More Like this

1. Dual State - Parameter Estimation for Series Arc Fault Detection on a DC Microgrid

   https://doi.org/10.1109/ECCE44975.2020.9235753  

   Gajula, Kaushik; Yao, Xiu; Herrera, Luis (October 2020, 2020 IEEE Energy Conversion Congress and Exposition (ECCE))

   null (Ed.)

   In this paper, a detection and localization technique based on dual State and Parameter Estimation (SE and PE respectively) for series dc arc faults is presented. Detection of series arc faults in dc microgrids is challenging due to its low fault current. By using the available set of sensor measurement data over a period of time, a Least Squares (LS) based SE algorithm estimates the dc microgrid's bus voltages and injection currents. Kalman Filter (KF) is then used to estimate the line conductances in the network, which are used to detect and localize (with respect to the faulted line) the series arc fault. Simulation results are presented with different case studies to demonstrate the robustness of the algorithm to normal operating conditions and different number and placement of sensors. Finally, Control Hardware in the Loop (CHIL) results are shown. 

   more » « less
2. Quickest Detection of Series Arc Faults on DC Microgrids

   https://doi.org/10.1109/ECCE47101.2021.9595315  

   Gajula, Kaushik; Le, Vu; Yao, Xiu; Zou, Shaofeng; Herrera, Luis (October 2021, IEEE Energy Conversion Congress and Expo (ECCE))

   In this paper we explore the problem of series arc fault detection and localization on dc microgrids. Through a statistical model of the microgrid obtained by nodal equation, the injection currents are modeled as a random vector whose distribution depends on the nodal voltages and the admittance matrix. A series arc fault causes a change in the admittance matrix, which further leads to a change in the data generating distribution of injection currents. The goal is to detect and localize faults on different lines in a timely fashion subject to false alarm constraints. The model is formulated as a quickest change detection problem, and the classical Cumulative Sum algorithm (CUSUM) is employed. The proposed framework is tested on a dc microgrid with active (constant power) loads. Furthermore, a case considering fault detection in the presence of an internal node is presented. Finally, we present an experimental result on a four node dc microgrid to verify the practical application of our approach. 

   more » « less
3. Fault location in microgrids: a communication‐based high‐frequency impedance approach

   https://doi.org/10.1049/iet-gtd.2018.5166  

   Beheshtaein, Siavash; Cuzner, Robert; Savaghebi, Mehdi; Golestan, Saeed; Guerrero, Josep_M (February 2019, IET Generation, Transmission & Distribution)

   This paper proposes a novel method to locate faults in an AC‐meshed microgrid. To this end, a set of features is first extracted and selected from the measured signals and fed to a Support Vector Machine (SVM) to detect the occurrence of fault. Then, the Distributed Generator (DG) with the lowest amount of fundamental voltage, which is the closest one to the fault, injects an appropriate voltage/current harmonic. As the faulted section has the lowest impedance value from the Point of Common Coupling of the DG, the harmonic current of the corresponding line has the highest value. Based on this fact, the first candidate DG sends a notification signal to the second candidate DG, in which the fault occurs between them. Finally, the impedances in the injected frequency are measured from these two DGs and fed into a multi‐class SVM to locate the faulted line. The proposed method has the ability to locate faults for islanded and grid‐connected microgrids with variable configurations. Real‐time simulation results are taken by OPAL‐RT to show the effectiveness of the proposed method in the meshed microgrid. 

   more » « less
4. A Modified Secondary-Control Based Fault Current Limiter for Four-Wire Three Phase DGs

   https://doi.org/10.1109/TIE.2018.2851970  

   Beheshtaein, Siavash; Savaghebi, Mehdi; Cuzner, Rob; Golestan, Saeed; Guerrero, Josep M. (July 2018, IEEE Transactions on Industrial Electronics)

   Fault Current Limiters (FCLs) are one of the main solutions to upcoming challenges in microgrid protection. Regarding the high penetration of distributed generations (DGs) in future power system, designing cheap and effective FCL is a necessity. The present study addresses this issue by proposing an embedded FCL operating based on modifying the secondary control of four-wire DG. As this method is presented for a four-wire system, besides very low implementing cost, it has independency and flexibility to only limit the current of DG faulted phase. This study also provides real-time simulation results by OPAL-RT to compare the proposed method with a virtual-impedance-based FCL to validate its effectiveness. Finally, experimental results are presented to validate the effectiveness of the proposed FCL. 

   more » « less
5. 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. 

   more » « less