Adequacy Assessment in Power Systems Using Genetic Algorithm and Dynamic Programming

In power system reliability analysis, state space pruning has been investigated to improve the efficiency of the conventional Monte Carlo Simulation (MCS). New algorithms have been proposed to prune the state space so as to make the Monte Carlo Simulation sample a residual state space with a higher density of failure states. This thesis presents a modified Genetic Algorithm (GA) as the state space pruning tool, with higher efficiency and a controllable stopping criterion as well as better parameter selection. This method is tested using the IEEE Reliability Test System (RTS 79 and MRTS), and is compared with the original GA-MCS method. The modified GA shows better efficiency than the previous methods, and it is easier to have its parameters selected. This thesis also presents a Dynamic Programming (DP) algorithm as an alternative state space pruning tool. This method is also tested with the IEEE Reliability Test System and it shows much better efficiency than using Monte Carlo Simulation alone

Nobel-Prize-winning papers are significantly more highly-cited but not more disruptive than non-prize-winning counterparts

Using citation data of 557 Nobel prize winning papers and the same number of their non-prize winning counterparts in the same journal issues, we examined if the prize-winning papers have higher academic disruption than their counterparts. The results show that overall, the former group is significantly more highly-cited but not more disruptive than the latter. Moreover, the results are not consistent with existing knowledge that the numbers of authors and references negatively correlate with the disruption of papers