Monitoring systems |
Below: Figure 4. Both the dynamic pressure plot (top) and the vibration plot (bottom) demonstrate increased hydraulic imbalance and vortex rope turbulence increase for the lower loads. Left: the dynamic pressure variation and amplitude in the draft tube of unit 1 at the unit is more pronounced at 80MW load than at 100MW load. Right: this plot shows the historical vibration variation of the four X-Y guide bearing sensors (shown in different colors) for the five different operating baseload values for the hydropower station over a period of one month. As can be seen in the vibration plot, the vibration is higher at the lower loads than for the higher loads. It is also obvious the turbine guide bearing vibration is more affected by the draft tube turbulence than the upper generator guide bearing. In any case, the vibration values shown are not enough to indicate a fully developed vortex turbulence
monitored in an enterprise-wide historian (or locally, if needed). Event notification, visualisation, and analytics are all done in the historian, using data from the monitoring system. One of the important requirements for a monitoring system to be used in this solution is the ability to store raw dynamic data in the historian in an efficient, effective manner. Not all monitoring systems can do that. A historian database is a practical solution since there is more data available than is provided by a single stand-alone system. This is ideal for correlating measurements with process data correlation, which makes diagnostics faster, easier and more reliable. Various monitoring systems and service providers can access this data for further processing, including for AI and machine learning. Analytics done in the historian are generally more transparent and easier to fine-tune than what can be done in the stand-alone monitoring system.
Some condition monitoring systems include
hardware that also provides machine protection in one system. In such a system the monitoring system has to be on-premise, since machine protection cannot be provided by remote services.
B&K Vibro monitoring solutions B&K Vibro offers both on-premise and off-premise
monitoring solutions to the hydropower industry, but in this case study, an on-premise rack-based monitoring system, SETPOINT, was installed. It has a native connection to the AVEVA™ PI System™ for condition monitoring. This same system also includes protection and advanced hydropower unit monitoring capability such as multiple machine states, specific plots and monitoring techniques for monitoring hydropower units (e.g. air gap, magnetic flux), and non-changing data reduction. Another special feature, a data ‘flight recorder,’ is used when the monitoring network is down.
Case study in Brazil A hydropower unit in Brazil features a storage-based
reservoir of 151m2 and is equipped with two 113MW
Francis turbines. Originally constructed in the 1970s, the unit underwent significant upgrades between 2013 and 2015. It operates primarily under a fixed load and synchronous condenser mode, with oversight provided by a Generation Operations Center. The primary challenges faced by the operator at this facility included increasing the availability of the turbines, reducing the frequency of inspections, and transitioning to a predictive maintenance strategy. To address these challenges, the project involved the integration of condition monitoring and diagnostics into the existing AVEVA™ PI System™, allowing for more efficient and effective operation. This integration enabled real-time monitoring and data analysis, significantly enhancing the plant’s operational reliability and maintenance processes. The operator can monitor the units (as with the units at their other hydropower stations) at partial load to load-specific alarm limits. This monitoring strategy enabled them to avoid subjecting the units to hydraulic disturbances such as vortex rope turbulence and cavitation, which occur at partial load, and yet still generate power for a wider range. They also perform analytics on the data – some of it driven by AI – to more closely keep an eye on the hydropower unit healthcare. One example of this includes taking SETPOINT vibration data (including harmonics and phase) in correlation with process data (temperatures, flow, pressure), and then using machine learning to detect developing faults earlier. The results of this analysis, of which several faults were accurately detected, are then put back into the AVEVA PI System. The algorithm for this diagnostic tool was trained over a period of eight months using data from nine hydropower stations and will be extended as experience is gained.
Summary Enterprise data management enhances efficiency
and reliability by eliminating the limitations and costs associated with proprietary and standalone systems. The hydropower industry has already embarked on digital transformation, leveraging off-premises and enterprise on-premises machine condition monitoring systems to deliver basic and advanced monitoring solutions for small, medium, and large hydropower plants. These systems enable early fault detection and automatic decision support with AI and machine learning, to improve machine healthcare and optimise operation and maintenance. Moreover, third-party service providers can easily access cyber-secure data to offer transparent analytics, such as thermodynamic performance calculations, which can be fine- tuned as experience grows. In contrast, proprietary systems often restrict access and rely on ‘black box’ calculations that are difficult to modify, highlighting the superior flexibility and adaptability of enterprise data management solutions. The case study in Brazil is an example of a well-developed digital transformation project that combines data provided by various systems, including the SETPOINT system in the AVEVA™ PI System™ historian. Process data is correlated with the monitored data for more accurate, reliable diagnostics.
30 | August 2025 |
www.waterpowermagazine.com
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