The detection of damage in wind turbine blades is critical for ensuring their operational efficiency and longevity. This study presents a novel method for wind turbine blade damage detection, utilizing Gramian Angular Field (GAF) transformations of vibration signals in combination with Convolutional Neural Networks (CNNs). The GAF method enables the transformation of vibration signals, which are captured using a triaxial accelerometer, into angular representations that preserve temporal dependencies and reveal distinctive texture patterns that can be associated with structural damage. This transformation facilitates the capability of CNNs to identify complex features correlated with crack severity in wind turbine blades, thereby enhancing the precision and effectiveness of turbine fault diagnosis. The GAF-CNN model achieved a notable classification accuracy over 99.9%, demonstrating its robustness and potential for automated damage detection. Unlike traditional methods, which rely on expert interpretation and are sensitive to noise, the proposed system offers a more efficient and precise tool for damage monitoring. The findings suggest that this method can significantly enhance wind turbine condition monitoring systems, offering reduced dependency on manual inspections and improving early detection capabilities.

The Alternating Current Optimal Power Flow (ACOPF) problem remains one of the most fundamental yet computationally challenging tasks in power systems operation and planning due to its nonconvex, nonlinear, and multimodal nature. This paper proposes a convex reformulation of the AC power flow problem by introducing auxiliary variables to isolate nonlinear terms, applying logarithmic transformations to exploit product-sum properties, and approximating with Bezier curves using a novel convexifying butterfly shaped function. This model is intended for assessing and operating weak power systems that face challenges with reactive power supply and overall network robustness. Its formulation closely mirrors the AC formulation, particularly regarding active and reactive power dispatch and network voltage levels. The proposed model achieves convergence on large test systems (e.g., IEEE 118 bus) in seconds and is validated against exact AC solutions. This convex formulation stands out not only for its mathematical transparency and intuitive structure but also for its ease of validation and implementation, making it an accessible and reliable tool for researchers and system operators for energy planning. The numerical analysis conducted on the IEEE 118 bus system yielded average percentage errors in the state variables specifically, the magnitudes and angles of nodal voltages of just 0.0008 percentage and 0.014 degree, respectively, when compared with the precise AC formulation. These results underscore the high accuracy and reliability of the proposed methodology.