A Comparative Assessment of Dimensionality Reduction Techniques for Diagnosing Faults in Smart Grids
Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Classification, Dimensionality Reduction, Fault Diagnosis, Machine Learning, Noisy Measurements, Smart Grids
Data-driven diagnostic frameworks for large-scale power grid networks usually deal with a large number of features collected by means of sparse measuring devices. As a pre-processing task, dimensionality reduction methods can improve the efficiency of data-driven diagnostic methods by extracting sets of informative and relevant features from the raw data through appropriate transformations. This work is devoted to studying the applicability of various well-known dimensionality reduction techniques in combination with four classification models in diagnosing open circuit faults in smart grids. By providing a comparative study, this work aims at finding the best combination of dimensionality reduction techniques and classification models for diagnosing faults under normal, high signal-to-noise-ratio, low sampling rate, and high fault-resistance conditions. Various fault scenarios have been simulated on the IEEE 39-bus system and a rigorous analysis of the attained results is fulfilled so as to determine the best combinations under different conditions.
Hassani, Hossein; Razavi-Far, Roozbeh; and Saif, Mehrdad. (2020). A Comparative Assessment of Dimensionality Reduction Techniques for Diagnosing Faults in Smart Grids. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 2020-October, 3618-3623.