Optimal placement of wind turbines within wind farm using binary particle swarm optimization with time-varying acceleration coefficients

This paper proposes a binary particle swarm optimization (BPSO) with time-varying acceleration coefficients (TVAC) for solving optimal placement of wind turbines within a wind farm. The objective is to extract the maximum turbine power output in a minimum investment cost within a wind farm. The BPSO–TVAC algorithm is applied to 100 square cells test site considering uniform wind and non-uniform wind speed with variable direction characteristics. Linear wake model is used to calculate downstream wind speed. Test results indicate that BPSO–TVAC investment cost per installed power of both uniform and non-uniform wind speed with variable wind direction are lower than those obtained from genetic algorithm and evolutive algorithm, BPSO–TVIW (time-varying inertia weight factor), BPSO–RANDIW (random inertia weight factor) and BPSO–RTVIWAC (random time-varying inertia weight and acceleration coefficients), leading to maximum power extracted in a least investment cost manner.

► BPSO-TVAC is developed to solve an optimal placement of wind turbines within a wind farm. ► It yields a better investment cost per installed power than those obtained from genetic algorithm and evolutive algorithm, BPSO-TVIW, BPSO-RANDIW and BPSO-RTVIWAC. ► Wake combination model was improved for partial wake effect, multiple wake effect, and reduced velocity calculation.