Behaviour of a One Cell Prestressed Concrete Box Girder Bridge Experimental Study

Note:An experimental study of the behaviour of a 1:7.10 scale direct physical model of a simply supported single span, one cell, prestressed concrete box girder bridge is presented. The experimental techniques developed for construction and the instrumentation required during testing of the bridge model are summarized. Variation of the flexural stiffness and the dynamic characteristics, such as the damping ratio and the fundamental natural frequency, of the bridge model at different level of damage are presented. […]L’étude porte sur le comportement d'un modèle à échelle réduite 1:7.10 d'un pont à poutres-caisson, simplement appuyé, en béton précontraint. Les techniques expérimentales développées pour la construction du modèle et l'instrumentation nécessaire pour les tests sont résumées. La variation de la rigidite en flexion et les caractéristiques dynamiques, comme le coefficient d'amortissement et la fréquence naturale fondamentale, du pant modèle à diffèrent niveau de détérioration sont présentées. […

Application des modèles mathématiques pour l’optimisation de l’énergie dans un système PV

International audienceThis paper proposes an approach used to optimize the energy for a stand-alone photovoltaic (PV) system in isolated regions. The intended objective is house energy comfort. The aim is to present the impact of flow energy of housing on the system reliability. The operation of stand-alone PV system is represented by a simulation program. This later describes the principle of energy equilibrium among diverse sub-systems, using different mathematical models of different parts of renewable energy system. The recommended models were implemented via Matlab-Simulink software with real input data. The reliability is achieved by reducing the loss power supply probability criteria, with improvement of the battery life cycle during the operating years of the PV system.Cet article propose une approche basée sur des modèles mathématiques validés, pour l’optimisation de l’énergie dans un système photovoltaïque (PV) autonome destiné à l’électrification d’un habitat dans une région isolée. L’objectif attendu est le confort énergétique de l’habitat. Le but du travail est de montrer à travers la modélisation mathématique l’impact du profil dynamique de la consommation énergétique sur la fiabilité du système PV. Le fonctionnement de ce dernier est représenté par un programme de simulation sous Simulink, qui décrit le principe de l'équilibre énergétique entre les sous-systèmes, en utilisant des modèles mathématiques validés avec des données d’entrées réelles. Les résultats obtenus ont montrés une bonne fiabilité des modèles utilisés, pour prévoir le fonctionnement optimal du système photovoltaïque

Improved Semi-Supervised Data-Mining-Based Schemes for Fault Detection in a Grid-Connected Photovoltaic System

Fault detection is a necessary component to perform ongoing monitoring of photovoltaic plants and helps in their safety, maintainability, and productivity with the desired performance. In this study, an innovative technique is introduced by amalgamating Latent Variable Regression (LVR) methods, namely Principal Component Regression (PCR) and Partial Least Square (PLS), and the Triple Exponentially Weighted Moving Average (TEWMA) statistical monitoring scheme. The TEWMA scheme is known for its sensitivity to uncovering changes of small magnitude. Nevertheless, TEWMA can only be utilized for monitoring single variables and ignoring the correlation among monitored variables. To alleviate this difficulty, the LVR methods (i.e., PCR and PLS) are used as residual generators. Then, the TEWMA is applied to the obtained residuals for fault detection purposes, where the detection threshold is computed via kernel density estimation to improve its performance and widen its applicability in practice. Real data with different fault scenarios from a 9.54 kW photovoltaic plant has been used to verify the efficiency of the proposed schemes. Results revealed the superior performance of the PLS-TEWMA chart compared to the PLS-TEWMA chart, particularly in detecting anomalies with small changes. Moreover, they have almost comparable performance for large anomalies

Ensemble Learning Techniques-Based Monitoring Charts for Fault Detection in Photovoltaic Systems

Over the past few years, there has been a significant increase in the interest in and adoption of solar energy all over the world. However, despite ongoing efforts to protect photovoltaic (PV) plants, they are continuously exposed to numerous anomalies. If not detected accurately and in a timely manner, anomalies in PV plants may degrade the desired performance and result in severe consequences. Hence, developing effective and flexible methods capable of early detection of anomalies in PV plants is essential for enhancing their management. This paper proposes flexible data-driven techniques to accurately detect anomalies in the DC side of the PV plants. Essentially, this approach amalgamates the desirable characteristics of ensemble learning approaches (i.e., the boosting (BS) and bagging (BG)) and the sensitivity of the Double Exponentially Weighted Moving Average (DEWMA) chart. Here, we employ ensemble learning techniques to exploit their capability to enhance the modeling accuracy and the sensitivity of the DEWMA monitoring chart to uncover potential anomalies. In the ensemble models, the values of parameters are selected with the assistance of the Bayesian optimization algorithm. Here, BS and BG are adopted to obtain residuals, which are then monitored by the DEWMA chart. Kernel density estimation is utilized to define the decision thresholds of the proposed ensemble learning-based charts. The proposed monitoring schemes are illustrated via actual measurements from a 9.54 kW PV plant. Results showed the superior detection performance of the BS and BG-based DEWMA charts with non-parametric threshold in uncovering different types of anomalies, including circuit breaker faults, inverter disconnections, and short-circuit faults. In addition, the performance of the proposed schemes is compared to that of BG and BS-based DEWMA and EWMA charts with parametric thresholds

Adaptive Fuzzy Logic-Based Control and Management of Photovoltaic Systems with Battery Storage

Renewable energy sources (RESs) such as solar photovoltaic (PV) systems are increasingly used as distributed generation for replacing the conventional energy. At the same time, energy storage systems like battery (BAT) must be applied for maintaining the balance between fluctuating energy production and load consumption. BAT’s state of charge (SOC) should be maintained within their design limits unaffected by RES intermittency and/or load power variations. This necessitates advanced power control and management methodologies for overcoming challenging conditions. This paper discusses and evaluates an optimal DC bus voltage regulation approach: an intelligent controller using an adaptive fuzzy logic controller (FLC) and a novel supervisory power management strategy for PV systems with BAT. The objectives are to keep a stable power flow in the system and guarantee the continuity of service by ensuring that the system components do not exceed their limits. In this manner, the DC bus voltage regulation of the PV/BAT system can be improved in comparison with conventional regulation. Therefore, the most important contributions of this work are as follows. (1) Development of comprehensive and modular novel energy management system (EMS): its originality is related to the inclusion of the control system limits with faster SOC balancing and smaller DC bus voltage fluctuation. (2) Providing a simple power flow management implementation that considers the optimal energy flow between PV system, BAT system, and load: a balance between minimal energy flow in the connecting line and the least requirements of BAT capacity is kept, reducing component constraints with a very straightforward structure. (3) Furthermore, FLC offers high robustness and smooth performances. FLC is added to the control strategy design requirements to reduce DC bus voltage deviation. (4) Real-time simulation/experimentation-based complete cases utilizing Matlab/Simulink and DSpace are illustrated to testify the effectiveness of the proposed FLC and EMS

Fuzzy logic approach based mppt for the dynamic performance improvement for PV systems

International audienceRenewable Energies (RE) are considered as an important alternative sources of energy for the generation of electricity such as hydrogen and photovoltaic energies. To ensure an efficient photovoltaic energy conversion several Maximum Power Point Tracking (MPPT) algorithms have been developed to incite the PV field to deliver maximum power. This paper presents a comprehensive comparative study of two classical MPPT algorithms against the Artificial Intelligence (AI)-based one under hypothetical and realistic atmospheric conditions. The suggested algorithms are Perturb and Observe (P&O) algorithm, Incremental of Conductance (IC) algorithm and Fuzzy Logic based Incremental Conductance (FL-IC). After the PV verification under hypothetical conditions such as slow and fast irradiance variations, the effectiveness of those numerous methods are evaluated in terms of stability, robustness and rapidity considering one-year realistic atmospheric irradiance data for Bouzareah region. Through MatlabTM-simulation results, the superiority of the FL-IC over other both classical algorithms is highlighted

Intelligent Monitoring of Photovoltaic Systems via Simplicial Empirical Models and Performance Loss Rate Evaluation under LabVIEW: A Case Study

The capacity of photovoltaic solar power installations has been boosted last years by reaching a new record with 175 GWdc of newly installed solar power in 2021. To guarantee reliable performances of photovoltaic (PV) plants and maintain target requirements, faults have to be reliably detected and diagnosed. A method for an effective and reliable fault diagnosis of PV plants based on the behavioral model and performance analysis under the LabVIEW environment is presented in this paper. Specifically, the first phase of this study consists of the behavioral modeling of the PV array and the inverter in order to estimate the electricity production and analyze the performance of the 9.54 kWp Grid Connected PV System (GCPVS). Here, the results obtained from the empirical models were validated and calibrated by experimental data. Furthermore, a user interface for modeling and analyzing the performance of a PV system under LabVIEW has been designed. The second phase of this work is dedicated to the design of a simple and efficient diagnostic tool in order to detect and recognize faults occurring in the PV systems. Essentially, the residuals obtained using the parametric models are analyzed via the performance loss rates (PLR) of four electrical indicators (i.e., DC voltage, DC current, DC power, and AC power). To evaluate the proposed method, numerous environmental anomalies and electrical faults affecting the GCPVS were taken into account. Results demonstrated the satisfactory prediction performance of the considered empirical models to predict the considered variables, including DC current, DC power, and AC power with an R2 of 0.99. Moreover, the obtained results show that the detection and recognition of faults were successfully achieved

Intelligent Monitoring of Photovoltaic Systems via Simplicial Empirical Models and Performance Loss Rate Evaluation under LabVIEW: A Case Study

The capacity of photovoltaic solar power installations has been boosted last years by reaching a new record with 175 GWdc of newly installed solar power in 2021. To guarantee reliable performances of photovoltaic (PV) plants and maintain target requirements, faults have to be reliably detected and diagnosed. A method for an effective and reliable fault diagnosis of PV plants based on the behavioral model and performance analysis under the LabVIEW environment is presented in this paper. Specifically, the first phase of this study consists of the behavioral modeling of the PV array and the inverter in order to estimate the electricity production and analyze the performance of the 9.54 kWp Grid Connected PV System (GCPVS). Here, the results obtained from the empirical models were validated and calibrated by experimental data. Furthermore, a user interface for modeling and analyzing the performance of a PV system under LabVIEW has been designed. The second phase of this work is dedicated to the design of a simple and efficient diagnostic tool in order to detect and recognize faults occurring in the PV systems. Essentially, the residuals obtained using the parametric models are analyzed via the performance loss rates (PLR) of four electrical indicators (i.e., DC voltage, DC current, DC power, and AC power). To evaluate the proposed method, numerous environmental anomalies and electrical faults affecting the GCPVS were taken into account. Results demonstrated the satisfactory prediction performance of the considered empirical models to predict the considered variables, including DC current, DC power, and AC power with an R2 of 0.99. Moreover, the obtained results show that the detection and recognition of faults were successfully achieved