
-
The M.S. University of Baroda Ph.D2008 - 2013
-
L. D. College of Engineering M.E. in Electrical Engineering1999 - 2001
-
L.D. College of Engineering B.E. in Electrical Engineering1996 - 1999
Projects & Funding
Projects & funding information is unavailable.
Publications (69)
- Advance technique for online condition monitoring of surge arresters Save
- Social Nudging for Sustainable Electricity Use: Behavioral Interventions in Energy Conservation Policy Save
- Physics-informed neural networks for predicting sediment transport in pressurized pipe flows Save
- Advancing Water Quality Management: An Integrated Approach Using Ensemble Machine Learning and Real-Time Interactive Visualization Save
- A Novel Approach to the Ageing Process of Metal Oxide Surge Arresters Save
- Empowering customers in local electricity market: A prosumer segmentation and operating envelope strategy for joint cost reduction and profit maximization Save
- Issues and Solutions for Optimum Overcurrent Relays Co-Ordination in Medium Voltage Radial Distribution System Save
- Optimum Overcurrent Relay Coordination for Radial Distribution Networks Using Improved Mathematical Formulation Save
- Overcurrent Relay Coordination and Adaptive Relay Setting of Distributed Network Using Multiple Standardized Tripping Relays and Improved Mathematical Formulation Save
- Multi-objective optimized high-strength concrete mix design using a hybrid machine learning and metaheuristic algorithm Save