8,165 research outputs found

    Dynamic Voltage Scaling Aware Delay Fault Testing

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    The application of Dynamic Voltage Scaling (DVS) to reduce energy consumption may have a detrimental impact on the quality of manufacturing tests employed to detect permanent faults. This paper analyses the influence of different voltage/frequency settings on fault detection within a DVS application. In particular, the effect of supply voltage on different types of delay faults is considered. This paper presents a study of these problems with simulation results. We have demonstrated that the test application time increases as we reduce the test voltage. We have also shown that for newer technologies we do not have to go to very low voltage levels for delay fault testing. We conclude that it is necessary to test at more than one operating voltage and that the lowest operating voltage does not necessarily give the best fault cover

    A droplet routing technique for fault-tolerant digital microfluidic devices

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    Abstract—Efficient droplet routing is one of the key approaches for realizing fault-tolerant microfluidic biochips. It requires that run-time diagnosis and fault recovery can be made possible in such systems. This paper describes a droplet routing technique for a fault-tolerant digital microfluidic platform. This technique features handling of many microfluidic operations simultaneously and uses on-chip sensors for diagnosis at run-time.\ud Once a fault is detected during the droplet routing, recovery procedures will be started-up immediately. Faulty units on the chip will be marked and isolated from the array so that the remaining droplets can still be routed along a fault-free path to their destinations. This method guarantees a non-stop fault-tolerant operation for very large microfluidic arrays.\u

    Analysis of drawbacks and constraints of classification algorithms for three-phase voltage dips

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    Voltage events are one of the most common and harmful disturbances of power electric systems. Voltage dips, swells and interruptions are included under this heading. Given the economic cost that these disturbances represent for electrical power transmission and distribution companies and the industry, it becomes imperative to detect and classify them properly. Several classification criteria and algorithms have been proposed in the literature as analysis tools to differentiate voltage events by their characteristics and, if possible, to determine their causes and consequences. Even though some of these approaches make a correct classification of the voltage events, there are certain operation conditions that are common in real electrical grids, in which the classification criteria, and their corresponding algorithms, make a wrong classification. These particular conditions, together with the lack of a fair comparison in a common scenario, have not been addressed in the specific field literature. This work explores in detail all these aspects by evaluating the symmetrical components criterion and ABC classification criterion, and rigorously analyzes three specific algorithms: the Symmetrical Components Algorithm, the Six Phases Algorithm and the Space Vector Algorithm. Drawbacks arise from both classification criteria and algorithms. The causes of the classification errors are described and discussed in detail in order to better understand the problem, and evidence the constraints of these classification methods.Fil: Strack, Jorge Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica; ArgentinaFil: Carugati, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica; ArgentinaFil: Orallo, Carlos Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica; ArgentinaFil: Donato, Patricio Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica; ArgentinaFil: Maestri, Sebastian Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica; ArgentinaFil: Carrica, Daniel Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica; Argentin

    Modeling and Recognition of Smart Grid Faults by a Combined Approach of Dissimilarity Learning and One-Class Classification

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    Detecting faults in electrical power grids is of paramount importance, either from the electricity operator and consumer viewpoints. Modern electric power grids (smart grids) are equipped with smart sensors that allow to gather real-time information regarding the physical status of all the component elements belonging to the whole infrastructure (e.g., cables and related insulation, transformers, breakers and so on). In real-world smart grid systems, usually, additional information that are related to the operational status of the grid itself are collected such as meteorological information. Designing a suitable recognition (discrimination) model of faults in a real-world smart grid system is hence a challenging task. This follows from the heterogeneity of the information that actually determine a typical fault condition. The second point is that, for synthesizing a recognition model, in practice only the conditions of observed faults are usually meaningful. Therefore, a suitable recognition model should be synthesized by making use of the observed fault conditions only. In this paper, we deal with the problem of modeling and recognizing faults in a real-world smart grid system, which supplies the entire city of Rome, Italy. Recognition of faults is addressed by following a combined approach of multiple dissimilarity measures customization and one-class classification techniques. We provide here an in-depth study related to the available data and to the models synthesized by the proposed one-class classifier. We offer also a comprehensive analysis of the fault recognition results by exploiting a fuzzy set based reliability decision rule

    STANDARDS IN CONTROL AND PROTECTION TEHNOLOGY FOR ELECTRIC POWER SYSTEMS

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    The features of the standard IEC 16850 with respect to intelligent applications in substations are summarized. It is shown how modeling of functions independently from its allocation to devices allows optimizing existing applications and opening up for future intelligent applications. The data model provides all information in a substation needed not only for control and protection functions but also about the IEDs and the switchgear configuration.electric power systems
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