Optimal decision-making method for equipment maintenance to enhance the resilience of power digital twin system under extreme disaster

Digital twins and the physical assets of electric power systems face the potential risk of data loss and monitoring failures owing to catastrophic events,causing surveillance and energy loss.This study aims to refine maintenance strategies for the monitoring of an electric power digital twin system post disasters.Initially,the research delineates the physical electric power system along with its digital counterpart and post-disaster restoration processes.Subsequently,it delves into communication and data processing mechanisms,specifically focusing on central data processing(CDP),communication routers(CRs),and phasor measurement units(PMUs),to re-establish an equipment recovery model based on these data transmission methodologies.Furthermore,it introduces a mathematical optimization model designed to enhance the digital twin system’s post-disaster monitoring efficacy by employing the branch-and-bound method for its resolution.The efficacy of the proposed model was corroborated by analyzing an IEEE-14 system.The findings suggest that the proposed branch-and-bound algorithm significantly augments the observational capabilities of a power system with limited resources,thereby bolstering its stability and emergency response mechanisms.

Analysis on the Problems in Start-up and Debugging of Two 600 MW Generating Units in Yangzhou No.2 Thermal Power Plant

The problems including excessive flow of attemperating water for boiler, failure of butterfly valve at the outlet of circulating water pump, burnt-out of thyristor for excitation regulator, load variation rate of CCS not complying with the contract target, etc. occurred during start-up and debugging of two 600 MW generating units in Yangzhou No.2 Thermal Power Plant. Through analysis on these problems. the remedial measures were put forward, to which can be referred for similar units.

Optimal Placement of Phasor Measurement Units Using a Modified Canonical Genetic Algorithm for Observability Analysis

This paper proposes a method for optimal placement of synchronized PMUs （phasor measurement units） in electrical power systems using a MCGA （modified canonical genetic algorithm）, which the goal is to determine the minimum number of PMUs, as well as the optimal location of these units to ensure the complete topological observability of the system. In case of more than one solution, a strategy of analysis of the design matrix rank is applied to determine the solution with the lower number of critical measurements. In the proposed method of placement, modifications are made in the crossover and mutation genetic operators, as well as in the formation of the subpopulation, and are considered restrictive hypotheses in the search space to improve the performance in solving the optimization problem. Simulations are performed using the IEEE 14-bus, IEEE 30-bus and New England 39-bus test systems. The proposed method is applied on the IEEE 118-bus test system considering the presence of observable zones formed by conventional measurements.

RELATIVE ASYMPTOTICS FOR ORTHOGONAL MATRIX POLYNOMIALS WITH RESPECT TO A PERTURBED MATRIX MEASURE ON THE UNIT CIRCLE

Given a positive definite matrix measure Ω supported on the unit circle T, then main purpose of this paper is to study the asymptotic behavior of L n()L n(Ω) -1 and Φ n(z;)Φ n(z;Ω) -1 where(z)=Ω(z)+Mδ(z-w); |w|>1,M is a positive definite matrix and δ is the Dirac matrix measure. Here, L n(·) means the leading coefficient of the orthonormal matrix polynomials Φ n(z;·). Finally, we deduce the asymptotic behavior of Φ n(w;)Φ n(w;Ω)* in the case when M=I.

HUID:DBN-Based Fingerprint Localization and Tracking System with Hybrid UWB and IMU

High-precision localization technology is attracting widespread attention in harsh indoor environments.In this paper,we present a fingerprint localization and tracking system to estimate the locations of the tag based on a deep belief network(DBN).In this system,we propose using coefficients as fingerprints to combine the ultra-wideband(UWB)and inertial measurement unit(IMU)estimation linearly,termed as a HUID system.In particular,the fingerprints are trained by a DBN and estimated by a radial basis function(RBF).However,UWB-based estimation via a trilateral method is severely affected by the non-line-of-sight(NLoS)problem,which limits the localization precision.To tackle this problem,we adopt the random forest classifier to identify line-of-sight(LoS)and NLoS conditions.Then,we adopt the random forest regressor to mitigate ranging errors based on the identification results for improving UWB localization precision.The experimental results show that the mean square error(MSE)of the localization error for the proposed HUID system reduces by 12.96%,50.16%,and 64.92%compared with that of the existing extended Kalman filter(EKF),single UWB,and single IMU estimation methods,respectively.

A Robust Asynchrophasor in PMU Using Second-Order Kalman Filter

Phasor Measurement Units(PMUs)provide Global Positioning System(GPS)time-stamped synchronized measurements of voltage and current with the phase angle of the system at certain points along with the grid system.Those synchronized data measurements are extracted in the form of amplitude and phase from various locations of the power grid to monitor and control the power system condition.A PMU device is a crucial part of the power equipment in terms of the cost and operative point of view.However,such ongoing development and improvement to PMUs’principal work are essential to the network operators to enhance the grid quality and the operating expenses.This paper introduces a proposed method that led to lowcost and less complex techniques to optimize the performance of PMU using Second-Order Kalman Filter.It is based on the Asyncrhophasor technique resulting in a phase error minimization when receiving the signal from an access point or from the main access point.The MATLAB model has been created to implement the proposed method in the presence of Gaussian and non-Gaussian.The results have shown the proposed method which is Second-Order Kalman Filter outperforms the existing model.The results were tested usingMean Square Error(MSE).The proposed Second-Order Kalman Filter method has been replaced with a synchronization unit into thePMUstructure to clarify the significance of the proposed new PMU.

Rapid Fault Analysis by Deep Learning-Based PMU for Smart Grid System

Smart Grids(SG)is a power system development concept that has received significant attention nationally.SG signifies real-time data for specific communication requirements.The best capabilities for monitoring and controlling the grid are essential to system stability.One of the most critical needs for smart-grid execution is fast,precise,and economically synchronized measurements,which are made feasible by Phasor Measurement Units(PMU).PMUs can pro-vide synchronized measurements and measure voltages as well as current phasors dynamically.PMUs utilize GPS time-stamping at Coordinated Universal Time(UTC)to capture electric phasors with great accuracy and precision.This research tends to Deep Learning(DL)advances to design a Residual Network(ResNet)model that can accurately identify and classify defects in grid-connected systems.As part of fault detection and probe,the proposed strategy uses a ResNet-50 tech-nique to evaluate real-time measurement data from geographically scattered PMUs.As a result of its excellent signal classification efficiency and ability to extract high-quality signal features,its fault diagnosis performance is excellent.Our results demonstrate that the proposed method is effective in detecting and classifying faults at sufficient time.The proposed approaches classify the fault type with a precision of 98.5%and an accuracy of 99.1%.The long-short-term memory(LSTM),Convolutional Neural Network(CNN),and CNN-LSTM algo-rithms are applied to compare the networks.Real-world data tends to evaluate these networks.

Curvature Quantified Douglas-Peucker-based Phasor Measurement Unit Data Compression Method for Power System Situational Awareness

Facing constraints imposed by storage and bandwidth limitations,the vast volume of phasor meas-urement unit(PMU)data collected by the wide-area measurement system(WAMS)for power systems cannot be fully utilized.This limitation significantly hinders the effective deployment of situational awareness technologies for systematic applications.In this work,an effective curvature quantified Douglas-Peucker(CQDP)-based PMU data compression method is proposed for situational awareness of power systems.First,a curvature integrated distance(CID)for measuring the local flection and fluc-tuation of PMU signals is developed.The Doug-las-Peucker(DP)algorithm integrated with a quan-tile-based parameter adaptation scheme is then proposed to extract feature points for profiling the trends within the PMU signals.This allows adaptive adjustment of the al-gorithm parameters,so as to maintain the desired com-pression ratio and reconstruction accuracy as much as possible,irrespective of the power system dynamics.Fi-nally,case studies on the Western Electricity Coordinat-ing Council(WECC)179-bus system and the actual Guangdong power system are performed to verify the effectiveness of the proposed method.The simulation results show that the proposed method achieves stably higher compression ratio and reconstruction accuracy in both steady state and in transients of the power system,and alleviates the compression performance degradation problem faced by existing compression methods.Index Terms—Curvature quantified Douglas-Peucker,data compression,phasor measurement unit,power sys-tem situational awareness.

Ultra-tight GPS/INS integration based long-range rocket projectile navigation method

Accurate navigation is important for long-range rocket projectile＇s precise striking. To obtain stable and high-per- formance navigation result, a ultra-tight global positioning system/inertial navigation system （GPS/INS） integration based nav- igation approach is proposed. The accurate short-time output of INS is used by GPS receiver to assist in acquisition of signal, and output information of INS and GPS is fused based on federated filter. Meanwhile, the improved cubature Kalman filter with strong tracking ability is chosen to serve as the local filter, and then the federated filter is enhanced based on vector sharing theory. Finally, simulation results show that the navigation accuracy with the proposed method is higher than that with traditional methods. It provides reference for long-range rocket projectile navigation.

Determination of Total Vector Error of the Phasor Measurement Unit (PMU) Using the Phase Angle Error of a Constant Amplitude Voltage Signal

This paper investigates the effect of the Phase Angle Error of a Constant Amplitude Voltage signal in determining the Total Vector Error (TVE) of the Phasor Measurement Unit (PMU) using MATLAB/Simulink. The phase angle error is measured as a function of time in microseconds at four points on the IEEE 14-bus system. When the 1 pps Global Positioning System (GPS) signal to the PMU is lost, sampling of voltage signals on the power grid is done at different rates as it is a function of time. The relationship between the PMU measured signal phase angle and the sampling rate is established by injecting a constant amplitude signal at two different points on the grid. In the simulation, 64 cycles per second is used as the reference while 24 cycles per second is used to represent the fault condition. Results show that a change in the sampling rate from 64 bps to 24 bps in the PMUs resulted in phase angle error in the voltage signals measured by the PMU at four VI Measurement points. The phase angle error measurement that was determined as a time function was used to determine the TVE. Results show that (TVE) was more than 1% in all the cases.

Robust and Accurate Monocular Visual Navigation Combining IMU for a Quadrotor

In this paper, we present a multi-sensor fusion based monocular visual navigation system for a quadrotor with limited payload, power and computational resources. Our system is equipped with an inertial measurement unit (IMU), a sonar and a monocular down-looking camera. It is able to work well in GPS-denied and markerless environments. Different from most of the keyframe-based visual navigation systems, our system uses the information from both keyframes and keypoints in each frame. The GPU-based speeded up robust feature (SURF) is employed for feature detection and feature matching. Based on the flight characteristics of quadrotor, we propose a refined preliminary motion estimation algorithm combining IMU data. A multi-level judgment rule is then presented which is beneficial to hovering conditions and reduces the error accumulation effectively. By using the sonar sensor, the metric scale estimation problem has been solved. We also present the novel IMU+3P (IMU with three point correspondences) algorithm for accurate pose estimation. This algorithm transforms the 6-DOF pose estimation problem into a 4-DOF problem and can obtain more accurate results with less computation time. We perform the experiments of monocular visual navigation system in real indoor and outdoor environments. The results demonstrate that the monocular visual navigation system performing in real-time has robust and accurate navigation results of the quadrotor. © 2014 Chinese Association of Automation.

THE EFFECT OF IMU INACCURACIES ON AIRBORNE SAR IMAGING

When using motion compensation approaches based on the measurement of motion sensors, the residual uncompensated motion errors due to measurement instrument inaccuracies contribute to phase errors and hence degrade Synthetic Aperture Radar (SAR) images. This paper presents a model to compute the phase error caused by Inertial Measurement Unit (IMU) measurement inaccuracies. By analyzing SAR motion compensation method and the effect of lever arm, this model derives the con-tribution of each term of IMU inaccuracies towards the residual uncompensated motion errors and provides a method to calculate each order of the residual phase error. According to the model, com-puted results of the airborne X-band SAR system with POS AV510 accord closely with the actual image quality.

Subsynchronous oscillation monitoring and alarm method based on phasor measurements

Owing to the large-scale grid connection of new energy sources, several installed power electronic devices introduce sub-/supersynchronous inter-harmonics into power signals, resulting in the frequent occurrence of subsynchronous oscillations(SSOs). The SSOs may cause significant harm to generator sets and power systems;thus, online monitoring and accurate alarms for power systems are crucial for their safe and stable operation. Phasor measurement units(PMUs) can realize the dynamic real-time monitoring of power systems. Based on PMU phasor measurements, this study proposes a method for SSO online monitoring and alarm implementation for the main station of a PMU. First, fast Fourier transform frequency spectrum analysis is performed on PMU current phasor amplitude data to obtain subsynchronous frequency components. Second, the support vector machine learning algorithm is trained to obtain the amplitude threshold and subsequently filter out safe components and retain harmful ones. Finally, the adaptive duration threshold is determined according to frequency susceptibility, amplitude attenuation, and energy accumulation to decide whether to transmit an alarm signal. Experiments based on field data verify the effectiveness of the proposed method.

Application of Total Least Square Algorithm in Static Decoupling of NGMIMU

The linear coupling of Non-gyro Micro Inertial Measurement Unit (NGMIMU) is akind of system error that affects the accuracy of measurement seriously. In this article, theauthor puts forward a new linear decoupling algorithm which simultaneously considers the error ofstandard input signal and output of accelerators when the coupling parameters are calculated. TheTotal Least Square (TLS) solutions of coupling parameters own the minimum characteristic to theinput and output values. Then these parameters are used to reconstruct the outputs of acceleratorsso as to realize the decoupling. The emulation result show that the ratio of decoupling error isless than 8 percent and verify the feasibility of this algorithm.

Hybrid ToA and IMU indoor localization system by various algorithms

In this paper, we integrate inertial navigation system (INS) with wireless sensor network (WSN) to enhance the accuracy of indoor localization. Inertial measurement unit (IMU), the core of the INS, measures the accelerated and angular rotated speed of moving objects. Meanwhile, the ranges from the object to beacons, which are sensor nodes with known coordinates, are collected by time of arrival (ToA) approach. These messages are simultaneously collected and transmitted to the terminal. At the terminal, we set up the state transition models and observation models. According to them, several recursive Bayesian algorithms are applied to producing position estimations. As shown in the experiments, all of three algorithms do not require constant moving speed and perform better than standalone ToA system or standalone IMU system. And within them, two algorithms can be applied for the tracking on any path which is not restricted by the requirement that the trajectory between the positions at two consecutive time steps is a straight line.

Intelligently Tuned Wavelet Parameters for GPS/INS Error Estimation

This paper presents a new algorithm for de-noising global positioning system （GPS） and inertial navigation system （INS） data and estimates the INS error using wavelet multi-resolution analysis algorithm （WMRA）-based genetic algorithm （GA） with a well-designed structure appropriate for practical and real time implementations because of its very short training time and elevated accuracy. Different techniques have been implemented to de-noise and estimate the INS and GPS errors. Wavelet de-noising is one of the most exploited techniques that have been recently used to increase the precision and reliability of the integrated GPS/INS navigation system. To ameliorate the WMRA algorithm, GA was exploited to optimize the wavelet parameters so as to determine the best wavelet filter, thresholding selection rule （TSR）, and the optimum level of decomposition （LOD）. This results in increasing the robustness of the WMRA algorithm to estimate the INS error. The proposed intelligent technique has overcome the drawbacks of the tedious selection for WMRA algorithm parameters. Finally, the proposed method improved the stability and reliability of the estimated INS error using real field test data.

Numerical algorithm for rigid body position estimation using the quaternion approach

This paper deals with rigid body attitude estimation on the basis of the data obtained from an inertial measurement unit mounted on the body. The aim of this work is to present the numerical algorithm, which can be easily applied to the wide class of problems concerning rigid body positioning, arising in aerospace and marine engineering, or in increasingly popular robotic systems and unmanned aerial vehicles. Following the considerations of kinematics of rigid bodies, the relations between accelerations of different points of the body are given. A rotation matrix is formed using the quaternion approach to avoid singularities. We present numerical procedures for determination of the absolute accelerations of the center of mass and of an arbitrary point of the body expressed in the inertial reference frame, as well as its attitude. An application of the algorithm to the example of a heavy symmetrical gyroscope is presented, where input data for the numerical procedure are obtained from the solution of differential equations of motion, instead of using sensor measurements.

Technological advancements in the analysis of human motion and posture management through digital devices

Technological development of motion and posture analyses is rapidly progressing,especially in rehabilitation settings and sport biomechanics.Consequently,clear discrimination among different measurement systems is required to diversify their use as needed.This review aims to resume the currently used motion and posture analysis systems,clarify and suggest the appropriate approaches suitable for specific cases or contexts.The currently gold standard systems of motion analysis,widely used in clinical settings,present several limitations related to marker placement or long procedure time.Fully automated and markerless systems are overcoming these drawbacks for conducting biomechanical studies,especially outside laboratories.Similarly,new posture analysis techniques are emerging,often driven by the need for fast and non-invasive methods to obtain high-precision results.These new technologies have also become effective for children or adolescents with non-specific back pain and postural insufficiencies.The evolutions of these methods aim to standardize measurements and provide manageable tools in clinical practice for the early diagnosis of musculoskeletal pathologies and to monitor daily improvements of each patient.Herein,these devices and their uses are described,providing researchers,clinicians,orthopedics,physical therapists,and sports coaches an effective guide to use new technologies in their practice as instruments of diagnosis,therapy,and prevention.

Adaptive filter for a miniature MEMS based attitude and heading reference system

An extended Kalman filter with adaptive gain was used to build a miniature attitude and heading reference system based on a stochastie model. The adaptive filter has six states with a time variable transition matrix. When the system is in the non-acceleration mode, the accelerometer measurements of the gravity and the compass measurements of the heading have observability and yield good eslimates of the states. When the system is in the high dynamic mode and the bias has converged to an aceurate estimate, the attitude caleulation will be maintained for a long interval of time. The adaptive filter tunes its gain automatically based on the system dynamics sensed by the accelerometers to yield optimal performance,

GPS Spoofing Attack Detection in Smart Grids Based on Improved CapsNet

This paper analyzes the influence of the global positionong system(GPS)spoofing attack(GSA)on phasor measurement units(PMU)measurements.We propose a detection method based on improved Capsule Neural Network(CapsNet)to handle this attack.In the improved CapsNet,the gated recurrent unit(GRU)is added to the front of the full connection layer of the CapsNet.The improved CapsNet trains and updates the network parameters according to the historical measurements of the smart grid.The detection method uses different structures to extract the temporal and spatial features of the measurements simultaneously,which can accurately distinguish the attacked data from the normal data,to improve the detection accuracy.Finally,simulation experiments are carried out on IEEE 14-,IEEE 118-bus systems.The experimental results show that compared with other detection methods,our method is proved to be more efficient.