Wind Micro-Turbine Networks for Urban Areas: Optimal Design and Power Scalability of Permanent Magnet Generators
Abstract
:1. Introduction
2. Main Components of the Proposed Electromechanical Energy Conversion System
2.1. Ducted Wind Turbine
2.2. Permanent Magnet Generator
- a segmented back iron, composed by one U-shaped core for each phase coil, can be used for the stator (as shown in Figure 5a); this solution allows to reduce the weight of the stator yoke and makes easier the manufacturing and assembly process, especially for machines with large diameters; and,
- for a given number of turns and thickness of the copper traces, the dimensions of the PCB in the axial direction can be shortened (as schematically shown in Figure 6); this is due to the reduction of the non-active parts of each phase coil.
3. Scalability Rules
4. Automated Design Tool
4.1. Multi-Objective Optimization
- two-dimensional (2D) magneto-static simulations are performed, considering few equally spaced rotor positions along one pole pitch;
- the electrical machine geometry is described by a limited number of parameters;
- a reduced portion of the entire geometry is analyzed, taking advantage of the machine symmetries; and,
- the computation is parallelized among all the cores of a workstation through the parallel toolbox provided by Matlab.
Meta-Heuristic Algorithm
- a combined population CP(i) is formed considering a set P(i) of Npop solutions originated from the (i − 1)-th iteration and a second set Q(i) of Npop solutions obtained from P(i), through crossover and mutation;
- the population CP(i), whose size is 2Npop, is sorted according to the non-domination criterion; therefore, a rank is assigned to each solution based on its non-domination level;
- Npop solutions are collected, starting from the 1st rank, in order to form the set P(i + 1); if the number of solutions belonging to the 1st rank is less than Npop, the remaining candidates are collected from the subsequent non-dominated set (i.e., the 2nd rank) and so on until the size Npop is reached; and,
- the combined population CP(i + 1) for the (i + 1)-th iteration is finally obtained considering the combination between the population P(i + 1) and the population Q(i + 1) originated from P(I + 1) through crossover and mutation.
4.2. Machine Parameterization
4.3. Cost Functions
- the material cost, which is proportional to the weight for both the PMs and the copper, is indirectly considered; and,
- the impact of the mechanical issues related to the rotor integrity, which are proportional to the rotor mass, can be estimated.
5. Case of Study
- Type A: machine with non-magnetic material for both stator and rotor yokes (ironless machine);
- Type B: machine with non-magnetic material for the stator yoke and ferromagnetic material (M-19 29 Ga) for the rotor yoke;
- Type C: machine with ferromagnetic material (M-19 29 Ga) for both stator and rotor yokes; and,
- Type D: machine with non-magnetic material for the rotor yoke and ferromagnetic material (M-19 29 Ga) for the stator yoke.
5.1. Comparison
5.2. Scalability
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Nomenclature
Symbols | |
A | Wind turbine rotor swept area |
C | Rotor circumference |
CP | Power coefficient |
D | Rotor diameter |
F | Force at airgap |
iSdq | Stator current in the dq-reference frame |
L | Axial length |
P | Output power |
R | Rotor radius |
T | Output torque |
λ | Tip Speed Ratio |
λdq | Flux linkage in the dq-reference frame |
ρ | Air density |
RS | Stator resistance |
Ttarget | Target torque |
va | Wind speed upstream the streamtube |
vp | Wind speed at the rotor |
vO | Wind speed downstream the streamtube |
vSdq | Stator voltage in the dq-reference frame |
ω | Rotational speed |
Acronyms | |
CP | Combined Population |
CFD | Computational Fluid Dynamic |
DAWT | Diffuser-Augmented Wind Turbines |
DC | Direct Current |
DL | Double Layer |
FE | Finite Element |
FEA | Finite Element Analysis |
FEMM | Finite Element Method Magnetics |
NSGA | Nondominated Sorting Genetic Algorithm |
PCB | Printed Circuit Board |
PM | Permanent Magnet |
p.u. | Per Unit |
S-HAWT | Shrouded Horizontal Axis Wind Turbines |
SL | Single Layer |
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Parameter | Lower Bound | Upper Bound | Unit |
---|---|---|---|
Magnet arc | 0.65 | 0.9 | p.u. |
Magnet thickness | 10 | 20 | mm |
PCB thickness | 1 | 3 | mm |
Parameter | N.7-Type A | N.7-Type C |
---|---|---|
Power [kW] | 1 | 1 |
Electromagnetic torque [Nm] | 14.4 | 14.4 |
Copper weight [kg] | 0.19 | 0.15 |
Magnet weight [kg] | 1.30 | 1.20 |
Thickness of the magnet [mm] | 18.1 | 19.2 |
Thickness of the PCB [mm] | 2.7 | 2.8 |
Axial length [mm] | 3.4 | 2.6 |
Magnet arc [p.u.] | 0.78 | 0.90 |
Copper loss [W] | 15 | 11 |
Iron loss [W] | 0 | 19 |
Efficiency [%] | 98.5 | 97 |
Parameter | Starting Machine | Half-Power Machine | Double-Power Machine |
---|---|---|---|
Target Power [kW] | 1 | 0.5 | 2 |
Turbine diameter [mm] | 1440 | 1020 | 2040 |
Rotational speed [rpm] | 663 | 937 | 468 |
Target torque [Nm] | 14.4 | 5.1 | 40.8 |
Generator axial length [mm] | 2.6 | 1.9 | 3.7 |
Copper weight [kg] | 0.15 | 0.08 | 0.31 |
Magnet weight [kg] | 1.20 | 0.64 | 2.55 |
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Palmieri, M.; Bozzella, S.; Cascella, G.L.; Bronzini, M.; Torresi, M.; Cupertino, F. Wind Micro-Turbine Networks for Urban Areas: Optimal Design and Power Scalability of Permanent Magnet Generators. Energies 2018, 11, 2759. https://doi.org/10.3390/en11102759
Palmieri M, Bozzella S, Cascella GL, Bronzini M, Torresi M, Cupertino F. Wind Micro-Turbine Networks for Urban Areas: Optimal Design and Power Scalability of Permanent Magnet Generators. Energies. 2018; 11(10):2759. https://doi.org/10.3390/en11102759
Chicago/Turabian StylePalmieri, Marco, Salvatore Bozzella, Giuseppe Leonardo Cascella, Marco Bronzini, Marco Torresi, and Francesco Cupertino. 2018. "Wind Micro-Turbine Networks for Urban Areas: Optimal Design and Power Scalability of Permanent Magnet Generators" Energies 11, no. 10: 2759. https://doi.org/10.3390/en11102759
APA StylePalmieri, M., Bozzella, S., Cascella, G. L., Bronzini, M., Torresi, M., & Cupertino, F. (2018). Wind Micro-Turbine Networks for Urban Areas: Optimal Design and Power Scalability of Permanent Magnet Generators. Energies, 11(10), 2759. https://doi.org/10.3390/en11102759