ECoalVis: Visual Analysis of Control Strategies in Coal-fired Power Plants

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Shuhan Liu, Di Weng, Yuan Tian, Zikun Deng, Haoran Xu, Xiangyu Zhu, Honglei Yin, Xianyuan Zhan, Yingcai Wu

 View presentation:2022-10-20T21:21:00ZGMT-0600Change your timezone on the schedule page
2022-10-20T21:21:00Z

Exemplar figure, described by caption below — The user interface of ECoalVis. (A) The filter view reveals the time series of key sensors and allows users to fuzzy query control strategies. (B) The graph view reveals the spatial propagation of control strategy impact across components, units and sensors. (C) The strategy view depicts the temporal cascading of control strategy impact, visualizing the topology of the strategy and the time-lag-aligned time series. (D) The detail view allows users to search for sensors and perform time series operations to find insights from the raw data.

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The live footage of the talk, including the Q&A, can be viewed on the session page, Infrastructure Management.

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Abstract

Improving the efficiency of coal-fired power plants has numerous benefits. The control strategy is one of the major factors affecting such efficiency. However, due to the complex and dynamic environment inside the power plants, it is hard to extract and evaluate control strategies and their cascading impact across massive sensors. Existing manual and data-driven approaches cannot well support the analysis of control strategies because these approaches are time-consuming and do not scale with the complexity of the power plant systems. Three challenges were identified: a) interactive extraction of control strategies from large-scale dynamic sensor data, b) intuitive visual representation of cascading impact among the sensors in a complex power plant system, and c) time-lag-aware analysis of the impact of control strategies on electricity generation efficiency. By collaborating with energy domain experts, we addressed these challenges with ECoalVis, a novel interactive system for experts to visually analyze the control strategies of coal-fired power plants extracted from historical sensor data. The effectiveness of the proposed system is evaluated with two usage scenarios on a real-world historical dataset and received positive feedback from experts.