An effective power system protection scheme has to be able to detect and locate all occurring faults corresponding to low and high impedance values. The latter category poses the greatest challenge for the protection schemes due to the low values of the related fault current. This paper extends previous work by the authors on the subject, aiming to achieve detection and location of high impedance faults (HIFs) in multiconductor overhead distribution networks utilizing power line communication (PLC) devices. Fault detection is proposed to be performed by a PLC device installed at the starting point of the monitored line and by using differences to the values of metrics related to input impedance at frequencies utilized by narrowband systems. Moreover, fault location can be derived by a response to impulse injection procedure utilized by all installed PLC devices along the line. The method is evaluated and validated in various simulation test cases concerning its ability to effectively detect and locate HIFs.

Modeling of battery energy storage systems (BESS) used for applications, such as electric vehicles and smart grids, emerged as a necessity over the last decade and depends heavily on the accurate estimation of battery states and parameters. Depending on the battery-cell type and operation, a combination of algorithms is used to identify battery parameters and define battery states. This paper deals with robust Li-ion batteries modeling with a specific focus on a hybrid approach for a more accurate state-of-charge (SOC) estimation. The analysis presents a detailed description of the state-of-the-art stand-alone SOC estimation methods and focuses on a hybrid SOC estimation technique to improve accuracy under varying conditions. Emphasis is given on performance improvements of the proposed hybrid approach compared to the conventional methods, whereas a thorough experimental validation is presented to evaluate the accuracy of the proposed method.

Smart-meter technology advancements have resulted in the generation of massive volumes of information introducing new opportunities for energy services and data-driven business models. One such service is non-intrusive load monitoring (NILM). NILM is a process to break down the electricity consumption on an appliance level by analyzing the total aggregated data measurements monitored from a single point. Most prominent existing solutions use deep learning techniques resulting in models with millions of parameters and a high computational burden. Some of these solutions use the turn-on transient response of the target appliance to calculate its energy consumption, while others require the total operation cycle. In the latter case, disaggregation is performed either with delay (in the order of minutes) or only for past events. In this paper, a real-time NILM system is proposed. The scope of the proposed NILM algorithm is to detect the turning-on of a target appliance by processing the measured active power transient response and estimate its consumption in real-time. The proposed system consists of three main blocks, i.e., an event detection algorithm, a convolutional neural network classifier and a power estimation algorithm. Experimental results reveal that the proposed system can achieve promising results in real-time, presenting high computational and memory efficiency.

This paper introduces the idea of unified unit commitment and economic dispatch modeling within a unique tool that performs economic dispatch with up to 24-hour look-ahead capability. The tool provides financially binding dispatch and ex-ante locational marginal prices (LMPs) for the next 5-min interval and advisory commitment, dispatch schedule and prices for the remaining scheduling horizon. Variable time resolution and variable modeling complexity are used in order to reduce computational requirements. A finer time resolution and detailed modeling are used during the first hours of the scheduling horizon while coarser time resolution and simplified modeling during the last ones. The viability of the method for medium-sized systems is demonstrated through its application to the Greek power system.

Power distribution network protection and restoration are significant issues in terms of quality and reliability of the supplied electrical energy. In this paper, an extended research in possible high-end protection and control methods is presented. The authors intend to show the efficiency of combining modern IT techniques with the equipment provided by distribution automation evolving technology. As a result, a flexible and versatile multiagent system (MAS) is proposed. The effected MAS is fault isolation and power restoration oriented, as shown in the case studies. The structure of the proposed system (software and hardware aspects) is introduced. Both microscopic and macroscopic procedures are revised. Power line communications technology is implemented. Results acquired from computer simulations are evaluated. Conclusions concerning the perspectives of implementing the proposed system are finally presented

The interference of power transmission lines to buried pipelines, sharing the same rights of way, has been a research subject for many years. Especially under fault conditions, large currents and voltages are induced on the pipelines, posing a threat to operating personnel, equipment, and the integrity of the pipeline. The soil structure is an important parameter that affects the level of this interference. In this study, the influence of a soil structure composed of layers with different resistivities, both horizontally and vertically, on the inductive part of this interference is investigated. The method used to determine the inductive interference comprises finite-element calculations and standard circuit analysis. The results show that good knowledge of the soil structure is necessary in order to estimate the above interference with minimum error. Therefore, it is desirable that soil resistivity measurements are made both at adequate depths and at locations far away from the rights-of-way.