The state estimation and the analysis of load flow are very important subjects in the analysis and management of Electrical Power Systems (EPS). This article describes the state estimation in EPS using the Extended Kalman Filter (EKF) and the method of Holt to linearize the process model and then calculates a performance error index as indicators of its accuracy. Besides, this error index can be used as a reference for further comparison between methodologies for state estimation in EPS such as the Unscented Kalman Filter, the Ensemble Kalman Filter, Monte Carlo methods, and others. Results of error indices obtained in the simulation process agree with the order of magnitude expected and the behavior of the filter is appropriate due to follows adequately  the true value of the state variables. The simulation was done using Matlab and the electrical system used corresponds to the IEEE 14 and 30 bus test case systems. State Variables to consider in this study are the voltage and angle magnitudes.

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In this article, a referential study of the sequential importance sampling particle filter with a systematic resampling and the ensemble Kalman filter is provided to estimate the dynamic states of several synchronous machines connected to a modified 14-bus test case, when a balanced three-phase fault is applied at a bus bar near one of the generators. Both are supported by Monte Carlo simulations with practical noise and model uncertainty considerations. Such simulations were carried out in MATLAB by the Power System Toolbox, whereas the evaluation of the Particle Filter and the Ensemble Kalman Filter by script files developed inside the toolbox. The results obtained show that the particle filter has higher accuracy and more robustness to measurement and model noise than the ensemble Kalman filter, which helps support the feasibility of the method for dynamic state estimation applications.

ResumenSe evalúa el impacto de los dominios de la personalidad y la experiencia de trabajo en el estilo de liderazgo transformacional, entregando nuevas evidencias de la relación causal entre ambos constructos. La investigación tuvo un enfoque cuantitativo con un diseño de investigación no experimental, de corte transversal y de alcance correlacional-causal. Se realizó una encuesta a 368 profesionales que cursan programas de posgrado en Administración de Empresas en el Ecuador. El instrumento denominado Inventario Revisado de Personalidad se utilizó para medir los cinco dominios de la personalidad: (a) extraversión, (b) amabilidad, (c) escrupulosidad, (d) neuroticismo, y (e) apertura a la experiencia. Por otra parte, el Cuestionario Multifactorial de Liderazgo (MLQ) fue el instrumento utilizado para medir el estilo de liderazgo transformacional. Para el análisis de datos se utilizó análisis de correlaciones y un modelo de regresión multivariado. Los resultados muestran que los dominios extraversión y escrupulosidad fueron los más importantes en la proyección del estilo de liderazgo transformacional. AbstractThe impact of personality domains and work experience in the transformational leadership style, providing new evidence of the causal relationship between both constructs is evaluated. The research had a quantitative approach with a no-experimental design, cross-sectional and correlational-causal scope. A survey was applied to 368 professionals of several Master of Business Administration programs in Ecuador. The instrument called Revised Personality Inventory was used to measure the five domains of personality: (a) extraversion, (b) agreeableness, (c) conscientiousness, (d) neuroticism, and (e) openness to experience. On the other hand, the Multifactor Leadership Questionnaire (MLQ) was the instrument used to measure the transformational leadership style. To analyze the data correlation analysis and a multivariate regression model were used. The results highlight that extraversion and conscientiousness domains proved to be the most important in the projection of transformational leadership style.