Microgrid Fault Research Articles

Fault Diagnosis of Power Components with Reliability Assessment in Extraterrestrial Microgrids

This research investigates the possible failures caused by aging and other environmental and external factors that could significantly impact the performance of extraterrestrial power systems. Additionally, it presents a reliability assessment model for the space microgrid based on fault tree analysis (FTA). The reliability assessment model developed in this paper represents a tool that can be used by engineers to harden the system design for operational and economic benefits. To improve the reliability of the system, this work provides a broad review of the different fault detection and diagnosis (FDD) algorithms used for power microgrids and space applications. Using data sets from the Habitat Simulator developed through the NASA-funded Resilient Extraterrestrial Habitat Institute, this paper compares the applicability and accuracy of the different FDD methods. The primary FDD approach proposed and assessed in this work is based on the Markov reliability model. It predicts and detects future faults in the space microgrids by using past data samples and categorizing them into different classes. Data-driven-based models such as artificial neural networks are also investigated, tested, and evaluated using simulation data sets. According to the simulation results and the broad FDD algorithm comparison, this study provides the crew or maintenance engineers with a clear methodology to detect and localize power system failures.

Computer Vision Search System for the Identification of Power Flow Faults in Micro Electric Networks

To improve the safety of microgrid operation, this paper uses a computer vision search system to study the identification of power flow faults in microgrids. Firstly, a power flow fault detection method based on a message transmission network was proposed, and the fault mapping from the microgrid fault to each node was constructed, and the power flow fault of each node in the microgrid was extracted. Then, by migrating the long short-term memory network to identify power flow faults in microgrids, the influence of computer vision search system on power flow fault identification in microgrids was comprehensively analyzed. The simulation results show that the proposed method can effectively improve the accuracy of power flow fault identification in the microgrid to improve the safety of the microgrid operation.

Early detection of arc faults in DC microgrids using wavelet-based feature extraction and deep learning

Early detection of arc faults in DC microgrids using wavelet-based feature extraction and deep learning

Microgrid Fault Detection and Classification using HHT and Decision Tree

Microgrid Fault Detection and Classification using HHT and Decision Tree

A Novel Observer-Centric Approach for Detecting Faults in Islanded AC Microgrids With Uncertainties

A Novel Observer-Centric Approach for Detecting Faults in Islanded AC Microgrids With Uncertainties

An advanced graph algorithm-based protection strategy for detecting kilometric and cross-country faults in DC microgrid

Low voltage DC microgrids (LVDC) are on rise, because of increase in usage of electronics-based utility loads. However, the protection and safety aspects of these grids remain unresolved due to the fault current magnitude, unnecessary tripping, and blinding of protection. In this paper, an adaptive differential protection system incorporated with an advanced graph algorithm is proposed for DC microgrid. This graph algorithm is a combination of Fenwick tree and Bidirectional Dijkstra algorithm. Fenwick tree algorithm is used to determine the network configuration and Bidirectional Dijkstra algorithm is used to determine the least distance from the fault location to the nearest distributed generations. The developed protection scheme is applied to a 7 bus, 400 V DC microgrid setup using Real Time simulator (control hardware in loop) to detect and clear the faults such as kilometric faults, and cross-country faults. In terms of efficiency, the proposed algorithm demonstrates superiority over the conventional algorithm by detecting and rectifying faults in 384 μs.The results depict the robustness of the protection setup in clearing the faults rapidly with minimum network disconnection.

A novel sequence component based fault detection index for microgrid protection

The paper introduces a novel protective relaying method using symmetrical components of current to detect asymmetrical faults in microgrid. This approach is focused on utilizing the complex magnitude-phase plane (MPP) of the fault detection index (FDI), which is computed using the positive and negative sequence components of the current signal retrieved at one end of the faulty line. The MPP obtained from the settling values of the FDI's magnitude (MFDI) and FDI's angle (AFDI) after the fault inception, is considered as the key indicator for fault detection. The scheme is comprehensively evaluated for a wide range of fault situations including variations in fault types, fault locations, fault resistances and DG penetrations in grid-connected (GC) and non-grid-connected (NGC) modes of operation. Several critical no fault cases are also tested to show the efficacy of the proposed logic for false operation. The fault study is conducted extensively on a modified low voltage standard Consortium for Electric Reliability Technology Solutions (CERTS) microgrid and a standard IEEE-34 bus distribution system including DGs. Furthermore, the proposed FDI-based scheme is tested on MATLAB/Simulink platform and validated on Typhoon HIL platform to assess the real-time performance. The test results show that this FDI-based scheme using the MPP concept can be a potent solution for the protection of microgrid.

Empirical Evaluation of Microgrid Fault Identification Models from a Statistical Perspective

Microgrid fault identification models are developed via integration of extensive data collection, pre-processing of collected data, current & voltage segmentation, feature representation, identification of variant feature sets, their classification & post-processing operations. Existing models that perform microgrid fault identification are either highly complex, or cannot be applied for heterogeneous fault types. Moreover, these models also showcase large variance in terms of their qualitative & quantitative performance levels. Due to these issues, it is difficult for researchers to identify optimum models for their performance-specific deployment use cases. To overcome these issues, a detailed review of different microgrid fault detection & mitigation models in needed, which can evaluate their performance in terms of qualitative & quantitative parameters. Thus, this text initially discusses characteristics of some of the recently proposed microgrid fault detection models in terms of their functional nuances, application specific advantages, deployment specific limitations, and context-specific future research scopes. After referring this discussion, it was observed that linear models that incorporate pattern recognition are highly useful for fault pre-emption and mitigation purposes. This text also compares these models in terms of their accuracy of detection, delay needed for fault identification, computational complexity, deployment cost, and scalability metrics.

Novel protection method for AC microgrids with multiple distributed generations using Unscented Kalman filter

Traditional protection methods were inadequate in multiple distributed generations (MDG) microgrids due to the bidirectional power flow/high fault current in the grid-connected (GC) mode, and reduced fault current in the islanded (ID) mode. Therefore, a reliable and robust protection method has become of paramount importance for such MDG-based microgrids. This article suggests an Unscented Kalman filter (UKF) to detect, classify, and locate faults in such MDG-based microgrids. Initially, the harmonic content is generated from the UKF-estimated current signal to compute the total harmonic RMS factor (THRF). Then the changes in the THRF of each phase are cross-checked to a pre-specified threshold level to distinguish the fault conditions. Furthermore, the cumulative covariance-dependent reactive energy (CCDRE) is computed from both the estimated state and covariance matrix of voltage and current signals provided by UKF. Finally, the directional trends in the CCDRE are used to identify the fault zone in the microgrids. The effectiveness of the proposed method is validated through ample simulations on CIGRE and IEC 61,850–7–420 microgrid test system via MATLAB® Simulink. The results illustrate that the presented strategy is robust in both the GC mode and the ID mode of operation in meshed and radial topologies with 99.9% accuracy.

A Comprehensive Fault Detection and Isolation Method for DC Microgrids Using Reduced-Order Unknown Input Observers

Fault diagnosis is of critical importance to the safety of power electronic devices in DC microgrids. To detect and isolate different component faults in DC microgrids, this paper introduces a comprehensive protection scheme using reduced-order unknown input observers (ROUIOs). As opposed to conventional protection strategies, the proposed method provides a centralized fault detection and isolation (FDI) solution for DC microgrids that covers multiple faults occurring in different components in a unified process. Moreover, it reduces the complexity of observer model and relaxes the requirements of measurement signals compared with existing observer-based FDI methods for DC microgrids. To this end, the state-space model of a multi-terminal DC microgrid with different faults is first established. On this basis, a bank of ROUIOs are designed with the aim of classifying different component faults in the system. At last, the performance of the proposed FDI method is verified through numerical simulations with MATLAB/Simulink and hardware tests. Test results show that the proposed method can accurately detect and isolate different component faults in DC microgrids in a short response time of 1 ms.

The Principles of Controlled DC-Reactor Fault Current Limiter for Battery Energy Storage Protection

The significance of battery energy storage systems (BESS) technology has been growing rapidly, mostly due to the need for microgrid applications and the integration of renewables. Relevant to the importance of utilization of BESS in microgrids, the protection of the BESS during microgrid faults has become a concern too. The short-circuit in a microgrid cause over-current for all of the integrated sources. BESS, as one of the sources in the microgrid, is heavily influenced by fault occurrence. The overcurrent can easily damage power electronic converter switches, battery management systems (BMS), and damage battery banks. Fault current limiters (FCLs) are appropriate protection devices that have been massively studied. This paper proposes a controllable reactor fault current limiter (CRFCL) to protect the BESS against fault currents. The proposed CRFCL can control the fault current value supplied by BESS during a fault condition as a current regulator. It is realized by means of the operation of solid-state switches and series DC reactor behavior. The main achievement of CRFCL is the protection of BESS against fault currents without delay. The simulations of the proposed structure are carried out in a MATLAB/SIMULINK platform, and they are confirmed and validated by experimental test results.

Machine Learning Approaches for Fault Detection in Renewable Microgrids

This paper presents a novel use of machine learning techniques for identifying faults in renewable microgrids within the field of decentralized energy systems. The study investigates the effectiveness of machine learning models in identifying abnormalities in dynamic and variable microgrid environments. It utilizes a comprehensive dataset that includes parameters such as solar, wind, and hydro power generation, energy storage status, and fault indicators. The investigation demonstrates a notable 94% precision in identifying faults, highlighting the superiority of machine learning compared to conventional rule-based approaches, which attained an accuracy rate of 80%. The precision and recall measures emphasize the well-balanced performance of the machine learning models, reducing both false positives and false negatives, and guaranteeing precise problem detection. The effect of faults on microgrid efficiency is significantly reduced, with an only 2% decrease recorded under fault situations, demonstrating the models’ ability to maintain an efficient energy supply. A comparative study reveals a 14% improvement in accuracy when compared to conventional techniques, emphasizing the benefits of adaptive and data-driven approaches in identifying intricate fault patterns. The sensitivity study validates the resilience of the machine learning models, demonstrating their capacity to adjust to different settings. The practical application of the models is validated by real-world testing in a simulated microgrid environment, which leads to their repeated improvement and improved performance. Ethical concerns play a crucial role in assuring ethical data use during research, particularly in the implementation of machine learning, by upholding privacy and security requirements. The study results indicate significant implications for identifying faults in renewable microgrids, providing a potential opportunity for the progress of robust and sustainable decentralized energy networks. The effectiveness of machine learning models stimulates further study in expanding their deployment for varied microgrid situations, including more machine learning approaches, and resolving obstacles associated with real-time application in operational settings.

Classification and Localization of Faults in AC Microgrids through Discrete Wavelet Transform and Artificial Neural Networks

Classification and Localization of Faults in AC Microgrids through Discrete Wavelet Transform and Artificial Neural Networks

Research on Generic Diagnostic Methods for Short-Circuit Faults in AC Microgrids

Research on Generic Diagnostic Methods for Short-Circuit Faults in AC Microgrids

Machine learning approaches for fault detection in renewable microgrids

This study focuses on investigating and using machine learning (ML) methods to identify faults in renewable microgrids. It highlights the difficulties and intricacies associated with these dynamic energy systems. The examination of real-world data obtained from solar and wind power production, battery storage status, fault signals, and machine learning model performance highlights the complex nature of fault detection techniques in renewable microgrids. An analysis of data on renewable energy production demonstrates oscillations in the outputs of solar and wind power, highlighting differences of about 5-10% across certain time periods, thereby illustrating the intermittent characteristics of renewable energy sources. Simultaneously, the energy stored in batteries inside the microgrid shows a progressive decrease of about 3-5% in stored energy levels across time intervals, indicating possible consequences for the stability of the system. The fault detection signals display erratic patterns, which emphasize the intricacies involved in finding and categorizing issues inside the system. The assessment of machine learning models, which includes both supervised and unsupervised learning methods, reveals many performance measures. Supervised models provide greater accuracy rates, often ranging from 85% to 90%. However, they are prone to occasional misclassifications. In contrast, unsupervised models provide a moderate level of accuracy, often ranging from 75% to 80%. They exhibit flexibility in detecting faults, but their precision is limited. The study highlights the need of using a combination of supervised and unsupervised machine learning models to improve the accuracy of fault detection in renewable microgrids. These results provide valuable understanding of the intricacies and difficulties of fault detection procedures, which may lead to further progress in improving the dependability and durability of renewable microgrid systems.

Detect, Classify, and Locate Faults in DC Microgrids Based on Support Vector Machines and Bagged Trees in the Machine Learning Approach

Detect, Classify, and Locate Faults in DC Microgrids Based on Support Vector Machines and Bagged Trees in the Machine Learning Approach

Microgrid Fault Detection Method Coordinated with a Sequence Component Current-Based Fault Control Strategy

Microgrid Fault Detection Method Coordinated with a Sequence Component Current-Based Fault Control Strategy

Dimension Reduction Techniques for Machine Learning-Based AC Microgrid Fault Diagnosis: A Systematic Review

Dimension Reduction Techniques for Machine Learning-Based AC Microgrid Fault Diagnosis: A Systematic Review

A Communication-Assisted Distance Protection for AC Microgrids considering the Fault-Ride-Through Requirements of Distributed Generators

The conventional overcurrent protection is ineffective in isolating faults in microgrids due to the low fault current levels contributed by inverter-interfaced distributed generations (IIDGs). To extend the microgrid protection methods, the distance protection is considered as a common alternative to detect faults. However, the fault-ride-through (FRT) requirements and the high impedance fault (HIF) detection challenge the application of the conventional distance protection. To solve the two problems, a new inverse-time distance (ITD) protection for medium-voltage AC microgrids was proposed in this paper. The primary ITD protection was designed to meet the FRT requirements, and the backup protection was coordinated to isolate faults. The fault resistance and distributed generation infeed effect on the measured impedance were mitigated by a communication-assisted method proposed in this paper. Owing to the shorter communication channel time in distribution feeders and lower communication investments without a synchronous clock, the proposed method can be used to improve the ITD protection performance. An 11 kV AC microgrid was simulated with different fault types in the islanded mode and grid-connected mode. Time domain simulation verified the effectiveness of the proposed ITD protection.

Fault Detection in a Single-Bus DC Microgrid Connected to EV/PV Systems and Hybrid Energy Storage Using the DMD-IF Method

Variations in fault currents, short times to clear the fault, and a lack of a natural current zero-crossing point are the most important challenges that DC microgrid protection faces. This challenge becomes more complicated with the presence of electric vehicles and energy storage systems due to their uncertainties. For this reason, in this paper, a new method for fault detection in DC microgrids with the presence of electric vehicles and energy storage systems is proposed. The new proposed method uses the combination of dynamic mode decomposition and instantaneous frequency for fault detection. In this method, first, a reference signal is made using the voltage and current signal sampled from the DC microgrid using the dynamic mode decomposition method. Next, in order to detect the fault, the instantaneous frequency value of the reference signal is calculated by the Hilbert transform. The simultaneous use of voltage and current signals reduces the transient effects of the control system on the proposed protection method. In order to measure voltage and current signals, only one intelligent electronic device unit is used in this paper. The proposed new method has been tested on a single-bus DC microgrid with the presence of electric vehicles and energy storage systems in MATLAB 2019b software. The results show that this method can detect all types of faults in DC microgrids, electric vehicles, and photovoltaics. Also, this method is immune to the uncertainties of the generation of distributed generation resources and the existence of noise distortions in the measured signals.