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Full text release has been delayed at the author's request until December 31, 2024

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Anomaly Detection in Distribution Power Grids Using Recurrent Neural Networks: A Digital Twin Simulation Approach

Upadhyaya, Barsha
http://orcid.org/0000-0002-3645-2696
http://rave.ohiolink.edu/etdc/view?acc_num=toledo1702048107529967

Abstract Details

2023, Master of Science, University of Toledo, Engineering (Computer Science).
Anomaly detection in power grids has become a significant challenge in recent years. The heterogeneous nature and integration of different smart grid appliances make it difficult to detect system faults or energy thefts leading to substantial cumulative losses over long periods. However, implementing the anomaly detection mechanism at every node in the grid network can be costly. Therefore, it is crucial to optimize the anomaly detection technique to not only detect local anomalies but also those further down the network. By doing so, we can ensure minimal resource usage and maximum reliability. In this study, we investigate anomaly detection capabilities at various levels in the distribution power system. We utilize Recurrent Neural Networks (RNNs) for anomaly detection and evaluate their performance compared to other machine learning techniques. The power grid analyzed in this research is a Digital Twin - a digital replica of a real-world power grid modeled using Gridlab-D and Helics. To ensure accurate simulation behavior and simulate with real consumption data and voltage properties. This paper presents two aspects of the study: building the Digital Twin and conducting anomaly detection in the Digital Twin Simulation at various grid levels.
Ahmad Y Javaid (Committee Chair)
Weiqing Sun (Committee Co-Chair)
Raghav Khanna (Committee Member)
67 p.
Computer Engineering; Electrical Engineering
Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), Anomaly Detection, Power Grid Simulation, Digital Twin

Recommended Citations

Citations

  • Upadhyaya, B. (2023). Anomaly Detection in Distribution Power Grids Using Recurrent Neural Networks: A Digital Twin Simulation Approach [Master's thesis, University of Toledo]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1702048107529967

    APA Style (7th edition)

  • Upadhyaya, Barsha. Anomaly Detection in Distribution Power Grids Using Recurrent Neural Networks: A Digital Twin Simulation Approach. 2023. University of Toledo, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=toledo1702048107529967.

    MLA Style (8th edition)

  • Upadhyaya, Barsha. "Anomaly Detection in Distribution Power Grids Using Recurrent Neural Networks: A Digital Twin Simulation Approach." Master's thesis, University of Toledo, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1702048107529967

    Chicago Manual of Style (17th edition)

toledo1702048107529967
© 2023, all rights reserved.
This open access ETD is published by University of Toledo and OhioLINK.