Abstract
The placement and configuration of wind turbines (WTs) are the key factors in determining the performance and energy output of a wind farm (WF). This involves considering various elements such as wind speed, wind direction, and the interspacing between turbines in the design process. To achieve an optimized and consistent wind farm layout optimization (WFLO) for maximum output power, a novel hybrid algorithm hybrid particle swarm optimization and genetic algorithm (HPSOGA), combining particle swarm optimization (PSO) and genetic algorithm (GA), is proposed. HPSOGA can effectively handle problems with multiple local optima, as PSO explores multiple regions and GA refines solutions found by PSO. The framework has two phases, where PSO improves initial parameters in the first phase, and parameters are adjusted in the second phase for improved fitness. The wake effect is analyzed using the Jenson-Wake model, and the objective function considers the total cost of WTs and the power output of the WF. The interspacing of WTs is evaluated by the rule of thumb. HPSOGA outperforms other methods such as GA, BPSO-TVAC, L-SHADE, BRCGA, and EO-PS, producing better results in terms of total output power generation. The simulation results validate the reliability of HPSOGA in WFLO.
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Data availability
The corresponding author will provide the datasets used and/or analyzed during the present work upon reasonable request.
Abbreviations
- r r :
-
Turbine rotor radius
- r w :
-
Radius of the downstream wake
- V 0 :
-
Freestream wind speed
- V 1 :
-
Wind speed behind the rotor
- X :
-
Downstream distance
- Prat:
-
Rated power
- A r :
-
Rotor area
- A overlap :
-
Overlap area
- V :
-
Wake wind speed at a downstream distance X.
- k w :
-
Wake decay constant
- Z hub :
-
Turbine hub height
- D :
-
Rotor diameter
- Z 0 :
-
Surface roughness
- V out:
-
Cut-out speed
- V in :
-
Cut-in speed
- V rat :
-
Rated wind speed
- C t :
-
Thrust coefficient
- C p :
-
Power coefficient
- k*:
-
Wake expansion constant
- ρ :
-
Air density
- P total :
-
Total power
- Costtotal :
-
Total cost
- Ntotal :
-
Total number of turbines
- GA:
-
Genetic algorithm
- RS:
-
Random search
- SBO:
-
Satin bowerbird optimization
- BPSO-TVAC:
-
Binary particle swarm optimization with time-varying acceleration coefficients
- DE:
-
Differential evolution
- GWO:
-
Grey wolf optimizer
- SSA:
-
Salp swarm algorithm
- WCA:
-
Water cycle algorithm
- BRCGA:
-
Binary real-coded genetic algorithm
- PSO:
-
Particle swarm optimization
- EO:
-
Equilibrium optimizer
- LEO:
-
Levy equilibrium optimizer
- EO-PS:
-
Equilibrium optimizer-pattern search
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Acknowledgements
This research has been carried out with the support of the National Institute of Technology (NIT), Bhopal, India. The authors would like to thank the National Institute of Wind Energy (NIWE), Chennai, India, for providing a wind data facility.
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Tarique Anwar Qureshi: writing original draft, visualization, conceptualization, methodology, software, investigation, and data curation
Vilas Warudkar: resources, supervision, writing, review, and editing.
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Qureshi, T.A., Warudkar, V. Wind farm layout optimization through optimal wind turbine placement using a hybrid particle swarm optimization and genetic algorithm. Environ Sci Pollut Res 30, 77436–77452 (2023). https://doi.org/10.1007/s11356-023-27849-7
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DOI: https://doi.org/10.1007/s11356-023-27849-7