Generator interconnection procedures at the Midwest ISO

The Midwestern US is a wind-rich resource and wind power is being developed in this region at a very brisk pace. Transporting this energy resource to load centers invariably requires massive transmission lines. This issue of developing additional transmission to support reliable integration of wind on to the power grid provides a multitude of interesting challenges spanning various areas of power systems such as transmission planning, real-time operations and cost-allocation for new transmission. The Midwest ISO as a regional transmission provider is responsible for processing requests to interconnect proposed generation on to the transmission grid under its purview. This paper provides information about some of the issues faced in performing interconnection planning studies and Midwest ISO's efforts to improve its generator interconnection procedures. Related cost-allocation efforts currently ongoing at the Midwest ISO to streamline integration of bulk quantities of wind power in to the transmission grid are also presented.

Security constrained economic dispatch considering wind energy conversion systems

The available wind power is stochastic and requires appropriate tools in the OPF model for economic and reliable power system operation. This paper exhibit the OPF formulation with factors involved in the intermittency of wind power. Weibull distribution is adopted to find the stochastic wind speed and power distribution. The reserve requirement is evaluated based on the wind distribution and risk of under/over estimation of the wind power. In addition, the Wind Energy Conversion System (WECS) is represented by Doubly Fed Induction Generator (DFIG) based wind farms. The reactive power capability for DFIG based wind farm is also analyzed. The study is performed on IEEE-30 bus system with wind farm located at different buses and with different wind profiles. Also the reactive power capacity to be installed in the wind farm to maintain a satisfactory voltage profile under the various wind flow scenario is demonstrated.

Intended and actual avoidance of road use situations by older drivers and pedestrians, by gender and age

• The Queensland context • Rationale and aims • Method • Demographics and basic data • Avoidance of driving and walking situations • Success of intended avoidance • Further analyses (preliminary results) • Implications

On robust face recognition via sparse encoding : the good, the bad, and the ugly

In the field of face recognition, Sparse Representation (SR) has received considerable attention during the past few years. Most of the relevant literature focuses on holistic descriptors in closed-set identification applications. The underlying assumption in SR-based methods is that each class in the gallery has sufficient samples and the query lies on the subspace spanned by the gallery of the same class. Unfortunately, such assumption is easily violated in the more challenging face verification scenario, where an algorithm is required to determine if two faces (where one or both have not been seen before) belong to the same person. In this paper, we first discuss why previous attempts with SR might not be applicable to verification problems. We then propose an alternative approach to face verification via SR. Specifically, we propose to use explicit SR encoding on local image patches rather than the entire face. The obtained sparse signals are pooled via averaging to form multiple region descriptors, which are then concatenated to form an overall face descriptor. Due to the deliberate loss spatial relations within each region (caused by averaging), the resulting descriptor is robust to misalignment & various image deformations. Within the proposed framework, we evaluate several SR encoding techniques: l1-minimisation, Sparse Autoencoder Neural Network (SANN), and an implicit probabilistic technique based on Gaussian Mixture Models. Thorough experiments on AR, FERET, exYaleB, BANCA and ChokePoint datasets show that the proposed local SR approach obtains considerably better and more robust performance than several previous state-of-the-art holistic SR methods, in both verification and closed-set identification problems. The experiments also show that l1-minimisation based encoding has a considerably higher computational than the other techniques, but leads to higher recognition rates.