Energy-noise trade-off to optimize the total number and the placement of wind turbines on wind farms: A hybrid approach

Mittal, P and Mitra, Kishalay (2017) Energy-noise trade-off to optimize the total number and the placement of wind turbines on wind farms: A hybrid approach. In: Indian Control Conference, ICC, 4-6 January, 2017, Indian Institute of Technology (IIT) Guwahati Guwahati; India.

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Wind energy is gaining importance as one the most progressive renewable energies due to rapid depletion of conventional energy resources. Micro-siting is the optimal way of placing turbines inside a wind farm to convert wind power into electrical energy avoiding constraints related to wake loss. Though a significant progress has been made towards proposing efficient methodologies for micro-siting, limited availability of land area has resulted in the construction of wind farms near to the human habitats causing a negative impact on the human health. Compared to the other effects, the effect of noise is a matter of immense concern for the wind farm designers, as it needs to be constrained within the mandatory limits. Using a well-established wake model and ISO-9613-2 noise calculation, this study performs a wind farm layout optimization (WFLO) based on multi-objective trade-off between minimization of the noise propagation and maximization of the energy generation. A novel hybrid methodology is proposed as a combination of probabilistic multi-objective evolutionary algorithm (NSGA-II) and a deterministic gradient based Normalized normal constraint (NNC) method. In contrast to previous studies, the generated Pareto Optimal (PO) front provides several options for a decision maker, where optimal number of turbines and their optimal layouts are obtained at the same time along with the alternative solutions.

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IITH Creators:
IITH CreatorsORCiD
Mitra, Kishalay
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: acoustic modelling; gradient based method; Normalized Normal constraint (NNC) method; wake propagation non-dominated sorting genetic algorithm (NSGA-II); Wind farm layout optimization (WFLO)
Subjects: Chemical Engineering > Biochemical Engineering
Divisions: Department of Chemical Engineering
Depositing User: Team Library
Date Deposited: 11 Apr 2017 06:00
Last Modified: 10 Nov 2017 05:06
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