Enhanced diagnostics and data analytics for your wind turbine operations
Operations and maintenance costs can be high due to failing wind turbine components such as gearboxes and blades. SCADA systems installed at each turbine contain information about various turbine parameters and errors encountered by the system. Data-mining the historical SCADA data gives us a robust approach to monitoring turbine performance, identifying patterns and predicting failures. This data is key to understanding plant performance, managing expectations of operational plants and identifying opportunities for performance optimization saving you time and money.
Determine wind turbine energy production performance with a power generation and variance report (PGVR)
A wide variety of factors can negatively impact renewable energy project performance. To understand how a plant is performing, we evaluate variance of the actual operational energy production from the pre-construction energy production estimates. The PGVR provides a comprehensive, independent overview of a renewable project's performance against expectations. A variance analysis of major aspects of project performance includes:
- Available resources
- Availability (contractual and commercial)
- Electrical losses
- Environmental losses
Additionally, the PGVR provides an asset-level evaluation with the goal of identifying and highlighting both the magnitude and possible causes of deviations from expected performance. A ranked view of priority assets and possible causes of underperformance provides operators with actionable intelligence, which can be translated to increased revenue.
Event log analysis report (ELAR) for detailed wind turbine asset-level analysis
The ELAR provides a detailed asset-level analysis of the production impacts of downtime at a wind or solar project, identifying the amount of lost energy and, as far as possible, lost revenue for each type of downtime. The report identifies the most relevant downtime causes and can enable an operator to prioritize remediation activities.
The ELAR is an introductory diagnostic of project performance and health status based on the statistical data mining of sensor alarms registered by the SCADA system for each asset in the project. It has been proven to be a solid basis for and complement to end-of-warranty inspections. The analysis provides:
- Downtime and loss of energy by downtime type, including its monetary impact
- Frequency of alarms per component or turbine
- Duration of O&M interventions
- Time between alarm activation and intervention
- Evolution of alarm logs
Methods consulting using wind turbine SCADA data
As an industry leader in operational assessments, UL can assist owners/operators with establishing in-house procedures to deal with any aspect of turbine or PV system operation. UL has an established track record with customers that can:
- Identify the best methods to estimate possible power and energy loss due to curtailment for wind plants
- Create test plans for wake optimization, which can be installed by operators
- Develop test plans – either side by side or before and after – to estimate the impact of power uprates (e.g., vortex generators) or control system changes by the turbine manufacturers
Methods consulting services can range from drafting procedures for in-house use to reviewing and commenting on procedures developed by owners or third parties who are implementing performance modifications.
Lidar technology has become increasingly important in performance assessments of wind projects. The lidar device is mounted on the rooftop of the nacelle and accurately aligned with it. By measuring wind speed and wind direction upwind of the rotor, the assessment provides higher-reliability data than the normal nacelle anemometer. As industry leaders in operational assessments, UL has invested in its own lidars and developed the experience, knowledge and tools to use them for the assessment of wind project performance.
Applications of the nacelle mounted lidar for wind farm performance assessments include:
Lidar-based yaw misalignment detection (YMD)
Yaw misalignment is a common cause of turbine underperformance in the operation of a wind project. In the short term, yaw misalignment causes production losses. In the long term, it can cause undesired loads that lead to higher maintenance and repair costs and/or a reduction in the wind turbine’s or components’ lifetime. The detection of yaw misalignment with nacelle-mounted lidar is accomplished by measuring the relative wind direction compared to the yaw position. The report identifies the average yaw misalignment angle, which can then be used for correction of the yaw direction. The measurement campaign takes usually between two and three weeks, depending on the effective wind during the measurement campaign period.
Lidar-based operational power curve
As the nacelle-mounted lidar measures the free wind speed in front of the rotor, one of its main applications is to calculate the power curve of the wind turbine with higher accuracy than is possible with the nacelle anemometer. In combination with the correction of the yaw misalignment, the power curve assessment can be used to identify the impact of yaw misalignment correction on energy production.
Lidar-based nacelle transfer function
An additional application of nacelle-based lidar is the calculation of the nacelle transfer function to improve the accuracy of the performance assessments against uncorrected nacelle anemometer measurements taken from the SCADA data.
Other supported certification schemes and requirements:
- US: UL 6141 (large WT), UL 6142 (small WT)
- Japan: JSWTA0001 (small WT)
- UK: IEC 61400-2, MCS (small WT)
- German type approval (DiBT)
- Korean type approval (KEA Scheme)