Virgilio A. Centeno

Chetan Mishra, James S. Thorp, Anamitra Pal et al.

There has been significant increase in penetration of renewable generation (RG) sources all over the world. Localized concentration of many such generators could initiate a cascade tripping sequence that might threaten the stability of the entire system. Understanding the impact of cascade tripping process would help the system planner identify trip sequences that must be blocked in order to increase stability. In this work, we attempt to understand the consequences of cascade tripping mechanism through a Lyapunov approach. A conservative definition for the stability region (SR) along with its estimation for a given cascading sequence using sum of squares (SOS) programming is proposed. Finally, a simple probabilistic definition of the SR is used to visualize the risk of instability and understand the impact of blocking trip sequences. A 3-machine system with significant RG penetration is used to demonstrate the idea.

Chetan Mishra, Anamitra Pal, Virgilio A. Centeno

From a stability perspective, a renewable generation (RG)-rich power system is a constrained system. As the quasistability boundary of a constrained system is structurally very different from that of an unconstrained system, finding the sensitivity of critical clearing time (CCT) to change in system parameters is very beneficial for a constrained power system, especially for planning/revising constraints arising from system protection settings. In this paper, we derive the first order sensitivity of a constrained power system using trajectory sensitivities of fault-on and post-fault trajectories. The results for the test system demonstrate the dependence between ability to meet angle and frequency constraints, and change in power system parameters such as operating conditions and inertia.

Yunchuan Liu, Lei Yang, Amir Ghasemkhani et al.

This paper studies robust event classification using imperfect real-world phasor measurement unit (PMU) data. By analyzing the real-world PMU data, we find it is challenging to directly use this dataset for event classifiers due to the low data quality observed in PMU measurements and event logs. To address these challenges, we develop a novel machine learning framework for training robust event classifiers, which consists of three main steps: data preprocessing, fine-grained event data extraction, and feature engineering. Specifically, the data preprocessing step addresses the data quality issues of PMU measurements (e.g., bad data and missing data); in the fine-grained event data extraction step, a model-free event detection method is developed to accurately localize the events from the inaccurate event timestamps in the event logs; and the feature engineering step constructs the event features based on the patterns of different event types, in order to improve the performance and the interpretability of the event classifiers. Based on the proposed framework, we develop a workflow for event classification using the real-world PMU data streaming into the system in real-time. Using the proposed framework, robust event classifiers can be efficiently trained based on many off-the-shelf lightweight machine learning models. Numerical experiments using the real-world dataset from the Western Interconnection of the U.S power transmission grid show that the event classifiers trained under the proposed framework can achieve high classification accuracy while being robust against low-quality data.