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In this study, we use the simulation software MATLAB (R2023b) and Simulink to simulate the power curves of a photovoltaic (PV) system under three different irradiance levels: 1000W/m², 800W/m², and 600W/m². This approach aims to verify the reliability and effectiveness of the simulation framework. By exporting the data simulated in Simulink to the MATLAB workspace, then we employ the Ant Colony Optimization (ACO) algorithm for Maximum Power Point Tracking (MPPT) in the MATLAB workspace. The simulation results are analyzed to explore the effects of different numbers of ants, specifically 20, 15, and 10 ants, and different iterations, specifically 20, 15, and 10 iterations, on the simulation outcomes.
Next, we employ a hybrid method combining the Perturb and Observe (P&O) algorithm with the Ant Colony Optimization (ACO) algorithm. Initially, the power curves are input into the Simulink module of the P&O algorithm to reduce the search range. Subsequently, the power curves are exported to the MATLAB workspace, where the ACO algorithm is used for Maximum Power Point Tracking (MPPT) to obtain the simulation results.
Finally, we compare the simulation results of the Ant Colony Optimization (ACO) algorithm and the hybrid method , demonstrating that the hybrid method achieves an improvement in average power output compared to the traditional Ant Colony Optimization (ACO) algorithm. This improvement is observed in both environments that are relatively easy to converge and those that are difficult to converge, with an increase ranging from approximately 0.4% to 9%. These results further validate that the hybrid method offers superior stability and efficiency compared to the traditional ACO algorithm.
致謝 I
摘要 II
Abstract III
目次 IV
圖次 VI
表次 VIII
第一章 前言 1
1.1 研究背景 1
1.2研究動機 2
1.3論文貢獻 5
1.4論文架構 5
第二章 文獻探討 6
2.1 最大功率點追蹤(MPPT) 6
2.2 擾動觀察法(P&O) 8
2.3 蟻群演算法(ACO) 15
2.3.1 蟻群演算法(ACO)的起源和沿革 15
2.3.2 蟻群演算法(ACO)的具體運行步驟 16
2.3.3 蟻群演算法(ACO)的參數意義與作用 17
2.3.4 蟻群演算法(ACO)的發展與應用 18
第三章 模擬架構與原理 19
3.1 Simulink介紹 19
3.1.1 Simulink主要功能與特點 19
3.1.2 Simulink模塊介紹 20
3.2太陽能光電(PV)模組 22
3.3 擾動觀察法(P&O)模組 26
3.4 蟻群演算法(ACO) 31
3.5 條件說明 32
第四章 模擬結果與分析 35
4.1 PV模組模擬結果 35
4.2 擾動觀察法(P&O)尋找最大功率點之模擬結果 38
4.3 蟻群演算法(ACO)尋找最大功率點(MPP)之模擬結果 40
4.3.1 輻照度1000W/m2的情況 40
4.3.2 輻照度800W/m2的環境 43
4.3.3 輻照度600W/m2的情況 45
4.4 混合方法(ACO+P&O)尋找最大功率點(MPP)之模擬結果 47
4.4.1 輻照度1000W/m2的情況 47
4.4.2 輻照度800W/m2的情況 50
4.4.3 輻照度600W/m2的情況 53
4.5 三種方法的分析與探討 56
4.5.1 螞蟻數量為20隻的情況 56
4.5.2 螞蟻數量為15隻的情況 59
4.5.3 螞蟻數量為10隻的情況 61
4.6 兩種方法的模擬結果比較 63
4.6.1 1000輻照度的條件下 63
4.6.2 800輻照度的條件下 64
4.6.3 600輻照度的條件下 65
4.7 三種方法的模擬結果比較 66
第五章 結論 68
5.1 結論 68
5.2 未來展望 69
參考文獻 70
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| : |
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|---|---|---|---|---|
| 研究生: |
陳鼎裕
|
|||
| 研究生(外文): |
CHEN, TING-YU
|
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| 論文名稱: | 基於MATLAB模型比較模擬不同方法以改善最大功率點追蹤(MPPT)效率之研究 | |||
| 論文名稱(外文): | The Study of Simulating and Comparing Different Methods to Improve Maximum Power Point Tracking (MPPT) Efficiency Based on MATLAB Model | |||
| 指導教授: |
張忠誠
、
何志傑
|
|||
| 指導教授(外文): | CHANG, CHUNG-CHENG 、 HO, JYH-JIER | |||
| 口試委員: | 張忠誠 、 何志傑 、 鄭遠東 、 王盛時 | |||
| 口試委員(外文): | CHANG, CHUNG-CHENG 、 HO, JYH-JIER 、 CHENG, YUANG-TUNG 、 WANG, SHENG-SHIH | |||
| 口試日期: | 2024-07-04 | |||
| 學位類別: | 碩士 | |||
| 校院名稱: |
國立臺灣海洋大學
|
|||
| 系所名稱: | 電機工程學系 | |||
| 學門: | 工程學門 | |||
| 學類: | 電資工程學類 | |||
| 論文種類: | 學術論文 | |||
| 論文出版年: | 2024 | |||
| 畢業學年度: | 112 | |||
| 語文別: | 中文 | |||
| 論文頁數: | 73 | |||
| 中文關鍵詞: | 最大功率點追蹤 、 蟻群演算法 、 擾動觀察法 、 太陽能光電系統 | |||
| 外文關鍵詞: | Maximum Power Point Tracking (MPPT) 、 Ant Colony Optimization (ACO) 、 Perturb and Observe (P&O) 、 Photovoltaic (PV) System | |||
| 相關次數: |
接著我們將擾動觀察法(P&O)和蟻群演算法(ACO)做為混合方法，透過先將功率曲線輸入至擾動觀察法(P&O)之Simulink模組中，降低搜索之範圍。隨後，將功率曲線匯出至MATLAB工作區，再以MATLAB工作區中的蟻群演算法(ACO)執行最大功率點追蹤(MPPT)，並得到其模擬結果。 最後，我們將蟻群演算法(ACO)和混合方法之模擬結果進行比較，展示不論是較容易收斂的環境下，或是難以收斂的環境下，混合方法之平均功率相較於傳統蟻群演算法(ACO)皆有提升，提升值約落在0.4%~9%不等。此結果進一步驗證了此混合方法相較於傳統的蟻群演算法(ACO)有較佳的穩定性和效率。 |
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Next, we employ a hybrid method combining the Perturb and Observe (P&O) algorithm with the Ant Colony Optimization (ACO) algorithm. Initially, the power curves are input into the Simulink module of the P&O algorithm to reduce the search range. Subsequently, the power curves are exported to the MATLAB workspace, where the ACO algorithm is used for Maximum Power Point Tracking (MPPT) to obtain the simulation results.
Finally, we compare the simulation results of the Ant Colony Optimization (ACO) algorithm and the hybrid method , demonstrating that the hybrid method achieves an improvement in average power output compared to the traditional Ant Colony Optimization (ACO) algorithm. This improvement is observed in both environments that are relatively easy to converge and those that are difficult to converge, with an increase ranging from approximately 0.4% to 9%. These results further validate that the hybrid method offers superior stability and efficiency compared to the traditional ACO algorithm.
致謝 I
摘要 II
Abstract III
目次 IV
圖次 VI
表次 VIII
第一章 前言 1
1.1 研究背景 1
1.2研究動機 2
1.3論文貢獻 5
1.4論文架構 5
第二章 文獻探討 6
2.1 最大功率點追蹤(MPPT) 6
2.2 擾動觀察法(P&O) 8
2.3 蟻群演算法(ACO) 15
2.3.1 蟻群演算法(ACO)的起源和沿革 15
2.3.2 蟻群演算法(ACO)的具體運行步驟 16
2.3.3 蟻群演算法(ACO)的參數意義與作用 17
2.3.4 蟻群演算法(ACO)的發展與應用 18
第三章 模擬架構與原理 19
3.1 Simulink介紹 19
3.1.1 Simulink主要功能與特點 19
3.1.2 Simulink模塊介紹 20
3.2太陽能光電(PV)模組 22
3.3 擾動觀察法(P&O)模組 26
3.4 蟻群演算法(ACO) 31
3.5 條件說明 32
第四章 模擬結果與分析 35
4.1 PV模組模擬結果 35
4.2 擾動觀察法(P&O)尋找最大功率點之模擬結果 38
4.3 蟻群演算法(ACO)尋找最大功率點(MPP)之模擬結果 40
4.3.1 輻照度1000W/m2的情況 40
4.3.2 輻照度800W/m2的環境 43
4.3.3 輻照度600W/m2的情況 45
4.4 混合方法(ACO+P&O)尋找最大功率點(MPP)之模擬結果 47
4.4.1 輻照度1000W/m2的情況 47
4.4.2 輻照度800W/m2的情況 50
4.4.3 輻照度600W/m2的情況 53
4.5 三種方法的分析與探討 56
4.5.1 螞蟻數量為20隻的情況 56
4.5.2 螞蟻數量為15隻的情況 59
4.5.3 螞蟻數量為10隻的情況 61
4.6 兩種方法的模擬結果比較 63
4.6.1 1000輻照度的條件下 63
4.6.2 800輻照度的條件下 64
4.6.3 600輻照度的條件下 65
4.7 三種方法的模擬結果比較 66
第五章 結論 68
5.1 結論 68
5.2 未來展望 69
參考文獻 70
[1]S. LaRose, "International Energy Outlook 2021 (IEO2021)," Washington, DC, 2021.
[2] I. Mead, "International energy outlook 2017," U.S. Energy Information Administration, 2017.
[3]M. A. Sattar, M. Sameeroddin, M. K. G. Deshmukh, and M. A.Sami,“Renewable energy and its industrial applications,” Int. Res. J. Eng. Technol. (IRJET), vol. 7, no. 6, pp. 6042-6046, 2020.
[4] V. Sebestyén, "Environmental impact networks of renewable energy power plants," Renewable and Sustainable Energy Reviews, vol. 151, 2021, Art. no. 111626. doi: 10.1016/j.rser.2021.111626.
[5]M. Shahabuddin, M. A. Alim, T. Alam, M. Mofijur, S. F. Ahmed, and G. Perkins,
"A critical review on the developmentand challenges of concentrated solar power technologies," Sustainable Energy Technologies and Assessments, vol. 47,p.101434, 2021.
[6]M. Sarvi and A. Azadian, "A comprehensive review and classified comparison of MPPT algorithms in PVsystems,“Energy Syst.,vol.13,pp.281320,2022.https://doi.
org/10.1007/s12667-021-00427-x
[7]A. S. Al-Ezzi and M. N. M. Ansari, "Photovoltaic Solar Cells: A Review," Applied System Innovation, vol. 5, no. 4,p. 67, 2022.https://doi.org/10.3390/asi5040067
[8]S. Bhattacharyya, D. S. Kumar P, S. Samanta and S. Mishra, "Steady Output and Fast Tracking MPPT (SOFT-MPPT) for P&O and InC Algorithms," IEEE Transactions on Sustainable Energy, vol. 12, no. 1, pp. 293-302, Jan. 2021,
doi: 10.1109/TSTE.2020.2991768.
[9]Z. Liu, Y. -P. Hsu and M. M. Hella, "A Thermal/RF Hybrid Energy Harvesting
System With Rectifying-Combination and Improved Fractional-OCV MPPT
Method," IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 67, no. 10, pp. 3352-3363, Oct. 2020, doi: 10.1109/TCSI.2020.2982403.
[10]A. Vijayakumari, "A non-iterative MPPT of PV array with online measured short
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