Abstract
In this paper, we present a Simulator Program that identifies the topologies and supports operation staff in decision-electrical power system operation consider load reestablishment procedures and topological possibilities to each event, offering a selection of the best settings. Facing a electrical power transmission system contingency, the Simulator considers the current state and uses special algorithms that detect the grid topology, interprets results and presents to the operators a rank of procedures with their respective degrees of reliability. For these actions the software Simulator make the circuits breakers states analysis and electric keys, load flow solution with the on line data, makes risk prediction and the mathematical analysis of remote sensing values. The software Simulator uses in some of his actions, special algorithms that are based on Paraconsistent Annotated Logic (PAL), which is a non-classical logic whose main property is to accept contradiction in their fundamentals. These algorithms based on PAL offer greater speed of processing and allows the Paraconsistent Simulator of topologies (ParaSimTop) to be implemented in real time.

Abstract
Industrial automation relies on PLC's to perform real-time critical tasks. These tasks are commonly implemented using IEC 61131-3 and tend to be very complex due to the current evolution of microprocessor capabilities. This complexity causes testing to be considered a very difficult task and the scope to be reduced to minimize effort. The application of automatic calibration techniques could allow the identification of optimal or near optimal solutions for the set of inputs to allow a better test coverage and simulation accuracy. This paper reviews current automatic simulation calibration methodologies and provides a description of an experiment of the application of search-based metaheuristics to IEC 61131-3 implemented algorithms in the context of power systems automation.

Abstract
Power systems are becoming more complex and the need for increased awareness at the lower voltage levels of the distribution grid requires new tools that provide a reliable and accurate estimation of the system state. This paper describes an innovative state estimation method for low voltage (LV) grids that analyses similarities between a real-time snapshot comprising only a subset of smart meters with real-time communications and fully observed system states present in historical data. Real-time estimates of voltage magnitudes are obtained by smoothing the most similar past snapshots with a data-driven methodology that does not relies on full knowledge of the grid topology and electrical characteristics. Moreover, the output of the LV state estimator is a conditional probability distribution obtained with kernel density estimation. The results show highly accurate estimation of voltage magnitude, even in a scenario characterized by a strong integration of photovoltaic (PV) microgeneration.

Abstract
This paper provides an answer to the problem of State Estimation (SE) with multiple simultaneous gross errors, based on Generalized Error Correntropy instead of Least Squares and on an interior point method algorithm instead of the conventional Gauss-Newton algorithm. The paper describes the mathematical model behind the new SE cost function and the construction of a suitable solver and presents illustrative numerical cases. The performance of SE with the data set contaminated with up to five simultaneous gross errors is assessed with confusion matrices, identifying false and missed detections. The superiority of the new method over the classical Largest Normalized Residual Test is confirmed at a 99% confidence level in a battery of tests. Its ability to address cases where gross errors fall on critical measurements, critical sets or leverage points is also confirmed at the same level of confidence.

Abstract
Classical Weighted Least Squares (WLS) State estimation (SE) in power systems is known for not performing well in the presence of Gross Errors (GE). The alternative using Correntropy proved to be appealing in dealing with outliers. Now, a novel SE method, generalized correntropy interior point method (GCIP) is being proposed, taking advantage of the properties of the Generalized Correntropy and of the Interior Point Method (IPM) as solver. This paper discusses how the choice of different central path neighborhoods, an essential concept in IPM, is critical in the identification of gross errors. The simulation results indicate that a one-sided infinity norm neighborhood successfully identifies outliers in the SE problem, making GCIP a competitive method. © 2019 IEEE.

Abstract
This paper aims to describe the main outcomes of the ADMS4LV project which stands for Advanced Distribution Management System for Active Management of LV Grids. ADMS4LV targets the development and demonstration of a framework with adequate tools to optimize the management and operation of Low Voltage (LV) networks towards the effective implementation of Smart Grids. This work details the main functionalities of ADMS4LV and discusses their implementation. The validation of the functionalities is presented from demonstrations in a laboratorial setup, namely regarding the algorithms which using advanced data analytics, accomplish to operate LV networks with low observability, (i.e., with few real-time measurements) and without having full knowledge of the networks' technical characteristics, such as the consumers' phase connection to the grid. The assessment of the results shows the adequacy of the ADMS4LV solutions for deployment in distribution networks with current infrastructures, differing unnecessary investments in sensory devices. © 2019 IEEE.