scholarly journals Smart Transmit abnormal Condition in High Voltage Substation by SCADA System between Protection Equipment’s and National Control Center

2016 ◽  
Vol 10 (10) ◽  
pp. 1-19
Author(s):  
Amer Elghaffar ◽  
Karim Ibrahim ◽  
Yehia Ali ◽  
Adel Mohamed
Author(s):  
Brett Christie ◽  
David Norris

The use of integrated offline training simulators within the liquids pipeline community has not been widespread. Some companies opt to use a stand-alone generic pipeline simulation, which has advantages of ease of set up and offer relatively lower capital cost. They can also be effective in training on basic hydraulics under both normal and abnormal operating conditions. However, since they do not train on the actual pipeline system of the company, trainees do not learn any of the specifics of their company’s pipeline. Also, if the system is not interfaced to the pipeline SCADA system, trainees may have difficulty transferring what they have learned into their day to day control of the pipeline. This paper outlines the major considerations for ensuring that the offline training environment is as a realistic depiction of the actual pipeline control center as possible. Techniques and guidelines, such as Gap analyses, cause & effect diagrams, and flow charts are presented.


Author(s):  
He´lio A. R. Aniceto

The National Control Center Operation TRANSPETRO created an information site that allows obtaining in real-time, using a system PIMS (Plant Information Management Systems), the information of process plants involved in the operations of our pipelines. The site provides different views for different clients. It also indicates the logistics transportation schedule progress, pipelines operation rates, comparative graphs (like global movement historical by period), transportation summaries by product or regions, covering all pipeline operations in Brazil. We also have links to process data of some plants long and short oil pipeline and for transfer of custody, obtaining information on pumps, valves, control valves, pressure and flow of the operation in progress. The pipeline location maps have a dynamic representation using geographic maps and showing the pipeline status. We are still developing applications to improve the information quality for clients, what give us feedback about the site’s stage progress. When we created the site of the National Control Center Operation TRANSPETRO we seek the principal function of technology PIMS “collect data from all areas of a plant and provides them for any type of application and makes it a great diffuser of information across the various organizational levels” [1].The benefits obtained from deploying the site using the PIMS technology brings potential gains support for the “decision-making of strategic and tactical levels of the enterprise” [2].


2012 ◽  
Vol 614-615 ◽  
pp. 971-975
Author(s):  
Ping He ◽  
Zhi Jie Zhu ◽  
Hong Cheng Jiang

In a centralized control center SCADA system, thousands of real-time data are sent to SCADA, including real-time signal, telemetering and alarm information. The intelligent analysis and processing of alarm signals is an important requirement. This article from the design point of intelligent alarm rules, explore an internal reasoning knowledge base and the external definition of rules combined design algorithm. Practical application results show that, the algorithm to power a variety of abnormal and fault has better capture and analysis ability.


Author(s):  
Nada Mohammed Ahmed Alamin

    The purpose of the research is to reach the forecast of monthly electricity consumption in Gezira state, Sudan for the period (Jun 2018 - Dec 2020) through the application to the historical data of electric power consumption (Jan 2006-May 2018) obtained from the National Control Center, which has been applied in the research methodology of seasonal Autoregressive Integrated Moving Average due to seasonal behavior in the data, good forecast has been given by SARIMA (2, 1, 7) (0, 1, 1), which has been examined its quality using the Thiel coefficient. The study recommended the use of the model of seasonal Autoregressive Integrated Moving Average in data with Seasonal behavior due to its simple application and accuracy of the results reached.    


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