A Comparative Assessment of Dimensionality Reduction Techniques for Diagnosing Faults in 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.

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