Calculation of parameter ranges for robust gain tuning of power system controllers

This work proposes a computational tool to assist power system engineers in the field tuning of power system stabilizers (PSSs) and Automatic Voltage Regulators (AVRs). The outcome of this tool is a range of gain values for theses controllers within which there is a theoretical guarantee of stability for the closed-loop system. This range is given as a set of limit values for the static gains of the controllers of interest, in such a way that the engineer responsible for the field tuning of PSSs and/or AVRs can be confident with respect to system stability when adjusting the corresponding static gains within this range. This feature of the proposed tool is highly desirable from a practical viewpoint, since the PSS and AVR commissioning stage always involve some readjustment of the controller gains to account for the differences between the nominal model and the actual behavior of the system. By capturing these differences as uncertainties in the model, this computational tool is able to guarantee stability for the whole uncertain model using an approach based on linear matrix inequalities. It is also important to remark that the tool proposed in this paper can also be applied to other types of parameters of either PSSs or Power Oscillation Dampers, as well as other types of controllers (such as speed governors, for example). To show its effectiveness, applications of the proposed tool to two benchmarks for small signal stability studies are presented at the end of this paper.

Coassimetria, cocurtose e as taxas de retorno das ações: uma análise com dados em painel

Modelos de apreçamento de ativos têm sido um tema sob constante investigação em finanças. Desde o capital asset pricing model (CAPM) proposto por Sharpe (1964), tais modelos relacionam, geralmente de maneira linear, a taxa de retorno esperada de um ativo ou carteira de ativos com fatores de risco sistêmico. Esta pesquisa apresenta um teste de um modelo de apreçamento, com dados brasileiros, introduzindo em sua formulação fatores de risco baseados em comomentos estatísticos. O modelo proposto é uma extensão do CAPM original acrescido da coassimetria e da cocurtose entre as taxas de retorno das ações das empresas que compõem a amostra e as taxas de retorno da carteira de mercado. Os efeitos de outras variáveis, como o valor de mercado sobre valor contábil, a alavancagem financeira e um índice de negociabilidade em bolsa, serviram de variáveis de controle. A amostra foi composta de 179 empresas brasileiras não financeiras negociadas na BM&FBovespa e com dados disponíveis entre os anos de 2003 a 2007. A metodologia consistiu em calcular os momentos sistêmicos anuais a partir de taxas de retornos semanais e em seguida testá-los em um modelo de apreçamento, a fim de verificar se há um prêmio pelo risco associado a cada uma dessas medidas de risco. Foi empregada a técnica de análise de dados em painel, estimada pelo método dos momentos generalizado (GMM). O emprego do GMM visa lidar com potenciais problemas de determinação simultânea e endogeneidade nos dados, evitando a ocorrência de viés nas estimações. Os resultados das estimações mostram que a relação das taxas de retorno dos ativos com a covariância e a cocurtose são estatisticamente significantes. Os resultados se mostraram robustos a especificações alternativas do modelo. O artigo contribui para a literatura por apresentar evidências empíricas brasileiras de que há um prêmio pelo risco associado aos momentos sistêmicos.

Subcutaneous Tissue Thickness is an Independent Predictor of Image Noise in Cardiac CT

Background: Few data on the definition of simple robust parameters to predict image noise in cardiac computed tomography (CT) exist. Objectives: To evaluate the value of a simple measure of subcutaneous tissue as a predictor of image noise in cardiac CT. Methods: 86 patients underwent prospective ECG-gated coronary computed tomographic angiography (CTA) and coronary calcium scoring (CAC) with 120 kV and 150 mA. The image quality was objectively measured by the image noise in the aorta in the cardiac CTA, and low noise was defined as noise < 30HU. The chest anteroposterior diameter and lateral width, the image noise in the aorta and the skin-sternum (SS) thickness were measured as predictors of cardiac CTA noise. The association of the predictors and image noise was performed by using Pearson correlation. Results: The mean radiation dose was 3.5 ± 1.5 mSv. The mean image noise in CT was 36.3 ± 8.5 HU, and the mean image noise in non-contrast scan was 17.7 ± 4.4 HU. All predictors were independently associated with cardiac CTA noise. The best predictors were SS thickness, with a correlation of 0.70 (p < 0.001), and noise in the non-contrast images, with a correlation of 0.73 (p < 0.001). When evaluating the ability to predict low image noise, the areas under the ROC curve for the non-contrast noise and for the SS thickness were 0.837 and 0.864, respectively. Conclusion: Both SS thickness and CAC noise are simple accurate predictors of cardiac CTA image noise. Those parameters can be incorporated in standard CT protocols to adequately adjust radiation exposure.