104 research outputs found

    Deep reinforcement learning approach for MPPT control of partially shaded PV systems in Smart Grids

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    Photovoltaic systems (PV) are having an increased importance in modern smart grids systems. Usually, in order to maximize the energy output of the PV arrays a maximum power point tracking (MPPT) algorithm is used. However, once deployed, weather conditions such as clouds can cause shades in the PV arrays affecting the dynamics of each panel differently. These conditions directly affect the available energy output of the arrays and in turn make the MPPT task extremely difficult. For these reasons, under partial shading conditions, it is necessary to have algorithms that are able to learn and adapt online to the changing state of the system. In this work we propose the use of deep reinforcement learning (DRL) techniques to address the MPPT problem of a PV array under partial shading conditions. We develop a model free RL algorithm to maximize the efficiency in MPPT control. The agent's policy is parameterized by neural networks, which take the sensory information as input and directly output the control signal. Furthermore, a PV environment under shading conditions was developed in the open source OpenAI Gym platform and is made available in an open repository. Several tests are performed, using the developed simulated environment, to test the robustness of the proposed control strategies to different climate conditions. The obtained results show the feasibility of our proposal with a successful performance with fast responses and stable behaviors. The best results for the presented methodology show that the maximum operating power point achieved has a deviation less than 1% compared to the theoretical maximum power point.Fil: Avila, Luis Omar. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: de Paula, Mariano. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; ArgentinaFil: Trimboli, Maximiliano Daniel. Universidad Nacional de San Luis. Facultad de Ingeniería y Ciencias Agropecuarias. Laboratorio de Control Automático; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Carlucho, Ignacio. State University of Louisiana; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

    Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects

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    From MDPI via Jisc Publications RouterHistory: accepted 2021-09-30, pub-electronic 2021-10-03Publication status: PublishedTo ensure the continuity of electric power generation for photovoltaic systems, condition monitoring frameworks are subject to major enhancements. The continuous uniform delivery of electric power depends entirely on a well-designed condition maintenance program. A just-in-time task to deal with several naturally occurring faults can be correctly undertaken via the cooperation of effective detection, diagnosis, and prognostic analyses. Therefore, the present review first outlines different failure modes to which all photovoltaic systems are subjected, in addition to the essential integrated detection methods and technologies. Then, data-driven paradigms, and their contribution to solving this prediction problem, are also explored. Accordingly, this review primarily investigates the different learning architectures used (i.e., ordinary, hybrid, and ensemble) in relation to their learning frameworks (i.e., traditional and deep learning). It also discusses the extension of machine learning to knowledge-driven approaches, including generative models such as adversarial networks and transfer learning. Finally, this review provides insights into different works to highlight various operating conditions and different numbers and types of failures, and provides links to some publicly available datasets in the field. The clear organization of the abundant information on this subject may result in rigorous guidelines for the trends adopted in the future

    Model-based Reinforcement Learning of Nonlinear Dynamical Systems

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    Model-based Reinforcement Learning (MBRL) techniques accelerate the learning task by employing a transition model to make predictions. In this dissertation, we present novel techniques for online learning of unknown dynamics by iteratively computing a feedback controller based on the most recent update of the model. Assuming a structured continuous-time model of the system in terms of a set of bases, we formulate an infinite horizon optimal control problem addressing a given control objective. The structure of the system along with a value function parameterized in the quadratic form provides flexibility in analytically calculating an update rule for the parameters. Hence, a matrix differential equation of the parameters is obtained, where the solution is used to characterize the optimal feedback control in terms of the bases, at any time step. Moreover, the quadratic form of the value function suggests a compact way of updating the parameters that considerably decreases the computational complexity. In the convergence analysis, we demonstrate asymptotic stability and optimality of the obtained learning algorithm around the equilibrium by revealing its connections with the analogous Linear Quadratic Regulator (LQR). Moreover, the results are extended to the trajectory tracking problem. Assuming a structured unknown nonlinear system augmented with the dynamics of a commander system, we obtain a control rule minimizing a given quadratic tracking objective function. Furthermore, in an alternative technique for learning, a piecewise nonlinear affine framework is developed for controlling nonlinear systems with unknown dynamics. Therefore, we extend the results to obtain a general piecewise nonlinear framework where each piece is responsible for locally learning and controlling over some partition of the domain. Then, we consider the Piecewise Affine (PWA) system with a bounded uncertainty as a special case, for which we suggest an optimization-based verification technique. Accordingly, given a discretization of the learned PWA system, we iteratively search for a common piecewise Lyapunov function in a set of positive definite functions, where a non-monotonic convergence is allowed. Then, this Lyapunov candidate is verified for the uncertain system. To demonstrate the applicability of the approaches presented in this dissertation, simulation results on benchmark nonlinear systems are included, such as quadrotor, vehicle, etc. Moreover, as another detailed application, we investigate the Maximum Power Point Tracking (MPPT) problem of solar Photovoltaic (PV) systems. Therefore, we develop an analytical nonlinear optimal control approach that assumes a known model. Then, we apply the obtained nonlinear optimal controller together with the piecewise MBRL technique presented previously

    Machine Learning for Photovoltaic Systems Condition Monitoring:A Review

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    Condition Monitoring of photovoltaic systems plays an important role in maintenance interventions due to its ability to solve problems of loss of energy production revenue. Nowadays, machine learning-based failure diagnosis is becoming increasingly growing as an alternative to various difficult physical-based interpretations and the main pile foundation for condition monitoring. As a result, several methods with different learning paradigms (e.g. deep learning, transfer learning, reinforcement learning, ensemble learning, etc.) have been used to address different condition monitoring issues. Therefore, the aim of this paper is at least, to shed light on the most relevant work that has been done so far in the field of photovoltaic systems machine learning-based condition monitoring

    MPPT algorithms based on artificial neural networks for PV system under partial shading condition

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    I pannelli fotovoltaici in condizione di parziale ombreggiatura presentano delle difficoltà nell'insgeguimento del punto di massima potenza MPP. A tal fine sono stati implementati due algoritmi basati su una rete neurale artificiale FeedForward. Gli algoritmi sono stati testati con Simulink, ed è stata verificata la caratteristica attesa da questi algoritmi: sono molto veloci nell'inseguimento del MPP. Questo comporta una maggiore efficienza finale.openEmbargo temporaneo per motivi editorial

    An overview of artificial intelligence applications for power electronics

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    Industrial Applications: New Solutions for the New Era

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    This book reprints articles from the Special Issue "Industrial Applications: New Solutions for the New Age" published online in the open-access journal Machines (ISSN 2075-1702). This book consists of twelve published articles. This special edition belongs to the "Mechatronic and Intelligent Machines" section
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