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20242026
most citedOptimal Parameter Design for Power Electronic Converters Using a Probabilistic Learning-Based Stochastic Surrogate Model

3 citations · 7 across the 14 of their papers we have counts for

collaborators

15 papers

eess.SY2026

Decision-Gated Surrogate-Assisted Stochastic Optimization with Independent High-Fidelity Certification for Photovoltaic Hosting-Capacity Planning

Ali Abubakar, Samuel Essamuah Assabil, Akhtar Hussain +1

High photovoltaic (PV) penetration planning requires repeated nonlinear PV and power-flow evaluations under uncertain irradiance and demand, particularly when two competing objecti…

eess.SY2026

A Hybrid Two-Stage Machine Learning Pipeline for Fault Detection and Classification in Power Transmission Systems

Sahil Manikshete, Atharva Gujarathi, Thanh Long Vu +2

Rapid and accurate fault detection in high-voltage transmission networks is essential for grid reliability and equipment protection. Transmission fault datasets are frequently imba…

eess.SY2026

An End-to-End Explainable AI Framework with Automated LLM-Based Natural Language Explanation Generation for Energy Systems

Venkata Sesha Sai Raj Nanduri, Akthar Hussain, Van-Hai Bui

Explainable AI (XAI) is important for deploying machine learning systems in domains where stakes are very high and where transparency, trust and accountability are critical. Althou…

eess.SY2026

Scalable Impedance Identification of Diverse IBRs via Cluster-Specialized Neural Networks

Quang Manh Hoang, Guilherme Vieira Hollweg, Bang Nguyen +3

Modern machine learning approaches typically identify the impedance of a single inverter-based resource (IBR) and assume similar impedance characteristics across devices. In modern…

eess.SY2025

Scalable Fairness Shaping with LLM-Guided Multi-Agent Reinforcement Learning for Peer-to-Peer Electricity Markets

Shrenik Jadhav, Birva Sevak, Srijita Das +3

Peer-to-peer (P2P) energy trading is becoming central to modern distribution systems as rooftop PV and home energy management systems become pervasive, yet most existing market and…

eess.SY2025

ANN-Based Grid Impedance Estimation for Adaptive Gain Scheduling in VSG Under Dynamic Grid Conditions

Quang-Manh Hoang, Van Nam Nguyen, Taehyung Kim +3

In contrast to grid-following inverters, Virtual Synchronous Generators (VSGs) perform well under weak grid conditions but may become unstable when the grid is strong. Grid strengt…

eess.SY2025

Enhancing Power Flow Estimation with Topology-Aware Gated Graph Neural Networks

Shrenik Jadhav, Birva Sevak, Srijita Das +2

Accurate and scalable surrogate models for AC power flow are essential for real-time grid monitoring, contingency analysis, and decision support in increasingly dynamic and inverte…

cs.LG2025

FairMarket-RL: LLM-Guided Fairness Shaping for Multi-Agent Reinforcement Learning in Peer-to-Peer Markets

Shrenik Jadhav, Birva Sevak, Srijita Das +3

Peer-to-peer (P2P) trading is increasingly recognized as a key mechanism for decentralized market regulation, yet existing approaches often lack robust frameworks to ensure fairnes…

eess.SY2025★ 3 cited

Optimal Parameter Design for Power Electronic Converters Using a Probabilistic Learning-Based Stochastic Surrogate Model

Akash Mahajan, Shivam Chaturvedi, Srijita Das +2

The selection of optimal design for power electronic converter parameters involves balancing efficiency and thermal constraints to ensure high performance without compromising safe…

eess.SY2025★ 2 cited

Enhancing Forecasting Accuracy in Dynamic Environments via PELT-Driven Drift Detection and Model Adaptation

Nikhil Pawar, Guilherme Vieira Hollweg, Akhtar Hussain +2

Accurate time series forecasting models are often compromised by data drift, where underlying data distributions change over time, leading to significant declines in prediction per…

eess.SY2025

Large Language Models for Solving Economic Dispatch Problem

Sina Mohammadi, Ali Hassan, Rouzbeh Haghighi +2

This paper investigates the capability of off-the-shelf large language models (LLMs) to solve the economic dispatch (ED) problem. ED is a hard-constrained optimization problem solv…

eess.SY2025

A Novel Topology Recovery Method for Low Voltage Distribution Networks

Sina Mohammadi, Van-Hai Bui, Wencong Su

Low voltage distribution networks (LVDNs) suffer from limited visibility due to sparse or nonexistent measurement systems, leaving distribution network service providers with incom…

eess.SY2025

Deep Reinforcement Learning-Based Optimization of Second-Life Battery Utilization in Electric Vehicles Charging Stations

Rouzbeh Haghighi, Ali Hassan, Van-Hai Bui +2

The rapid rise in electric vehicle (EV) adoption presents significant challenges in managing the vast number of retired EV batteries. Research indicates that second-life batteries…

eess.SY2025

PSO-based Sliding Mode Current Control of Grid-Forming Inverter in Rotating Frame

Quang-Manh Hoang, Guilherme Vieira Hollweg, Akhtar Hussain +3

The Grid-Forming Inverter (GFMI) is an emerging topic that is attracting significant attention from both academic and industrial communities, particularly in the area of control de…

eess.SY2024★ 2 cited

A Critical Review of Safe Reinforcement Learning Techniques in Smart Grid Applications

Van-Hai Bui, Srijita Das, Akhtar Hussain +2

The high penetration of distributed energy resources (DERs) in modern smart power systems introduces unforeseen uncertainties for the electricity sector, leading to increased compl…