Multi-Objective Optimal Cloud Model Design of Vehicle-to-Grid Connected Systems Based on the Multiple Performance Characteristic Index Method †
Abstract
:1. Introduction
2. Principle of Vehicle-to-Grid Connected System Characteristics
3. Initial Design of a Single-Phase Full-Bridge Vehicle-to-Grid Connected System
4. Cloud Model Formulation for Optimal Controller Design
4.1. Control Factors and Noise Factors for Selection of Cloud Data
4.2. Cloud Model Design by Using Combined Array Method
4.3. The Fuzzy Inference with MPCI in Cloud Model
4.4. Regressive Analysis and Response Surface Derivation
5. Optimization by Orthogonal Particle Swarm Method in Cloud Model
5.1. OPSO Modeling in Cloud Model
5.2. Orthogonal Array Algorithm in Cloud Model
5.3. Optimization Results of Cloud Model
5.4. Testing Results for the Cloud Model
6. Discussion
7. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Level | Level 1 | Level 2 | Level 3 | |
---|---|---|---|---|
Factor | ||||
Control factor A, Kp | 12,767 | 32,767 | 52,767 | |
Control factor B, Ki | 367 | 567 | 767 | |
Control factor C, Kd | 1 × 10−5 | 1 × 10−6 | 1 × 10−7 | |
Noise factor 1, ESR (Ω) | 2 × 10−10 | 4 × 10−10 | 6 × 10−10 | |
Noise factor 2, rL (Ω) | 1 × 10−6 | 3 × 10−6 | 5 × 10−6 |
Run | Kp | Ki | Kd | ESR (Ω) | rL (Ω) |
---|---|---|---|---|---|
1 | −1 | −1 | −1 | −1 | 1 |
2 | 1 | −1 | −1 | −1 | −1 |
3 | −1 | 1 | −1 | −1 | −1 |
4 | 1 | 1 | −1 | −1 | 1 |
5 | −1 | −1 | 1 | −1 | −1 |
6 | 1 | −1 | 1 | −1 | 1 |
7 | −1 | 1 | 1 | −1 | 1 |
8 | 1 | 1 | 1 | −1 | −1 |
9 | −1 | −1 | −1 | 1 | −1 |
10 | 1 | −1 | −1 | 1 | 1 |
11 | −1 | 1 | −1 | 1 | 1 |
12 | 1 | 1 | −1 | 1 | −1 |
13 | −1 | −1 | 1 | 1 | 1 |
14 | 1 | −1 | 1 | 1 | −1 |
15 | −1 | 1 | 1 | 1 | −1 |
16 | 1 | 1 | 1 | 1 | 1 |
Run | Kp | Ki | Kd | ESR (Ω) | rL (Ω) |
---|---|---|---|---|---|
1 | 0 | 0 | 0 | 0 | 0 |
2 | 0 | 0 | 0 | 0 | 0 |
3 | 0 | 0 | 0 | 0 | 0 |
4 | 0 | 0 | 0 | 0 | 0 |
Output MPCI, C | Input A | |||
---|---|---|---|---|
S | M | L | ||
Input B | S | VS | S | M |
M | S | M | L | |
L | M | L | VL |
Reactive Power | Total Harmonic Distortion | MPCI |
---|---|---|
0.14514 | 0.31646 | 0.69786 |
0.13587 | 0.27624 | 0.26379 |
0.12019 | 0.28329 | 0.21765 |
0.16835 | 0.29762 | 0.69497 |
0.1368 | 0.31746 | 0.59794 |
0.13587 | 0.27624 | 0.26379 |
0.14347 | 0.28329 | 0.41040 |
0.13947 | 0.29762 | 0.46805 |
0.15625 | 0.3268 | 0.83963 |
0.12285 | 0.30675 | 0.44023 |
0.1269 | 0.28818 | 0.27368 |
0.14409 | 0.30211 | 0.65908 |
0.12048 | 0.32787 | 0.56093 |
0.13123 | 0.30675 | 0.48839 |
0.11669 | 0.27473 | 0.25740 |
0.14599 | 0.30211 | 0.59001 |
Reactive Power | Total Harmonic Distortion | MPCI |
---|---|---|
0.13831 | 0.29762 | 0.44420 |
0.13699 | 0.29851 | 0.44330 |
0.13736 | 0.29674 | 0.42078 |
0.13717 | 0.29851 | 0.44466 |
Item | Degree of Freedom | Sum of Squares, SS | Mean Square, MS | F-Statistics | Significance, p |
---|---|---|---|---|---|
Regressive treatments | 14 | 0.4981 | 0.0356 | 3.1052 | 0.1086 |
Error | 5 | 0.0573 | 0.0115 | - | - |
Total | 19 | 0.5553 | - | - | - |
Item | Coefficient | Standard Error | Statistic, t | Significance, p |
---|---|---|---|---|
Intercept | 0.4738 | 0.0239 | 19.7976 | 0.000006 |
x1 | 0.0008 | 0.0268 | 0.0299 | 0.9773 |
x2 | −0.0363 | 0.0268 | −1.3578 | 0.2326 |
x3 | −0.0281 | 0.0268 | −1.0510 | 0.3414 |
z1 | 0.0309 | 0.0268 | 1.1559 | 0.3000 |
z2 | 0.0087 | 0.0268 | 0.3269 | 0.7570 |
x1x2 | 0.1558 | 0.0268 | 5.8231 | 0.0021 |
x1x3 | −0.0029 | 0.0268 | −0.1067 | 0.9192 |
x2x3 | 0.0132 | 0.0268 | 0.4927 | 0.6431 |
x1z1 | 0.0300 | 0.0268 | 1.1195 | 0.3138 |
x2z1 | −0.0323 | 0.0268 | −1.2068 | 0.2815 |
x3z1 | −0.0114 | 0.0268 | −0.4246 | 0.6888 |
x1z2 | 0.0050 | 0.0268 | 0.1855 | 0.8601 |
x2z2 | 0.0371 | 0.0268 | 1.3870 | 0.2241 |
x3z2 | −0.0071 | 0.0268 | −0.2645 | 0.8020 |
Item | Reactive Power Experiment | THD Experiment | MPCI Experiment |
---|---|---|---|
Analysis method | Response surface method | Response surface method | MPCI fuzzy inference response surface method |
Objective function | Smaller-the-better | Smaller-the-better | Smaller-the-better |
Optimal combination | (1.0, 1.0, −1.0) | (−1.0, −1.0, −0.75) | (−1.0, −1.0, −0.99) |
Optimal PID parameters | (52767, 767, 1 × 10−5) | (12767, 367, 1 × 10−5) | (12767, 367, 1 × 10−5) |
Data in combined array | 5.94 (Var) 6.94 (Var) | 3.16 (%) 3.06 (%) | 6.89 (Var), 3.16 (%) 6.40 (Var), 3.06 (%) |
OPSO solution | 6.52 (Var) | 3.10 (%) | 6.48 (Var), 3.08 (%) |
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Kuo, J.-L. Multi-Objective Optimal Cloud Model Design of Vehicle-to-Grid Connected Systems Based on the Multiple Performance Characteristic Index Method. Energies 2019, 12, 1041. https://doi.org/10.3390/en12061041
Kuo J-L. Multi-Objective Optimal Cloud Model Design of Vehicle-to-Grid Connected Systems Based on the Multiple Performance Characteristic Index Method. Energies. 2019; 12(6):1041. https://doi.org/10.3390/en12061041
Chicago/Turabian StyleKuo, Jian-Long. 2019. "Multi-Objective Optimal Cloud Model Design of Vehicle-to-Grid Connected Systems Based on the Multiple Performance Characteristic Index Method" Energies 12, no. 6: 1041. https://doi.org/10.3390/en12061041
APA StyleKuo, J.-L. (2019). Multi-Objective Optimal Cloud Model Design of Vehicle-to-Grid Connected Systems Based on the Multiple Performance Characteristic Index Method. Energies, 12(6), 1041. https://doi.org/10.3390/en12061041