Question

 

Describe the purposes within the organization for data collection by the SCADA historian

Answer 1

In Supervisory Control And Data Acquisition (SCADA) systems, data acquisition begins with Programmable Logic Controllers (PLC) or Remote TerminalUnits (RTU) which retrieve measurements from metering devices and equipmentstatus reports. These data elements – called tags or points – represent a single in-put or output value monitored or controlled by the system. Tags usually appearas value-timestamp pairs.

After generation, data are eventually sent to other automatons, or monitor-ing servers to let human operators make supervisory decisions. Coincidentally,data may also be fed to a data historian to allow trending and other analytical auditing.

Data historians – like InfoPlus by AspenTech, PI by OSIsoft or Wonderware Historian by Invensys – are proprietary software designed to archive and query industrial automation time series data. They store time series following a hierarchical data model which reflect the operating environment. This data model should be consistent with the plant organization to ease browsing and group similar time series by subsystem.

Data historians receive data generated, for the most part, by industrial process control – Distributed Control Systems (DCS) or SCADA systems. For these purposes, they provide some business-oriented features which are not typically found within other data management systems: they support industrial communi- cation protocols and interfaces – like OPC [7], Modbus or device manufacturers proprietary protocols – to acquire data and communicate with other DCS or SCADA software. They also receive data from other systems, occasionally provided by external entities, like production requirements or pricing informations from the Transmission System Operator, as well as meteorological forecasts. Additionally, manual insertions may occur to store measurements made by human operators.

Data historians provide fast insertion rates, with capacities reaching tens of thousand of tags processed per second. These performances are allowed by specific buffer designs, which keep recent values in volatile memory, to later write data on disk sorted by increasing timestamps. Acquired data that do not fall in the correct time window are written on reserved areas, with reduced performances, or even discarded.

To store large amounts of data with minimum disk usage and acceptable approximation errors, data historians often rely on efficient data compression engines, lossy or lossless. Each tag is then associated with rules conditioning new values archival – for example: storage at each modification, with a sampling interval, or with constant or linear approximation deviation thresholds.

Regarding information retrieval, data historians are fundamental intermediary in the technical information systems, providing data for plant operating applications – like device monitoring or system maintenance – and business intelligence – like decision support, statistics publication or economic monitoring.

These applications might benefit from data historians time series specific features, especially interpolation and re-sampling, or retrieve values pre-computed from raw data. Values not measured directly, auxiliary power consumption or fuel cost for example, key performance indicators, diagnostics or informations on availability may be computed and archived by data historians.

Visualization features are dispensed by standard clients supplied with data historians. They ease exploitation of archived data by displaying plots, tables, statistics or other synoptics. These clients allow efficient time series trending by retrieving only representative inflection points for the considered time range.

Data historians also provide a SQL interface, with proprietary extensions for their specific features, and offer some continuous queries capabilities, to trigger alarms for instance.

Roughly speaking, data historians can be characterized by:

–a simple schema structure, based on tags,

–a SQL interface,

–a NoSQL interface for insertions, but also to retrieve data from time series, eventually with filtering, resampling or aggregate calculations,

–a design for high volume append-only data,

–built-in specialized applications for industrial data,

–no support for transactions,

–a centralized architecture