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Effective monitoring and management applications on modern distribution networks (DNs) require a sound network model and the knowledge of line parameters. Network line impedances are used, among other things, for state estimation and protection relay setting. Phasor measurement units (PMUs) give synchronized voltage and current phasor measurements, referred to a common time reference (coordinated universal time). All synchrophasor measurements can thus be temporally aligned and coordinated across the network. This feature, along with high accuracy and reporting rates, could make PMUs useful for the evaluation of network parameters. However, instrument transformer behavior strongly affects the parameter estimation accuracy. In this paper, a new PMU-based iterative line parameter estimation algorithm for DNs, which includes in the estimation model systematic measurement errors, is presented. This method exploits the simultaneous measurements given by PMUs on different nodes and branches of the network. A complete analysis of uncertainty sources is also performed, allowing the evaluation of estimation uncertainty. Issues related to operating conditions, topology, and measurement uncertainty are thoroughly discussed and referenced to a realistic model of a DN to show how a full network estimator is possible. [ABSTRACT FROM AUTHOR]

Distribution systems require ad hoc estimators, distribution system state estimation (DSSE) techniques, to acquire knowledge about the system status. An incorrect evaluation of the accuracy of the DSSE creates decision risks in network management. The possible variations in the network parameter values and the decays of the metrological characteristics of the measurement system elements are uncertainty sources very often not considered. Considering these possible lacks of accuracy, this paper focuses on the robustness of distributed measurement systems aimed to obtain accurate DSSE results. The problem of the proper assessment of the accuracy of the DSSE results obtained through a weighted least squares (WLS) approach is faced. A method capable of including different uncertainty sources in the uncertainty estimation of the WLS approach is presented. Furthermore, this paper proposes an optimal meter placement algorithm robust with respect to possible malfunctions in measurement system components. The results obtained on a portion of an Italian distribution network, along with their accuracy, are presented and discussed. [ABSTRACT FROM AUTHOR]

In this paper, we analyze and compare the properties of different well-known and also new nonparametric discrete Fourier transform (DFT)-based methods for resonant frequency and logarithmic decrement estimation in application to mechanical spectroscopy. We derive a new DFT interpolation algorithm for a signal analyzed with Rife–Vincent class-I windows and also propose new formulas that extend Bertocco and Yoshida methods. We study errors of the resonant frequency and logarithmic decrement estimation in realistic conditions that include measurement noise and a zero-point drift. We also investigate the systematic errors of the estimation methods of interest. A nonlinear least squares time-domain parametric signal fitting is used to determine the boundaries of statistical efficiency in all tests. [ABSTRACT FROM AUTHOR]

This paper considers the problem of density estimation and clustering in sensor networks. It is assumed that measurements of the sensors can statistically be modeled by a common Gaussian mixture model (GMM). In this paper, a distributed expectation maximization (DEM) algorithm is developed to estimate the model order and the parameters of this model. Scalability and fault tolerance are two important advantages of this method. In the E-step of this algorithm, each node calculates local sufficient statistics using its local observations. A distributed averaging approach is then used to diffuse local sufficient statistics to neighboring nodes and estimate global sufficient statistics in each node. In the M-step, each node updates parameters of the GMM using the estimated global sufficient statistics. Diffusion speed and convergence of the proposed algorithm are also studied. The proposed method is then used for environmental monitoring and also distributed target classification. Simulation results approve the promising performance of this algorithm. [ABSTRACT FROM PUBLISHER]

In some complex environments, nonline-of-sight (or multipath) usually introduces an additional path delay to the pseudorange, which is known to be one of the most dominant sources of accuracy degradation in global navigation satellite systems (GNSSs). The additional path error is non-Gaussian. Different techniques, including antenna-based, receiver-based, and poster-receiver, have been proposed for mitigating the influence of multipath. However, the estimated additional path delay obtained by classical algorithms, such as maximum likelihood or Bayesian estimation, often has a certain deviation from the real-additional path delay. For more accurate estimation, we first restrict the additional path error in a relatively wide interval; then, the position estimation is converted into a constrained separable nonlinear least squares problem. We propose, in this paper, a two-stage estimation algorithm for global positioning system positioning. At the first stage, we ignore the additional path error and obtain a relatively accurate initial position. Then, based on the result obtained at the first stage, we include the additional path error in the estimation problem and estimate it by variable projection (VP) method. Numerical results show that the proposed algorithm based on the VP method can effectively mitigate multipath interference. [ABSTRACT FROM AUTHOR]

In this paper, the problem of the simultaneous estimation of frequency and power components is investigated. It is solved through a new recursive estimator capable of estimating the unknown model parameters during severe dynamic changes in the system. By this, a simple nonlinear parameter model for the instantaneous power, taking into account the fundamental components of voltage and current, is used as a starting point for the power components and frequency estimation. In the model, the system frequency is considered as an unknown model parameter, and it is simultaneously estimated with other unknown parameters. This resulted in an efficient numerical algorithm for the measurement of power components, which is not sensitive to variations of the system frequency. This is an important achievement, which improves the accuracy of the method during network transients. The new estimator has been improved by the strategy for tuning its forgetting factor according to the system dynamics, and it is tested through computer simulations and by using data records obtained under laboratory conditions. The algorithm was developed for a single-phase system, and the approach might be easily extended to multiphase systems. [ABSTRACT FROM AUTHOR]

This paper presents a performance comparison of two classes of synchrophasor estimators recently proposed in the literature, i.e., the frequency-domain algorithms known as interpolated discrete Fourier transform (IpDFT), and the time-domain algorithms based on the weighted least squares (WLSs) approach. The analysis is focused on fast phasor estimation only and it is performed under steady-state, dynamic, and transient conditions, when the length of the observation interval is equal to one or two nominal cycles of the acquired electric waveform. The considered testing conditions include not only the worst-case scenarios specified in the IEEE Standard C37.118.1-2011, but they also combine the effect of static and dynamic disturbances. As accuracy performance parameters, the total vector error as well as the amplitude and phase estimation errors are considered and evaluated. Estimator responsiveness is analyzed instead in terms of response times, when amplitude or phase steps occur. The preferable one- and two-cycles IpDFT and WLS algorithms are first determined by choosing the windows and the number of Taylor's series terms (in the WLS case only) assuring the best accuracy. It is shown that, when the time-domain approach is considered and one-cycle or two-cycle intervals are observed, the best accuracy is achieved by the WLS algorithm based on the rectangular window or the two-term one-cycle maximum image tone interference rejection (MIR) window, respectively. Conversely, when the frequency-domain approach is adopted, the best accuracy is achieved by the IpDFT-based on the MIR window related to the observed interval length. The estimates produced by the selected WLS and IpDFT algorithms are then compared against the boundaries specified in the Standard IEEE C37.118.1-2011 for P-and M-class compliance. During the discussion, advantages and disadvantages of both time-domain and frequency-domain approaches are highlighted. [ABSTRACT FROM AUTHOR]

It is well known that in a photovoltaic (PV) plant, the modules are connected to switch-mode power converters to enhance the power output in every environmental condition. This task is performed by the maximum power point tracker (MPPT), which provides a current or voltage reference to the converter. Traditional perturb and observe or incremental conductance algorithms are not efficient in rapidly changing conditions, whereas a model-based (MB) MPPT offers a better dynamic performance. Because it is relatively easy to obtain an accurate model of a single PV panel, thus predicting the maximum power point voltage for given environmental conditions, MB MPPTs seemed to be attractive for employing in module integrated converters. Conventional MB MPPT algorithms, however, usually require an expensive pyranometer to properly operate. In this paper, starting from a new set of equations modeling a PV module, a novel MB MPPT technique, which does not require the direct measurement of the solar radiation, is proposed and experimentally validated. [ABSTRACT FROM PUBLISHER]

This paper presents a novel iterative calibration algorithm for triaxial magnetometers. The proposed algorithm estimates and compensates the effects of deterministic interference parameters using only nine distinct measurements. The results of our extensive simulations and empirical evaluations confirm that the proposed method outperforms conventional ellipsoid fitting based models both in terms of accuracy and reliability even in the presence of a moderate wideband noise. The algorithm also achieves considerably faster convergence, which makes it suitable for real-time applications. The algorithm performance is also shown to be independent of the initial guesses of the interference parameters. [ABSTRACT FROM PUBLISHER]

An algorithm for improving the accuracy of the estimation of parameters that characterize a multifrequency damped sinusoidal signal is presented. The core ideas of the algorithm is that the signal is weighted using Hann window before discrete Fourier transform and the frequencies, amplitudes, phases, and damping factors of the signal are obtained by frequency domain interpolation. It is shown that the purpose of improving the accuracy of parameter estimation is achieved using Hann window, which reduces long-range leakage and frequency-domain interpolation that eliminates the effect of the picket fence. The sensitivity analysis of the changing of the signal parameters, noise and spectral resolution, shows that both the effects of strong noise and spectral interference are considered, proving the reliability and high-accuracy parameters estimation in a number of engineering applications. Otherwise, the characteristic of efficiency computational and low memory demands are advantageously adopted for the poor computing resources situations. Extracting the impact signal of the rotor test bed using this method is illustrated. The result shows that the method can be an optional method for the fault features extraction of the mechanical impact. [ABSTRACT FROM PUBLISHER]