On the accuracy versus transparency trade-off of data-mining models for fast-response PMU-based catastrophe predictors

Summary form only given. In all areas of engineering, modelers are constantly pushing for more accurate models and their goal is generally achieved with increasingly complex, data-mining-based black-box models. On the other hand, model users which include policy makers and systems operators tend to favor transparent, interpretable models not only for predictive decision-making but also for after-the-fact auditing and forensic purposes. In this paper, we investigate this trade-off between the accuracy and the transparency of data-mining-based models in the context of catastrophe predictors for power grid response-based remedial action schemes, at both the protective and operator levels. Wide area severity indices (WASI) are derived from PMU measurements and fed to the corresponding predictors based on data-mining models such as decision trees (DT), random forests (RF), neural networks (NNET), support vector machines (SVM), and fuzzy rule based models (Fuzzy_DT and Fuzzy_ID3). It is observed that while switching from black-box solutions such as NNET, SVM, and RF to transparent fuzzy rule-based predictors, the accuracy deteriorates sharply while transparency and interpretability are improved.