arXiv subjects

Rock Agon

Publications and source records attributed to Rock Agon.

3 recordsLinked to original sources

arXiv2026

Efficient MPC for LC-Filtered Three-Level NPC Inverters in Vehicle-to-Load Applications

Finite control-set model predictive control (FCS-MPC) offers fast, modulator-free voltage regulation but is sensitive to the fidelity of its internal model. This sensitivity is usually countered by algorithmic augmentation, disturbance observers, adaptive weighting, or learning-based estimators, at the cost of extra tuning and computation. This paper instead improves fidelity at the source. For a three-level neutral-point-clamped (3L-NPC) inverter with an LC output filter in vehicle-to-load operation, a coupled alpha-beta-frame predictive model carries the capacitor voltage and filter current jointly through the state vector, so that the second-order LC coupling is retained without enlarging the 27-candidate search. The cost function additionally regulates dc-link neutral-point balance and switching effort. One-step prediction accuracy, parametric sensitivity, and computational cost are bounded in closed form. Simulation of a V2L system yields a voltage total harmonic distortion of 0.72%, a steady-state tracking error of 1.6%, and transient recovery within one fundamental cycle, meeting the applicable IEEE power-quality limits with margin, without observers, parameter estimation, or learning-based augmentation.

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arXiv2026

Hierarchical Agglomerative Clustering for Efficient Annual Voltage Security Assessment in Very-High RES Penetrated Power Systems

Voltage security assessment in power systems with high renewable energy source (RES) penetration requires analyzing many operating conditions to capture variability and uncertainty, but simulating a full year of operating points is computationally costly - motivating the selection of representative operating points (ROPs). Most existing methods cluster demand and generation profiles, but similarity in these profiles does not guarantee similarity in voltage behavior, since reactive power limits, voltage-control actions, and nonlinear network interactions shape voltage response in ways that cannot be inferred from power profile patterns. This paper proposes an unsupervised learning framework that selects ROPs based on the system's actual voltage response: each operating point is represented by system-wide voltage-risk indices from AC power-flow solutions, Principal Component Analysis reduces dimensionality, and Hierarchical Agglomerative Clustering with Ward linkage identifies representative voltage regimes. A comprehensive set of evaluation criteria then measures how well the selected ROPs reproduce the full year's voltage-security characteristics under normal and contingency conditions. On the IEEE Voltage Test System under very high RES penetration, the framework reduces the annual operating point set by 99.66 percent while reproducing full-year voltage behavior with 98.3 percent reconstruction accuracy in steady state and 93.4 percent in post-contingency response, outperforming existing injection-space clustering and heuristic sampling.

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arXiv2026

Scalable Voltage-Stability Dataset Generation Via Boundary-Proximity Indicators Clustering

This paper proposes a scalable framework for voltage-stability dataset generation. Voltage-stability-constrained planning increasingly relies on machine-learning surrogates but training them requires large datasets labelled by continuation power flow (CPF) results, which is computationally costly. To address this, this paper proposes a framework that uses hierarchical clustering on boundary-proximity indicators to reduce the number of required CPF evaluations. The proposed approach combines (i) uniform sampling of feasible operating space using Hit-and-Run Markov Chain Monte Carlo, (ii) structured stress directions via maximin Latin hypercube sampling (LHS), (iii) sensitivity-guided perturbations to target weak buses, and (iv) clustering-based representative CPF labelling that reconstructs the voltage stability margins of unlabelled operating points from representative cluster medoids. Results on the IEEE 39-bus system show that the proposed framework significantly reduces CPF evaluations by 95.45% while preserving high accuracy and boundary fidelity for both regression and classification tasks. The reduced surrogates remain structurally consistent with their full-CPF dataset counterparts, demonstrating the suitability and scalability of the proposed approach for operation and planning optimization.

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