Hongwei Zhang

h-index28
2papers
5,838citations

2 Papers

2.0SYJun 23
Restricting Voltage Deviation of DC Microgrids with Critical and Ordinary Nodes: A Generalized Consensus Approach

Handong Bai, Peng Li, Hongwei Zhang

Restricting bus voltage deviation is crucial for normal operation of multi-bus DC microgrids, yet it has received insufficient attention due to the conflict between two main control objectives in DC microgrids, i.e., voltage regulation and current sharing. By revealing a necessary and sufficient condition for achieving these two objectives, this paper proposes a novel consensus-based current sharing control law that can achieve the compromised control objective, balancing both current sharing and voltage deviation restriction. Additionally, we examine the effectiveness of the proposed control scheme for DC Microgrids that include both critical nodes and ordinary nodes, where there is a simultaneous requirement for voltage deviation limits on critical nodes and accurate current sharing among ordinary nodes. Theoretical results are verified by simulations, and the effectiveness in handling plug-and-play operations of distributed generators is also illustrated.

2.2IRJun 25, 2024
Learning to Rank for Maps at Airbnb

Malay Haldar, Hongwei Zhang, Kedar Bellare et al.

As a two-sided marketplace, Airbnb brings together hosts who own listings for rent with prospective guests from around the globe. Results from a guest's search for listings are displayed primarily through two interfaces: (1) as a list of rectangular cards that contain on them the listing image, price, rating, and other details, referred to as list-results (2) as oval pins on a map showing the listing price, called map-results. Both these interfaces, since their inception, have used the same ranking algorithm that orders listings by their booking probabilities and selects the top listings for display. But some of the basic assumptions underlying ranking, built for a world where search results are presented as lists, simply break down for maps. This paper describes how we rebuilt ranking for maps by revising the mathematical foundations of how users interact with search results. Our iterative and experiment-driven approach led us through a path full of twists and turns, ending in a unified theory for the two interfaces. Our journey shows how assumptions taken for granted when designing machine learning algorithms may not apply equally across all user interfaces, and how they can be adapted. The net impact was one of the largest improvements in user experience for Airbnb which we discuss as a series of experimental validations.