Bulk system reliability evaluation in a deregulated power industry

The basic function of an electric power system is to supply its customers with electric energy as economically as possible and with a reasonable degree of continuity and quality. Power system reliability evaluation techniques are now highly developed through the work of many researchers and engineers. It is expected that the application of power system reliability evaluation in bulk power systems will continue to increase in the future especially in the newly deregulated power industry. This thesis presents research conducted on the three areas of incorporating multi-state generating unit models, evaluating system performance indices and identifying transmission deficiencies in composite system adequacy assessment. The research was done using a previously developed software package designated as MECORE. Many generating companies in both the traditionally regulated and newly deregulated electrical power industry have large generating units that can operate in one or more derated states. In this research work, load point and system reliability indices are evaluated using two-state and multi-state generating unit models to examine the impact of incorporating multi-state generating unit models in composite system adequacy assessment. The intention behind deregulation in the power industry is to increase competition in order to obtain better service quality and lower production costs. This research illustrates how Canadian power systems have performed in the past using data compiled by the Canadian Electricity Association. A procedure to predict similar indices is presented and used to estimate future performance and the effects of system modifications. The incentives for market participants to invest in new generation and transmission facilities are highly influenced by the market risk in a deregulation environment. An adequate transmission system is a key element in a dynamic competitive market. This thesis presents a procedure to identify transmission deficiencies in composite generation and transmission system. The research work illustrated in this thesis is focused on the application of probabilistic techniques in composite system adequacy assessment and particularly in the newly deregulated electric power industry. The conclusions and the techniques presented should prove valuable to those responsible for power system planning

How to Train Your Dragon: Tamed Warping Network for Semantic Video Segmentation

Real-time semantic segmentation on high-resolution videos is challenging due to the strict requirements of speed. Recent approaches have utilized the inter-frame continuity to reduce redundant computation by warping the feature maps across adjacent frames, greatly speeding up the inference phase. However, their accuracy drops significantly owing to the imprecise motion estimation and error accumulation. In this paper, we propose to introduce a simple and effective correction stage right after the warping stage to form a framework named Tamed Warping Network (TWNet), aiming to improve the accuracy and robustness of warping-based models. The experimental results on the Cityscapes dataset show that with the correction, the accuracy (mIoU) significantly increases from 67.3% to 71.6%, and the speed edges down from 65.5 FPS to 61.8 FPS. For non-rigid categories such as "human" and "object", the improvements of IoU are even higher than 18 percentage points

Quasi-Optimality of an Adaptive Finite Element Method for Cathodic Protection

In this work, we derive a reliable and efficient residual-typed error estimator for the finite element approximation of a 2d cathodic protection problem governed by a steady-state diffusion equation with a nonlinear boundary condition. We propose a standard adaptive finite element method involving the D\"{o}rfler marking and a minimal refinement without the interior node property. Furthermore, we establish the contraction property of this adaptive algorithm in terms of the sum of the energy error and the scaled estimator. This essentially allows for a quasi-optimal convergence rate in terms of the number of elements over the underlying triangulation. Numerical experiments are provided to confirm this quasi-optimality

Verification of Infection Prevention Control Using a Spatial Random Walk Model

To stop pandemic of the 2019 novel coronavirus (COVID-19), "an 80 percent reduction of person to person contact opportunities" was proposed by the Japanese government. This guideline was based on the result of macroscopic differential equation model akin to the SIR (Susceptible-Infected-Recovered) model. For the purpose of indicating person to person’s infection mechanism intuitively, we built a new model to calculate infections between two persons who are in contact each other. This model adopted a spatial random walk model to express random movement of people in a specific 2-D geographical space. This model was applied to verify the effect of the proposed infection control procedure, "80 percent reduction". The result of the numerical simulation supported a proposed infection control procedure of "an 80 percent reduction" derived by the SIR model