Neural Network Inverse Modeling for Optimization

In this chapter, artificial neural networks (ANNs) inverse model is applied for estimating the thermal performance () in parabolic trough concentrator (PTC). A recurrent neural network architecture is trained using the Kalman Filter learning from experimental database obtained from PTCs operations. Rim angle (φr), inlet (Tin), outlet (Tout) fluid temperatures, ambient temperature (Ta), water flow (Fw), direct solar radiation (Gb) and the wind velocity (Vw) were used as main input variables within the neural network model in order to estimate the thermal performance with an excellent agreement (R2=0.999) between the experimental and simulated values. The optimal operation conditions of parabolic trough concentrator are established using artificial neural network inverse modeling. The results, using experimental data, showed that the recurrent neural network (RNN) is an excellent tool for modeling and optimization of PTCs

Neural Model with Particle Swarm Optimization Kalman Learning for Forecasting in Smart Grids

This paper discusses a novel training algorithm for a neural network architecture applied to time series prediction with smart grids applications. The proposed training algorithm is based on an extended Kalman filter (EKF) improved using particle swarm optimization (PSO) to compute the design parameters. The EKF-PSO-based algorithm is employed to update the synaptic weights of the neural network. The size of the regression vector is determined by means of the Cao methodology. The proposed structure captures more efficiently the complex nature of the wind speed, energy generation, and electrical load demand time series that are constantly monitorated in a smart grid benchmark. The proposed model is trained and tested using real data values in order to show the applicability of the proposed scheme. \ua9 2013 Alma Y. Alanis et al

Environmental regulation of carbon isotope composition and crassulacean acid metabolism in three plant communities along a water availability gradient

Expression of crassulacean acid metabolism (CAM) is characterized by extreme variability within and between taxa and its sensitivity to environmental variation. In this study, we determined seasonal fluctuations in CAM photosynthesis with measurements of nocturnal tissue acidification and carbon isotopic composition (δ13C) of bulk tissue and extracted sugars in three plant communities along a precipitation gradient (500, 700, and 1,000 mm year−1) on the Yucatan Peninsula. We also related the degree of CAM to light habitat and relative abundance of species in the three sites. For all species, the greatest tissue acid accumulation occurred during the rainy season. In the 500 mm site, tissue acidification was greater for the species growing at 30% of daily total photon flux density (PFD) than species growing at 80% PFD. Whereas in the two wetter sites, the species growing at 80% total PFD had greater tissue acidification. All species had values of bulk tissue δ13C less negative than −20‰, indicating strong CAM activity. The bulk tissue δ13C values in plants from the 500 mm site were 2‰ less negative than in plants from the wetter sites, and the only species growing in the three communities, Acanthocereus tetragonus (Cactaceae), showed a significant negative relationship between both bulk tissue and sugar δ13C values and annual rainfall, consistent with greater CO2 assimilation through the CAM pathway with decreasing water availability. Overall, variation in the use of CAM photosynthesis was related to water and light availability and CAM appeared to be more ecologically important in the tropical dry forests than in the coastal dune

A wind speed neural model with particle swarm optimization Kalman learning

This paper deals with a novel training algorithm for a neural network architecture for wind speed time series prediction. The proposed training algorithm is based in an extended Kalman filter (EKF) improved using particle swarm optimization (PSO) to compute the design parameters The EKF-PSO based algorithm is employed to update the synaptic weights of the neural network. The size of the regression vector is determined by means of the Cao methodology. The proposed structure captures more efficiently the complex nature of the wind speed time series. The proposed model is trained and tested using real wind speed data values. In order to show the applicability of the proposed scheme Simulation results are included. \ua9 2012 TSI Press

Microambientes de luz, crecimiento y fotosíntesis de la pitahaya (Hylocereus Undatus) en un agrosistema de Yucatán, México

Con el fin de incrementar el crecimiento de los tallos de una cactácea en las primeras etapas de una plantación comercial se estudió el efecto del microambiente de luz sobre la fisiología de Hylocereus undatus (Haworth) Britton & Rose en Yucatán, México. Se colocaron esquejes de tallos en cuatro tratamientos de luz para evaluar su crecimiento durante 55 semanas. La elongaciónde los tallos fue 67% mayor con 36 a 48% de flujo de fotonespara fotosíntesis (FFF) diario incidente que en tallos con 25% y90% de FFF. Durante el periodo de lluvias la acidez tisular (unamedida de la actividad fotosintética de plantas con metabolismoácido) fue 44% mayor en los individuos bajo 36 a 48% del FFF diario incidente, que en aquellos con menor o mayor radiación. Durante el periodo de nortes (previo a la sequía con eventos esporádicos de fuertes vientos y poca lluvia) la acidez fue similar bajo 36%, 48% y 90% del FFF diario incidente, pero 44% menor en el tratamiento con menor radiación. La eficiencia cuántica máxima indicó que plantas expuestas sufrían fotoinhibición durante el periodo de nortes. El periodo de lluvias presenta condiciones ambientales óptimas para la fotosíntesis de H. undatus, por las bajas temperaturas del aire y del déficit de presión de vapor por la noche

Transient Differentiation Maximum Power Point Tracker (Td-MPPT) for Optimized Tracking under Very Fast-Changing Irradiance: A Theoretical Approach for Mobile PV Applications

This work presents an algorithm for Maximum Power Point Tracking (MPPT) that measures transitory states to prevent drift issues and that can reduce steady-state oscillations. The traditional MPPT algorithms can become confused under very fast-changing irradiance and perform tracking in the wrong direction. Errors occur because these algorithms operate under the assumption that power changes in the system are triggered exclusively due to perturbations introduced by them. However, the power increase triggered by irradiance changes could be more significant than those caused by the perturbation effect. The proposed method modifies the Perturb and Observe algorithm (P&O) with an additional measurement stage performed close to the maximum overshoot peak after the perturbation stage. By comparing power changes between three measurement points, the algorithm can accurately identify whether the perturbation was made in the correct direction or not. Furthermore, the algorithm can use additional information to determine if the operating point after the perturbation stage is beyond the maximum power point (MPP) and perturb in the opposite direction for the next iteration. Thus, the proposed algorithm shows reduced steady-state oscillations and improved tracking under fast irradiance changes compared to conventional P&O and P&O with power differences (dP-P&O). The design is validated via simulations using fast-changing irradiance tests based on the standard EN 50530 accelerated by a factor of 100×. The proposed algorithm achieved 99.74% of global efficiency versus 97.4% of the classical P&O and 99.54% of the dP-P&O under the tested conditions

Transient Differentiation Maximum Power Point Tracker (Td-MPPT) for Optimized Tracking under Very Fast-Changing Irradiance: A Theoretical Approach for Mobile PV Applications

This work presents an algorithm for Maximum Power Point Tracking (MPPT) that measures transitory states to prevent drift issues and that can reduce steady-state oscillations. The traditional MPPT algorithms can become confused under very fast-changing irradiance and perform tracking in the wrong direction. Errors occur because these algorithms operate under the assumption that power changes in the system are triggered exclusively due to perturbations introduced by them. However, the power increase triggered by irradiance changes could be more significant than those caused by the perturbation effect. The proposed method modifies the Perturb and Observe algorithm (P&O) with an additional measurement stage performed close to the maximum overshoot peak after the perturbation stage. By comparing power changes between three measurement points, the algorithm can accurately identify whether the perturbation was made in the correct direction or not. Furthermore, the algorithm can use additional information to determine if the operating point after the perturbation stage is beyond the maximum power point (MPP) and perturb in the opposite direction for the next iteration. Thus, the proposed algorithm shows reduced steady-state oscillations and improved tracking under fast irradiance changes compared to conventional P&O and P&O with power differences (dP-P&O). The design is validated via simulations using fast-changing irradiance tests based on the standard EN 50530 accelerated by a factor of 100×. The proposed algorithm achieved 99.74% of global efficiency versus 97.4% of the classical P&O and 99.54% of the dP-P&O under the tested conditions