Continuing a track record of fleet-wide O&M optimization in US wind, the partnership has seen ONYX Insight identify and quantify yearly costs of individual lost energy issues across 122 of Evergy's 2.3MW onshore Siemens turbines. The findings have empowered Evergy's smart O&M decision-making, allowing the utility to prioritise spending on specific maintenance cases, increasing its overall operational profitability.
ONYX detected rotor overspeed protection systems triggering regular shutdowns in certain turbines, requiring adjustments to their sensitivity. Power reduction was recommended to extend the life of turbines whose bearings wore down too quickly — allowing follow-up projects to optimize accordingly. Yaw misalignments, which can drastically reduce speed and power, were identified and calculated for correction.
The result has been a data-led, joined-up approach to increasing turbine health and output. With wind farm efficiencies across the industry averaging 30-45%, untold gigawatts and financial returns will continue to be left on the table as long as inefficient approaches to lost energy problems persist. In parallel with their efforts to recover that energy, 62% of wind industry stakeholders believe that access to data is their biggest barrier to advancement.
Sam Larson, of Evergy, said, "No turbine is perfect. We knew energy was being lost somewhere along the way, but needed to uncover the specific issues responsible and the precise actions needed to recover it. ONYX's SCADA data and analytics expertise helped us advance to the next level of O&M, giving us the means and foresight to plug the leaks in our wind energy pipeline in the most efficient way possible."
Ashley Crowther of ONYX Insight, said, "We're delighted to have been able to help Evergy with their lost energy issues. Armed with our data and the building blocks to create a fleet-level O&M strategy, we've been able to give Evergy the tools to target maintenance investigations and unmask the true performance of its portfolio by benchmarking against industry standards.
"With a database of over 22,000 turbines to generate a model of failure modes and rates, we can show operators not just how their assets are performing now, but how well they can — and should — be performing in the long run. The wind industry needs to keep building on prior learning to grow optimally and eliminate power wastage; and to do that, we need to combine rich data sources and stringent methodologies to distil clear, long-lasting solutions."
