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2019 IEEE International Conference on Power Data Science (ICPDS)最新文献

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Real-time Monitoring and Optimization Modification for Turbine Performance Based on Data Driven Model 基于数据驱动模型的汽轮机性能实时监测与优化修正
2019 IEEE International Conference on Power Data Science (ICPDS) Pub Date : 2019-11-01 DOI: 10.1109/ICPDS47662.2019.9017184
Jiannan Kang, Jiakui Shi, Y. Gao, Junfeng Fu, Libo Li, Fengliang Wang, Wei Wang, J. Wan
{"title":"Real-time Monitoring and Optimization Modification for Turbine Performance Based on Data Driven Model","authors":"Jiannan Kang, Jiakui Shi, Y. Gao, Junfeng Fu, Libo Li, Fengliang Wang, Wei Wang, J. Wan","doi":"10.1109/ICPDS47662.2019.9017184","DOIUrl":"https://doi.org/10.1109/ICPDS47662.2019.9017184","url":null,"abstract":"The performance of the unit is in a state of realtime degradation, and the traditional proprietary test method cannot accurately monitor this performance degradation in time. Aiming at the above problems, a real-time monitoring system and optimization scheme for steam turbine performance based on data driven model is proposed. Firstly, a steam turbine performance prediction model based on pattern recognition and prediction function is established, which can realize medium and long-term prediction of turbine operating economy and provide early warning for performance degradation. Then, performance analysis is performed for units performance degradation, respectively in thermal system and communication. The corresponding optimization scheme is proposed in the flow design. Finally, the 600MW supercritical unit is taken as the research case. The results show that the above method is effective and feasible in practice.","PeriodicalId":130202,"journal":{"name":"2019 IEEE International Conference on Power Data Science (ICPDS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134607844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Category Data Visualization Based on Obstacle Avoidances 基于避障的类别数据可视化
2019 IEEE International Conference on Power Data Science (ICPDS) Pub Date : 2019-11-01 DOI: 10.1109/ICPDS47662.2019.9017167
Rongtao Qian, Sitong Fang, Yinhui Ge, Lijun Wang, Yuzhe Xiang
{"title":"Category Data Visualization Based on Obstacle Avoidances","authors":"Rongtao Qian, Sitong Fang, Yinhui Ge, Lijun Wang, Yuzhe Xiang","doi":"10.1109/ICPDS47662.2019.9017167","DOIUrl":"https://doi.org/10.1109/ICPDS47662.2019.9017167","url":null,"abstract":"Obstacle avoidance algorithm is often used in data visualization to connect lines between data items or perform route planning in 3D volume visualization. The traditional obstacle avoidance algorithm is often designed to find a shortest path between two data items. It is not suitable to be used in visualization, because it needs to achieve a more artistic and smooth effect. Well-designed visualization often provides user-friendly, effective, and efficient manipulations and interactions. In this paper, we use an obstacle avoidance algorithm to connect lines between data items, which can be used to visualize set information present in category data (or set data). Specifically, we use A-star algorithm to conduct obstacle avoidance between data items, then we make the lines more artistic and smooth by introducing a series pivot points. Finally, we visualize the data items by connecting the lines to show the category information and sub-category information or other information. Experiments show that the proposed approach is capable of revealing the set information present in category data.","PeriodicalId":130202,"journal":{"name":"2019 IEEE International Conference on Power Data Science (ICPDS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132830574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A General Data Renewal Model for Prediction Algorithms in Industrial Data Analytics 工业数据分析中预测算法的通用数据更新模型
2019 IEEE International Conference on Power Data Science (ICPDS) Pub Date : 2019-08-22 DOI: 10.1109/ICPDS47662.2019.9017196
Hongzhi Wang, Yijie Yang, Yang Song
{"title":"A General Data Renewal Model for Prediction Algorithms in Industrial Data Analytics","authors":"Hongzhi Wang, Yijie Yang, Yang Song","doi":"10.1109/ICPDS47662.2019.9017196","DOIUrl":"https://doi.org/10.1109/ICPDS47662.2019.9017196","url":null,"abstract":"In industrial data analytics, one of the fundamental problems is to utilize the temporal correlation of the industrial data to make timely predictions in the production process, such as fault prediction and yield prediction. However, the traditional prediction models are fixed while the conditions of the machines have changed over time, thus making the errors of predictions increase with the lapse of time. In this paper, we propose a general data renewal model to deal with it. Combined with the similarity function and the loss function, the data renewal model estimates the time of updating the existing prediction model, and then updates it according to the evaluation function iteratively and adaptively. We have applied the data renewal model to two prediction algorithms. The experiments demonstrate that the data renewal model can effectively identify the changes of data, update and optimize the prediction model so as to improve the accuracy of prediction.","PeriodicalId":130202,"journal":{"name":"2019 IEEE International Conference on Power Data Science (ICPDS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129410073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
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