Overview on topology identification technologies for a low-voltage distribution network

• Autorzy: Haotian Ge , Jiuming Zhong

Identyfikatory

DOI

10.24425/aee.2023.147424

• Języki publikacji: EN

Abstrakty

EN

The topology identification of low-voltage distribution networks is an important foundation for the intelligence of low-voltage distribution networks. Its accuracy fundamentally determines the effectiveness of functions such as power system state estimation, operational control, optimization planning, and intelligent electricity consumption. The low-voltage distribution network is composed of transformers, lines, and end users. The key task of topology identification is to distinguish the connection relationship between distribution transformers, low-voltage lines, and phase sequence with end users, which can be divided into transformer user relationship, line user relationship, and phase user relationship. At present, the main methods of low-voltage network topology identification can be divided into signal injection method and data analysis method. The signal injection method requires a large number of additional terminal devices and is difficult to promote. The data analysis method combines the characteristics of switch state, voltage, current, electrical energy, and other data to perform topology analysis. The commonly used methods include correlation analysis and feature learning. Finally, typical problems that urgently need to be solved in topology recognition and representation were proposed, providing a reference for the research and development of low-voltage distribution network topology automatic recognition technology.

Słowa kluczowe

EN

data driven method; low-voltage distribution network; signal injection method; topology identification

• Wydawca: Polish Academy of Sciences, Committee on Electrical Engineering
• Czasopismo: Archives of Electrical Engineering
• Rocznik: 2023
• Tom: Vol. 72, nr 4
• Strony: 1017--1034
• Opis fizyczny: Bibliogr. 94 poz., fig., tab.

Twórcy

autor

Haotian Ge

• Hainan Normal University China

autor

Jiuming Zhong

• Hainan Normal University China

Bibliografia

• [1] Xu B.Y., Relaying protection and automation of distribution networks, Beijing: China Electric Power Press, ISBN 9787519804237 (2017).
• [2] Xu B.Y., Li T.Y., Xue Y.R., Smart distribution grid and distribution automation, Automation of Electric Power Systems, vol. 33, no. 17, pp. 38–41 (2009), DOI: 10.3321/j.issn:1000-1026.2009.17.008.
• [3] Wang C.S., Li P., Development and challenges of distributed generation, the micro-grid and smart distribution system, Automation of Electric Power Systems, vol. 34, no. 2, pp. 10–14¸23 (2010), https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CJFD&dbname=CJFD2010&filename=DLXT201002004&uniplatform=NZKPT&v=Ekk2Q1D19c2s85J65VjfRtXAyec3Ylvy1GfIbD2E8msPQH-TaQcZCtdPxVfMlNFT.
• [4] Liu Z.Y., Global energy internet, Beijing: China Electric Power Press, ISBN: 9787512370524 (2015).
• [5] China Southern Power Grid News, China Southern Power Grid realizes full coverage of smart meters and low-voltage centralized reading, available online: http://www.csg.cn/xwzx/2018/gsyw/201812/t20181224_174006.html.
• [6] Zhang X.H., Exploration and practice of intelligent distribution network of State Grid Corporation of China, available online: http://www.chinasmartgrid.com.cn/news/20180918/630050.shtml.
• [7] Thomas A.S., Electric Power Distribution Handbook, CRC Press (2014), DOI: 10.1201/9780203486504.
• [8] Kersting W., Distribution System Modeling and Analysis, CRC Press (2017), DOI: 10.1201/9781315120782.
• [9] Xu B.Y., Li T.Y., Investigations to Some Distribution Automation Issues, Automation of Electric Power Systems, vol. 34, no. 9, pp. 81–86 (2010), https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CJFD&dbname=CJFD2010&filename=DLXT201009018&uniplatform=NZKPT&v=Ekk2Q1D19c2R9PD0GeVv6sjlYCgE5oSGgDoaSnFEMk6wHqycOAADN2ls_ThmrsTY.
• [10] Guo J.B., Challenges of high-proportion new energy power systems in the future, Hangzhou City, Zhejiang Province: Chinese Society of Electrical Engineering (2020).
• [11] Chen G.P., Li M.J., Xu T. et al., Study on Technical Bottleneck of New Energy Development, Proceedings of the CSEE, vol. 37, no. 1, pp. 20–27 (2017), DOI: 10.13334/j.0258-8013.pcsee.161892.
• [12] Ge Haotian, Technologies for low voltage distribution network topological automatic identification, Ph.D. Thesis, School of Agricultural Engineering, Shandong University of Technology, Zibo China (2000).
• [13] IEC 61970, Energy management system application program interface (EMS-API)-Part 301: Common information model (CIM) base, Edition 7.0, IEC 61970-301 (2020).
• [14] Wang X.F., Schulz N.N., Neumann S., CIM Extensions to Electrical Distribution and CIM XML for the IEEE Radial Test Feeders, IEEE Trans on Power Systems, vol. 18, no. 3, pp. 1021–1028 (2003), DOI: 10.1109/TPWRS.2003.814857.
• [15] Han G., Xu B., Circuit Oriented Topology Analysis of Medium-Voltage Distribution Network, Nanfang Dianwang Jishu, vol. 5, no. 1, pp. 61–64 (2011), DOI: 10.3969/j.issn.1674-0629.2011.01.015.
• [16] Ma J., Leng H., Zhu J., Tang H., Extended common information model for distribution network production repair platform, International Journal of Internet Protocol Technology, vol. 11, no. 2, pp. 71–79 (2018), DOI: 10.1504/IJIPT.2018.092463.
• [17] IEC 61968, Application integration at electric utilities – System interfaces for distribution management Part 11: Common information model (CIM) extensions for distribution, IEC 61968-11 (2013).
• [18] IEC 61968, Application integration at electric utilities – System interfaces for distribution management Part 13: CIM RDF Model exchange format for distribution, IEC 61968-13 (2008).
• [19] IEC 61850, Communication networks and systems for power utility automation-Part 90-6: Use of IEC 61850 for Distribution Automation Systems, IEC 61850-90-6 (2018).
• [20] Cong W., Zheng Y., Zhang Z.J. et al., Distributed Storage and Management Method for Topology Information of Smart Distribution Network, Automation of Electric Power Systems, vol. 41, no. 13, pp. 111–118 (2017), DOI: 10.7500/AEPS20161228008.
• [21] Zhu G.F., Shen P.F., Wang Y. et al., Dynamic identification method of feeder topology for distributed feeder automation based on topological slices, Power System Protection and Control, vol. 46, no. 14, pp. 152–157 (2018), DOI: 10.7667/PSPC171001.
• [22] Zhu Z., Xu B., Brunner C., Guise L., Han G., Distributed topology processing solution for distributed controls in distribution automation systems, IET Generation, Transmission & Distribution, vol. 11, no. 3, pp. 776–784 (2017), DOI: 10.1049/iet-gtd.2016.0421.
• [23] International Electrotechnical Commission, Communication networks and systems for power utility automation: configuration description language for communication in electrical substation related to IEDs, IEC 61850-6 (2017).
• [24] Wang Z.H., Chen Y., Xu B.Y. et al., Logical Node Based Topology Identification of Distributed Feeder Automation, Automation of Electric Power Systems, vol. 44, no. 12, pp. 124–130 (2020), DOI: 10.1515/ijeeps-2021-0396.
• [25] Zhou L., Intelligent topology identification and voltage management optimization of low voltage distribution network based on data mining, Ph.D. Thesis, College of Electric Power, South China University of Technology, Guangzhou (2021).
• [26] Sang Z.Z., Zhang H.F., Pan Z.C. et al., Protection for single phase to earth fault line selection for ungrounded power system by injecting signal, Automation of Electric Power Systems, vol. 20, no. 2, pp. 11–12 (1996), DOI: 10.1007/BF02943174.
• [27] Sang Z.Z., Pan Z.C., Li L. et al., A new approach of fault line identification, fault distance measurement and fault location for single phase-to-ground fault in small current neutral grounding system, Power System Technology, no. 10, pp. 50–52¸55 (1997), DOI: CNKI: SUN: DWJS.0.1997-10-015.
• [28] Zeng X.J., Yin X.G., Yu Y.Y. et al., New methods for control and protection relay in a compensated medium voltage distribution network based on injecting various frequency current, Proceedings of the Chinese Society for Electrical Engineering, no. 1, pp. 30–33¸37 (2000), DOI: CNKI:SUN:ZGDC.0.2000-01-007.
• [29] Pan Z.C., Zhang H.F., Zhang F. et al., Analysis and Modification of Signal Injection Based Fault Line Selection Protection, Automation of Electric Power Systems, no. 4, pp. 71–75 (2007), DOI: 10.3321/ j.issn:1000-1026.2007.04.015.
• [30] Lu H.Y., Cai Y.M., Liu M.X. et al., Topology Identification and Check Method for Low-voltage Distribution Areas, Electrical Automation, vol. 42, no. 3, pp. 95–98 (2020), DOI: CNKI:SUN:DQZD.0.2020-03-028.
• [31] Wang H., Ai Q., Wu J.H. et al., Bi-Level Distributed Optimization for Microgrid Clusters Based on Alternating Direction Method of Multipliers, Power System Technology, vol. 42, no. 6, pp. 1718–1727 (2018), DOI: 10.13335/j.1000-3673.pst.2017.2939.
• [32] Zhong J.Y., Xiong X.F., He Y.C. et al., Plug-Play and Topology Identification Method for Intelligent Terminal in Distribution Station Area, Automation of Electric Power Systems, vol. 45, no. 10, pp. 166–173 (2021), DOI: 10.7500/AEPS20200629007.
• [33] Li X., Wang W.F., Ge Y.L. et al., Research on user-transformer relation identification method based on characteristic current, Electrical Measurement & Instrumentation, vol. 58, no. 9, pp. 115–121 (2021), DOI: 10.19753/j. issn1001-1390.2021.09.017.
• [34] Ge H.T., Xu B.Y., Zhang X.H. et al., Topology identification of low voltage distribution network based on current injection method, Archives of Electrical Engineering, vol. 70, no. 2, pp. 297–306 (2021), DOI: 10.24425/aee.2021.136985.
• [35] Ge H.T., Xu B.Y., Zhang X.H., Low-voltage overhead lines topology identification method based on high-frequency signal injection, Archives of Electrical Engineering, vol. 70, no. 4, pp. 794–800 (2021), DOI: 10.24425/aee.2021.138261.
• [36] Wu W.C., Zhang B.M., A graphic database based network topology and its application, Power System Technology, vol. 26, no. 2, pp. 14–18 (2002), DOI: 10.3321/j.issn:1000-3673.2002.02.004.
• [37] Yehsakul P.D., Dabbaghchi I., A topology-based algorithm for tracking network connectivity, IEEE Trans on Power Systems, vol. 10, no. 1, pp. 339–346 (1995), DOI: 10.1109/59.373954.
• [38] Monticelli A., State Estimation in Electric Power Systems-A Generalized Approach, Massachusetts: Khwer Academic Publisher (1999), DOI: 10.1007/978-1-4615 4999-4.
• [39] Wan H., Li N.H., Chen H. et al., Fast topology analysis based on breadth-first, Power Systems and Automation, vol. 7, no. 2, pp. 17–23 (1995), https://kns.cnki.net/kcms2/article/abstract?v=k4jT09spKVgtscxZDg5_NYqdbHXneXJicOB0meAQOCXQqeQssQY94D5cvXmCpqh2memvuO0dSvRlBRSrl63JDWWdzD_TuMTEAs1HCFQ62wEwWWbV41Bpln8-Vu56tjju&uniplatform=NZKPT&flag=copy.
• [40] Chen X.Y., Sun S.J., Qian F., A fast power system network topology based on tracking technology, Power System Technology, vol. 28, no. 5, pp. 31–34 (2004), DOI: CNKI:SUN:DWJS.0.2004-05-006.
• [41] Zhang M.Y., Research on fast identification of power network topology analysis, Ph.D. Thesis Beijing: China Electric Power University (2014).
• [42] Mei N., Shi D.Y., Duan X.Z., A Novel Method for Fast Power Network Topology Formation and Partial Revision Based on Graph Theory, Power System Technology, no. 13, pp. 35–39 (2018), DOI: CNKI: SUN: DWJS.0.2008-13-012.
• [43] Zhou Y., Zhou B.X., Xiong Y., Graphical power network topology analysis based on adjacency matrix, Power System Protection and Control, vol. 37, no. 17, pp. 49–52¸56 (2009), DOI: 10.3969/j.issn.1674-3415.2009.17.011.
• [44] Wang Z.P., Zhang J.F., Zhang Y.G., A Novel Substation Configuration Identification Algorithm Based on the Set of Breaker-path Functions, Proceedings of the CSEE, vol. 33, no. 1, pp. 137–145 (2013), DOI: 10.13334/j.0258-8013.pcsee.2013.01.012.
• [45] Ma J., Zhang M.Y., Ma W. et al., Power Network Topological Analysis Based on Incidence Matrix Notation Method and Loop Matrix, Automation of Electric Power Systems, no. 12, pp. 74–80 (2014), DOI: 10.7500/AEPS20131212003.
• [46] Yao Y.B., Determination of network topology by fast quasi-square of the adjacency matrix, Power System Protection and Control, vol. 40, no. 6, pp. 17–21¸29 (2012), DOI: 10.3969/j.issn.1674-3415.2012.06.004.
• [47] Yao Y.B., Xuan J., Yu N. et al., Determination of network topology by quasi-square of the connectivitymatrix, Power System Protection and Control, vol. 39, no. 5, pp. 31–34 (2011), DOI: 10.3969/j.issn.1674-3415.2011.05.007.
• [48] Hua J., Han X.S., Wang J.Q. et al., Application of improved Gaussian elimination algorithm in power system topology analysis, Power System Technology, no. 23, pp. 57–61 (2007), DOI: 10.1002/jrs.1570.
• [49] Yao Y.B., Wang D., Wu Z.L. et al., Network topology analysis by solving equations, Electric Power Automation Equipment, vol. 30, no. 1, pp. 79–83 (2010), DOI: 10.3969/j.issn.1006-6047.2010.01.017.
• [50] Xue Y.S., Lai Y.N., Integration of Macro Energy Thinking and Big Data Thinking Part One Big Data and Power Big Data, Automation of Electric Power Systems, vol. 40, no. 1, pp. 1–8 (2016), DOI: 10.7500/AEPS20151208005.
• [51] Xue Y.S., Lai Y.N., Integration of Macro Energy Thinking and Big Data Thinking Part Two Applications and Explorations, Automation of Electric Power Systems, vol. 40, no. 8, pp. 1–13 (2016), DOI: 10.7500/AEPS20160311004.
• [52] Hu Y.A., Volt/var control strategy for larger-scale distributed photovoltaic access to rural power grid, Ph.D. Thesis Beijing: North China Electric Power University (2021).
• [53] Hu J., Vasilakos A.V., Energy big data analytics and security: challenges and opportunities, IEEE Transactions on Smart Grid, vol. 7, no. 5, p. 1 (2016), DOI: 10.1109/TSG.2016.2563461.
• [54] Wang P., Lin J.Y., Guo S. et al., Distribution System Data Analytics and Applications, Power System Technology, vol. 41, no. 10, pp. 3333–3340 (2017), DOI: 10.13335/j.1000-3673.pst.2017.1624.
• [55] Liu Y.X., Zhang N., Kang C.Q., A Review on Data-driven Analysis and Optimization of Power Grid, Automation of Electric Power Systems, vol. 42, no. 6, pp. 157–167 (2018), DOI: 10.7500/AEPS20170922003.
• [56] Bolognani S., Bof N., Michelotti D., Muraro R., Schenato L., Identification of power distribution network topology via voltage correlation analysis, 52nd IEEE Conference on Decision and Control, pp. 1659–1664 (2013), DOI: 10.1109/CDC.2013.6760120.
• [57] Pignati M., Zanni L., Romano P., Cherkaoui R., Paolone M., Fault Detection and Faulted Line Identification in Active Distribution Networks Using Synchrophasors-Based Real-Time State Estimation, IEEE Transactions on power delivery, vol. 32, no. 1, pp. 381–392 (2017), DOI: 10.1109/TP-WRD.2016.2545923.
• [58] Singh R., Manitsas E., Pal B.C., Strbac G., A Recursive Bayesian Approach for Identification of Network Configuration Changes in Distribution System State Estimation, IEEE Transactions on Power Systems, vol. 25, no. 3, pp. 1329–1336 (2010), DOI: 10.1109/TPWRS.2010.2040294.
• [59] Short T., A. Advanced metering for phase identification, transformer identification, and secondary modeling, IEEE Transactions on Smart Grid, vol. 4, no. 2, pp. 651–658 (2013), DOI: 10.1109/TSG.2012.2219081.
• [60] Watson J.D., Welch J., Watson N.R., Use of smart meter data to determine distribution system topology, The Journal of Engineering, vol. 5, pp. 94–101 (2016), DOI: 10.1049/joe.2016.0033.
• [61] Zhang M., Luan W., Guo S. et al., Topology identification method of distribution network based on smart meter measurements, 2018 China International Conference on Electricity Distribution (2018), DOI: 10.1109/CICED.2018.8592228.
• [62] Luan W., Peng J., Maras M. et al., Smart meter data analytics for distribution network connectivity verification, Transactions on Smart Grid, vol. 6, no. 4, pp. 1964–1971 (2015), DOI: 10.1109/TSG.2015.2421304.
• [63] Weng Y., Liao Y., Rajagopal R., Distributed energy resources topology identification via graphical modeling, IEEE Transactions on Power Systems, vol. 32, no. 4, pp. 2682–2694 (2016), DOI: 10.1109/TPWRS.2016.2628876.
• [64] Mitra R., Kota R., Bandyopadhyay S. et al., Voltage correlations in smart meter data, 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2015), DOI: 10.1145/2783258.2788594.
• [65] Wang W., Yu N., Foggo B. et al., Phase identification in electric power distribution systems by clustering of smart meter data, 15th IEEE International Conference on Machine Learning and Applications (2016), DOI: 10.1109/ICMLA.2016.0050.
• [66] Zhang R., Sun X.L., He Z.X. et al., Phase Identification Method for Distribution Area Users Based on Outlier Detection and Improved K-means Algorithm, Smart Power, vol. 48, no. 1, pp. 91–96 (2020), DOI: CNKI:SUN:XBDJ.0.2020-01-016.
• [67] Wu Q., Chen X., Zhou H. et al., Transformer Area User Recognition Method Based on Principal Component Analysis and Improved K-means Algorithm, Electrical Automation, vol. 42, no. 5, pp. 55–57 (2020), DOI: 10.3969/j.issn.1000-3886.2020.05.017.
• [68] Ni F., Liu J.Q., Wei F. et al., Phase identification in distribution systems by data mining methods, IEEE Conference on Energy Internet and Energy System Integration (2017), DOI: 10.1109/EI2.2017.8245748.
• [69] Olivier F., Sutera A., Geurts P. et al., Phase identification of smart meters by clustering voltage measurements, Power Systems Computation Conference (2018), DOI: 10.23919/PSCC.2018.8442853.
• [70] Deka D., Chertkov M., Backhaus S., Structure learning in power distribution networks, IEEE Transactions on Control of Network Systems (2017), DOI: 10.1109/TCNS.2017.2673546.
• [71] Geng J.C., Zhang X.F., Wan D.M. et al., Automatic phase identification of low-voltage users based on voltage curves cluster analysis, Power Systems and Big Data, vol. 22, no. 12, pp. 1–8 (2019), DOI: CNKI:SUN:GZDJ.0.2019-12-001.
• [72] Lian Z.K., Yao L., Liu S.Y. et al., Phase and Meter Box Identification for Single-phase Users Based on t-SNE Dimension Reduction and BIRCH Clustering, Automation of Electric Power Systems, vol. 44, no. 8, pp. 176–184 (2020), DOI: 10.7500/AEPS20190621001.
• [73] Ma Y., Fan X., Tang R. et al., Phase identification of smart meters by spectral clustering, 2nd IEEE Conference on Energy Internet and Energy System Integration (2018), DOI: 10.1109/EI2.2018.8582318.
• [74] Blakely L., Reno M.J., Feng W., Spectral clustering for customer phase identification using AMI voltage time series, IEEE Power and Energy Conference at Illinois (2019), DOI: 10.1109/PECI.2019.8698780.
• [75] Liu S., Cui X., Lin Z. et al., Practical method for mitigating three-phase unbalance based on data-driven user phase identification, IEEE Transactions on Power Systems, vol. 35, no. 2, pp. 1653–1656 (2020), DOI: 10.1109/TPWRS.2020.2965770.
• [76] Dilek M., Broadwater R.P., Sequin R., Phase prediction in distribution systems, IEEE Power Engineering Society Winter Meeting (2002), DOI: 10.1109/PESW.2002.985153.
• [77] Arya V., Seetharam D., Kalyanaraman S. et al., Phase identification in smart grids, IEEE International Conference on Smart Grid Communications (2011), DOI: 10.1109/SmartGridComm.2011.6102329.
• [78] Gao Z.P., Zhao Y., Yu Y.L. et al., Low-voltage distribution network topology identification method based on knowledge graph, Power System Protection and Control, vol. 48, no. 2, pp. 34–43 (2020), DOI: 10.19783/j.cnki.pspc.190379.
• [79] Luo J.T., Zhang J.M., Yao L. et al., Modeling and Application of Phase Identification Optimization for Low-voltage Customer Based on Voltage and Power Data, Automation of Electric Power Systems, vol. 45, no. 7, pp. 123–131 (2021), DOI: 10.7500/AEPS20200107007.
• [80] Luo J.T., Zhang J.M., Chen Y.J. et al., Identification of Low-voltage User Meter Box Combined with Known Phase and Address Information, Automation of Electric Power Systems, vol. 45, no. 9, pp. 115–121 (2021), https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CJFD&dbname=CJFDLAST2021&filename=DLXT202109014&uniplatform=NZKPT&v=lzPv3VaLE-kW0WnAYWkk26TfeMRo_Vq5 wal1dP8QOcuezvVhC1uf4zfwwW5Vv6Un.
• [81] Jayadev S.P., Rajeswaran A., Bhatt N.P. et al., A novel approach for phase identification in smart grids using graph theory and principal component analysis, American Control Conference (2016), DOI: 10.1109/ACC.2016.7526150.
• [82] Pappu S.J., Bhatt N., Pasumarthy R. et al., Identifying topology of low voltage distribution networks based on smart meter data, IEEE Transactions on Smart Grid, vol. 9, no. 5, pp. 5113–5122 (2018), DOI: 10.1109/TSG.2017.2680542.
• [83] Kekatos V., Giannakis G.B., Baldick R., Online energy price matrix factorization for power grid topology tracking, IEEE Transactions on Smart Grid, vol. 7, no. 3, pp. 1239–1248 (2016), DOI: 10.1109/TSG.2015.2469098.
• [84] Korres G.N., Manousakis N.M., A state estimation algorithm for monitoring topology changes in distribution systems, 2012 IEEE Power & Energy Society General Meeting (2012), DOI: 10.1109/PESGM.2012.6345126.
• [85] Alam S.S., Natarajan B., Pahwa A., Distribution grid state estimation from compressed measurements, IEEE Transactions on Smart Grid, vol. 5, no. 4, pp. 1631–1642 (2014), DOI: 10.1109/TSG.2013.2296534.
• [86] Tang X., Milanovic J.V., Phase identification of LV distribution network with smart meter data, IEEE PES General Meeting (2018), DOI: 10.1109/PESGM.2018.8586483.
• [87] Zhang L.Q., Cong W., Dong G. et al., Method for single-phase electric meter phase identification based on multiple linear regression, Electric Power Automation Equipment. vol. 40, no. 5, pp. 144–156 (2020), DOI: 10.1109/APAP47170.2019.9225024.
• [88] Xu M.H., Li R., Li F.R., Phase Identification with Incomplete Data, IEEE Transactions on Smart Grids, vol. 9, no. 4, pp. 2777–2785 (2018), DOI: 10.1109/TSG.2016.2619264.
• [89] Sebastian G., Javier M. et al., Phase topology identification in low-voltage distribution networks: A Bayesian approach, International Journal of Electrical Power & Energy Systems, vol. 144, 108525 (2023), DOI: 10.1016/j.ijepes.2022.108525.
• [90] Hosseini Z.S., Khodaei A., Paaso A., Machine Learning-Enabled Distribution Network Phase Identification, IEEE Transactions on Power Systems, vol. 36, no. 2, pp. 842–850 (2021), DOI: 10.1109/TP-WRS.2020.3011133.
• [91] Victor A.G, Adrian W., Sebastian R., Phase identification and substation detection using data analysis on limited electricity consumption measurements, Electric Power Systems Research, vol. 187 (2020), DOI: 10.1016/j.epsr.2020.106450.
• [92] Victor Adrian Jimenez, Adrian Will, A new data-driven method based on Niching Genetic Algorithms for phase and substation identification, Electric Power Systems Research, vol. 199 (2021), DOI: 10.1016/j.epsr.2021.107434.
• [93] Tang J., Cai Y.Z., Zhou L. et al., Data-driven based identification method of feeder-consumer connectivity in low-voltage distribution network, Automation of Electric Power Systems, vol. 44, no. 11, pp. 127–137 (2020), DOI: 10.7500/AEPS20191021007.
• [94] Berry T., Guise L., IEC61850 for distribution feeder automation, IET International Conference on Resilience of Transmission and Distribution Networks (RTDN) (2015), DOI: 10.1049/cp.2015.0887.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).