Anomaly-Resistant Decentralized State Estimation Under Minimum Error Entropy With Fiducial Points for Wide-Area Power Systems

This paper investigates the anomaly-resistant decentralized state estimation(SE) problem for a class of wide-area power systems which are divided into several non-overlapping areas connected through transmission lines. Two classes of measurements(i.e., local measurements and edge measurements) are obtained, respectively, from the individual area and the transmission lines. A decentralized state estimator, whose performance is resistant against measurement with anomalies, is designed based on the minimum error entropy with fiducial points(MEEF) criterion. Specifically, 1) An augmented model, which incorporates the local prediction and local measurement, is developed by resorting to the unscented transformation approach and the statistical linearization approach;2) Using the augmented model, an MEEF-based cost function is designed that reflects the local prediction errors of the state and the measurement;and 3) The local estimate is first obtained by minimizing the MEEF-based cost function through a fixed-point iteration and then updated by using the edge measuring information. Finally, simulation experiments with three scenarios are carried out on the IEEE 14-bus system to illustrate the validity of the proposed anomaly-resistant decentralized SE scheme.

An Image Fingerprint and Attention Mechanism Based Load Estimation Algorithm for Electric Power System

With the rapid development of electric power systems,load estimation plays an important role in system operation and planning.Usually,load estimation techniques contain traditional,time series,regression analysis-based,and machine learning-based estimation.Since the machine learning-based method can lead to better performance,in this paper,a deep learning-based load estimation algorithm using image fingerprint and attention mechanism is proposed.First,an image fingerprint construction is proposed for training data.After the data preprocessing,the training data matrix is constructed by the cyclic shift and cubic spline interpolation.Then,the linear mapping and the gray-color transformation method are proposed to form the color image fingerprint.Second,a convolutional neural network(CNN)combined with an attentionmechanism is proposed for training performance improvement.At last,an experiment is carried out to evaluate the estimation performance.Compared with the support vector machine method,CNN method and long short-term memory method,the proposed algorithm has the best load estimation performance.

Robust state of charge estimation of lithium-ion battery via mixture kernel mean p-power error loss LSTM with heap-based-optimizer

The state of charge(SOC)estimation of lithium-ion battery is an important function in the battery management system(BMS)of electric vehicles.The long short term memory(LSTM)model can be employed for SOC estimation,which is capable of estimating the future changing states of a nonlinear system.Since the BMS usually works under complicated operating conditions,i.e the real measurement data used for model training may be corrupted by non-Gaussian noise,and thus the performance of the original LSTM with the mean square error(MSE)loss may deteriorate.Therefore,a novel LSTM with mixture kernel mean p-power error(MKMPE)loss,called MKMPE-LSTM,is developed by using the MKMPE loss to replace the MSE as the learning criterion in LSTM framework,which can achieve robust SOC estimation under the measurement data contaminated with non-Gaussian noises(or outliers)because of the MKMPE containing the p-order moments of the error distribution.In addition,a meta-heuristic algorithm,called heap-based-optimizer(HBO),is employed to optimize the hyper-parameters(mainly including learning rate,number of hidden layer neuron and value of p in MKMPE)of the proposed MKMPE-LSTM model to further improve its flexibility and generalization performance,and a novel hybrid model(HBO-MKMPE-LSTM)is established for SOC estimation under non-Gaussian noise cases.Finally,several tests are performed under various cases through a benchmark to evaluate the performance of the proposed HBO-MKMPE-LSTM model,and the results demonstrate that the proposed hybrid method can provide a good robustness and accuracy under different non-Gaussian measurement noises,and the SOC estimation results in terms of mean square error(MSE),root MSE(RMSE),mean absolute relative error(MARE),and determination coefficient R2are less than 0.05%,3%,3%,and above 99.8%at 25℃,respectively.

Traction power substation balance and losses estimation in AC railways using a power transfer device through Monte Carlo analysis

The high dynamic power requirements present in modern railway transportation systems raise research challenges for an optimal operation of railway electrification. This paper presents a Monte Carlo analysis on the application of a power transfer device installed in the neutral zone and exchanging active power between two sections. The main analyzed parameters are the active power balance in the two neighbor traction power substations and the system power losses. A simulation framework is presented to comprise the desired analysis and a universe of randomly distributed scenarios are tested to evaluate the effectiveness of the power transfer device system. The results show that the density of trains and the relative branch length of a traction power substation should be considered in the evaluation phase of the best place to install a power transfer device, towards the reduction of the operational power losses, while maintaining the two substations balanced in terms of active power.

Hover performance estimation and validation of battery powered vertical takeoff and landing aircraft

Battery powered vertical takeoff and landing(VTOL) aircraft attracts more and more interests from public, while limited hover endurance hinders many prospective applications. Based on the weight models of battery, motor and electronic speed controller, the power consumption model of propeller and the constant power discharge model of battery, an efficient method to estimate the hover endurance of battery powered VTOL aircraft was presented. In order to understand the mechanism of performance improvement, the impacts of propulsion system parameters on hover endurance were analyzed by simulations, including the motor power density, the battery capacity, specific energy and Peukert coefficient. Ground experiment platform was established and validation experiments were carried out, the results of which showed a well agreement with the simulations. The estimation method and the analysis results could be used for optimization design and hover performance evaluation of battery powered VTOL aircraft.

Residential PV capacity estimation and power disaggregation using net metering measurements

As the intermittency and uncertainty of photovoltaic(PV)power generation poses considerable challenges to the power system operation,accurate PV generation estimates are critical for the distribution operation,maintenance,and demand response program implementation because of the increasing usage of distributed PVs.Currently,most residential PVs are installed behind the meter,with only the net load available to the utilities.Therefore,a method for disaggregating the residential PV generation from the net load data is needed to enhance the grid-edge observability.In this study,an unsupervised PV capacity estimation method based on net metering data is proposed,for estimating the PV capacity in the customer’s premise based on the distribution characteristics of nocturnal and diurnal net load extremes.Then,the PV generation disaggregation method is presented.Based on the analysis of the correlation between the nocturnal and diurnal actual loads and the correlation between the PV capacity and their actual PV generation,the PV generation of customers is estimated by applying linear fitting of multiple typical solar exemplars and then disaggregating them into hourly-resolution power profiles.Finally,the anomalies of disaggregated PV power are calibrated and corrected using the estimated capacity.Experiment results on a real-world hourly dataset involving 260 customers show that the proposed PV capacity estimation method achieves good accuracy because of the advantages of robustness and low complexity.Compared with the state-of-the-art PV disaggregation algorithm,the proposed method exhibits a reduction of over 15%for the mean absolute percentage error and over 20%for the root mean square error.

Probability distribution of wind power volatility based on the moving average method and improved nonparametric kernel density estimation

In the process of large-scale,grid-connected wind power operations,it is important to establish an accurate probability distribution model for wind farm fluctuations.In this study,a wind power fluctuation modeling method is proposed based on the method of moving average and adaptive nonparametric kernel density estimation(NPKDE)method.Firstly,the method of moving average is used to reduce the fluctuation of the sampling wind power component,and the probability characteristics of the modeling are then determined based on the NPKDE.Secondly,the model is improved adaptively,and is then solved by using constraint-order optimization.The simulation results show that this method has a better accuracy and applicability compared with the modeling method based on traditional parameter estimation,and solves the local adaptation problem of traditional NPKDE.

Joint DOA and polarization estimation for unequal power sources based on reconstructed noise subspace

In most literature about joint direction of arrival（DOA） and polarization estimation, the case that sources possess different power levels is seldom discussed. However, this case exists widely in practical applications, especially in passive radar systems. In this paper, we propose a joint DOA and polarization estimation method for unequal power sources based on the reconstructed noise subspace. The invariance property of noise subspace（IPNS） to power of sources has been proved an effective method to estimate DOA of unequal power sources. We develop the IPNS method for joint DOA and polarization estimation based on a dual polarized array. Moreover, we propose an improved IPNS method based on the reconstructed noise subspace, which has higher resolution probability than the IPNS method. It is theoretically proved that the IPNS to power of sources is still valid when the eigenvalues of the noise subspace are changed artificially. Simulation results show that the resolution probability of the proposed method is enhanced compared with the methods based on the IPNS and the polarimetric multiple signal classification（MUSIC） method. Meanwhile, the proposed method has approximately the same estimation accuracy as the IPNS method for the weak source.

Recursive estimation algorithms for power controls of wireless communication networks

Power control problems for wireless communication networks are investigated in direct-sequence codedivision multiple-access （DS/CDMA） channels. It is shown that the underlying problem can be formulated as a constrained optimization problem in a stochastic framework. For effective solutions to this optimization problem in real time, recursive algorithms of stochastic approximation type are developed that can solve the problem with unknown system components. Under broad conditions, convergence of the algorithms is established by using weak convergence methods.

State Estimation of Regional Power Systems with Source-Load Two-Terminal Uncertainties

The development and utilization of large-scale distributed power generation and the increase of impact loads represented by electric locomotives and new energy electric vehicles have brought great challenges to the stable operation of the regional power grid.To improve the prediction accuracy of power systems with source-load twoterminal uncertainties,an adaptive cubature Kalman filter algorithm based on improved initial noise covariance matrix Q0 is proposed in this paper.In the algorithm,the Q0 is used to offset the modeling error,and solves the problem of large voltage amplitude and phase fluctuation of the source-load two-terminal uncertain systems.Verification of the proposed method is implemented on the IEEE 30 node system through simulation.The results show that,compared with the traditional methods,the improved adaptive cubature Kalman filter has higher prediction accuracy,which verifies the effectiveness and accuracy of the proposed method in state estimation of the new energy power system with source-load two-terminal uncertainties.

Lifetime estimation of IGBT module using square-wave loss discretization and power cycling test

The insulated gate bipolar transistor(IGBT)module is one of the most age-affected components in the switch power supply, and its reliability prediction is conducive to timely troubleshooting and reduction in safety risks and unnecessary costs. The pulsed current pattern of the accelerator power supply is different from other converter applications;therefore, this study proposed a lifetime estimation method for IGBT modules in pulsed power supplies for accelerator magnets. The proposed methodology was based on junction temperature calculations using square-wave loss discretization and thermal modeling.Comparison results showed that the junction temperature error between the simulation and IR measurements was less than 3%. An AC power cycling test under real pulsed power supply applications was performed via offline wearout monitoring of the tested power IGBT module. After combining the IGBT4 PC curve and fitting the test results,a simple corrected lifetime model was developed to quantitatively evaluate the lifetime of the IGBT module,which can be employed for the accelerator pulsed power supply in engineering. This method can be applied to other IGBT modules and pulsed power supplies.

Power Estimation of Acoustic Sources by Sensor Array Processing

A constant problem is to localize a number of acoustic sources, to separate their individual signals and to estimate their strengths in a propagation medium. An acoustic receiving array with signal processing algorithms is then used. The most widely used algorithm is the conventional beamforming algorithm but it has a very low resolution and high sidelobes that may cause a signal leakage problem. Several new signal processors for arrays of sensors are derived to evaluate the strengths of acoustic signals arriving at an array of sensors. In particular, we present the covariance vector estimator and the pseudoinverse of the array manifold matrix estimator. The covariance vector estimator uses only the correlations between sensors and the pseudoinverse of the array manifold matrix estimator operates with the minimum eigenvalues of the covariance matrix. Numerical and experimental results are presented.

Error induced by the estimation of the corneal power and the effective lens position with a rotationally asymmetric refractive multifocal intraocular lens

AIM : To evaluate the prediction error in intraocular lens(IOL) power calculation for a rotationally asymmetric refractive multifocal IOL and the impact on this error of the optimization of the keratometric estimation of the corneal power and the prediction of the effective lens position(ELP).METHODS: Retrospective study including a total of 25 eyes of 13 patients(age, 50 to 83y) with previous cataract surgery with implantation of the Lentis Mplus LS-312 IOL(Oculentis Gmb H, Germany). In all cases, an adjusted IOL power(P IOLadj) was calculated based on Gaussian optics using a variable keratometric index value(n kadj) for the estimation of the corneal power(P kadj) and on a new value for ELP(ELP adj) obtained by multiple regression analysis.This P IOLadj was compared with the IOL power implanted(P IOLReal) and the value proposed by three conventional formulas(Haigis, Hoffer Q and Holladay Ⅰ).RESULTS: P IOLReal was not significantly different than P IOLadj and Holladay IOL power(P 】0.05). In the Bland and Altman analysis, P IOLadj showed lower mean difference(-0.07 D) and limits of agreement(of 1.47 and-1.61 D)when compared to P IOLReal than the IOL power value obtained with the Holladay formula. Furthermore, ELP adj was significantly lower than ELP calculated with other conventional formulas(P 【0.01) and was found to be dependent on axial length, anterior chamber depth and P kadj. CONCLUSION: Refractive outcomes after cataract surgery with implantation of the multifocal IOL Lentis Mplus LS-312 can be optimized by minimizing thekeratometric error and by estimating ELP using a mathematical expression dependent on anatomical factors.

Power Balance Estimation in Long Duration Discharges on QUEST

Fully non-inductive plasma start-up was successfully achieved by using a well- controlled microwave source on the spherical tokamak, QUEST. Non-inductive plasmas were maintained for approximately 3-5 min, during which time power balance estimates could be achieved by monitoring wall and cooling-water temperatures. Approximately 70%-90% of the injected power could be accounted for by calorimetric measurements and approximately half of the injected power was found to be deposited on the vessel wall, which is slightly dependent on the magnetic configuration. The power distribution to water-cooled limiters, which are expected to be exposed to local heat loads, depends significantly on the magnetic configuration, however some of the deposited power is due to energetic electrons, which have large poloidal orbits and are likely to be deposited on the plasma facing components.

Bayesian estimation of a power law process with incomplete data

Due to the simplicity and flexibility of the power law process,it is widely used to model the failures of repairable systems.Although statistical inference on the parameters of the power law process has been well developed,numerous studies largely depend on complete failure data.A few methods on incomplete data are reported to process such data,but they are limited to their specific cases,especially to that where missing data occur at the early stage of the failures.No framework to handle generic scenarios is available.To overcome this problem,from the point of view of order statistics,the statistical inference of the power law process with incomplete data is established in this paper.The theoretical derivation is carried out and the case studies demonstrate and verify the proposed method.Order statistics offer an alternative to the statistical inference of the power law process with incomplete data as they can reformulate current studies on the left censored failure data and interval censored data in a unified framework.The results show that the proposed method has more flexibility and more applicability.

A HIGH RESOLUTION TIME DELAY ESTIMATION BASED ON THE MAXIMUM ENTROPY POWER SPECTRUM ESTIMATON

Multipath time delay estimation is constrained by the width of the signal correlation function when using correlation based methods. This paper obtains a high resolution time delay estimation by introducing Burg algorithm and Marple algorithm of the maximum entropy power spectral estimation to non-resolvable multipath time delay estimatoin. The principles, the performaces and the results of computer simulation are given.

Accuracy Improvement in CCT Estimation of Power Systems by iRprop-RAN Hybrid Neural Network

This paper proposes a new Initial CCT (Critical Clearing Time) estimation method using a hybrid neural network composed of iRprop (Improving the Resilient back PROPation Algorithm) and RAN (Resource Allocation Network). In transient stability study, CCT evaluation is very important but time consuming due to the fact it needs many iteration of time domain simulations gradually increasing the fault clearing time. The key to reduce the required computing time in this process is to find accurate initial estimation of CCT by a certain handy method before going to the iterative stage. As one of the strongest candidates of this handy method is the utilization of the pattern recognition ability of neural networks, which enable us to jump to a close estimation of the real CCT without any heavy computing burden. This paper proposes a new hybrid neural network which is a combination of the well-known iRprop and RAN. In the proposed method, the outputs of the hidden units of RAN are modified by multiplying the contribution factors calculated by an additional iRprop network. Numerical studies are done using two different test systems for the purpose of confirming the validity of the proposal. The result of the proposed method is the best. Properly evaluating the contribution of each input to the hidden units, the estimation error obtained by the proposed method is improved further than the original RAN based estimation.

Logic Picture-Based Dynamic Power Estimation for Unit Gate-Delay Model CMOS Circuits

In this research, a fast methodology to calculate the exact value of the average dynamic power consumption for CMOS combinational logic circuits is developed. The delay model used is the unit-delay model where all gates have the same propagation delay. The main advantages of this method over other techniques are its accuracy, as it is deterministic and it requires less computational effort compared to exhaustive simulation approaches. The methodology uses the Logic Pictures concept for obtaining the nodes’ toggle rates. The proposed method is applied to well-known circuits and the results are compared to exhaustive simulation and Monte Carlosimulation methods.

Methods of Power Balance Analysis and Estimation of Current Density Profile for LHCD and IBW Discharges in HT-7 Tokamak

In this paper a simple code has been developed to analyze power balance and qualitatively evaluate current profiles for discharges with lower hybrid current drive （LHCD） and ion Bernstein wave （IBW） heating in the HT-7 tokamak. Electron and ion thermal diffusivity, profiles of the bootstrap current density and total plasma current density can be estimated by this code using the experimental data. This code offers an easy and reasonable means to understand plasma transport in HT-7.

The Measuring System for Estimation of Power of Wind Flow Generated by Train Movement and Its Experimental Testing

The measuring system for estimation of power of wind flow generated by the train movement has been created. The advantages of the proposed system are the cheapness and simple design. With its simplicity of design and easy build-up of channels, designed measuring system can be used for a wide range of technical problems. This paper describes the design process, validation and conducting the first field test of this measuring system.