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1. WAMs Based Eigenvalue Space Model for High Impedance Fault Detection

   https://doi.org/10.3390/app112412148  

   Paramo, Gian ; Bretas, Arturo S. ( December 2021 , Applied Sciences)

   High impedance faults present unique challenges for power system protection engineers. The first challenge is the detection of the fault, given the low current magnitudes. The second challenge is to locate the fault to allow corrective measures to be taken. Corrective actions are essential as they mitigate safety hazards and equipment damage. The problem of high impedance fault detection and location is not a new one, and despite the safety and reliability implications, relatively few efforts have been made to find a general solution. This work presents a hybrid data driven and analytical-based model for high impedance fault detection in distribution systems. The first step is to estimate a state space model of the power line being monitored. From the state space model, eigenvalues are calculated, and their dynamic behavior is used to develop zones of protection. These zones of protection are generated analytically using machine learning tools. High impedance faults are detected as they drive the eigenvalues outside of their zones. A metric called eigenvalue drift coefficient was formulated in this work to facilitate the generalization of this solution. The performance of this technique is evaluated through case studies based on the IEEE 5-Bus system modeled in Matlab. Test results are encouraging indicating potential for real-life applications. 

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2. Prognostic Health Monitoring of DC Microgrid with Fault Detection and Localization using Machine Learning Techniques

   https://doi.org/10.1109/ECCE53617.2023.10362739  

   Bohara, Bharat ; Krishnamoorthy, Harish S. ( October 2023 , IEEE)

   DC microgrids incorporate several converters for distributed energy resources connected to different passive and active loads. The complex interactions between the converters and components and their potential failures can significantly affect the grids' resilience and health; hence, they must be continually assessed and monitored. This paper presents a machine learning-assisted prognostic health monitoring (PHM) and diagnosis approach, enabling progressive interactions between the converters at multiple nodes to dynamically examine the grid's (or micro-grid's) health in real time. By measuring the resulting impedance at the power converters' terminals at various grid nodes, a neural network-based classifier helps detect the grid's health condition and identify the potential fault-prone zones, along with the type and location of the fault type in the grid topology. For a faulty grid, a Naive Bayes and a support vector machine (SVM)-based classifiers are used to locate and identify the faulty type, respectively. A separate neural network-based regression model predicts the source power delivered and the loads at different terminals in a healthy grid network. The proposed concepts are supported by detailed analysis and simulation results in a simple four-terminal DC microgrid topology and a standard IEEE 5 Bus system. 

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3. Impact of Solar Inverter Dynamics during Grid Restoration Period on Protection Schemes Based on Negative-Sequence Components

   https://doi.org/10.3390/en15124360  

   Ekic, Almir ; Wu, Di ; Jiang, John N. ( June 2022 , Energies)

   The growing penetration of renewable resources such as wind and solar into the electric power grid through power electronic inverters is challenging grid protection. Due to the advanced inverter control algorithms, the inverter-based resources present fault responses different from conventional generators, which can fundamentally affect the way that the power grid is protected. This paper studied solar inverter dynamics focused on negative-sequence quantities during the restoration period following a grid disturbance by using a real-time digital simulator. It was found that solar inverters can act as negative-sequence sources to inject negative-sequence currents into the grid during the restoration period. The negative-sequence current can be affected by different operating conditions such as the number of inverters in service, grid strength, and grid fault types. Such negative-sequence responses can adversely impact the performance of protection schemes based on negative-sequence components and potentially cause relay maloperations during the grid restoration period, thus making system protection less secure and reliable. 

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4. Optimized contact geometries for high speed disconnect switches

   https://doi.org/10.1109/CEIDP.2017.8257488  

   Lim, Gyu Cheol ; Damle, T. ; Graber, L. ( October 2017 , 2017 IEEE Conference on Electrical Insulation and Dielectric Phenomenon (CEIDP))

   Fast mechanical disconnect switches are an integral part of hybrid circuit breakers, which are proposed as protection devices to clear faults in medium voltage distribution systems. Compared to their conventional counterparts, hybrid circuit breakers can have the ability to limit the fault current, which could allow more interconnections between substations with advantages with respect to grid reliability and resiliency. Furthermore, they enable the integration of distributed generation such as small solar power installations without expensive changes to the grid infrastructure. The proposed design of an ultrafast mechanical disconnect switch operates in vacuum, carries continuous current similar to conventional vacuum interrupters, opens at current zero, features minimum moving mass, and has an open contact separation of less than a millimeter. The limited separation distance requires an optimized contact geometry to keep the electric field within safe limits, minimize the moving mass, and reduce contact resistance. This paper proposes different contact geometries and uses finite element analysis to compare the contact geometries with respect to maximum electric field, mass, and contact resistance. Reductions of mass by 50% and reduction of contact resistance of 10% have been achieved. 

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5. Machine-Learning-Based High-Resolution Earthquake Catalog Reveals How Complex Fault Structures Were Activated during the 2016–2017 Central Italy Sequence

   https://doi.org/10.1785/0320210001  

   Tan, Yen Joe ; Waldhauser, Felix ; Ellsworth, William L. ; Zhang, Miao ; Zhu, Weiqiang ; Michele, Maddalena ; Chiaraluce, Lauro ; Beroza, Gregory C. ; Segou, Margarita ( April 2021 , The Seismic Record)

   Abstract The 2016–2017 central Italy seismic sequence occurred on an 80 km long normal-fault system. The sequence initiated with the Mw 6.0 Amatrice event on 24 August 2016, followed by the Mw 5.9 Visso event on 26 October and the Mw 6.5 Norcia event on 30 October. We analyze continuous data from a dense network of 139 seismic stations to build a high-precision catalog of ∼900,000 earthquakes spanning a 1 yr period, based on arrival times derived using a deep-neural-network-based picker. Our catalog contains an order of magnitude more events than the catalog routinely produced by the local earthquake monitoring agency. Aftershock activity reveals the geometry of complex fault structures activated during the earthquake sequence and provides additional insights into the potential factors controlling the development of the largest events. Activated fault structures in the northern and southern regions appear complementary to faults activated during the 1997 Colfiorito and 2009 L’Aquila sequences, suggesting that earthquake triggering primarily occurs on critically stressed faults. Delineated major fault zones are relatively thick compared to estimated earthquake location uncertainties, and a large number of kilometer-long faults and diffuse seismicity were activated during the sequence. These properties might be related to fault age, roughness, and the complexity of inherited structures. The rich details resolvable in this catalog will facilitate continued investigation of this energetic and well-recorded earthquake sequence. 

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