An Ordered Lasso and Sparse Time-Lagged Regression

We consider regression scenarios where it is natural to impose an order constraint on the coefficients. We propose an order-constrained version of L1-regularized regression for this problem, and show how to solve it efficiently using the well-known Pool Adjacent Violators Algorithm as its proximal operator. The main application of this idea is time-lagged regression, where we predict an outcome at time t from features at the previous K time points. In this setting it is natural to assume that the coefficients decay as we move farther away from t, and hence the order constraint is reasonable. Potential applications include financial time series and prediction of dynamic patient out- comes based on clinical measurements. We illustrate this idea on real and simulated data.Comment: 15 pages, 6 figure

An Ordered Lasso and Sparse Time-lagged Regression

<div><p>We consider regression scenarios where it is natural to impose an order constraint on the coefficients. We propose an order-constrained version of ℓ₁-regularized regression (Lasso) for this problem, and show how to solve it efficiently using the well-known Pool Adjacent Violators Algorithm as its proximal operator. The main application of this idea is to time-lagged regression, where we predict an outcome at time <i>t</i> from features at the previous <i>K</i> time points. In this setting it is natural to assume that the coefficients decay as we move farther away from <i>t</i>, and hence the order constraint is reasonable. Potential application areas include financial time series and prediction of dynamic patient outcomes based on clinical measurements. We illustrate this idea on real and simulated data.</p></div

Large-Signal Stabilization Method for Islanded DC Microgrids Considering Battery and Supercapacitor Hybrid Energy Storage Systems

Islanded DC microgrids composed of distributed generators (DGs), constant power loads (CPLs), parallel converters, batteries and supercapacitors (SCs) are typical nonlinear systems, and guaranteeing large-signal stability is a key issue. In this paper, the nonlinear model of a DC microgrid with a hybrid energy storage system (HESS) is established, and large-signal stability criteria are obtained. The HESS consists of batteries and SCs. The derived criteria reveal the influences of the filter parameters, CPL power, DG power and the proportional control parameters of the battery converter and the SC converter on the system large-signal stability. Furthermore, important large-signal stabilization methods for regulating the HESS converter’s control parameters can easily achieve the large-signal stabilization of islanded DC microgrids without extra equipment. The paper is summarized as follows: First, the topology of and control strategy for a DC microgrid with an HESS and CPLs are proposed. Then, according to the characteristics of the HESS, the DGs and the CPLs, the system is equivalently simplified. Finally, the nonlinear model and large-signal stability criteria are both derived using the mixed potential theory, and a large-signal stabilization design method for the HESS converter’s control parameters is proposed. The experimental and simulation results show the effectiveness of the proposed large-signal stabilization method

Large-Signal Stabilization Method for Islanded DC Microgrids Considering Battery and Supercapacitor Hybrid Energy Storage Systems

Islanded DC microgrids composed of distributed generators (DGs), constant power loads (CPLs), parallel converters, batteries and supercapacitors (SCs) are typical nonlinear systems, and guaranteeing large-signal stability is a key issue. In this paper, the nonlinear model of a DC microgrid with a hybrid energy storage system (HESS) is established, and large-signal stability criteria are obtained. The HESS consists of batteries and SCs. The derived criteria reveal the influences of the filter parameters, CPL power, DG power and the proportional control parameters of the battery converter and the SC converter on the system large-signal stability. Furthermore, important large-signal stabilization methods for regulating the HESS converter&rsquo;s control parameters can easily achieve the large-signal stabilization of islanded DC microgrids without extra equipment. The paper is summarized as follows: First, the topology of and control strategy for a DC microgrid with an HESS and CPLs are proposed. Then, according to the characteristics of the HESS, the DGs and the CPLs, the system is equivalently simplified. Finally, the nonlinear model and large-signal stability criteria are both derived using the mixed potential theory, and a large-signal stabilization design method for the HESS converter&rsquo;s control parameters is proposed. The experimental and simulation results show the effectiveness of the proposed large-signal stabilization method