Voltage-Regulated Sparse Optimization for Proactive Diagnosis of Voltage Collapses
Qinghua Ma, Seyyedali Hosseinalipour, Ming Shi, Jan Drgona, Shimiao Li

TL;DR
This paper introduces a voltage-regulated sparse optimization method to proactively diagnose and mitigate voltage collapse risks in power systems by identifying minimal corrective actions at key locations.
Contribution
It presents a novel sparse optimization approach that efficiently finds critical bus locations for voltage correction, scalable to large power systems.
Findings
Effectively mitigates voltage collapses with minimal corrective actions.
Scales efficiently to large systems, taking less than 4 minutes for 2000+ buses.
Identifies key vulnerable locations for proactive voltage management.
Abstract
This paper aims to proactively diagnose and manage the voltage collapse risks, i.e., the risk of bus voltages violating the safe operational bounds, which can be caused by extreme events and contingencies. We jointly answer two resilience-related research questions: (Q1) Survivability: Upon having an extreme event/contingency, will the system remain feasible with voltage staying within a (preferred) safe range? (Q2) Dominant Vulnerability: If voltage collapses, what are the dominant sources of system vulnerabilities responsible for the failure? This highlights some key locations worth paying attention to in the planning or decision-making process. To address these questions, we propose a voltage-regulated sparse optimization that finds a minimal set of bus locations along with quantified compensations (corrective actions) that can simultaneously enforce AC network balance and voltage…
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Taxonomy
TopicsOptimal Power Flow Distribution · Power System Optimization and Stability · Power Systems Fault Detection
