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About

Vladimiro Miranda, PhD, is presently Full Professor at the University of Porto, Portugal, and also one of the Directors of INESC Porto since 2000, where previously and for a number of years he had been the coordinator of the Power Systems area. He has been also Full Professor in the University of Macau RAEM (China) and President of INESC Macau, a Portuguese-Chinese joint institution. He is an IEEE Fellow (the highest grade of this prestigious worldwide institution from the USA) and author of over 200 publications, among which one should highlight the papers on the application of computational intelligence to power systems, namely fuzzy sets, neural networks and evolutionary algorithms. Several computer applications running models inspired in his contributions are in use in a diversity of utilities in the world. He was responsible for the participation of INESC Porto in a number of European Union projects e several of its research programmes, as well as for projects with the industry and utilities, especially in Portugal and Brazil. He has and is serving in the Administration board of spin-off companies generated by INESC and has a dense personal experience in the management of science and technology in interface institutions. He has supervised, co-supervised or cooperated in the supervision of a large number of PhD and MSc theses e power systems in several countries and universities such as in Portugal, Brazil, Argentina, Bosnia, China, Ecuador, Norway or Sweden. He has served as evaluator of research projects in power systems in Portugal, Argentina, Croatia, Norway and South Africa for the governmental authorities supervising the local university systems. He has been a member (at times the chairman) of the organizing or scientific committees of several important conferences in his areas of expertise such as PMAPS, ISAP, IEEE Power Tech, etc. A reference to many of his papers may be found in http://www.inescporto.pt, in the publications section.

Interest
Topics
Details

Details

005
Publications

2017

Mitigation in the Very Short-term of Risk from Wind Ramps with Unforeseen Severity

Authors
Pinto, M; Miranda, V; Saavedra, O; Carvalho, L; Sumaili, J;

Publication
JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS

Abstract
This paper addresses a critical analysis of the impact of the wind ramp events with unforeseen magnitude in power systems at the very short term, modeling the response of the operational reserve against this type of phenomenon. A multi-objective approach is adopted, and the properties of the Pareto-optimal fronts are analyzed in cost versus risk, represented by a worst scenario of load curtailment. To complete this critical analysis, a study about the usage of the reserve in the event of wind power ramps is performed. A case study is used to compare the numerical results of the models based on stochastic programming and models that take a risk analysis view in the system with high level of wind power. Wind power uncertainty is represented by scenarios qualified by probabilities. The results show that the reliability reserve may not be adequate to accommodate unforeseen wind ramps and therefore the system may be at risk.

2017

Successful Large-scale Renewables Integration in Portugal: Technology and Intelligent Tools

Authors
Miranda, V; University of Porto,;

Publication
CSEE JOURNAL OF POWER AND ENERGY SYSTEMS

Abstract
Portugal is seen worldwide as a case of success in the large-scale integration of renewables in its power system, especially for wind power. Consistent policies and sound management decisions are fundamental, but a sustainable process is not possible without the development of endogenous knowledge. This paper summarizes a set of models, both applied by the industry and representing actual technologic advancement, denoting the context of research and innovation in the country that helps to explain such success. Novelties arise in reliability assessment for systems with renewables, active and reactive power control, integration of wind farms, storage, electric vehicle integration, wind and solar power forecasting and distribution operation and state estimation taking advantage of smart grid structures. In all cases, one relevant trait is evident: the pervasive use of computational intelligence tools.

2017

Mean shift densification of scarce data sets in short-term electric power load forecasting for special days

Authors
Rego, L; Sumaili, J; Miranda, V; Frances, C; Silva, M; Santana, A;

Publication
ELECTRICAL ENGINEERING

Abstract
Short-term load forecasting plays an important role to the operation of electric systems, as a key parameter for planning maintenances and to support the decision making process on the purchase and sale of electric power. A particular case in this respect is the consumption forecasting on special days, which can be a complex task as it presents unusual load behavior, when compared to regular working days. Moreover, its reduced number of samples makes it hard to properly train and validate more complex and nonlinear prediction algorithms. This paper tackles this problem by proposing a new approach to improve the accuracy of the predictions amidst existing special days, employing an Information Theoretic Learning Mean Shift algorithm for pattern discovery, classifying and densifying the available scarce consumption data. The paper describes how this methodology was applied to an electrical load forecasting problem in the northern region of Brazil, improving the previously obtained accuracy held by the power company.

2017

Merging conventional and phasor measurements in state estimation: A multi-criteria perspective

Authors
Tavares, B; Freitas, V; Miranda, V; Costa, AS;

Publication
2017 19th International Conference on Intelligent System Application to Power Systems (ISAP)

Abstract

2017

Robust State Estimation Based on Orthogonal Methods and Maximum Correntropy Criterion

Authors
Freitas, V; Coasta, AS; Miranda, V;

Publication
2017 IEEE MANCHESTER POWERTECH

Abstract
This paper presents an orthogonal implementation for power system state estimators based on the Maximum Correntropy Criterion (MCC). The proposed approach leads to a numerically robust estimator which exhibits self -healing properties, in the sense that gross errors in analog measurements are automatically rejected. As a consequence, robust estimates are produced without the need of running the state estimator again after bad data identification and removal. Numerical robustness is achieved by means of a specialized orthogonal algorithm based on fast Givens Rotations, which is able to handle the dynamic measurement weighting mechanism implied by the Parzen window concept associated to MCC. Results for a 3 -bus test system are presented to properly illustrate the Correntropy principles, and several case studies conducted on the IEEE 30 -bus and 57 -bus benchmark systems are used to validate the proposed methodology.

Supervised
thesis

2017

Synergies between Electric Vehicles and Dispersed Renewable generation in a GIS Environment under Information Theory Criteria

Author
Fabian Heymann

Institution
UP-FEUP

2016

Synergies between Electric Vehicles and Dispersed Renewable generation in a GIS Environment under Information Theory Criteria

Author
Fabian Heymann

Institution
UP-FEUP

2016

Sensory fusion applied to power system state estimation considering information theory concepts

Author
Bruna Daniela Costa Tavares

Institution
UP-FEUP

2016

Microgrid Reliability Assessment

Author
José Miguel Gomes Campos Costa

Institution
UP-FEUP

2016

CROSS Monte Carlo Simulation and Cross-Entropy

Author
João Meneses Figueiredo Costa

Institution
UP-FEUP