Redundant Measurements Research Articles

Allocation of Optimal PMUs for Power System Observability Using PROMETHEE Approach

Phasor measurement units (PMUs) are becoming a vital measurement device in wide-area monitoring, operation, and control. The allocation of PMU at each bus will make that bus directly observable. However, considering the high installation costs, it is not feasible to place PMU at each bus. Thus, placing the PMUs at optimal locations is extremely important. In this study, the Preference Ranking Organization Method for Enrichment of Evaluation (PROMETHEE)-based multi-criteria decision-making (MCDM) technique has been applied for the optimal allocation of phasor measurement units (PMUs) with the aim of achieving full system observability. Along with the entire network observability, the proposed approach provides maximum measurement redundancy (MR) too. Unlike some previous popular MCDM techniques, the proposed approach obtains optimal PMU placement (OPP) solution without performing pruning operations. Different criterion has been formulated to construct a decision matrix (DM). This DM helps in calculating the net outranking flow (NOF) of all the buses during the PROMETHEE approach. Based on the maximum NOF value, the PMUs are placed at those buses. The proposed approach also considers the inclusion of zero injection buses (ZIBs). Further, cases such as single PMU outage and existence of conventional measurements have been considered while determining optimal locations of PMUs. The proposed algorithm is demonstrated on IEEE 14-bus, 30-bus, 57-bus, and 118-bus systems, one Indian practical utility, i.e., Northern Regional Power Grid (NRPG) 246-bus system, and larger Polish 2383-bus system. To prove the effectiveness of the proposed algorithm, it has been compared with some of the popular existing techniques.

Lithium Battery Remaining Useful Life Prediction Based on Multi-Kernel Support Vector Regression With Hybrid Optimization Algorithm

Abstract Accurately and reliably predicting the remaining useful life (RUL) of lithium battery is very important for the lithium battery health management system. However, most of the existing methods rely on complex multidimensional input features, which require a large number of sensors, increase the application cost and introduce redundant measurement errors. Therefore, this paper, only based on the battery capacity curve itself, proposes a method to construct a prediction model of support vector regression (SVR) by fusing multiple kernel functions. The linear equation coefficients of multiple kernel function combinations are optimized by the hybrid optimization algorithm. It is found that the hybrid kernel function can effectively overcome the problem that the single-kernel function is not capable of mapping the capacity fading trend of lithium battery. Hybrid optimization algorithm can avoid the problems of local optimization and global search ability deficiency. The proposed method is validated by experiments using the battery attenuation datasets from NASA, the University of Maryland, and a high-rate lithium battery in the laboratory stage. It can be seen from the experimental results that the prediction accuracy of this method is high. The mean prediction error, mean RMSE, and mean MAE are 2%, 0.0198, and 0.0157.

An automated structural health monitoring system developed for Ekaterinburg Arena

Introduction. The paper presents those essential Structural Health Monitoring (SHM) implementation details that have not been sufficiently documented in Russian national guides or regulations. The study aims to systematize and share the experience of applying the previously developed SHM theoretical principles to the existing large-span unique sports facility Ekaterinburg Arena. The paper provides information about determining monitored structural elements, instrumentation alignment, and monitoring parameters’ threshold value.
 Materials and methods. The long-term SHM system of the large-span structure of the Arena is organized around hardware and software for processing the data constantly incoming from tiltmeters, strain gauges, accelerometers, a motorized total station, and a weather station.
 Results. A unified information system for automated SHM of the Arena has been created. It comprised modules for data acquisition, high-performance databases for storing metrics, metadata, and the project information, services for evaluating and visualizing structural parameters, components for the instrumentation self-diagnosis, an application development interface for sophisticated data analysis, and a 3D model of the structure. Engineering methods for evaluating the initial values of the controlled parameters and their thresholds acceptable for regular operation are proposed. The technique of calculating internal forces based on the strain gauge data with a redundant measurement pattern has been tested.
 Conclusions. The obtained monitoring results have become a valuable source of initial data for developing automated methods for detecting and locating damages in structures, improving structural analysis and design methods, predictive maintenance, surveys, and solving other knowledge-intensive tasks. Applying the SHM theoretical principles to the real structure can significantly contribute to developing the current immature scientific and regulatory framework for implementing similar monitoring systems at complicated construction facilities.

Robust Extended Kalman Filtering for Systems With Measurement Outliers

Outliers can be caused by sensor errors, model uncertainties, changes in the ambient environment, data loss, or malicious cyberattacks to contaminate the measurement process of many nonlinear dynamic systems. When the extended Kalman filter (EKF) is applied to such systems for state estimation, the outliers can seriously reduce the estimation accuracy. This brief proposes an innovation saturation mechanism to make the EKF robust against outliers. This mechanism applies a saturation function to the innovation process that the EKF leverages to correct the state estimation. As such, when outliers occur, the distorted innovation is saturated, so as not to undermine the state estimation. The mechanism features an adaptive adjustment of the saturation bounds. The design leads to the development of robust EKF approaches for both continuous- and discrete-time systems. The stability of the proposed approaches when applied to linear systems is characterized, showing that they are capable of performing bounded-error estimation in the presence of bounded outlier disturbances in this case. A simulation study about mobile robot localization is presented to illustrate the efficacy of the proposed design. Compared to existing methods, the proposed approaches can effectively reject outliers of various magnitudes, types, and durations, at significant computational efficiency and without requiring additional measurement redundancy.

Redundancy cancellation of compressed measurements by QRS complex alignment.

The demand for long-term continuous care has led healthcare experts to focus on development challenges. On-chip energy consumption as a key challenge can be addressed by data reduction techniques. In this paper, the pseudo periodic nature of ElectroCardioGram(ECG) signals has been used to completely remove redundancy from frames. Compressing aligned QRS complexes by Compressed Sensing (CS), result in highly redundant measurement vectors. By removing this redundancy, a high cluster of near zero samples is gained. The efficiency of the proposed algorithm is assessed using the standard MIT-BIH database. The results indicate that by aligning ECG frames, the proposed technique can achieve superior reconstruction quality compared to state-of-the-art techniques for all compression ratios. This study proves that by aligning ECG frames with a 0.05% unaligned frame rate(R-peak detection error), more compression could be gained for PRD > 5% when 5-bit non-uniform quantizer is used. Furthermore, analysis done on power consumption of the proposed technique, indicates that a very good recovery performance can be gained by only consuming 4.9μW more energy per frame compared to traditional CS.

Critical analysis of risks factors in using public-private partnership in building critical infrastructure resilience: a systematic review

PurposePublic–private partnership (PPP) has been adopted in many areas especially within the architecture, engineering and construction research domain. However, the PPP in critical infrastructure resilience (CIR) has not received the needed attention even though it has been acclaimed to be the panacea for building infrastructure resilience. This paper aims to adopt a systematic review to proactively identify the risks factors that pertains to using PPP as a mechanism to build the resilience of critical infrastructure.Design/methodology/approachUsing a systematic methodology, a total record of 51 academic publications and 5 institutional reports from reputable organizations were identified and analyzed.FindingsThe selected literature was subjected to content analysis to retrieve 46 risk factors in PPP in CIR. The outcome of the systematic revealed the topmost risks as corruption, natural and unavoidable catastrophes, wars, terrorism, sabotage, cost overrun issues, a lack of centralized mechanism for coordinating integrated actions, inconsistent government policies, inadequate supervision, high operational cost due to robust and redundant measure, lack of supporting infrastructure, lack of open and integrated communication, unstable government, political interference, lack of PPP experience and legislation change. A conceptual framework was developed by grouping the identified risks under 13 categories.Research limitations/implicationsThe outcome of this study will be a guide for decision makers and stakeholders with the responsibility of building the resilience of critical infrastructure.Originality/valueThe study contributes to CIR research area by providing an in-depth knowledge on risks that are inherent in PPP in CIR.

Optimal sensor placement method for wastewater treatment plants based on discrete multi-objective state transition algorithm

Parameters monitoring is essential to maintain the stability and efficiency of the wastewater treatment process, which has spurred ubiquitous installation of sensors in wastewater treatment plants (WWTPs). As the rich process data of WWTPs is not effectively transformed into actionable knowledge for system optimization due to improper sensor installation, the sensor placement scheme needs to be optimized. In this paper, a weighted sensor placement optimization model based on sensor cost, information richness and reliability is established to transform the sensor optimization problem to a nonlinear mathematical programming problem. Then a discrete multi-objective state transition algorithm is proposed to find the Pareto optimal solutions. Finally, an evaluation strategy is designed to select the most suitable solution for industrial application. The results of simulation experiments on three different WWTPs demonstrate the validity and superiority of the proposed method, increasing the degree of variable observability and measurement redundancy while keeping the sensor cost at a low level.

Ultrasonic Atomization: New Spray Characterization Approaches

In particle engineering, spray drying is an essential technique that depends on producing sprays, ideally made of equal-sized droplets. Ultrasonic sprays appear to be the best option to achieve it, and Faraday waves are the background mechanism of ultrasonic atomization. The characterization of sprays in this atomization strategy is commonly related to the relation between characteristic drop sizes and the capillary length produced by the forcing frequency of wavy patterns on thin liquid films. However, although this atomization approach is practical when the intended outcome is to produce sprays with droplets of the same size, drop sizes are diverse in real applications. Therefore, adequate characterization of drop size is paramount to establishing the relations between empirical approaches proposed in the literature and the outcome of ultrasonic atomization in actual operating conditions. In this sense, this work explores new approaches to spray characterization applied to ultrasonic sprays produced with different solvents. The first two introduced are the role of redundancy in drop size measurements to avoid resolution limitation in the measurement technique and compare using regular versus variable bin widths when building the histograms of drop size. Another spray characterization tool is the Drop Size Diversity to understand the limitations of characterizing ultrasonic sprays solely based on representative diameters or moments of drop size distributions. The results of ultrasonic spray characterization obtained emphasize: the lack of universality in the relation between a characteristic diameter and the capillary length associated with Faraday waves; the variability on drop size induced by both liquid properties and flow rate on the atomization outcome, namely, lower capillary lengths produce smaller droplets but less efficiently; the higher sensibility of the polydispersion and heterogeneity degrees in Drop Size Diversity when using variable bin widths to build the histograms of drop size; the higher drop size diversity for lower flow rates expressed by the presence of multiple clusters of droplets with similar characteristics leading to multimodal drop size distributions; and the gamma and log-normal mathematical probability functions are the ones that best describe the organization of drop size data in ultrasonic sprays.

The establishment of a reference geodetic network made of closed polygonometric quadrangles and designated for landslide slopes

Introduction. A reference geodetic network in the form of a closed polygonometric quadrangle is established at the Karamyshevsky landslide slope in Moscow for the purpose of repeated geodetic observations of landslide displacements. The purpose of the study is to show that the reference geodetic network in the form of closed polygonometric quadrangles can be implemented for landslide slopes along with other linear and angular methods.
 Materials and methods. Accurate or high-precision electronic total stations, such as Leica, Sokkia, Topcon, etc. are used to take field measurements. In this case, all four corners and all four sides are to be measured. There are three redundant measurements in this polygon of polygonometry to ensure the reliable control of the field work quality. It is advisable to equalize this polygon using the correlate type of the least squares method for the two reasons. First, to calculate the acceptable residuals, it is necessary to make an appropriate conditional equation, i.e. implement the most important and time-consuming part of the correlate method algorithm. Second, in the process of the algorithm implementation, the correlate method allows to obtain the inverse weights of equalized measurements, whose comparison with the inverse weights of unbalanced measurements provides a visual representation of the quality of the geometry of the designed network.
 Results. The mathematical processing of the results of geodetic measurements of the reference network in the form of closed polygonometric quadrangles using the method of least squares is quite complex and requires a computer technology and appropriate software. However if they are available, the problem is solved unambiguously and very accurately, which particularly important.
 Conclusions. The construction of reference geodetic networks in the form of closed polygonometric quadrangles for observing landslide displacements can be recommended for other landslide slopes.

Topological Approach for Identifying Critical Measurements and Sets in State Estimation

Power system state estimation (SE) is an energy management system application responsible for providing a consistent real-time database, instrumental in monitoring the system. SE input data are redundant observations (measurements) of the system state (complex bus voltages) taken in a given network configuration. Measurement redundancy is an essential requirement for the SE results' reliability, determined by the quantity, location, and type of measurements received for processing. There are two ways to approach the observability/ criticality analysis, namely topological (a graph theory-based) and numerical (performed by arithmetic operations on matrices). From the conceptual viewpoint, topological methods are adequate since the problem in question is considered structural-natured, dependent on the network topology and the type/placement of measurements. Criticality analysis has been considered vital to reveal the different network observability degrees established by the measuring system. The most severe conditions are related to the occurrence of single critical measurements and critical sets of measurements. These conditions refer to imminent network unobservability and SE limitation to detect/identify the presence of spurious measurements. This paper proposes a graph theory-based method devoted to identifying essential elements to SE. Conventional measurements (branch power flows and bus power injections) coming from SCADA systems, as well as synchrophasors (phase angles and branch current measurements), are considered. Simulation results carried out on the IEEE 14-bus test system are provided.

Effective connectivity determines the critical dynamics of biochemical networks.

Living systems comprise interacting biochemical components in very large networks. Given their high connectivity, biochemical dynamics are surprisingly not chaotic but quite robust to perturbations—a feature C.H. Waddington named canalization. Because organisms are also flexible enough to evolve, they arguably operate in a critical dynamical regime between order and chaos. The established theory of criticality is based on networks of interacting automata where Boolean truth values model presence/absence of biochemical molecules. The dynamical regime is predicted using network connectivity and node bias (to be on/off) as tuning parameters. Revising this to account for canalization leads to a significant improvement in dynamical regime prediction. The revision is based on effective connectivity, a measure of dynamical redundancy that buffers automata response to some inputs. In both random and experimentally validated systems biology networks, reducing effective connectivity makes living systems operate in stable or critical regimes even though the structure of their biochemical interaction networks predicts them to be chaotic. This suggests that dynamical redundancy may be naturally selected to maintain living systems near critical dynamics, providing both robustness and evolvability. By identifying how dynamics propagates preferably via effective pathways, our approach helps to identify precise ways to design and control network models of biochemical regulation and signalling.

Optimal Configuration Based on Lever Arm Effect Rejection Performance Index for Redundant Inertial Sensors

Redundant inertial sensor systems improve navigation performance and guarantee fault detection or fault isolation functions. However, they may cause unexpected side effects owing to their enlarged size. The misalignment between the center of gravity of the sensor system and the rotational origin of the vehicle results in the lever arm effect inducing significant acceleration errors. To minimize the lever arm effect error, the configuration of redundant sensors should be carefully considered, as their performance is highly dependent on its configuration. Thus, the figure-of-merit (FOM), which is considered a performance index or cost function, is proposed to obtain the optimal configuration for minimizing the lever arm effect error. Further, to define an FOM the angular velocity and angular acceleration are considered as random variables and to quantify them, they were replaced as variances. The lever arm effect error was found to be completely eliminated when a redundant inertial measurement unit (RIMU) with more than eight single-axis inertial sensors was configured using the proposed FOM. Further, a case study and simulation were conducted to verify the proposed FOM and configuration, and it was found that the proposed FOM can be used to obtain the optimal configuration of the RIMU and as an indicator of the lever arm effect error.

Scale-Wise Variance Minimization for Optimal Virtual Signals: An Approach for Redundant Gyroscopes

The increased use of low-cost gyroscopes within inertial sensors for navigation purposes, among others, has brought to the development of a considerable amount of research in improving their measurement precision. Aside from developing methods that allow to model and account for the deterministic and stochastic components that contribute to the measurement errors of these devices, an approach that has been put forward in recent years is to make use of arrays of such sensors in order to combine their measurements thereby reducing the impact of individual sensor noise. Nevertheless combining these measurements is not straightforward given the complex stochastic nature of these errors and, although some solutions have been suggested, these are limited to certain specific settings which do not allow to achieve solutions in more general and common circumstances. Hence, in this work we put forward a non-parametric method that makes use of the wavelet cross-covariance at different scales to combine the measurements coming from an array of gyroscopes in order to deliver an optimal measurement signal without needing any assumption on the processes underlying the individual error signals. As a result of this work we also study an appropriate non-parametric approach for the estimation of the asymptotic covariance matrix of the wavelet cross-covariance estimator which has important applications beyond the scope of this work. The theoretical properties of the proposed approach are studied and are supported by simulations and real applications, indicating that this method represents an appropriate and general tool for the construction of optimal virtual signals that are particularly relevant for arrays of gyroscopes. Moreover, the results of this work can support the creation of optimal signals for other types of inertial sensors other than gyroscopes as well as for redundant measurements in other domains other than navigation.

Flash LiDAR-Based Super-Resolution Mapping for Small Solar System Body Exploration

Safe spacecraft navigation on small solar system bodies requires an accurate and high-resolution representation of the surface. This work develops a super-resolution mapping algorithm based on flash LiDAR measurements for unstructured terrain. It integrates a novel approach of processing multiple range values per pixel per measurement to increase the measurement redundancy and thus the map accuracy. The proposed algorithm is thus able to overcome the limitations of the low flash LiDAR resolution. Micro- and macro-motion strategies are recommended to guarantee subpixel displacements crucial for the super-resolution algorithm, and measurement constraints are introduced to ensure high measurement quality. Computational experiments based on an advanced Flash LiDAR model in a realistic simulation environment of comet 67P/Churyumov-Gerasimenko compare the resulting digital elevation model (DEM) with a baseline DEM and demonstrate the advantages of our algorithm.

Resonant sensors for multi-axis force and torque estimation in collaborative robotics

This paper presents a complete design methodology for a multi-axis resonant force sensor. The proposed device has been designed to be useable in a context of physical interaction between robots and humans. The proposed solution allows the three force components and the three torque components to be measured simultaneously and can be inserted into an interface handle or a robot end-effector. The information on wrench can then be used to detect and control interactions for cooperative tasks between humans and robots. In the context of sensors for robotic co-manipulation, it is imperative to guarantee not only a certain level of performance (accuracy, dynamics, size) but also to guarantee certain non-functional specifications associated with safety, measurement redundancy or functional integration of the sensor into its environment. The consideration of all these design specifications, both functional and non-functional, led to the principles and technologies used to develop this sensor. Multi-axis resonant sensors have excellent performance and safety characteristics, two key elements for collaborative robotics, but the literature review did not reveal any significant previous work in this area. Thus, this paper proposes an original sensor based on a breakthrough technology in the crucial field of multi-axis force sensors. The performances obtained on our prototype are already close to the performances of the best existing 6-axis industrial sensors, which suggests very good prospects for this new technology. In addition, and compared to alternative technologies, resonance technology offers new possibilities in terms of sensor fault detection, thus improving the intrinsic safety of these devices.

Allocation of phasor measurement unit using an admissible searching‐based algorithm A‐star and binary search tree for full interconnected power network observability

AbstractIn this research work, an admissible searching‐based algorithm A‐star and binary search tree is proposed to unravel the optimal phasor measurement unit (PMU) placement problem (OPPP) for achieving full interconnected power network observability considering contingencies. The proposed method is an advanced version of the best‐first search method that finds minuscule paths to the best solution. Moreover, it is an optimal and complete search method. Optimal signifies that the proposed method is guaranteed to unravel the minimum cost from origin to the terminus point and Complete denotes that it is going to determine all the possible paths that are available to us from the origin to the destination. The binary tree is used for this purpose as its structural design is well‐matched with the proposed method. Integrating an A‐star algorithm and binary search tree enhances the optimization and performance toward unraveling the OPPP. Also, for more than one solution of OPPP, redundancy measurement is taken into consideration. The proposed method is applied on standard IEEE‐14, ‐30, ‐118, New England‐39, and northern regional power grid (NRPG) 246‐bus test systems. Furthermore, to check the scalability of the proposed method, the Polish 2383‐bus system is considered. The obtained experimental results were compared to established procedures published in peer‐reviewed journals, and the proposed methodology outperformed the other comparison approaches. Thus, the proposed method successfully accomplishes complete observability of the power networks.

Optimization of target alignment in inertial confinement fusion facilities based on redundant measurements

Target alignment technology is one of the most essential technologies in inertial confinement fusion facilities, and is directly related to the results of high-energy physics experiments. In this study, we examine the error in the target alignment scheme, classify the error based on the source and performance, minimize the error through redundant measurement methods (target fusion and sensor fusion), and improve the precision of the target alignment scheme. By integrating these methods, the alignment error of the target is considerably improved and the position uncertainty is reduced.

Data Fusion in Redundant Inertial Measurement Unit Using a Fruit-Fly-Optimized Weighted Least Squares Algorithm

Data Fusion in Redundant Inertial Measurement Unit Using a Fruit-Fly-Optimized Weighted Least Squares Algorithm

Application of State Estimation in Distribution Systems with Embedded Microgrids

In this paper, a weighted least square (WLS) state estimation algorithm with equality constraints is proposed for smart distribution networks embedded with microgrids. Since only a limited number of real-time measurements are available at the primary or secondary substations and distributed generation sites, load estimates at unmeasured buses remote from the substations are needed to execute state estimation. The load information can be obtained by forecasted and historical data or smart real-time meters. The proposed algorithms can be applied in either grid-connected or islanded operation mode and can efficiently identify breaker status errors at the main substations and feeders, where sufficient measurement redundancy exists. The impact of the accuracy of real and pseudo-measurements on the estimated bus voltages is tested with a 55-bus distribution network including distributed generation.

A multi-objective grey wolf optimizer (GWO)-based multi-layer perceptrons (MLPs) trainer for optimal PMUs placement

PurposeThis paper aims to find the minimum possible number of phasor measurement units (PMUs) to achieve maximum and complete observability of the power system and improve the redundancy of measurements, in normal cases (with and without zero injection bus [ZIB]), and then in conditions of a single PMU failure and outage of a single line.Design/methodology/approachAn efficient approach operates adequately and provides the optimal solutions for the PMUs placement problem. The finest function of optimal PMUs placement (OPP) should be mathematically devised as a problem, and via that, the aim of the OPP problem is to identify the buses of the power system to place the PMU devices to ensure full observability of the system. In this paper, the grey wolf optimizer (GWO) is used for training multi-layer perceptrons (MLPs), which is known as Grey Wolf Optimizer (GWO) based Neural Network (“GW-NN”) to place the PMUs in power grids optimally.FindingsFollowing extensive simulation tests with MATLAB/Simulink, the results obtained for the placement of PMUs provide system measurements with less or at most the same number of PMUs, but with a greater degree of observability than other approaches.Practical implicationsThe efficiency of the suggested method is tested on the IEEE 14-bus, 24-bus, New England 39-bus and Algerian 114-bus systems.Originality/valueThis paper proposes a new method for placing PMUs in the power grids as a multi-objective to reduce the cost and improve the observability of these grids in normal and faulty cases.