TY - GEN
T1 - A novel adaptive genetic algorithm for wine farm layout optimization
AU - Liu, Feng
AU - Wang, Zhifang
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/13
Y1 - 2017/11/13
N2 - In this paper we propose an adaptive genetic algorithm (AGA) to optimize wind farm layout in order to achieve a highest aggregate wind power conversion efficiency with the presence of wake effect impacts. Instead of using random 'crossover' in each iteration as in conventional GA, our proposed adaptive algorithm introduces novel relocation of 'bad' turbines so that turbines experiencing worst wake effect impacts in a layout will be relocated to some new and more efficient positions. Therefore each iteration of the AGA can more effectively improve the aggregate wind power conversion efficiency and greatly accelerate the algorithm convergence. We experiment the proposed AGA and compare its performance with conventional GA based on a number of scenarios such as multi-directional wind distribution, offshore and inland wind distribution, with sparse and crowded farm settings. Numerical results verify the effectiveness of the proposed AGA algorithm which is able to locate an optimal layout at a much faster convergence speed and achieve a higher aggregate wind power output from a farm.
AB - In this paper we propose an adaptive genetic algorithm (AGA) to optimize wind farm layout in order to achieve a highest aggregate wind power conversion efficiency with the presence of wake effect impacts. Instead of using random 'crossover' in each iteration as in conventional GA, our proposed adaptive algorithm introduces novel relocation of 'bad' turbines so that turbines experiencing worst wake effect impacts in a layout will be relocated to some new and more efficient positions. Therefore each iteration of the AGA can more effectively improve the aggregate wind power conversion efficiency and greatly accelerate the algorithm convergence. We experiment the proposed AGA and compare its performance with conventional GA based on a number of scenarios such as multi-directional wind distribution, offshore and inland wind distribution, with sparse and crowded farm settings. Numerical results verify the effectiveness of the proposed AGA algorithm which is able to locate an optimal layout at a much faster convergence speed and achieve a higher aggregate wind power output from a farm.
KW - genetic algorithm
KW - wake effect
KW - wind farm layout optimization problem (WFLOP)
UR - http://www.scopus.com/inward/record.url?scp=85040568127&partnerID=8YFLogxK
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U2 - 10.1109/NAPS.2017.8107410
DO - 10.1109/NAPS.2017.8107410
M3 - Conference contribution
AN - SCOPUS:85040568127
T3 - 2017 North American Power Symposium, NAPS 2017
BT - 2017 North American Power Symposium, NAPS 2017
T2 - 2017 North American Power Symposium, NAPS 2017
Y2 - 17 September 2017 through 19 September 2017
ER -