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Publications

Publications by Vladimiro Miranda

2005

GIS spatial analysis applied to electric line routing optimization

Authors
Monteiro, C; Ramirez Rosado, IJ; Miranda, V; Zorzano Santamaria, PJ; Garcia Garrido, E; Fernandez Jimenez, LA;

Publication
IEEE TRANSACTIONS ON POWER DELIVERY

Abstract
This paper presents a new methodology for auto- mated route selection for the construction of new power lines, based on geographic information systems (GIS). It uses a dynamic programming model for route optimization. Environmental restrictions are taken into account together with all of the operating, maintenance, and equipment installation costs, including a new approach to the costs associated with the slope of the terrain crossed by the power lines. The computing and visual representation capacities of GIS are exploited for the selection of economic corridors, keeping the total costs under a threshold imposed by the user. Intensive simulation examples illustrate the power and flexibility of the proposed methodology.

2008

Error entropy and mean square error minimization algorithms for neural identification of supercritical extraction process

Authors
Soares, RPDO; Castro, ARG; De Oliveira, RCL; Miranda, V;

Publication
Proceedings - 10th Brazilian Symposium on Neural Networks, SBRN 2008

Abstract
In this paper, Artificial Neural Networks (ANN) are used to model an extraction process that uses a supercritical fluid as solvent which its pilot installation is located at the Institute of Experimental and Technological Biology - IBET in Oeiras - Lisbon - Portugal. A strategy is used to complement the experimental data collected in laboratory during extraction procedures of useful compositions for the pharmaceutical industry using Black Agglomerate Residues (BAR) originating from of the cork production as raw material. The strategy involves fitting of data obtained during an operation of extraction. Two neural models are presented: the neural model trained using a Mean Square Error (MSE) minimization algorithm and the neural model which the learning was based on the error entropy minimization. A comparison of the performance of the two models is presented. © 2008 IEEE.

2011

On the use of information theoretic mean shift for electricity load patterns clustering

Authors
Sumaili, J; Keko, H; Miranda, V; Chicco, G;

Publication
2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011

Abstract
This paper analyzes the application of the Information Theoretic (IT) Mean Shift algorithm for modes finding in order to provide the classification of Electricity Customer Load Patterns. The impact of the algorithm parameters is discussed and then clustering indices are used in order to make a comparison with the classical methods available. Results show a good capability of the modes found in capturing the data structure, aggregating similar load patterns and identifying the uncommon patterns (outliers). © 2011 IEEE.

2011

Reliability impact on bulk generation system considering high penetration of electric vehicles

Authors
Da Rosa, MA; Heleno, M; Miranda, V; Matos, M; Ferreira, R;

Publication
2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011

Abstract
This paper presents a generation adequacy evaluation based on analytical calculation, considering electric vehicles. The scenarios used are exploring electric vehicles penetrations in six different European countries, in order to assess their impact on the security of supply. An analytical method is developed to perform this evaluation. Afterwards, a discussion about the accuracy of this methodology is done and the differences between this approach and a flexible Sequential Monte Carlo Simulation are identified © 2011 IEEE.

2009

Evolutionary Algorithm EPSO Helping Doubly-Fed Induction Generators in Ride-Through-Fault

Authors
Leite, H; Barros, J; Miranda, V;

Publication
2009 IEEE BUCHAREST POWERTECH, VOLS 1-5

Abstract
A tuning process of the PI (Proportional-Integral) controller gains of a doubly-fed induction generator's (DFIG) rotor side converter is described in this work. The purpose is to tune PI controllers to help the DFIG to survive to network faults, avoiding being tripped-off by under-voltage relays. The ride-through-fault capability of DFIGs improves system reliability and allows them to participate in the control and stabilization of a power system following system disturbances. The robust tuning of the DFIG rotor side converter's PI controllers may avoid undesired disconnections from the grid by, for instance, preventing over-currents in its variable frequency AC/DC/AC converter. This work presents an Evolutionary Particle Swarm Optimization-based (EPSO) approach to this tuning, with the aim of helping to limit the line-to-line voltage dip at the DFIG's terminals after a short-circuit, in order to avoid its tripping-off. The EPSO-based algorithm developed is validated at a typical Portuguese 15 kV Distribution Network with the integration of a DFIG, using the transient electromagnetic software package PSCAD/EMTDC (TM).

2001

Spatial decision support system for site permitting of distributed generation facilities

Authors
Monteiro, C; Miranda, V; Ramirez Rosado, IJ; Morais, C; Garcia Garrido, E; Mendoza Villena, M; Fernandez Jimenez, LA; Martinez Fernandez, A;

Publication
2001 IEEE Porto Power Tech Proceedings

Abstract
Distributed Generation (DG) facilities require, like other energy projects, a sitting review process to acquire the permits and approval needs for construction and operation. In this process different groups and individuals with different roles, interests and priorities are involved. This paper presents a Spatial Decision Support System (SDSS) that helps to identify permissible areas to install DG facilities. Wind energy facilities are used in this paper to exemplify the use of the SDSS. © 2001 IEEE.

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