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State-of-the art physics-model based dynamic state estimation generally relies on the assumption that the system's transition matrix is always correct, the one that relates the states in two different time instants, which might not hold always on real-life applications. Further, while making such assumptions, state-of-the-art dynamic state estimation models become unable to discriminate among different types of anomalies, as measurement gross errors and sudden load changes, and thus automatically leads the state estimator framework to inaccuracy. Towards the solution of this important challenge, in this work, a hybrid adaptive dynamic state estimator framework is presented. Based on the Kalman Filter formulation, measurement innovation analytical-based tests are presented and integrated into the state estimator framework. Gross measurement errors and sudden load changes are automatically detected, identified, and corrected, providing continuous updating of the state estimator. Towards such, the asymmetry index applied to the measurement innovation is introduced, as an anomaly discrimination method, which assesses the physics-model-based dynamic state estimation process in different piece-wise stationary levels. Comparative tests with the state-of-the-art are presented, considering the IEEE 14, IEEE 30, and IEEE 118 test systems. Easy-to-implement-model, without hard-to-design parameters, build-on the classical Kalman Filter solution, highlights potential aspects towards real-life applications. [ABSTRACT FROM AUTHOR]

• A 2D Densenet does not need prior extraction of features for PQD signals by some mathematical method, which eliminates the 2-step based model and reduces computational burden. • Spatio-temporal environment characteristics are considered in a data driven based framework. A 2D Densenet solution allows for the reduction in the quantity of filters per convolutional layer of Densenet used to classify PQDs. • Due the densely connected layers, the 2D Densenet size is smaller leading it to a faster classification time. The fast and accurate diagnosis of power quality disturbances (PQD) aids in avoiding shutdowns and unnecessary procedures, concerning electric energy distribution systems. As such, a number of techniques have been tested and applied in order to reach this objective. Majority of the techniques applied are two-step based. On the first step, power quality disturbances features are extracted. Second step, considering features extracted, disturbance classification is implemented. Recently, relevant literature has presented data-driven signal processing-based approaches, as deep convolutional neural networks (DCNN), which can implement both processing steps while providing automated recognition of patterns and outliers in data. However, not considered by state-of-art, power quality disturbances are evolving in nature, while all possible regularities might not be represented in the dataset. In this work a 2 Dimension Densely Connected Convolutional Network (2D-DenseNet) framework is presented. Case study with synthetic disturbance events are analyzed. Easy-to-implement formulation, built on the 2D-DenseNet, without hard-to-design parameters, highlight potential aspects for real-life implementation. [ABSTRACT FROM AUTHOR]

• A Multi-agent System (MAS) is suggested for distribution systems fault section estimation;. • The authors discussed the use of smart meters in a smart grid environment;. • Smart meters are considered to be agents and communicate via MAS;. • The proposed method provides a fast and efficient technique for fault section estimation in distribution systems. Multi-Agent Systems (MAS) are seen from different areas as one of the paramount trends for the next generation of power systems. Numerous published studies about MAS discuss its utilization in power distribution networks but none focuses on the prior step to restoration and self-healing that is fault section estimation. This paper aims to show how MAS can improve the utilities' reliability indexes and consumer satisfaction by overcoming the multiple fault section estimation problem. In order to do this, the authors considered using MAS as a means of communication between smart meters. The purpose of smart meters usage is to employ devices that are already present in smart grids, mainly because of their reading and saving data capacity. The proposed method was tested on a radial feeder generated by the authors. The network was built on HYPERSIM, a software platform of OPAL-RT Technologies. The simulation results show that this MAS provides speed, efficiency, and automation for the process of fault section estimation. [ABSTRACT FROM AUTHOR]

• Data-driven time-domain protection method based on Support Vector Machine. • Machine learning-based protection scheme implemented in hardware. • Energization events, internal and external faults real-time classification. • Proposed method was scarcely affected by CT saturation. • External-to-internal evolving fault identification. This paper presents a power transformer differential protection scheme based on support vector machines (SVM) combined with high-frequency features extracted with the real-time boundary stationary wavelet transform (RT-BSWT). SVM models are derived with synthetic data, considering a wide variety of events, such as inter-turn faults, external faults during CT saturation, and evolving external-to-internal faults. A comparative performance assessment is carried out considering accuracy and other reliability indices, as well as operating time, and good results were achieved. The simplicity of the presented SVM-based relay, without hard-to-derive parameters, built on the classical differential protection framework, highlights potential aspects towards real-life implementation. [ABSTRACT FROM AUTHOR]