Fault Mitigation Research Articles

Design Considerations of Bootstrap Gate Driver With Fault Mitigation for Si- and GaN-Based High-Current Converters

Design Considerations of Bootstrap Gate Driver With Fault Mitigation for Si- and GaN-Based High-Current Converters

Fault Diagnosis Review of Proton Exchange Membrane Fuel Cell Systems: Fault Mechanisms, Detection and Identification, and Fault Mitigation

Proton exchange membrane fuel cell (PEMFC) has become a hotspot due to its high efficiency, compact structure, and good dynamic operation efficiency. However, problems such as poor reliability and short lifespan create bottlenecks in its large‐scale applications. There has been a large amount of research on fault diagnosis and health management techniques dedicated to addressing the lifespan issues of PEMFC systems. This article provides an in‐depth analysis on the fault mechanism of PEMFC and systematically sorts out the types, causes, and impacts of faults. On this basis, the research progress of PEMFC fault diagnosis technology is summarized, and the measurement characterization methods for fuel cell status monitoring and fault detection are summarized. The literatures of model‐based and data‐driven fault identification methods are summarized and compared. The relevant mitigation measures for PEMFC faults are discussed. Finally, based on the challenges in the current research of fault diagnosis, people mainly conduct research on fault model, online diagnostic technology, and improving diagnostic mechanisms. Overall, this article can provide useful summary and guidance for future research.

Digital twin-enabled autonomous fault mitigation in diesel engines: An experimental validation

Due to the growing demand for robust autonomous systems, automating maintenance and fault mitigation activities has become essential. If an unexpected fault occurs during the travel, the system should be able to manage that fault autonomously and continue its mission. Thus, a robust fault mitigation system is needed that can quickly reconfigure itself in an optimal way. This paper presents a novel digital twin-based fault mitigation strategy that uses hierarchical control architecture. Here, a computationally efficient high-fidelity hybrid engine model is developed to simulate actual engine behavior. This hybrid engine model includes a neural network model representing the cylinder combustion process and well-studied physics-based analytical equations describing the remaining subsystems. This architecture uses a feedback controller on top of the control calibration map, generated offline using the hybrid model, to mitigate faults and modeling errors. The fault mitigation strategies are calibrated and validated through model-in-loop (MIL) and hardware-in-loop (HIL) simulations for various operating points using the Navistar 7.6 liters six-cylinder engine. The effectiveness of the proposed architecture in handling injector nozzle clogging, intake manifold leaks, and pressure shift faults is illustrated. The results demonstrate that the proposed architecture can completely overcome faults and maintain the desired torque in a few seconds. Moreover, the average accuracy of 96% is observed for the engine model compared to experimental data. It is anticipated that the proposed end-to-end architecture will be easily deployable on unmanned marine vessels and can be extended to accommodate other component faults.

Fault classification and mitigation in grid-connected solar PV system using hybrid whale optimization algorithm with ANFIS

ABSTRACT Generally, fault mitigation and classification technique has become an important issue in grid-connected PV (GCPV) systems. This study intends a control topology for fault grouping and mitigation scheme in GCPV systems. Whale optimization algorithm (WOA)-ANFIS controllers are applied to uphold the DC-link voltage in UPQC as a perpetual limit. The ANFIS algorithm is employed to attain the apprising procedure of the WOA. The ANFIS controller is working based on the ANN (Artificial Neural Network) and FIS (Fuzzy Interference system). Additionally, the fault classification is proposed in this paper. The fault classification is attained through the use of DWT and ANFIS. Initially, the normal signals are analyzed; the fault is created and extracted in the consumption of DWT. Then the extracted characteristics are fed to the ANFIS for classification. The ANFIS classified faults in the consumption of fuzzy rules. The goals of the projected model are to classify and mitigate the faults in GCPV systems. The projected system is executed using MATLAB/Simulink. So as to investigate the efficacy of the projected technique, the several categories of concerns are examined and verified with the prevailing tactics for instance, UPQC with PSO-ANFIS, UPQC with WOA-ANN method.

Taking the MPI standard and the open MPI library to exascale

The Open MPI for Exascale (OMPI-X) project was one of two in the Exascale Computing Project (ECP) focused on advancing the MPI ecosystem. The OMPI-X team worked with other MPI Forum members to champion several important features for inclusion in the MPI 4.0, 4.1, and upcoming 5.0 MPI standard versions, in support of the needs of exascale applications and systems. The team also worked with the larger Open MPI community to bring implementations of these new features and other enhancements into Open MPI, one of the leading open-source implementations of the MPI interface. This paper describes the motivation for the work of the OMPI-X project in the context of exascale computing needs, the nature of the resulting new capabilities in the MPI standard, and how they were implemented in the Open MPI library. Features include improved support for “MPI + X” programming models through partitioned communications and support for user-level threading, sessions, fault tolerance through the user-level fault mitigation (ULFM) and Reinit models, and other features. We also discuss enhancements to Open MPI providing improved performance and scalability for existing features, such as collective operations, one-sided operations, support for the Slingshot-11 interconnect of the initial exascale systems, and how the OMPI-X team worked to improve quality assurance for the Open MPI library, particularly on platforms of interest to the Department of Energy community.

Machine Learning-Based Renewable Energy Systems Fault Mitigation and Economic Assessment

In an era increasingly focused on sustainability, the adoption of renewable energy stands as a promising avenue for fostering local economic growth. This study presents a novel approach, merging advanced fault mitigation techniques and machine learning, to assess the economic impact of renewable energy systems (RES) at the local level. Leveraging random forest, support vector machines (SVM), and gradient boosting, customized algorithms are deployed for regression analysis and defect identification. Hyperparameter optimization ensures optimal performance, with a linear regression meta-learner facilitating the fusion of predictions. An advanced anomaly detection component effectively identifies and rectifies errors within RES. Performance evaluation metrics, including an root mean square error (RMSE) of 2.18 and an overall system efficiency of 98%, underscore the success of the fault mitigation strategy. Precision, recall, and F1-score metrics further highlight its robustness. This comprehensive framework not only provides precise estimates of the financial impact of renewable energy adoption but also enhances the reliability of RES through sophisticated fault mitigation. Empowering decision-makers with actionable insights, it facilitates sustainable energy planning, effective policy implementation, and the establishment of resilient energy systems.

Physics-informed deep learning to quantify anomalies for real-time fault mitigation in 3D printing

In 3D printing processes, there are many thermal stress related defects that can have a significant negative impact on the shape and size of the structure. Such anomalies in the heat transfer of the printing process need to be detected at an early stage. Understanding heat transfer is crucial, and simulation models can offer insights while reducing the need for costly experiments. Traditional numerical solvers for heat transfer can be complex to adapt to diverse printed part geometries, and their reliance on predefined mathematical models limits their flexibility. Our physics-informed deep learning (PIDL) approach eliminates the need for discretization, simplifying the analysis of complex geometries and enabling automation. The drawback of parametric PIDL is their scalability for high-dimensional problems. Computational time, energy and cost of training prevent real-time analysis. It often takes only a few seconds to print a single layer. We can show an energy efficient transfer and training strategy to reduce the computational effort of PIDL significantly. The approach is able to quantify relevant effects of thermal stresses and mitigate errors during selective laser melting (SLM). To this end, heat transfer is modelled, simulated and analysed using high-dimensional data obtained from printing experiments with different geometries of metal components. The proposed method is applied to the solving forward problem of heat transfer prediction. The governing results are based on the heat equation, which is integrated into a deep neural network (DNN).

Investigation and mitigation of unbalanced hall sensor signal faults in sensored brushless DC motor drives

Investigation and mitigation of unbalanced hall sensor signal faults in sensored brushless DC motor drives

Fault mitigation mechanism to pave the way to accommodate over 90% renewable energy in electric power systems

Renewable energy plays a key role in reducing carbon emissions. The electric power system is widely considered a platform to accommodate renewable energy because of the high efficiency in transmitting energy through electricity tie lines. However, most renewable energy generation is weather-dependent, bringing difficulties in balancing generations and electricity demands. There are two major solutions to address the imbalance problem. The first is to balance the fluctuation of renewable energy in time scales by deploying energy storage, but with huge costs and security risks. The other is to balance the fluctuation in spatial scales by enhancing the interconnection among electric power systems, which however results in a sharp increase in the fault current in many countries. The high level of fault current threatens the security of power system. In other words, the overrating fault current of electric power systems is one of the key bottlenecks for carbon neutrality. To mitigate the high-level fault current because of the interconnection, we propose a novel mechanism, namely fault current release. It achieves new functionalities with proven equipment at low cost and will create no impact on the electric power systems in normal operation. The simulation selected six representative electric power systems, including those in the USA, the UK, China, Brazil, Nigeria and IEEE reliable test system. Results show that the fault current release method enables an increase of renewable energy accommodation from 19.2% ∼ 47.2% to over 90%, much higher than the existing methods can do. Correspondingly, the emissions of CO2, NOX and SOX in these systems decrease by 67.84% ∼ 88.07%, 61.17% ∼ 88.24% and 58.25% ∼ 88.33%, respectively. The proposed method is also economical in that it can accommodate renewable energy at about one-tenth the cost of energy storage. Our findings hence suggest a promising way to build a carbon-free energy system.

Design and Control for High-Reliability Power Electronics: State-of-the-Art and Future Trends

Electrification is a mega-trend in the modern economy, urged by the need for a more ecological and sustainable impact of new technologies, among many other merits related to the process of electrification. The widespread adoption of renewable energy, transportation electrification, energy storage systems and smart manufacturing is remarkable. As a result, power electronics has become the critical enabling technology for improving energy efficiency and sustainability. In recent years, the reliability of power electronic equipment has been under scrutiny, especially in mission-critical applications of power electronics converters. Recently, special attention has been paid to the development of lifetime models of the main electronic components, which could then be applied to probabilistic analysis based on the mission profile of system-level reliability. This prediction methodology has some limitations because it does not consider external factors and agents other than the wear itself. Redundancy has been the typical approach to improve the availability of mission critical power electronic systems, which could be associated with considerable extra cost. Fault-tolerant converters were proposed as a more cost-effective alternative solution that could also extend the service life. The maintenance cost could be reduced by using active thermal control, which allows the balancing of wear damage in modular power electronic converters and, consequently, the scheduling of maintenance events. A further step could be taken with the application of real-time condition monitoring of systems, which has evolved into the concepts of predictive and prescriptive maintenance that allows for prediction and mitigation of faults. These techniques are under active development and are expected to benefit from new methods and technologies from rapidly progressing areas of artificial intelligence and digital twin. This paper provides a detailed overview on the recent trends and advances in the field of high-reliability power electronics systems.

Fault Mitigation in Seven-Level Diode Clamped with Static Switch Based Fourth Leg Inverter Topology for Induction Motor Drives

Fault Mitigation in Seven-Level Diode Clamped with Static Switch Based Fourth Leg Inverter Topology for Induction Motor Drives

Intelligent optimisation in smart and sustainable compressed air systems: Towards support for decision-making under faulty conditions

The monitoring of complex industrial systems through the implementation of smart approaches provides unique opportunities, such as the characterisation of their real-time performance. Within this scope, there exists the need to support decision-making during maintenance processes, due to the presence of a multitude of faults in real-world systems and the difficulty of identifying the appropriate mitigating solutions. The proposed solution uses intelligent optimisation techniques to identify the ideal control solution within these complex systems when faults arise. This paper presents a framework based on an intelligent optimisation approach, which provides a workflow process for the support of decision-making during faulty situations. It is adapted and implemented to the demand-side of a Compressed Air System (CAS), thus providing a holistic approach in automating fault mitigation during real-time system operation. In implementing this framework, multiple intelligent optimisation techniques such as the Genetic Algorithm and the Particle Swarm Optimisation algorithms were adopted and implemented. Both algorithms were successful in providing the ideal control solution under fault conditions. For a typical production case study, the proposed optimisation approach results in a reduction of 40% in its air consumption, which directly improves its environmental performance and energy costs. This result demonstrates that this approach contributes a suitable control strategy for CAS when experiencing a pneumatic fault, which has a direct positive effect on the operational energy performance and costs.

Evaluation of Fault Mitigation Techniques Based on Approximate Computing Under Radiation

Evaluation of Fault Mitigation Techniques Based on Approximate Computing Under Radiation

A deep learning-based transformer model for photovoltaic fault forecasting and classification

According to the US-based National Renewable Energy Lab (NREL), solar energy losses due to faults were 3.5 % in 2004, which increased to 17.5 % in 2018. Therefore, the fault prediction mechanism will enable PV practitioners to reduce losses effectively, enhancing the solar system's efficiency and power output. This paper proposes a deep learning-based Transformer model for robust fault prediction in photovoltaic. Transformer uses attention mechanism that considers data points as a language units “word” and learn dependencies among them to predict upcoming data points. Unlike other forecasting algorithms, our proposed approach does not rely on previous trends. In case of PV faults, trends do not exist. The proposed algorithm utilizes rate of change of solar cell parameters for establishing a trend to forecast faults, enabling proactive fault mitigation. It also classifies faults with different severity levels to identify the level of predictive maintenance required. The proposed approach is extensively evaluated using MATLAB on datasets of several faults with low, medium, and high severity levels. The proposed Transformer model achieves a forecasting mean average error (MAE) of 0.09377. Performance of the proposed forecasting and classification algorithm is compared with existing machine learning-based regression and classification techniques such as KNN, SVM, and NN, where proposed approach outperforms state-of-the-art approaches.

A Passive Inter Turn Short Circuit Fault Mitigation Technique for Electric Machines

A Passive Inter Turn Short Circuit Fault Mitigation Technique for Electric Machines

Intelligent Fault Detection and Classification Schemes for Smart Grids Based on Deep Neural Networks

Effective fault detection, classification, and localization are vital for smart grid self-healing and fault mitigation. Deep learning has the capability to autonomously extract fault characteristics and discern fault categories from the three-phase raw of voltage and current signals. With the rise of distributed generators, conventional relaying devices face challenges in managing dynamic fault currents. Various deep neural network algorithms have been proposed for fault detection, classification, and location. This study introduces innovative fault detection methods using Artificial Neural Networks (ANNs) and one-dimension Convolution Neural Networks (1D-CNNs). Leveraging sensor data such as voltage and current measurements, our approach outperforms contemporary methods in terms of accuracy and efficiency. Results in the IEEE 6-bus system showcase impressive accuracy rates: 99.99%, 99.98% for identifying faulty lines, 99.75%, 99.99% for fault classification, and 98.25%, 96.85% for fault location for ANN and 1D-CNN, respectively. Deep learning emerges as a promising tool for enhancing fault detection and classification within smart grids, offering significant performance improvements.

Integrating Non-Positional Numbering Systems into E-Commerce Platforms: A Novel Approach to Enhance System Fault Tolerance

In the dynamic landscape of electronic commerce, the robustness of platforms is a critical determinant of operational continuity and trustworthiness, necessitating innovative approaches to fault tolerance. This study pioneers an advanced strategy for enhancing fault tolerance in e-commerce systems, utilizing non-positional numbering systems (NPNS) inspired by the mathematical robustness of the Chinese Remainder Theorem (CRT). Traditional systems rely heavily on positional numbering, which, despite its ubiquity, harbors limitations in flexibility and resilience against computational errors and system faults. In contrast, NPNS, characterized by their independence, equitability, and residue independence, introduce a transformative potential for system architecture, significantly increasing resistance to disruptions and computational inaccuracies. Our discourse extends beyond theoretical implications, delving into practical applications within contemporary e-commerce platforms. We introduce and elaborate on new terminologies, concepts, and a sophisticated classification system for fault-tolerance mechanisms within the framework of NPNS. This nuanced approach not only consolidates understanding but also identifies underexplored pathways for resilience in digital commerce infrastructure. Furthermore, this research highlights the empirical significance of adopting NPNS, offering a methodologically sound and innovative avenue to safeguard against system vulnerabilities. By integrating NPNS, platforms can achieve enhanced levels of redundancy and fault tolerance, essential for maintaining operational integrity in the face of unforeseen system failures. This integration signals a paradigm shift, emphasizing proactive fault mitigation strategies over reactive measures. Conclusively, this study serves as a seminal reference point for subsequent scholarly endeavors, advocating for a shift towards NPNS in e-commerce platforms. The practical adaptations suggested herein are poised to redefine stakeholders’ approach to system reliability, instigating a new era of confidence in e-commerce engagements.

Low-voltage ride-through capability in a DFIG using FO-PID and RCO techniques under symmetrical and asymmetrical faults

The power grid faults study is crucial for maintaining grid reliability and stability. Understanding these faults enables rapid detection, prevention, and mitigation, ensuring uninterrupted electricity supply, safeguarding equipment, and preventing potential cascading failures, ultimately supporting the efficient functioning of modern society. This paper delves into the intricate challenge of ensuring the robust operation of wind turbines (WTs) in the face of fault conditions, a matter of substantial concern for power system experts. To navigate this challenge effectively, the implementation of symmetrical fault ride-through (SFRT) and asymmetrical fault ride-through (AFRT) control techniques becomes imperative, as these techniques play a pivotal role in upholding the stability and dependability of the power system during adverse scenarios. This study addresses this formidable challenge by introducing an innovative SFRT–AFRT control methodology based on rotor components optimization called RCO tailored for the rotor side converter (RSC) within a doubly-fed induction generator (DFIG) utilized in wind turbine systems. The proposed control strategy encompasses a two-fold approach: firstly, the attenuation of both positive and negative components is achieved through the strategic application of boundary constraints and the establishment of reference values. Subsequently, the optimization of the control characteristic ‘beta’ is accomplished through the utilization of a particle swarm optimization (PSO) algorithm integrated within an optimization loop. This intricate interplay of mechanisms aims to optimize the performance of the RSC under fault conditions. To measure the efficacy of the proposed control technique, a comparative analysis is conducted. Fractional-order (FO) proportional–integral–derivative (PID) controllers are employed as an additional method to complement the novel approach. By systematically juxtaposing the performance of the proposed SFRT–AFRT control technique with the FO-PID controllers, a comprehensive evaluation of the proposed approach's effectiveness is attained. This comparative assessment lends valuable insights into the potential advantages and limitations of the novel control technique, thereby contributing to the advancement of fault mitigation strategies in WT systems. Finally, the paper highlights the economic viability of the proposed control method, suggesting its suitability for addressing broader power network issues, such as power quality, in future wind farm research.

An Enhanced Lightweight Security Gateway Protocol for the Edge Layer

With the rapid expansion of the Internet of Things (IoT), the necessity for lightweight communication is also increasing due to the constrained capabilities of IoT devices. This paper presents the design of a novel lightweight protocol called the Enhanced Lightweight Security Gateway Protocol (ELSGP) based on a distributed computation model of the IoT layer. This model introduces a new type of node called a sub-server to assist edge layer servers and IoT devices with computational tasks and act as a primary gateway for dependent IoT nodes. This paper then introduces six features of ELSGP with developed algorithms that include access token distribution and validation, authentication and dynamic interoperability, attribute-based access control, traffic filtering, secure tunneling, and dynamic load distribution and balancing. Considering the variability of system requirements, ELSGP also outlines how to adopt a system-defined policy framework. For fault resiliency, this paper also presents fault mitigation mechanisms, especially Trust and Priority Impact Relation for Byzantine, Cascading, and Transient faults. A simulation study was carried out to validate the protocol’s performance. Based on the findings from the performance evaluation, further analysis of the protocol and future research directions are outlined.

Reconfigurable tolerant control of nonlinear Euler–Lagrange systems under actuator fault: A reinforcement learning-based fixed-time approach

This paper presents a novel fixed-time adaptive Fault Tolerant Control (FTC) framework for MIMO nonlinear Euler-Lagrange systems using sliding mode-based strategy, reinforcement learning (RL) and fixed-time disturbance observer. The primary objective is to enhance system reliability in the presence of actuator faults, uncertainties and disturbances. The proposed RL algorithm incorporates an actor-critic neural network (NN), where the actor NN estimates the uncertainty and the critic NN evaluates the performance cost function. Additionally, a fixed-time adaptive observer is designed to estimate the lumped term of faults and disturbances. To achieve high precision trajectory tracking within a fixed-time interval, a nonsingular fast terminal sliding mode scheme is designed. This scheme ensures fixed-time convergence of the tracking error and facilitates disturbance attenuation and fault mitigation, which are key features of the proposed fixed-time secure control strategy. Furthermore, the closed-loop system's fixed-time stability is analyzed using Lyapunov theory. Experimental results demonstrate the effectiveness of the proposed FTC framework in mitigating the adverse effects of faults, uncertainties and disturbances, thereby enhancing system performance and reliability.