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This article proposes a voltage compensation control strategy for the distribution network based on the unified power quality conditioner (UPQC) with battery energy storage systems (BESS) to achieve comprehensive management of the power quality and improve the system dynamic responses. Specifically, aiming at that amplitude and phase jump of the terminal voltage, a capacity configuration compensation scheme is proposed to achieve voltage vector compensation. The series converter is designed to compensate for the amplitude and phase of the load voltage. The parallel converter is designed to realize power compensation and ensure that the grid voltage and current are sinusoidal and in phase. A voltage control approach based on the nonlinear disturbance observer (NDO) is developed for BESS to stabilize the DC bus voltage and improve the anti-interference performance of the current. An NDO is designed to accomplish current disturbance tracking and provide disturbance feedforward compensation. Simulations validate that the proposed approach is effective and feasible. [ABSTRACT FROM AUTHOR]

To reduce the cost and improve the efficiency of cohort studies, case-cohort design is a widely used biased-sampling scheme for time-to-event data. In modeling process, we may encounter the situation that actual covariates are not directly observed, but are contaminated with a multiplicative factor which is determined by a smooth unknown function of an observed covariate. In this paper, we study the inference methods for case-cohort data under the additive hazards model with covariate adjustment. We propose to estimate the distorting function by nonparametric regressing the contaminated covariates on the distorting factor. The estimators for the parameters can then be obtained based on the estimated covariates. A series of simulation studies are conducted to assess the finite-sample performance of the proposed estimator. An application to a pulmonary exacerbation study demonstrates the practicability of the proposed methods. [ABSTRACT FROM AUTHOR]

The case-cohort design is used in large cohort study to improve the efficiency and reduce the cost. In modeling process, we meet the situation that some covariates in a regression model are distorted by an unknown function of an observable confounding variable in a multiplicative form. In this paper, we consider fitting covariate-adjusted proportional hazards model for case-cohort studies. We propose to estimate the distorted function by non parametrically regressing the observed covariates on the distorted confounder, and then an estimator for the regression parameter is obtained by using the estimated covariates. Under some mild assumptions, we establish the asymptotic properties of the proposed estimator. The results from both artificial and real data demonstrate good performance and practicality of the proposed method. [ABSTRACT FROM AUTHOR]

When covariate information is collected through opinion surveys, experimental science, medical research and so on, incomplete records may result in missing information. Expectile regression is used in many fields; however, many fewer developments have been made with expectile regression for data with missing information. In this paper, we develop a weighted expectile regression approach for estimating the conditional expectile when covariates are missing at random (MAR). The asymptotic normality of the proposed weighted estimators is established. A proximal alternating direction method of multipliers (pADMM) algorithm is developed for the calculation of the weighted estimator. Simulation studies are conducted to assess the finite-sample performance of the proposed method. We illustrate the application of the method by analyzing a real data set from Beijing Olympic sports center monitoring stations. [ABSTRACT FROM AUTHOR]