Redundant Measurements Research Articles

Computation-Efficient Decoupled Multiparameter Estimation of PMSMs From Massive Redundant Measurements

Comprehensive parameters testing and analysis are critical to high-performance modeling and control of permanent magnet synchronous machines (PMSMs). In this article, a novel decoupled approach for dual three-phase PMSM parameter estimation including winding resistance, machine inductances, and PM flux linkage is proposed for comprehensive parameter testing. An improved machine model considering magnetic saturation and inverter nonlinearity is proposed at first, in which a quadratic equation is employed to model the nonlinear variation of machine inductances and inverter voltage distortion is also modeled. Thereafter, a novel decoupled estimation model is proposed to decouple multiparameter estimation into four simplified estimations using least squares method. This decoupled model can effectively reduce the cross influences between parameters and improve the computation efficiency. Moreover, it is capable of dealing with massive redundant measurements for accurate and computation-efficient parameter estimations, which is especially suitable for obtaining machine parameters over a wide operation range during machine testing, such as inductance maps under different operating conditions.

Diverging identification of the poor: A non-random process. Chile 1992–2017

Abstract This paper investigates the degree of association in the identification of the poor between the standard monetary FGT poverty measure and the Alkire-Foster Multidimensional Poverty Index. For this purpose, we use a measure of redundancy in the identification of the poor between the two poverty measures (R0). In Chile, over the past 25 years, R0 has declined at a rate of 1.5% per year. The decline is unimportant during the 1990s, a decade of rapid economic growth, while it is notable thereafter, in a period characterized by modest economic growth and the progressive introduction and deepening of social policies. The conditional correlation between socio-economic and demographic characteristics with R0 is examined at the province and household levels. After controlling for the household non-eligibility across some of the indicators of the multidimensional poverty index, we find that the divergence in the identification of the poor seems to be a real process which is not randomly distributed across the population. It is correlated with education improvements, increasing urbanization, and reduction in household size. On the basis of our results, we argue that this divergence may be a more general phenomenon that tends to occur in countries undergoing demographic transition, urbanization, and progress in education. If so, and given the fact that poverty alleviation strategies are adopted partly on the basis of poverty statistics, the diverging identification of the poor might have distributive consequences for the poor.

Methodology for Reliability Analysis of Cyber–Physical MTdc Grids

The MTdc grid can be considered as a cyber–physical system as they are dependent on the cyber infrastructure for optimal coordination of its resources irrespective of the adopted control strategy (master–slave, voltage margin, or droop). Hence, the reliability of the MTdc grids depends not only on the reliability of the power electronic components of the converter but also on the cyber infrastructure. This article proposes a reliability evaluation method based on a continuous-time Markov chain (CTMC) for cyber–physical MTdc grids. The detailed failure models of the cyber–physical components of the MTdc grids are presented. A method is proposed to derive the multidomain reliability of the MTdc grid. Finally, the proposed method is applied for the reliability analysis of a three-terminal dc grid. The impact of specific redundancy measures of the cyber components on the reliability of the MTdc grid control is presented.

Quantum Data-Syndrome Codes

Performing active quantum error correction to protect fragile quantum states highly depends on the correctness of error information--error syndromes. To obtain reliable error syndromes using imperfect physical circuits, we propose the idea of quantum data-syndrome (DS) codes that are capable of correcting both data qubits and syndrome bits errors. We study fundamental properties of quantum DS codes, including split weight enumerators, generalized MacWilliams identities, and linear programming bounds. In particular, we derive Singleton and Hamming-type upper bounds on degenerate quantum DS codes. Then we study random DS codes and show that random DS codes with a relatively small additional syndrome measurements achieve the Gilbert-Varshamov bound of stabilizer codes. Constructions of quantum DS codes are also discussed. A family of quantum DS codes is based on classical linear block codes, called syndrome measurement codes, so that syndrome bits are encoded in additional redundant stabilizer measurements. Another family of quantum DS codes is CSS-type quantum DS codes based on classical cyclic codes, and this includes the Steane code and the quantum Golay code.

Performance Analysis of Distributed Estimation for Data Fusion Using a Statistical Approach in Smart Grid Noisy Wireless Sensor Networks.

Internet of Things (IoT) can significantly enhance various aspects of today’s electric power grid infrastructures for making reliable, efficient, and safe next-generation Smart Grids (SGs). However, harsh and complex power grid infrastructures and environments reduce the accuracy of the information propagating through IoT platforms. In particularly, information is corrupted due to the measurement errors, quantization errors, and transmission errors. This leads to major system failures and instabilities in power grids. Redundant information measurements and retransmissions are traditionally used to eliminate the errors in noisy communication networks. However, these techniques consume excessive resources such as energy and channel capacity and increase network latency. Therefore, we propose a novel statistical information fusion method not only for structural chain and tree-based sensor networks, but also for unstructured bidirectional graph noisy wireless sensor networks in SG environments. We evaluate the accuracy, energy savings, fusion complexity, and latency of the proposed method by comparing the said parameters with several distributed estimation algorithms using extensive simulations proposing it for several SG applications. Results prove that the overall performance of the proposed method outperforms other fusion techniques for all considered networks. Under Smart Grid communication environments, the proposed method guarantees for best performance in all fusion accuracy, complexity and energy consumption. Analytical upper bounds for the variance of the final aggregated value at the sink node for structured networks are also derived by considering all major errors.

Measuring 3D indoor air velocity via an inexpensive low-power ultrasonic anemometer

The ability to inexpensively monitor indoor air speed and direction on a continuous basis would transform the control of environmental quality and energy use in buildings. Air motion transports energy, ventilation air, and pollutants around building interiors and their occupants, and measured feedback about it could be used in numerous ways to improve building operation. However indoor air movement is rarely monitored because of the expense and fragility of sensors. This paper describes a unique anemometer developed by the authors, that measures 3-dimensional air velocity for indoor environmental applications, leveraging new microelectromechanical systems (MEMS) technology for ultrasonic range-finding. The anemometer uses a tetrahedral arrangement of four transceivers, the smallest number able to capture a 3-dimensional flow, that provides greater measurement redundancy than in existing anemometry. We describe the theory, hardware, and software of the anemometer, including algorithms that detect and eliminate shielding errors caused by the wakes from anemometer support struts. The anemometer has a resolution and starting threshold of 0.01 m/s, an absolute air speed error of 0.05 m/s at a given orientation with minimal filtering, 3.1° angle- and 0.11 m/s velocity errors over 360° azimuthal rotation, and 3.5° angle- and 0.07 m/s velocity errors over 135° vertical declination. It includes radio connection to internet and is able to operate standalone for multiple years on a standard battery. The anemometer also measures temperature and has a compass and tilt sensor so that flow direction is globally referenced regardless of anemometer orientation. The retail cost of parts is $100 USD, and all parts snap together for ease of assembly.

A Novel Algorithm for the Fault Diagnosis of a Redundant Inertial Measurement Unit

Although a number of fault diagnosis algorithms for inertial sensors have been proposed in previous decades, the performance of these algorithms needs to be improved with regard to small faults. In this paper, we introduce a data driven-based algorithm, namely, SaPD, for the anomaly detection and output reconstruction of a redundant inertial measurement unit (RIMU). SaPD implements the fault identification of an inertial apparatus by combining an artificial neural network with the Q contribution plots method in parity space. To improve the performance of the fault detection part, in particular for small faults, we introduce a novel hyperplane that measures the distances between inputs and the primary-neuron set obtained from a self-organizing incremental neural network (SOINN). We also employ the Q contribution plots of sensors in the fault isolation part by analyzing historical data with principal component analysis (PCA). We perform quantitative evaluations in a realistic simulation environment, which demonstrates that the proposed SaPD algorithm outperforms other related algorithms in terms of the fault identification accuracy of tiny faults with an acceptable computational complexity.

A Two-Station Passive Locating Solution Independent of the Baseline Length

The two-station positioning system based on time difference and azimuth measurement has measurement redundancy. Therefore, not only can a positioning solution which is completely independent of the baseline length between two stations be derived, but also the baseline length can be solved as an unknown quantity. These findings not only enhance the performance of the two-station positioning system, but also provide a design basis for the construction of a self-organizing dynamic intelligent positioning system.

An incremental meter placement method for state estimation considering collinear measurements and high leverage points

Abstract The performance of the power system state estimation (SE) is influenced by the configuration of the meters and measurement redundancy. Therefore, the measurement set needs to be updated by installing new SCADA meters and phasor measurement units for improving the quality of the SE solution. However, the potential inconsistency between the existing meters and the new meters should be addressed. Otherwise, the additional meters may lead to numerical problems such as collinearity (linear dependence due to duplicated measurements) and the existence of high leverage points (HLPs) (influential measurements). Hence, this paper proposes an incremental meter placement method. The proposed method utilizes the HLPs and aims to improve the numerical performance of the SE and facilitate the elimination of bad data. The cuckoo search optimization is used for selecting the optimal locations and the numbers of the new meters. The performance of the proposed algorithm is tested on UK 18-bus, the IEEE 30-bus, and 118-bus systems and simulation results show improvements in the quality of the SE solution.

Feature Redundancy Based on Interaction Information for Multi-Label Feature Selection

Recent years, multi-label feature selection has gradually attracted significant attentions from machine learning, statistical computing and related communities and has been widely applied to diverse problems from music recognition to text mining, image annotation, etc. However, traditional multi-label feature selection methods employ cumulative summation strategy to design methods, which suffers from the problem of overestimation the redundancy of some candidate features. Additionally, the cumulative summation strategy will lead to the high value of the goal function when one candidate feature is completed related with one or few already-selected features, but it is almost independent from the majority of already-selected features. To address these issues, we propose a new multi-label feature selection method named Feature Redundancy Maximization (FRM), which combines the cumulative summation of conditional mutual information with the `maximum of the minimum' criterion. Additionally, FRM can be rewritten as another form of multi-label feature selection method that employs interaction information as a measure of feature redundancy that obtains an accurate score of feature redundancy as the number of already-selected features increases. Finally, extensive experiments are implemented on fourteen benchmark multi-label data sets in comparison to six state-of-the-art methods. The experimental results demonstrate the superiority of the proposed method.

Optimal PMUs placement considering ZIBs and single line and PMUs outages

Phasor measurement unit (PMU) is among the most important measurement devices in modern power systems. It measures the voltage and current phasors which have both magnitude and phase angle using a common timing reference. These measurements are utilized in real time applications of electrical power systems. The main drawback of PMUs is its high cost so if PMUs are used to collect or send online real data from a system to a data centre or a decision maker, the used number of PMUs should be minimum with full observability of system measurements. This paper proposes a flower pollination algorithm (FPA) to find the optimal number and locations of PMUs in power systems. The optimization objectives of the work are the minimization of PMUs number, the achievement of complete observability of power system states, and the maximization of measurement redundancy. Existence of zero-injection buses in the system decreases the number of PMUs that is required to fulfil the complete observability of system states. Furthermore, additional constraints for remaining system full observable following failure of single PMU and single line outage are also included to increase the system reliability. The performance and efficiency of the proposed FPA is tested on IEEE standard networks such as 14-bus, 30-bus, 57-bus and 118-bus and new England 39-bus network. The obtained simulation results approve the ability of the proposed optimization method to find the optimal allocation of PMUs in different cases with fulfilling complete observability and reliability of electric power systems. The superiority of FPA is also verified by comparing its results of other optimization algorithms.

Secure Market Operation in Presence of Critical Model Parameters in State Estimation

This paper is concerned about the impact of network parameter errors on the reliable operation and management of electricity markets. Specifically, the paper investigates the so-called critical parameters in a network model whose errors cannot be detected or estimated due to the lack of local measurement redundancy. Due to this property of critical parameters, it will be impossible to detect, identify and correct errors in these parameters. Given the fact that electricity market applications are heavily model-dependent, the locational marginal prices (LMPs) can be shown to be seriously distorted in the presence of critical parameter errors. Furthermore, if such errors are maliciously injected by adversaries, they will go undetected. Meanwhile, prices and revenues associated with power transactions may be strategically manipulated. An approach for quantifying the impact of critical parameters on the management of electricity markets is proposed. Conditions related to network topology and measurement configuration leading to the appearance of critical parameters are classified, and meter placement strategies for avoiding critical parameters are presented as well. Simulation results obtained by using IEEE test systems are given to verify the proposed analysis and design methods.

Measurements and models of the correlation of redundant spatial coherence measurements for the incoherently scattered field.

Sonar systems that exploit correlation for navigation, such as correlation velocity logs and micronavigation for synthetic aperture sonar, often make redundant estimates of the spatial coherence of the scattered field at several spatial lags. Two models for the correlation of these redundant measurements are described. First, an analytical model is derived using the assumption of stationary Gaussian statistics. Next, a numerical model is described that accounts for non-stationary processes present in measurements of seafloor scattering. These models are compared to normal-incidence scattering data collected at Seneca Lake, NY. Both models show good agreement with the measurements when the spatial separation between redundant hydrophone pairs is less than the coherence length. At greater spatial separation, the analytical model diverges from the measurements. This disagreement is explained by a lack of stationarity in the measured data which is captured by the numerical model. Finally, spatial variations in the volume scattering strength of the sediment are identified as a source of the non-stationarity in the measurements.

Simultaneous Visible Light Communication and Distance Measurement Based on the Automotive Lighting

Visible light communication could be an interesting complement to the IEEE 802.11p-based vehicle-to-vehicle communication systems that are sensitive to interferences and delays in dense traffic scenarios such as platooning. Visible light could also provide a redundant distance measure that is crucial for path control in this application. In this paper, a system called visible light communication rangefinder and performing simultaneously vehicle-to-vehicle communication and range-finding using the headlamps and taillights is proposed for the first time. By exchanging a clock signal contained in Manchester-encoded signals, both the following and leading vehicles can share information and estimate their inter-distance through phase-shift measurement. The system is first presented theoretically and it is shown in particular that the Doppler effect has no significant impact on both functions. Then, it is modeled and validated using Simulink. Finally, both the range-finding and communication function are validated experimentally. The range-finding function is functional up to 25 m, and has a resolution of around 24 cm at 10 m, whereas the communication function provides a 500 kbps link with a BER below $10^{-6}$ up to 30 m.

The excess measurements influence analysis in creating geodesic networks by the polygonometry method

The article analyzes the polygonometric moves elements accuracy in the redundant measurements implementation to ensure accuracy in the geodetic networks’ construction.

Experimental design for parameter estimation in steady-state linear models of metabolic networks

Metabolic networks are typically large, containing many metabolites and reactions. Dynamical models that aim to simulate such networks will consist of a large number of ordinary differential equations, with many kinetic parameters that must be estimated from experimental data. We assume these data to be metabolomics measurements made under steady-state conditions for different input fluxes. Assuming linear kinetics, analytical criteria for parameter identifiability are provided. For normally distributed error terms, we also calculate the Fisher information matrix analytically to be used in the D-optimality criterion. A test network illustrates the developed tool chain for finding an optimal experimental design. The first stage is to verify global or pointwise parameter identifiability, the second stage to find optimal input fluxes, and finally remove redundant measurements.

PANG-NAV: a tool for processing GNSS measurements in SPP, including RAIM functionality

Global Navigation Satellite Systems (GNSSs) are theoretically able to provide accurate, three-dimensional, and continuous positioning to an unlimited number of users. An important shortcoming of GNSS is the lack of integrity, defined as the ability of a system to provide timely warnings in case of malfunction; this problem is especially felt in safety-of-life applications such as aviation. A common way to fill this gap is the use of Receiver Autonomous Integrity Monitoring (RAIM) techniques, which are able to provide integrity information by analyzing redundant measurements. A possible RAIM functionality is the ability to identify, and so discard, anomalous measurements; this functionality has made RAIM very useful also in case of severe signal degradation, such as in urban or dense vegetation areas, where blunders are common. PANG-NAV is a tool, developed by the PArthenope Navigation Group, able to process GNSS measurements (from RINEX files) in order to obtain a position solution. The core of PANG-NAV is the single point positioning (SPP) technique, including a RAIM functionality. A multi-constellation solution, with GPS and Galileo, can be provided. Both static processing and kinematic processing are possible, and in cases where ground truth is available, error analysis can be carried out.

Research on Optimal D-PMU Placement Technology to Improve the Observability of Smart Distribution Networks

With the continuous development of smart distribution networks, their observable problems have become more serious. Research on the optimal placement of the distribution phasor measurement unit (D-PMU) is an important way to improve the measurability, observability and controllability of a smart distribution network. In this paper, the optimal D-PMU placement methods and implementation technology were studied to determine the optimal D-PMU placement scheme. Considering the bus vulnerability index and the different operating states of the system, the more practical one-time optimal placement methods to ensure complete system observability was proposed. On this basis, the system's measurement redundancy and unobservable depth were considered to realize the multistage optimal D-PMU placement. The corresponding mathematical model and solution flow were given. Then the implementation technology of the methods was studied and the optimal D-PMU placement assistant decision-making software for smart distribution network was developed. Thereby, the structure and requirements of different distribution networks can be satisfied. The application analysis, functional architecture and the overall design process were given. Finally, the methods and software were analyzed by using the IEEE 33 bus system and an actual project, the Guangzhou Nansha Yuan'an Substation. The verification results showed that the method and software mentioned in this paper can provide convenient and quick operation for optimal D-PMU placement, improve the efficiency of smart distribution network planning work, and promote the theoretical application level of smart distribution network planning results.

Distribution System State Estimation to Support Coordinated Voltage-Control Strategies by Using Smart Meters

In this paper, a novel distribution system state estimation method based on the information provided by smart meters to support coordinated voltage control strategies in real time is presented. Because the measurement redundancy in a distribution system is very low compared with the transmission system, the proposed method is able to mitigate the effects of temporal communication link failures, thanks to the combination of the Weighted Least Square method and the Levenberg–Marquardt algorithm with a new power flow formulation using the available information from the smart meters. The test to validate the method and show its robustness was made for three representative different systems: the IEEE 30-bus and 57-bus systems, and a 30 smart home LV distribution system. To illustrate the applicability of the proposed method, a real-time digital simulator for smart distribution power grids with a coordinated voltage control system is used. The results show that the proposed method is in effective estimating the missing values in the case of communication problems between smart meters and the coordinated control system allowing the use of central voltage control strategies in real time.

Intelligent Operation of Small-Scale Interconnected DC Grids via Measurement Redundancy

Interconnected dc grids are studied in this paper, which comprise resistive and constant-power loads (CPLs) fed by photovoltaic (PV) units. All the sources and CPLs are connected to the grid via dc–dc converters. Nonlinear behavior of PV units in addition to the effect of negative-resistance CPLs can destabilize the dc grid. Thus, the decentralized nonlinear model and intelligent control are proposed using adaptive output-feedback controller to stabilize the grid. The use of the output-feedback control makes possible the utilization of other available signals, in case of loss of main signal, at the converter location and creates measurement redundancy that improves reliability of the dc network. The switching between measured signals of different types are performed through using the neural network (NN) controllers without the need to further tuning. The stability of the entire network is assured through Lyapunov stability method while each converter employs only local measurements. The adaptive NNs are utilized to overcome the unknown dynamics of the dc–dc converters at distributed energy resources and CPLs and those of the interconnected network imposed on the converters. Simulation and experimental results are provided on a small-scale dc grid to show the effectiveness of the developed model and the proposed controller.