Anomaly Detection and Optimization in IoT Connected Smart Grids

Authors

  • Nirup Kumar Reddy Pothireddy   Independent Researcher, USA

DOI:

https://doi.org/10.32628/IJSRST23419547

Keywords:

Smart Grids, Internet of Things (IoT), Anomaly Detection, Optimization, Machine Learning, Cybersecurity, Data Analytics, Predictive Maintenance, Energy Management, Artificial Intelligence.

Abstract

IoT-enabled smart grids use data from smart meters, transformers, and power-distribution units to optimize energy efficiency and seamless power management. However, when there are sensor faults, unauthorized energy consumption, or sudden fluctuations in demand, operations are compromised with inefficient or potential failures. This research proposes an AI-based anomaly detection system for real-time identification of energy theft, voltage fluctuation, and device malfunction. Secondly, a recommendation engine integrates load balancing, predictive maintenance, and energy distribution strategies, which enhances grid reliability, resilience, and sustainability.

References

  1. A. Dal Pozzolo, O. Caelen, R. A. Johnson, and G. Bontempi, "Calibrating Probability with Undersampling for Unbalanced Classification," in 2015 IEEE Symposium Series on Computational Intelligence, Cape Town, South Africa, 2015, pp. 159-166.
  2. "Learned Lessons in Credit Card Fraud Detection from a Practitioner Perspective," Expert Systems with Applications, vol. 41, no. 10, pp. 4915-4928, Aug. 2014.
  3. V. S. Subrahmanian, "Final Report for the DARPA ADAMS Project," University of Maryland, College Park, MD, USA, May 2015.
  4. "Anomaly Detection at Multiple Scales (ADAMS) Broad Agency Announcement DARPA-BAA-11-04," General Services Administration, Washington, DC, USA, Oct. 2010.
  5. A. G. Akoglu, M. McGlohon, and C. Faloutsos, "OddBall: Spotting Anomalies in Weighted Graphs," in Advances in Knowledge Discovery and Data Mining, PAKDD 2010, Hyderabad, India, 2010, pp. 410-421.
  6. F. T. Liu, K. M. Ting, and Z.-H. Zhou, "Isolation Forest," in Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, Pisa, Italy, 2008, pp. 413-422.
  7. S. Ramaswamy, R. Rastogi, and K. Shim, "Efficient Algorithms for Mining Outliers from Large Data Sets," in Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, Dallas, TX, USA, 2000, pp. 427-438.
  8. M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander, "LOF: Identifying Density-Based Local Outliers," in Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, Dallas, TX, USA, 2000, pp. 93-104.
  9. V. Chandola, A. Banerjee, and V. Kumar, "Anomaly Detection: A Survey," ACM Computing Surveys, vol. 41, no. 3, pp. 1-58, Jul. 2009.
  10. M. Goldstein and S. Uchida, "A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data," PLOS ONE, vol. 11, no. 4, pp. 1-31, Apr. 2016.
  11. E. Eskin, "Anomaly Detection over Noisy Data Using Learned Probability Distributions," in Proceedings of the Seventeenth International Conference on Machine Learning, Stanford, CA, USA, 2000, pp. 255-262.
  12. M. Sabokrou, M. Fathy, M. Hoseini, and R. Klette, "Real-Time Anomaly Detection and Localization in Crowded Scenes," in 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Boston, MA, USA, 2015, pp. 56-62.
  13. J. An and S. Cho, "Variational Autoencoder Based Anomaly Detection Using Reconstruction Probability," Special Lecture on IE, vol. 2, no. 1, pp. 1-18, 2015.
  14. D. P. Kingma and M. Welling, "Auto-Encoding Variational Bayes," in Proceedings of the 2nd International Conference on Learning Representations (ICLR), Banff, AB, Canada, 2014, pp. 1-14.
  15. J. Hawkins, S. Ahmad, and D. Dubinsky, "Hierarchical Temporal Memory Including HTM Cortical Learning Algorithms," Numenta, Inc., Redwood City, CA, USA, Tech. Rep. 0.2.1, 2011.
  16. S. Ahmad, A. Lavin, S. Purdy, and Z. Agha, "Unsupervised Real-Time Anomaly Detection for Streaming Data," Neurocomputing, vol. 262, pp. 134-147, Nov. 2017.
  17. R. Chalapathy and S. Chawla, "Deep Learning for Anomaly Detection: A Survey," arXiv preprint arXiv:1901.03407, Jan. 2019.
  18. T. K. Ho, "Random Decision Forests," in Proceedings of 3rd International Conference on Document Analysis and Recognition, Montreal, QC, Canada, 1995, pp. 278-282.
  19. L. Breiman, "Random Forests," Machine Learning, vol. 45, no. 1, pp. 5-32, Oct. 2001.
  20. I. Goodfellow, J. Shlens, and C. Szegedy, "Explaining and Harnessing Adversarial Examples," in Proceedings of the 3rd International Conference on Learning Representations (ICLR), San Diego, CA, USA, 2015, pp. 1-11.

International Journal of Scientific Research in Science and Technology

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Published

2019-12-30

Issue

Volume 6, Issue 6, November-December-2019

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Nirup Kumar Reddy Pothireddy "Anomaly Detection and Optimization in IoT Connected Smart Grids" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 6, Issue 6, pp.422-443, November-December-2019. Available at doi : https://doi.org/10.32628/IJSRST23419547