Parallel operation of distributed generation is an important topic for microgrids, which can provide a highly reliable electric supply service and good power quality to end customers when the utility is unavailable. However, there is a well-known limitation: the power sharing accuracy between distributed generators in a parallel operation. Frequency and voltage droop is a well-established control method for improving power sharing performance. In this method, the active and reactive power calculations are used to adjust the frequency and amplitude of the output voltage. This paper describes the digital implementation of a droop method, and analyzes the influence of power calculation on droop method performance. According to the analysis, the performance of droop control in a digital control system is limited by the accuracy and speed of the power calculation method. We propose an improved power calculation method based on p-q theory to improve the performance of the droop control method, and we compare our new method with two traditional power calculation methods. Finally, simulation results and experimental results from a three single-phase 1-kW-inverter system are presented, which validate the performance of our proposed method.

Power consumption has become a major concern since the integrated circuit industry entered the nanometer design regime. Due to the increasing process variation, deterministic leakage power analysis becomes inadequate and thus statistical analysis is required. The challenges of statistical leakage analysis are that the huge number of random variables make trivial computation of the variance inO(N2) time impractical for realistic designs and that knowing only the first two moments is not sufficient to obtain the distribution of the full-chip leakage. In this paper, we introduce efficient linear time algorithms for statistical leakage analysis. To enable those algorithms, a fast matrix vector product technique is crucial, being applied not only to compute the second moment of the total leakage, but also, combined with a comonotonic approximation, to estimate the distribution function of the total leakage power. The computational complexity of the proposed algorithms is provablyO(N), and the experimental result is presented with detailed discussion, indicating promising improvement in terms of accuracy.

Random process variations are often composed of location dependent part and distance dependent correlated part. While an accurate extraction of process variation is a prerequisite of both process improvement and circuit performance prediction, it is not an easy task to characterize such complicated spatial random process from a limited number of silicon data. For this purpose, kriging model was introduced to silicon society. This work forms a modified kriging model with L1-norm penalty which offers improved robustness. With the help of Least Angle Regression (LAR) in solving a core optimization sub-problem, this model can be characterized efficiently. Some promising results are presented with numerical experiments where a 3X improvement in model accuracy is shown.

Passivity enforcement is an important issue for macro-modeling for passive systems from measured or simulated data. Existing convex programming based methods are too expensive and thus are ruled out for realistic application. Other methods based on iteratively fixing the passivity through perturbing the eigenvalues of the Hamiltonian matrix either suffer from convergence issue or lack optimality which will sometimes lead to unacceptable error. In this paper we propose a novel framework for macro-modeling. In addition to the traditional two-stage (fixing plus enforcement) schemes, we propose a post-enforcement optimization, which takes a passive, while potentially not-so-accurate model, as the starting point, and performs local search to find the local optimum with passivity constraint or build-in passivity guarantee. A simple yet stable passive modeling generator is proposed to produce the starting model for optimization. Two algorithms are proposed for performing constrained and unconstrained optimizations. Experiments show that the accuracy of passivity-fixed model can be significantly improved with the proposed methods.

Existing approaches to statistical leakage analysis focus only on calculating the mean and variance of the total leakage. In practice, however, what concerns most is the tail behavior of the sum distribution, as it tells that to what extent the design will be safe or reliable. However, computing the tail distribution is much more difficult than computing the mean and variance. In this paper, we tackle this problem by making use of the recent developments in the area of financial and insurance analysis, as well as the fast evaluation algorithm for the variance of spatially correlated random sums. The proposed algorithm is provably of O(N) complexity. Experiments show that the algorithm provides 1% accuracy in modeling the tail behavior and it is 10X more accurate compared with existing methods that approximate the distribution by matching the moments of a lognormal distribution.