Search Results for author: Subham Sahoo

Found 20 papers, 0 papers with code

Uncertainty-Aware Artificial Intelligence for Gear Fault Diagnosis in Motor Drives

no code implementations2 Dec 2024 Subham Sahoo, Huai Wang, Frede Blaabjerg

This paper introduces a novel approach to quantify the uncertainties in fault diagnosis of motor drives using Bayesian neural networks (BNN).

Decision Making Fault Diagnosis +1

Data-Driven Graph Switching for Cyber-Resilient Control in Microgrids

no code implementations12 Nov 2024 Suman Rath, Subham Sahoo

Distributed microgrids are conventionally dependent on communication networks to achieve secondary control objectives.

Spike Talk in Power Electronic Grids -- Leveraging Post Moore's Computing Laws

no code implementations12 Nov 2024 Yubo Song, Subham Sahoo

Emerging distributed generation demands highly reliable and resilient coordinating control in microgrids.

On Noise Resiliency of Neuromorphic Inferential Communication in Microgrids

no code implementations25 Jul 2024 Yubo Song, Subham Sahoo, Xiaoguang Diao

Neuromorphic computing leveraging spiking neural network has emerged as a promising solution to tackle the security and reliability challenges with the conventional cyber-physical infrastructure of microgrids.

Inferring Ingrained Remote Information in AC Power Flows Using Neuromorphic Modality Regime

no code implementations20 Jul 2024 Xiaoguang Diao, Yubo Song, Subham Sahoo

In this paper, we infer remote measurements such as remote voltages and currents online with change in AC power flows using spiking neural network (SNN) as grid-edge technology for efficient coordination of power electronic converters.

Learning Theory Philosophy

Spike Talk: Genesis and Neural Coding Scheme Translations

no code implementations16 Jul 2024 Subham Sahoo

In this paper, we introduce a novel architecture Spike Talk via a unified representation of power and information as a means of data normalization using spikes for coordinated control of microgrids.

Philosophy

Stability-Oriented Prediction Horizons Design of Generalized Predictive Control for DC/DC Boost Converter

no code implementations25 Apr 2024 Yuan Li, Subham Sahoo, Sergio Vazquez, Yichao Zhang, Tomislav Dragicevic, Frede Blaabjerg

This paper introduces a novel approach in designing prediction horizons on a generalized predictive control for a DC/DC boost converter.

A Data-Driven Condition Monitoring Method for Capacitor in Modular Multilevel Converter (MMC)

no code implementations20 Apr 2024 Shuyu Ou, Mahyar Hassanifar, Martin Votava, Marius Langwasser, Marco Liserre, Ariya Sangwongwanich, Subham Sahoo, Frede Blaabjerg

The modular multilevel converter (MMC) is a topology that consists of a high number of capacitors, and degradation of capacitors can lead to converter malfunction, limiting the overall system lifetime.

A Gray-Box Stability Analysis Mechanism for Power Electronic Converters

no code implementations15 Apr 2024 Rui Kong, Subham Sahoo, Yubo Song, Frede Blaabjerg

This paper proposes a gray-box stability analysis mechanism based on data-driven dynamic mode decomposition (DMD) for commercial grid-tied power electronics converters with limited information on its control parameters and topology.

Semiconductor Devices Condition Monitoring Using Harmonics in Inverter Control Variables

no code implementations15 Apr 2024 Shuyu Ou, Ariya Sangwongwanich, Subham Sahoo, Frede Blaabjerg

The health status of power semiconductor devices in power converters is important but difficult to monitor.

Neuromorphic Event-Driven Semantic Communication in Microgrids

no code implementations28 Feb 2024 Xiaoguang Diao, Yubo Song, Subham Sahoo, Yuan Li

Synergies between advanced communications, computing and artificial intelligence are unraveling new directions of coordinated operation and resiliency in microgrids.

Semantic Communication

A Monolithic Cybersecurity Architecture for Power Electronic Systems

no code implementations21 Feb 2024 Kirti Gupta, Subham Sahoo, Bijaya Ketan Panigrahi

This unified approach concurrently compensates for the intrusion challenges posed by cyber attacks by reconstructing signals using the dynamics of the inner control layer.

Delay-Aware Semantic Sampling in Power Electronic Systems

no code implementations21 Feb 2024 Kirti Gupta, Subham Sahoo, Bijaya Ketan Panigrahi

In power electronic systems (PES), attacks on data availability such as latency attacks, data dropouts, and time-synchronization attacks (TSAs) continue to pose significant threats to both the communication network and the control system performance.

Chasing the Intruder: A Reinforcement Learning Approach for Tracking Intruder Drones

no code implementations10 Sep 2023 Shivam Kainth, Subham Sahoo, Rajtilak Pal, Shashi Shekhar Jha

Our proposed solution uses computer vision techniques interleaved with the policy learning framework of reinforcement learning to learn a control policy for chasing the intruder drone.

reinforcement-learning Reinforcement Learning

Digital Twins for Moving Target Defense Validation in AC Microgrids

no code implementations24 Jul 2023 Suman Rath, Subham Sahoo, Shamik Sengupta

Cyber-physical microgrids are vulnerable to stealth attacks that can degrade their stability and operability by performing low-magnitude manipulations in a coordinated manner.

Impact Assessment of Data Integrity Attacks in MVDC Shipboard Power Systems

no code implementations31 May 2023 Kirti Gupta, Subham Sahoo, Bijaya Ketan Panigrahi, Charalambos Konstantinou

The development of power electronics-based medium voltage direct current (MVDC) networks has revolutionized the marine industry by enabling all-electric ships (AES).

Atomic Anatomy of Low-Inertia Power Systems

no code implementations21 May 2023 Subham Sahoo, Arpan Malkhandi, Kristian Skafte Jensen

In this article, we determine a fundamental anatomical modeling parallelism between low-inertia power systems and Bohr's atomic model.

Anatomy Quantization

Optimizing a Digital Twin for Fault Diagnosis in Grid Connected Inverters -- A Bayesian Approach

no code implementations7 Dec 2022 Pavol Mulinka, Subham Sahoo, Charalampos Kalalas, Pedro H. J. Nardelli

In this paper, a hyperparameter tuning based Bayesian optimization of digital twins is carried out to diagnose various faults in grid connected inverters.

Bayesian Optimization Fault Detection +1

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