Partial Knowledge Data-Driven Event Detection for Power Distribution Networks

Abstract

The power system has been incorporating increasing amount of unconventional generations and loads, such as distributed renewable resources, electric vehicles, and controllable loads. The induced dynamic and stochastic power flow require high-resolution monitoring technology and agile decision support techniques for system diagnosis and control. This paper discusses the application of micro-phasor measurement unit ( $\mu$ PMU) data for power distribution network event detection. A novel data-driven event detection method, namely hidden structure semi-supervised machine (HS3M), is established. HS3M only requires partial expert knowledge: it combines unlabeled data and partly labeled data in a large margin learning objective to bridge the gap between supervised learning, semi-supervised learning, and learning with hidden structures. To optimize the non-convex learning objective, a novel global optimization algorithm, namely parametric dual optimization procedure, is established through its equivalence to a concave programming. Finally, the proposed method is validated on an actual distribution feeder with installed $\mu$ PMUs, and the result justifies the effectiveness of the learning-based event detection framework, as well as its potential to serve as one of the core algorithms for power system security and reliability.