Spatiotemporal Deep Learning for Power System Applications: A Survey

Abstract

Understanding spatiotemporal correlations in power systems is crucial for maintaining grid stability, reliability, and efficiency. By discerning connections between spatial and temporal dimensions, operators can anticipate and address issues such as congestion, voltage instability, and equipment failures. Recent advancements in power system analysis have leveraged spatiotemporal correlations through sophisticated data-driven algorithms. In this survey paper, we conduct a comprehensive examination of deep learning frameworks tailored to tackle the complexities inherent in spatiotemporal data analysis within power systems. We categorize machine learning methodologies into discriminative, generative, and reinforcement learning, providing a structured overview of their mathematical foundations, advantages, and limitations in processing dynamic power system measurements. Through empirical evaluations, we assess the performance of these methodologies across various spatiotemporal applications, including cyber attack detection, fault identification, demand response, and renewable energy forecasting, offering insights into their efficacy and applicability. Additionally, we identify emerging topics within the machine learning domain that hold promise for future endeavors in power systems analysis.

Keywords

• Spatiotemporal correlations
• power systems applications
• deep neural architectures