Isolation Scheme Research Articles

A Decentralized Model-Based Fault Detection and Isolation Scheme for MVDC Shipboard Power Systems

A Decentralized Model-Based Fault Detection and Isolation Scheme for MVDC Shipboard Power Systems

Seismic evaluation of isolation performance on single layer cylindrical reticulated shells supported along four sides

This study investigated the dynamic characteristics and seismic isolation performance of a single-layer cylindrical grid shell structure with lower support structures and different seismic isolation schemes, which encompass the installation of layered rubber bearings and new three-dimensional isolation bearings. Seven reduced-scale models of the structure were tested on a shaking table, with acceleration, strain, and displacement sensors positioned in typical locations. The results showed that the seismic response of the upper cylindrical grid shell structure was amplified by the lower support structure, which indicates that separate seismic designs for both the upper structure and lower support structure are unsafe. The use of layered rubber bearings at the foundation effectively reduced horizontal seismic response but amplified vertical seismic response. To address this issue, the new three-dimensional isolation bearing was proposed, which showed better overall seismic isolation performance compared to the rubber bearings. Additionally, a new seismic isolation scheme was proposed, using a combination of three-dimensional isolation bearings at both the foundation and column tops. This scheme showed superior seismic isolation efficiency compared to other schemes and can reduce the volume and installation difficulty of the three-dimensional isolation bearing. Considering the functional requirements, cost, and technical difficulty is crucial when selecting suitable seismic isolation measures since different schemes have their advantages and disadvantages and perform differently under various seismic conditions.

Design of an intelligent decision model for power grid fault location and isolation based on topology analysis

Detecting and isolating power grid faults quickly and efficiently ensures a constant power flow. The lack of speed and accuracy in conventional approaches to fault management highlights the importance of creating a smart decision model. The suggested approach uses topology analysis to better localize and isolate faults, reducing downtime and boosting grid performance. Real-time fault detection, precise fault location identification, and choosing the best isolation schemes are all difficulties in power grid fault management. Furthermore, significant issues need innovative solutions, such as managing the grid's complexity, responding to changing load conditions and guaranteeing cyber security. The present research unveils a new approach combining the Intelligent Decision Model integrated with the Supervisory Control and Data Acquisition system (SC-DAS). Here, Real-time decision-making, control, and monitoring are made possible by the SC-DAS system, which mediates between the intelligent decision model and the physical grid. In SC-DAS, Phasor Measurement Units (PMUs) are used for the real-time monitoring of the grid's electrical properties, which are collected using SCADA (Supervisory Control and Data Acquisition) systems. Further, SC-DAS uses decision-making models for instantaneous modifications and remedial measures of the grid's functioning. The grid's reliability uses Topological DAS for fault detection, localization, and isolation. Topological DAS represents a revolutionary approach that could significantly improve power grid reliability, reduce interruption frequency, and boost overall system performance in microgrids and renewable energy sources. The grid layouts, load circumstances, and fault scenarios used in the simulations help to improve the decision-making strategies in a controlled environment.

Embryo rescue in stone fruit breeding: specifics and application success

The development of stone fruit varieties that meet modern production requirements is a labor-intensive process involving low yields of progeny obtained through controlled intraspecific and interspecific hybridization, which can take up to 25 years. The non-viability of hybrid seeds is caused by the abortion of the embryo that does not have time to complete its morphophysiological development and endosperm abortion in interspecific hybrids. It is possible to preserve hybrids having a unique combination of traits by removing embryos derived from interspecific and intergeneric crossing that are unable to survive in vivo or when using conventional methods for breeding embryos at early developmental stages and placing them under in vitro conditions for further development. On the basis of literary sources, the study aims to analyze growth media, conditions of their application, and time of embryo placement under in vitro conditions designed to preserve immature embryos and create hybrids of stone fruit crops, as well as to identify the most effective in vitro embryo isolation schemes for further use in the breeding process. The analytical review used open sources containing information on the composition of the most commonly used growth media, their compositions, modifications, application conditions, time of embryo placement under in vitro conditions for common peach (Prunus persica (L.) Batsch.), common apricot (P. armeniaca L.), common plum (P. domestica L.), Chinese plum (P. salicina Lindl.), cherry plum (P. cerasifera Ehrh.), sloe (P. spinosa L.), sour cherry (P. cerasus L.), sweet cherry (P. avium (L.) L.), as well as electronic scientific libraries (eLibrary, Scopus, and Google Scholar). The performed analysis of works revealed the following media as optimal for sustaining the development of embryos: common peach – modified growth medium Woody Plant (up to 100 % of preserved embryos); common apricot – modified Monnier (up to 92.4 % of preserved embryos); sour cherry, sweet cherry, plum, and interspecific hybrids – different variants of Murashige and Skoog (over 70 % of preserved embryos). The used modifiers include 6-benzylaminopurine (6-BAP), gibberellic acid (GA), kinetin, peracetic acid, carbohydrates, L-glutamine, iron chelate, ascorbic acid, thiamine, glutamic acid, tryptophan, casein hydrolysate, etc. The time of placement under in vitro conditions varies from 28 days (sour cherry) to 99 days (common peach) depending on the crop. Stratification in the dark at +4…+5 ℃ prior to development increases the yield of viable embryos. The optimal photoperiod for in vitro embryo cultivation ranges from 12 (common peach) to 16 (cherry plum, sour cherry, sweet cherry, and interspecific hybrids) hours. As a result of selection from the fund of hybrids obtained through embryo preservation, one common peach variety (Krymskii Shedevr) and three sweet cherry varieties (Vesnyany Naspivy, Prizerka, and Uslada), recommended for the North Caucasus region, were included in the State Register of Breeding Achievements approved for use; two common plum varieties (Tulitsa and Velichavaya) were included into the Register of Protected Breeding Achievements for the Central Region.

BIM and Computational Fluid Dynamics Analysis for Thermal Management Improvement in Data Centres

One of the most energy-intensive facilities requiring a comprehensive and well-optimised cooling system is the data centre. Air containment across the data centre is a key thermal management and energy-saving strategy that enhances the performance of data centres. The majority of modern energy-efficient data centres use some type of air containment. The primary advantage of aisle separation and containment is the decrease in the air temperature at the server inlet by reducing the mixing of hot air with cold air. In order to ascertain the volume of literature relating to corridor insulation, we conducted a literature review. Currently, there have been numerous articles regarding the application of computational fluid dynamics (CFD) analysis, however, publications delineating the integration of building information modelling (BIM) principles for corridor separation are still limited. Research specifically targeting data centre corridor insulation is somewhat limited. As a result of this analysis, the most common methods used to isolate hot or cold aisles within a data centre were identified. To determine the most effective type of corridor insulation, the BIM family was created in Autodesk Revit. The model includes 15 telecom cabinets containing information technology (IT) equipment, eight inter-row air conditioners, and one UPS. The model was used for the CFD analysis of the air temperature in different zones of the room. Visualisation of the results using gradient temperature distributions at different levels provides a complete picture of the microclimate formation in the room and allowed the advantage of the hot aisle isolation scheme to be demonstrated.

Data-driven distributed collaborative fault detection and isolation for large-scale dynamic processes in simultaneous-fault cases

This paper proposes a data-driven distributed collaborative fault detection and isolation scheme for large-scale dynamic processes, interconnected by heterogeneous subsystems with both actuator and sensor faults. The residual generator in each subsystem is directly constructed with both local and neighboring process data based on subspace identification. Utilizing the rank deficiency property, the healthy mode residual covariance matrix is estimated with noisy data, and thus the test statistic and detection threshold are obtained. Then, a collaborative simultaneous fault isolation method is developed, where the local residual is decoupled with neighboring actuator fault’s effects and a series of robust residual generators are constructed by solving corresponding optimization problems. In each subsystem, both local fault isolation and neighboring sensor fault isolation are realized. Thus, sensor fault detection and isolation for the whole process can be achieved with a reduced number of monitoring subsystems. Finally, case studies on the Tennessee Eastman (TE) process and a real hot strip mill process (HSMP) verify the feasibility and good performance of the proposed scheme.

Protection of Line Faults in HVDC Grids Through Convexity Detection in Backward Traveling Wave Voltages

Short-circuit faults in high-voltage DC (HVDC) grids must be timely isolated. Conventional fault isolation schemes for HVDC grids typically rely on numerical simulations or analytical calculations for threshold setting, which complicate engineering implementation. To solve this issue, this paper first introduces the convex point of backward traveling wave (BTW) voltage as the reference for the faulty line isolation in HVDC grids, which is interpreted as the singular feature in the second derivative (SD) of BTW voltage. The singularity in the SD of BTW voltage is independent of system or fault parameters, having good generality in HVDC grids. Through recognizing the unique pattern in the wavelet transform modulus maxima of the singular feature, the proposed protection method can identify faulty lines in HVDC grids in the initial phase of faults. Numerical studies carried out with PSCAD/EMTDC and MATLAB/Simulink demonstrate the effectiveness, robustness and applicability of the proposed method in detecting and isolating various single- and double-pole faults in HVDC grids.

Cell culture of <i>Scorzonera hispanica</i> L., strain SFR-SH1. Major secondary metabolites

Plant cell cultures are widely used in the world pharmaceutical industry as a raw material for the production of biologically active compounds. Their certain advantages, such as independence from seasonal conditions, diseases and their transmitter, contribute to the development of this area of biotechnology. In addition, it is possible to obtain the required quantity of the desired product with standard quality characteristics. Since the biosynthesis of new compounds does not occur in the intact plant, another advantage of this method arises. It consists in obtaining new substances due to the peculiarities of cell culture biology. The conducted review represents secondary metabolites isolated from the cell culture of Scorzonera hispanica L., strain SFR-SH1, possessing biological activity. In addition, the history of developing the selected strain and schemes for isolation and purification of secondary metabolites are demonstrated. For a clearer discussion, the main secondary metabolites found in cells are broadly divided into three groups. The first group includes compounds typical of most plants: β-sitosterol and its glucoside, caffeic acid methyl ester and oleanic acid. The second group contains syringaresinol monoglucoside, which is relatively rare in plant objects. The third group includes two compounds isolated from natural objects for the first time: sesquiterpene glucoside, scorzoside and neolignan scorzonoside. The results obtained were compared with the available literature data on the chemical composition of native plants of S. hispanica and other Scorzonera genera species. Due to its high biological activity, Syringaresinol monoglycoside attracts more attention than other compounds detected in cultured cells. Long-term cultivation of Monoglycoside leads to an alteration in the physicochemical characteristics of this compound for an unknown reason.

Hybrid robust fault detection and isolation of satellite reaction wheel actuators

In this paper, a combined robust fault detection and isolation scheme is studied for satellite system subject to actuator faults, external disturbances, and parametric uncertainties. The proposed methodology incorporates a residual generation module, including a bank of filters, into an intelligent residual evaluation module. First, residual filters are designed based on an improved nonlinear differential algebraic approach so that they are not affected by external disturbances. The residual evaluation module is developed based on the suggested series and parallel forms. Further, a new ensemble classification scheme defined as blended learning integrates heterogeneous classifiers to enhance the performance. A wide range of simulations is carried out in a high-fidelity satellite simulator subject to the constant and time-varying actuator faults in the presence of disturbances, manoeuvres, uncertainties, and noises. The obtained results demonstrate the effectiveness of the proposed robust fault detection and isolation method compared to the traditional nonlinear differential algebraic approach.

Deterministic learning-based sensor fault detection, isolation and accommodation of nonlinear systems

This paper presents a systematic deterministic learning-based rapid sensor fault detection, isolation and accommodation (SFDIA) scheme for a class of nonlinear systems with unmodeled output dynamics. First, locally accurate approximations of sensor faults and unmodeled output dynamics are achieved by using a deterministic learning-based neural network observer. Second, a sensor fault pattern bank which stores the knowledge of sensor faults and unmodeled output dynamics is established. A set of observers utilising the learned knowledge are constructed to generate residuals for rapid SFDIA. The contributions are: (1) the fault detectability and isolability conditions are rigorously analysed and verified in the simulation; (2) using the proposed fault accommodation approach, compared with online parameter adaptation techniques, better tracking performance can be achieved in the presence of sensor faults. Simulation results show the effectiveness of the proposed method.

A novel semi-supervised fault detection and isolation method for battery system of electric vehicles

The detection and isolation of early and minor faults in vehicle battery systems is vital to safe driving and improving power utilization. This paper proposes a data-driven model to achieve accurate, early, and economical fault detection and isolation. The model is based on kernel principal component analysis (KPCA), which maps complex nonlinear data from the input space into a high-dimensional feature space to gain a detection model with good performance. To overcome the difficulty of hyperparameter selection, KPCA is trained using Bayesian Optimization (BO) iterations with a small amount of labeled data and a large amount of unlabeled data. This step can obtain the optimal hyperparameter to greatly improve the model fault detection capability, which is beneficial for detecting both early faults and minor faults. In addition, a unified contribution graph based on the partial differentiation of KPCA was adopted to build a reasonable isolation scheme. The semi-supervised model of KPCA based on Bayesian Optimization and contribution graph is developed to reveal the relationship between fault and variable. Finally, the proposed method is fully tested on four fault datasets and the results prove the excellent detection capability in the early stage of faults compared with other methods and the accurate fault isolation capability from the occurrence to the end of the fault.

Design and Implementation of a Fuzzy Classifier for FDI Applied to Industrial Machinery.

In the present work, the design and the implementation of a Fault Detection and Isolation (FDI) system for an industrial machinery is proposed. The case study is represented by a multishaft centrifugal compressor used for the syngas manufacturing. The system has been conceived for the monitoring of the faults which may damage the multishaft centrifugal compressor: instrument single and multiple faults have been considered as well as process faults like fouling of the compressor stages and break of the thrust bearing. A new approach that combines Principal Component Analysis (PCA), Cluster Analysis and Pattern Recognition is developed. A novel procedure based on the statistical test ANOVA (ANalysis Of VAriance) is applied to determine the most suitable number of Principal Components (PCs). A key design issue of the proposed fault isolation scheme is the data Cluster Analysis performed to solve the practical issue of the complexity growth experienced when analyzing process faults, which typically involve many variables. In addition, an automatic online Pattern Recognition procedure for finding the most probable faults is proposed. Clustering procedure and Pattern Recognition are implemented within a Fuzzy Faults Classifier module. Experimental results on real plant data illustrate the validity of the approach. The main benefits produced by the FDI system concern the improvement of the maintenance operations, the enhancement of the reliability and availability of the compressor, the increase in the plant safety while achieving reduction in plant functioning costs.

Seismic isolation analysis and design of a plane irregular building structure in high earthquake intensity area

Taking the large commercial complex building of Changzhou Golden Eagle Plaza as the research object, this paper analyzes various parameters of typical special-shaped space structure under different isolation schemes under the conditions of earthquake protection, rare earthquake and extremely rare earthquake, compares three isolation layer design combinations of rubber isolation bearing, lead isolation bearing, rubber isolation bearing + lead isolation bearing, and studies the influence of different isolation layer arrangement schemes on the steel core and mass center of the building structure. The results show that the reasonable isolation design can obtain the reasonable arrangement pattern and rule of the plane irregular building structure in a high seismic intensity area by comparing and analyzing the arrangement schemes of different types and forms of isolation bearings in the isolation structure model.

Study on the seismic mitigation effects of inerter isolated storage tanks

Liquefied Natural Gas (LNG) storage tanks can ensure national energy sufficiency, but they are vulnerable to earthquakes. Although ordinary seismic isolation bearings can effectively reduce the base shear force and overturning moment, they tend to amplify the height of the sloshing wave. In this paper, a hybrid vibration control method for LNG storage tanks is proposed, paralleling the series viscous mass damper (SVMD) devices with Lead-core rubber bearings (LRB). The seismic mitigation effects of this parallel seismic isolation scheme were investigated. First, three-dimensional finite element models of fixed base, LRB isolated and SVMD isolated tanks were established, and nonlinear time history analyses were performed for the three types of tanks to compare the seismic responses. It was concluded that SVMD can effectively control the sloshing wave height. In addition, the effects of site types, storage liquid heights, and SVMD's performance parameters on the seismic mitigation effects were also studied. It was found that the softer the site soil, the higher the storage liquid height would make seismic isolation less efficient. The increase of SVMD's additional equivalent mass reduces the sloshing wave height, increases the shear-to-weight ratio, and has little influence on the base displacement. The increase of SVMD's additional equivalent damping reduces the sloshing wave height, base shear force, and base displacement of the tank. The final conclusion is that SVMD can effectively control the sloshing wave height and has a good seismic mitigation effect.

Characterization of Timing-based Software Side-channel Attacks and Mitigations on Network-on-Chip Hardware

Modern network-on-chip (NoC) hardware is an emerging target for side-channel security attacks. A recent work implemented and characterized timing-based software side-channel attacks that target NoC hardware on a real multicore machine. This article studies the impact of system noise on prior attack setups and shows that high noise is sufficient to defeat the attacker. We propose an information theory-based attack setup that uses repetition codes and differential signaling techniques to de-noise the unwanted noise from the NoC channel to successfully implement a practical covert-communication attack on a real multicore machine. The evaluation demonstrates an attack efficacy of 97%, 88%, and 78% under low, medium, and high external noise, respectively. Our attack characterization reveals that noise-based mitigation schemes are inadequate to prevent practical covert communication, and thus isolation-based mitigation schemes must be considered to ensure strong security. Isolation-based schemes are shown to mitigate timing-based side-channel attacks. However, their impact on the performance of real-world security critical workloads is not well understood in the literature. This article evaluates the performance implications of state-of-the-art spatial and temporal isolation schemes. The performance impact is shown to range from 2–3% for a set of graph and machine learning workloads, thus making isolation-based mitigations practical.

Research on circuit breaker failure protection and the secondary accelerated fault isolation scheme of VSC-HVDC grids

When the direct current circuit breaker (DCCB) fails to operate, the fault range will further expand, endangering the safe operation of the power gird. Therefore, this study proposes a DCCB failure protection method and a secondary accelerated fault isolation scheme of voltage source converter-based high-voltage direct current (VSC-HVDC) grids. On the basis of considering the influence of DCCB disconnection, characteristics of fault current after the failure of the DCCB are analyzed, and a circuit breaker failure protection method based on “increase current” discrimination is proposed. By analyzing the logical relationship among failed breaker re-tripping, adjacent DCCB action, blocking of the converter station, and AC circuit breaker action, a secondary accelerated fault isolation scheme is proposed to minimize the fault isolation area. The scheme achieves the accelerated isolation of DC faults after the failure of the DCCB and avoids the trip of the AC circuit breaker effectively. Simulation results verify the effectiveness of the proposed scheme.

To Investigate Different Parameters of Economic Sliding Based Seismic Isolation System

ABSTRACT Masonry structures are frequently constructed in developing countries because of their affordability and ease of construction, even though they are vulnerable and perform poorly during earthquakes. Experimental and numerical studies were carried out to determine the optimum thickness of the isolation layer for a recently developed, low-cost seismic isolation system known as the Reinforced-Cut-Wall (RCW). The parameters of the isolation layer were optimized during the experimental and numerical investigations using hollow concrete blocks. Quasi-static cyclic tests were performed on a 1:3 reduced-scale brick masonry wall incorporating the RCW isolation scheme, validating the better performance of the proposed isolation technique.

Actuator FDI Scheme for a Wind Turbine Benchmark Using Sliding Mode Observers

This paper proposes a fault detection and isolation (FDI) scheme for a wind turbines subject to actuator faults in both the pitch system and the drive train system. The proposed scheme addresses fault detection and isolation problems using a fault estimation approach. The proposed approach considers the use of a particular class of sliding mode observers (SMOs) designed to maintain the sliding motion even in the presence of actuator faults. The fault detection problem is solved by reconstructing the actuator faults through an appropriate analysis of the nonlinear output error injection signal, which is required to keep the SMO in a sliding motion. To ensure accurate fault reconstruction, only two conditions are required, namely that the faults are bounded and they meet the matching condition. A scheme based on a bank of SMOs is proposed to solve the fault detection and isolation problem in the pitch system. For the drive train system, a scheme using only one SMO is proposed. The performance of the proposed FDI scheme is validated by using a wind turbine benchmark model subjected to several actuator faults. Normalized root mean square error (NRMSE) analysis is performed to evaluate the accuracy of the actuator fault estimations.

A Novel Scheme for Full Bottom Dielectric Isolation in Stacked Si Nanosheet Gate-All-Around Transistors.

In this paper, a novel scheme for source/drain-first (S/D-first) full bottom dielectric isolation (BDI), i.e., Full BDI_Last, with integration of a sacrificial Si0.5Ge0.5 layer was proposed and demonstrated in a stacked Si nanosheet gate-all-around (NS-GAA) device structure using TCAD simulations. The proposed full BDI scheme flow is compatible with the main process flow of NS-GAA transistor fabrication and provides a large window for process fluctuations, such as the thickness of the S/D recess. It is an ingenious solution to insert the dielectric material under the source, drain and gate regions to remove the parasitic channel. Moreover, because the S/D-first scheme decreases the problem of high-quality S/D epitaxy, the innovative fabrication scheme introduces full BDI formation after S/D epitaxy to mitigate the difficulty of providing stress engineering in the full BDI formation before S/D epitaxy (Full BDI_First). The electrical performance of Full BDI_Last is demonstrated by a 4.78-fold increase in the drive current compared to Full BDI_First. Furthermore, compared to traditional punch through stoppers (PTSs), the proposed Full BDI_Last technology could potentially provide an improved short channel behavior and good immunity against parasitic gate capacitance in NS-GAA devices. For the assessed inverter ring oscillator (RO), applying the Full BDI_Last scheme allows the operating speed to be increased by 15.2% and 6.2% at the same power, or alternatively enables an 18.9% and 6.8% lower power consumption at the same speed compared with the PTS and Full BDI_First schemes, respectively. The observations confirm that the novel Full BDI_Last scheme incorporated into an NS-GAA device can be utilized to enable superior characteristics to benefit the performance of integrated circuits.

An incremental fuzzy approach to risk-based evaluation of process equipment isolation schemes

Any time that process equipment needs to be out of service for inspection, maintenance, cleaning, or repair, sufficient isolation should be applied to isolate the equipment from the hazardous energies. Opting and implementing a proper scheme to isolate such equipment is critical to prevent accidents in process plants.Conventional risk-based methods to determine the isolation schemes are not so effective in dealing with subjective errors and uncertainties. Therefore, in this study we have developed an incremental fuzzy system to cope with the aforesaid limitations of conventional risk-based methods. In the proposed model, a numerical technique is applied for developing membership functions to reduce the uncertainty. In the incremental fuzzy model, three fuzzy inference systems in three levels are generated to calculate a release factor, outcome factor, and isolation level.Applying the proposed model to a three-phase separator, the results of the proposed model were compared with those of a conventional risk-based method, demonstrating that the proposed model can effectively handle the uncertainty while keeping the simplicity of the conventional approaches. The results are more transparent, detailed, and thus of greater value in identifying isolation schemes. Therefore, the proposed model can be an effective alternative to the conventional methods currently used in process plants for determination of isolation schemes.