Inherent Reliability Research Articles

Design and initial test results of a space-bound flux pump to energize the Hēki mission’s superconducting magnet

Despite repeated proposals to utilize superconducting magnets in space since at least the 1970s, examples of their use remain scant. One of the technical challenges is to maintain suitable cryogenic temperatures on a spacecraft. This challenge can be alleviated by the use of flux pumps to reduce the required cryogenic cooling power needed to energize the superconducting magnet. This paper describes the design and initial test results of the flux pump to fulfill the requirements of the Hēki mission that will operate a high-temperature superconducting magnet on an external platform of the International Space Station. A transformer-based, self-rectifier architecture was chosen for the flux pump. An effective circuit model used to design its electromagnetic properties and finite-element modelling used in its mechanical and thermal design. Liquid nitrogen tests were used to demonstrate that the electrical performance of the flux pump meets requirements. Higher-fidelity tests using flight-like copies of the hardware and software were undertaken and validated the thermal modelling. These tests also featured the continuous operation of the flux pump in a conduction-cooled setting for over 100 hours, reflecting an inherent reliability of this technology. Whilst further testing and flight qualification remains to be completed, we anticipate an on-orbit demonstration of this flux pump technology in February 2025. Such a demonstration will signal a maturing of this emerging superconducting technology for both in-space and terrestrial applications.

Algorithms of Cross-Domain Redundancy Management for Resilient of Dual-Priority Critical Communication Systems

The paper presents models for managing cross-domain redundancy to enhance the reliability of two priority communication channels within critical infrastructure systems. Employing Markov chain models, the paper analyzes the impact of two distinct redundancy management strategies: a unified reserve pool and a separate pool approach with cross-domain resource sharing. The study introduces reliability improvement factors to quantify system performance, exploring their dependency on the number of additional redundant elements, their inherent reliability, and the chosen strategy for managing cross-domain redundancy. An air traffic control system serves as a case study of the application of the proposed management algorithms. Results indicate that the integration of resources from different priority domains significantly improves communication reliability. The findings may be useful for the design and operation of secure communication networks.

Condition-based switching, loading, and age-based maintenance policies for standby systems

Standby techniques are widely incorporated in structural design to enhance the inherent reliability of systems. To further leverage the system performance during operation, decision-makers can adopt operational policies to manage system degradation. Specifically, at the system level, unit switching that dynamically determines the online unit contributes to avoiding unexpected shutdowns. At the unit level, adjusting load levels to manage the trade-off between condition degradation and revenue accumulation is crucial for maximizing profit. Additionally, adopting age-based maintenance as a tactical decision, which effectively facilitates the integration of maintenance resources, can be implemented to restore a degraded system. For instance, maintenance is typically scheduled at fixed moments for multi-generator power systems located in remote areas. In between maintenance moments, the proactive switching of generators can ensure uninterrupted output, and the adjustment of load levels for online generators helps to maximize output. Motivated by such engineering practices, this paper investigates condition-based switching, loading, and age-based maintenance policies for standby systems to maximize the expected profit rate in the long-run horizon. The problem is formulated as a Markov decision process. The structural properties of the control-limit switching and monotone loading policies are analyzed for easy policy implementation and efficient problem solutions. For comparative purposes, several heuristic policies are proposed and evaluated. Finally, numerical examples are presented to validate theoretical results and illustrate the superiority of the proposed risk control policy.

Extraction of pre-earthquake anomalies from borehole strain data using Graph WaveNet: a case study of the 2013 Lushan earthquake in China

Abstract. On 20 April 2013, Lushan experienced an earthquake with a magnitude of 7.0. In seismic assessments, borehole strainmeters, recognized for their remarkable sensitivity and inherent reliability in tracking crustal deformation, are extensively employed. However, traditional data-processing methods encounter challenges when handling massive dataset-s. This study proposes using a Graph WaveNet graph neural network to analyze borehole strain data from multiple stations near the earthquake epicenter and establishes a node graph structure using data from four stations near the Lushan epicenter, covering the years 2010–2013. After excluding the potential effects of pressure, temperature, and rainfall, we statistically analyzed the pre-earthquake anomalies. Focusing on the Guza, Xiaomiao, and Luzhou stations, which are the closest to the epicenter, the fitting results revealed two acceleration events of anomalous accumulation that occurred before the earthquake. Occurring approximately 4 months before the earthquake event, the first acceleration event indicated the pre-release of energy from a weak fault section. Conversely, the acceleration event observed a few days before the earthquake indicated a strong fault section that reached an unstable state with accumulating strain. We tentatively infer that these two anomalous cumulative accelerations may be related to the preparation phase for a large earthquake. This study highlights the considerable potential of graph neural networks in conducting multistation studies of pre-earthquake anomalies.

An Improved Positive Displacement Pump Model Accounting for Suction Cavitation

Abstract Positive displacement (PD) pumps are widely employed in industrial settings due to their inherent simplicity and reliability, serving a variety of applications from slurry transport to jet washing. Although their operational principles are straightforward, the fluid dynamics of the pumped medium exhibit nontrivial characteristics, including intricate transient phenomena. Consequently, a comprehensive fluid dynamic description, such as a three-dimensional fluid analysis, presents challenges due to its demanding computational requirements. While simpler analytical PD pump models are available, they often fail to adequately represent the primary system behaviors, particularly when dealing with cavitation. Motivated by these challenges, this study aims to develop a novel one-dimensional model for PD pumps, offering a representation of essential fluid phenomena without imposing significant computational burdens. After assessing the relative importance of the fluid dynamic behaviors that the model must capture, we construct a pump model based on a one-dimensional fluid description and solve it using a second-order in time and space monotone upwind scheme for conservative law and total variation diminishing (MUSCL-TVD) scheme. The model's validity is confirmed by its application to both single-chamber and three-chamber diaphragm PD pumps, which are instrumented for experimental validation. The results of the one-dimensional model exhibit strong agreement with physical experiments, both in controlled laboratory environments and field conditions. This success suggests a promising approach for industrial applications.

A general framework for the design of efficient passive pitch systems

Mitigating the impact of variable inflow conditions is critical for a wide range of engineering systems such as drones or wind and tidal turbines. Passive control systems are of increasing interest for their inherent reliability, but a mathematical framework to aid the design of such systems is currently lacking. To this end, in this paper a two-dimensional rigid foil that passively pitches in response to changes in the flow velocity is considered. Both an analytical quasi-steady model and a dynamic low-order model are developed to investigate the pivot point position that maximizes unsteady load mitigation. The paper focuses on streamwise gusts, but the proposed methodology would apply equally to any change in the inflow velocity (speed and/or direction). The quasi-steady model shows that the force component in any arbitrary direction can be kept constant if the pivot lies on a particular line, and that the line coordinates depend on the gust and the foil characteristics. The dynamic model reveals that the optimum distance of the pivot location from the foil increases with decreasing inertia. For a foil at small angles of incidence, the optimum pivot point is along the extended chord line. This knowledge provides a methodology to design optimum passively pitching systems for a plethora of applications, including flying and swimming robotic vehicles, and provides new insights into the underlying physics of gust mitigation.

Synergistic effects of La-doping on ZnO nanostructured photocatalysts for enhanced MB dye degradation

In this work, we present the design and characterization of pristine and La-doped ZnO photocatalysts, prepared via a facile co-precipitation technique. A comprehensive range of characterization techniques was employed to analyze the physicochemical properties of these synthesized nanostructured photocatalysts. The slight shift in the position of peaks in the X-ray diffraction (XRD) pattern confirms successful doping (La3+) of ZnO. Additionally, the identified wurtzite phase further validates the presence of ZnO nanostructures. Subsequent photocatalytic investigations were conducted using model solutions of water and methylene blue (MB) dyes under solar irradiation, meticulously comparing the performance of pure ZnO with La3+ doped ZnO. Over the time span of 90 min, the 5 % La-doped ZnO photocatalysts exhibited an impressive 98 % degradation efficiency for MB dye, surpassing the performance of the pristine ZnO counterpart (0 % La), which achieved only 51 % degradation. This emphasizes the pronounced enhancement resulting from the incorporation of La ions. The photoluminescence (PL) spectra insights with 5 % La3+doping exhibit significant quenching, caused by the suppression of charge carrier recombination, which enhances the photocatalyst's charge transport. X-ray photoelectron spectroscopy (XPS) spectra provided crucial insights into the elemental composition and chemical states of the 5 % La3+ doped ZnO nanostructures. Through systematic reusability assessments, it was established that the 5 % La-doped ZnO photocatalysts maintain their efficiency for up to four cycles, without displaying a significant reduction in removal efficacy. This emphasizes the inherent reliability and robustness of the developed photocatalysts.

Critical Examination of 3D Building Modelling through UAV Frame and Video Imaging

Abstract. Data capture in UAV photogrammetry is carried out using two main methodologies: frame frame-based and video video-based. Frame Frame-based data gathering is the preferred method among UAV projects because to its inherent reliability in calibration. Nonetheless, circumstances involving moving objects or occlusions inside the measured region may produce unsatisfactory results utilizing this method. I In response to these challenges, video video-based data collecting appears as a potential option, capable of creating a series of successive images that together alleviate the constraints outlined above. In this study we aim to compare the usefulness of frame and video images in building 3D models, using both oblique and vertical image orientations. Rigorous evaluations produced many outputs, including dense point clouds, digital surface models (DSM), meshes, and orthophotographs. The evaluation criteria included da ta acquisition velocity, processing efficiency, calibration precision, distortion analysis, residual plots, scale correctness, and reprojection error. The empirical results demonstrated the advantages of video video-frame captures in improving the quality of the resulting 3D models. Notably, the use of video frames resulted in a significant reduction in reprojection error by 16%, calibration residuals by 36%, distortions by up to 51%, and processing time by 27%. Thus, it seems that integrating video frames improves data gathering accuracy and speeds up processing, replacing standard frame images with video counterparts for increased efficacy.

Monte Carlo simulation study on the reliability of concrete-filled HSS beam-column design provisions

Revisions were recently proposed to the way in which concrete-filled hollow structural section (HSS) members are handled in CSA S16. These revisions were based on previous research, comparisons to experiments, and a first-order reliability method analysis of existing provisions for compression and flexural members. In this paper, this topic is further expanded by using Monte Carlo Simulations (MCS) to determine the inherent reliability of the previous and new design rules for concrete-filled rectangular hollow section (RHS) and circular hollow section (CHS) beam-columns. A representative set of concrete-filled RHS and CHS members with variations in concrete strength, wall slenderness, effective length, and loading eccentricity are analyzed. Using MCS, the reliability index (β+) of each is determined over a range of live-to-dead load (L/D) ratios. Inherent β+ values are compared to the code-specified target (i.e., β+ = 3.0 per Annex B of CSA S16) and the CSA S16:19 and AISC 360-16 provisions.

A novel entropy-driven dual-output mode integrated with DNAzyme for enhanced microRNA detection

Accurate microRNA (miRNA) detection is pivotal in the diagnosis and monitoring of cancer. Entropy-driven catalysis (EDC) has attracted widespread attention as an enzyme-free, isothermal technique for miRNA detection owing to its inherent simplicity and reliability. However, conventional EDC is a single-output mode, limiting the efficiency of signal amplification. In this study, a novel EDC dual-output mode was employed in conjunction with DNAzyme, resulting in the development of an EDC dual-end DNAzyme (EDC-DED) approach for highly sensitive miRNA detection. In this system, miRNA-21 initiated the EDC reaction, producing a large amount of catalytically active dual-end Mg2+-dependent DNAzyme. The DNAzyme further cleaved the reporter cyclically, generating a notably amplified fluorescence signal. The proposed method achieved a low detection limit of 2 pM. Compared with the traditional EDC single-end DNAzyme (EDC-SED) strategy, the present method exhibited superior amplification efficiency, enhancing detection sensitivity by approximately 46.5-fold. Furthermore, this platform demonstrated ideal specificity, satisfactory reproducibility and acceptable detection capabilities in clinical serum samples. Therefore, the straightforward and convenient strategy is a potential tool for miRNA analysis, which may provide a new perspective for biological analysis and clinical application.

A novel consensus model considering individual and social behaviors under the social trust network

In practical decision-making scenarios, the challenges lie in obtaining the collective opinion that the majority of decision makers are willing to accept, thereby arriving at the final decision result. Among them, both the inherent uncertainty and reliability embedded in the information may lead to a deviation from the fact in the final decision result. Moreover, individual and social behaviors have the potential to influence the acquisition of effective decision result. Based on this, this paper constructs a novel social network-based consensus model under discrete Z-number environment, taking into account the individual and social behaviors of experts. First, novel trust propagation and trust aggregation operators with discrete Z-numbers are developed, which aim to form a complete social trust network. Then, unit consensus cost function and compromise boundary function based on social trust network are given to objectively determine the unit adjustment costs and compromise boundaries of each decision maker. In addition, a minimum cost consensus model considering the coexistence and interaction between individual and social behaviors is explored. Last, the feasibility and superiority of the model are proved through a case of the agricultural supply chain. Through simulation and comparative analysis, the results show the effectiveness of novel trust propagation and aggregation operators. And the necessity of considering individual and social behaviors for consensus reaching process is presented.

HR-Pro: Point-Supervised Temporal Action Localization via Hierarchical Reliability Propagation

Point-supervised Temporal Action Localization (PSTAL) is an emerging research direction for label-efficient learning. However, current methods mainly focus on optimizing the network either at the snippet-level or the instance-level, neglecting the inherent reliability of point annotations at both levels. In this paper, we propose a Hierarchical Reliability Propagation (HR-Pro) framework, which consists of two reliability-aware stages: Snippet-level Discrimination Learning and Instance-level Completeness Learning, both stages explore the efficient propagation of high-confidence cues in point annotations. For snippet-level learning, we introduce an online-updated memory to store reliable snippet prototypes for each class. We then employ a Reliability-aware Attention Block to capture both intra-video and inter-video dependencies of snippets, resulting in more discriminative and robust snippet representation. For instance-level learning, we propose a point-based proposal generation approach as a means of connecting snippets and instances, which produces high-confidence proposals for further optimization at the instance level. Through multi-level reliability-aware learning, we obtain more reliable confidence scores and more accurate temporal boundaries of predicted proposals. Our HR-Pro achieves state-of-the-art performance on multiple challenging benchmarks, including an impressive average mAP of 60.3% on THUMOS14. Notably, our HR-Pro largely surpasses all previous point-supervised methods, and even outperforms several competitive fully-supervised methods. Code will be available at https://github.com/pipixin321/HR-Pro.

Design and safety features of SALUS-100: A long fuel-cycled Sodium-cooled fast reactor

The paper introduces the innovative development of an SFR-based Small Modular Reactor (SMR) known as SALUS-100, with a 100 MWe electrical output, developed by KAERI. This innovative design, derived from the previous TRU transmutation burner SFR concept, represents a significant step forward in establishing a new model for SMRs and offers diverse applications, including on– and off-grid electricity generation, district heating, and industrial heat supply. The reactor features of SALUS-100 incorporate an integrated pool-type primary system configuration and a long fuel-cycle core concept with a metallic alloy nuclear fuel. Its design concept prioritizes preventing severe accidents by capitalizing on the inherent safety and reliability features achieved through emphasizing passive design characteristics. The paper describes the engineering design and safety analyses of SALUS-100, highlighting advanced design concepts in reactor core, fuel, materials, and systems. Additionally, the key research and development endeavors, focusing on vital areas such as enhancing system design capability, validating designs, demonstrating safety, and innovating in metal fuel technology, are briefly discussed, emphasizing the potential of SALUS-100 in revolutionizing the SMR market and utilizing efficient nuclear energy in the future.

Damage Detection of Gantry Crane with a Moving Mass Using Artificial Neural Network

Gantry cranes play a pivotal role in various industrial applications, and their reliable operation is paramount. While routine inspections are standard practice, certain defects, particularly in less accessible components, remain challenging to detect early. In this study, first a finite element model is presented, and the damage is introduced using random changes in the stiffness of different parts of the structure. Contrary to the assumption of inherent reliability, undetected defects in crucial structural elements can lead to catastrophic failures. Then, the vibration equations of healthy and damaged models are analyzed to find the displacement, velocity, and acceleration of the different crane parts. The learning vector quantization neural network is used to train and detect the defects. The output is the location of the damage and the damage severity. Noisy data are then used to evaluate the network performance robustness. This research also addresses the limitations of traditional inspection methods, providing early detection and classification of defects in gantry cranes. The study’s relevance lies in the need for a comprehensive and efficient damage detection method, especially for components not easily accessible during routine inspections.

ICH Q14-inspired novel approach to establish an SFC-based purity method for carbamazepine.

The proposed ICH Q14 guideline "Analytical procedure development" describes science and risk-based approaches for development and maintenance of analytical procedures suitable for the assessment of the quality of drug substances and drug products. As a case study, the systematic development and validation of a supercritical fluid chromatography (SFC)-based purity method for carbamazepine is presented. Systematic analytical quality by design (AQbD) principles were applied using the software package Fusion QbD to the method development approach. The relationship between chromatographic parameters and the responses of interest were examined to improve the reliability of the method by understanding, reducing, and controlling sources of variability. Method performance qualification in terms of method robustness was finally carried out with the parameters that were classified as critical after method development and a validation study met previously set acceptance criteria. The developed SFC purity method for carbamazepine demonstrated readiness as a viable alternative to the official HPLC method published in the Ph.Eur. with improved peak resolution, improved peak symmetry, and faster analysis times (3min vs. 80 min for the official method). Its inherent reliability illustrates the superiority of AQbD in method development and application for drug quality assurance.

Notice of Violation of IEEE Publication Principles: Washout Filter-Based Distributed Resilient Current Control for Cyber-Physical Microgrids

Notice of Violation of IEEE Publication Principles: <br><br>“Washout Filter-Based Distributed Resilient Current Control for Cyber-Physical Microgrids," <br>by X. Lu and J. Lai, <br> in IEEE Systems Journal, Early Access <br><br>After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles. <br><br>This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. <br><br>"A Combined Distributed Cooperative Method and Washout Filter-Based Method for Power Sharing and Voltage Balancing in Bipolar DC Microgrids" <br> by Ramin Babazadeh-Dizaji, Mohsen Hamzeh, Nima Mahdian Dehkordi, <br>submitted to IEEE Transactions on Energy Conversion, August 2020. <br><br> <br/> This article proposes a washout filter-based distributed resilient current control scheme that can achieve current sharing of distributed generations (DG) and voltage restoration of dc bus almost surely under communication channel noise interferences. Since the cyber channels are exposed to inherent noise interferences, the stability and reliability of cyber-physical dc microgrids may terribly be reduced. To dispel the adverse influences of noise interferences, a novel discrete time washout filter is developed. Accordingly, a discrete-time-distributed noise-resiliency control algorithm is developed for dc microgrids based on washout filters, in which only the current state variable is allowed to exchange among DGs with the local decentralized voltage controller. This makes the proposed control mode different from the conventional parallel mode usually requiring a leader–follower consensus-based voltage control by exchanging two state variables (i.e., terminal voltage and current output). Thus, the proposed control mode decreases the complexity to the formulation and enhances the robustness to the leader communication failure. By introducing stochastic theory and Lyapunov stability function, the sufficient conditions considering noise interferences is derived to ensure the stability operation of the whole closed-loop dc network. The obtained results show the effectiveness of the proposed strategy by a tested dc microgrid in OPAL-RT real-time simulator.

ECS an endeavor towards providing similar cache reliability behavior in different programs

The reliability of embedded processors is one of the major concerns in safety-critical applications. Reliability, in particular, is expressed within the cache memories which are the largest element of processors, and consequently one of the most vulnerable components that can remarkably affect the reliability, especially in deep transistor scaling. Therefore, evaluating the cache vulnerability is crucial in the design of a reliable processor, especially for safety-critical applications.It has been shown that using the same cache size for different programs leads to non-identical vulnerability patterns/phases through them. According to the literature, most of the related research works have exploited identical cache sizes for different programs in their reliability evaluations, while the cache reliability strictly depends on the cache size and program behavior. State-of-the-art attempts to find an appropriate cache size for different programs require a huge and complex design space exploration.In this work, we have introduced a fast-obtaining criterion for determining the Effective Cache Size (ECS) for embedded processors which considers inherent programs' reliability and performance properties based on the memory footprints of programs. According to the results, using the ECS for the experimented programs in a processor with reconfigurable cache, the reliability would be increased up to 43× on average with acceptable performance degradations (21 % on average).

Multi-modal deep convolutional dictionary learning for image denoising

Leveraging the capabilities of traditional dictionary learning (DicL) and drawing upon the success of deep neural networks (DNNs), the recently proposed framework of deep convolutional dictionary learning (DCDicL) has exhibited remarkable behaviours in image denoising. Note that, the application of the DCDicL method is confined to single modality scenarios, whereas the images in practice often originate from diverse modalities. In this paper, to broaden the application scope of the DCDicL method, we design a multi-modal version of it, dubbed MMDCDicL. Specifically, within the mathematical model of MMDCDicL, we adopt an analytical approach to tackle the sub-problem linked to the guidance modality, harnessing its inherent reliability. Meanwhile, like in DCDicL, we utilize a network-based learning approach for the noisy modality to extract trustworthy information from the data. Based on the solution, we establish an interpretable network structure for MMDCDicL. Additionally, wherein, we design a multi-kernel channel attention block (MKCAB) in the structure to efficiently integrate the information from diverse modalities. Experimental results suggest that MMDCDicL can reconstruct higher-quality outcomes both quantitatively and perceptually. Code is available at http://www.diplab.net/lunwen/mmdcdicl.htm.

Evaluation of the New Multi-HTLV Serological Assay: Improvement for HTLV-2 Detection.

Despite the accuracy of confirmatory tests for the diagnosis of human T cell lymphotropic virus (HTLV), inconclusive or false-negative results still occur when diagnosing human T cell lymphotropic virus type 2 (HTLV-2)-positive patients. The goal of this study was to evaluate the sensitivity and accuracy of a confirmatory immunoassay, the Multi-HTLV assay. A total of 246 plasma samples were tested by real-time polymerase chain reaction (qPCR) and used to calculate the sensitivity and typing accuracy of the Multi-HTLV assay. Of the 246 plasma samples, 127 were positive for human T cell lymphotropic virus type 1 (HTLV-1), 112 were positive for HTLV-2, and 7 were positive for both HTLV-1 and HTLV-2. Thereafter, the nonparametric Mann-Whitney U test was used to calculate the concordance between the qPCR test and Multi-HTLV assay in 12 samples with discrepant and inconclusive qPCR results. The Multi-HTLV assay showed high performance in identifying HTLV-1 and HTLV-2 with sensitivities of 97% [95% confidence interval (CI): 0.92-0.98] and 94% (0.87-0.96), respectively. However, due to typing performance (98% for HTLV-1 and 94% for HTLV-2), it had 95% agreement with positive HTLV-1 qPCR results (95% CI: 90.07-97.81) and 86% (78.04-91.01) of HTLV-2 qPCR results were positive. Moreover, this test was able to recognize 80% of indeterminate samples and all HTLV-2 positive samples that showed false-negative qPCR results. Our findings, derived from a substantial number of HTLV-positive samples, underscore the inherent reliability and feasibility of the Multi-HTLV assay, regardless of the molecular testing facilities. Furthermore, the distinctive multiparametric nature of this assay, combined with its straightforward procedural execution, introduces novel perspectives for analyzing specific serological profiles in each patient, as well as the potential for immunological monitoring of disease progression.

Fault-tolerant strategies in MMC-based high power magnet supply for particle accelerator

Many particle accelerators require to supply chains of magnets with high quality, high magnitude, cycling currents. To do this, the power converters need to provide high output voltages, reaching in some cases tens of kilovolts. Additionally, converters are required to store the magnet energy during de-magnetization cycles. For such application, Full-bridge Modular Multilevel Converters (FB-MMC) could be used given their capacity to store energy and their inherent reliability. In this sense, one of the most interesting features of the proposed topology is the possibility of bypassing one or several submodules in the event of a fault or malfunction. By doing this, it is possible to ride-through the failure of a component and avoid the interruption of the accelerator operation.However, when the number of submodules is small, this operation could lead to an excessive charge of the healthy cells, increasing the risk of secondary failures. Besides, undesired harmonic content could appear on the output current, degrading the operation of the accelerator. It is then necessary to implement strategies that allow to remove a faulty cell without significantly impacting the operation of the remaining ones and of the converter itself.Accordingly, the purpose of this article is to investigate several of these strategies and assess them. By means of detailed computer simulations, the behaviour of the converter during normal and submodule fault conditions is analysed. Then, several fault-tolerant strategies are described, verified and compared with the aid of simulation tools. The results show the effectiveness of the analysed strategies in avoiding the overvoltage on the healthy submodules after a cell bypass and the little impact of this operation on the quality of the converter output current.