Knowing the exact location of a short-circuit fault in a power distribution system (PDS) is essential for rapid restoration of service to customers and has a direct impact on the operational costs and reliability of the system. In this paper a phasor measurement unit (PMU) based method for fault localization and classification is presented. By introducing the concept of a virtual bus, the exact fault location on the line is determined rather than just a bus closest to the fault. Moreover, after determining the fault location, a generic fault model (GFM) is introduced to classify the type of fault by solving a minimization problem, which also provides fault impedances as a result. These can then be further used to determine if the fault occurred on the lateral instead of the main feeder without using additional PMU at the end of that lateral. The effectiveness of the proposed approach is verified by simulating various fault scenarios in two test systems based on real PDSs using a real-time digital simulator. In addition, a sensitivity analysis of the method is performed for different noise levels in the input parameters to verify its applicability to the real system.

The detection and localization of faults plays a huge role in every electric power system, be it a transmission network (TN) or a distribution network (DN), as it ensures quick power restoration and thus enhances the system’s reliability and availability. In this paper, a framework that supports phasor measurement unit (PMU)-based fault detection and localization is presented. Besides making the process of fault detecting, localizing and reporting to the control center fully automated, the aim was to make the framework viable also for DNs, which normally do not have dedicated fiber-optic connectivity at their disposal. The quality of service (QoS) for PMU data transmission, using the widespread long-term evolution (LTE) technology, was evaluated and the conclusions of the evaluation were used in the development of the proposed edge-cloud framework. The main advantages of the proposed framework can be summarized as: (a) fault detection is performed at the edge nodes, thus bypassing communication delay and availability issues, (b) potential packet losses are eliminated by temporally storing data at the edge nodes, (c) since the detection of faults is no longer centralized, but rather takes place locally at the edge, the amount of data transferred to the control center during the steady-state conditions of the network can be significantly reduced.

This paper analyzes the challenges and requirements of establishing energy data ecosystems (EDEs) as data-driven infrastructures that overcome the limitations of currently fragmented energy applications. It proposes a new data- and knowledge-driven approach for management and processing. This approach aims to extend the analytics services portfolio of various energy stakeholders and achieve two-way flows of electricity and information for optimized generation, distribution, and electricity consumption. The approach is based on semantic technologies to create knowledge-based systems that will aid machines in integrating and processing resources contextually and intelligently. Thus, a paradigm shift in the energy data value chain is proposed towards transparency and the responsible management of data and knowledge exchanged by the various stakeholders of an energy data space. The approach can contribute to innovative energy management and the adoption of new business models in future energy data spaces.

We investigate the transport mechanism of poly-Si-based carrier-selective junctions using the two-dimensional numerical semiconductor device simulations. The detailed transport model considers the charge carrier transport through the pinholes as well as tunneling through a very thin silicon oxide simultaneously. For the verification of the simulation model, the complete temperature dependent transfer length method is modeled and its results are verified with measurements of two different samples. By means of rigorous simulations, the influence of different pinhole geometrical and material parameters on junction resistivity are investigated and explained in detail. From the presented results, the fundamental understanding needed for optimizing the poly-Si-based carrier selective junction in respect to the main design parameters such as doping level in poly-Si, annealing time, silicon oxide thickness, and pinhole density is given. The detailed analysis shows the pinhole channel plays the most crucial role in the design of poly-Si-based carrier-selective junctions if the silicon oxide layer thickness is larger than 2 nm.