無人機巡檢太陽能發電站之最佳路徑規劃

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邱郁鈞(Yu-Chun Chiu) 查詢紙本館藏

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工業管理研究所

論文名稱

無人機巡檢太陽能發電站之最佳路徑規劃
(Path Planning for Solar Power Plant Inspection by Using Drone)

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摘要(中)

人類文明發展在帶來進步的同時，亦由於我們對化石能源的過度依賴而產生的大量溫室氣體，引發了全球暖化效應。這種人為氣候變遷不但會助長極端天氣的發生，更可能衝擊生態造成物種滅絕或糧食危機等嚴峻問題。因此，減少溫室氣體排放成為控制全球升溫的一項重要策略。據統計，電力與熱力生產是產生最高碳排放的經濟活動。基於現今社會對能源的需求只將有增無減，利用可再生能源取代傳統發電的做法逐漸被重視，而當中，尤以太陽能發電極速增長成為近年的發展趨勢。當今太陽能市場中最主流的光伏發電方式之核心設備便是我們常聽聞的太陽能板。一個太陽能發電站由大面積的太陽能板組成，並藉此將陽光轉換為可用電力。而太陽能板的裂痕、表面玻璃破碎、熱斑、異物遮擋或污漬覆蓋等都可能影響面板的發電狀況及使用壽命，因此定期檢測是維持其最佳狀態的必要手段。然而，隨著不同形式的太陽能板陸續問世，倚靠人力的傳統維護方式不僅費時、危險且難以實現，如臺灣因建築模式與有限土地等因素，近年以水域型太陽能發電站為發展方向，此便為一不便於以人工巡檢的發電廠種類。因此，近年逐漸成熟的無人機技術成為了巡檢太陽能板的新出路。
綜上所述，本研究針對無人機巡檢水域型太陽能發電站之路徑規劃進行相關研究，自面對現實情境應如何進行問題轉換，至獲得問題網絡圖後應考慮哪些限制建構演算法的流程與方法之探討。文中鎖定巡檢任務中之必行經定位點、充電站位置以及巡檢最佳路徑求解三個議題，建立了一個針對現實情境轉換問題網絡的標準流程，使議題一得以獲得結果，並基於基因演算法提出一調整算法，使其符合本問題情境，可對此無人機路徑問題進行求解，並於結果中得到一組推薦的巡檢任務飛行路徑與推薦之最佳充電站設置位置，完成對議題二、三的討論。最後，本研究在考慮發電廠場域涵蓋面積與分佈狀況、後續影像分析的需求，以及無人機的使用型號三種資訊的結合，並將重疊率視為問題轉換關鍵之下，成功由彰濱工業區之彰濱崙尾東一暨二號電廠轉換出一個具4,798個節點之網絡圖。且於將基因演算法迭代最低限制設為300下，以表現良好的初始解出發，在確保過程中所得解之可行性下，於有限迭代內收斂得到一耗時1,605分鐘之可接受路徑解，以及一個相對穩定的充電站建議設置位置（987.76, 438.62）。
關鍵字：水域型太陽能發電站、光伏發電、光伏檢測、無人航空載具、無人機、無人機路徑規劃。

摘要(英)

While the development of human civilization has brought progress, it’s also caused global warming due to the large emissions of GHGs generated by our over-reliance on fossil fuels. This human-induced climate change not only will escalate the occurrence of extreme weather, but also may impact the ecology, causing serious problems such as species extinction or food crisis. Therefore, reducing GHG emissions has become an important strategy to control global warming. According to statistics data, electricity and heat production is the economic sector that produces the highest carbon emissions. As the demand for energy in today′s society will only increase, the methods of using renewable energy to replace traditional power generation have gradually been valued. Among these means, the rapidly growing solar power generation has become a trend in recent years. The solar panel that we often hear is the core equipment of photovoltaic power generation which is the most mainstream generation method in the solar energy market nowadays. A solar power plant consists of large areas of solar panels that convert sunlight into electricity. However, cracks, broken surface glass, hot spots, foreign objects, or stains on the solar panel may affect its generation and life, hence regular inspection is necessary to keep it in its best condition. Nevertheless, with the different types of solar panels coming out, the traditional maintenance method that relies on manual labor is time-consuming, dangerous, and difficult. Thus, drone technology, which has recently matured, has become a new way to inspect solar panels.
To sum up, this research focuses on the path planning for floating solar plant inspection by using drone. Discuss the processes and methods from how to convert problem in realistic situation, to what constraints should be considered to build the algorithm after obtaining the network. The must-be-passed points in the inspection task, the location of the recharging station, and the optimal path for the drone are the three main issues we aim at. For that, we established a standard process for the network conversion of the realistic situation to answer the first issue, and proposed an adjusted algorithm based on Genetic Algorithm which can fit the condition to solve this drone routing problem. After the algorithm, there′s a set of recommended flight paths for inspection task and the recommended optimal recharging station setting positions can be obtained, thus the second and third issues can be completed. Finally, this study considers the combination of three types of information, including the coverage area and distribution of the power plant site, the need for subsequent image analysis, and the use model of drone, and considers the overlap rate as the key to problem conversion. The Zhang Bin solar panel plant has successfully been converted into a network with 4,798 nodes. And under the condition that the minimum limit of Genetic Algorithm iterations set to 300, the algorithm starts from a good initial solution while ensuring the feasibility of the solution obtained in the process, converges into an acceptable path solution that takes 1,605 minutes within a limited iteration, and recommends a relatively stable recharging station position (987.76, 438.62).
Keywords: floating solar plant; solar photovoltaics; photovoltaic inspection; unmanned aerial vehicles (UAV); drone; drone routing problem.

關鍵字(中)

★ 水域型太陽能發電站
★ 光伏發電
★ 光伏檢測
★ 無人航空載具
★ 無人機
★ 無人機路徑規劃

關鍵字(英)

★ floating solar plant
★ solar photovoltaics
★ photovoltaic inspection
★ unmanned aerial vehicles (UAV)
★ drone
★ drone routing problem

論文目次

摘要 i
Abstract ii
目錄 iv
圖目錄 v
表目錄 vi
第一章　研究問題 1
1.1 全球暖化 1
1.2 研究動機 4
1.3 研究問題 10
第二章　文獻探討 14
2.1 無人機 14
2.2 路徑規劃問題 16
2.3 無人機巡檢基礎設施 18
第三章　研究方法 22
3.1 問題分析 22
3.2 模型與參數 26
3.3 研究方法 29
第四章　個案分析與結果 43
4.1 情境描述與問題轉換 43
4.2 分析結果 51
第五章　結論 55
5.1 結論 55
5.2 未來方向 56
參考文獻 58
中文文獻 58
英文文獻 59

參考文獻

中文文獻
[1] 今天頭條（2019）。無人機的發展歷史。今天頭條。檢自https://twgreatdaily.com/1HSMTG4BMH2_cNUgdjA7.html（訪問於2021年12月23日）。
[2] 方惠民、張崑宗、蕭松山、錢軒宇、蘇育弘（2019）。應用無人熱紅外線影像於近海域溫排水擴散之研究。第41屆海洋工程研討會，高雄市，臺灣。
[3] 王聰榮、林正軒（2020）。電推進無人機市場與技術發展趨勢。機械工業雜誌，先進馬達與電推進無人機技術專輯（448），20-28。
[4] 台灣科技媒體中心（2018）。水面太陽光電相關科學資訊。台灣科技媒體中心。檢自https://smctw.tw/3618/（訪問於2022年1月20日）。
[5] 先創國際（2021）。維護DJI大疆無人機電池健康安全的提示。先創國際。檢自https://www.esentra.com.tw/2021/01/%E7%B6%AD%E8%AD%B7dji-%E5%A4%A7%E7%96%86%E7%84%A1%E4%BA%BA%E6%A9%9F%E9%9B%BB%E6%B1%A0%E5%81%A5%E5%BA%B7%E5%AE%89%E5%85%A8%E7%9A%84%E6%8F%90%E7%A4%BA/（訪問於2022年5月25日）。
[6] 交通部民用航空局（2018）。民用航空法遙控－無人機專章。交通部民用航空局。檢自https://www.caa.gov.tw/Article.aspx?a=2194&lang=1（訪問於2022年1月20日）。
[7] 辰亞能源股份有限公司（2021）。太陽能電廠建置及光電教育推廣並進，辰亞能源樹立永續發展新標竿。辰亞能源股份有限公司。檢自https://www.chenya-energy.com/%e5%a4%aa%e9%99%bd%e8%83%bd%e9%9b%bb%e5%bb%a0%e5%bb%ba%e7%bd%ae%e5%8f%8a%e5%85%89%e9%9b%bb%e6%95%99%e8%82%b2%e6%8e%a8%e5%bb%a3%e4%b8%a6%e9%80%b2-%e8%be%b0%e4%ba%9e%e8%83%bd%e6%ba%90%e6%a8%b9/（訪問於2022年1月19日）。
[8] 邱家琳（2021）。開發水上型光電還不夠！辰亞能源將積極參與漁電共生。上報。檢自https://www.upmedia.mg/news_info.php?Type=5&SerialNo=117610（訪問於2022年2月16日）。
[9] 洪永杰（2019）。新及再生能源前瞻技術掃描評估及研發推動—搭載輕量型監測太陽光電裝置之發電數據異常類型診斷系統創新前瞻計畫。財團法人資訊工業策進會。
[10] 深圳大疆創新科技有限公司。發電巡檢。DJI Enterprise。檢自https://enterprise.dji.com/zh-tw/electricity/power-generation-management（訪問於2021年11月25日）。
[11] 深圳大疆創新科技有限公司（2021）。Mavic 2 Enterprise Advanced使用者手冊。DJI。檢自https://www.dji.com/tw/downloads/products/mavic-2-enterprise-advanced（訪問於2022年6月22日）。
[12] 強將實業（2020）。無人機大時代：太陽能板空拍巡檢。強將實業－鴻大非破壞性檢測顧問。檢自https://www.ir.com.tw/newsdetail_tw.php?id=2518（訪問於2022年2月16日）。
[13] 康智堯（2021）。太陽能發電火災案例研析。內政部消防署。檢自http://monthly.nfa.gov.tw/article.php?id=189（訪問於2022年2月16日）。
[14] 程式人生。無人機系列之發展史（2019）。程式人生。檢自https://www.itread01.com/content/1548371542.html（訪問於2021年12月23日）。
[15] 翔隆航太股份有限公司（2021）。太陽能巡檢無人機設備推薦。Dragonfly UAS。檢自https://www.dragonflyuas.com.tw/post/drone-in-solar（訪問於2022年1月19日）。
[16] 楊明德、莊子毅、韓仁毓（2018）。結合光學與紅外線熱影像正射鑲嵌處理。航測及遙測學刊，23（2），71-81。https://doi.org/10.6574/JPRS.201806_23(2).0001。
[17] 鍾晨沅（2020）。太陽能系統自動蓄電，提升無人機續航力。大學報。檢自https://unews.nccu.edu.tw/unews/%EF%BC%88%EF%BD%86%EF%BC%89%E6%87%89%E7%94%A8%E6%96%BC%E7%84%A1%E4%BA%BA%E6%A9%9F%E8%BC%89%E5%85%B7%E4%B9%8B%E9%AB%98%E6%95%88%E8%83%BD%E5%A4%AA%E9%99%BD%E8%83%BD%E5%85%89%E4%BC%8F%E5%85%85%E9%9B%BB/（訪問於2022年1月19日）。
英文文獻
[18] Allahyari, S., Salari M. & Vigo, D. (2015). A Hybrid Metaheuristic Algorithm for the Multi-depot Covering Tour Vehicle Routing Problem. European Journal of Operational Research, 242(3), 756-768. https://doi.org/10.1016/j.ejor.2014.10.048.
[19] Askri, G. (2018). State of art of the Capacitated Vehicle Rooting Problem. Medium. Retrieved from https://medium.com/cmsa-algorithm-for-the-service-of-the-capacitated/state-of-art-of-the-capacitated-vehicle-rooting-problem-30ad4de6c2e9 (Accessed Apr. 20, 2022).
[20] Baik, H. & Valenzuela, J. (2018). Unmanned Aircraft System Path Planning for Visually Inspecting Electric Transmission Towers. Journal of Intelligent & Robotic Systems, 95, 1097-1111. https://doi.org/10.1007/s10846-018-0947-9.
[21] Belfiore, P. & Yoshizaki, H. (2009). Scatter Search for a Real-life Heterogeneous Fleet Vehicle Routing Problem with Time Windows and Split Deliveries in Brazil. European Journal of Operational Research, 199(3), 750-758. https://doi.org/10.1016/j.ejor.2008.08.003.
[22] Besada, J. A., Bergesio, L., Campaña, I., Vaquero-Melchor, D., López-Araquistain, J., Bernardos, A. M. & Casar, J. R. (2018). Drone Mission Definition and Implementation for Automated Infrastructure Inspection Using Airborne Sensors. Sensors, 18(4), 1170. https://doi.org/10.3390/s18041170.
[23] Blanton, J. L. & Wainwright, R. L. (1993). Multiple Vehicle Routing with Time and Capacity Constraints Using Genetic Algorithms. Proceedings of the 5th International Conference on Genetic Algorithms, Urbana-Champaign, IL, USA.
[24] Brakels, R. (2017). Solar Panel Maintenance: How often should they be inspected. Retrieved from https://www.solarquotes.com.au/blog/solar-panel-maintenance-often-inspected/#comments (Accessed Jun. 19, 2022).
[25] Chung, H.-M., Maharjan, S., Zhang, Y., Eliassen, F. & Strunz, K. (2021). Placement and Routing Optimization for Automated Inspection with Unmanned Aerial Vehicles: A Study in Offshore Wind Farm. IEEE Transactions on Industrial Informatics, 17(5), 3032-3043. https://doi.org/10.1109/TII.2020.3004816.
[26] Clarke, G. & Wright, J. W. (1964). Scheduling of Vehicles from a Central Depot to a Number of Delivery Points. Operations Research, 12(4), 568-581. https://doi.org/10.1287/opre.12.4.568.
[27] Dantzig, G. B. & Ramser, J. H. (1959). The Truck Dispatching Problem. Management Science, 6(1), 80-91. https://doi.org/10.1287/mnsc.6.1.80.
[28] Drexl, M. (2012). Rich Vehicle Routing in Theory and Practice. Logistics Research, 5, 47-63. https://doi.org/10.1007/s12159-012-0080-2.
[29] Elliott, J. A. (2012). An Introduction to Sustainable Development (4th ed.). Routledge.
[30] Energy Information Administration. Renewable Energy Explained. Retrieved from https://www.eia.gov/energyexplained/renewable-sources/ (Accessed Oct. 28, 2021).
[31] Environmental Protection Agency. Global Greenhouse Gas Emissions Data. Retrieved from https://www.epa.gov/ghgemissions/global-greenhouse-gas-emissions-data (Accessed Oct. 28, 2021).
[32] Gandreau, M., Guertin, F., Potvin, J.-Y. & Seguin, R. (2006). Neighborhood Search Heuristics for a Dynamic Vehicle Dispatching Problem with Pick-ups and Deliveries. Transportation Research Part C: Emerging Technologies, 14(3), 157-174. https://doi.org/10.1016/j.trc.2006.03.002.
[33] Gutin, G. & Punnen, A. P. (2002). The Traveling Salesman Problem and Its Variations. Kluwer Academic Publishers.
[34] Intergovernmental Panel on Climate Change (2014). Climate Change 2014: Mitigation of Climate Change. Cambridge University Press.
[35] Intergovernmental Panel on Climate Change (2018). Global Warming of 1.5°C. World Meteorological Organization.
[36] Intergovernmental Panel on Climate Change (2021). Climate Change 2021: Global warming Basis. Cambridge University Press (in press).
[37] International Energy Agency (2019). Solar Energy: Mapping the Road Ahead. IEA.
[38] Jabir, E., Panicker, V. V. & Sridharan, R. (2017). Design and Development of a Hybrid Ant Colony-Variable Neighborhood Search Algorithm for a Multi-depot Green Vehicle Routing Problem. Transportation Research Part D: Transport and Environment, 57, 422-457. https://doi.org/10.1016/j.trd.2017.09.003.
[39] Kumar, S. N. & Panneerselvam, R. (2012). A Survey on the Vehicle Routing Problem and Its Variants. Intelligent Information Management, 4(3), 66-74. https://doi.org/10.4236/iim.2012.43010.
[40] Lee, D. H. & Park, J. H. (2019). Developing Inspection Methodology of Solar Energy Plants by Thermal Infrared Sensor on Board Unmanned Aerial Vehicles. Energies 2019, 12(15), 2928. https://doi.org/10.3390/en12152928.
[41] Li, Y., Yang, W., Huang, B. (2020). Impact of UAV Delivery on Sustainability and Costs under Traffic Restrictions. Mathematical Problems in Engineering, 2020. https://doi.org/10.1155/2020/9437605.
[42] Liu, Y., Shi, J., Liu, Z., Huang, J. & Zhou, T. (2019). Two-layer Routing for High-voltage Powerline Inspection by Cooperated Ground Vehicle and Drone. Energies, 12(7), 1385. https://doi.org/10.3390/en12071385.
[43] Muntwyler, U., Schuepbach, E. & Lanz, M. (2015). Infrared (IR) Drone for Quick and Cheap PV Inspection. 31st European Photovoltaic Solar Energy Conference and Exhibition, Hamburg, Germany.
[44] Nagata, Y. & Bräysy, O. (2009). A Powerful Route Minimization Heuristic for the Vehicle Routing Problem with Time Windows. Operations Research Letters, 37(2009), 333-338. https://doi.org/10.1016/j.orl.2009.04.006.
[45] Natural Sciences and Engineering Research Council of Canada & the Department of Combinatorics and Optimization at the University of Waterloo (2021). The Traveling Salesman Problem. Solving TSPs. Retrieved from https://www.math.uwaterloo.ca/tsp/index.html (Accessed Jun. 22, 2022).
[46] Nazif, H. & Lee, L. S. (2012). Optimised Crossover Genetic Algorithm for Capacitated Vehicle Routing Problem. Applied Mathematical Modelling, 36(5), 2110-2117. https://doi.org/10.1016/j.apm.2011.08.010.
[47] Renewables Now (2021). The 2021 edition of the Renewables Global Status Report. REN21.
[48] Ritchie, H. & Roser, M. (2020). CO₂ and Greenhouse Gas Emissions. Retrieved from https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions (Accessed Oct. 28, 2021).
[49] Ritchie, H. & Roser M. (2020). Energy. Retrieved from https://ourworldindata.org/energy (Accessed Oct. 28, 2021).
[50] Seo, J., Duque, L. & Wacker, J. (2018). Drone-enabled Bridge Inspection Methodology and Application. Automation in Construction, 94(2018), 112-126. https://doi.org/10.1016/j.autcon.2018.06.006.
[51] Single European Sky ATM Research 3 Joint Undertaking (2017). European Drones Outlook Study: Unlocking the Value for Europe. Publications Office.
[52] Solar Energy Industries Association. Solar Energy. Retrieved from https://www.seia.org/initiatives/about-solar-energy (Accessed Nov. 11, 2021).
[53] Toth, P. & Vigo, D. (2002). The Vehicle Routing Problem. SIAM.
[54] United Nations (1992). United Nations Framework Convention on Climate Change.
[55] United Nations (2015). Paris Agreement.
[56] World Commission on Environment and Development (1987). Our Common Future. Oxford University Press.
[57] World Resources Institute (2021). Climate Watch Historical GHG Emissions. Retrieved from https://www.climatewatchdata.org/ghg-emissions?breakBy=sector&end_year=2017&gases=all-ghg§ors=total-including-lucf&start_year=1990 (Accessed Oct. 28, 2021).
[58] Zachariadisa, E. E., Tarantilisb, C. D. & Kiranoudis, C. T. (2009). A Hybrid Metaheuristic Algorithm for the Vehicle Routing Problem with Simultaneous Delivery and Pick-up Service. Expert Systems with Applications, 36(2), 1070-1081. https://doi.org/10.1016/j.eswa.2007.11.005.

指導教授

王啟泰(Chi-Thai Wang)

審核日期

2022-7-13

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