Kamal Chandra Paul

Online Monitoring and Control of Power Converter Junction Temperature in Wave Energy Systems Using Artificial Neural-Network-Based Model Predictive Control

Qiang Mu, Kamal Chandra Paul, Zaheen Mustakin, Jiale Zhou et autres

Ocean waves are more consistent and predictable than solar or wind energy, enabling power generation for up to 90% of the time. Power converters represent one of the most vulnerable elements in wave energy systems, and their malfunction may cause severe system …

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Hybrid Autoencoder-LSTM Augmentation for Improved Arc Fault Classification Under Data Scarcity

Shangze Chen, Kamal Chandra Paul, Tiefu Zhao

Arc faults pose serious risks in modern electrical systems, resulting in electrical failures and safety hazards. Although recent advances in artificial intelligence have boosted detection accuracy, obtaining diverse and sufficient fault data remains challenging. In this paper, we propose a hybrid Autoencoder-Long …

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PV Arc Fault Circuit Interrupter with Knowledge Distillation-Based Lightweight Convolutional Neural Network and SSCB Integration

Kamal Chandra Paul, Shen-En Chen, Tiefu Zhao

DC arc faults in photovoltaic (PV) systems pose serious safety risks, including fire hazards and power disruption, making accurate and timely detection essential. This paper presents PArcNet, a lightweight convolutional neural network (CNN) architecture optimized through knowledge distillation for efficient arc fault …

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Public sentiment analysis of roadway work zones using social media data and machine learning models

Md Abu Sayed, Md. Amjad Hossain, Md. Mokhlesur Rahman, G. G. Md. Nawaz Ali et autres

The construction and maintenance of roadway infrastructure contribute positively to social and economic development and improve traffic safety. However, roadway work zones (WZs) present safety issues for construction workers and travelers, and adversely affect vehicular movement. By collecting and analyzing Twitter data …

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Artificial Intelligence for DC Arc Fault Detection in Photovoltaic Systems: A Comprehensive Review

Kamal Chandra Paul, Disnebio Waldmann, Chen Chen, Yao Wang et autres

Photovoltaic (PV) systems are increasingly used for renewable energy generation but remain vulnerable to series arc faults, which can cause serious safety risks and system failures. Detecting these faults in DC circuits is challenging due to their subtle electrical signatures and the …

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LArcNet: Lightweight Neural Network for Real-Time Series AC Arc Fault Detection

Kamal Chandra Paul, Chen Chen, Yao Wang, Tiefu Zhao

Detecting series ac arc faults in diverse residential loads is challenging due to variations in load characteristics and noise. While traditional artificial intelligence-based algorithms can be effective, they often involve high computational complexity, limiting their real-time implementation on resource-constrained edge devices. This …

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Enhancing Arc Fault Detection Performance through Data Augmentation with Artificial Intelligence Technology: An Approach to Time Series Dataset Enlargement

Shangze Chen, Kamal Chandra Paul, Tiefu Zhao

Arc faults present a significant risk to electrical systems and pose a challenge for detection using machine learning methodologies. This study proposes a novel approach that enhances detection performance by employing an Autoencoder and Long Short-Term Memory network to augment time series …

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Efficient and Lightweight Convolutional Neural Network-based Series DC Arc Fault Protection for PV Systems

Kamal Chandra Paul, Jiale Zhou, Shen-En Chen, Chen Chen et autres

Series DC arc faults in photovoltaic (PV) systems pose substantial fire risks and compromise electrical safety and reliability. Traditional AI-based arc fault detection methods, while highly accurate, are computationally burdensome to integrate into resource-constrained, low-cost microcontrollers for real-time applications. This research introduces …

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Explainability Approach-Based Series Arc Fault Detection Method for Photovoltaic Systems

Yao Wang, Jiawang Zhou, Kamal Chandra Paul, Tiefu Zhao et autres

Arc fault detection devices are mandatory worldwide for mitigating DC series arc faults in photovoltaic systems. However, they are prone to nuisance tripping. Artificial intelligence-based approaches can be a solution, but they are "black boxes" and challenging to modify. This paper proposes …

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Pandemic vulnerability index of US cities: A hybrid knowledge-based and data-driven approach

Kamal Chandra Paul, Jim Samuel, Jean‐Claude Thill, Md. Amjad Hossain et autres

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Machine Learning Based Public Sentiment Analytics on Roadway Work-Zone Tweets

Md Abu Sayed, Md. Amjad Hossain, Md. Mokhlesur Rahman, Gohar Ali et autres

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Series AC Arc Fault Detection Using Decision Tree-Based Machine Learning Algorithm and Raw Current

Kamal Chandra Paul, L. Schweizer, Tiefu Zhao, Chen Chen et autres

Series AC arc fault is dangerous because it may lead to electrical fire hazards. It is very challenging to detect it accurately. This paper proposes a decision tree-based machine learning algorithm, random forest (RF), to detect series AC arc faults. The proposed …

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