A Software-based Low-Jitter Servo Clock for Inexpensive Phasor Measurement Units

This paper presents the design and the implementation of a servo-clock (SC) for low-cost Phasor Measurement Units (PMUs). The SC relies on a classic Proportional Integral (PI) controller, which has been properly tuned to minimize the synchronization error due to the local oscillator triggering the on-board timer. The SC has been implemented into a PMU prototype developed within the OpenPMU project using a BeagleBone Black (BBB) board. The distinctive feature of the proposed solution is its ability to track an input Pulse-Per-Second (PPS) reference with good long-term stability and with no need for specific on-board synchronization circuitry. Indeed, the SC implementation relies only on one co-processor for real-time application and requires just an input PPS signal that could be distributed from a single substation clock

A Servo-Clock Model for Chains of Transparent Clocks Affected by Synchronization Period Jitter

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A methodology for the design of dynamic accuracy operators by runtime back bias

Mobile and IoT applications must balance increasing processing demands with limited power and cost budgets. Approximate computing achieves this goal leveraging the error tolerance features common in many emerging applications to reduce power consumption. In particular, adequate (i.e., energy/quality-configurable) hardware operators are key components in an error tolerant system. Existing implementations of these operators require significant architectural modifications, hence they are often design-specific and tend to have large overheads compared to accurate units. In this paper, we propose a methodology to design adequate data-path operators in an automatic way, which uses threshold voltage scaling as a knob to dynamically control the power/accuracy tradeoff. The method overcomes the limitations of previous solutions based on supply voltage scaling, in that it introduces lower overheads and it allows fine-grain regulation of this tradeoff. We demonstrate our approach on a state-of-the-art 28nm FDSOI technology, exploiting the strong effect of back biasing on threshold voltage. Results show a power consumption reduction of as much as 39% compared to solutions based only on supply voltage scaling, at iso-accuracy

Adaptive Task Migration Policies for Thermal control in MPSoCs

In deep submicron circuits, high temperatures have created critical issues in reliability, timing, performance, coolings costs and leakage power. Task migration techniques have been proposed to manage efficiently the thermal distribution in multi-processor systems but at the cost of important performance penalties. While traditional techniques have focused on reducing the average temperature of the chip, they have not considered the effect that temperature gradients have in system reliability. In this work, we explore the benefits of thermal-aware task migration techniques for embedded multi-processor systems. We propose several policies that are able to reduce the average temperature of the chip and the thermal gradients with a negligible performance overhead. With our techniques, hot spots and temperature gradients are decreased up to 30% with respect to state-of-the-art thermal management approache

Management of Measurement Uncertainty for Effective Statistical Process Control

In the context of quality assurance strategies, statistical process control techniques and conformance testing are necessary to perform a correct quality auditing of process outcomes. However, data collection is based on measurements and every measurement is intrinsically affected by uncertainty. Even if adopted instruments are in a condition of metrological confirmation, random and systematic measurement errors can not be completely eliminated. Moreover, the consequence of wrong measurement–based decisions can seriously decrease company profits because of larger repairing and shipping costs, as well as for the loss of reputation due to customers’ dissatisfaction. This paper deals with a theoretical analysis aimed at estimating the growth in decisional risks due to both random and systematic errors. Also, it provides some useful guidelines about how to choose the Test Uncertainty Ratio (TUR) of industry–rated measurement instruments in order to bound the risk of making wrong decisions below a preset maximum value

Management of Distributed Measurement Systems Based on Abstract Client-Server Paradigms

This paper describes in detail a Java-based, clientserver architecture specifically conceived to allow a flexible control of remote devices. The main attributes of the proposed solution are portability and flexibility. The former feature is assured by the employment of the TCP/IP protocol suite and by the Java language properties. The latter is due to the high level of abstraction of the system implementation, that addresses multi-user issues and a wide range of possible applications with a high code reusability. In particular, the proposed architecture can be easily upgraded so as to fit different kinds of devices, by simply adding a limited amount of code on the server-side of the overall system

Measurement Uncertainty and Metrological Confirmation in Quality-Oriented Organizations

The effect of measurement uncertainty on estimates and decisions performed under a regime of quality control and improvement, is considered in this paper. Standard statistical quality tools are analyzed such as control charts and instrument calibration procedures. Their performance is characterized under the assumption of both normally and uniformly distributed measurement uncertainty. Exact and approximate expressions are derived that allow the design of suitable procedures including the contribution of measurement uncertainty

Alternative methods to estimate measurand values: models and operative implications

This paper compares two alternative averaging methods commonly adopted to estimate measurand values, in agreement with the guidelines of the Guide to the Expression of Uncertainty in Measurement (GUM). According to the proposed analysis, validated in a simple case study, the choice of best estimation method should depend not only on the amount of nonlinearity of the measurement model, but also on the amount of definitional and acquisition uncertainty of the input measurands.3-5 September 200

Measurand value estimation in nonlinear models: a comparison between two averaging methods

The problem of how to choose the best estimation method for a measurand value when repeated measurements of the input quantities to a nonlinear model are performed is not clearly addressed in the guide to the expression of uncertainty in measurement. In this paper, this issue is tackled by analyzing and comparing the effect of both definitional and acquisition uncertainty contributions on the overall uncertainty resulting from the application of the two averaging methods mentioned in the Guide. The achieved theoretical results, validated by means of some simulations, provide effective suggestions about which method should be used in different contexts.2008-07-2

A Distribution System State Estimator Based on an Extended Kalman Filter Enhanced with a Prior Evaluation of Power Injections at Unmonitored Buses

In the context of smart grids, Distribution Systems State Estimation (DSSE) is notoriously problematic because of the scarcity of available measurement points and the lack of real-time information on loads. The scarcity of measurement data influences on the effectiveness and applicability of dynamic estimators like the Kalman filters. However, if an Extended Kalman Filter (EKF) resulting from the linearization of the power flow equations is complemented by an ancillary prior least-squares estimation of the weekly active and reactive power injection variations at all buses, significant performance improvements can be achieved. Extensive simulation results obtained assuming to deploy an increasing number of next-generation smart meters and Phasor Measurement Units (PMUs) show that not only the proposed approach is generally more accurate and precise than the classic Weighted Least Squares (WLS) estimator (chosen as a benchmark algorithm), but it is also less sensitive to both the number and the metrological features of the PMUs. Thus, low-uncertainty state estimates can be obtained even though fewer and cheaper measurement devices are used