{"title":"水管理中的SCADA与建模","authors":"I. Stoian, D. Capatina, S. Ignat, O. Ghiran","doi":"10.1109/AQTR.2014.6857920","DOIUrl":null,"url":null,"abstract":"In the field of water management it operate with hydrologic, hydraulic models, and the river basin GIS component, that take into account the real-time measured parameters, and climate prognosis. The SCADA system associated to a river basin, by acquired historical data, constitutes the support for models calibration and validation, by their training on representative data sets, with the purpose of establishing model associated parameters. But not all acquired data from sensors is qualified for interaction with the models. These must pass through filtering, compensation, plausibility processes. The data stored in databases complies with these requirements, being validated when loaded into historical databases or by post-processing. For managing some dynamic conditions, alerts or certain extreme conditions - floods or prolonged drought - the models have to be shifted during running, depending on instant values of process parameters - run-time models. The selection of the most suitable model for on-line erroneous data may result in disastrous results. Therefore, at the level of on-line data there is required rapid checking, involving specialized processors or demanding software applications, running on parallel threads. Presented paper achieves SCADA data analysis with the purpose of make them compatible with predictive models, for ensuring of decisional support mechanisms, that sustain proactive measures, by proposing o set of solutions regarding their efficient usage.","PeriodicalId":297141,"journal":{"name":"2014 IEEE International Conference on Automation, Quality and Testing, Robotics","volume":"63 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"SCADA and modeling in water management\",\"authors\":\"I. Stoian, D. Capatina, S. Ignat, O. Ghiran\",\"doi\":\"10.1109/AQTR.2014.6857920\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the field of water management it operate with hydrologic, hydraulic models, and the river basin GIS component, that take into account the real-time measured parameters, and climate prognosis. The SCADA system associated to a river basin, by acquired historical data, constitutes the support for models calibration and validation, by their training on representative data sets, with the purpose of establishing model associated parameters. But not all acquired data from sensors is qualified for interaction with the models. These must pass through filtering, compensation, plausibility processes. The data stored in databases complies with these requirements, being validated when loaded into historical databases or by post-processing. For managing some dynamic conditions, alerts or certain extreme conditions - floods or prolonged drought - the models have to be shifted during running, depending on instant values of process parameters - run-time models. The selection of the most suitable model for on-line erroneous data may result in disastrous results. Therefore, at the level of on-line data there is required rapid checking, involving specialized processors or demanding software applications, running on parallel threads. Presented paper achieves SCADA data analysis with the purpose of make them compatible with predictive models, for ensuring of decisional support mechanisms, that sustain proactive measures, by proposing o set of solutions regarding their efficient usage.\",\"PeriodicalId\":297141,\"journal\":{\"name\":\"2014 IEEE International Conference on Automation, Quality and Testing, Robotics\",\"volume\":\"63 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE International Conference on Automation, Quality and Testing, Robotics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AQTR.2014.6857920\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE International Conference on Automation, Quality and Testing, Robotics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AQTR.2014.6857920","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In the field of water management it operate with hydrologic, hydraulic models, and the river basin GIS component, that take into account the real-time measured parameters, and climate prognosis. The SCADA system associated to a river basin, by acquired historical data, constitutes the support for models calibration and validation, by their training on representative data sets, with the purpose of establishing model associated parameters. But not all acquired data from sensors is qualified for interaction with the models. These must pass through filtering, compensation, plausibility processes. The data stored in databases complies with these requirements, being validated when loaded into historical databases or by post-processing. For managing some dynamic conditions, alerts or certain extreme conditions - floods or prolonged drought - the models have to be shifted during running, depending on instant values of process parameters - run-time models. The selection of the most suitable model for on-line erroneous data may result in disastrous results. Therefore, at the level of on-line data there is required rapid checking, involving specialized processors or demanding software applications, running on parallel threads. Presented paper achieves SCADA data analysis with the purpose of make them compatible with predictive models, for ensuring of decisional support mechanisms, that sustain proactive measures, by proposing o set of solutions regarding their efficient usage.