Criteria Selection Using Machine Learning (ML) for Communication Technology Solution of Electrical Distribution Substations
Source abstract
Abstract
In the future, as populations grow and more end-user applications become available, the current traditional electrical distribution substation will not be able to fully accommodate new applications that may arise. Consequently, there will be numerous difficulties, including network congestion, latency, jitter, and, in the worst-case scenario, network failure, among other things. Thus, the purpose of this study is to assist decision makers in selecting the most appropriate communication technologies for an electrical distribution substation through an examination of the criteria’s in-fluence on the selection process. In this study, nine technical criteria were selected and processed using machine learning (ML) software, RapidMiner, to find the most optimal technical criteria. Several ML techniques were studied, and Naïve Bayes was chosen, as it showed the highest performance among the rest. From this study, the criteria were ranked in order of importance from most important to least important based on the average value obtained from the output. Seven technical criteria were identified as being important and should be evaluated in order to determine the most appropriate communication technology solution for electrical distribution substation as a result of this study.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: IoT Networks and Protocols · IoT and Edge/Fog Computing · Smart Grid Security and Resilience
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
UNITEN R & D Sdn. Bhd. (URND), Universiti Tenaga Nasional, Kajang 43000, Malaysia
Electrical and Electronics Engineering Department, College of Engineering, Universiti Tenaga Nasional, Kajang 43000, Malaysia
The Institute of Informatics and Computing in Energy, Universiti Tenaga Nasional, Kajang 43000, Malaysia
Asset Strategy and Policy, Asset Management, Distribution Network Division, Tenaga Nasional Berhad, Kuala Lumpur 56000, Malaysia
Funding
TNB Seed Fund
References (74)
Show source-provided references
1. Li, R. (2019). Chapter 4—Protection and control technologies of connecting to the grid for distributed power resources. Distributed Power Resources, Academic Press.10.1016/B978-0-12-817447-0.00004-3 ↗
2. (2021, October 11). Transmission vs. Distribution, Available online: https://www.osha.gov/etools/electric-power/generation-transmission-distribution/transmission-distribution.
3. (2021, October 11). What Is a Substation—Definition, Types of SubStations. Available online: https://www.elprocus.com/what-is-a-substation-definition-types-of-substations/.
4. Standardization and deployment scenario of next generation NB-PLC technologies10.1016/j.rser.2016.07.060 ↗
5. Study of Smart Grid Communication Network Architectures and Technologies10.4236/jcc.2019.73003 ↗
6. Kuzlu, M., and Pipattanasomporn, M. (2013, January 24–27). Assessment of communication technologies and network requirements for different smart grid applications. Proceedings of the 2013 IEEE PES Innovative Smart Grid Technologies Conference, Washington, DC, USA.10.1109/ISGT.2013.6497873 ↗
7. Kabalci, E., and Kabalci, Y. (2019). Introduction to Smart Grid Architecture. Smart Grids and Their Communication Systems, Springer.10.1007/978-981-13-1768-2 ↗
8. Kabalci, E., and Kabalci, Y. (2019). Standards and Communication Systems in Smart Grid. Smart Grids and Their Communication Systems, Springer.10.1007/978-981-13-1768-2 ↗
9. Smart choice for the smart grid: Narrowband internet of things (NB-IoT)10.1109/JIOT.2017.2781251 ↗
10. A review of wireless communications for smart grid10.1016/j.rser.2014.08.036 ↗
11. Li, L., Hu, X., Chen, K., and He, K. (2011, January 21–23). The applications of WiFi-based Wireless Sensor Network in Internet of Things and Smart Grid. Proceedings of the 2011 6th IEEE Conference on Industrial Electronics and Applications, Beijing, China.10.1109/ICIEA.2011.5975693 ↗
12. Lalle, Y., Fourati, L.C., Fourati, M., and Barraca, J.P. (2019, January 18–20). A Comparative Study of LoRaWAN, SigFox, and NB-IoT for Smart Water Grid. Proceedings of the 2019 Global Information Infrastructure and Networking Symposium, Paris, France.10.1109/GIIS48668.2019.9044961 ↗
13. Division, E.M. (2021, October 11). Communication Network Solutions for Transmission and Distribution Grids. Available online: https://assets.new.siemens.com/siemens/assets/api/uuid:8b4809cf50679ccae32f511471c3eb92d064c814/version:1501223616/cgem-160662-communication-network-solutions-16-seiter-row-lowres-v080rz.pdf.
14. Filho, H.G.S., Filho, J.P., and Moreli, V.L. (2016, January 12–15). The adequacy of LoRaWAN on smart grids: A comparison with RF mesh technology. Proceedings of the IEEE 2nd International Smart Cities Conference: Improving the Citizens Quality of Life, Trento, Italy.
15. The Role of Satellite Communications in the Smart Grid10.1109/MWC.2017.1600251 ↗
16. Evolution of communication technologies for smart grid applications10.1016/j.rser.2012.11.002 ↗
17. Vegdani, M., Bahadornejad, M., and Nair, N. (2021, October 11). Smart Grid Communications Infrastructure: A Discussion on Technologies and Opportunities. Available online: https://www.google.com.hk/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwj5qM6t6ZT2AhVaxosBHRILADAQFnoECAUQAQ&url=https%3A%2F%2Fwiki.auckland.ac.nz%2Fdownload%2Fattachments%2F88902304%2FGreenGrid_ICT_WhitePaper_2013.pdf%3Fversion%3D1%26modificationDate%3D1415756060000%26api%3Dv2&usg=AOvVaw0vroo1AB6_ZxwkPCXQPHVi.
18. A survey on smart metering and smart grid communication10.1016/j.rser.2015.12.114 ↗
19. Investigation of Narrow-Band Power-Line Carrier Communication System Performance in Rural Distribution Grids
20. Smith, D. (2021, July 05). What Is the Frequency Range in Optical Fibre Communication?. Quora, Available online: https://www.quora.com/What-is-the-frequency-range-in-optical-fibre-communication.
21. (2021, July 05). Calculating Fiber Loss and Distance Estimates. FOSCO, Available online: https://www.fiberoptics4sale.com/blogs/archive-posts/95049798-calculating-fiber-loss-and-distance-estimates.
22. Smart Grid Communication Infrastructure, Automation Technologies and Recent Trends10.11648/j.epes.20180703.11 ↗
23. Baimel, D., Tapuchi, S., and Baimel, N. (2016, January 22–24). Smart grid communication technologies- overview, research challenges and opportunities. Proceedings of the 2016 International Symposium on Power Electronics, Electrical Drives, Automation and Motion, SPEEDAM, Capri, Italy.10.1109/SPEEDAM.2016.7526014 ↗
24. Trends in smart power grid communication and networking
25. A comparative study of wireless protocols: Bluetooth, UWB, ZigBee, and Wi-Fi
26. Parikh, P.P., Kanabar, M.G., and Sidhu, T.S. (2010, January 25–29). Opportunities and challenges of wireless communication technologies for smart grid applications. Proceedings of the IEEE PES General Meeting, Minneapolis, MN, USA.10.1109/PES.2010.5589988 ↗
27. Challenges and research opportunities in wireless communication networks for smart grid10.1109/MWC.2013.6549287 ↗
28. Mulla, A., Baviskar, J., Khare, S., and Kazi, F. (2015, January 4–6). The wireless technologies for smart grid communication: A review. Proceedings of the 2015 5th International Conference on Communication Systems and Network Technologies, Gwalior, India.10.1109/CSNT.2015.146 ↗
29. Aravinthan, V., Karimi, B., Namboodiri, V., and Jewell, W. (2011, January 24–28). Wireless communication for smart grid applications at distribution level—Feasibility and requirements. Proceedings of the 2011 IEEE Power and Energy Society General Meeting, Detroit, MI, USA.10.1109/PES.2011.6039716 ↗
30. (2021, November 25). 4 GHz vs. 5 GHz Wireless Frequency. Internet Broadband Deals, Reviews and Guides, Available online: https://checkmybroadbandspeed.online/2-4-ghz-vs-5-ghz-wireless-frequency/.
31. On Mobile Mesh Networks
32. The search for a convergent option to deploy smart grids on iot scenario10.25046/aj020373 ↗
33. (2021, November 12). What Frequency Spectrum Will 5G Technology Use and How Does This Compare to 4G?. Available online: https://www.arrow.com/en/research-and-events/articles/what-frequency-spectrum-will-5g-technology-use-and-how-does-this-compare-to-4g.
34. Triggs, R. (2021, November 13). What Is LTE? Everything You Need to Know. Android Authority, Available online: https://www.androidauthority.com/what-is-lte-283296/.
35. Parvin, J.R. (2019). An Overview of Wireless Mesh Networks. Wireless Mesh Networks—Security, Architectures and Protocols, IntechOpen.
36. Malaysian Communications and Multimedia Commission (2021, October 11). Press Release: Final Report on Allocation of Spectrum Bands for Mobile Broadband Service in Malaysia. Cyberjaya, Available online: https://www.mcmc.gov.my/en/media/press-releases/final-report-on-allocation-of-spectrum-bands-for-m.
37. Practical Aspects for the Integration of 5G Networks and IoT Applications in Smart Cities Environments10.1155/2019/5710834 ↗
38. 5G network-based Internet of Things for demand response in smart grid: A survey on application potential10.1016/j.apenergy.2019.113972 ↗
39. The impact of internet of things supported by emerging 5G in power systems: A review
40. Gaurav, G., Semra, B., and Christine, C. (2021, November 17). 5G Powered Utility Transformation. Available online: https://www.infosys.com/iki/insights/5g-powered-utility.html.
41. Qualcomm (2021, November 20). Everything You Need to Know about 5G. Available online: https://www.qualcomm.com/5g/what-is-5g.
42. Malaysian Communications and Multimedia Commission (2021, November 17). Requirements for Mobile Cellular Services Operating in the Frequency Band from 452.000 MHz to 456.475 MHz and 462.000 MHz to 466.475 MHz, Available online: https://www.mcmc.gov.my/skmmgovmy/files/attachments/SRSP541MCS.pdf.
43. A comparative study of LPWAN technologies for large-scale IoT deployment10.1016/j.icte.2017.12.005 ↗
44. Impact of foliage on LoRa 433MHz propagation in tropical environment
45. (2021, November 25). EMBS LoRa 433 Benefits. Available online: https://openrb.com/wp-content/uploads/2019/02/EMBS-LoRa-433-benefits_security.pdf.
46. LoRa® Alliance (2021, November 25). A Technical Overview of LoRa® and LoRaWANTM. San Ramon. Available online: https://www.tuv.com/content-media-files/master-content/services/products/1555-tuv-rheinland-lora-alliance-certification/tuv-rheinland-lora-alliance-certification-overview-lora-and-lorawan-en.pdf.
47. Cotrim, J.R., and Kleinschmidt, J.H. (2020). LoRaWAN Mesh Networks: A Review and Classification of Multihop Communication. Sensors, 20.10.3390/s20154273 ↗
48. Malaysian Communications and Multimedia Commission (2021, June 10). Satellite Industry Developments, Available online: https://www.mcmc.gov.my/skmmgovmy/media/General/pdf/Satellite_Industry_Developments_compressed.pdf.
49. European Telecommunications Standards Institute (2021, June 10). ETSI TR 103 401 V1.1.1 (2016-11) Smart Grid Systems and Other Radio Systems Suitable for Utility Operations, and Their Long-Term Spectrum Requirements. Available online: https://www.etsi.org/deliver/etsi_tr/103400_103499/103401/01.01.01_60/tr_103401v010101p.pdf.
50. Adams, T. (2021, June 10). SCADA Systems Intermediate Overview. Arlington. Available online: https://www.cedengineering.com/userfiles/SCADA%20Systems.pdf.
51. (2021, June 10). Machine Learning. IBM Cloud Education. Available online: https://www.ibm.com/my-en/cloud/learn/machine-learning.
52. Anisimova, A. (2021, June 10). Types of Machine Learning Out There. IDAP. Available online: https://idapgroup.com/blog/types-of-machine-learning-out-there/.
53. Dhall, D., Kaur, R., and Juneja, M. (2019, January 8–9). Machine Learning: A Review of the Algorithms and Its Applications. Proceedings of the ICRIC 2019, Jammu, India.10.1007/978-3-030-29407-6_5 ↗
54. Criteria evaluation and selection in non-native language MBA students admission based on machine learning methods10.1007/s12652-019-01490-0 ↗
55. Machine learning: A review of classification and combining techniques10.1007/s10462-007-9052-3 ↗
56. Application of Support Vector Machine, Random Forest, and Genetic Algorithm Optimized Random Forest Models in Groundwater Potential Mapping10.1007/s11269-017-1660-3 ↗
57. Glen, S. (2021, November 03). Decision Tree vs. Random Forest vs. Gradient Boosting Machines: Explained Simply. Data Science Central. Available online: https://www.datasciencecentral.com/profiles/blogs/decision-tree-vs-random-forest-vs-boosted-trees-explained.
58. RapidMiner (2021, November 03). Gradient Boosted Trees. Available online: https://docs.rapidminer.com/latest/studio/operators/modeling/predictive/trees/gradient_boosted_trees.html.
59. Sharma, T., Gupta, P., Nigam, V., and Goel, M. (2020, January 21–23). Customer Churn Prediction in Telecommunications Using Gradient Boosted Trees. Proceedings of the International Conference on Innovative Computing and Communications, New Delhi, India.10.1007/978-981-15-0324-5_20 ↗
60. An Effective Two-Step Intrusion Detection Approach Based on Binary Classification and k-NN10.1109/ACCESS.2017.2787719 ↗
61. Resampling methods: Concepts, applications, and justification
62. RapidMiner (2021, November 07). Cross Validation. Available online: https://docs.rapidminer.com/latest/studio/operators/validation/cross_validation.html.
63. Sanjay, M. (2021, November 05). Why and how to Cross Validate a Model?. Available online: https://towardsdatascience.com/why-and-how-to-cross-validate-a-model-d6424b45261f.
64. Ranganathan, S., Gribskov, M., Nakai, K., and Schönbach, C. (2019). Cross-Validation. Encyclopedia of Bioinformatics and Computational Biology, Academic Press.
65. Prediction of fatty liver disease using machine learning algorithms10.1016/j.cmpb.2018.12.032 ↗
66. Brownlee, J. (2021, November 03). A Gentle Introduction to k-fold Cross-Validation. Machine Learning Mastery. Available online: https://machinelearningmastery.com/K-fold-cross-validation/.
67. Kumar, S. (2021, November 03). Understanding 8 Types of Cross-Validation. Available online: https://towardsdatascience.com/understanding-8-types-of-cross-validation-80c935a4976d.
68. Great Learning Team (2021, November 07). What is Cross Validation in Machine Learning? Types of Cross Validation. Great Learning. Available online: https://www.mygreatlearning.com/blog/cross-validation.
69. Tamilarasi, P., and Rani, R.U. (2020, January 11–13). Diagnosis of Crime Rate against Women using k-fold Cross Validation through Machine Learning. Proceedings of the 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), Erode, India.10.1109/ICCMC48092.2020.ICCMC-000193 ↗
70. Lyashenko, V. (2021, November 14). Cross-Validation in Machine Learning: How to Do It Right. Neptune Blog. Available online: https://neptune.ai/blog/cross-validation-in-machine-learning-how-to-do-it-right.
71. Cross-Validation: A Method Every Psychologist Should Know10.1177/2515245919898466 ↗
72. Kuhn, M., and Johnson, K. (2013). Over-Fitting and Model Tuning. Applied Predictive Modeling, Springer.10.1007/978-1-4614-6849-3 ↗
73. RapidMiner (2021, November 10). Explain Predictions. Available online: https://docs.rapidminer.com/9.0/studio/operators/scoring/explain_predictions.html.
74. (2021, November 13). How to Decide when to Use Naive Bayes for Classification. Analytics Vidhya. Available online: https://discuss.analyticsvidhya.com/t/how-to-decide-when-to-use-naive-bayes-for-classification/5720/2.