Mayank Baranwal
Tata Consultancy Services
76 Papers
127 Citations
Mayank Baranwal is an academic researcher from Tata Consultancy Services. The author has contributed to research in topics: Computer science & Voltage regulation. The author has an hindex of 8, co-authored 52 publications. Previous affiliations of Mayank Baranwal include University of Illinois at Urbana–Champaign & University of Michigan.
Chat about Author
Papers
A deep learning architecture for metabolic pathway prediction.
TL;DR: This paper applies a hybrid machine learning approach consisting of graph convolutional networks used to extract molecular shape features as input to a random forest classifier and shows that simple linear/logistic regression models can predict the values of these global features from the shape features extracted using the framework.
96
A Distributed Architecture for Robust and Optimal Control of DC Microgrids
TL;DR: The proposed control design seamlessly accommodates communication architectures that range from the centralized to decentralized scenarios with graceful degradation of the performance with lessened communication ability and are applicable to the case where the desired proportion in which the sources provide the power varies with time.
74
Robust Distributed Fixed-Time Economic Dispatch Under Time-Varying Topology
Mayank Baranwal,Kunal Garg,Dimitra Panagou,Alfred O. Hero +3 more
- 01 Oct 2021
TL;DR: A fixed-time convergent, fully distributed economic dispatch algorithm for scheduling optimal power generation among a set of DERs is proposed, which incorporates both load balance and generation capacity constraints.
29
Clustering and supervisory voltage control in power systems
TL;DR: A rule-based decentralized control strategy is proposed for effective management of bus voltages in the weakly coupled zones that are obtained as a result of the clustering process, which results in excellent identification of mutually decoupled sub-networks within a large power network.
24
A Fixed-Time Convergent Distributed Algorithm for Strongly Convex Functions in a Time-Varying Network
Kunal Garg,Mayank Baranwal,Dimitra Panagou +2 more
- 14 Dec 2020
TL;DR: In this article, a distributed nonlinear protocol for minimizing the sum of convex objective functions in a fixed time under time-varying communication topology is presented, where each node in the network has access only to its private objective function, while exchange of local information, such as, state and gradient values, is permitted between the immediate neighbors.
24