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Haozhe Xu

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2 papers

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EAAI Journal 2026 Journal Article

Classification and detection of direct current power quality disturbances for grid operation implications using deep learning models

Hui Hwang Goh
Haozhe Xu
Dongdong Zhang
Wei Dai
Shen Yuong Wong
Tonni Agustiono Kurniawan
Kai Chen Goh

In direct current power systems, the introduction of new energy generation and non-linear loads can cause power quality disturbances, which can degrade power quality and jeopardise the system's functionality. Due to the dearth of research on direct current power quality disturbance standards, this paper identifies seven essential direct current power quality disturbances. This study proposes hybrid solutions for classifying direct current power quality disturbances based on deep learning. The method first augments the dataset with a generative adversarial network, then transforms the one-dimensional data into two-dimensional data using the Gramian angular field and finally feeds the data into a convolutional neural network based on the GoogleNet framework to identify and classify disturbances and introduces an attention mechanism to enhance the network's efficiency. The mathematical models are used to generate 50 disturbances for the training phase dataset, while the microgrid simulation system is sampled to produce the inference phase dataset. The proposed model attained 98. 66 % classification accuracy during the training phase and maintained classification accuracy of 98. 41 %, 98. 11 % and 97. 84 % despite 70 dB, 60 dB and 50 dB noise interference respectively. In addition, the classification precision of the inference phase is 92. 18 %. The experimental outcomes demonstrate that the proposed model has outstanding performance and real-time grid operation implications.

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ICLR Conference 2024 Conference Paper

HAZARD Challenge: Embodied Decision Making in Dynamically Changing Environments

Qinhong Zhou
Sunli Chen
Yisong Wang
Haozhe Xu
Weihua Du
Hongxin Zhang
Yilun Du
Joshua B. Tenenbaum

Recent advances in high-fidelity virtual environments serve as one of the major driving forces for building intelligent embodied agents to perceive, reason and interact with the physical world. Typically, these environments remain unchanged unless agents interact with them. However, in real-world scenarios, agents might also face dynamically changing environments characterized by unexpected events and need to rapidly take action accordingly. To remedy this gap, we propose a new simulated embodied benchmark, called HAZARD, specifically designed to assess the decision-making abilities of embodied agents in dynamic situations. HAZARD consists of three unexpected disaster scenarios, including fire, flood, and wind, and specifically supports the utilization of large language models (LLMs) to assist common sense reasoning and decision-making. This benchmark enables us to evaluate autonomous agents' decision-making capabilities across various pipelines, including reinforcement learning (RL), rule-based, and search-based methods in dynamically changing environments. As a first step toward addressing this challenge using large language models, we further develop an LLM-based agent and perform an in-depth analysis of its promise and challenge of solving these challenging tasks. HAZARD is available at https://vis-www.cs.umass.edu/hazard/.

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