Given a bounded linear operator A on a Banach space, Kreiss and Tadmor-Ritt are two well-known resolvent conditions related to power-boundedness of A, that is, ‖Ak‖ is bounded by some M(A) for all k≥0. The classical Kreiss matrix theorem gives a finite-dimensional characterization up to a dimension-dependent constant. This dependence on dimension makes the theorem unsuitable as a Banach-space criterion. One main goal of this paper is to introduce a Banach-space version of the Kreiss constant that is equivalent to M(A) for all power-bounded operators A. For a Tadmor-Ritt operator A, one has M(A)≤CT(A)log(1+T(A)). We show that the log term cannot be removed in general, resolving a conjecture that has been open for 23 years.
The high penetration of Inverter-Based Resources (IBRs) necessitates computationally intensive Electro-Magnetic Transient (EMT) simulations to analyze controller interactions. A practical challenge is that these studies are considerably time-consuming for large systems, and need to be done for different operating conditions and contingencies. Reducing the order of the model to form a reduced-order equivalent is a viable approach to speed up the simulation. In such models, it is important to ensure that (a) the reduced order model is accurate over a wide range of frequencies, and (b) the equivalent system is passive to reduce the chance of instability of the simulation. To address this issue, this paper presents a novel, passivity-preserving model order reduction framework that achieves wide-band accuracy. The proposed approach extends the classical moment-matching approach to match the system’s response at multiple frequency points to construct a passivity-preserving reduced-order model. The efficacy of the proposed technique is validated on the IEEE 39-bus system. Results demonstrate that the reduced models provide high fidelity over a wide frequency range, enabling a significant reduction in EMT simulation time without compromising the stability of the simulation.
Line Commutated Converter (LCC)-based High Voltage Direct Current (HVdc) systems connected to weak ac networks face significant challenges in maintaining voltage and frequency stability. While synchronous condensers (SCs) provide reactive power and inertia support, their energy exchange is limited, and their voltage regulation response is slow.This paper employs a proposed SC configuration, called “Back-to-Back Synchronous Condenser” (BtB-SC), which interfaces the SC with the ac network via reconfigurable back-to-back (BtB) voltage source converters (VSCs). This setup dynamically adjusts the system’s apparent inertia and enhances damping, offering superior frequency and voltage regulation compared to standalone SCs.The BtB-SC’s controller parameters are optimized using non-linear optimization techniques to achieve rapid post-disturbance recovery. Electromagnetic Transient (EMT) simulations validate the system’s effectiveness, demonstrating significant improvements in inertia, damping, and overall grid stability for weak LCC-HVdc systems.
Computational models of memory have achieved considerable success by formalizing how traces are encoded, cued, and retrieved. However, the role that individual linguistic experience plays in shaping representational structure has been relatively understudied. Where the issue has been examined, most models assume that a common population-level semantic space suffices, effectively treating individual variation in language experience as noise rather than signal. We test the theoretical adequacy of this assumption. In a large-scale experiment (N = 478), participants completed a cued recall task in which identical target words were paired with cues drawn from eight different genre-specific semantic spaces (mystery, fantasy, horror, literary fiction, romance, science fiction, thriller, and non-fiction). Participants reported their reading habits across the eight genres, and personalized semantic representations were constructed by weighting genre-specific corpora proportionally to each participant's reading profile. Two complementary analyses were conducted. In a model-free analysis, cosine similarity computed within each participant's personalized semantic space predicted recall and omission outcomes more reliably than similarity derived from generic semantic spaces. In a computational analysis, substituting personalized representations into the embedded Computational Framework of Memory improved cue–target-level predictions over generic alternatives, accounting for approximately 4 percentage points more explained variance. These findings provide proof of concept that variation in individual linguistic experience shapes semantic structure in ways that are both behaviourally detectable and computationally tractable. More broadly, they suggest that the predictive limits of memory models may reside not only in their retrieval mechanisms but also in the fidelity of the representations they assume.
Wildfire smoke introduces fine particulate matter into the atmosphere, altering the insulating properties of air used in HV AC transmission systems. This study experimentally evaluates how wildfire smoke affects air-gap insulation under controlled laboratory conditions. A sealed smoke chamber was used to measure breakdown voltage, partial discharge inception voltage, phase-resolved discharge behavior, and partial discharge time-frequency characteristics at varying smoke and humidity conditions. Breakdown measurements using sphere–sphere gaps showed no significant deviation across all smoke conditions. In contrast, partial discharge inception voltage in needle–plane gap increased consistently with smoke density, rising by nearly 100% at the highest concentrations. Phase-resolved partial discharge patterns and partial discharge indicators show that smoke suppresses negative polarity discharges at low voltage, whereas at higher voltage it enhances partial discharge activity and increases pulse rate. Time–frequency analysis further revealed waveform-shape changes under combined smoke–humidity conditions.
