Roberto Chouhy Leborgne

Sistemas de Distribuição de Energia Elétrica estão constantemente expostos a perturbações que afetam a Qualidade da Energia fornecida. Dentre estas perturbações os afundamentos de tensão são os de maior frequência de ocorrência e os que mais impacto provocam no funcionamento de equipamentos modernos. Neste trabalho são estudados afundamentos de tensão a partir dos resultados de simulações de faltas realizadas em um sistema radial de 14 barras, operado com e sem Geração Distribuída. O sistema foi modelado no programa A TPDraw. Os resultados de correntes obtidos com o programa ATP serviram de referência para comparar resultados obtidos em uma aplicação em Matlab desenvolvida utilizando os métodos de Componentes Simétricas e de Componentes de Fases. Também servem de referência no desenvolvimento de um programa computacional próprio. O trabalho mostra que com o Método de Componentes Simétricas os resultados se afastam da referência quando se incrementa o desequilíbrio do sistema. As tensões resultantes do ATP permitiram a análise de afundamentos de tensão. A análise foi realizada a partir da elaboração de matrizes booleanas que consideram o estado de todas as barras. Dois índices relacionados com os afundamentos de tensão são propostos no trabalho, um que caracteriza o sistema como um todo e outro que utiliza como referência a barra onde esteja localizado um gerador independente. A partir destas matrizes e índices mapas de contorno e com cores são obtidos.

Geomagnetically induced currents (GICs) can cause impacts on power systems operation through transformer saturation, increased reactive power demand, and voltage drops. This paper presents a mitigation strategy based on the automatic insertion of a high-impedance capacitor at the transformer neutral, controlled by a logical control block (LCB). The LCB continuously monitors GIC conditions and voltage deviations to trigger the insertion of the blocking capacitor only when necessary. The method is tested on the IEEE 9-bus system using PSCAD simulations. Results demonstrate that the proposed technique significantly reduces reactive power losses and ensures voltage levels remain within ANSI C84.1 limits, improving voltage regulation during geomagnetic disturbances.

Geomagnetically induced currents (GICs) are a significant challenge to the safe operation of electric power systems, cause step-up transformer saturation and consequently high harmonic distortion. This paper assesses the versatility and applicability of the Nodal Admittance Matrix (NAM) method, implemented in PowerWorld, for modeling GICs and evaluating a mitigation strategy. A case study on a modified WSCC 9-bus system analyzes the effects of induced electric fields ($0-20 ~\mathrm{V} / \text{km}$) in northward and eastward directions, identifying critical vulnerabilities in step-up transformers and autotransformers. Results show that incorporating high-impedance capacitors ($\mathbf{1} \mathbf{M} \boldsymbol{\Omega}$) in step-up transformer neutrals can reduce reactive power losses by up to 90 % and mitigate voltage sags in critical buses, raising voltages from 0.885 pu to 0.95 pu in some cases. However, notable side effects were observed, such as the redistribution of GICs toward autotransformers, highlighting the need for further in-depth studies on mitigation strategies. Overall, this study validates the applicability of the NAM method not only for quantifying the impacts of geomagnetic storms, but also for evaluating mitigation devices in power networks.

Geomagnetically induced currents (GIC) cause the half-cycle saturation of transformers that inject high harmonic distortion into the grid. Most of the studies cover the analysis of GIC for extreme latitude locations. However, GIC might also impact power grids at mid and low latitudes. Solar energetic particles at the South Atlantic Anomaly (SAA), which covers latitudes between 15° and 45° South, can penetrate deeply into Earth's atmosphere and even reach surface level. Therefore, GIC signatures might be found in harmonic measurements in power grids at SAA. Moreover, the high harmonic distortion can even lead to the mal-trip of protection relays at SAA. This work extracts anomalies in long-term harmonics measurements in a transmission grid (525 kV) in the South of Brazil (latitude 33° South) to evaluate the possible impact of GIC inside SAA. The anomalies are extracted by a deep learning method in combination with expert rules. The harmonic anomalies are validated by cross-checking with Space Weather data and the simultaneity with anomalies in Sweden (latitude 63° North). The main contributions of this work are: (a) validating that the signatures of harmonics due to GIC at SAA are similar to the ones extracted in high latitude; (b) demonstrating that anomalies in harmonics due to GIC are a potential cause of protection mal-trips at SAA.

Microgrid sizing optimization is often formulated as a black-box optimization problem. This allows modeling the microgrid with a realistic temporal simulation of the energy flows between components. Such models are usually optimized with gradient-free methods, because no analytical expression for gradient is available. However, the development of new Automatic Differentiation (AD) packages allows the efficient and exact computation of the gradient of black-box models. Thus, this work proposes to solve the optimal microgrid sizing using gradient-based algorithms with AD packages. However, physical realism of the model makes the objective function discontinuous which hinders the optimization convergence. After an appropriate smoothing, the objective is still nonconvex, but convergence is achieved for more that 90 % of the starting points. This suggest that a multi-start gradient-based algorithm can improve the state-of-the-art sizing methodologies.

This article proposes a framework that uses analytical assessment of reliability to guide the expansion planning of power distribution systems (PDS) considering reliability criteria. The framework allows the estimation of reliability indices with and without the execution of expansion projects, thus supporting the decision-making process on investments in expansion projects. In the analytical assessment of reliability, failure rates of zones and restoration times are calculated from past data of interruptions in the primary distribution network. In addition, the estimated reliability indices are adjusted to historical values through failure rates proportionate to the length of each zone. To test and validate the proposed framework, it was applied to the distribution network at bus 5 of the Roy Billinton Test System (RBTS) and also to a real distribution feeder located in Brazil. The results indicated that the proposed framework can help define the most attractive investments leading to improvements in reliability indices and reduction in unsupplied energy. The estimation of reliability indices and energy not supplied, considered the following expansion alternatives: (i) the installation of normally-closed sectionalizing switches, (ii) the installation of normally-open switches with interconnection to adjacent feeders, (iii) the automation of switches, and (iv) the reconductoring of zones of the primary distribution network. Nevertheless, the proposed framework allows the inclusion of other expansion alternatives. Finally, the proposed framework proved to be handy and useful for real-life applications.

The Electromagnetic Time Reversal (EMTR) theory has been applied to fault location with effectiveness. The method works with the electromagnetic transients (EMTs) induced by the fault incipience. However, using the fault-induced EMTs has some shortcomings, as those related to the fault-inception angle. Thus, this work proposes an alternative EMTR fault-location method, where the fault-clearing EMTs compose the working signal. The study is performed with fault simulations in a single-feed transmission system. The method's accuracy is tested under different conditions of faults. The fault type, fault resistance, and fault-inception angle are changed. Also, both single- and three-pole tripping are used. Evaluating the method through two criteria, the average relative error on the fault location estimation observed is even null for single-line-to-ground faults. The method fully overcomes the issue related to low fault-inception angles.

Microgrids need a robust residential load forecasting. As a consequence, this highlights the problem of predicting electricity consumption in small amounts of households. The individual demand curve is volatile, and more difficult to forecast than the aggregated demand curve. For this reason, Mean Absolute Percentage Error (MAPE) varies in a large range (of 1% to 45%), depending on the number of consumers analyzed. Different levels of aggregation of household consumers that can be used in microgrids are analyzed; the load forecasting of the single consumer and aggregated consumers are compared. The forecasting methodology used is the most consolidated of Recurrent Neural Networks, i.e., LSTM. The dataset used contains 920 residential consumers belonging to the Commission for Energy Regulation (CER), a control group that is in the Irish Social Science Data Archive (ISSDA) repository. The result shows that the forecasting of groups of more than 20 aggregated consumers has a lower MAPE that individual forecasting. On the other hand, individual forecasting is better for groups with fewer than 10 consumers.