The use of photovoltaics is still considered to be challenging because of certain reliability issues and high dependence on the global horizontal irradiance (GHI). GHI forecasting has a wide application from grid safe...
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The use of photovoltaics is still considered to be challenging because of certain reliability issues and high dependence on the global horizontal irradiance (GHI). GHI forecasting has a wide application from grid safety to supply-demand balance and economic load dispatching. Given a data set, a multi-layer perceptron neural network (MLPNN) is a strong tool for solving the forecasting problems. Furthermore, noise detection and feature selection in a data set with numerous variables including meteorological parameters and previous values of GHI are of crucial importance to obtain the desired results. This paper employs density-based spatial clustering of applications with noise (DBSCAN) and non-dominated sorting geneticalgorithmii (NSGA ii) algorithms for noise detection and feature selection, respectively. Tuning the neural network is another important issue that includes choosing the hidden layer size and activation functions between the layers of the network. Previous studies have utilized a combination of different parameters based on trial and error, which seems to be inefficient in terms of accurate selection of the desired features and also tuning of the neural network. In this research, two different methodsnamely, particle swarm optimization (PSO) algorithm and geneticalgorithm (GA)are utilized in order to tune the MLPNN, and the results of one-hour-ahead forecasting of the GHI are subsequently compared. The methodology is validated using the hourly data for Elizabeth City located in North Carolina, USA, and the results demonstrated a better performance of GA in comparison with PSO. The GA-tuned MLPNN reported a normalized root mean square error (nRMSE) of 0.0458 and a normalized mean absolute error (nMAE) of 0.0238.
This paper presents an optimum design procedure for the coordinated tuning of machine side converter (MSC) and grid side converter (GSC) controllers of grid connected permanent magnet synchronous generator (PMSG). Mod...
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This paper presents an optimum design procedure for the coordinated tuning of machine side converter (MSC) and grid side converter (GSC) controllers of grid connected permanent magnet synchronous generator (PMSG). Model based predictive controller (MBPC) is used to control the MSC. MBPC based speed control design consists of two steps. A linearized state space model is employed to predict the future output (rotor speed). An optimal control law is derived by minimizing a quadratic performance index that considers the control effort and the difference between the predicted rotor speed and the reference rotor speed. A proportional-integral (PI) controller is used to control the GSC. The MSC and GSC controller parameters are determined by simultaneously optimizing the controller performance indices. The coordinated controller design is carried out in two steps. The analytical expression that relates the performance indices and the controller parameters is arrived using response surface methodology (RSM). The determination of controller parameters is posed as a constrained multi-objective optimization problem and solved employing NSGA-ii (non-dominated sorted genetic algorithm ii). The proposed methodology is tested on a sample power system with PMSG based WECS (Wind Energy Conversion System). Simulation results demonstrate the effectiveness of the proposed methodology.
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