SIMULATION

We develop four simulation-based algorithms for finite-horizon Markov decision processes. Two of these algorithms are developed for finite state and compact action spaces while the other two are for finite state and finite action spaces. Of the former two, one algorithm uses a linear parameterization for the policy, resulting in reduced memory complexity. Convergence analysis is briefly sketched and illustrative numerical experiments with the four algorithms are shown for a problem of flow control in communication networks.

In modern interconnected power systems, almost 70—90% of faults in high voltage Power Transmission Lines (PTLs) are intrinsically transient. The necessity of rapid fault clearing results in fast developing of protection equipments. Moreover, need for reliable supplying of loads, lead to improvements in single-phase autoreclosure (SPAR) equipments. An ADAptive LInear NEuron (ADALINE) is suitable for important applications such as protection of power systems and digital relays. In this paper, a novel simple adaptive SPAR algorithm is introduced. This algorithm is based on learning error function of an ADALINE. It can be distinguished by fault type (transient fault or a permanent fault), and if the fault is permanent, autoreclosure should be blocked. This leads to improve the performance and efficiency of SPAR. Electromagnetic transients program-based simulation results show that the autoreclosure scheme based on learning error function of ADALINE on a typical 400 kV circuit for various system and fault conditions improves the reliability of fault discrimination.

The very simple model known as Stupid Model was proposed in 2005. Stupid Model could be implemented within a couple of hours, and was later extended to demonstrate the use of common ABM platform functionality. Implementations of the model in several agent-based modeling platforms were provided, and platforms were compared for ease of implementation and performance. In this paper, Stupid Model is implemented in the EcoLab simulation platform (a C++ based modeling platform), demonstrating that it is a feasible platform for these sorts of models. The performance of the implementation is compared with the Repast, Mason and Swarm versions.

This paper presents a novel analytical approach to calculate the saturation throughput of the Distributed Queuing Medium Access Control protocol for Ad Hoc Networks (DQMAN) in single-hop scenarios. DQMAN integrates a near-optimum MAC protocol into a passive clustering algorithm based on Carrier Sensing Multiple Access (CSMA). Therefore, based on models of both CSMA and centralized MAC protocols, a new model is presented to accurately calculate the saturation throughput of DQMAN in single-hop networks. In addition, the model presented can be extended to any MAC protocol based on the spontaneous clustering concept of DQMAN. The accuracy of the overall model has been assessed through link-level computer simulations. A performance evaluation of the protocol under different configurations is also presented together with a performance comparison with the IEEE 802.11 standard.