Short Term Hydro Thermal Scheduling by Artificial Fish Swarm Algorithm - Prusty, Ramesh Chandra;Mondal, Shovan;Majhi, Santosh Kumar
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Operation of a system having both hydro and thermal plants is far more complex than the system having only thermal plants. The hydro plants have negligible operating cost, but are required to operate under constraints of water available for hydro generation in a given period of time. The problem of minimizing the operating cost of a hydrothermal system can be viewed as one of minimizing the fuel cost of thermal plants under the constraints of water availability (storage and inflows) for hydro generation over a given period of operation several AI methods, such as Genetic Algorithm,…mehr

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Produktbeschreibung
Operation of a system having both hydro and thermal plants is far more complex than the system having only thermal plants. The hydro plants have negligible operating cost, but are required to operate under constraints of water available for hydro generation in a given period of time. The problem of minimizing the operating cost of a hydrothermal system can be viewed as one of minimizing the fuel cost of thermal plants under the constraints of water availability (storage and inflows) for hydro generation over a given period of operation several AI methods, such as Genetic Algorithm, Evolutionary programming, Particle swarm Optimization and Differential Evolution are utilized to find out the optimal solution of constrained optimization problem but, because of certain drawbacks such as drastic growth of computational and dimensional requirements and insecure convergence properties, these optimization technique are not suitable for such problem. Thus Artificial Fish Swarm Algorithm (AFSA) a new optimization technique has been adopted for solving the hydro thermal scheduling problems.
Autorenporträt
Dr. Ramesh Chandra Pusty and Santosh Kumar Majhi are working as Asst Professor in V.S.S University of Technology, Burla, India and Shovan Mondal is working as Asst Engineer for West Bengal Government.