New asset performance management tools to focus on wind power performance improvement
By Edward Wagner
Asset reporting, data visualization and performance monitoring have become standard for wind power plant owners, asset managers and financial stakeholders to ensure wind turbines are running safely and meeting performance expectations.
Asset Performance Management solutions (APMs) began to gain popularity about a decade ago with the goal of monitoring and reporting wind plant Key Performance Indicators (KPIs) like downtime and availability. This was driven in part by investor reporting requirements, as well as for tracking availability guarantees from OEMs. Many larger wind companies have invested substantial resources in developing custom software and dashboards to manage their wind assets, with varying success.
APMs have garnered a lot of attention from investors, from private equity and venture capital in addition to strategic players already in the wind market seeking a shorter path to bespoke analytics programs. Despite the perceived need for an APM from across the market, APMs often fail to detect issues that impact turbine reliability and efficiency, ultimately failing to improve asset performance. To support the green energy transition, it’s time for the wind energy industry to rethink what Asset Performance Management means, and move beyond visualization and monitoring towards performance improvement.
Monitoring Is No Longer Enough
Market conditions over the past few years have not been kind to the wind energy industry. Pandemic-induced supply chain delays have pushed back the commissioning of new wind power plants around the world. According to a recent report by American Clean Power, over 8 GW of clean energy capacity that was expected to come online in the U.S. during the second quarter of 2022 was delayed.
OEMs, meanwhile, have nearly universally reported declining financial performance over the past several quarters, due in part to delivery delays and increases in commodity prices. And while the industry and governments seem laser-focused on expanding wind capacity worldwide, existing assets continue to deliver lower-than-expected returns for investors.
According to analysis done by WindESCo over the past several years, there is a five year upside potential of $8M-$10M per gigawatt of installed capacity on operating assets from issues that can be detected using high speed SCADA data.
Today’s APM solutions provide a unified view to asset owners and investors for reporting. Ten-minute data that is generally used by APMs is sufficient for understanding turbine and fleet output over time and for internal and external reports. APMs fall short however, on actually detecting issues that lead to turbine underperformance and in fixing those issues.
Stakeholders in wind energy are seeking solutions to improve the margins of their portfolios. Fortunately, there are opportunities to (1)Median 5-year present value using a discount rate of 9% capture turbine performance, improve reliability and increase margins without expensive retrofits.
Identifying Issues that Impact Margins
Solutions need to go beyond monitoring turbine performance to uncover actionable issues, recommend fixes, and measure improvements over time. For example, Find, Fix, Measure continuously detects 60+ anomalies in turbines at scale. Below are examples of some of the most common issues detected by Find, Fix, Measure.
Non-optimal cut in behavior - Turbines may attempt to start up thousands of times, with only one successful start. Non-optimal cut in behavior creates problems when wind speeds are sufficient to maintain operation, but the turbine does not start, missing potential energy production. This cannot be seen in 10-minute data, because no significant power curve behavior is observed due to the turbine not producing power.
Rated Power Oscillation - A given turbine is exhibiting fluctuations (oscillations) in the rated power signal. These fluctuations may be periodic oscillations, or they may be changes in the rated power, unrelated to wind speed changes, on the order of 10s of seconds or faster. If a turbine is operating below rated power, and it is not expected, this will directly impact turbine output.
Static yaw misalignment - Occurs when the measured yaw error is 0°, but the turbine is not pointing directly into the wind. This happens because the turbine cannot see the difference between measurement and reality, making the misalignment invisible to the turbine controller. Yaw misalignment cannot be detected using 10-minute data typically used in power curves and displayed by APMs.
Prioritizing Improvement Opportunities
Wind teams are up against long lists to keep their assets running and their teams safe, pushing Annual Energy Production (AEP) improvement programs lower on the list of priorities. In order to get the most out of the high speed data’s detection, performance issues must be prioritized by comparing the amount of increase in AEP to the likelihood of quickly fixing the issue. For example, static yaw misalignment requires only parameter changes in the controller to recover lost AEP and can be handled immediately. Adjustments to turbine parameters to address seasonal wind changes can be planned for in advance. Issues that require a trip up tower can be scheduled with other regular maintenance over time to best utilize farm resources. With this new type of technology, asset owners will be able to get the most out of their machines and maintenance cycles.
Edward Wagner is the Chief Revenue Officer of WindESCo, a leading renewable energy performance analytics company (www.windesco.com).