由於近年來隨著油價高漲與環保議題受到關注，使得世界各國均積極推動替代能源分散式發電系統的開發。在小型分散式發電系統中，具有低電壓輸出特性的光伏電池，扮演著甚為重要的角色。太陽光伏發電系統之電壓、電流等特性會隨溫度與照度呈現非線性的變化，給予不同的操作電壓、電流將得到不同的功率，若能給予其適當的操作電壓、電流，便能達到較好的發電功率，因此其最大功率追蹤乃一重要研究議題。直線近似法可在不同照度時快速追蹤最大功率點，然而其方法之直線斜率並未針對溫度改變作修正，因此當太陽能電池之溫度改變時，系統並非操作在最大功率點上。本論文更深入一層探討溫度對直線近似法最大功率追蹤的影響，並提出修正技術以解決上述困境。
本論文主要探討最大功率追蹤之方法，並針對所應用的太陽能發電系統提出一適用的演算法，先探討太陽能電池輸出功率與電壓電流之關係。接著提出一能隨溫度變化自動修正之直線近似法，在溫度改變時能自動修正直線近似法中最大功率點直線之斜率，讓光伏系統能在不同溫度與照度下準確操作在最大功率點，並經由模擬驗證此方法之可行性，然後以微處理器硬體實測，驗證此法是否能真實應用於所需控制的太陽能發電系統，確認演算法的可行性之後，更進一步將演算法實現成晶片。晶片的設計實現採用TSMC 1P6M COMS 0.18um 製程，操作電壓為1.8V，晶片佈局面積為1.553*1.553mm2，數位核心電路的操作速度為25MHz，完整晶片系統的功率消耗約為10mW。在文末的量測結果亦證實，此演算法可以順利修正直線近似法的斜率。

[1]
N. Femia, G. Petrone, G. Spagnuolo, and M. Vitelli, “Optimization of Perturb and Observe Maximum Power Point Tracking Method,” IEEE Transactions on Power Electronics, vol. 20, no. 4, pp. 963-973, Jul. 2005.
[2]
O. Wasynczuk, “Dynamic Behavior of a Class of Photovoltaic Power Systems,” IEEE Transactions on Power Apparatus and Systems, vol. 102, no. 9, pp. 3031-3037, Sep. 1983.
[3]
K. Ohniwa and T. Sato, “A simplified maximum power tracking method for photovoltaic solar system,” IEEJ Trans. Power Syst., vol. 106-B, no. 7, pp. 72–78, 1984.
[4]
I. H. Altas and A. M. Sharaf, “A novel on-line MPP search algorithmfor PV arrays,” IEEE Transactions on Energy Conversion, vol. 11, pp. 748–754,Nov. 1996.
[5]
Y. C. Kuo, T. J. Liang, and J. F. Chen, “Novel Maximum-Power-Point Tracking Controller for Photovoltaic Energy Conversion System,” IEEE Transactions on Industrial Electronics, vol. 48, no. 3, pp. 594-601, Jun. 2001.
[6]
F. Liu, S. Duan, F. Liu, B. Liu, and Y. Kang, “A Variable Step Size INC MPPT Method for PV Systems,” IEEE Transactions on Industrial Electronics, vol. 55, no. 7, pp. 2622-2628, July 2008.
[7]
C. T. Pan, J. Y. Chen, C. P. Chu, and Y. S. Huang, “A Fast Maximum Power Point Tracker for Photovoltaic Power Systems,” IEEE Industrial Electronics Conference, pp. 21-24, 1999.
[8]
T. Esram and P. L. Chapman, “Comparison of Photovoltaic Array Maximum Power Point Tracking Techniques,” IEEE Transactions on Energy Conversion, vol. 22, no. 2, pp. 439-449, June. 1985.
[9]
M. A. S. Masoum, H. Dehbonei, and E. F. Fuchs, “Theoretical and Experimental Analyses of Photovoltaic Systems with Voltage and Current-Based Maximum Power-Point Tracking,” IEEE Transactions on Energy Conversion, vol. 17, no. 4, pp. 514-522, Dec. 2002.
[10]
H. Sugimoto and H. Dong, “A New Scheme for Maximum Photovoltaic Power Tracking Control,” Power Conversion Conference, pp. 691-696, Aug. 1997.
[11]
S. J. Chiang, K. T. Chang, and C. Y. Yen, “Residential Photovoltaic Energy Storage System,” IEEE Transactions on Industrial Electronics, vol. 45, no. 3, pp. 385-394, Jun. 1998.
[12]
B. K. Bose, P. M. Szxzdsny, and R. L. Steigerwald, “Microcomputer Control of a Residential Photovoltaic Power Conditioning System,” IEEE Transactions on Industry Applications, vol. 1A-21, no. 5, pp. 1182-1191, 1985.
[13]
Veerachary, M., Senjyu, T., and Uezato, K., "Neural-network-based maximum power-point tracking of coupled-inductor interleaved-boost-converter-supplied PV system using fuzzy controller," Industrial Electronics, IEEE Transactions on, vol. 50, no. 4, pp. 749-758, 2003.
[14]
Hilloowala, R. M. and Sharaf, A. M., "A Rule-based Fuzzy Logic Controller for a PWM Inverter in a Stand Alone Wind Energy Conversion Scheme," IEEE Transactions on Industry Applications, vol. 32, no. 1, pp. 57-65, 1996.
[15]
Hilloowala, R. M. and Sharaf, A. M., “A Rule-based Fuzzy Logic Controller for a PWM Inverter in a Stand Alone Wind Energy Conversion Scheme,” IEEE Transactions on Industry Applications, vol.32, no. 1, pp. 57-65, 1996.
[16]
Veerachary, M., Senjyu, T., and Uezato, K., “Neural-network-based maximum
-power-point tracking of coupled-inductor interleaved-boost-converter-supplied PV system using fuzzy controller,” IEEE Transactions on Industrial Electronics, vol. 50, no. 4, pp. 749-758, 2003.
[17]
Chen, H. and Murray, A. F., "A Continuous Restricted Boltzmann Machine with an Implementable Training Algorithm," IEEE Proceedings of Vision, Image and Signal Processing, vol. 150, no. 3, pp. 153-158, 2003.
[18]
Lu, C. C. and Chen, H., “Current-mode Computation with Noise in a Scalable and Programmable Probabilistic Neural VLSI System,” Artificial Neural Networks ICANN 2009, pp.401-409, 2009.
[19]
Cauwenberghs, G. and Bayoumi, M. A., Learning on Silicon : Adaptive Neural VLSI Systems, 1 ed. Boston: Kluwer Academic Publishers, 1999.
[20]
C. M. Lai, Ching-Tsai Pan, and M. C. Cheng, “High Efficiency Modular High Step-Up Interleaved Boost Converter for DC-Microgrid Applications,” IEEE Transactions on Industry Applications, Vol. 48, No. 1, pp.161-171, Jan./Feb. 2012.