arXiv subjects

Abdallah Alalem Albustami

Publications and source records attributed to Abdallah Alalem Albustami.

4 recordsLinked to original sources

arXiv2026

The Iberian Blackout: A Black Swan or a Gray Rhino? A Protection-Aware Dynamic Voltage Security Assessment

On 28 April 2025, the Iberian mainland power system collapsed after a rapid voltage rise, widespread generation disconnections, and loss of synchronism. The ENTSO-E Expert Panel final report attributes the blackout to multiple interacting factors including ineffective voltage control, fixed power factor reactive behavior, fast generation ramps, protection settings not aligned with requirements, slow or unavailable reactive absorption, and limited observability outside the transmission system. This paper uses the incident as a motivating case for a broader operational voltage security problem: given the present grid state, can the next plausible trip, ramp, topology action, or shunt action push protected downstream voltages above relay thresholds before available voltage controls can respond? We develop a protection-aware dynamic voltage security assessment for this question. Starting from a nonlinear hybrid differential-algebraic equation (DAE) model, we derive mode wise finite window voltage maps that include automatic voltage regulators (AVRs), inverter-based resources (IBRs), static synchronous compensators (STATCOMs), high-voltage direct-current (HVDC) links, loads, shunts, transformers, protection functions, and limiter behavior whenever the corresponding models are available. We define normalized overvoltage margin erosion at the protection measurement side and time resolved lower bounds on useful control response. We then develop a monotone pickup cascade screen, robust data-limited certificates under uncertain relay and protected-voltage data, and a mitigation optimization that computes the minimum fast reactive action needed to keep protected voltages below relay thresholds. Case studies on a 2000-bus mechanism replica and multiple dynamic benchmark systems show that the screen predicts nonlinear cascade propagation.

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arXiv2026

DAE-Aware Bayesian Inference for Joint Generator-Network Parameter Estimation

This paper addresses the classic problem of parameter estimation (PE) in multimachine power system models. Such models are typically described by a set of nonlinear differential-algebraic equations (DAE), where generator physics and network power flow equations are coupled. DAE models are well established in classic power system textbooks, but parameter identification and estimation of generator inertia and damping together with network branch resistances and reactances for these models remain relatively underexplored. In contrast to prior approaches that rely on ODE approximations, this paper develops a joint Bayesian inference framework to perform PE of generator and network parameters while exploiting grid DAE models. It further combines physics-aware statistical modeling with computationally efficient posterior sampling to make joint Bayesian calibration practical. Results on the IEEE 9-bus system show accurate parameter recovery with well-behaved posterior uncertainty, while a short 39-bus study provides evidence that the framework remains effective on a materially larger joint-estimation problem. These results are obtained without requiring overly conservative priors.

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arXiv2025

Breaking the Flow and the Bank: Stealthy Cyberattacks on Water Network Hydraulics

As water distribution networks (WDNs) become increasingly connected with digital infrastructures, they face greater exposure to cyberattacks that threaten their operational integrity. Stealthy False Data Injection Attacks (SFDIAs) are particularly concerning, as they manipulate sensor data to compromise system operations while avoiding detection. While existing studies have focused on either detection methods or specific attack formulations, the relationship between attack sophistication, system knowledge requirements, and achievable impact remains unexplored. This paper presents a systematic analysis of sensor attacks against WDNs, investigating different combinations of physical constraints, state monitoring requirements, and intrusion detection evasion conditions. We propose several attack formulations that range from tailored strategies satisfying both physical and detection constraints to simpler measurement manipulations. The proposed attacks are simple and local -- requiring knowledge only of targeted sensors and their hydraulic connections -- making them scalable and practical. Through case studies on Net1 and Net3 benchmark networks, we demonstrate how these attacks can persistently increase operational costs and alter water flows while remaining undetected by monitoring systems for extended periods. The analysis provides utilities with insights for vulnerability assessment and motivates the development of protection strategies that combine physical and statistical security mechanisms.

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arXiv2025

Unmasking Stealthy Attacks on Nonlinear DAE Models of Power Grids

Smart grids are inherently susceptible to various types of malicious cyberattacks that have all been documented in the recent literature. Traditional cybersecurity research on power systems often utilizes simplified models that fail to capture the interactions between dynamic and steady-state behaviors, potentially underestimating the impact of cyber threats. This paper presents the first attempt to design and assess stealthy false data injection attacks (FDIAs) against nonlinear differential algebraic equation (NDAE) models of power networks. NDAE models, favored in industry for their ability to accurately capture both dynamic and steady-state behaviors, provide a more accurate representation of power system behavior by coupling dynamic and algebraic states. We propose novel FDIA strategies that simultaneously evade both dynamic and static intrusion detection systems while respecting the algebraic power flow and operational constraints inherent in NDAE models. We demonstrate how the coupling between dynamic and algebraic states in NDAE models significantly restricts the attacker's ability to manipulate state estimates while maintaining stealthiness. This highlights the importance of using more comprehensive power system models in cybersecurity analysis and reveals potential vulnerabilities that may be overlooked in simplified representations. The proposed attack strategies are validated through simulations on the IEEE 39-bus system.

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