SCADA software-based techniques for the management and improvement of industrial efficiency

SCADA, DCS and BMS systems are prevalent across a range of large industrial and commercial installations. The core research contribution of this thesis was to examine whether suitable, non-time critical, algorithms could be developed for deployment on these style of systems. The objective being to use the existing industry standard low frequency signals, for fault detection and diagnosis, condition based control and performance monitoring. This has indicated the potential for applying academic research in a new fashion across industry to improve operational efficiency. A representative SCADA system was used and the work focussed on the industrial water infrastructure in a deep bed filtration plant, a coal fired power station and a gas turbine research establishment. In the water filtration plant innovative software was developed which diagnosed the location of pipe work blockages. A second programme was developed which passively monitored system variables, giving an indication of filter bed fouling and detecting abnormal system conditions. This functionality was used to provide a robust fault tolerant condition based backwash strategy for the filters. A third programme utilised a novel, threshold based, approach to diagnose the individual severity of combined blockages, allowing condition based back wash to continue, even under extreme abnormal blockage conditions. The second area considered was based upon a cooling process located in a power station. An ideal condenser performance surface was successfully assimilated into SCADA software infrastructure, thus vastly improving on existing manual monitoring approaches and providing operators with real time efficiency information. Associated work at a gas turbine research facility demonstrated the further scope for gathering and displaying efficiency information using SCADA software. The work undertaken proved that a research approach can be encapsulated in non-time critical, low frequency algorithms suitable for application to supervisory systems.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

SCADA software-based techniques for the management and improvement of industrial efficiency

SCADA, DCS and BMS systems are prevalent across a range of large industrial and commercial installations. The core research contribution of this thesis was to examine whether suitable, non-time critical, algorithms could be developed for deployment on these style of systems. The objective being to use the existing industry standard low frequency signals, for fault detection and diagnosis, condition based control and performance monitoring. This has indicated the potential for applying academic research in a new fashion across industry to improve operational efficiency. A representative SCADA system was used and the work focussed on the industrial water infrastructure in a deep bed filtration plant, a coal fired power station and a gas turbine research establishment. In the water filtration plant innovative software was developed which diagnosed the location of pipe work blockages. A second programme was developed which passively monitored system variables, giving an indication of filter bed fouling and detecting abnormal system conditions. This functionality was used to provide a robust fault tolerant condition based backwash strategy for the filters. A third programme utilised a novel, threshold based, approach to diagnose the individual severity of combined blockages, allowing condition based back wash to continue, even under extreme abnormal blockage conditions. The second area considered was based upon a cooling process located in a power station. An ideal condenser performance surface was successfully assimilated into SCADA software infrastructure, thus vastly improving on existing manual monitoring approaches and providing operators with real time efficiency information. Associated work at a gas turbine research facility demonstrated the further scope for gathering and displaying efficiency information using SCADA software. The work undertaken proved that a research approach can be encapsulated in non-time critical, low frequency algorithms suitable for application to supervisory systems