There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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[서평]「Computer Organization and Design, The Hardware/Software Interface」

The Internet of Things (IoT) provides a virtual view, via the Internet Protocol, to a huge variety of real life objects, ranging from a car, to a teacup, to a building, to trees in a forest. Its appeal is the ubiquitous generalized access to the status and location of any “thing” we may be interested in. Wireless sensor networks (WSN) are well suited for long-term environmental data acquisition for IoT representation. This paper presents the functional design and implementation of a complete WSN platform that can be used for a range of long-term environmental monitoring IoT applications. The application requirements for low cost, high number of sensors, fast deployment, long lifetime, low maintenance, and high quality of service are considered in the specification and design of the platform and of all its components. Low-effort platform reuse is also considered starting from the specifications and at all design levels for a wide array of related monitoring applications.

Design of a WSN Platform for Long-Term Environmental Monitoring for IoT Applications

In the last decade, the research on and the technology for outdoor tracking have seen an explosion of advances. It is expected that in the near future, we will witness similar trends for indoor scenarios where people spend more than 70% of their lives. The rationale for this is that there is a need for reliable and high-definition real-time tracking systems that have the ability to operate in indoor environments, thus complementing those based on satellite technologies, such as the Global Positioning System (GPS). The indoor environments are very challenging, and as a result, a large variety of technologies have been proposed for coping with them, but no legacy solution has emerged. This paper presents a survey on indoor wireless tracking of mobile nodes from a signal processing perspective. It can be argued that the indoor tracking problem is more challenging than the problem on indoor localization. The reason is simple: From a set of measurements, one has to estimate not one location but a series of correlated locations of a mobile node. The paper illustrates the theory, the main tools, and the most promising technologies for indoor tracking. New directions of research are also discussed.

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Indoor Tracking: Theory, Methods, and Technologies

Transition to a sustainable energy environment results in aggregated generator and load dynamics in the distribution network. State estimation is a key function in building adequate network models for online monitoring and analyzes. The requirements of distribution system state estimation (DSSE) is becoming stringent because of the needs of new system modeling and operation practices associated with integration of distributed energy resources and the adoption of advanced technologies in distribution network. This paper summarizes the state-of-the-art technology, major hurdles, and challenges in DSSE development. The opportunities, paradigm shift, and future research directions that could facilitate the need of DSSE are discussed.

A Review on Distribution System State Estimation

Synchrophasor estimation accuracy is a well-known critical issue in systems for smart grid monitoring and control. This paper deals with an in-depth analysis of the effect of both steady-state and dynamic disturbances on single-cycle and multicycle windowed discrete Fourier transform (DFT)-based synchrophasor estimators. Unlike other qualitative or simulation-based results found in the literature, this work provides two accurate and easy-to-use analytical expressions that can be used to determine the worst case range of variation of the total vector error (TVE) due to off-nominal frequency deviations. In such conditions, estimation accuracy is limited by two factors, i.e., the infiltration caused by the input signal image frequency and the scalloping loss associated with the spectrum main lobe of the chosen window. Starting from the aforementioned general analysis, a new two-term window minimizing the detrimental effects of image frequency tone is proposed. The accuracy of the related DFT-based synchrophasor estimator is evaluated under both static and dynamic conditions, which is the most interesting scenario for future smart grids. Moreover, the effect of waveform frequency measurement uncertainty on scalloping loss compensation is quantified. Several simulation results (including the effects of noise, harmonic distortion, and amplitude and phase modulation) confirm that the proposed window can significantly improve the accuracy achievable with a simple single-cycle DFT estimator. Indeed, TVE values much smaller than 1% can be achieved even in the worst case conditions reported in the standard IEEE C37.118.1-2011, when the frequency waveform deviations are within ±4% of the nominal value. In addition, the proposed solution could be useful to improve the performance of more complex dynamic phasor estimators, e.g., those in which the first- and second-order terms of the phasor Taylor series expansion result from the differences of consecutive DFT-based phasor estimates.

Accuracy Analysis and Enhancement of DFT-Based Synchrophasor Estimators in Off-Nominal Conditions

Next-generation phasor measurement units (PMUs) are expected to play a key role for monitoring the behavior of future smart grids. While most of the PMUs used nowadays in transmission networks rely on static phasor models, more sophisticated representations and stricter accuracy requirements are needed to track amplitude, phase, and frequency changes of power waveforms in strongly dynamic scenarios as those expected in future distribution systems. In this paper, a discrete Fourier transform (DFT)-based algorithm based on a dynamic phasor model (referred to as interpolated dynamic DFT-based synchrophasor estimator) is used to estimate not only amplitude and phase of the collected waveforms, but also their frequency and rate of change of frequency. The performances of the proposed method are evaluated through multiple simulations in different steady-state and transient conditions described in the Standard IEEE C37.118.1-2011.

A Frequency-Domain Algorithm for Dynamic Synchrophasor and Frequency Estimation

In this work we address the problem of static state estimation (SE) in distribution grids by leveraging historical meter data (pseudo-measurements) with real-time measurements from synchrophasors (PMU data). We present a Bayesian linear estimator based on a linear approximation of the power flow equations for distribution networks, which is computationally more efficient than standard nonlinear weighted least squares (WLS) estimators. We show via numerical simulations that the proposed strategy performs similarly to the standard WLS estimator on a small distribution network. A key advantage of the proposed approach is that it provides explicit off-line computation of the estimation error confidence intervals, which we use to explore the tradeoffs between number of PMUs, PMU placement and measurement uncertainty. Since the estimation error in distribution systems tends to be dominated by uncertainty in loads and scarcity of instrumented nodes, the linearized method along with the use of high-precision PMUs may be a suitable way to facilitate on-line state estimation where it was previously impractical.

/pdf/bayesian-linear-state-estimation-using-smart-meters-and-pmus-2znanz5x1d.pdf

Bayesian linear state estimation using smart meters and PMUs measurements in distribution grids

Indoor localization and tracking of moving human targets is a task of recognized importance and difficulty. In this paper, we describe a position measurement technique based on the fusion of various sensor data collected using a wearable embedded platform. Since the accumulated measurement uncertainty affecting inertial data (especially due to the on-board accelerometer) usually makes the measured position values drift away quickly, a heuristic approach is used to keep velocity estimation uncertainty in the order of a few percent. As a result, unlike other solutions proposed in the literature, localization accuracy is good when the wearable platform is worn at the waist. Unbounded uncertainty growth is prevented by injecting the position values collected at a very low rate from the nodes of an external fixed infrastructure (e.g., based on cameras) into an extended Kalman filter. If the adjustment rate is in the order of several seconds and if such corrections are performed only when the user is detected to be in movement, the infrastructure remains idle most of time with evident benefits in terms of scalability. In fact, multiple platforms could work simultaneously in the same environment without saturating the communication channels.

Flexible Indoor Localization and Tracking Based on a Wearable Platform and Sensor Data Fusion

The real-time high-accuracy measurement of waveform phasors is one of the many open challenges that need to be addressed in future smart grids. In this paper, the accuracy of four recently proposed synchrophasor estimators is analyzed and compared with the well-known one-cycle discrete Fourier transform estimator under the effect of static frequency offsets, amplitude modulation, phase modulation, harmonic distortion, and wideband noise. Two of the considered techniques track the phasor variations through finite-difference equations that estimate the first- and second-order derivatives of the phasor itself. The other two methods are instead based on a least squares estimation of the coefficients of the phasor Taylor's series expansion. The analysis reported in this paper covers the main scenarios described in the Standard IEEE C37.118.1-2011. In particular, the influence of different signal parameters on the total vector error (TVE) values is quantified and used to determine the maximum TVE increments associated with distinct parameters and the corresponding upper bounds.

David Macii

Papers

Accuracy Analysis and Enhancement of DFT-Based Synchrophasor Estimators in Off-Nominal Conditions

A Frequency-Domain Algorithm for Dynamic Synchrophasor and Frequency Estimation

Bayesian linear state estimation using smart meters and PMUs measurements in distribution grids

Flexible Indoor Localization and Tracking Based on a Wearable Platform and Sensor Data Fusion

Synchrophasor Estimators Accuracy: A Comparative Analysis