Percorrer por autor "Pestana, Rui"
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- Particle swarm optimization-based algorithm for optimal reactive power dispatchPublication . Menezes, R.; Fonte, Pedro M; Pestana, RuiOptimal Reactive Power Dispatch (ORPD) is very important for the security and economy of power systems. It is a mixed integer nonlinear optimization problem for which metaheuristic methods have proven to be effective in its solution. This paper presents an implementation of the particle swarm optimization algorithm (PSO) for the solution of the ORPD. Another approach, called Fitness-Distance Ratio PSO (FDR-PSO), is implemented to improve the results of the basic PSO. One other version called Second Order PSO (SO-PSO) is implemented for the same purpose. This second-order principle was combined with the FDR-PSO, making it the SO-FDR-PSO. These versions were tested on the IEEE 14 bus and IEEE 39 bus systems. The results show that the tested PSO algorithm and its improved versions can converge to good global optimal solutions (the best optimal solution the PSO could find), effectively solving the ORPD, with increasing degrees of success.
- Wind power forecasting with machine learning: single and combined methodsPublication . Rosa, J.; Pestana, Rui; Leandro, Carlos; Brás-Geraldes, Carlos; Esteves, João; Carvalho, D.In Portugal, wind power represents one of the largest renewable sources of energy in the national energy mix. The investment in wind power started several decades ago and is still on the roadmap of political and industrial players. One example is that by 2030 it is estimated that wind power is going to represent up to 35% of renewable energy production in Portugal. With the growth of the installed wind capacity, the development of methods to forecast the amount of energy generated becomes increasingly necessary. Historically, Numerical Weather Prediction (NWP) models were used. However, forecasting accuracy depends on many variables such as on-site conditions, surrounding terrain relief, local meteorology, etc. Thus, it becomes a challenge to obtain improved results using such methods. This article aims to report the development of a machine learning pipeline with the objective of improving the forecasting capability of the NWP’s to obtain an error lower than 10%.