MCS algorithm

Abstract: This paper proposes a new approach to evaluate loss of load indices in composite generation and transmission systems. The main idea is to combine a Cross-Entropy (CE)-based optimization process and nonsequential Monte Carlo Simulation (MCS) to obtain an auxiliary sampling distribution, which can minimize the variance of the reliability index estimators. This auxiliary sampling distribution will properly modify the original unavailabilities of both generation and transmission equipment, so that important failure events are sampled more often. As a result, the MCS algorithm can reach convergence faster and with fewer samples, leading to significant gains in computational performance, especially when dealing with very reliable system configurations. The proposed method is tested using several composite power systems, including the IEEE RTS 79, IEEE RTS 96, and a configuration of the Brazilian system.

Abstract: Traditional shaking–table testing has been limited by the effectiveness of conventional fixed–gain algorithms used in their control. These algorithms are normally based on linear models of the shaking table and specimen, whose parameters are assumed to be fixed for the duration of the test. Although the influence of the specimen in the overall system dynamics can be partly removed by fine–tuning the linear controller, this process cannot deal with nonlinear effects and is limited in scope by the expertise of the operator. The minimal control synthesis (MCS) algorithm is a form of adaptive control, which was originally and successfully employed to cope with the nonlinear problems in the field of robotics. The MCS algorithm can tune the controller in real–time without any parametric knowledge of the system to be controlled. This paper describes how MCS has been incorporated within both analog and digital controllers for shaking tables and shows some of the results achieved on tables at the University of Bristol and at Athens Technical University. In both cases, the introduction of adaptive control has noticeably improved the performance of the shaking table, correcting errors by more than 5 dB in some experiments.

Abstract: In this paper we consider the concept of modelling dynamical systems using numerical–experimental substructuring. This type of modelling is applicable to large or complex systems, where some part of the system is difficult to model numerically. The substructured model is formed via the adaptive minimal control synthesis (MCS) algorithm. The aim of this paper is to demonstrate that substructuring can be carried out in real time, using the MCS algorithm. Thus, we reformulate the MCS algorithm into a substructuring form. We introduce the concepts of a transfer system, and carry out numerical simulations of the substructuring process using a coupled three mass example. These simulations are compared with direct simulations of a three mass system. In addition we consider the stability of the substructuring algorithm, which we discuss in detail for a class of second-order transfer systems. A numerical–experimental system is considered, using a small-scale experimental system, for which the substructuring algorithm is implemented in real time. Finally we discuss these results, with particular reference to the future application of this method to modelling large-scale structures subject to earthquake excitation. Copyright © 2001 John Wiley & Sons, Ltd.

Abstract: This paper shows various schemes to change Mod- ulation and Coding Schemes(MCS) adaptively for multicasting. Applying Adaptive Modulation and Coding(AMC) scheme to multicasting will show better performance in view of spectrum efficiency aspect than fixing MCS level. Since there exist mul- tiple receivers for a data in multicasting, there is much more difficulty in determining the overall MCS level which satisfies all the receivers. Based on the channel quality information of the receivers, the source can determine the overall MCS level by three schemes; minimum MCS algorithm, average MCS algorithm, and weighted average MCS algorithm. Here, a tradeoff between spectrum efficiency and robustness exists: minimum MCS algorithm chooses robustness that guarantees low error rate to all the receivers, while average MCS algorithm chooses spectrum efficiency to improve throughput performance. A compromise between spectrum efficiency and robustness can be made by introducing weights to averaging MCS. Depending on the parameters related to the weights, more robustness or spec- trum efficiency can be achieved. This paper presents numerical analysis showing the performance of the proposed schemes and a conventional scheme. Also simulation is presented to verify the numerical analysis results and shows the performance of weighted MCS algorithm.