Browsing by Author "Oliveira, P. B. Moura"
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- Automated design of microwave discrete tuning differential capacitance circuits in Si-integrated technologiesPublication . Mendes, Luís; Pires, E. J. Solteiro; Vaz, João C.; Rosário, Maria J.; Oliveira, P. B. Moura; Machado, J. A. TenreiroA genetic algorithm used to design radio-frequency binary-weighted differential switched capacitor arrays (RFDSCAs) is presented in this article. The algorithm provides a set of circuits all having the same maximum performance. This article also describes the design, implementation, and measurements results of a 0.25 lm BiCMOS 3-bit RFDSCA. The experimental results show that the circuit presents the expected performance up to 40 GHz. The similarity between the evolutionary solutions, circuit simulations, and measured results indicates that the genetic synthesis method is a very useful tool for designing optimum performance RFDSCAs.
- Automated synthesis procedure of RF discrete tuning differential capacitance circuitsPublication . Mendes, Luís; Pires, E. J. Solteiro; Vaz, João C.; Rosário, Maria J.; Oliveira, P. B. Moura; Machado, J. A. Tenreiro; Ferreira, Nuno M. F.The paper presents a RFDSCA automated synthesis procedure. This algorithm determines several RFDSCA circuits from the top-level system specifications all with the same maximum performance. The genetic synthesis tool optimizes a fitness function proportional to the RFDSCA quality factor and uses the epsiv-concept and maximin sorting scheme to achieve a set of solutions well distributed along a non-dominated front. To confirm the results of the algorithm, three RFDSCAs were simulated in SpectreRF and one of them was implemented and tested. The design used a 0.25 mum BiCMOS process. All the results (synthesized, simulated and measured) are very close, which indicate that the genetic synthesis method is a very useful tool to design optimum performance RFDSCAs.
- A Clustering Neural Network Model Applied to Electricity Price Range ForecastPublication . Azevedo, Filipe; Vale, Zita; Oliveira, P. B. MouraWith electricity markets birth, electricity price volatility becomes one of the major concerns for their participants and in particular, for the producers. Whether or not to hedge, what type of portfolio is ade-quate, and how to manage that portfolio are important considerations for electricity market agents. To achieve that, load and electricity price forecast have a high impor-tance. This paper provides an approach applied to price range forecast. Making use of artificial neural networks (ANN), the methodology presented here has as main con-cern finding the maximum and the minimum System Mar-ginal Price (SMP) for a specific programming period, with a certain confidence level. To train the neural networks, probabilistic information from past years is used. To in-crease accuracy and turning ANN training more efficient, a K-Means clustering method is previously applied. Re-sults from real data are presented and discussed in detail.
- A decision-support system based on particle swarm optimization for multiperiod hedging in electricity marketsPublication . Azevedo, Filipe; Vale, Zita; Oliveira, P. B. MouraThis paper proposes a particle swarm optimization (PSO) approach to support electricity producers for multiperiod optimal contract allocation. The producer risk preference is stated by a utility function (U) expressing the tradeoff between the expectation and variance of the return. Variance estimation and expected return are based on a forecasted scenario interval determined by a price range forecasting model developed by the authors. A certain confidence level is associated to each forecasted scenario interval. The proposed model makes use of contracts with physical (spot and forward) and financial (options) settlement. PSO performance was evaluated by comparing it with a genetic algorithm-based approach. This model can be used by producers in deregulated electricity markets but can easily be adapted to load serving entities and retailers. Moreover, it can easily be adapted to the use of other type of contracts.
- Dynamical modelling of a genetic algorithmPublication . Pires, E. J. Solteiro; Machado, J. A. Tenreiro; Oliveira, P. B. MouraThis work addresses the signal propagation and the fractional-order dynamics during the evolution of a genetic algorithm (GA). In order to investigate the phenomena involved in the GA population evolution, the mutation is exposed to excitation perturbations during some generations and the corresponding fitness variations are evaluated. Three distinct fitness functions are used to study their influence in the GA dynamics. The input and output signals are studied revealing a fractional-order dynamic evolution, characteristic of a long-term system memory.
- Entropy diversity in multi-objective particle swarm optimizationPublication . Pires, E. J. Solteiro; Machado, J. A. Tenreiro; Oliveira, P. B. MouraMulti-objective particle swarm optimization (MOPSO) is a search algorithm based on social behavior. Most of the existing multi-objective particle swarm optimization schemes are based on Pareto optimality and aim to obtain a representative non-dominated Pareto front for a given problem. Several approaches have been proposed to study the convergence and performance of the algorithm, particularly by accessing the final results. In the present paper, a different approach is proposed, by using Shannon entropy to analyzethe MOPSO dynamics along the algorithm execution. The results indicate that Shannon entropy can be used as an indicator of diversity and convergence for MOPSO problems.
- Fractional order dynamics in a particle swarm optimization algorithmPublication . Pires, E. J. Solteiro; Oliveira, P. B. Moura; Machado, J. A. Tenreiro; Jesus, Isabel S.This article reports the study of fractional dynamics during the evolution of Particle Swarm Optimization (PSO) algorithm. Some initial swarm particles are randomly changed, for simulating the system response, and its effect is compared with a non-perturbed reference. The perturbation effect in the PSO evolution is observed in the perspective of the fitness time behavior of the best particle. the dynamics is represented throught the median of a sample of experiments, while adopting the Fourier analysis for describing the phenomena. The influence of the PSO parameters influence upon the global dynamics is also analyzed.
- Long-term price range forecast applied to risk management using regression modelsPublication . Azevedo, Filipe; Vale, Zita; Oliveira, P. B. MouraLong-term contractual decisions are the basis of an efficient risk management. However those types of decisions have to be supported with a robust price forecast methodology. This paper reports a different approach for long-term price forecast which tries to give answers to that need. Making use of regression models, the proposed methodology has as main objective to find the maximum and a minimum Market Clearing Price (MCP) for a specific programming period, and with a desired confidence level α. Due to the problem complexity, the meta-heuristic Particle Swarm Optimization (PSO) was used to find the best regression parameters and the results compared with the obtained by using a Genetic Algorithm (GA). To validate these models, results from realistic data are presented and discussed in detail.
- A long-term risk management tool for electricity markets using swarm intelligencePublication . Azevedo, Filipe; Vale, Zita; Oliveira, P. B. Moura; Khodr, H. M.This paper addresses the optimal involvement in derivatives electricity markets of a power producer to hedge against the pool price volatility. To achieve this aim, a swarm intelligence meta-heuristic optimization technique for long-term risk management tool is proposed. This tool investigates the long-term opportunities for risk hedging available for electric power producers through the use of contracts with physical (spot and forward contracts) and financial (options contracts) settlement. The producer risk preference is formulated as a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance of return and the expectation are based on a forecasted scenario interval determined by a long-term price range forecasting model. This model also makes use of particle swarm optimization (PSO) to find the best parameters allow to achieve better forecasting results. On the other hand, the price estimation depends on load forecasting. This work also presents a regressive long-term load forecast model that make use of PSO to find the best parameters as well as in price estimation. The PSO technique performance has been evaluated by comparison with a Genetic Algorithm (GA) based approach. A case study is presented and the results are discussed taking into account the real price and load historical data from mainland Spanish electricity market demonstrating the effectiveness of the methodology handling this type of problems. Finally, conclusions are dully drawn.
- Manipulator trajectory planning using a MOEAPublication . Pires, E. J. Solteiro; Oliveira, P. B. Moura; Machado, J. A. TenreiroGenerating manipulator trajectories considering multiple objectives and obstacle avoidance is a non-trivial optimization problem. In this paper a multi-objective genetic algorithm based technique is proposed to address this problem. Multiple criteria are optimized considering up to five simultaneous objectives. Simulation results are presented for robots with two and three degrees of freedom, considering two and five objectives optimization. A subsequent analysis of the spread and solutions distribution along the converged non-dominated Pareto front is carried out, in terms of the achieved diversity.