Anomaly Detection in Smart Distribution Grids with Deep Neural Network

  • Author / Creator
    Zhou, Ming
  • With the rapid development of smart grids, the detection of anomalies is essential to improve the quality and security protection of the grid. The identification of anomalies not only saves valuable time but also reduces maintenance costs. Due to the increasing deployment of distributed energy resources, traditional methods of protecting the grid that rely on simple linear models and manual inspections are no longer sufficient. Meanwhile, the massive amount of data generated by smart meters and phasor measurement units provide opportunities to better monitor and control power grids in real-time. Due to this advantage of data availability, various machine learning and deep learning methods have been proposed and are currently demonstrating successful results in anomaly detection in power systems.
    While previously proposed artificial intelligence techniques can successfully de- tect anomalies, most of them tend to require large amounts of simulated data of all different types of anomalies for training their framework. However, anomalous data may be rare in power distribution systems. In addition, their static training model makes them vulnerable to new data from different distributions entering the system. To address these drawbacks, we propose data-driven frameworks based on deep learning network models to directly detect anomalies in power distribution systems. Anomalies are generally defined as observations that deviate from the standard, normal or expected values. Specifically, this work is divided into two phases. In the first phase, we consider anomalies as events caused by changes in the distribution system load, such as customer disconnection from the grid. A long short-term memory network is proposed to predict the next time step of the voltage magnitude of all buses in the distribution system. A threshold function based on Euclidean distance is then used to detect voltage anomalies by utilizing only normal data. The results corresponding to this proposed framework have been successfully tested using a real distribution network.
    In the second phase, we aim to classify faults and locate faulted lines in partially observable distribution systems using convolutional neural networks. To improve the robustness of the classification and localization performance, we extract feature vectors with measurements in the observable buses as inputs to the proposed classifier. In addition, we incorporate an online continuous learning algorithm to accommodate variations in the level of integration of distributed energy resources and changes in the load of the distribution system over time. Unlike previous data-driven approaches, the proposed method also deals with imbalanced learning tasks, as fault data are often rare. The performance of the method has been tested and validated by simulating ten faults on a real distribution feeder model.

  • Subjects / Keywords
  • Graduation date
    Fall 2022
  • Type of Item
  • Degree
    Master of Science
  • DOI
  • License
    This thesis is made available by the University of Alberta Library with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.