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.Note to Practitioners-The objective of this article is to address the significant effects of inaccuracies in the measurements for fault location determination in transmission lines in power systems. These inaccuracies increase the cost and duration of the search process for the actual fault location, and they, thus, also enlarge the outage duration and reduce the power system reliability. This article aims to analyze and reduce the influence of the uncertainties in the measurements in order to obtain a much more accurate fault location estimate when a fault has occurred along the transmission line. One of the key contributions of this Manuscript
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-timescale 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.Index Terms-Distribution network postdisaster restoration, monitoring and inspection coordination, real-time routing for unpredictable events, unmanned aerial vehicles routing strategy (UAVRS).
I. INTRODUCTIOND ISASTERS, e.g., hurricanes, floods, and earthquakes, can damage components of distribution networks. To reduce Manuscript
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