Abstract: One of the existing most popular technologies for finding open bypass circuits to prevent hotspots is the measurement of surface temperature with an IR camera. However, this solution has defects. For example, the thermal image is affected by the reflection of surrounding structures such as antennas and buildings, and so on, and it is difficult to measure the true surface temperature of PV modules with an IR camera. To solve the problems mentioned previously, we developed new detection technology for open-fault bypass circuits. The stationary state reflection effect is deleted, and the position of the open fault part is identified exactly by the development technology. However, the reflection effect of moving clouds is not deleted. Therefore, we studied the advanced detection technology to delete the reflection effect of moving clouds. In this paper, the outline and effect of a new proposed technology is described.

Abstract: With the advancement of computational resources and methodologies, computational materials science has significantly reinforced experimental efforts and accelerated materials research and development. However, a significant disparity exists between experimental observations and theoretical calculations, primarily because of the structural simplifications often employed in computational models to enhance feasibility. Bridging this gap is challenging, especially when dealing with large, complex systems such as nanoparticles and interfaces. This requires solutions that extend computational simulations to emulate actual systems. In this study, we propose a method that utilizes the moment tensor potential (MTP) combined with active learning techniques for highly reliable and large-scale simulations of alloy nanoparticle catalysts and reactive dynamics at electrode interfaces.

Abstract: As an important component of the smart grid energy storage system, high-precision state of health estimation of lithium-ion batteries is crucial for ensuring the power quality and supply capacity of the smart grid. To achieve this goal, improved integrated smart algorithms are proposed to estimate the SOH of Lithium-ion batteries. Kernel function parameters are used to simulate the update of particle position and speed, and genetic algorithm is introduced to select, cross and mutate particles. The improved particle swarm optimization is used to optimize the extreme value to improve prediction accuracy and model stability. The cycle data of different specifications are processed to construct the traditional high-dimensional health feature dataset and the low-dimensional fusion feature dataset, and each version of the constructed network is trained and tested separately. The results of the multi-indicator comparison show that the proposed algorithms can track the true value stably and accurately with satisfactory high accuracy and strong robustness, providing guarantees for the efficient and stable operation of the smart grid.