D
Dipayan Guha
Researcher at Motilal Nehru National Institute of Technology Allahabad
Publications - 69
Citations - 1444
Dipayan Guha is an academic researcher from Motilal Nehru National Institute of Technology Allahabad. The author has contributed to research in topics: Electric power system & PID controller. The author has an hindex of 15, co-authored 48 publications receiving 820 citations. Previous affiliations of Dipayan Guha include National Institute of Technology, Durgapur & Dr. B.C. Roy Engineering College, Durgapur.
Papers
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Load frequency control of interconnected power system using grey wolf optimization
TL;DR: Simulation results show that GWO has better tuning capability than CLPSO, EPSDE and other similar population-based optimization techniques.
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Load frequency control of large scale power system using quasi-oppositional grey wolf optimization algorithm
TL;DR: Time domain simulation results confirm the potentiality and efficacy of the proposed QOGWO method over other intelligent methods like fuzzy logic, artificial neural network (ANN) and adaptive neuro-fuzzy interface system (ANFIS) controller.
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Optimal tuning of 3 degree-of-freedom proportional-integral-derivative controller for hybrid distributed power system using dragonfly algorithm
TL;DR: The efficacy of proposed DA over different reported algorithms is established in terms of convergence rate, minimum fitness value and dynamic performance of the system, and the robustness of the 3-DOF PID-controller is ascertained.
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Study of differential search algorithm based automatic generation control of an interconnected thermal-thermal system with governor dead-band
TL;DR: The extensive results presented in this article demonstrate that proposed DSA can effectively improve system dynamics and may be applied to real-time LFC problem.
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Quasi-oppositional symbiotic organism search algorithm applied to load frequency control
TL;DR: The theory of quasi-oppositional based learning (Q-OBL) is integrated with original SOS and used to solve the LFC problem and the success of QOSOS algorithm is established by comparing the dynamic performances of concerned power system with those obtained by some recently published algorithms available in the literature.