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- Design and Implementation of MPPT System Based on PSO AlgorithmPublication . Calvinho, Gonçalo; Pombo, José Álvaro Nunes; Mariano, S.; Calado, M. Do RosárioThis paper presents a method for maximum power point tracking (MPPT) based on the particle swarm optimization (PSO) algorithm with variable step size in order to prevent steady state oscillations. This will avoid the impact of partial shading conditions in the efficiency of photovoltaic (PV) systems. To optimize power output of the solar panels a DC-DC boost converter is used. Special emphasis is put on software development and implementation in the TMS320F28027 microcontroller in Texas Instruments Code Composer Studio.
- A procedure to specify the weighting matrices for an optimal load-frequency controllerPublication . Mariano, S.; Pombo, José Álvaro Nunes; Calado, M. Do Rosário; Ferreira, Luís António Fialho MarcelinoThe linear quadratic optimal regulator is one of the most powerful techniques for designing multivariable control systems. The performance of the system is specified in terms of a cost, which is the integral of a weighted quadratic function of the system state and control inputs, that is to be minimized by the optimal controller. The components of the state cost weighting matrix, Q, and the control cost weighting matrix, R, are ours to choose in mathematically specifying the way we wish the system to perform. Changing these matrices, we can modify the transient behavior of the closed-loop system. This paper addresses the stabilization and performance of the load-frequency controller by using the theory of the optimal control. A new technique, based on pole placement using optimal regulators, to overcome the difficulties of specifying weighting matrices Q and R is proposed. The design method employs successive shifting of either a real pole or a pair of complex conjugate poles at a time. The proposed technique builds Q and R in such a way that the system response also obeys conventional criteria for the system pole location. The effectiveness of the proposed method is illustrated by numerical examples.
- Damping of Power System Oscillations with Optimal RegulatorPublication . Mariano, S.; Pombo, José Álvaro Nunes; Calado, M. Do Rosário; Souza, J. A. M. Felippe DeThis chapter presents a study of the small signal stability applied to an electric power system, with the consideration of the Power System Stabilizer and using the optimal control theory. A new technique is proposed, which is based on pole placement using optimal state feedback for damping electromechanical oscillation under small signal. The proposed technique builds the weighting matrices of the quadratic terms for the state vector Q and control vector R in such a way that the system response also obeys conventional criteria for the system pole location. Besides, when the number of output variables is less than the order of the system, it is proposed an optimal output feedback approach, where a set of closed-loop system poles is allocated to an arbitrary position by means of a suitable output feedback. The Power Sensitivity Model is used to represent the electric power system. Information about the stability of the electric power system, when subjected to small disturbances, is illustrated by using numerical examples.
- Collaborative swarm intelligence to estimate PV parametersPublication . Nunes, H.G.G.; Pombo, José Álvaro Nunes; Bento, P.M.R.; Mariano, S.; Calado, M. Do RosárioTo properly evaluate, control and optimize photovoltaic (PV) systems, it is crucial to accurately estimate the equivalent electric circuit parameters from the respective mathematical models that characterize the PV cells or modules behavior. This is currently a hot research topic that has attracted the attention of numerous researchers. In this paper, we propose a new hybrid methodology that combines diversification and intensification mechanisms from different metaheuristics (MHs) to estimate PV parameters precisely. The proposed methodology has the capacity to adapt to the specific optimization problem and maintain diversity when building solutions, thus mitigating premature convergence and population stagnation. This methodology can incorporate several MHs (two or more swarms) with different potentialities, enabling a good balance between diversification and intensification mechanisms. Furthermore, it is able to explore a multidimensional search space in different regions simultaneously. To validate its performance, the proposed methodology was compared with other wellestablished MHs in several benchmark functions, and used to estimate PV parameters in single and double-diode models in two case studies, the first using standard literature data, and the second using measured data from a real application with and without the occurrence of partial shading. The proposed methodology was able to find highly accurate solutions with reduced computational cost and high reliability. Comparisons with the other MHs demonstrate that the proposed methodology presents a very competitive performance when solving the PV parameter estimation problem.
- Optimization of neural network with wavelet transform and improved data selection using bat algorithm for short-term load forecastingPublication . Bento, P.M.R.; Pombo, José Álvaro Nunes; Calado, M. Do Rosário; Mariano, S.Short-term load forecasting is very important for reliable power system operation, even more so under electricity market deregulation and integration of renewable resources framework. This paper presents a new enhanced method for one day ahead load forecast, combing improved data selection and features extraction techniques (similar/recent day-based selection, correlation and wavelet analysis), which brings more “regularity” to the load time-series, an important precondition for the successful application of neural networks. A combination of Bat and Scaled Conjugate Gradient Algorithms is proposed to improve neural network learning capability. Another feature is the method's capacity to fine-tune neural network architecture and wavelet decomposition, for which there is no optimal paradigm. Numerical testing using the Portuguese national system load, and the regional (state) loads of New England and New York, revealed promising forecasting results in comparison with other state-of-the-art methods, therefore proving the effectiveness of the assembled methodology.
- Short-Term Load Forecasting using optimized LSTM Networks via Improved Bat AlgorithmPublication . Bento, P.M.R.; Pombo, José Álvaro Nunes; Mariano, S.; Calado, M. Do RosárioShort-term load forecasting plays a preponderant role in the daily basis system's operation and planning. The state- of-the-art comprises a far-reaching set of methodologies, which are traditionally based on time-series analysis and multilayer neural networks. In particular, the existence of countless neural network architectures has highlighted its ability to cope with 'hard' nonlinear approximation tasks, thus making them appropriate to perform load forecasts. Following this successful path, long short-term memory networks were employed in an optimized arrangement as forecasters, this type of recurrent neural networks has received in recent years a renewed interest for machine learning tasks. Firstly, a preprocessing stage takes place, where through the selection of similar days and correlation analysis, meaningful statistics and characteristics are extracted from the load time-series, to assemble the proper training sets. Then, Bat Algorithm is used to excel the long short-term memory network functioning, by fine-tuning its size and its learning hyperparameters. Numerical testing conducted on the Portuguese load time-series reveals promising forecasting results in an overall assessment, when compared with other state-of-the-art methods.
- Lookup Table Based Intelligent Charging and Balancing Algorithm for Li-ion Battery PacksPublication . Velho, Ricardo Lopes; Pombo, José Álvaro Nunes; Fermeiro, J.B.L.; Calado, M. Do Rosário; Mariano, S.In this article, an energy storage system (ESS) and its entire monitoring system are implemented. A new charging algorithm is developed based on the parameters of the battery pack in real time, extending the battery lifespan, improving the capacity usage and performance of the ESS. The proposed algorithm analyses, in real time, the difference between the desired voltage and the mean voltage of the cells, the temperature of the pack, and the difference of voltage between cells. Based on the obtained information, by trilinear interpolation, the algorithm calculates the charging current. The proposed charging algorithm also combines a balancing algorithm. This represents an innovation compared to the existing methods in the literature. The experimental results demonstrate that the proposed algorithm can successfully charge Li-ion battery packs with different capacities and life cycles, providing a better charging time and a much lower temperature increase.