blog Lightning Data Optimizes Pattern Energy's Wind Operations Naomi Stringfield Marketing Manager Share Published: Mar 29, 2016 Wind and Solar Energy Reducing Blade Maintenance and Inspection Costs Across a Large Wind Fleet Once a wind farm becomes operational, it is important to keep it running as efficiently as possible. This includes regular inspection and repair of turbine blades to make sure they are operating at an optimal level. Did you know that lightning damage accounts for nearly a quarter of reported insurance claims in the U.S. wind sector, while turbine blades have the highest failure rate of any single component? Blade damage due to lightning strikes can result in a significant amount of downtime for inspection and repair, not to mention the cost to repair or replace a blade. A new case study explains how leading wind power company Pattern Energy uses Vaisala lightning detection information to improve operational efficiency and its inspection process across all of its projects in lightning-prone areas of the U.S. and Canada. Using the Fault Analysis Lightning Location System (FALLS), powered by real-time data from Vaisala's unmatched North American Lightning Detection Network (NALDN), Pattern Energy can pinpoint the location of recent and historic lightning strikes at its sites to determine exactly which turbines have been affected and the extent of the damage. This information helps it streamline time and costs associated with inspection and blade assessments as well as complete detailed insurance and warranty-based claims. Read the case study to learn: How historical Vaisala lightning data helps assess risk How Pattern Energy uses Vaisala data to identify and repair blades damaged by lightning before they need 10x more costly replacements Why Pattern says Vaisala is "the source for lightning data and well worth the investment." Do you know how lightning is affecting your assets? Read the Pattern Energy case study and consider how lightning data can improve your own operations and inspection processes.