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The efficient acquisition and processing of large-scale terrain data has always been a focal point in the field of photogrammetry. Particularly in complex mountainous regions characterized by clouds, terrain, and airspace environments, the window for data collection is extremely limited. This paper investigates the use of airborne millimeter-wave InSAR systems for efficient terrain mapping under such challenging conditions. The system’s potential for technical application is significant due to its minimal influence from cloud cover and its ability to acquire data in all-weather and all-day conditions. Focusing on the key factors in airborne InSAR data acquisition, this study explores advanced route planning and ground control measurement techniques. Leveraging radar observation geometry and global SRTM DEM data, we simulate layover and shadow effects to formulate an optimal flight path design. Additionally, the study examines methods to reduce synchronous ground control points in mountainous areas, thereby enhancing the rapid acquisition of terrain data. The results demonstrate that this approach not only significantly reduces field work and aviation costs but also ensures the accuracy of the mountain surface data generated by airborne millimeter-wave InSAR, offering substantial practical application value by reducing field work and aviation costs while maintaining data accuracy.

Sparse matrix converter (SMC) is an indirect AC-to-AC power electronic converter that has a fictitious DC link between rectification and inversion stages in which neither a capacitor nor an inductor, as the storage element, is utilized. Due to this advantage, SMC is used in AC drives, marine thrust systems, aerospace industry, as well as in wind energy applications. On the other hand, permanent magnet synchronous generator (PMSG) is competitive in wind turbine applications due to their prominent features. In this work, a fuzzy fractional-order PID (FFOPID) controller is designed for a PMSG based wind energy conversion system (WECS) which employs a three-phase three-level SMC. The FFOPID controller is chosen to combine the salient features of the fractional-order calculus and fuzzy logic operations to enhance the dynamic response of classical PID controller with fixed gains. The simulation results taken under different case studies are analyzed in detail, which demonstrate the superiority of the designed FFOPID controller over classical PID control approach in tracking d- and q-axis current references of the SMC at the output. With the designed control approach, the smooth control of the real and reactive power injections into the grid from the WECS are ensured with acceptable transient response.

In view of the complex and changeable operating environment of pumped storage power stations and the noise and outliers in the modeling data, this study proposes a sparse robust least squares support vector machine (LSSVM) model based on the hybrid backtracking search algorithm for the model identification of a pumped turbine governing system. By introducing the maximum linearly independent set, the sparsity of the support vectors of the LSSVM model are realized, and the complexity is reduced. The robustness of the identification model to noise and outliers is enhanced using the weighted function based on improved normal distribution. In order to further improve the accuracy and generalization performance of the sparse robust LSSVM identification model, the model input variables, the kernel parameters, and the regularization parameters are optimized synchronously using a binary-real coded backtracking search algorithm. Experiments on two benchmark problems and a real-world application of a pumped turbine governing system in a pumped storage power station in China show that the proposed sparse robust LSSVM model optimized by the hybrid backtracking search algorithm can not only obtain higher identification accuracy, it also has better robustness and a higher generalization performance compared with the other existing models.