Diseño y simulación de un Sistema de Tracking basado en redes neuronales para mantener la máxima eficiencia de paneles solares

This research work focuses on the use of artificial neural networks (ANN) as a promising tool to improve the efficiency and stability of solar photovoltaic systems. Although photovoltaic systems harness a clean and renewable energy source, they face challenges due to variations in solar radiation, temperature and environmental conditions. These factors cause fluctuations in the output current and voltage of the solar panels, affecting the power generated. To address this problem, it is necessary to implement control strategies that maximize power extraction from the photovoltaic field. The main focus of this work is the maximum power point (MPP), which represents the optimal power transfer point on the current-voltage (I-V) characteristic curve of a solar panel. The challenge lies in adapting to changing conditions and achieving accurate monitoring of the MPP to improve system efficiency. Although there are different proposed tracking algorithms, they have shown limitations in terms of tracking rates and steady-state oscillations. To overcome these deficiencies, the applications of ANNs in the design of control algorithms are explored. ANNs stand out for their high dynamic response and ability to adapt to non-linear conditions. However, obtaining accurate training data for the controller is one of the main challenges. In this study, important variables such as solar radiation, temperature and optimal voltage are considered as inputs to the controller.Este trabajo de investigación se centra en el uso de redes neuronales artificiales (RNA) como una herramienta prometedora para mejorar la eficiencia y estabilidad de los sistemas solares fotovoltaicos. Aunque los sistemas fotovoltaicos aprovechan una fuente de energía limpia y renovable, enfrentan desafíos debido a las variaciones en la radiación solar, temperatura y condiciones ambientales. Estos factores ocasionan fluctuaciones en la corriente y tensión de salida de los paneles solares, afectando la potencia generada. Para abordar este problema, se requiere implementar estrategias de control que maximicen la extracción de potencia del campo fotovoltaico. El enfoque principal de este trabajo es el punto de máxima potencia (MPP), que representa el punto óptimo de transferencia de potencia en la curva de características corriente-voltaje (I-V) de un panel solar. El desafío radica en adaptarse a las condiciones cambiantes y lograr un seguimiento preciso del MPP para mejorar la eficiencia del sistema. Aunque existen diferentes algoritmos de seguimiento propuestos, han mostrado limitaciones en términos de tasas de seguimiento y oscilaciones en estado estacionario. Para superar estas deficiencias, se exploran las aplicaciones de las RNA en el diseño de algoritmos de control. Las RNA se destacan por su alta respuesta dinámica y capacidad para adaptarse a condiciones no lineales. Sin embargo, obtener datos precisos de entrenamiento para el controlador es uno de los principales desafíos. En este estudio, se consideran variables importantes como radiación solar, temperatura y voltaje óptimo como entradas para el controlador

Investigation of Some Self-Optimizing Control Problems for Net-Zero Energy Buildings

Green buildings are sustainable buildings designed to be environmentally responsible and resource efficient. The Net-Zero Energy Building (NZEB) concept is anchored on two pillars: reducing the energy consumption and enhancing the local energy generation. In other words, efficient operation of the existing building equipment and efficient power generation of building integrated renewable energy sources are two important factors of NZEB development. The heating, ventilation and air conditioning (HVAC) systems are an important class of building equipment that is responsible for large portion of building energy usage, while the building integrated photovoltaic (BIPV) system is well received as the key technology for local generation of clean power. Building system operation is a low-investment practice that aims low operation and maintenance cost. However, building HVAC and BIPV are systems subject to complicated intrinsic processes and highly variable environmental conditions and occupant behavior. Control, optimization and monitoring of such systems desire simple and effective approaches that require the least amount of model information and the use of smallest number but most robust sensor measurements. Self-optimizing control strategies promise a competitive platform for control, optimization and control integrated monitoring for building systems, and especially for the development of cost-effective NZEB. This dissertation study endorses this statement with three aspects of work relevant to building HVAC and BIPV, which could contribute several small steps towards the ramification of the self-optimizing control paradigm. This dissertation study applies self-optimizing control techniques to improve the energy efficiency of NZEB from two aspects. First, regarding the building HVAC efficiency, the dither based extremum seeking control (DESC) scheme is proposed for energy efficient operation of the chilled-water system typically used in the commercial building ventilation and air conditioning (VAC) systems. To evaluate the effectiveness of the proposed control strategy, Modelica based dynamic simulation model of chilled water chiller-tower plant is developed, which consists of a screw chiller and a mechanical-draft counter-flow wet cooling tower. The steady-state performance of the cooling tower model is validated with the experimental data in a classic paper and good agreement is observed. The DESC scheme takes the total power consumption of the chiller compressor and the tower fan as feedback, and uses the fan speed setting as the control input. The inner loop controllers for the chiller operation include two proportional-integral (PI) control loops for regulating the evaporator superheat and the chilled water temperature. Simulation was conducted on the whole dynamic simulation model with different environment conditions. The simulation results demonstrated the effectiveness of the proposed ESC strategy under abrupt changes of ambient conditions and load changes. The potential for energy savings of these cases are also evaluated. The back-calculation based anti-windup ESC is also simulated for handling the integral windup problem due to actuator saturation. Second, both maximum power point tracking (MPPT) and control integrated diagnostics are investigated for BIPV with two different extremum seeking control strategies, which both would contribute to the reduction of the cost of energy (COE). In particular, the Adaptive Extremum Seeking Control (AESC) is applied for PV MPPT, which is based on a PV model with known model structure but unknown nonlinear characteristics for the current-voltage relation. The nonlinear uncertainty is approximated by a radial basis function neural network (RBFNN). A Lyapunov based inverse optimal design technique is applied to achieve parameter estimation and gradient based extremum seeking. Simulation study is performed for scenarios of temperature change, irradiance change and combined change of temperature and irradiance. Successful results are observed for all cases. Furthermore, the AESC simulation is compared to the DESC simulation, and AESC demonstrates much faster transient responses under various scenarios of ambient changes. Many of the PV degradation mechanisms are reflected as the change of the internal resistance. A scheme of detecting the change of PV internal shunt resistance is proposed using the available signals in the DESC based MPPT with square-wave dither. The impact of the internal resistance on the transient characteristics of step responses is justified by using the small-signal transfer function analysis. Simulation study is performed for both the single-string and multi-string PV examples, and both cases have demonstrated successful results. Monotonic relationship between integral error indices and the shunt internal resistance is clearly observed. In particular, for the multi-string, the inter-channel coupling is weak, which indicates consistent monitoring for multi-string operation. The proposed scheme provides the online monitoring ability of the internal resistance condition without any additional sensor, which benefits further development of PV degradation detection techniques

Maximisation en temps réel de la puissance de sortie de piles à combustible microbiennes en utilisant l'optimisation par essaims particulaires

L’industrie se tourne de plus en plus vers les sources d’énergie alternatives, et les piles à combustible microbiennes (PCM) s’inscrivent dans cette optique, car elles utilisent des microbes pour transformer l’énergie contenue dans la matière organique en énergie électrique. Les PCM ont cependant une densité de puissance de sortie très faible, en plus d’une dynamique interne très lente, ce qui rend difficile la récolte de leur énergie. En effet, il n’est possible de récolter le maximum d’énergie d’une PCM que si sa charge externe est adaptée à sa résistance interne. De plus, la dynamique interne lente des PCM implique que leur résistance interne peut varier au fil du temps. Ainsi, afin de résoudre ces problèmes, il faut se tourner vers des techniques d’optimisation en temps réel (OTR). Les algorithmes d’optimisation par essaims particulaires (OEP) et d’optimisation par perturbations et observations (P&O) sont souvent utilisés pour l’OTR de la puissance de systèmes photovoltaïques (PV). Le P&O a aussi été appliqué sur des PCM, mais l’OEP ne l’a pas encore été, bien que cette approche présente plusieurs avantages par rapport au P&O. Ainsi, la première contribution de ce travail de recherche est une évaluation de la possibilité d’utiliser les algorithmes basés sur l’OEP pour l’OTR de la puissance de sortie de PCM. La deuxième contribution de ce mémoire est la conception d’un nouvel algorithme d’OTR, soit l’optimisation par essaims particulaires parallélisée avec classificateur (OEPPC). Les résultats de simulation ont démontré que les algorithmes basés sur l’OEP ont beaucoup de potentiel pour l’OTR de la puissance de PCM et qu’ils surpassent les performances du P&O. Les résultats expérimentaux sur des PCM ont quant à eux démontré que, si l’optimum de puissance varie très lentement, il est nécessaire de modifier l’OEP pour l’adapter à un tel contexte. L’OEPPC avec diversité (OEPPCD) a été proposée pour combler ce manque, et des simulations ont démontré que l’OEPPCD performerait mieux que le P&O. L’algorithme d’OEPPC a aussi été validé expérimentalement sur un système composé de 15 cellules PV. Les résultats expérimentaux ont démontré que l’OEPPC performe mieux que l’OEP et que le P&O, deux algorithmes éprouvés sur les systèmes PV. Ainsi, l’OEPPC a prouvé être un algorithme efficace pouvant être appliqué à différents types de système