Contribution à la modélisation et au contrôle de trajectoire de Trackers photovoltaïques à haute concentration (HCPV)

This work focuses on improving the accuracy and on reducing the cost of the tracker generating trajectory strategy, in order to maximize the production and to reduce the installation and the maintenance cost of a solar tracker orienting high concentrated photovoltaic modules (HCPV). Initially, we propose a behavioral modeling of the HCPV module mounted on a dual axis tracker in order to study the influence of the tracking performance on the module power production. Then, this simulator can be used to test control strategies and to compare their performance. Firstly, a classical control strategy is implemented in the simulator. It is based on a hybrid control operating an astronomical calculation to follow the sun path, and a sun sensor to correct the tracking error. A sensorless strategy is proposed in this work to reduce the cost of the HCPV tracker control. This strategy is based on a gradient optimization algorithm to generate the tracker trajectory and to catch the sun path. Tested on the simulator, this strategy presents the same accuracy as the classical strategy while being less costly. The last study proposed in this thesis work concerns maximum power point tracking (MPPT) algorithms, in order to respond to a given problem relating to the practical implementation of gradient algorithm. In this context, we propose an original optimization of the P&O MPPT control with a neural network algorithm leading to a significant reduction of the computational cost required to train it. This approach, which is ensuring a good compromise between accuracy and complexity is sufficiently fast to not affect the quality of the evaluation of the gradient.Dans une optique de maximisation de la production et de réduction des coûts d’installation, de maintenance et d’entretien des trackers solaires, qui permettent d’orienter les modules photovoltaïques à haute concentration (HCPV), ces travaux de thèse se focalisent sur l’amélioration de la précision et la réduction du coût de la stratégie de génération de la trajectoire du tracker. Dans un premier temps, un simulateur de tracker HCPV est développé offrant une étude de l’influence de la performance du suivi du soleil sur la production des modules HCPV, permettant ainsi une étude et une comparaison des stratégies de génération de trajectoires. Le simulateur est basé sur un modèle comportemental de module HCPV monté sur tracker permettant de prédire la puissance maximale du module HCPV en fonction de l’erreur de position du tracker face au soleil, de l’ensoleillement direct et de la température. Une première stratégie de commande dite de référence a été implémentée sur ce simulateur. C’est une commande hybride qui repose sur un viseur solaire pour corriger l’erreur de poursuite par un calcul astronomique. Ensuite, afin d’améliorer les performances et de réduire les coûts de cette stratégie, une nouvelle approche sans capteur est développée en se basant sur une méthode d’optimisation du gradient de puissance pour la génération de la trajectoire du tracker. Une étude complémentaire est également exposée afin de mettre en évidence des algorithmes de recherche de la puissance maximale (MPPT) pouvant offrir des temps de réponse suffisamment rapides pour ne pas affecter la qualité de l’évaluation du gradient de puissance. Dans ce contexte, une commande MPPT P&O améliorée par un réseau de neurones à complexité réduite est proposée, assurant un compromis entre précision, simplicité et rapidit

Maximum power point tracking using P&O control optimized by a neural network approach: a good compromise between accuracy and complexity

In the field of power optimization of photovoltaic panels (PV), there exist many maximum power point tracking (MPPT) control algorithms, such as: the perturb and observe (P&O) one, the algorithms based on fuzzy logic and the ones using a neural network approaches. Among these MPPT control algorithms, P&O is one of the most widely used due to its simplicity of implementation. However, the major drawback of this kind of algorithm is the lack of accuracy due to oscillations around the PPM. Conversely, MPPT control using neural networks have shown to be a very efficient solution in term of accuracy. However, this approach remains complex. In this paper we propose an original optimization of the P&O MPPT control with a neural network algorithm leading to a significant reduction of the computational cost required to train it, ensuring a good compromise between accuracy and complexity. The algorithm has been applied to the models of two different types of solar panels, which have been experimentally validated

Maximum power point tracking using P&O control optimized by a neural network approach: a good compromise between accuracy and complexity

In the field of power optimization of photovoltaic panels (PV), there exist many maximum power point tracking (MPPT) control algorithms, such as: the perturb and observe (P&O) one, the algorithms based on fuzzy logic and the ones using a neural network approaches. Among these MPPT control algorithms, P&O is one of the most widely used due to its simplicity of implementation. However, the major drawback of this kind of algorithm is the lack of accuracy due to oscillations around the PPM. Conversely, MPPT control using neural networks have shown to be a very efficient solution in term of accuracy. However, this approach remains complex. In this paper we propose an original optimization of the P&O MPPT control with a neural network algorithm leading to a significant reduction of the computational cost required to train it, ensuring a good compromise between accuracy and complexity. The algorithm has been applied to the models of two different types of solar panels, which have been experimentally validated

Contribution to the modeling and control of high concentrated Photovoltaic tracker (hcpv)

Dans une optique de maximisation de la production et de réduction des coûts d’installation, de maintenance et d’entretien des trackers solaires, qui permettent d’orienter les modules photovoltaïques à haute concentration (HCPV), ces travaux de thèse se focalisent sur l’amélioration de la précision et la réduction du coût de la stratégie de génération de la trajectoire du tracker. Dans un premier temps, un simulateur de tracker HCPV est développé offrant une étude de l’influence de la performance du suivi du soleil sur la production des modules HCPV, permettant ainsi une étude et une comparaison des stratégies de génération de trajectoires. Le simulateur est basé sur un modèle comportemental de module HCPV monté sur tracker permettant de prédire la puissance maximale du module HCPV en fonction de l’erreur de position du tracker face au soleil, de l’ensoleillement direct et de la température. Une première stratégie de commande dite de référence a été implémentée sur ce simulateur. C’est une commande hybride qui repose sur un viseur solaire pour corriger l’erreur de poursuite par un calcul astronomique. Ensuite, afin d’améliorer les performances et de réduire les coûts de cette stratégie, une nouvelle approche sans capteur est développée en se basant sur une méthode d’optimisation du gradient de puissance pour la génération de la trajectoire du tracker. Une étude complémentaire est également exposée afin de mettre en évidence des algorithmes de recherche de la puissance maximale (MPPT) pouvant offrir des temps de réponse suffisamment rapides pour ne pas affecter la qualité de l’évaluation du gradient de puissance. Dans ce contexte, une commande MPPT P&O améliorée par un réseau de neurones à complexité réduite est proposée, assurant un compromis entre précision, simplicité et rapiditéThis work focuses on improving the accuracy and on reducing the cost of the tracker generating trajectory strategy, in order to maximize the production and to reduce the installation and the maintenance cost of a solar tracker orienting high concentrated photovoltaic modules (HCPV). Initially, we propose a behavioral modeling of the HCPV module mounted on a dual axis tracker in order to study the influence of the tracking performance on the module power production. Then, this simulator can be used to test control strategies and to compare their performance. Firstly, a classical control strategy is implemented in the simulator. It is based on a hybrid control operating an astronomical calculation to follow the sun path, and a sun sensor to correct the tracking error. A sensorless strategy is proposed in this work to reduce the cost of the HCPV tracker control. This strategy is based on a gradient optimization algorithm to generate the tracker trajectory and to catch the sun path. Tested on the simulator, this strategy presents the same accuracy as the classical strategy while being less costly. The last study proposed in this thesis work concerns maximum power point tracking (MPPT) algorithms, in order to respond to a given problem relating to the practical implementation of gradient algorithm. In this context, we propose an original optimization of the P&O MPPT control with a neural network algorithm leading to a significant reduction of the computational cost required to train it. This approach, which is ensuring a good compromise between accuracy and complexity is sufficiently fast to not affect the quality of the evaluation of the gradient

Maximum power point tracking using P&O control optimized by a neural network approach: a good compromise between accuracy and complexity

International audienceIn the field of power optimization of photovoltaic panels (PV), there exist many maximum power pointtracking (MPPT) control algorithms, such as: the perturb and observe (P&O) one, the algorithms based onfuzzy logic and the ones using a neural network approaches. Among these MPPT control algorithms,P&O is one of the most widely used due to its simplicity of implementation. However, the majordrawback of this kind of algorithm is the lack of accuracy due to oscillations around the PPM.Conversely, MPPT control using neural networks have shown to be a very efficient solution in term ofaccuracy. However, this approach remains complex.In this paper we propose an original optimization of the P&O MPPT control with a neural networkalgorithm leading to a significant reduction of the computational cost required to train it, ensuring a goodcompromise between accuracy and complexity. The algorithm has been applied to the models of twodifferent types of solar panels, which have been experimentally validated