ML-based Data Anomaly Mitigation and Cyber-Power Transmission Resiliency Analysis

Abstract

In recent years, the cyber and physical extreme events have increased and impacted the power system operations. Although there are multiple work reported for improving the resiliency of the power grid systems, there are a limited number of resiliency management tools available to the grid operators. Addressing the data quality issue is critical before feeding the measurements for situational awareness and decision-making using resiliency management tools. In this work, we describe an automated ML-based measurement data anomaly mitigation technique that uses regression, clustering, deep learning techniques as a base detector. Maximum Likelihood Criterion (MLE) based ensemble of these base detectors helps in anomaly detection and mitigation using SyncAED tool and feeding data for enhanced resiliency using a tool: Cyber-Physical Transmission Resiliency Assessment Metric (CP-TRAM). CP-TRAM utilizes real-time power grid data and aims to assist operators in measuring resiliency and ensuring the energy supply to critical loads given a cyber-attack or a natural disaster. This paper discusses the multiple ML algorithms for data anomaly detection, the basis of software design considerations, open-source software components, and use cases for the prototype developed tools.