Daguo Cheng

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Analysis of the overall solution of centralized and cluster inverter of large grid-connected photovoltaic power station in mountainous area

Guanghui Zhang Daguo Cheng

As the focus of modern society construction and development, large-scale grid-connected photovoltaic power station is also the energy utilization technology with the most development potential in the future. The one hand, can reduce the battery energy storage and release process, effectively control the wastage of the energy economy, on the other hand can improve the quality and efficiency of power station transmission power, effective control of the space and capital consumption, further promote social and economic development for the education. At present, in the large-scale construction and promotion of grid-connected photovoltaic power stations in mountainous areas of China, centralized or cluster inverter schemes are used. Although there are technical differences between them, their fundamental purpose is to ensure the safe and stable operation of power stations. Therefore, on the basis of understanding the current construction and development status of large grid-connected photovoltaic power stations in China, this paper makes a comparative analysis of the centralized and cluster inverter schemes to clarify the application differences of the two in large grid-connected photovoltaic power stations, so as to provide basic guarantee for future urban residents.

10.1117/12.2646630

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Luori: Active Probing and Evaluation of Internet-Wide IPv6 Fully Responsive Prefixes

Daguo Cheng Lin He Wei Chen Qilei Yin Bao Jin

Prefix

10.1109/icnp61940.2024.10858548

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Efficient and Robust KPI Outlier Detection for Large-Scale Datacenters

IEEE Transactions on Computers (2023)

Yongqian Sun Daguo Cheng Tiankai Yang Yuhe Ji Shenglin Zhang

To ensure the performance of large-scale datacenters, operators need to monitor up to tens of millions of various-type KPIs, e.g., CPU utilization, memory utilization. For each KPI, it is crucial but challenging to detect outliers that deviate from its historical patterns or the patterns of other KPIs in the same period. In this work, we propose OutSpot , an unsupervised outlier detection framework that integrates hierarchical agglomerative clustering (HAC) with conditional variational autoencoder (CVAE), which significantly improves computational efficiency and comprehensively learns the above two patterns. Additionally, two simple yet effective techniques, soft threshold and median filter, are applied to precisely determine outlier KPIs. Using two real-world datasets collected from the datacenters owned by a top-tier global short video service provider and a top-tier domestic operator,respectively. It demonstrates that OutSpot achieves the best F1 score of 0.95 and 0.91, AUC of 0.99 and 0.99 on the two datasets, significantly outperforming seven baseline outlier detection methods.

Autoencoder

Performance indicator

10.1109/tc.2023.3272288

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Robust Anomaly Clue Localization of Multi-Dimensional Derived Measure for Online Video Services

IEEE Transactions on Services Computing (2022)

Yongqian Sun Daguo Cheng Pengxiang Jin Quan Ding Shenglin Zhang

Anomaly clue localization of multi-dimensional derived measure is vitally important for the reliability of online video services. In this paper, we propose RobustSpot, an end-to-end framework for localizing the clues to anomalous multi-dimensional derived measures. RobustSpot integrates two novel indicators, i.e., “Anomaly Degree” and “Contribution Ability”, with a simple yet effective method, weighted association rule mining (WARM), to automatically mine the hidden relationships across data dimensions for localizing the most likely clues to the root cause. Using 135 real-world cases collected from a top-tier global online video service provider

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with 170+ million monthly active users, we demonstrate that RobustSpot achieves high accuracy (Top-5 accuracy of 98%), significantly outperforming state-of-the-art methods. The average localization time of RobustSpot is 1.83s, which is satisfying in our scenario. We have open-sourced the implementation of RobustSpot as well as the data used in the evaluation experiments.

Anomaly (physics)

10.1109/tsc.2022.3155500

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