臺灣博碩士論文加值系統

對於當代的電力系統而言，如何能快速且精準地辨識出各類型故障及其發生地點一直都是電力運作者的最重要課題之一。近年來電力系統由於綠色能源等第三方電力供應者的開放加入，各類型電源的非預定注入電力導致電力傳輸變得更加複雜，因此故障診斷將更為困難。智慧電表為智慧電網重要的元件，具有蒐集電力資訊和雙向傳輸的功能。為避免因上述電力源的非預期性加入電網而造成故障種類和地點的誤判，故在本文中提出利用自適應模糊類神經推論系統ANFIS，結合智慧型電表來進行故障類型辨識與位置判斷的方法。在系統中，ANFIS利用來自智慧電表所蒐集之電力資訊如電壓和故電壓變化進行學習後，即可透過顯示代碼來辨識故障類型與位置，並透過物聯網傳遞故障排除指令，啟動斷路器將故障點隔離於系統俾益系統繼續維持正常運作。由模擬結果顯示，本文所提出的方法除了對於非預期性電源(如風力發電)所造成之干擾極具強健性外，還能精確且迅速地辨識出故障類型及發生位置。此外和在傳統上以測距電驛故障排除故障的方法相較，更可以有效地域縮小斷電區域。對於當代的電力系統而言，如何能快速且精準地辨識出各類型故障及其發生地點一直都是電力運作者的最重要課題之一。近年來電力系統由於綠色能源等第三方電力供應者的開放加入，各類型電源的非預定注入電力導致電力傳輸變得更加複雜，因此故障診斷將更為困難。智慧電表為智慧電網重要的元件，具有蒐集電力資訊和雙向傳輸的功能。為避免因上述電力源的非預期性加入電網而造成故障種類和地點的誤判，故在本文中提出利用自適應模糊類神經推論系統ANFIS，結合智慧型電表來進行故障類型辨識與位置判斷的方法。在系統中，ANFIS利用來自智慧電表所蒐集之電力資訊如電壓和故電壓變化進行學習後，即可透過顯示代碼來辨識故障類型與位置，並透過物聯網傳遞故障排除指令，啟動斷路器將故障點隔離於系統俾益系統繼續維持正常運作。由模擬結果顯示，本文所提出的方法除了對於非預期性電源(如風力發電)所造成之干擾極具強健性外，還能精確且迅速地辨識出故障類型及發生位置。此外和在傳統上以測距電驛故障排除故障的方法相較，更可以有效地域縮小斷電區域。

In modern power system, how to identify the fault type and location rapidly and precisely would be one of the most important issues. Due to the open access of power system to the three parties in recent years, the unscheduled power injections from several kinds of green powers result in the power transmission no more be single direction. That is, the fault identification would become more difficult. Smart meters with the functions of power information collection and bidirectional transmission, are one of the main devices for smart grid. In order to avoid the misidentification on fault type and location resulting from above unscheduled access, the method by integrating (Adaptive Fuzzy Neural Inference System, ANFIS) and smart meters for fault type and location identification is proposed in this thesis. In the system, based on the power information such as Bus voltages and powers collected and transferred from smart meters and through ANFIS learning, the fault type and location can be identified. In addition, through the IoT (Internet of Things) to transfer the instructions of fault removal, start circuit breakers, and isolate the fault point from the system to facilitate the system maintaining in normal operation. The simulation results show that the proposed method is not only with the robustness against the perturbations resulted from unscheduled power, but also with the preciseness and rapidness on the identification of both fault type and location. Furthermore, to compare with the traditional method by using distance relays to isolate the fault point, it is more efficient to narrow down the power-off area.

摘要...............................................I
Abstract.........................................II
致謝..............................................IV
目次.............................................V
表目次..........................................VII
圖目次..........................................IX
縮寫代號與符號............................XII
一、緒論 ......................................1
1.1 研究背景.................................1
1.2 研究動機.................................5
1.3 研究目的.................................6
1.4 研究步驟.................................6
1.5 主要貢獻.................................7
1.6 本文架構.................................7
二、文獻回顧...............................10
2.1 電力系統故障回顧..................10
2.2 故障辨識系統發展..................11
2.3 機械學習................................12
2.4 人工神經網路.........................13
2.5 自適應神經模糊系統..............14
2.6 物聯網技術應用.....................15
三、故障辨識與定位系統結構.....16
3.1 系統設計................................16
3.2 模擬流程................................20
3.3 電網差異................................21
3.4 負載曲線................................24
3.5 ANFIS結構.............................25
3.6 特徵擷取................................26
3.7 ANFIS訓練.............................33
3.8 故障類型與位置辨識系統.......40
3.9 故障區域隔離系統..................45
四、模擬結果與分析....................47
4.1 加入風力3 Bus系統模擬........47
4.2 加入風力發電30 Bus系統......53
4.3 物聯網系統模擬.....................64
五、結論探討與未來展望.............68
5.1 結論探討................................68
5.2 未來展望................................69
參考文獻......................................70
附錄 A .........................................76
附錄 B .........................................77

參考文獻

[1]C.-T. Hsiao, C.-S. Liu, D.-S. Chang, and C.-C. Chen, "Dynamic modeling of the policy effect and development of electric power systems: A case in Taiwan," Energy Policy, vol. 122, pp. 377-387, 2018/11/01/ 2018.
[2]H. Farhangi, "The path of the smart grid," IEEE Power and Energy Magazine, vol. 8, no. 1, pp. 18-28, 2010.
[3]T. Ackermann, G. Andersson, and L. Söder, "Distributed generation: a definition1In addition to this paper, a working paper entitled ‘Distributed power generation in a deregulated market environment’ is available. The aim of this working paper is to start a discussion regarding different aspects of distributed generation. This working paper can be obtained from one of the authors, Thomas Ackermann.1," Electric Power Systems Research, vol. 57, no. 3, pp. 195-204, 2001/04/20/ 2001.
[4]V. H. Ferreira et al., "A survey on intelligent system application to fault diagnosis in electric power system transmission lines," Electric Power Systems Research, vol. 136, pp. 135-153, 2016/07/01/ 2016.
[5]A. Colmenar-Santos, C. Reino-Rio, D. Borge-Diez, and E. Collado-Fernández, "Distributed generation: A review of factors that can contribute most to achieve a scenario of DG units embedded in the new distribution networks," Renewable and Sustainable Energy Reviews, vol. 59, pp. 1130-1148, 2016/06/01/ 2016.
[6]J. Zhang, Z. Y. He, S. Lin, Y. B. Zhang, and Q. Q. Qian, "An ANFIS-based fault classification approach in power distribution system," International Journal of Electrical Power & Energy Systems, vol. 49, pp. 243-252, 2013/07/01/ 2013.
[7]B. K. Chaitanya and A. Yadav, "An intelligent fault detection and classification scheme for distribution lines integrated with distributed generators," Computers & Electrical Engineering, vol. 69, pp. 28-40, 2018/07/01/ 2018.
[8]Z. Gao, C. Cecati, and S. X. Ding, "A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches," IEEE Transactions on Industrial Electronics, vol. 62, no. 6, pp. 3757-3767, 2015.
[9]K. Zhou, C. Fu, and S. Yang, "Big data driven smart energy management: From big data to big insights," Renewable and Sustainable Energy Reviews, vol. 56, pp. 215-225, 2016/04/01/ 2016.
[10]Y. Wang, M. Liu, and Z. Bao, "Deep learning neural network for power system fault diagnosis," in 2016 35th Chinese Control Conference (CCC), 2016, pp. 6678-6683.
[11]R. A. Sowah et al., "Design of Power Distribution Network Fault Data Collector for Fault Detection, Location and Classification using Machine Learning," in 2018 IEEE 7th International Conference on Adaptive Science & Technology (ICAST), 2018, pp. 1-8.
[12]O. A. S. Youssef, "Combined fuzzy-logic wavelet-based fault classification technique for power system relaying," IEEE Transactions on Power Delivery, vol. 19, no. 2, pp. 582-589, 2004.
[13]A. Rafinia and J. Moshtagh, "A new approach to fault location in three-phase underground distribution system using combination of wavelet analysis with ANN and FLS," International Journal of Electrical Power & Energy Systems, vol. 55, pp. 261-274, 2014/02/01/ 2014.
[14]M. M. A. S. Mahmoud and Z. Qurbanov, "Review of Fuzzy and ANN Fault Location Methods for Distribution Power System in Oil and Gas Sectors," IFAC-PapersOnLine, vol. 51, no. 30, pp. 263-267, 2018/01/01/ 2018.
[15]Y. Tang, H. F. Wang, R. K. Aggarwal, and A. T. Johns, "Fault indicators in transmission and distribution systems," in DRPT2000. International Conference on Electric Utility Deregulation and Restructuring and Power Technologies. Proceedings (Cat. No.00EX382), 2000, pp. 238-243.
[16]J. Teng, W. Huang, and S. Luan, "Automatic and Fast Faulted Line-Section Location Method for Distribution Systems Based on Fault Indicators," IEEE Transactions on Power Systems, vol. 29, no. 4, pp. 1653-1662, 2014.
[17]Q. Sun et al., "A Comprehensive Review of Smart Energy Meters in Intelligent Energy Networks," IEEE Internet of Things Journal, vol. 3, no. 4, pp. 464-479, 2016.
[18]A. A. Cecilia and K. Sudarsanan, "A survey on smart grid," in 2016 International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS), 2016, pp. 1-7.
[19]H. Cui and K. Zhou, "Industrial power load scheduling considering demand response," Journal of Cleaner Production, vol. 204, pp. 447-460, 2018/12/10/ 2018.
[20]J. N. Bharothu, M. Sridhar, and R. S. Rao, "A literature survey report on Smart Grid technologies," in 2014 International Conference on Smart Electric Grid (ISEG), 2014, pp. 1-8.
[21]D. B. Avancini, J. J. P. C. Rodrigues, S. G. B. Martins, R. A. L. Rabêlo, J. Al-Muhtadi, and P. Solic, "Energy meters evolution in smart grids: A review," Journal of Cleaner Production, vol. 217, pp. 702-715, 2019/04/20/ 2019.
[22]Y. Wang et al., "Energy management of smart micro-grid with response loads and distributed generation considering demand response," Journal of Cleaner Production, vol. 197, pp. 1069-1083, 2018/10/01/ 2018.
[23]X. Wu, X. Hu, S. Moura, X. Yin, and V. Pickert, "Stochastic control of smart home energy management with plug-in electric vehicle battery energy storage and photovoltaic array," Journal of Power Sources, vol. 333, pp. 203-212, 2016/11/30/ 2016.
[24]A. Rawea and S. Urooj, "Power Energy Management for Grid-Connected Hybrid Renewable Energy System in Yemen Using Fuzzy Logic," in Smart Computing and Informatics, Singapore, 2018, pp. 183-191: Springer Singapore.
[25]L. Mehigan, J. P. Deane, B. P. Ó. Gallachóir, and V. Bertsch, "A review of the role of distributed generation (DG) in future electricity systems," Energy, vol. 163, pp. 822-836, 2018/11/15/ 2018.
[26]G. Cerullo, G. Mazzeo, G. Papale, B. Ragucci, and L. Sgaglione, "Chapter 4 - IoT and Sensor Networks Security," in Security and Resilience in Intelligent Data-Centric Systems and Communication Networks, M. Ficco and F. Palmieri, Eds.: Academic Press, 2018, pp. 77-101.
[27]G. N. Ericsson, "Cyber Security and Power System Communication—Essential Parts of a Smart Grid Infrastructure," IEEE Transactions on Power Delivery, vol. 25, no. 3, pp. 1501-1507, 2010.
[28]D. S. Markovic, D. Zivkovic, I. Branovic, R. Popovic, and D. Cvetkovic, "Smart power grid and cloud computing," Renewable and Sustainable Energy Reviews, vol. 24, pp. 566-577, 2013/08/01/ 2013.
[29]H. Fernando and B. Surgenor, "An unsupervised artificial neural network versus a rule-based approach for fault detection and identification in an automated assembly machine," Robotics and Computer-Integrated Manufacturing, vol. 43, pp. 79-88, 2017/02/01/ 2017.
[30]M. A. Alsheikh, S. Lin, D. Niyato, and H.-P. Tan, "Machine learning in wireless sensor networks: Algorithms, strategies, and applications," IEEE Communications Surveys & Tutorials, vol. 16, no. 4, pp. 1996-2018, 2014.
[31]M. Kezunovic, "Smart Fault Location for Smart Grids," IEEE Transactions on Smart Grid, vol. 2, no. 1, pp. 11-22, 2011.
[32]M. H. Moradi and Y. Mohammadi, "Voltage sag source location: A review with introduction of a new method," International Journal of Electrical Power & Energy Systems, vol. 43, no. 1, pp. 29-39, 2012/12/01/ 2012.
[33]A. Prasad, J. Belwin Edward, and K. Ravi, "A review on fault classification methodologies in power transmission systems: Part—I," Journal of Electrical Systems and Information Technology, vol. 5, no. 1, pp. 48-60, 2018/05/01/ 2018.
[34]S. S. Gururajapathy, H. Mokhlis, and H. A. Illias, "Fault location and detection techniques in power distribution systems with distributed generation: A review," Renewable and Sustainable Energy Reviews, vol. 74, pp. 949-958, 2017/07/01/ 2017.
[35]M. P. Tcheou et al., "The Compression of Electric Signal Waveforms for Smart Grids: State of the Art and Future Trends," IEEE Transactions on Smart Grid, vol. 5, no. 1, pp. 291-302, 2014.
[36]A. Prasad, J. Belwin Edward, and K. Ravi, "A review on fault classification methodologies in power transmission systems: Part-II," Journal of Electrical Systems and Information Technology, vol. 5, no. 1, pp. 61-67, 2018/05/01/ 2018.
[37]E. Mocanu, P. H. Nguyen, and M. Gibescu, "Chapter 7 - Deep Learning for Power System Data Analysis," in Big Data Application in Power Systems, R. Arghandeh and Y. Zhou, Eds.: Elsevier, 2018, pp. 125-158.
[38]C. Delpha, D. Diallo, H. Al Samrout, and N. Moubayed, "Multiple incipient fault diagnosis in three-phase electrical systems using multivariate statistical signal processing," Engineering Applications of Artificial Intelligence, vol. 73, pp. 68-79, 2018/08/01/ 2018.
[39]V. Vapnik, The nature of statistical learning theory. Springer science & Business media, 2013.
[40]H. M. M. Maruf, F. Müller, M. S. Hassan, and B. Chowdhury, "Locating Faults in Distribution Systems in the Presence of Distributed Generation using Machine Learning Techniques," in 2018 9th IEEE International Symposium on Power Electronics for Distributed Generation Systems (PEDG), 2018, pp. 1-6.
[41]J. Cai, J. Luo, S. Wang, and S. Yang, "Feature selection in machine learning: A new perspective," Neurocomputing, vol. 300, pp. 70-79, 2018/07/26/ 2018.
[42]A. P. Marugán, F. P. G. Márquez, J. M. P. Perez, and D. Ruiz-Hernández, "A survey of artificial neural network in wind energy systems," Applied Energy, vol. 228, pp. 1822-1836, 2018/10/15/ 2018.
[43]S. K. Saha, P. Das, and A. K. Chakrabothy, "An ANN based relay design for identification faults of 400kv high voltage AC transmission line," International Journal of Computer Applications, vol. 975, p. 8887, 2012.
[44]S. Kesharwani and D. K. Singh, "Simulation of fault Detection for protection of Transmission line using neural network," International Journal of Science, Engineering and Technology Research (IJSETR), vol. 3, no. 5, pp. 1367-1371, 2014.
[45]F. Abid and L. Hamami, "A survey of neural network based automated systems for human chromosome classification," Artificial Intelligence Review, vol. 49, no. 1, pp. 41-56, 2018/01/01 2018.
[46]F. Rodríguez, A. Fleetwood, A. Galarza, and L. Fontán, "Predicting solar energy generation through artificial neural networks using weather forecasts for microgrid control," Renewable Energy, vol. 126, pp. 855-864, 2018/10/01/ 2018.
[47]J. R. Jang, "ANFIS: adaptive-network-based fuzzy inference system," IEEE Transactions on Systems, Man, and Cybernetics, vol. 23, no. 3, pp. 665-685, 1993.
[48]F. Zhu and Y. Wu, "A rapid structural damage detection method using integrated ANFIS and interval modeling technique," Applied Soft Computing, vol. 25, pp. 473-484, 2014/12/01/ 2014.
[49]M. M. Ismail and A. F. Bendary, "Protection of DFIG wind turbine using fuzzy logic control," Alexandria Engineering Journal, vol. 55, no. 2, pp. 941-949, 2016/06/01/ 2016.
[50]A. Arabi, N. Bourouba, A. Belaout, and M. Ayad, "An accurate classifier based on adaptive neuro-fuzzy and features selection techniques for fault classification in analog circuits," Integration, vol. 64, pp. 50-59, 2019/01/01/ 2019.
[51]M. Centenaro, L. Vangelista, A. Zanella, and M. Zorzi, "Long-range communications in unlicensed bands: the rising stars in the IoT and smart city scenarios," IEEE Wireless Communications, vol. 23, no. 5, pp. 60-67, 2016.
[52]I. Lee and K. Lee, "The Internet of Things (IoT): Applications, investments, and challenges for enterprises," Business Horizons, vol. 58, no. 4, pp. 431-440, 2015/07/01/ 2015.
[53]F. A. Alaba, M. Othman, I. A. T. Hashem, and F. Alotaibi, "Internet of Things security: A survey," Journal of Network and Computer Applications, vol. 88, pp. 10-28, 2017/06/15/ 2017.
[54]S. D. T. Kelly, N. K. Suryadevara, and S. C. Mukhopadhyay, "Towards the Implementation of IoT for Environmental Condition Monitoring in Homes," IEEE Sensors Journal, vol. 13, no. 10, pp. 3846-3853, 2013.
[55]A. Kotsev, S. Schade, M. Craglia, M. Gerboles, L. Spinelle, and M. Signorini, "Next Generation Air Quality Platform: Openness and Interoperability for the Internet of Things," Sensors, vol. 16, no. 3, 2016.
[56]S. i. Konomi and G. Roussos, Enriching Urban Spaces with Ambient Computing, the Internet of Things, and Smart City Design. IGI Global, 2016.
[57]Y. Zhang, L. Wang, Y. Xiang, and C. Ten, "Power System Reliability Evaluation With SCADA Cybersecurity Considerations," IEEE Transactions on Smart Grid, vol. 6, no. 4, pp. 1707-1721, 2015.
[58]D. Grozev, G. Spasov, M. Shopov, N. Kakanakov, and G. Petrova, "Experimental study of Cloud Computing based SCADA in Electrical Power Systems," in 2016 XXV International Scientific Conference Electronics (ET), 2016, pp. 1-4.
[59]K. Sayed and H. A. Gabbar, "Chapter 18 - SCADA and smart energy grid control automation," in Smart Energy Grid Engineering, H. A. Gabbar, Ed.: Academic Press, 2017, pp. 481-514.
[60]R. Leszczyna, "Standards on cyber security assessment of smart grid," International Journal of Critical Infrastructure Protection, vol. 22, pp. 70-89, 2018/09/01/ 2018.
[61]A. M. L. Azad, A. Khursheed, and S. V. Singh, "Operation and Control of Micro Sources in Island Mode of a Microgrid," Int J Innov Technol Explor Eng (IJITEE), vol. 4, no. 3, 2014.
[62]X. Wang, L. Chen, and W. Tao, "Research on load classification based on user's typical daily load curve," in 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2), 2017, pp. 1-4.
[63]K. Mets, F. Depuydt, and C. Develder, "Two-stage load pattern clustering using fast wavelet transformation," IEEE Transactions on Smart Grid, vol. 7, no. 5, pp. 2250-2259, 2015.
[64]A. Parey and A. Singh, "Gearbox fault diagnosis using acoustic signals, continuous wavelet transform and adaptive neuro-fuzzy inference system," Applied Acoustics, vol. 147, pp. 133-140, 2019/04/01/ 2019.
[65]B. P. Lin, W. Tsai, C. C. Wu, P. H. Hsu, J. Y. Huang, and T. Liu, "The Design of Cloud-Based 4G/LTE for Mobile Augmented Reality with Smart Mobile Devices," in 2013 IEEE Seventh International Symposium on Service-Oriented System Engineering, 2013, pp. 561-566.
[66]何秉衡, "輸電系統暫態特性及負序電流分析," 2011.
[67]Y. Zhan and Q. P. Zheng, "A multistage decision-dependent stochastic bilevel programming approach for power generation investment expansion planning," IISE Transactions, vol. 50, no. 8, pp. 720-734, 2018.