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The utility industry is in a state of flux as renewable energy farms share more and more of the grid with traditional energy generating plants. Distributed generation growth and the diversification of power sources, although positive developments, have exacerbated the issues of load management, lack of switching flexibility and the potential for reverse power flow. As a result, service organizations are questioning older methods of utility infrastructure maintenance while new “predictive” maintenance approaches are increasing their marketplace presence. In fact, Navigant Research estimates that utilities will spend almost $50 billion on asset management and grid monitoring technologies by 2023. But how do these new approaches benefit utilities stakeholders in a practical sense?
Consider a recent study that was undertaken by Schneider Electric. During that study, the asset maintenance record of a 110MW steam model turbine with seven bearings was analyzed. Over a one-year period, the turbine experienced sporadic turbine bearing vibration issues, followed by a catastrophic breakdown that resulted in the shutdown of the unit. After corrective maintenance, a similar cycle of sporadic vibration issues began again.
This unit’s raw historical data was then analyzed with an up-to-date predictive analytics tool. It was determined that if predictive asset analytics had been in place, plant personnel would have received early warning of the turbine’s thermal expansion issues. Undetected, the issue became chronic over the year. Through a modeling exercise, the tool was able to detect the fault patterns with early warnings six months prior to failure. The model showed that the bearing vibrations were a symptom of thermal expansion which, in fact, acted as the primary cause of the problem. Had the thermal expansion issue been proactively addressed far enough in advance, the bearing vibration issue and the shutdown of the unit would have been avoided. The analysis team estimated a cost savings in the millions of dollars – a result of 35 days of avoided downtime and associated repair costs.
Predictive analytics tools transform raw data into easy-to-understand and actionable insights resulting in improved availability, reliability and decision-making. Risk assessment becomes a more exact science and potential behaviors of monitored assets can be leveraged to better prioritize capital and operational expenditures.
These benefits have a ripple effect on how utilities operations personnel perform their work:
- Immediate shut down of a section of the power plant can be avoided, and a problematic situation can be assessed for more controlled outcomes.
- Loads can be shifted to reduce asset strain or the necessary maintenance can be scheduled during a planned outage.
- Planning can be improved which, in turn, reduces maintenance costs.
- Parts can be ordered and shipped without the need for stressful rush and equipment can continue running while the problem is being addressed.
- Maintenance windows can be lengthened to better accommodate equipment condition and performance.
- Asset utilization can improve and the ability to identify underperforming assets can be enhanced
Knowledge capture is another benefit of these predictive analytics tools. In an environment where transitioning workforces are becoming more prevalent, knowledge capture ensures that maintenance decisions and processes are repeatable. Therefore, when experienced personnel leave the company, their years of accumulated knowledge remain available to incoming staff.
The amount of “big data” available today is providing utilities with an opportunity to overcome some of the industry’s disruptive growing pains Learn more by visiting Schneider Electric’s entire suite of best practice maintenance services.
