Clustering Techniques Performance for the Coordination of Adaptive Overcurrent Protections

Inclusion of distributed generation and topological changes in a network originate several operating scenarios. For this reason, techniques that adjust the configuration of overcurrent relays have been developed in order to provide protection coordination strategies capable of operating in different schemes. However, the adjustments allowed by these devices are limited. Thus, scenario grouping techniques are proposed to reduce the number of required configurations. This paper aims to evaluate the performance of different grouping techniques with input parameters for coordination strategies of electrical overcurrent protections, where it is required to associate the different modes of operation of a distribution network. For the clustering process, unsupervised learning techniques such as K-means, K-medoids and Agglomerative Hierarchical Clustering were employed. Additionally, for the input characteristics, fault currents, nominal currents and other parameters obtained from the electrical system were taken into account. From the results obtained when evaluating different combinations of techniques and inputs, it is important to mention that the characteristics that describe the different modes of operation necessary for the grouping are decisive for the coordination strategies of electrical protections and that it is not possible to establish a significant difference between the clustering techniques evaluated. Lastly, the combination that presents the best performance was K-means: Manhattan and maximum short-circuit phase currents per relay with a sum of operation time of 428.72s and zero restriction violation. © 2022 IEEE

Passivity-based control applied of a reaction wheel pendulum: An IDA-PBC approach

This paper presents the development of a nonlinear controller for the reaction wheel pendulum (RWP) via an interconnection and damping assignment passivity-based control (IDA-PBC) approach. The IDA-PBC approach works with the port-Hamiltonian open-loop dynamics of the RWP to propose a nonlinear controller that preserves the Hamiltonian structure in closed-loop by guaranteeing stability properties in the sense of Lyapunov. Numerical results confirm the theoretical development presented throughout simulations in Simulink package from MATLAB. Comparison with a Lyapunov-based approach is also provide

Economic Dispatch of BESS and renewable generators in DC microgrids using voltage-dependent load models

This paper addresses the optimal dispatch problem for battery energy storage systems (BESSs) in direct current (DC) mode for an operational period of 24 h. The problem is represented by a nonlinear programming (NLP) model that was formulated using an exponential voltage-dependent load model, which is the main contribution of this paper. An artificial neural network was employed for the short-term prediction of available renewable energy from wind and photovoltaic sources. The NLP model was solved by using the general algebraic modeling system (GAMS) to implement a 30-node test feeder composed of four renewable generators and three batteries. Simulation results demonstrate that the cost reduction for a daily operation is drastically affected by the operating conditions of the BESS, as well as the type of load model used. © 2019 MDPI AG. All rights reserved

Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids

Smart networks such as active distribution network (ADN) and microgrid (MG) play an important role in power system operation. The design and implementation of appropriate protection systems for MG and ADN must be addressed, which imposes new technical challenges. This paper presents the implementation and validation aspects of an adaptive fault detection strategy based on neural networks (NNs) and multiple sampling points for ADN and MG. The solution is implemented on an edge device. NNs are used to derive a data-driven model that uses only local measurements to detect fault states of the network without the need for communication infrastructure. Multiple sampling points are used to derive a data-driven model, which allows the generalization considering the implementation in physical systems. The adaptive fault detector model is implemented on a Jetson Nano system, which is a single-board computer (SBC) with a small graphic processing unit (GPU) intended to run machine learning loads at the edge. The proposed method is tested in a physical, real-life, low-voltage network located at Universidad del Norte, Colombia. This testing network is based on the IEEE 13-node test feeder scaled down to 220 V. The validation in a simulation environment shows the accuracy and dependability above 99.6%, while the real-time tests show the accuracy and dependability of 95.5% and 100%, respectively. Without hard-to-derive parameters, the easy-to-implement embedded model highlights the potential for real-life applications. © 2013 State Grid Electric Power Research Institute

Data-driven topology detector for self-healing strategies in Active Distribution Networks

The integration of distributed energy resources requires the implementation of control and automation functionalities in distribution networks, which allow them to operate in a more flexible, efficient, and reliable way. The operation of these functionalities causes topological changes on the network that must be identified since these affect protection, volt/var control, and state estimation, among others. This paper presents a data-driven topology detector for self-healing strategies in Active Distribution Networks (ADN). This approach uses machine learning (ML) techniques to obtain a trained model as a topology detector. The ML models are integrated into the Intelligent Electronic Device (IED) of ADN so that using the voltage and current signals measured locally determine the network’s topology. A features selection and tuning technique based on a multiverse optimizer are proposed to improve the ML model accuracy. This approach allows it to be implemented in decentralized architectures since each IED detects the system’s topology from local measurements and does not depend on the availability of the communication system. The proposed topology detector was validated on a modified IEEE 123 nodes test feeder considering six topology changes, five DER outages, and five load variations. The results obtained show accuracy values above 96%, which evidences a highlighted potential for real-life applications

Master-slave strategy based in artificial intelligence for the fault section estimation in active distribution networks and microgrids

Fault location plays an essential role in the integration of self-healing functionalities in active distribution networks and microgrids. However, the fault location methods formulation presents great challenges for these types of networks because the operating changes that occur them, such as changes in topology, DER connection/ disconnection and microgrids operating modes. Several fault location solutions have been proposed; nevertheless, these are strongly dependent on robust communication systems. This paper presents an artificial intelligence-based master–slave strategy for the estimation of the fault section in active distribution networks and microgrids using dispersed measurements. The strategy is composed by two stages. The master stage uses a genetic algorithm that determines the location and number of devices which maximize the faulted location e performance. The slave stage uses artificial neural networks to predict the fault section by using local voltage and current measurements trough an intelligent electronic device (IED). This approach is useful because it neglects the need of a robust communication systems and synchronization process between measurements. Here, each IED estimates the faulted section and then sends it through the single communication system to the distribution system operator control center. The presented method is validated on the modified IEEE 34-nodes test feeder where the accuracy of the strategy was 95%. The results obtained and its easy implementation indicate potential for real-life applications

Adaptive Impedance-Based Fault Location Algorithm for Active Distribution Networks

Modern fault location methods are robust; however, they depend strongly on the availability of the measurements given by Distributed Energy Resources (DER). If the communication or synchronism of this information is lost, the fault location is not possible. This paper proposes an adaptive impedance-based fault location algorithm for active distribution systems. The proposal combines information provided by Intelligent Electronic Devices (IEDs) located at the substation, the knowledge of the network topology and parameters, as well as the distributed power sources, to estimate the fault location. Its adaptive feature is given by the use of a Distributed Energy Resources (DER) electrical model. This model is used to estimate the DER current contribution to the fault, in case the information provided by a local IED is not available. The method takes two types of DER technologies into account: Inverter non-interfaced DER (INIDER) and Inverter-interfaced DER (IIDER). The proposed method is validated on a modified IEEE 34-node test feeder, which was simulated with ATP/EMTP. The results obtained using the IEDs information, presented a maximum error of 0.8%. When this information is not available, the method’s performance decreases slightly, obtaining a maximum error of 1.1%. The proposed method showed better performance when compared with two state of the art methods, indicating potential use for real-life applications

Magnetic hyperthermia in brick-like Ag@Fe3O4 core-shell nanoparticles

FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOHeating efficiency of multifunctional Ag@Fe3O4 brick-like nanoparticles under alternating magnetic field was investigated by means of specific absorption rate (SAR) measurements, and compared with equivalent measurements for plain magnetite and dimer heteroparticles. The samples were synthesized by thermal decomposition reactions and present narrow size polydispersity and high degree of crystallinity. The SAR values are analyzed using the superparamagnetic theory, in which the basic morphology, size and dispersion of sizes play key roles. The results suggest that these novel brick-like nanoparticles are good candidates for hyperthermia applications, displaying heating efficiencies comparable with the most efficient plain nanoparticles. (C) 2015 Elsevier B.V. All rights reserved.Heating efficiency of multifunctional Ag@Fe3O4 brick-like nanoparticles under alternating magnetic field was investigated by means of specific absorption rate (SAR) measurements, and compared with equivalent measurements for plain magnetite and dimer heteroparticles. The samples were synthesized by thermal decomposition reactions and present narrow size polydispersity and high degree of crystallinity. The SAR values are analyzed using the superparamagnetic theory, in which the basic morphology, size and dispersion of sizes play key roles. The results suggest that these novel brick-like nanoparticles are good candidates for hyperthermia applications, displaying heating efficiencies comparable with the most efficient plain nanoparticles.3972027FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOFAPESP [2013/13275-8, 2011/01235-6]2011/01235-6, 2013/13275-8Sem informaçãoThis work has been funded by the brazilian agencies FAPESP and CNPq. We thank TEM facilities of C2NANO-Brazilian Nanotechnology National Laboratory (LNNano) at Centro Nacional de Pesquisa em Energia e Materiais (CNPEM)/MCT (# 14825 and 14827). R.L.R. acknowledges FAPESP grant 2013/13275-8. D.M acknowledges FAPESP grant 2011/01235-6

A Fault Localization Method for Single-phaseto Ground Faults in LV Smart Distribution Grids

https://doi.org/10.1007/978-3-030-37161-6_24International audienceA fault localization method for single-phase to ground short-circuit (SC) faults in low voltage (LV) smartdistribution grids is presented in this paper. Both the use of rms voltage phase measurements and an analysis of symmetrical components of the voltage were investigated and compared in this study. Phase measurements were found to be more suitable for single-phase to ground faults. The described method is a three-step process beginning with the identification of the faulty branch, followed by the localization of the sector in which the fault occurred and concluding with the estimation of the fault distance from the beginning of the feeder. Fault resistance values of 0.1, 1, 5, 10, 50, 100, 500 and 1000 W were tested. An heterogeneity analysis was performed to test the effect of the use of various conductors on the method. Faults in all three phases were implemented and simulated on a real-case of a semi-rural LV distribution network of Portugal, provided by Efacec. Finally, the method presented an average estimation accuracy of 89.33% and an increased accuracy of 93.11% for low impedance faults (up to 10 W of fault resistance)