Posters

Abstract

There are many possible strategies to maintain a windfarm, and we propose to share how RES is using data and predictive maintenance to optimize the assets' performance and the maintenance costs. The three main possible maintenance strategies, could be: * Corrective / Run to failure: wait for a failure to complete a maintenance. * Preventive / Maintenance to schedule: provide a global schedule with preventive maintenance, according to the turbine manufacturer * Predictive / Reliability centered maintenance based on condition monitoring and engineering: to be proactive on turbine analysis to monitor some data and identify failure before it occurs. The choice of maintenance approach typically depends on the criticality of the component. However, RES focusses predominantly on predictive maintenance which is a financial and technical key in asset optimization: * Reduce loss of production and cost of repair 1. Limit turbines downtimes 2. Reduce the risk on the main components and minimize chances of catastrophic failures 3. Safe improvement of O&M operation (less urgencies) * Verify operation within requirement (Bird/Noise curtailment) 1. Ensure optimal production and regulatory obligations (right level every time) Predictive maintenance can be performed using SCADA data or additional monitoring systems (CMS, ION, NETBITER, EWON, ...). The following analysis could be performed to realize the best predictive maintenance service: reactive, particles, temperature, warnings/alarms, curtailment, icing, power curve, solar performance ratio, CMS. The poster will illustrate three cases study: * two case studies to illustrate how predictive maintenance can be used to improve the preventive maintenance plan (case 1 and 2) * One case study to illustrate the benefits bring by the predictive maintenance (case 3) * Case study 1: Downtime analysis shows a significant number of alarms related to errors in yaw position. After analysis of high frequency data, we detected that clutches (safety mechanical fuse of the yaw system) were not working properly. We have identified this issue on several turbines of the same manufacturer platform and RES worked with the OEM to modify the preventive maintenance service to maintain properly the system. This solved the issues in a durable way. * Case study 2: Based on turbines' alarms, site inspection campaigns, and experience on yaw ring failure, RES identified that the yaw ring inspection frequency was not optimal. RES worked with O&M contractor to change this frequency because yaw ring damage evolution is exponential. The purpose is to detect damage at an early stage when damage can be repaired. The benefits are: 1. limitation of O&M cost (140K€ of saving for the O&M provider) 2. less turbine downtimes (128K€ of saving for the Owner) * Case Study 3: Following a number of turbine stops and several high temperature alerts on a gearbox, RES identified the root cause of the issue - poor cable connections resulting in poor temperature regulation of the gearbox. The rapid response by RES avoided further downtime and damage induced to gearbox components.