Damage detection of wind turbine blades is difficult due to their large sizes and complex geometries. Additionally, economic restraints limit the viability of high-cost monitoring methods. While it is possible to monitor certain global signatures through modal analysis, obtaining useful measurements over a blade's surface using off-the-shelf sensing technologies is difficult and typically not economical. A solution is to deploy dedicated sensor networks fabricated from inexpensive materials and electronics. The authors have recently developed a novel large-area electronic sensor measuring strain over very large surfaces. The sensing system is analogous to a biological skin, where local strain can be monitored over a global area. In this paper, we propose the utilization of a hybrid dense sensor network of soft elastomeric capacitors to detect, localize, and quantify damage, and resistive strain gauges to augment such dense sensor network with high accuracy data at key locations. The proposed hybrid dense sensor network is installed inside a wind turbine blade model and tested in a wind tunnel to simulate an operational environment. Damage in the form of changing boundary conditions is introduced into the monitored section of the blade. Results demonstrate the ability of the hybrid dense sensor network, and associated algorithms, to detect, localize, and quantify damage.