Data-driven Power Flow Method Based on Exact Linear Regression Equations

Power flow(PF)is one of the most important calculations in power systems.The widely-used PF methods are the Newton-Raphson PF(NRPF)method and the fast-decoupled PF(FDPF)method.In smart grids,power generations and loads become intermittent and much more uncertain,and the topology also changes more frequently,which may result in significant state shifts and further make NRPF or FDPF difficult to converge.To address this problem,we propose a data-driven PF(DDPF)method based on historical/simulated data that includes an offline learning stage and an online computing stage.In the offline learning stage,a learning model is constructed based on the proposed exact linear regression equations,and then the proposed learning model is solved by the ridge regression(RR)method to suppress the effect of data collinearity.In online computing stage,the nonlinear iterative calculation is not needed.Simulation results demonstrate that the proposed DDPF method has no convergence problem and has much higher calculation efficiency than NRPF or FDPF while ensuring similar calculation accuracy.

Estimations of Parametric Functions under a System of Linear Regression Equations with Correlated Errors

Estimations of parametric functions under a system of linear regression equations with correlated errors across equations involve many complicated operations of matrices and their generalized inverses. In the past several years, a useful tool -- the matrix rank method was utilized to simplify various complicated operations of matrices and their generalized inverses. In this paper, we use the matrix rank method to derive a variety of new algebraic and statistical properties for the best linear unbiased estimators （BLUEs） of parametric functions under the system. In particular, we give the necessary and sufficient conditions for some equalities, additive and block decompositions of BLUEs of parametric functions under the system to hold.

Body Composition Estimatives by Anthropometry, Bioelectrical Impedance and Deuterium Oxide Dilution in Obese Adolescents

To evaluate the relationship between measures of body composition in obese adolescents by the methods of bioelectrical impedance analysis, deuterium oxide dilution and anthropometric measures, proposing an equation. The variables were weight, height, BMI, triceps and subscapular skinfold thickness, waist and arm muscle circumference, lean body mass, fat mass and total body water by bioelectrical impedance and deuterium oxide dilution methods. The study included 40 obese adolescents, 45% male, age distribution was 2.42 ± 1.19 years and females 55%, and the predominant age was 12.61 ± 1.78. Linear regression equations were developed, capable of predicting body composition from information supplied by the method of deuterium oxide dilution (gold standard), bioelectrical impedance and anthropometry. The variables gender, age, height, arm circumference, triceps and suprailiac skin fold thickness, resistance and reactance were used to estimate lean body mass, fat mass and total body water by the method of deuterium and significantly correlated with variables, resistance, reactance, sex and total body water (TBW) by bioimpedance method. Among the equations developed, five were suitable for this sample, therefore, it is suggested that more studies should be done to test the applicability of the equations in other samples so that we can validate the equations encountered in obese adolescents.