Intelligent fault location algorithms for distributed generation distribution networks: a review

• Autorzy: Giral-Ramírez Diego Armando , Hernández-Suarez Cesar Augusto , Cortes-Torres José David

Identyfikatory

DOI

10.15199/48.2022.07.23

Warianty tytułu

PL

Inteligentne algorytmy lokalizacji uszkodzeń dla sieci dystrybucyjnych generacji rozproszonej: przegląd

• Języki publikacji: EN

Abstrakty

EN

Distributed Generation (DG) is a small-scale technology linked to consumers through the distribution system and has a high potential for technical, economic, and environmental benefits. The incorporation of generation at demand points produces a variety of load flow and fault currents, changing unidirectional flows to bidirectional structures and altering the characteristics of fault currents. The traditional methods for fault location that are implemented correspond to the traveling wave method and the impedance method. DG inclusion establishes new challenges, so it is necessary to propose or adopt models that improve the location process. During the last years, several Artificial Intelligence (AI) techniques have been introduced, where it presents good results due to its high performance and capacity to provide a fast response. This paper reviews AI-based techniques for fault location in distribution networks with DG. Although the advances are promising, many questions still need to be answered; the permanent work is to identify the advances in AI to obtain better results. Additionally, the implemented strategies must be scalable to ease the computational load and to be able to solve problems of greater complexity.

PL

Generacja rozproszona (DG) to technologia na małą skalę powiązana z konsumentami za pośrednictwem systemu dystrybucyjnego, która ma wysoki potencjał korzyści technicznych, ekonomicznych i środowiskowych. Włączenie generacji w punktach odbioru wytwarza różnorodne przepływy obciążenia i prądy zwarciowe, zmieniając przepływy jednokierunkowe na struktury dwukierunkowe i zmieniając charakterystyki prądów zwarciowych. Zaimplementowane tradycyjne metody lokalizacji zwarcia odpowiadają metodzie fali biegnącej i metodzie impedancyjnej. Dyrekcja Generalna ds. integracji stawia nowe wyzwania, dlatego konieczne jest zaproponowanie lub przyjęcie modeli usprawniających proces lokalizacji. W ciągu ostatnich lat wprowadzono kilka technik sztucznej inteligencji (AI), które dają dobre wyniki ze względu na wysoką wydajność i zdolność do szybkiego reagowania. W niniejszym artykule dokonano przeglądu opartych na sztucznej inteligencji technik lokalizacji uszkodzeń w sieciach dystrybucyjnych z DG. Chociaż postęp jest obiecujący, wiele pytań wciąż wymaga odpowiedzi; stałą pracą jest identyfikacja postępów w sztucznej inteligencji w celu uzyskania lepszych wyników. Dodatkowo wdrażane strategie muszą być skalowalne, aby zmniejszyć obciążenie obliczeniowe i móc rozwiązywać problemy o większej złożoności.

Słowa kluczowe

EN

intelligent optimization; distributed generation; artificial intelligence; machine learning; fault location

PL

lokalizacja uszkodzeń; sieć rozproszona; sztuczna inteligencja

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2022
• Tom: R. 98, nr 7
• Strony: 137--144
• Opis fizyczny: Bibliogr. 55 poz., rys., tab.

Twórcy

autor

Giral-Ramírez Diego Armando

• Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

• https://orcid.org/0000-0001-9983-4555

autor

Hernández-Suarez Cesar Augusto

• Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

• https://orcid.org/0000-0002-7974-5560

autor

Cortes-Torres José David

• Universidad Industrial de Santander, Bucaramanga, Colombia

• https://orcid.org/0000-0001-9232-6785

Bibliografia

• [1] U. del V. Ministerio de Minas y Energía, Universidad Nacional de Colombia, UPME, Observatorio Colombiano de Energía. Aproximación a las condiciones para su conformación. 2018.
• [2] R. Naghizadeh, H. Afrakhte, and M. Ziapour, “Smart Distribution Network Reconfiguration Based on Optimal Planning of Distributed Generation Resources Using Teaching Learning Based Algorithm to Reduce Generation Costs, Losses and Improve Reliability,” in Electrical Engineering (ICEE), Iranian Conference on, 2018, pp. 1125–1131, doi: 10.1109/ICEE.2018.8472451.
• [3] M.B. de las Casas, H.R. Quintero, I.O. Sosa, D.S. Morales, and L.E.L. Mendoza, “Influencia de la generación distribuida en los niveles de cortocircuito y en las protecciones eléctricas en subestaciones de 110/34, 5 kV,” Ing. Energética, vol. 30, no. 1, pp. 3-a, 2009.
• [4] T.C. Srinivasa Rao, S.S. Tulasi Ram, and J.B.V. Subrahmanyam, “Fault Signal Recognition in Power Distribution System using Deep Belief Network,” J. Intell. Syst., vol. 29, no. 1, pp. 459–474, Dec. 2019, doi: 10.1515/jisys-2017-0499.
• [5] N. Jenkins, J., Ekanayake, and G. Strbac, Distributed Generation. India, 2014.
• [6] T. Adefarati and R. C. Bansal, “Integration of renewable distributed generators into the distribution system: a review, ”IET Renew. Power Gener., vol. 10, no. 7, pp. 873–884, 2016.
• [7] C.L. Trujillo et al., Microrredes eléctricas, Primera Ed. Bogotá, 2015.
• [8] L. Strezoski, I. Stefani, and B. Brbaklic, “Active Management of Distribution Systems with High Penetration of Distributed Energy Resources,” in IEEE EUROCON 2019 -18th International Conference on Smart Technologies, 2019, pp. 1–5, doi: 10.1109/EUROCON.2019.8861748.
• [9] D. Gaonkar, Distributed generation. Croatia: BoD–Books on Demand, 2010.
• [10] X. Chen and Z. Jiao, “Accurate Fault Location Method of Distribution Network with Limited Number of PMUs,” in 2018 China International Conference on Electricity Distribution (CICED), 2018, pp. 1503–1507, doi: 10.1109/CICED.2018.8592074.
• [11] H. Sun, H. Yi, F. Zhuo, X. Du, and G. Yang, “Precise Fault Location in Distribution Networks Based on Optimal Monitor Allocation,” IEEE Trans. Power Deliv., vol. 35, no. 4, pp. 1788–1799, 2020, doi: 10.1109/TPWRD.2019.2954460.
• [12] S. F. Alwash, V. K. Ramachandaramurthy, and N. Mithulananthan, “Fault-Location Scheme for Power Distribution System with Distributed Generation,” IEEE Trans. Power Deliv., vol. 30, no. 3, pp. 1187–1195, 2015, doi: 10.1109/TPWRD.2014.2372045.
• [13] A. Tashakkori, P.J. Wolfs, S. Islam, and A. Abu-Siada, “Fault Location on Radial Distribution Networks via Distributed Synchronized Traveling Wave Detectors,” IEEE Trans. Power Deliv., vol. 35, no. 3, pp. 1553–1562, 2020, doi: 10.1109/TPWRD.2019.2948174.
• [14] S. Seghir and T. Bouthiba, “Impedance correction method of distance relay on high voltage transmission line,” Przegląd Elektrotechniczny, vol. 97, 2021.
• [15] IEEE, “Guide for Determining Fault Location on AC Transmission and Distribution Lines,” C37.114-2014. pp. 1–76, 2015, doi: 10.1109/IEEESTD.2015.7024095.
• [16] S. Das, S. Santoso, and S.N. Ananthan, Fault Location on Transmission and Distribution Lines: Principles and Applications. John Wiley & Sons, 2021.
• [17] J. Herlender, J. Iżykowski, and E. Rosołowski, “Impedance differential relay as a transmission line fault locator,” Przegląd Elektrotechniczny, vol. 93, 2017.
• [18] S. Dadary and H. Afrakhte, “Accuracy improvement of impedance-based fault locating method in distribution systems with DGs considering loss of laterals and load variations,” Int. Trans. Electr. Energy Syst., vol. 27, no. 11, p. e2420, Nov. 2017, doi: https://doi.org/10.1002/etep.2420.
• [19] H.H. Goh et al., “Fault location techniques in electrical power system: A review,” Indones. J. Electr. Eng. Comput. Sci., vol. 8, no. 1, pp. 206–212, 2017.
• [20] International Electrotechnical Commission, “IEC 60909 - Short circuit currents in three-phase,” vol. 0. 2016.
• [21] B. Zhang, H. Liu, J. Song, and J. Zhang, “Simulation on Grounding Fault Location of Distribution Network Based on Regional Parameters,” in 2019 IEEE 19th International Symposium on High Assurance Systems Engineering (HASE), 2019, pp. 216–221, doi: 10.1109/HASE.2019.00040.
• [22] B. Jiang, X. Dong, S. Shi, and B. Wang, “Fault line identification of Single Line to Ground fault for non-effectively grounded distribution networks with double-circuit lines,” in IEEE Power and Energy Society General Meeting, Jul. 2015, vol. 2015-Septe, no. 51120175001, pp. 1–5, doi: 10.1109/PESGM.2015.7286346.
• [23] A. Bahmanyar, S. Jamali, A. Estebsari, and E. Bompard, “A comparison framework for distribution system outage and fault location methods,” Electr. Power Syst. Res., vol. 145, pp. 19–34, 2017, doi: https://doi.org/10.1016/j.epsr.2016.12.018.
• [24] R. Kumar and D. Saxena, “A Literature Review on Methodologies of Fault Location in the Distribution System withDistributed Generation,” Energy Technol., vol. 8, no. 3, p. 1901093, Mar. 2020, doi: https://doi.org/10.1002/ente.201901093.
• [25] N. Gana, N.F. Ab Aziz, Z. Ali, H. Hashim, and B. Yunus, “A comprehensive review of fault location methods for distributionpower system,” Indones. J. Electr. Eng. Comput. Sci., vol. 6, no. 1, 2017, doi: http://doi.org/10.11591/ijeecs.v6.i1.pp185-192.
• [26] K.S.M.H. Ibrahim, Y.F. Huang, A.N. Ahmed, C.H. Koo, and A. El-Shafie, “A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting, ”Alexandria Eng. J., 2021.
• [27] S. Barja-Martinez, M. Aragüés-Peñalba, Í. Munné-Collado, P. Lloret-Gallego, E. Bullich-Massagué, and R. Villafafila-Robles, “Artificial intelligence techniques for enabling Big Data services in distribution networks: A review,” Renew. Sustain. Energy Rev., vol. 150, p. 111459, 2021, doi: https://doi.org/10.1016/j.rser.2021.111459.
• [28] D. H. Wolpert and W. G. Macready, “No free lunch theorems for optimization,” IEEE Trans. Evol. Comput., vol. 1, no. 1, pp. 67–82, 1997, doi: 10.1109/4235.585893.
• [29] Y. Shi, Y.E. Sagduyu, T. Erpek, K. Davaslioglu, Z. Lu, and J.H. Li, “Adversarial Deep Learning for Cognitive Radio Security: Jamming Attack and Defense Strategies,” in 2018 IEEE International Conference on Communications Workshops (ICC Workshops), 2018, pp. 1–6, doi: 10.1109/ICCW.2018.8403655.
• [30] C. Hernández, D. Giral, and H. Marquez, “Evolutive Algorithm for Spectral Handoff Prediction in Cognitive Wireless Networks,” HIKARI Ltd, vol. 10, no. 14, pp. 673–689, 2017, doi: 10.12988/ces.2017.7766.
• [31] M. Bkassiny, Y. Li, and S. K. Jayaweera, “A Survey on Machine-Learning Techniques in Cognitive Radios,” IEEE Commun. Surv. Tutorials, vol. 15, no. 3, pp. 1136–1159, 2013, doi: 10.1109/SURV.2012.100412.00017.
• [32] M. S. Ibrahim, W. Dong, and Q. Yang, “Machine learning driven smart electric power systems: Current trends and new perspectives,” Appl. Energy, vol. 272, p. 115237, 2020, doi: https://doi.org/10.1016/j.apenergy.2020.115237.
• [33] A. Sharifzadeh, M. T. Ameli, and S. Azad, “Power System Challenges and Issues,” in Application of Machine Learning and Deep Learning Methods to Power System Problems, Springer, 2021, pp. 1–17.
• [34] F. Tao, L. Zhang, and Y. Laili, Configurable intelligent optimization algorithm. Springer, 2016.
• [35] K.-C. Chang, R. Zhang, H. Deng, F.-H. Chang, H.-C. Wang, and G. D. K. Amesimenu, “Chaotic Particle Swarm Optimization Algorithm for Fault Location of Distribution Network with DG BT,” in International Conference on Advanced Intelligent Systems and Informatics, 2022, pp. 256–266.
• [36] W.-C. Lin, W.-T. Huang, K.-C. Yao, H.-T. Chen, and C.-C. Ma, “Fault Location and Restoration of Microgrids via Particle Swarm Optimization,” Applied Sciences , vol. 11, no. 15. 2021, doi: 10.3390/app11157036.
• [37] W. Bao, Q. Fang, P. Wang, W. Yan, and P. Pan, “A Fault Location Method for Active Distribution Network with DGs,” in International Conference on Smart Grid and Electrical Automation (ICSGEA), 2021, pp. 6–10, doi: 10.1109/ICSGEA53208.2021.00010.
• [38] W. Li, J. Su, X. Wang, J. Li, and Q. Ai, “Fault location of distribution networks based on multi-source information,” Glob. Energy Interconnect., vol. 3, no. 1, pp. 76–84, 2020, doi: https://doi.org/10.1016/j.gloei.2020.03.005.
• [39] H. Yang, Y. Guo, and X. Liu, “Fault Section Location of Active Distribution Network based on Wolf Pack and Differential Evolution Algorithms.,” Int. J. Performability Eng., vol. 16, no. 1, 2020.
• [40] H. Lala, S. Karmakar, and S. Ganguly, “Detection and localization of faults in smart hybrid distributed generation systems: A Stockwell transform and artificial neural network-based approach,” Int. Trans. Electr. Energy Syst., vol. 29, no. 2, p. e2725, Feb. 2019, doi: 10.1002/etep.2725.
• [41] S. Beheshtaein, R. Cuzner, M. Savaghebi, S. Golestan, and J. M. Guerrero, “Fault location in microgrids: a communication based high-frequency impedance approach,” IET Gener. Transm. Distrib., vol. 13, no. 8, SI, pp. 1229–1237, 2019, doi: 10.1049/iet-gtd.2018.5166.
• [42] S. K. Yellagoud and P. R. Talluri, “Assessment of Fault Location Methods for Electric Power Distribution Networks,” in 2018 4th International Conference for Convergence in Technology (I2CT), 2018, pp. 1–8, doi: 10.1109/I2CT42659.2018.9058148.
• [43] D. Sonoda, A. C. Z. de Souza, and P. M. da Silveira, “Fault identification based on artificial immunological systems,” Electr. Power Syst. Res., vol. 156, pp. 24–34, 2018, doi: https://doi.org/10.1016/j.epsr.2017.11.012.
• [44] X. Wang, X. Yu, Y. Xue, Y. Zhu, and J. Fu, “Application of Improved Quantum Genetic Algorithm in Fault Location of Distribution Network,” in Proceedings of 2018 IEEE 3rd advanced information technology, electronic and automation control conference (IAEAC 2018), 2018, pp. 2524–2529.
• [45] 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, doi: 10.1109/PEDG.2018.8447728.
• [46] C. Darab, R. Tarnovan, A. Turcu, and C. Martineac, “Artificial Intelligence Techniques for Fault Location and Detection in Distributed Generation Power Systems,” in 2019 8th International Conference on Modern Power Systems (MPS), 2019, pp. 1–4, doi: 10.1109/MPS.2019.8759662.
• [47] X. He, Q. Qian, Y. Wang, Y. Wang, and S. Shi, “Adaptive traveling waves based protection of distribution lines,” in 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2), 2018, pp. 1–5, doi: 10.1109/EI2.2018.8582627.
• [48] B. Nuthalapati and U. K. Sinha, “Fault Detection and Location of Broken Power Line Not Touching the Ground,” Int. J. Emerg. Electr. Power Syst., vol. 20, no. 3, pp. 2–11, Jul. 2019, doi: 10.1515/ijeeps-2018-0321.
• [49] R. Sharma, O. P. Mahela, and S. Agarwal, “Detection of Power System Faults in Distribution System Using Stockwell Transform,” in 2018 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), 2018, pp. 1–5, doi: 10.1109/SCEECS.2018.8546879.
• [50] V. Veerasamy et al., “High-impedance fault detection in medium-voltage distribution network using computational intelligence-based classifiers,” Neural Comput. Appl., vol. 31, no. 12, pp. 9127–9143, Dec. 2019, doi: 10.1007/s00521-019-04445-w.
• [51] A. Khaleghi, M. Oukati Sadegh, M. Ghazizadeh-Ahsaee, and A. Mehdipour Rabori, “Transient fault area location and fault classification for distribution systems based on wavelet transform and adaptive neuro-fuzzy inference system (ANFIS),” Adv. Electr. Electron. Eng., vol. 16, no. 2, pp. 155–166, Jun. 2018, doi: 10.15598/aeee.v16i2.2563.
• [52] Y. Aslan and Y. E. Yağan, “ANN based fault location for medium voltage distribution lines with remote-end source,” in 2016 International Symposium on Fundamentals of Electrical Engineering (ISFEE), 2016, pp. 1–5, doi: 10.1109/ISFEE.2016.7803203.
• [53] Z. Sun, Q. Wang, and Z. Wei, “Fault location of distribution network with distributed generations using electrical synaptic transmission-based spiking neural P systems,” Int. J. Parallel, Emergent Distrib. Syst., vol. 0, no. 0, pp. 1–17, Oct. 2019, doi: 10.1080/17445760.2019.1682145.
• [54] W. Fei and P. Moses, “Fault current tracing and identification via machine learning considering distributed energy resources in distribution networks,” Energies, vol. 12, no. 22, p. 4333, Nov. 2019, doi: 10.3390/en12224333.
• [55] Y. Hui, X. Yan, Q. Bin, and W. Qi, “Fault Location Method for DC Distribution Network Based on Particle Swarm Optimization,” in 2019 IEEE 2nd International Conference on Electronics Technology (ICET), 2019, pp. 335–338, doi: 10.1109/ELTECH.2019.8839476.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).