arXiv subjects

Syed Islam

Publications and source records attributed to Syed Islam.

5 recordsLinked to original sources

arXiv2026

Coordinated Optimal Power Quality Management in Distribution Systems Using The Residual Capacity of Community IBRs

This letter proposes a network-wide coordinated optimization model to mitigate voltage unbalance (VU) by unleashing the remaining capacity of community inverter-based resources (IBRs). Existing single-sequence strategies ignore coupled capacity constraints and cause idle headroom. Meanwhile, they fail to harness the collective governance capabilities of community IBRs. To solve this discrepancy and exploit the unused potential, we developed a sequence-domain network model in dual commonly shared synchronous reference frames. Strict phase current and apparent power limits are formulated and convexified via polyhedral approximations. A quadratic objective function flexibly balances sequence capacity allocation. Simulation and experimental results validate the effectiveness of the proposed strategy.

eess.SY↗

arXiv2026

Full-Time-Scale Power Management Strategy for Hybrid AC/DC/DS Microgrid with Dynamic Concatenation and Autonomous Frequency / Voltage Restorations

Hybrid AC/DC microgrids with distributed energy storage (DS) improve power reliability in remote areas. Existing power management methods either focus on steady-state power sharing or transient inertia support, but rarely combine both. They also often ignore frequency and voltage deviations caused by droop control, which can harm sensitive loads. To overcome these issues, this paper proposes a full-time-scale (FTS) power management strategy that unifies transient inertia sharing and steady-state power allocation through a novel dynamic concatenator. It also introduces autonomous frequency/voltage restoration to eliminate steady-state deviations in each subgrid. Additionally, a global equivalent circuit model (GECM) is developed to simplify system analysis and design. Experiments confirm that the approach maintains nominal frequency and voltage in steady state while enabling seamless transition between transient inertia support and proportional power sharing across all time scales.

eess.SY↗

arXiv2023

Networkwide Traffic State Forecasting Using Exogenous Information: A Multi-Dimensional Graph Attention-Based Approach

Traffic state forecasting is crucial for traffic management and control strategies, as well as user- and system-level decision making in the transportation network. While traffic forecasting has been approached with a variety of techniques over the last couple of decades, most approaches simply rely on endogenous traffic variables for state prediction, despite the evidence that exogenous factors can significantly impact traffic conditions. This paper proposes a multi-dimensional spatio-temporal graph attention-based traffic prediction approach (M-STGAT), which predicts traffic based on past observations of speed, along with lane closure events, temperature, and visibility across the transportation network. The approach is based on a graph attention network architecture, which also learns based on the structure of the transportation network on which these variables are observed. Numerical experiments are performed using traffic speed and lane closure data from the California Department of Transportation (Caltrans) Performance Measurement System (PeMS). The corresponding weather data were downloaded from the National Oceanic and Atmospheric Administration (NOOA) Automated Surface Observing Systems (ASOS). For comparison, the numerical experiments implement three alternative models which do not allow for the multi-dimensional input. The M-STGAT is shown to outperform the three alternative models, when performing tests using our primary data set for prediction with a 30-, 45-, and 60-minute prediction horizon, in terms of three error measures: Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). However, the model's transferability can vary for different transfer data sets and this aspect may require further investigation.

cs.LG↗

arXiv2022

A proximal subgradient algorithm with extrapolation for structured nonconvex nonsmooth problems

In this paper, we consider a class of structured nonconvex nonsmooth optimization problems, in which the objective function is formed by the sum of a possibly nonsmooth nonconvex function and a differentiable function whose gradient is Lipschitz continuous, subtracted by a weakly convex function. This type of structured problems has many practical applications in machine learning and statistics such as compressed sensing, signal recovery, sparse dictionary learning, clustering, matrix factorization, and others. We develop a flexible extrapolated proximal subgradient algorithm for solving these problems with guaranteed subsequential convergence to a stationary point. The global convergence of the whole sequence generated by our algorithm is also established under the Kurdyka-Lojasiewicz property. To illustrate the promising numerical performance of the proposed algorithm, we conduct numerical experiments on two important nonconvex models. This includes a least squares problem with a nonconvex regularization and an optimal power flow problem with distributed energy resources.

math.OC↗

arXiv2017

Improved Parameter Estimation Techniques for Induction Motors using Hybrid Algorithms

The performance of Newton-Raphson, Levenberg-Marquardt, Damped Newton-Raphson and genetic algorithms are investigated for the estimation of induction motor equivalent circuit parameters from commonly available manufacturer data. A new hybrid algorithm is then proposed that combines the advantages of both descent and natural optimisation algorithms. Through computer simulation, the hybrid algorithm is shown to significantly outperform the conventional algorithms in terms of convergence and squared error rates. All of the algorithms are tested on a large data set of 6,380 IEC (50Hz) and NEMA (60Hz) motors.

eess.SY↗