Power generators are being challenged to operate plants differently from how legacy assets were designed to run. To contend with the variability of renewable power generation, fossil fuel plants must be flexible enough to ramp up or down production to fill the gap. This frequent cycling is contributing to asset degradation, breakdowns and increased heat rate and emissions. Plant performance intelligence software, combined with predictive maintenance recommendations, can help power generators run plants more efficiently and prevent unplanned shutdowns.
Enhanced visibility to equipment degradation
More frequent startups and cycling at conventional power plants causes additional stress and strain on rotating and non-rotating equipment. Under these new operating conditions, traditional planned maintenance intervals and condition-based maintenance are not enough to keep up with critical degradation issues. Adding to the mix are renewable asset types that mixed-generation operators have to monitor and operate. These assets may require sensors to be upgraded for better accuracy or new process conditions.
Added complexity typically can’t be addressed with more personnel. Companies need to find a way to increase efficiency while monitoring and troubleshooting multiple asset types and new operating conditions. By using machine learning that incorporates different operating states, operators can get better insights and identify potential failures earlier. They can also plan maintenance more appropriately for when their plants are not operating.
Move from reacting to breakdowns to predicting and preventing them
When a breakdown occurs, the process to troubleshoot a unit that has multiple operating modes is more involved and time-consuming. Anticipating the types of failures in an environment with different running conditions is exponentially more difficult, often resulting in generating an overengineered solution to compensate for unknown variables.
Reliability software that provides proactive diagnostics advisories, while also helping you contextualize and filter the data, helps offset this added complexity. Maintenance engineers will no longer need to reactively interpret data and develop engineering solutions on the fly to keep the same failure from occurring again. Software can level the playing field and allow plant teams to focus on analyzing the pre-treated data. Power engineers can make faster and more confident decisions with a unified dashboard that provides a view of optimal conditions for their plant and across the fleet.
Planning for shutdowns with better intelligence
Unplanned shutdowns can cost companies millions of dollars in lost time and power production. And when production does restart after a shutdown, you often have not yet identified all the asset variables that can cause future issues. A digital twin can pinpoint micro-anomalies and micro-trend differences, especially coming out of a shutdown, so a user can proactively investigate key areas of the asset. By finding these micro-anomalies early on, it gives adequate time to investigate them, inspect assets onsite, and plan accordingly to have the right people and parts ready when the plant is in a planned shutdown.
When predictive analytics are paired with performance models that are customized to equipment designs and plant dispatch profile, plant employees can identify assets that are underperforming and causing excess heat rate and emissions. With this knowledge and actionable recommendations for how to address them, planned maintenance shutdowns can be scheduled and asset repairs and adjustments can be prioritized.
To learn more about how power generators can be both flexible and reliable in a variable generation environment, watch our on-demand webinar about achieving optimal performance amid variable power generation with POWER Magazine.