In underwater navigation of autonomous underwater vehicles (AUVs), communication delays frequently occur, leading to a reduction in positioning accuracy. To mitigate this challenge, this work introduces a novel method for relative angle correction, aiming to reconstruct measurement information. Initially, Doppler measurement data are assimilated into the reconstruction of measurement equations to determine the relative angle between the AUV and the observatory. Subsequently, the obtained angle information is integrated into the Extended Kalman Filter (EKF) for the reconstruction of measurement equations. The proposed method effectively reduces positioning errors caused by hydroacoustic communication delays, consequently enhancing AUV positioning accuracy. The efficacy of the proposed method is demonstrated through a simulation study. Simulation results reveal that the incorporation of Doppler angle correction in the reconstructed measurement information method significantly decreases the localization error by approximately 50% compared to EKF and by around 20% compared to the method lacking angle correction.

With the advancement of intelligence in Active Distribution Networks (ADNs), effective fault recovery methods have become increasingly crucial. In this study, a reconfiguration method combining the immune mechanism and Northern Goshawk Optimization algorithm (NGO) is proposed, aimed at swiftly restoring power post-fault, maximizing the recovery of lost power areas within ADN, and minimizing losses. Firstly, an identification model within the immune mechanism is crafted to precisely match failures in ADNs. Then, the successful matched failure types can be used to restore power supply by the direct invocation of the recovery strategy from the library of strategies. Secondly, the immune response of ADNs is modeled, known as the reconfiguration model. For faults beyond the recovery strategy library, NGO is leveraged to address distribution network failures, with restoration solutions integrated into the library. Additionally, a reversed learning approach and stochastic variation strategy enhance the robustness of algorithm, preventing it from converging to suboptimal solutions. Finally, through simulation experiments, it is demonstrated that the recovery scheme obtained using the algorithm can be used to recover failure as well as reduce network losses in an effective manner. When similar or identical faults recur, ADN failure recovery becomes swift and efficient.