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- Control of the archimedes wave swing using neural networksPublication . Beirão, Pedro; Mendes, Mário J. G. C.; Valério, Duarte; Costa, José Sá daThis paper addresses the control of the Archimedes Wave Swing, a fully-submerged Wave Energy Converter (WEC), of which a prototype has already been built and tested. Simulation results are presented in which Internal Model Control (IMC) is used, both with linear models and with non-linear neural network (NN) models. To the best of our knowledge this is the first time NN-based control is being applied to design a controller for a WEC. NNs are a mathematical tool suitable to model the behaviour of dynamic systems, both linear and non-linear (as in our case). Significant absorbed wave energy increases were found, both using linear models and NNs. Results were better when IMC with NNs was employed (with a nearly sixfold increase against a fivefold increase), except for the May—September period, when IMC with linear models performs better.
- Comparison of control strategies performance for a Wave Energy ConverterPublication . Valério, Duarte; Beirão, Pedro; Mendes, Mário J. G. C.; Costa, José Sá daThe Archimedes Wave Swing (AWS) is a a fully submerged Wave Energy Converter (WEC), that is to say, a device that converts the kinetic energy of sea waves into electricity. A first prototype of the AWS has already been built and tested. This paper presents simulation results of the performance of several control strategies applied to this device, including PID control, reactive control, phase and amplitude control, latching control, feedback linearisation control, internal model control, switching control, and combinations thereof. Linear, white-box nonlinear, and neural network models were employed. Significant (above threefold) increases in yearly energy production were found to be possible with properly designed control strategies.
- Fault detection system for the Évora irrigation canalPublication . Louro, Diogo; Mendes, Mário J. G. C.; Valério, Duarte; Costa, José Sá daA model-based fault detection (FD) system was developed for a Simulink simulation of a four pool irrigation canal in ´Evora, Portugal. Incipient and abrupt faults in the gates, the water off-take valves and the water level sensors were considered. Neural Networks were used to model the canal and find the residue. The training algorithm employed for the NNs was found to be an important factor determining the success of the FD system.
- Development of a multi-agent management system for an intelligent charging network of electric vehiclesPublication . Miranda, João; Borges, José; Mendes, Mário J. G. C.; Valério, DuarteThis paper addresses the modelling and simulation of a battery charging infrastructure for electric vehicles, with the objective of pro-actively scheduling the charging of up to fifty vehicles so as not to overcharge the electrical network. Benefits of having the charging stations differ (as much as possible while satisfying end-user requirements) battery charging for those hours when electricity consumption is otherwise low include rendering electricity consumption more uniform along the day. A multi-agent system was used to design a distributed, modular, coordinated and collaborative multi-agent management system for this infrastructure. Simulation results show the effectiveness of this approach under the conditions of four real-life scenarios.
- Identification and control of the AWS using neural network modelsPublication . Valério, Duarte; Mendes, Mário J. G. C.; Beirão, Pedro; Costa, José Sá daThe Archimedes Wave Swing (AWS) is a a fully-submerged Wave Energy Converter (WEC), that is to say, a device that converts the energy of sea waves into electricity. A first prototype of the AWS has already been built and tested. In this paper, neural network (NN) models for this AWS prototype are developed. NNs are then used together with proven control strategies (phase and amplitude control, internal model control and switching control) to maximise energy production. Simulations show an yearly average electricity production increase of 160% over the performance of the original AWS controller.
- Robustness assessment of model-based control for the archimedes wave swingPublication . Valério, Duarte; Beirão, Pedro; Mendes, Mário J. G. C.; Costa, José Sá daIn this paper the robustness of three model-based control strategies—internal model control (IMC) with linear models, IMC with neural network models, and feedback linearisation control—for the Archimedes Wave Swing (AWS), a device designed to produce electricity from the energy of sea waves, is assessed by checking how their performance, optimised for a neutral tide with a standard atmospheric pressure, changes under high and low tides, and under atmospheric pressure variations. The original AWS controller and latching control are used as a term of comparison. Simulation results show that, as a rule, low tides and lower atmospheric pressures lead to higher power productions, while high tides and higher atmospheric pressures lead to lower power productions; but, in spite of model maladjustments, model-based control strategies are not at disadvantage when compared with latching control.
- GA optimized fractional controller for a wind turbine ride through pitch malfunctionPublication . Pandiyan, Surya; Valério, Duarte; Melicio, Rui; Mendes, VictorThis paper is about better integration of wind energy into an electric grid, avoiding wind turbine pitch malfunction to become a failure. A fractional-order controller is used in the two-level converters of the wind turbine to reduce the voltage drops during the malfunction. The reduction is attained by an optimization problem for selection of the parameters of the fractional-order control. The optimization problem is a non-convex one, solved by a genetic algorithm together with a model of the wind turbine pitch malfunction. Kriging metamodeling is used to assist in output prediction due its lower computational requirements and its ability to provide a value for the uncertainty of the estimate. A comparison between the Kriging metamodeling and the complete model is presented and conclusions are stated to show the advantage of the Kriging metamodeling.
- Switching control of the archimedes wave swingPublication . Valério, Duarte; Costa, José Sá da; Mendes, Mário J. G. C.; Beirão, PedroControl switching is applied to the Archimedes Wave Swing (AWS), a device designed to convert the energy of sea waves into electricity. Previous simulations showed that energy production can be significantly increased using Internal Model Control, together with direct and inverse Elman Neural Network (NN) models of the AWS, and a reference based upon the phase and amplitude control strategy. Since the best performance was achieved by different NN models depending on the month of the year, further simulations were carried out showing that switching between diferente controllers, corresponding to diferente models, according to the spectrum of the incoming wave,further increases energy production.