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1 Journal of Achievements in Materials and Manufacturing Engineering

1 2007

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Development of real-time data filtering for SCADA system

Wiliem L., Hargreaves D., Stapelberg R. F., Yarlagadda P. K. D. V.

Journal of Achievements in Materials and Manufacturing Engineering

2007

Vol. 21, nr 2

89-92

Purpose: Purpose to develop a suitable algorithm to filter data from the SCADA system. Design/methodology/approach: A real-time filtering method for SCADA system is developed by capturing the occurence of data change in SCADA data, which is followed by recording several data before this data change occurs. Then, the algorithm is modeled and developed and in the final step an experiment to verify the algorithm is conducted. Finally, the result from the experiment is analysed to check the effectiveness of the algorithm. Findings: As a result, SCADA data analysis will be easier to conduct since only essential information is left. In fact, in comparison to the entire data collection, only around 8-22% of data is changed. Research limitations/implications: By utilizing this algorithm, data will be easier to conduct since only the essential information as a starting point of analyses is left. However, this paper only describes the reasons and steps of data filtering algorithm development, how the algorithm works and the result after it is implemented to analyse data from the SCADA system. Further analyses to the data filtering results haven't been done yet. The next step will be to analyse the results in order to establish the root cause of why the data is changing. Originality/value: It has been noted in many research papers that the SCADA system is able to increase the efficiency of the monitoring itself. However, the SCADA system creates a huge amount of data which is difficult to analyse. This paper proposes a real-time data filtering for the SCADA system. The philosophy that is applied in this algorithm is only to "catch" the occurrence of data change in SCADA data, which is followed by recording several data before this data changes. As a result, SCADA data analysis will be easier to be conducted since only essential information is left. In fact compared to the entire data collection, only around 8-22% of data is changed. Therefore, this method is highly suitable for the SCADA system.