All-in-one image restoration has recently developed to be a new research trend in the low-level computer vision field, aiming to tackle multiple image degradation types simultaneously in a unified model. As a typical multi-task learning, existing approaches focus on modeling either the specificity or commonality among different image restoration tasks. To exploit the unique strengths of both worlds, we propose a method of Image Pyramid Transformer coupled with Information Loss Regularization (IPT-ILR), in which the multi-scale architecture structure can excavate more information for multiple restoration tasks concurrently, while the learning strategy can identify the difference among multiple restoration tasks depending on the degree of information loss in each restoration task. Specifically, it first establishes a new Image Pyramid Transformer Network (IPT-Network) to accommodate multiple image restoration tasks. Given original degraded images, the IPT-Network exploits the image pyramid technique to establish a series of images with different scales, which are then restored by transformer-like auto-encoders. Moreover, the restored image on a low-level scale is referenced to assist restoring the degraded image on a high-level scale. Next, Information Loss Regularization (ILR) is presented to optimize the IPT-Network. ILR calculates the average distance between degraded images and their clean counterparts as the weights, which automatically implement different penalties for different image restoration tasks, thus avoiding the short-cut phenomenon for the easy task while encouraging the hard task. Extensive experiments have been conducted with 6 image restoration tasks in the all-in-one setting. The results show our method performs favorably against numerous state-of-the-art methods across most tasks, including image denoising, image deblurring, image dehazing, image deraining, image desnowing, as well as low-light enhancement.

In this paper, an improved finite-control-set model predictive flux control (FCS-MPFC) is developed and implemented to eliminate the circulating current in the semicontrolled open-winding permanent magnet synchronous generator (SOW-PMSG) supplied by a single dc power. Firstly, the references of torque, flux and zero-sequence current are combined into the stator flux vector reference along with the implementation of maximum torque per ampere (MTPA) control to eliminate the weighting factors of the cost function in the conventional finite-control-set model predictive torque control (FCS-MPTC). Then, to further improve the steady-state performance, a developed double-vector method based on the uncontrollable side voltage vector adjustment is conducted, which has the advantage of low computation burden. Moreover, a modified duty cycle control strategy is presented, in a manner where the reference value of flux is superseded with the measurement one to effectively calculate the duration of the optimal vector. Finally, experimental results verify the effectiveness and superiority of the proposed FCS-MPFC scheme.

For an electric power grid that has large penetration levels of variable renewable energy including wind generation and photovoltaics, the system frequency stability is jeopardized, which is manifest in lowering frequency nadir and settling frequency. This paper suggests an enhanced primary frequency response strategy of a doubly-fed induction generator (DFIG) in association with pitch angle control. The DFIG works in de-loaded operation with a certain reserve power via pitch angle control prior to disturbances for frequency regulation. To address this, a function of the pitch angle is employed that decreases the pitch angle with time to slowly feed the active power to the power gird. The simulation results demonstrate the effectiveness and feasibility of the proposed primary frequency response strategy including the settling frequency and frequency nadir.

Power Line Autonomous Inspection (PLAI) plays a crucial role in the construction of smart grids due to its great advantages of low cost, high efficiency, and safe operation. PLAI is completed by accurately detecting the electrical components and defects in the aerial images captured by Unmanned Aerial Vehicles (UAVs). However, the visible quality of aerial images is inevitably degraded by adverse weather like haze, rain, or snow, which are found to drastically decrease the detection accuracy in our research. To circumvent this problem, we propose a new task of Power Line Aerial Image Restoration under Adverse Weather (PLAIR-AW), which aims to recover clean and highquality images from degraded images with bad weather thus improving detection performance for PLAI. In this context, we are the first to release numerous corresponding datasets, namely, HazeCPLID, HazeTTPLA, HazeInsPLAD for power line aerial image dehazing, RainCPLID, RainTTPLA, RainInsPLAD for power line aerial image deraining, SnowCPLID, SnowInsPLAD for power line aerial image desnowing, which are synthesized upon the public power line aerial image datasets of CPLID, TTPLA, InsPLAD following the mathematical models. Meanwhile, we select numerous state-of-the-art methods from image restoration community as the baseline methods for PLAIRAW. At last, we conduct large-scale empirical experiments to evaluate the performance of baseline methods on the proposed datasets. The proposed datasets and trained models are available at https://github.com/ntuhubin/PLAIR-AW

Power Line Autonomous Inspection (PLAI) plays a crucial role in the construction of smart grids due to its great advantages of low cost, high efficiency, and safe operation. PLAI is completed by accurately detecting the electrical components and defects in the aerial images captured by Unmanned Aerial Vehicles (UAVs). However, the visible quality of aerial images is inevitably degraded by adverse weather like haze, rain, or snow, which are found to drastically decrease the detection accuracy in our research. To circumvent this problem, we propose a new task of Power Line Aerial Image Restoration under Adverse Weather (PLAIR-AW), which aims to recover clean and high-quality images from degraded images with bad weather thus improving detection performance for PLAI. In this context, we are the first to release numerous corresponding datasets, namely, HazeCPLID, HazeTTPLA, HazeInsPLAD for power line aerial image dehazing, RainCPLID, RainTTPLA, RainInsPLAD for power line aerial image deraining, SnowCPLID, SnowInsPLAD for power line aerial image desnowing, which are synthesized upon the public power line aerial image datasets of CPLID, TTPLA, InsPLAD following the mathematical models. Meanwhile, we select numerous state-of-the-art methods from image restoration community as the baseline methods for PLAIR-AW. At last, we conduct large-scale empirical experiments to evaluate the performance of baseline methods on the proposed datasets. The proposed datasets and trained models are available at https://github.com/ntuhubin/PLAIR-AW.

An inertial response emulated control strategy of doubly-fed induction generators (DFIGs) is able to arrest their frequency decline following a severe frequency event. Nevertheless, the control coefficient is unchanged, so as to limit the benefit potentiality of improving the inertial response capability for various disturbances and provide less of a benefit for boosting the frequency nadir. This paper addresses an enhanced inertial response emulated control scheme for a DFIG to improve the maximum frequency deviation and maximum rate of change of frequency for various disturbances. To this end, the control coefficient is coupled with the system frequency deviation so as to regulate the control coefficient according to the system frequency deviation (i.e., sizes of the disturbance). Results clearly indicate that the proposed inertial response emulated control strategy provides better performance in terms of improving the maximum rate of change of frequency and maximum frequency deviation under various sizes of disturbance and random wind speed conditions.