After a natural disaster, a quick inspection of all damaged components is crucial to recover the functionality of distribution networks. Unmanned aerial vehicles (UAVs) can perform inspection tasks, particularly for damages that are difficult to access for human repair crews. Additionally, UAVs can monitor the transmission lines to find potential dangers and early-stage damages, and to monitor the road infrastructure to provide real-time information about traffic conditions so that repair crews can select the best ways to reach damages. Besides, due to unpredictable events during restoration, the UAV routing strategy (UAVRS) needs to be updated in real time. Thus, the proposed UAVRS in this article determines the optimal routes for the UAVs allocated to inspect damages as well as the optimal routes for the UAVs to monitor transmission lines and roads in real time for distribution networks. To tackle the multi-time-scale characteristic of the proposed UAVRS, a two-layer decision-making architecture is proposed. A bilevel programming problem is solved in the first layer for the large-time-scale problem, and a mixed-integer linear programming problem is solved for the small-time-scale problem in the second layer. A case study based on the distribution network in Zaltbommel and its neighbor areas, in The Netherlands, illustrates the effectiveness of our real-time method compared to the offline methods. Furthermore, different solvers are studied and compared in view of the real-time requirement.

Preventive maintenance is applied in distribution networks to prevent failures by performing maintenance actions on components that are at risk. Distributed generators (DGs) and batteries can be used to support power to nearby loads when they are isolated due to maintenance. In this paper, a novel short-term preventive maintenance method is proposed that explicitly considers the support potential of DGs and batteries as well as uncertainties in the power generated by the DGs. Two major issues are addressed. To deal with the large-scale complexity of the network, a depth-first-search clustering method is used to divide the network into zones. Moreover, a method is proposed to capture the influence of maintenance decisions in the model of the served load from DGs and batteries via generation of topological constraints. Then a stochastic scenario-based mixed-integer non-linear programming problem is formulated to determine the short-term maintenance schedule. We show the effectiveness and efficiency of the proposed approach via a case study based on a modified IEEE-34 bus distribution network, where we also compare a branch-and-bound and a particle swarm optimization solver. The results also show that the supporting potential of DGs and batteries in preventive maintenance scheduling allows a significant reduction of load losses.

Fault location algorithms for transmission lines use the parameters of the transmission line to locate faults after the faults have occurred along the line. Although these parameters can be estimated by the phasor measurement units (PMUs) at the terminal(s) of the transmission line continuously, the uncertainty in the measurements will give rise to stochastic errors in the measured values. Thus, the uncertainty in measurements definitely influences the estimations of the parameters of the transmission line, which, in turn, influences the results of fault location algorithms. Inaccurate results of fault location algorithms may lead to costly maintenance fees and prolonged outage time. Therefore, in this article, we estimate the parameters of the transmission line considering the uncertainty in the measurements so that a more accurate fault location can be derived. The uncertainty in the measurements will be modeled as a stochastic distribution, and the maximum likelihood estimation (MLE) method will be adopted to reduce the uncertainty in the measurements. In addition, as an illustration, the telegrapher's equations will be used to calculate the parameters of the transmission line, and the two-terminal positive sequence network fault location algorithm will be used to locate the fault. In a simulation, a case study of a real-life transmission line the influence of the uncertainty in the measurements on the transmission line parameter estimations and the effectiveness of the MLE method for estimations are simulated and analyzed. The results show that the influence of the uncertainty in the measurements on the positive sequence network fault location algorithm should not be neglected and that the proposed method is very effective in significantly reducing the influence of the uncertainty in the measurements.

Distributed model predictive control (DMPC) has become a focus in the energy management of shipboard power systems due to its capabilities for privacy preservation, robustness, and distributing computing burdens to local processors. DMPC determines control actions in a distributed manner based on the predictions of system statuses. However, the performance of DMPC is affected by inaccurate predictions resulting from uncertain parameters in nominal prediction models. Particularly, these inaccuracies in predicting propulsion loads and solar panel generation powers can lead to power imbalances when implementing the control actions determined by DMPC. To address this challenge, this paper proposed a novel reinforcement learning compensated DMPC (RL-C-DMPC) to distributively compensate for the control actions determined by DMPC baseline control, thereby rectifying the power imbalances caused by uncertain parameters in nominal prediction models. A value-decomposition-network-based training and distributed testing mechanism is designed for our proposed RL-C-DMPC. Furthermore, a method for range selection of compensation rate is specifically proposed for the energy management of shipboard power systems. To validate the effectiveness of our proposed RL-C-DMPC, we conduct a comprehensive case study utilizing real-life voyage data and historical solar power generation data in the area of the voyage to build the environment for training and testing. By comparing power imbalances between DMPC and RL-C-DMPC, our results indicate significant reductions in power imbalances so that frequency stability can be better ensured. Furthermore, via the case study, we also evaluate the communication robustness of RL-C-DMPC.

Predictive maintenance scheduling of modular-multilevel-converters (MMCs) requires estimations of the failure probabilities of MMCs during a period of time in the future. Particularly, the predicted future failure probabilities are influenced by two main factors, the mission profiles of the MMCs and the maintenance decisions on the MMCs during the prediction period. This paper proposes a failure probability prediction model (FPM) for MMCs by considering these two factors. First, the expectations of the failure probabilities of the components for all the scenarios of mission profiles are obtained. Second, in predictive maintenance scheduling problems, the decisions to perform the maintenance actions are represented by binary variables. When the number of submodules is very large, using the binomial probability form currently used in reliability engineering to express the “r-out-of-n” failure probability of arms of the MMCs is intractable. Thus, this paper proposes a tractable form (T-form) in FPM by observing that the submodules on one arm are homogeneous. Furthermore, an approximation method, i.e., clustering and assignment (C&A), is proposed to reduce the computation times for calculating the parameters needed by the proposed T-form. Then, we perform a case study that assesses the accuracy and computation time of the C&A approach. The results show that the accuracy of the C&A approach is high and that the computation time is reduced significantly compared with the accurate method. We also show that the computation time for solving the predictive maintenance scheduling problem can be reduced hugely by using the T-form instead of the binomial probability form.