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Abraiz Khattak

Researcher at University of the Sciences

Publications -  59
Citations -  573

Abraiz Khattak is an academic researcher from University of the Sciences. The author has contributed to research in topics: Engineering & Silicone rubber. The author has an hindex of 9, co-authored 43 publications receiving 249 citations. Previous affiliations of Abraiz Khattak include COMSATS Institute of Information Technology & City University of Science and Information Technology.

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Optimal Placement of Electric Vehicle Charging Stations in the Active Distribution Network

TL;DR: An optimized combination of all three types of EV chargers for efficiently managing the EV load while minimizing installation cost, losses, and distribution transformer loading is demonstrated.
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Optimal placement, sizing and coordination of FACTS devices in transmission network using whale optimization algorithm

TL;DR: The whale optimization algorithm (WOA) is employed not only to find an ideal ratings for these devices but also the optimal coordination of SVC, TCSC and UPFC with the reactive power sources already present in the network.
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Accelerated aging investigation of silicone rubber/silica composites for coating of high-voltage insulators

TL;DR: In this article, the effect of silica on the aging behavior of room temperature vulcanized silicone rubber (RTV-SiR) was analyzed with two silica-based composites with 10% micro, 5% nano and 15% microsilica.
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Comparison of integer and fractional order robust controllers for DC/DC converter feeding constant power load in a DC microgrid

TL;DR: In this article, an integer and fractional order terminal sliding mode (TSMC) controllers for DC/DC boost converter feeding the constant power load (CPL) in a typical DC microgrid configuration is proposed and its stability is guaranteed.
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Hierarchical Control of Microgrid Using IoT and Machine Learning Based Islanding Detection

TL;DR: In this article, a three layers hierarchical control of inverter-based microgrid was developed using cloud-based IoT infrastructure and machine learning (ML) based islanding detection scheme.