Aldo José Rubiales

Stabilization of the Generalized Benders Decomposition applied to Short-Term Hydrothermal Coordination Problem

Solving the Short Term Hydrothermal Coordination (STHTC) Problem considers the resolution of both the Unit Commitment (UC) and the Economic Dispatch (ED) for thermal and hydraulic units. In order to avoid post-dispatch corrections, the transmission network is modeled with a high level of detail considering an AC power flow. These facts lead to a very complex optimization problem which is solved by using a novel decomposition approach which combines Generalized Benders Decomposition (GBD) with Bundle methods presented in [LS97]. This proposed method resembles a stabilized version of the cutting planes method, which drastically reduces the well-known tailing-off effect that Benders methods have. The approach presented in this work allows the decomposition of the whole problem in a quadratic mixed integer master problem, and in a non-linear subproblem which should be separable. The former defines the state and the active power dispatched by each unit whereas the latter determines the reactive power to meet the electrical constraints through a modified AC optimal power flow (OPF). These approaches were applied to a 9-bus test case, and to the IEEE 24-bus test case. The problem is solved for a time horizon of a day with a one-hour step.

A Shadow Removal Approach for a Background Subtraction Algorithm

This paper presents preliminary results of an algorithm for shadow detection and removal in video sequences. The proposal is that from the base of the background subtraction with the Visual Background Extraction (ViBE), which identifies areas of movement, to apply a post processing to separate pixels from the real object and those of the shadow. As the areas of shadows have similar characteristics to those of the objects in movement, the separation becomes a difficult task. Consequently, the algorithms used for this classification may produce several false positives. To solve this problem, we set to use information of the object involved such as the size and movement direction, to estimate the most likely position of the shadow. Furthermore, the analysis of similarity between the present frame and the background model are realized, by means of the traditional indicator of normalized cross correlation to detect shadows. The algorithm may be used to detect both people and vehicles in applications for safety of cities, traffic monitoring, sports analysis, among others. The results obtained in the detection of objects show that it is highly likely to separate the shadow in a high percentage of effectiveness and low computational cost; allowing improving steps of further processing, such as object recognition and tracking.