Interactive Feature Extraction using Implicit Knowledge Elicitation : Application to Power System Expertise

Abstract

Industrial systems such as power networks are continuously monitored by human experts who quickly identify potentially dangerous situations by their experience. As current energy trends increase the complexity of day-to-day grid operations, it becomes necessary to assist experts in their monitoring tasks. This paper proposes an interactive approach to create human-readable analytical expressions that describe physical phenomena by their most impacting quantities. We present an interactive platform that brings experts in the training loop to guide the expression search using their expertise. It uses an evolutionary approach based on Probabilistic Grammar Guided Genetic Programming with expertly created and updated grammars. Interactivity is multi-level: users can distill their knowledge both within and between evolutionary runs. We proposed two usage scenarios on a real-world dataset where the non-interactive algorithm either provides (case 1) or not (case 2) satisfactory solutions. We show improvements regarding the solution's precision (case 1) and complexity (case 2).