Interval State Estimation in Active Distribution Systems Considering Multiple Uncertainties.

Distribution system state estimation (DSSE) plays a significant role for the system operation management and control. Due to the multiple uncertainties caused by the non-Gaussian measurement noise, inaccurate line parameters, stochastic power outputs of distributed generations (DG), and plug-in electric vehicles (EV) in distribution systems, the existing interval state estimation (ISE) approaches for DSSE provide fairly conservative estimation results. In this paper, a new ISE model is proposed for distribution systems where the multiple uncertainties mentioned above are well considered and accurately established. Moreover, a modified Krawczyk-operator (MKO) in conjunction with interval constraint-propagation (ICP) algorithm is proposed to solve the ISE problem and efficiently provides better estimation results with less conservativeness. Simulation results carried out on the IEEE 33-bus, 69-bus, and 123-bus distribution systems show that the our proposed algorithm can provide tighter upper and lower bounds of state estimation results than the existing approaches such as the ICP, Krawczyk-Moore ICP(KM-ICP), Hansen, and MKO

Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency Detection

Co-saliency detection aims to discover the common and salient foregrounds from a group of relevant images. For this task, we present a novel adaptive graph convolutional network with attention graph clustering (GCAGC). Three major contributions have been made, and are experimentally shown to have substantial practical merits. First, we propose a graph convolutional network design to extract information cues to characterize the intra- and interimage correspondence. Second, we develop an attention graph clustering algorithm to discriminate the common objects from all the salient foreground objects in an unsupervised fashion. Third, we present a unified framework with encoder-decoder structure to jointly train and optimize the graph convolutional network, attention graph cluster, and co-saliency detection decoder in an end-to-end manner. We evaluate our proposed GCAGC method on three cosaliency detection benchmark datasets (iCoseg, Cosal2015 and COCO-SEG). Our GCAGC method obtains significant improvements over the state-of-the-arts on most of them.Comment: CVPR202

Transmission of TE-polarized light through metallic nanoslit arrays assisted by a quasi surface wave

Optically thick metallic nanoslit arrays are opaque to TE-polarized light, in contrast to enhanced transmission of TM-polarized light. Here, we numerically show that, by introducing an ultrathin high-index dielectric coating on the metal surfaces, a quasi surface wave can be excited at the metasurfaces to enhance the transmission of TE-polarized light. The quasi surface wave is shown to behave like surface plasmon waves, and enhance the transmission in similar mechanisms as surface plasmon waves do for TM-polarized light. In this work, we suggest a way of manipulating TE-polarized light in metallic subwavelength structures. ? 2014 The Japan Society of Applied Physics

Robust state estimation for power systems via moving horizon strategy

In this paper, I propose a re-weighted moving horizon estimation (RMHE) to improve the robustness for power systems. The RMHE reduces its sensitivity to the outliers by updating their error variances real-time and re-weighting their contributions adaptively for robust power system state estimation (PSSE). Compared with the common robust state estimators such as the Quadratic-Constant (QC), Quadratic-Linear (QL), Square-Root (SR), Multiple-Segment (MS) and Least Absolute Value (LAV) estimator, one advance of RMHE is that the RMHE incorporates the uncertainty of process model and the arrival cost term during the optimization process. Constraints on states are also taken into account. The influence of the outliers can be further mitigated. Simulations on the IEEE 14-bus system show that the RMHE can obtain estimated results with smaller errors even when the outliers are present.NRF (Natl Research Foundation, S’pore)Accepted versio

Robust and distributed state estimation for power systems

Power system state estimation (PSSE) plays an important role in power system operation. The Gaussian noise assumption is commonly made in PSSE. However, this assumption is only an approximation to reality. Outliers that are far away from the expected Gaussian distribution function can give rise to erroneous estimation results. Robust estimators such as Quadratic-Constant (QC), Quadratic-Linear (QL), Square-Root (SR), Multiple-Segment (MS) and Schweppe-Huber Generalized-M (SHGM) have been introduced in the literature to solve the outlier problem in power systems. In this thesis, an analytical equation is derived using the Influence Function (IF), a tool from robust statistics, to calculate approximately the variances of the estimates of these robust estimators. This variance formula has many advantages: (i) It can be used to express the variance of state estimate as a function of measurement variances thus enabling the selection of sensors for specified estimator precision; (ii) It can be used to design an optimal estimator; (iii) Although numerical methods can also be used to find variance, the derived equation as a mathematical function is more insightful and requires less computational effort. For robust PSSE, this thesis proposes a robust estimator based on the maximum likelihood criterion, and a noise model with t-distribution probability density function (pdf). The thick tail property of t-distribution down weights outliers so that the proposed estimator is robust to outliers. Instead of solving the optimization problem numerically, the IF is employed to give an approximate solution to reduce computational load. In addition, a robust estimator based on the moving horizon estimation (MHE) technique is proposed for PSSE. This robust estimator is called re-weighted MHE. The proposed estimator reduces its sensitivity to the outliers by updating their error covariances in real time and then uses these re-weighted error covariances for robust PSSE. Compared with other robust state estimators such as MS and Least Absolute Value (LAV) estimator, one advantage of the proposed estimator is that it can directly incorporate constraints on the states to mitigate the outliers. If Phasor Measurement Units (PMUs) are used, the measurement model becomes linear. Then the proposed estimator can be formulated as a quadratic programming (QP) and solved by Alternating Direction Method of Multipliers (ADMM) algorithm efficiently. When the measurement model is nonlinear, the iterated RMHE (iRMHE) algorithm is proposed. Finally, the centralized estimator is not applicable when the size of power system becomes very large. Two distributed versions of the proposed robust estimator based on MHE are considered: distributed MHE (DMHE) and partitioned MHE (PMHE). For DMHE, each local area will obtain the states of the whole system. It is suitable for the advanced applications such as wide-area monitoring systems (WAMSs) that require the system-wide state to be available to all the regional transmission organizations (RTOs). For PMHE, each local area only uses its local measurements and the states of border buses exchanged from its neighborhoods. It solves a smaller optimization problem to obtain the states of local states. Therefore, the communication load and computational load are reduced.Doctor of Philosophy (EEE

A Gated Multiscale Multitask Learning Model Using Time-Frequency Representation for Health Assessment and Remaining Useful Life Prediction

Health assessment and remaining useful life prediction are usually seen as separate tasks in industrial systems. Some multitask models use common features to handle these tasks synchronously, but they lack the usage of the representation in different scales and time-frequency domain. A lack of balance also exists among these scales. Therefore, a gated multiscale multitask learning model known as GMM-Net is proposed in this paper. By using the time-frequency representation, GMM-Net can obtain features of different scales via different kernels and compose the features by a gating network. A detailed loss function whose weight can be searched in a smaller scale is designed. The model is tested with different weights in the total loss function, and an optimal weight is found. Using this optimal weight, it is observed that the proposed method converges to a smaller loss and has a smaller model size than long short-term memory (LSTM) and gated recurrent unit (GRU) with less training time. The experiment results demonstrate the effectiveness of the proposed method

A Gated Multiscale Multitask Learning Model Using Time-Frequency Representation for Health Assessment and Remaining Useful Life Prediction

Health assessment and remaining useful life prediction are usually seen as separate tasks in industrial systems. Some multitask models use common features to handle these tasks synchronously, but they lack the usage of the representation in different scales and time-frequency domain. A lack of balance also exists among these scales. Therefore, a gated multiscale multitask learning model known as GMM-Net is proposed in this paper. By using the time-frequency representation, GMM-Net can obtain features of different scales via different kernels and compose the features by a gating network. A detailed loss function whose weight can be searched in a smaller scale is designed. The model is tested with different weights in the total loss function, and an optimal weight is found. Using this optimal weight, it is observed that the proposed method converges to a smaller loss and has a smaller model size than long short-term memory (LSTM) and gated recurrent unit (GRU) with less training time. The experiment results demonstrate the effectiveness of the proposed method

Dose institutional quality influences the relationship between urbanization and CO2 emissions?

As a result of rapid economic expansion, increased energy use, and urbanization, global warming and climate change have become serious challenges in recent decades. Institutional quality can be the remedy to impede the harmful effect of factors on environmental quality. This study investigates the impact that urbanization and institutional quality on environmental quality in in the Belt and Road Initiative (BRI) countries from 2002 to 2019. By using two step generalized method of moment, the findings shows that urbanization leads to an increase in carbon dioxide emissions and a decline in environmental quality. On the other hand, the square term of urbanization indicates that an increase in urbanization leads to a reduction in emissions at a later stage after reach a certain level. Education, on the other hand, has the reverse impact of increasing carbon emissions; economic growth, foreign direct investment, and government effectiveness all boost carbon emissions. In a similar vein, the interaction between urbanization and the effectiveness of the government is unfavorable, underscoring the transformative role that the effectiveness of the government plays in leading to environmental sustainability. Finally, the findings of this study have considerable policy implication for the sample countries

Distributed State Estimation Using a Modified Partitioned Moving Horizon Strategy for Power Systems

In this paper, a distributed state estimation method based on moving horizon estimation (MHE) is proposed for the large-scale power system state estimation. The proposed method partitions the power systems into several local areas with non-overlapping states. Unlike the centralized approach where all measurements are sent to a processing center, the proposed method distributes the state estimation task to the local processing centers where local measurements are collected. Inspired by the partitioned moving horizon estimation (PMHE) algorithm, each local area solves a smaller optimization problem to estimate its own local states by using local measurements and estimated results from its neighboring areas. In contrast with PMHE, the error from the process model is ignored in our method. The proposed modified PMHE (mPMHE) approach can also take constraints on states into account during the optimization process such that the influence of the outliers can be further mitigated. Simulation results on the IEEE 14-bus and 118-bus systems verify that our method achieves comparable state estimation accuracy but with a significant reduction in the overall computation load

Effects of Type Ia Supernovae Absolute Magnitude Priors on the Hubble Constant Value

We systematically explore the influence of the prior of the peak absolute magnitude ( M ) of Type Ia supernovae (SNe Ia) on the measurement of the Hubble constant ( H _0 ) from SNe Ia observations. We consider five different data-motivated M priors, representing varying levels of dispersion, and assume the spatially flat ΛCDM cosmological model. Different M priors lead to relative changes in the mean values of H _0 from 2% to 7%. Loose priors on M yield H _0 estimates consistent with both the Planck 2018 result and the SH0ES result at the 68% confidence level. We also examine the potential impact of peculiar velocity subtraction on the value of H _0 and show that it is insignificant for the SNe Ia observations with redshift z > 0.01 used in our analyses. We also repeat the analysis in the cosmography model and find very similar results. This suggests that our results are robust and model independent