Fault Detection in Microgrids

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Detection of Sensor Fault in a DC Microgrid Using Supertwisting

The existing work on sensor faults detection techniques is mainly based on additive faults, and multiplicative sensor faults are less considered. In Bansal and Sodhi ( 2018 ), sensor fault detections in a grid-connected or islanded mode DC microgrid are discussed using model-based and data-driven-based fault detection.

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Islanding Fault Detection in Microgrids—A Survey

This paper provides an overview of islanding fault detection in microgrids. Islanding fault is a condition in which the microgrid gets disconnected from the microgrid unintentionally due to any fault in the utility grid. This paper surveys the extensive literature concerning the development of islanding fault detection techniques which can be classified into

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Fault detection and location in medium-voltage DC microgrids

Fast dc fault detection method is required in medium-voltage dc (MVDC) microgrids to avoid severe damage to the interfacing converters. Ensuring selectivity and sensitivity of the protection system within a few milliseconds is a major challenge.

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Microgrid fault detection methods: Reviews, issues and future trends

A critical review of various fault detection techniques is provided, and to categorize them based on the model based and data-driven based methods. Globally, microgrid (MG) technologies have become an important paradigm for integrating distributed resources (DR) into power systems. Growing cost, burdens associated with transmission and distribution infrastructure, and the

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An Improved High-Resistance Fault Detection Method in DC

High-resistance faults in direct current (DC) microgrids are small and thus difficult to detect. Such faults may be "invisible" in that grid operation continues for a considerable time, which damages the grid. It is essential to detect and remove high-resistance faults; we present a detection method herein. First, the transient DC current during the fault is subjected

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Data-driven fault detection and isolation in DC microgrids without

The lack of fault data is the major constraint on data-driven fault detection and isolation schemes for DC microgrids. To solve this problem, this paper develops an adversarial

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A Novel Machine Learning-Based Approach for Fault

DC microgrids have gained significant attention in recent years due to their potential to enhance energy efficiency, integrate renewable energy sources, and improve the resilience of power distribution systems. However,

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Recursive Least Squares and Adaptive Kalman Filter-Based

In this article, we present a recursive least squares (RLS) and adaptive Kalman filter (AKF)-based state and parameter estimation (SE and PE) for series arc fault (SAF) detection and identification on dc microgrids. It is evident from the state-of-the-art research on dc SAFs that due to the lack of zero crossings and low current of the fault, the detection/identification of a

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Resilient Event-Based Fuzzy Fault Detection for DC Microgrids

This paper addresses the problem of fault detection in DC microgrids in the presence of denial-of-service (DoS) attacks. To deal with the nonlinear term in DC microgrids, a Takagi-Sugeno (T-S) model is employed. In contrast to the conventional approach of utilizing current sampling data in the tradi

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Intelligent Fault Detection Scheme for Microgrids With Wavelet

Fault detection is essential in microgrid control and operation, as it enables the system to perform fast fault isolation and recovery. The adoption of inverter-interfaced distributed generation in microgrids makes traditional fault detection schemes inappropriate due to their dependence on significant fault currents. In this paper, we devise an intelligent fault detection

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Integrating fault detection and classification in microgrids using

The proposed method, which performs fault detection and classification together, just requires local information and functions effectively to discriminate faulty from normal conditions...

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Fault detection and location in medium‐voltage DC

This study proposes a new technique based on fault launched travelling-waves (TWs) to detect, classify, and locate different dc fault types in MVDC microgrids. Unlike the existing TW-based protection and fault location

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High‐speed algorithm for fault detection and location in DC microgrids

This study proposes a novel protection algorithm using traveling waves (TWs) for fault detection and localization. The high-order synchrosqueezing transform (FSSTH) is applied to precisely identify TWs at the relay location. FSSTH offers a sharp time–frequency representation, enhancing the accuracy and speed of fault detection.

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High Impedance Fault Detection and Isolation in DC Microgrids

This paper focuses on designing a cost-effective protection system for fast identification, selective isolation of high impedance faults and system restoration in such dc microgrids through proper coordination of source converters with sectionalizers, and with no solid state circuit breakers in action. Faults in dc microgrids require quick interruption than in

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Model-Based Fault Detection and Isolation in DC Microgrids

Abstract: DC microgrids require advanced protection techniques for fault detection and isolation (FDI). In this work, an FDI method able to respond to different types of component faults is

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Model-Based Fault Detection in DC Microgrids

The proposed model-based fault detection filter, named H_/H∞ based filter, and Kalman based filter can minimize disturbance effects and maximize the fault effects on the so-called residual signal in DC-MGs. Recently DC Microgrids (DC-MGs) are more attractive and effective in renewable energy resources (RERs). In this paper, for the protection of devices and

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Model-Based Fault Detection and Isolation in DC Microgrids

DC microgrids require advanced protection techniques for fault detection and isolation (FDI). In this work, an FDI method able to respond to different types of component faults is developed based

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Recent Developments and Challenges on AC Microgrids Fault Detection

Microgrids Fault Detection and Protection. Systems–A Review. Noor Hussain 1,2, Mashood Nasir 1, Juan Carlos V asquez 1 and Josep M. Guerrero 1, *

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Bayesian-optimized LSTM-DWT approach for reliable fault detection

Jayamaha, D., Lidula, N. & Rajapakse, A. D. Wavelet-multi resolution analysis based ANN architecture for fault detection and localization in DC microgrids. IEEE Access 7, 145371–145384 (2019).

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Model-Based Fault Detection and Isolation in DC Microgrids

The proposed FDI method is proved to be effective to detect and isolate different faults in DC microgrids with a response time of 1 ms. DC microgrids require advanced protection techniques for fault detection and isolation (FDI). In this work, an FDI method able to respond to different types of component faults is developed based on system modeling. First,

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Unknown Input Observer-Based Series DC Arc Fault Detection in

In this article, a fault detection and isolation technique for series arc faults in dc microgrids with multiple power electronics loads is proposed using unknown input observers

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Fault detection and classification in hybrid energy-based multi

Due to their reliance on sizable fault currents, classic fault detection techniques are no longer suitable for microgrids that employ inverter-interfaced distributed generation.

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Fault Detection in a Single-Bus DC Microgrid

In addition, the effect of low line impedance and fault resistance may make fault detection in DC microgrids a challenge . The high amplitude of the DC fault current may damage the converters [13,14]. Another challenging issue

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Fault detection and location in medium‐voltage DC

Fast dc fault detection method is required in medium-voltage dc (MVDC) microgrids to avoid severe damage to the interfacing converters. Ensuring selectivity and sensitivity of the protection system within a few

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Integrating discrete wavelet transform with neural networks and

DOI: 10.1016/j.ijepes.2023.109616 Corpus ID: 265072105; Integrating discrete wavelet transform with neural networks and machine learning for fault detection in microgrids @article{Cano2024IntegratingDW, title={Integrating discrete wavelet transform with neural networks and machine learning for fault detection in microgrids}, author={Antonio Cano and

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Fault detection and location in medium‐voltage DC microgrids

A new technique based on fault launched travelling-waves (TWs) to detect, classify, and locate different dc fault types in MVDC microgrids is proposed, which utilises the frequency of TW reflections, rather than their arrival time. Fast dc fault detection method is required in medium-voltage dc (MVDC) microgrids to avoid severe damage to the interfacing

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Machine Learning Approaches for Fault Detection in Renewable Microgrids

problems in Fault Detection in Renewable Microgrids: Renewable microgrids provide special problems for fault detection owing to the fluctuation in energy output and the complex relationships between renewable sources. Traditional fault detection methods, which often depend on predetermined thresholds and rule-based

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Early detection of arc faults in DC microgrids using wavelet-based

While, the study proposes a new fault detection method for DC microgrids using traveling wave analysis with discrete wavelet transform (DWT). While simulations show effectiveness in both grid-connected and isolated modes with fast detection times and high resistance fault tolerance, the method''s reliance on accurate DWT feature extraction and its

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High‐speed algorithm for fault detection and location

This study proposes a novel protection algorithm using traveling waves (TWs) for fault detection and localization. The high-order synchrosqueezing transform (FSSTH) is applied to precisely identify TWs at

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An efficient protection scheme for critical fault detection in

To validate the protection scheme, diverse operating conditions have been used with different AC faults in the microgrid where achieved accuracy for mode identification

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A novel sequence component based fault detection index for

The complex magnitude-phase plane (MPP) of the fault detection index (FDI), is verified as a key indicator for fault detection in microgrid. The magnitude and angle of the FDI

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AI-Enhanced Inverter Fault and Anomaly Detection System for

This research emphasizes the critical role of effective fault detection in microgrids, particularly as they play an increasingly vital role in grid modernization. The study focused on identifying fault patterns not only in external power lines but also within internal components such as inverters, which are central to power electronics-based

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Fault Detection and Location in DC Microgrids by Recurrent

Fast detection of dc faults in medium-voltage dc (MVDC) microgrids poses a challenge as such faults can cause severe damage to voltage-sourced converters within few milliseconds.

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Fault classification and localization in microgrids: Leveraging

This research applies the algorithms DTE and LDA to detect faults in microgrids and faulty feeders, determine fault location in the faulted feeder, and discriminate between

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About Fault Detection in Microgrids

About Fault Detection in Microgrids

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6 FAQs about [Fault Detection in Microgrids]

Do DC microgrids require advanced protection techniques for fault detection and isolation?

Abstract: DC microgrids require advanced protection techniques for fault detection and isolation (FDI). In this work, an FDI method able to respond to different types of component faults is developed based on system modeling. First, the state-space representation of a multiterminal dc microgrid with component faults is derived.

Why is data-driven fault detection a major constraint for DC microgrids?

Good robustness against measurement noises and changes in system configurations. The lack of fault data is the major constraint on data-driven fault detection and isolation schemes for DC microgrids.

Does a dc microgrid have fault-like features?

The principle of the proposed TL scheme is to extract fault-like features from normal operating data. For this reason, those operating disturbances that perturb DC microgrids in similar ways to faults are the focus of this study. In this section, the current features in a DC microgrid during a fault and such a non-fault disturbance are analyzed.

How effective is FDI method for detecting faults in DC microgrids?

The performance of the proposed FDI method is verified under the real-time (RT) simulation of a three-terminal low-voltage dc microgrid and with a small-scale laboratory dc grid. The proposed FDI method is proved to be effective to detect and isolate different faults in dc microgrids with a response time of 1 ms.

Can a deep transfer learning model detect short-circuit faults in DC microgrids?

The lack of fault data is the major constraint on data-driven fault detection and isolation schemes for DC microgrids. To solve this problem, this paper develops an adversarial-based deep transfer learning model that can detect and classify short-circuit faults in DC microgrids without using historical fault data.

How can FDI be used in a multiterminal DC microgrid?

First, the state-space representation of a multiterminal dc microgrid with component faults is derived. Then, an FDI function based on observers is designed. To achieve the desired selectivity in fault isolation, the linear matrix inequality (LMI) optimization approach is adopted in the observer design.

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