Explainable AI for Power Monitoring Systems
XAI methods for interpreting deep learning models in real-time power monitoring
Modern power systems are characterized by a high degree of complexity and uncertainties that may jeopardize their stability, due to the continuous integration of nonlinear distributed generators and loads. Furthermore, with the rapid growth of power measurement units, there is an urgent need for efficient and near real-time algorithms to analyze and make better use of all the available data.
Abstract
Accordingly, advances in deep learning now enable more accurate and scalable algorithms for power-monitoring applications. However, despite the evident success of such algorithms, an inherent difficulty is that since machine learning models are often very complex, it may not be clear how or why they make certain decisions, and how they treat real-world data....
In this light, the main objective of this talk is to present Explainable Artificial Intelligence (XAI) techniques for power monitoring systems and to highlight the potential of using XAI in this context.
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