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Shammya Shananda Saha

Researcher at Arizona State University

Publications -  31
Citations -  210

Shammya Shananda Saha is an academic researcher from Arizona State University. The author has contributed to research in topics: Computer science & AC power. The author has an hindex of 6, co-authored 22 publications receiving 101 citations. Previous affiliations of Shammya Shananda Saha include Bangladesh University of Engineering and Technology.

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Empowering Smart Communities: Electrification, Education, and Sustainable Entrepreneurship in IEEE Smart Village Initiatives

TL;DR: In this paper, the United Nations Sustainable Development Goals (SDGs) have outlined the most effective ways to provide billions of people with clean water, sanitation, access to education, medical services, and communication technologies.
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A secure distributed ledger for transactive energy: The Electron Volt Exchange (EVE) blockchain

TL;DR: Work herein introduces the Electron Volt Exchange framework with the following characteristics: a distributed protocol for pricing and scheduling prosumers’ production/consumption while keeping constraints and bids private, and a distributed algorithm to prevent theft that verifies pros consumers’ compliance to scheduled transactions and mitigates the impact of false data injection attacks.
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Lossy DistFlow Formulation for Single and Multiphase Radial Feeders

TL;DR: A line loss approximation via parametrization is developed to improve performance of the simplified Baran and Wu DistFlow method, while maintaining a linear set of equations, to improve the accuracy of multiphase distribution system calculations.
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Prioritizing Energy Blockchain Use Cases Using Type-2 Neutrosophic Number Based EDAS

TL;DR: This study proposes a Type-2 Neutrosophic Number (T2NN) based Evaluation based on Distance from Average Solution (EDAS) to evaluate and rank a set of existing use cases of an energy blockchain system.
Posted Content

Deep Reinforcement Learning for DER Cyber-Attack Mitigation

TL;DR: This work considers deep reinforcement learning as a tool to learn the optimal parameters for the control logic of a set of uncompromised DER units to actively mitigate the effects of a cyber-attack on a subset of network DER.