Fault Recovery Research Articles

Retracted: Realization of Single-Phase Grounding Fault Location and Recovery Technology in Distribution Network

Retracted: Realization of Single-Phase Grounding Fault Location and Recovery Technology in Distribution Network

Frequency and voltage regulation control strategy of Wind Turbine based on supercapacitors under power grid fault

As the penetration rate of wind turbines gradually increases, the fluctuation of system voltage and frequency significantly increases under power grid fault. In severe cases, wind turbines will disconnect from the grid, leading to large-scale power outages. In order to improve the stability of wind turbine grid connection, a large number of literature has studied the frequency and voltage regulation strategies. However, the existing fault ride through control methods mainly solve the problem of reactive power support during faults, while frequency regulation schemes focus on the frequency changes caused by load fluctuations. There is little research on methods that simultaneously consider voltage ride through and frequency regulation under power grid fault. This paper proposes a voltage and frequency regulation control strategy of wind turbines based on supercapacitors to address the above issues. By switching the functional modes of the energy storage device at different stages, wind turbines achieve the fault through and frequency recovery, thereby enhancing the fault tolerance ability. In the strategy, control process is divided into power grid fault period and fault recovery stage, and it adjusts the auxiliary priority level of energy storage devices based on the electrical response degree of different stages. During the fault, energy storage device is in voltage regulation stage because the voltage drop degree is greater than the frequency fluctuation, and it can ensure the wind turbine does not run off the grid by providing dynamic reactive power support to power grid. In the fault recovery stage, energy storage device is in the frequency regulation stage because of the large frequency deviation, it output active power to accelerate the system frequency recovery rating. Finally, the effectiveness of the control strategy proposed in this paper is verified through simulation.

Collaborative Service Restoration with Network Reconfiguration for Resilience Enhancement in Integrated Electric and Heating Systems

Coordinated fault recovery is essential for the resilience enhancement of integrated electric and heating systems (IEHS) following natural catastrophes as the linkage of the power distribution system (PDS) and district heating system becomes tighter. DHS reconfiguration is a viable method for service restoration because it could adjust the energy between energy sources and achieve uninterrupted energy supplies. In this paper, a collaborative service restoration model considering DHS reconfiguration is proposed to achieve better recovery after natural disasters. DHS reconfiguration could guarantee interrupted power supply in non-fault regions by shifting electric loads between power sources and accomplish optimal service restoration by adjusting the power output of combined heat and power units. Numerous case studies are undertaken to demonstrate the performance of coordinated reconfiguration on resilience enhancement and to confirm the efficacy of the proposed paradigm.

Optimal intelligent fuzzy TID controller for an uncertain level process with actuator and system faults: Population-based metaheuristic approach

For the Conical Tow-Tank Non-Interacting Level (CTTNL) prototype system, this article discusses the design of an unique optimal fractional order TID controller employing a type-1 fuzzy set. To generate a linearized model transfer function for the nonlinear CTTNL process, local linearization around the equilibrium state was performed. This work compares the performance of a fuzzy tilted-integral-derivative (FTID) controller for the CTTNL system to a traditional proportional–integral–derivative (PID) controller. The parameters of the FTID controller were optimized using the Chicken Search Optimization (CSO) algorithm in this study, and those values were used to create a PID controller. Thereafter, the FTID and PID controllers’ performance is compared. The investigation shows that the FTID controller beats the PID controller in terms of IAE, ISE, ITAE, and ITSE integral errors. The suggested controller’s fault-tolerant performance was also assessed using the fault recovery time (Frt) parameter. The FTID controller outperforms traditional PID controllers in terms of transient and steady-state performance. The fault tolerance capabilities of the FTID controller will be tested on the CTTNL system in the future, using additive and multiplicative failures. Further research will include a comprehensive examination of robustness in the context of model parameter uncertainties.

A two-layer optimal configuration approach of energy storage systems for resilience enhancement of active distribution networks

Introducing energy storage systems (ESSs) into active distribution networks (ADNs) has attracted increasing attention due to the ability to smooth power fluctuations and improve resilience against fault disturbances. This paper proposes a methodology for simultaneously optimizing the configuration of battery ESSs and the operation of ADNs, and the goal is to increase the resilience of the ADNs withstanding multi-faults. Firstly, based on random sampling and K-means clustering, a generation strategy of typical N-1 and N-2 fault scenarios is designed for the ADNs. Then, a two-layer optimization model is established, where the inner model is to optimize the fault recovery performance from the operational perspective, and the outer model is to obtain the optimal site and size of ESSs from the economic perspective. Further, the second-order cone relaxing (SOCR) method and the hybrid gray wolf optimal and particle swarm optimal (GWO-PSO) algorithm are applied to solve the optimization model. Using MATLAB, the modified IEEE 33-node and 118-node systems are built to check the proposed approach's performance. Different periods are considered to show the multi-faults' development, and by introducing a resilience assessment system with node voltage deviation, fault recovery rate, and network loss rate, the resilience of the ADNs is analyzed. From the comparative results, the proposed approach can optimally configure the battery ESSs, and adjust the network structure as well as the distributed generation outputs. Following the ESS configuration cost reduction of 53.19% and 9.8%, the resilience of the ADNs against the multi-faults will increase by 13.36% and 8.25% for the 33-node and 118-node systems.

Safety Assessment of an Unmanned Air Vehicle Flight Control System in accordance with SAE ARP 4761

Certification Technologies of Small Unmanned Aircraft System (CTsUAS) is an Unmanned Air Vehicle, which is on development based on KC-100. KC-100 is a four-seater civil aircraft developed by the Korea Aerospace Industries (KAI) with the type certification of the US Federal Aviation Administration (FAA). For the type certification of CTsUAS, system safety assessment and analysis have been conducted in accordance with SAE ARP 4761. This paper presents the partial results of safety assessment of the flight control system of CTsUAS, focusing on the fault detection and recovery functions. Through the lessons from the results, additional considerations needed to support safety assessment and safety compliance determination of an unmanned aircraft system have been derived.

Research On Multi-source Collaborative Distribution Network Fault Recovery Technology

When a power outage occurs in the power system, multiple distributed power sources and energy storage systems connected to the distribution network can be coordinated to restore power to important lost loads in a priority and fast manner. However, the generation output of intermittent power sources such as wind and photovoltaic is uncertain, which makes it difficult to provide a continuous and stable power supply to the loads in isolated islands. In this paper, we first explore the improvement effect of multi-source cooperative fault recovery on distribution system resilience, consider the distribution system with distributed power sources, energy storage systems, and controllable loads, and propose a distribution system recovery model with the goal of maximizing the power consumption of the restored loads for a relatively short period of time after a power outage incident. Then, the power output characteristics and power supply capacity of various distributed power sources are analyzed, and the intermittent power output fluctuations are compensated using controllable loads. Finally, the proposed model and method are validated using a modified IEEE 33-node distribution network as an example.

An integrated risk assessment model for the multi-perspective vulnerability of distribution networks under multi-source heterogeneous data distributions

The reliability of distribution network transmission corridors are vulnerable to different risk factors in both internal operation parameters and surrounding environmental conditions, and thus pinpoint evaluations of these impacting factors can further enhance their safety. To this end, this paper proposes an integrated assessment method, that is the multi-dimensional assessment with integrated fuzzy association (MDATFA). In this model, healthiness and importance indices of transmission lines are both defined to reflect the self-robustness and potential damages, respectively, and therefore to enable a two-dimensional assessment to incorporate the multi-source heterogeneous inputs. Firstly, healthiness indices for environmental characteristics are analyzed. On the one hand, discrete input features are processed by the established association rule mining model with rare variables (ARMrv) to mine and diagnose rarely occurred elements, and their healthiness contribution values can then be calculated. On the other hand, continuous features are handled by the designed hierarchical probabilistic fuzzy systems (HPFS). The weight assignments of these two expert models are adjusted by a gating network. Secondly, the importance indices for electrical characteristics are calculated. Under the condition that electrical signal constraints are satisfied, the amount of load loss and lost users can be solved by the built distribution network fault recovery model which is according to heuristic rule and second-order cone (HRSO). Finally, the validity and robustness of this operational condition assessment model is verified by an empirical analysis in an actual 39-bus power distribution system.

Analysis of low voltage ride-through capability and optimal control strategy of doubly-fed wind farms under symmetrical fault

Given the “carbon neutralization and carbon peak” policy, enhancing the low voltage ride-through (LVRT) capability of wind farms has become a current demand to ensure the safe and stable operation of power systems in the context of a possible severe threat of large-scale disconnection caused by wind farms. Currently, research on the LVRT of wind farms mainly focuses on suppressing rotor current and providing reactive current support, while the impact of active current output on LVRT performance has not been thoroughly discussed. This paper studies and reveals the relationship between the limit of reactive current output and the depth of voltage drop during LVRT for doubly-fed induction generator (DFIG) based wind farms. Specifically, the reactive current output limit of the grid-side converter is independent of the depth of voltage drop, and its limit is the maximum current allowed by the converter, while the reactive current output limit of the DFIG stator is a linear function of the depth of voltage drop. An optimized scheme for allocating reactive current among the STATCOM, DFIG stator, and grid-side converter is proposed. The scheme maximizes the output of active current while satisfying the standard requirements for reactive current output. Compared to traditional schemes, the proposed LVRT optimization strategy can output more active power during the LVRT period, effectively suppressing the rate of rotor speed increase, and improving the LVRT performance and fault recovery capability of wind farms. Simulation results verify the effectiveness of the proposed scheme.

The path towards increasing RAMS for novel complex missions based on CubeSat technology

The paper presents the initial outcomes of a project, currently ongoing under the supervision of the European Space Agency, having the main objective to specify and design a Fault Detection Isolation and Recovery (FDIR) system by making use of relevant RAMS (Reliability, Availability, Maintainability, Safety) analyses for missions in non-deterministic environment with limited resources. The initial project tasks have been to select a study case represented by a CubeSat complex mission, analyse in detail both its mission and system requirements and, based on them, define a set of relevant RAMS analyses to be carried out in the second phase of the project, as inputs for the development of a FDIR concept aimed at a careful balance of the limited spacecraft resources in case of critical failures. Two possible study cases have been identified: LUMIO, a 12U CubeSat mission for the observation of micro-meteoroid impacts on the Lunar farside, and M-ARGO, a 12U deep-space CubeSat which will rendezvous with a near-Earth asteroid and characterize its physical properties for the presence of in-situ resources. Although both missions are characterized by a high level of autonomy and complexity in a harsh environment, LUMIO has been eventually selected as study case for the project. In the paper, the challenges and features of this mission are shortly presented. The specificities of the RAMS analysis and FDIR concept for this specific class of small satellite missions (including the selected study case) are highlighted in the paper, looking in particular at aspects such as the improvement of reliability while maintaining the CubeSat philosophy, the tuning of mission and system requirements in view of facilitating the design and implementation of the FDIR concept, and the current gaps within the RAMS/FDIR body of knowledge. The conclusions drawn during this first project phase provide a real view of how systems engineering must work in tandem with RAMS analyses and FDIR to achieve a more robust and functional mission architecture, thus improving the mission reliability.

An Adaptive Phase Locked Oscillator to Improve the Performance of Fault Recovery from Commutation Failure in LCC-Based HVDC Systems

An adaptive control of phase lock oscillator (PLO) is proposed to increase the ability of LCC-based HVDC systems to successfully recover from commutation failure, where the dynamic performance during the recovery process is improved. The phase lock oscillator is one of the most important parts of the control system, which is used to trace the phase angle of commutating voltage. However, the PLO with constant parameters cannot provide accurate information under both steady-state operation and large disturbances. In our work, the control parameters of PLO can be adaptively adjusted, following the states of commutating voltage. When the system is operating in a steady state, the PLO selects parameters that exhibit improved small-signal stability, while parameters prioritizing dynamic behaviors with high-tracing accuracy are adopted during large disturbances. Case studies based on simulations in PSCAD/EMTDC and RT-LAB show that the proposed control strategy can improve the performance of fault recovery from CF.

Fault Recovery Strategy for Power–Communication Coupled Distribution Network Considering Uncertainty

In the face of multiple failures caused by extreme disasters, the power and communication sides of the distribution network are interdependent in the fault recovery process. To improve the post-disaster recovery efficiency of the distribution network, this paper proposes a coordinated optimization strategy for distribution network reconfiguration and repair, which integrates the power and communication aspects. First, the recovery process is divided into islanding–reconfiguration and dynamic emergency repair. The coupling relationship between power and communication is considered; that is, power failure may cause communication nodes to lose power, and communication failure may affect the effective operation of remote control devices. Based on this, the fault recovery process is optimized with the objective of maximizing load transfer and direct recovery while introducing a stochastic model predictive control method to handle the uncertainty of distributed power generation by rolling optimization of typical scenarios. Finally, the effectiveness of the proposed strategy is verified using an improved IEEE33-node distribution network system. The simulation results show that the proposed method can recover power to the maximum extent and reduce loss while ensuring the safe and stable operation of the distribution system.

A stochastic process of software fault detection and correction for business operations

Automatic software fault detection and repair is made possible by autonomic software recovery. By incorporating this function, the software will run more efficiently and aggressively while requiring much less time and resources for maintenance. The focus of this article is on a suggested automated approach to Software Fault Detection and Recovery. The Software Fault Detection and Recovery (SFDR) procedure identifies when a software component has been damaged due to a fault and then restores the damaged component so that the software can continue functioning normally. During the design process, the SFDR is examined and created independently from the intended program. The proposed technique was implemented into an application that demonstrates the SFDR's performance and effectiveness to guarantee its practicality in real-world scenarios. The results of this experiment were encouraging. Results from experiments and comparisons to prior works show that the proposed methodology is successful.

Reliable Actors with Retry Orchestration

Cloud developers have to build applications that are resilient to failures and interruptions. We advocate for a fault-tolerant programming model for the cloud based on actors, retry orchestration, and tail calls. This model builds upon persistent data stores and message queues readily available on the cloud. Retry orchestration not only guarantees that (1) failed actor invocations will be retried but also that (2) completed invocations are never repeated and (3) it preserves a strict happen-before relationship across failures within call stacks. Tail calls can break complex tasks into simple steps to minimize re-execution during recovery. We review key application patterns and failure scenarios. We formalize a process calculus to precisely capture the mechanisms of fault tolerance in this model. We briefly describe our implementation. Using an application inspired by a typical enterprise scenario, we validate the functional correctness of our implementation and assess the impact of fault preparedness and recovery on performance.

Enhancing low‐priority traffic reconfiguration designs in mixed‐critical avionics networks

AbstractIn avionics system networking, mixed‐critical networks are commonly used to integrate system applications with different levels of guarantees for various traffic classes on a shared resource platform, effectively addressing issues such as cabling, cost, weight, volume, power consumption, etc. However, current fault recovery and reconfiguration techniques mainly focus on high‐priority time‐triggered (TT) traffic, often neglecting the impact of TT traffic reconfiguration on low‐priority traffic. This paper proposes an avionics mixed‐critical networking approach based on the IEEE 802.1CB protocol, which is shown to be more effective for the post‐failure recovery of Rate‐Constrained (RC) traffic (a typical type of low‐priority traffic in TT networks). Furthermore, an elastic reconfiguration algorithm is presented to ensure quality of service for RC traffic during and after reconfiguration, preventing arbitrary application and traffic shutdown triggered by TT traffic reconfiguration. Simulations demonstrate that the proposed approach and algorithm can significantly improve the post‐failure recovery ratio of RC traffic, as well as guarantee quality of service and network throughput of RC traffic during and after reconfiguration to a certain extent.

Design for product resilience: concept, characteristics and generalisation

Potentially high-impact disturbances always disrupt normal product use, cause structural or functional failure, and shorten product life. How to design products with better fault tolerance and recovery capabilities to effectively deal with such complex disturbances will be a key factor in product innovation and intelligence in the future. Resilience is a system's ability to rapidly recover to its full function after disruption, and it is applied across multiple industries. As manufacturing systems and products diversify, this has resulted in an increasing variety of different definitions that threatens to dilute the concept and lead to ineffective implementations of resilient product design. This paper reviews the different definitions and scope of application of resilience in existing research and proposes two general definitions of resilient products from broad and narrow perspectives. On this basis, the author compared product resilience with product reliability, robustness, and adaptability, and proposed four main characteristics of product resilience, including visibility, agility, redundancy, and adaptability. Next, a design principle for product resilience is introduced, and future needs and opportunities for product resilience are outlined.

Survivability Mapping Strategy for Virtual Wireless Sensor Networks for Link Failures in the Internet of Things

In the case of virtual wireless sensor networks, a link-failure-oriented survivable virtual sensor network mapping algorithm (F-SVNE) is proposed to address the issue of link failure in the underlying wireless sensor network. First, the algorithm utilizes the fast routing strategy and creates a backup route set based on a multi-path selection algorithm to reduce the delay in fault recovery caused by path selection. Second, the survivable virtual sensor network mapping algorithm is adopted based on the routing set. Finally, in the mapping stage, the efficiency and reliability of the algorithm are comprehensively considered, and the path with the largest survival probability is selected from the backup route set of the faulty link to remap the virtual link affected by the link failure. Empirical results demonstrate that the F-SVNE algorithm can efficaciously lessen the failure recovery delay, and improve the long-term average revenue–cost ratio and average failure recovery rate.

The named entity recognition of vessel power equipment fault using the multi-details embedding model

The fault diagnosis of vessel power equipment is established by the manual work with low efficiency. The knowledge graph(KG) usually is applied to extract the experience and operation logic of controllers into knowledge, which can enrich the means of fault judgment and recovery decision. As an important part of KG building, the performance of named entity recognition (NER) is critical to the following tasks. Due to the challenges of information insufficiency and polysemous words in the entities of vessel power equipment fault, this study adopts the fusion model of Bidirectional Encoder Representations from Transformers (BERT), revised Convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and conditional random field (CRF). Firstly, the adjusted BERT and revised CNN are respectively adopted to acquire the multiple embeddings including semantic information and contextual glyph features. Secondly, the local context features are effectively extracted by adopting the channel-wised fusion structures. Finally, BiLSTM and CRF are respectively adopted to obtain the semantic information of the long sequences and the prediction sequence labels. The experimental results show that the performance of NER by the proposed model outperforms other mainstream models. Furthermore, this work provides the foundation of the tasks of intelligent diagnosis and NER in other fields.

A Personalized Alarm Distribution Approach Based on Collaborative Filtering in New-type Power System

Due to the complexity and heterogeneous characteristics of the information system architecture of the new-type power system, once a fault occurs during operation, a large number of alarm information will be generated. In order to improve the efficiency of operation and maintenance (O&M) personnel and minimize the loss caused by failures, a collaborative filtering algorithm is introduced to provide personalized alarm information distribution. Each alarm is preferentially assigned to O&M personnel with high processing probability, so that O&M personnel can receive alarm information they can handle. In addition, the minimum distribution number is limited to ensure that each alarm can be received by enough O&M personnel. Finally, experiments show that the proposed method can effectively improve the O&M efficiency and improve the fault recovery capability of the new-type power system.

Fed-TSN: Joint Failure Probability-Based Federated Learning for Fault-Tolerant Time-Sensitive Networks

Industrial Internet of Things (IIoT) applications have diverse network session requirements. Certain critical applications, such as emergency alert relays, as well as industrial floor evacuation and surveillance systems, require fresh updates that can maintain the most recently delivered packets. This requires high reconfigurability to an extent where the system can measure the impact of an event and adapt the network accordingly. Recent research has demonstrated that network failures can undermine the sustainability of Industry 4.0 or Industrial IoT in general. In this paper, we design an intelligent Federated learning based Time-Sensitive Networking (Fed-TSN) controller framework to optimize the failure recovery. In industrial IoT scenarios, such as emergency evacuations on factory floors due to natural disasters, there can be multiple link failures with no disjoint paths which require a sustainable recovery solution. Accordingly, we consider multiple simultaneous link failures, both for networks with and without disjoint paths. The typically probabilistic network failures on a factory floor call for designing a mechanism that can search for routes with minimum joint failure probability (JFP). We formulate the JFP minimization problem as a non-linear integer program. We design a Software Defined Networking (SDN) controller that runs an application to produce near-optimal solutions for providing enhanced sustainability in a wide range of Industry 4.0 scenarios. We employ this non-linear integer program solution as input to our intelligent Fed-TSN fault recovery strategy that predicts the migration location based on the changes in the TSN gate schedule. We conduct simulations to quantify the improvements achieved with Fed-TSN compared to state-of-the-art approaches.