Fault Mitigation Research Articles

Mitigating Misfire and Fire-through Faults in Hybrid Renewable Energy Systems Utilizing Dynamic Voltage Restorer

Recently, there was a great focus on integrating renewable energy sources (RESs) into electrical power systems (hybrid systems) due to their many environmental and economic advantages. The output of most of these RESs is DC; some power electronic devices, including inverters, must be used to integrate these RESs into the electrical grid. Any maloperation, faults, or improper control in these power electronic devices will enormously affect these hybrid systems’ performance. This paper aims to mitigate the misfire and fire-through faults that occur at the switching of the inverter that connects three renewable sources: PV, wind, and the fuel cell to the grid. This mitigation of such inverter faults (misfire and fire-through) is performed through optimal tuning of the PI controller driving a dynamic voltage restorer (DVR) connected at the system’s AC side. The optimization technique used is particle swarm optimization (PSO). While mitigating these two inverter faults using the PI-PSO controller for the DVR, improved system performance through voltages, currents, and powers waveforms is achieved. Besides, the three renewable sources were kept in continuous operation without disconnection from the system during these faults.

Reactive Hybrid Model for Fault Mitigation in Real-Time Cloud Computing

The Fourth Industrial Revolution is driven by cloud computing for increased productivity and efficiency. Despite the influence of cost-effectiveness, on-demand service and scalability, cloud computing is faced with many challenges such as security, performance, and fault tolerance. This research proposes a reactive mitigation model using checkpointing and replication techniques to overcome the challenges. The developed model has four layers: client/user, controller, fault tolerance (hybrid) and virtual machines layers. The functionalities of the developed model were compared to existing system using the same simulation conditions. The results showed a relative improvement over the existing system in terms of response time and computational cost and the developed system is able to react to faults by transferring requests to stable VMs and resume task execution at the last checkpoint. The developed model proved to work efficiently under various simulation conditions.

Design of a quality of service‐aware fault‐proof secure Q‐learning‐based Internet of Things kernel patch with multipath and multichannel capabilities

SummaryIn recent times, the expeditious growth of Internet of Things (IoT) offers applications to ease day‐to‐day activities with minimum human effort. Once the IoT application installed, the connected devices perform their tasks without human intervention. Hence, the need of performance optimization and security enhancement is vital to minimize end‐to‐end communication delay, improve kernel‐level security, mitigate faults adaptively, and have suitable backup options in case of node failure. This paper proposes a quality of service (QoS)‐aware fault‐proof secure Q‐learning‐based IoT (QIoT) kernel‐level protocol that integrates multipath aggregation and fuzzy authentication for security, with multichannel communication for improved QoS. Especially, this protocol integrates a source‐level clustering mechanism based on Q‐learning that aims at reducing route search delay. In order to provide fault tolerance, the kernel is equipped with real‐time fault‐tolerance mechanism that is activated in case of node‐level faults. Due to integration of Q‐learning, computational overheads are reduced by over 15% when compared with Zephyr, AliOS, and RTX kernels. This reduction in computational overheads facilitates light‐weight behavior of the kernel, due to which other QoS parameters like energy consumption, throughput, and routing overhead are reduced. The proposed QIoT kernel‐level protocol is compared with standard kernel modules, and performance evaluation showcases an improvement in authentication security by 8%, end‐to‐end delay by 5%, energy efficiency by 25%, and fault mitigation by 18%, thereby assisting the use of the proposed kernel for real‐time deployments.

Transmissibility-Based Health Monitoring of the Future Connected Autonomous Vehicles Networks

Transmissibility is a mathematical model that relates a subset of a system’s outputs to another subset of outputs of the same system without knowledge of the external excitation or the dynamics of the system. This study investigates fault detection, localization, and mitigation of connected autonomous vehicles (CAV) platoons using transmissibility operators. A CAV platoon is a network of connected autonomous vehicles that communicate together to move in a specific path with the desired velocity. Failure in a physical component of a vehicle, or failure in the form of an internal delay, a cyber-attack, or a communication time-delay affects the safety and security of the CAV platoons. In this paper, we use measurements from sensors available in CAV platoons to identify transmissibility operators, which are used for health monitoring, fault localization, and fault mitigation in the platoon. We first consider the case of vehicle-to-cloud communication (V2C) to monitor the platoon’s health. Then, we assume that the platoon loses communication with the cloud, and we monitor the health of the platoon based on vehicle-to-vehicle (V2V) communication. We apply the proposed technique to a model of the platoon obtained using the bond graph approach, and an experimental setup consisting of three connected autonomous robots.

Mismatching and partial shading identification in photovoltaic arrays by an artificial neural network ensemble

Photovoltaic arrays may suffer from a number of temporary and permanent faults. Partial shading and soiling belong to the former group, while cell cracking and delamination fall within the latter one. In these cases, the shape of the current vs voltage curve around the maximum power point shows features that are different from those ones of an array operating in normal conditions. The shape change should allow triggering, in case of a temporary fault, control actions that might improve the electrical power production. Instead, if the fault is permanent, the identification of the modified shape should activate a procedure for a more in-depth analysis of the problem and a maintenance action. In this paper, the conditions leading to a change in the array behavior during its delivering of the maximum power are examined. The change of curvature of the current vs voltage curve around its maximum power point is suitably detected to trigger the fault mitigation action. The feature is caught through an ensemble of artificial neural networks, which analyzes the current vs voltage curve and classifies the module as healthy or faulty. It is demonstrated that few samples around the maximum power point are required, this meaning that the proposed approach is compatible with the operation of any perturbative maximum power point tracking algorithm and its application does not lead to any power production drop. In addition, the approach does not require neither temperature nor irradiance measurements as inputs. The neural networks are trained through synthetic data, so that their application is not limited to arrays including a specific photovoltaic module. The method is also validated through experimental data.

Closure to Discussion on “Mitigation of Fault Induced Delayed Voltage Recovery (FIDVR) by PV-STATCOM”

This paper deals with the use of utility scale solar PV systems as PV-STATCOM in high voltage transmission systems. In this case a 75 MW solar PV system is considered in a 138 kV transmission system.

Discussion on “Mitigation of Fault Induced Delayed Voltage Recovery (FIDVR) by PV-STATCOM”

The authors of [1] presented a novel controller for PV-STATCOM. The theory posits dynamic modulation of PV inverters’ active/reactive power through non-unitary power factor operation. The proposed approach is applied to mitigate the effects of fault-induced delayed voltage recovery (FIDVR) experienced in power systems with high penetration of solar PVs and induction motor loads. The structure of the proposed control algorithm is based on 4 modes: 1) normal operation, 2) active power control, 3) reactive power control, and 4) ramp-up active power process. The methodology is tested and comprehensively validated on a radial distribution network (RDN) for numerous scenarios of LLL-G faults, X/R ratios, and night-time operation. For clarity, the authors’ insights on the following technical points are appreciated: 1)The authors meticulously adhered to many standards (IEEE std. 1547-2018, German grid code - BDEW and NERC) to formulate the PV-STATCOM and RDNs’ operational constraints. As per the IEEE std. 1547-2018, the PV inverter design is recommended to be oversized by 11% to address numerous operational challenges [2]. Did the authors consider this standard for the solar farm placed at Bus 3 of the RDN as depicted in Fig. 1? 2)In [Section IV-A (3 and 4)], the authors introduced the values of variables “$k_2$” and “$k_1$” as 1.1 and 0.96 that were used in Mode 2 and Mode 3 for active and reactive power control of the PV-STATCOM respectively as depicted in Fig. 2. What is the selection criteria and/or the range(s) of these variables? Will these values be the same under different RDN scenarios or different RDN networks (for instance, IEEE 34 feeder test system)? 3)In [Section VII], the authors comprehensively presented in-depth analytical case studies for the proposed PV-STATCOM. Most of these studies considered only a single solar farm. Practically, the active-reactive power coordination strategies through PV inverters mostly consider high PV penetration (at each bus) in RDNs [3]. Therefore, how will the proposed PV-STATCOM framework coordinate with more than one solar farm? 4)In [Section VII-E], the author validated the economical dominance of the proposed PV-STATCOM over conventional STATCOM in terms of equivalent size. Nevertheless, the operation of PV inverters as STATCOM negatively affects the inverter lifetime and hence impacts their economical significance [4]. In this respect, is the inverter of the PV-STATCOM economically dominant for long-term operation as well? 5)In [Section VII-G], the authors presented 5 cases to study the voltage recovery rate of the proposed PV-STATCOM based on different fault locations. These faults are LLL-G (for 100 ms) induced at various distances from the substation and the PV solar farm, high voltage side, and low voltage side of the 138/12.6 kV transformer. Considering these 5 cases, will the obtained recovery time be different if the PV-STATCOM was placed at different bus of the study system considered. In other words, is the placement of PV-STATCOM optimal? 6)Finally, the authors proposed a novel application of PV-STATCOM in the field of PV-integrated power network. Voltage stability requirement is one of the key parameters that affects the permissibility of integrating high-scale, inertia-less, and unpredictable PVs in a power network [5]. Therefore, with the proposed PV-STATCOM configuration, what is the maximum achievable hosting capacity of the RDN under consideration?

Study on a Fault Mitigation Scheme for Rub-Impact of an Aero-Engine Based on NiTi Wires.

The aim of this study was to solve the frequently occurring rotor-stator rub-impact fault in aero-engines without causing a significant reduction in efficiency. We proposed a fault mitigation scheme, using shape memory alloy (SMA) wire, whereby the tip clearance between the rotor and the stator is adjusted. In this scheme, an acoustic emission (AE) sensor is utilized to monitor the rub-impact fault. An active control actuator is designed with pre-strained two-way SMA wires, driven by an electric current via an Arduino control board, to mitigate the rub-impact fault once it occurs. In order to investigate the feasibility of the proposed scheme, a series of tests on the material properties of NiTi wires, including heating response rate, ultimate strain, free recovery rate, and restoring force, were carried out. A prototype of the actuator was designed, manufactured, and tested under various conditions. The experimental result verifies that the proposed scheme has the potential to mitigate or eliminate the rotor-stator rub-impact fault in aero-engines.

Guest Editors' Words

This Special Issues brings four invited papers that describe the state of the art techniques to improve fault tolerance on complex designs. Integrated circuits operating under radiation can experience undesirable faults that must be evaluated and mitigated. Mitigation can be implemented by redundancy in hardware or in software, and by selecting and protecting the most critical parts of the system. Radiation effects test and analysis also play an important step in identifying the criticality of the system and helping designers to better apply fault mitigation techniques.

Innovative T&D Switching Equipment and Development of its Testing Technology

Abstract A short overview of the developments in the overall transmission and distribution industry in South East Europe is presented in this contribution. Furthermore, as the present journal focuses on breaking and switching in HV and MV switchgears, several technology innovations in T&D switchgear have been identified, at global level. In almost all cases, the ongoing energy transition is the main driver of these innovations. In this contribution, based on test-experience and overview of the industry, innovations are grouped by drivers, with each of them having its impact on testing technology. These drivers are the following: 1. Increase of ratings: the ongoing trend of co-existence of micro-, local- and super-grids. The consequences for testing of switchgear for UHV and very large current are highlighted: 2. Offshore transmission: compactness and reliability are requirements stretched to the limit for this application. Very long cables for AC and HVDC power transmission, even in a multi-terminal topology will have impact on switchgear. 3. Health safety and environment: here, the major switchgear related discussion is on SF6 replacement and reduction of electrical losses. Various issues related to SF6 replacement will be highlighted. In addition, a recent project intended to quantify the hazard of aluminium bus bars in power stations affected by long duration fault arcs is described. 4. HVDC and power electronics: circuit breakers are going to be installed in multi-terminal HVDC grids. Testing of HVDC breakers, regarding the complete interruption current process is described. 5. Digitalization: switchgear is going to be communicating with the IEC 61850 communication protocol. This will impact testing for which laboratories need to prepare. The introduction of smaller micro-electronics (sensors, IED) closer to primary HV components and its high-frequency transients need careful consideration regarding operation, endurance, and lifetime. 6. Resilience and fault mitigation: Resilience against severe weather conditions, vandalism and cyber-attacks calls for resistant equipment and robust mobile equ ipment/substations that can 6be deployed very rapidly still having an extreme degree of readiness and availability. 7. Quality assurance of products in the face of a pandemic and a lockdown requires adequate methods of a remote digital access to test, and factory sites for type testing and inspection.

Fault-Tolerant FPGA-Based Nanosatellite Balancing High-Performance and Safety for Cryptography Application

With the growth of the nano-satellites market, the usage of commercial off-the-shelf FPGAs for payload applications is also increasing. Due to the fact that these commercial devices are not radiation-tolerant, it is necessary to enhance them with fault mitigation mechanisms against Single Event Upsets (SEU). Several mechanisms such as memory scrubbing, triple modular redundancy (TMR) and Dynamic and Partial Reconfiguration (DPR), can help to detect, isolate and recover from SEU faults. In this paper, we introduce a dynamically reconfigurable platform equipped with configuration memory scrubbing and TMR mechanisms. We study their impacts when combined with DPR, providing three different execution modes: low-power, safe and high-performance mode. The fault detection mechanism permits the system to measure the radiation level and to estimate the risk of future faults. This enables the possibility of dynamically selecting the appropriate execution mode in order to adopt the best trade-off between performance and reliability. The relevance of the platform is demonstrated in a nano-satellite cryptographic application running on a Zynq UltraScale+ MPSoC device. A fault injection campaign has been performed to evaluate the impact of faulty configuration bits and to assess the efficiency of the proposed mitigation and the overall system reliability.

Sensor-Assisted Fault Mitigation in Quantum Computation

We propose a method to assist fault mitigation in quantum computation through the use of sensors co-located near physical qubits. Specifically, we consider using transition edge sensors co-located on silicon substrates hosting superconducting qubits to monitor for energy injection from ionizing radiation, which has been demonstrated to increase decoherence in transmon qubits. We generalize from these two physical device concepts and explore the potential advantages of co-located sensors to assist fault mitigation in quantum computation. In the simplest scheme, co-located sensors beneficially assist rejection of calculations potentially affected by environmental disturbances. Investigating the potential computational advantage further required development of an extension to the standard formulation of quantum error correction. In a specific case of the standard three-qubit, bit-flip quantum error-correction code, we show that given a 20% overall error probability per qubit, approximately 90% of repeated calculation attempts are correctable. However, when sensor-detectable errors account for 45% of overall error probability, the use of co-located sensors uniquely associated with independent qubits boosts the fraction of correct final-state calculations to 96%, at the cost of rejecting 7% of repeated calculation attempts.

Novel lockstep-based fault mitigation approach for SoCs with roll-back and roll-forward recovery

All-Programmable System-on-Chips (APSoCs) constitute a compelling option for employing applications in radiation environments thanks to their high-performance computing and power efficiency merits. Despite these advantages, APSoCs are sensitive to radiation like any other electronic device. Processors embedded in APSoCs, therefore, have to be adequately hardened against ionizing-radiation to make them a viable choice of design for harsh environments. This paper proposes a novel lockstep-based approach to harden the dual-core ARM Cortex-A9 processor in the Xilinx Zynq-7000 APSoC against radiation-induced soft errors by coupling it with a MicroBlaze TMR subsystem in the programmable logic (PL) layer of the Zynq. The proposed technique uses the concepts of checkpointing along with roll-back and roll-forward mechanisms at the software level, i.e. software redundancy, as well as processor replication and checker circuits at the hardware level (i.e. hardware redundancy). Results of fault injection experiments show that the proposed approach achieves high levels of protection against soft errors by mitigating around 98% of bit-flips injected into the register files of both ARM cores while keeping timing performance overhead as low as 25% if block and application sizes are adjusted appropriately. Furthermore, the incorporation of the roll-forward recovery operation in addition to the roll-back operation improves the Mean Workload between Failures (MWBF) of the system by up to ≈19% depending on the nature of the running application, since the application can proceed faster, in a scenario where a fault occurs, when treated with the roll-forward operation rather than roll-back operation. Thus, relatively more data can be processed before the next error occurs in the system.

Emulating Radiation-Induced Multicell Upset Patterns in SRAM FPGAs With Fault Injection

Radiation-induced multiple-cell upsets (MCUs) are events that account for more than 50% of failures on triple modular redundancy (TMR) designs in SRAM field programmable gate array (FPGA). It is important to understand these events and their impact on FPGA designs to develop improved fault mitigation techniques. This article describes an enhanced fault injection (FI) method for SRAM-based FPGAs that injects MCUs within the configuration memory of an FPGA based on MCU information extracted from previous radiation tests. The improved FI technique uncovers 3× more failures than is observable in conventional single-bit FI approaches. The results from several MCU FI experiments also show that injecting MCUs can replicate the failures observed in the radiation beam test and identify new failure mechanisms.

Fault mitigation in real-time cloud computing

The introduction of computers has been a huge plus to human life in its entirety because it provides both the world of business and private an easy and fast means to process, generate and exchange information. However, the proliferation of networked devices, internet services and the amount of data being generated frequently is enormous. This poses a major challenge, to the procurement cost of high performing computers and servers capable of processing and housing the big data. This called for the migration of organizational and/or institutional data upload to the cloud for highlevel of productivity at a low cost. Therefore, with high demand for cloud services and resources by users who migrated to the cloud, cloud computing systems have experienced an increase in outages or failures in real-time cloud computing environment and thereby affecting its reliability and availability. This paper proposes and simulates a system comprising four components: the user, task controller, fault detector and fault tolerance layers to mitigate the occurrence of fault combining checkpointing and replication techniques using cloud simulator (CloudSim).

Adaptive Robust Fault-Tolerant Control Design for Wind Turbines Subject to Pitch Actuator Faults

This paper proposes an adaptive fault tolerant control (FTC) design for a variable speed wind turbine (WT) operating in the high wind speeds region. It aims at mitigating pitch actuator faults and regulating the generator power to its rated value, thereby reducing the mechanical stress in the high wind speeds region. The proposed FTC design implements a sliding mode control (SMC) approach with an adaptation law that estimates the upper bounds of the uncertainties. System stability and uniform boundedness of the outputs was proven using the Lyapunov stability theory. The proposed approach was validated on a 5 MW three-blade wind turbine modeled using the National Renewable Energy Laboratory’s (NREL) Fatigue, Aerodynamics, Structures and Turbulence (FAST) wind turbine simulator. The controller’s performance was assessed in the presence of several pitch actuator faults and turbulent wind conditions. Its performance was also compared to that of a standard SMC approach. Mitigation of blade pitch actuator faults, generation of uniform power, smoother pitching actions and reduced chattering compared to standard SMC approach are among the main features of the proposed design.

Safety Enhancement of UAVs from the Signal Processing’s Perspectives: A Bird’s Eye View

Unmanned air vehicles (UAVs) or drones have gained popularity in recent years. However, the US Federal Aviation Administration (FAA) is still hesitant to open up the national air space (NAS) to UAVs due to safety concerns because UAVs have several orders of magnitude of more accidents than manned aircraft. To limit the scope in this paper, we focus on large, heavy, and expensive UAVs that can be used for cargo transfer and search and rescue operations, not small radio-controlled toy drones. We first present a general architecture for enhancing the safety of UAVs. We then illustrate how signal processing technologies can help enhance the safety of UAVs. In particular, we provide a bird’s eye view of the application of signal processing algorithms on condition-based maintenance, structural health monitoring, fault diagnostics, and fault mitigation, which all play critical roles in UAV safety. Some practical applications are used to illustrate the importance of the various algorithms.

Preemptive Medium-Low Voltage Arc Flash Detection With Geometric Distribution Analysis on Magnetic Field

As power distribution systems become increasingly interconnected and concentrated with distributed energy resources (DER) and high potential equipment, effectively avoiding the medium-low voltage caused safety risk, drawing more attention. Therefore, the accurate, fast, and reliable preemptive arc detection and location method is essential to be developed. However, due to the issues such as low sensitivity, delayed detection, complex structure, and susceptible to interference by other sources, the traditional approaches are not up to the detection and location job, for weak system source and high arcing resistance featured medium-low voltage arc flash. To overcome the limitations, this article explores a novel arc flash preemptive detection and location mechanism. With the application of geometric distribution analysis on magnetic density flux and current density, mathematical modeling incorporated magnetohydrodynamic approach is developed. With a series of actual cases incorporated simulations were performed, the criterion of arc initiation and concisely numerical model on arc fault location estimation is derived, which shows a good ability to catch the transient dynamics of arcing current in the early initiating stage, and also proves the reliability and accuracy on fast detection and location uses. These results offered valuable information for medium-low arc fault mitigation and hazard prevention.

Hybrid Optimization Toward Proactive Resilient Microgrid Scheduling

Microgrids are one important lever to increase power system resilience and to tightly integrate renewable energies at the same time. Commonly, an optimization-based proactive scheduling controls assets in advance in a cost-effective way and ensures that contingencies may be successfully mitigated. However, often strong simplifications are introduced to manage the high computational complexity of scheduling, which can adversely impact fault mitigation. To consider essential phenomena such as power flow limitations and low-level control capabilities in detail, a novel hybrid scheduling approach is presented that integrates mathematical programming and arbitrary nonlinear constraint models via decision trees. A detailed case study compares the new method to an extended hybrid scheduling approach from literature. It is demonstrated that hybrid optimization can efficiently solve proactive resilient scheduling problems and that the tree-based algorithm provides a feasible solution, even in case the reference algorithm fails. Details on the convergence of both algorithms give further insights into the working principles and show that the novel method quickly finds a feasible solution that is successively improved afterwards. By the novel combination of highly-developed solvers for both mathematical programming and detailed asset models it is expected that this study further supports the operation of power systems and reduces costly reserve requirements.

Active Fault-Tolerant Control Design for Actuator Fault Mitigation in Robotic Manipulators

This paper proposes an active fault-tolerant control (FTC) scheme for robotic manipulators subject to actuator faults. Its main objective is to mitigate actuator faults and maintain system performance and stability, even under faulty conditions. The proposed FTC design combines the robustness and finite time convergence of non-singular terminal synergetic control with the optimization properties of an interval type-2 fuzzy satin bowerbird algorithm. System stability is established via the Lyapunov stability criteria. An adaptive state-augmented extended Kalman filter is implemented as the fault detection and diagnosis (FDD) module, to provide the controller with necessary information about faults in real time. This FDD scheme is based on the simultaneous estimation of the faulty parameters and system states. The effectiveness of the proposed approach is assessed using a simulated two-degree-of-freedom robotic manipulator subject to various faulty scenarios.