Development of a photovoltaic MPPT control based on neural network

The Maximum Power Point Tracking (MPPT) is an important factor to increase the efficiency of the solar photovoltaic (PV) system. This paper presents a solar PV system containing a solar PV array, a DC/DC boost converter and a load. Di↵erentMPPT algorithms have been established with their features. The conventional algorithms (Perturb and Observe, Incremental Conductance and Open Circuit Voltage) show a lot of drawbacks. The major issue is the tracking of the Maximum Power Point (MPP) when environmental conditions change faster. So, a MPPT technique based on Neural Network (NN) was developed and which can enhance the efficiency and gathers the advantages of a lot of techniques. A multi layer neural network with back-propagation algorithm is used in order to have a small Mean Squared Error (MSE). The inputs of NN are irradiance, temperature and the output is the duty cycle that controls the boost converter. Finally, it is discussed the results and made comparison in terms of performance of the di↵erent algorithms, covering the overshoot, time response, oscillation and stability.info:eu-repo/semantics/publishedVersio

Development of a photovoltaic MPPT control based on neural network

The Maximum Power Point Tracking (MPPT) is an important factor to increase the efficiency of the solar photovoltaic (PV) system. This paper presents a solar PV system containing a solar PV array, a DC/DC boost converter and a load. Different MPPT algorithms have been established with their features. The conventional algorithms (Perturb and Observe, Incremental Conductance and Open Circuit Voltage) show a lot of drawbacks. The major issue is the tracking of the Maximum Power Point (MPP) when environmental conditions change faster. So, a MPPT technique based on Neural Network (NN) was developed and which can enhance the efficiency and gathers the advantages of a lot of techniques. A multi layer neural network with back-propagation algorithm is used in order to have a small Mean Squared Error (MSE). The inputs of NN are irradiance, temperature and the output is the duty cycle that controls the boost converter. Finally, it is discussed the results and made comparison in terms of performance of the different algorithms, covering the overshoot, time response, oscillation and stability.info:eu-repo/semantics/publishedVersio

Compressive Strengths of Concrete Formulated with Algerian Local Materials

In the developed countries, concrete undergoes not only an improvement of its performance in both fresh and hardened states, but also a good control of the quality of its components. This is not the case in most of the building sites in our country. The factors affecting the quality of concrete during its production and implementation are numerous. Due to this fact, we aim to study the influence of the nature of the components on the mechanical property of the mixture, in particular the compressive strength which remains, for the engineer, the most significant property of material, if one excludes the indicators of durability (Mounanga et al., 2006). For this purpose, an extensive experimental program is conducted in our laboratory where more than 1500 cylindrical specimens 16x32 cm, using local materials, are tested. The mixtures are obtained using the Dreux-Gorisse’s method and the cure of the specimens is done both out in the open and immersed in water. We show on the one hand, that the intrinsic properties of the components of the studied concrete and particularly the broken up particles, offer to the concrete complete satisfactory resistances and on the other hand, the cement proportioning for the selected class does not offer notable differences as regards to compressive strengths. The whole experimental results obtained constituted a data bank. The latter enabled us to establish an abacus of formulation the use of which appears easy

Development of technical economic analysis for optimal sizing of a hybrid power system: a case study of an industrial site in Tlemcen Algeria

The current study aimed to develop an optimal sizing simulation model for an off-grid photovoltaic-wind hybrid power system of an industrial site in Algeria. The loss of power supply probability algorithm was used for sizing our hybrid system. The technical and economic evaluation for the case study showed that the storage system occupied the most critical part of the total investment cost of the hybrid system. The investment cost analysis indicated a unique optimal configuration for each size of the batteries bank. For one day's autonomy, the best size of the hybrid system corresponded to 61 PV panels and 9 wind turbines. Based on a levelized cost of energy analysis, the cost of the batteries represented for this combination is 52% of the total investment cost. The wind turbines accounted for 42% and the PV panels for only 3%. This combination of the hybrid system resulted in an energy cost that was very competitive with most European countries. However, the public energy grid cost in the case study region was still six times lower due to government subsidies. The findings are very encouraging and can help decision-makers adopt alternative and more sustainable solutions in energy policy. These results will aid in determining future research directions in Algeria's hybrid renewable energy systems.National funds funded Luís Frölén Ribeiro through FCT - Fundação para a Ciência e Tecnologia, through project UIDB/50022/2020 – LAETAinfo:eu-repo/semantics/publishedVersio

An innovative optimization approach for energy management of a microgrid system

The local association of electrical generator including renewable energies and storage technologies approximately installed to the client made way for a small-scale power grid called a microgrid. In certain cases, the random nature of renewable energy sources, combined with the variable pattern of demand, results in issues concerning the sustainability and reliability of the microgrid system. Furthermore, the cost of the energy coming from conventional sources is considering as matter to the private consumer due to its high fees. An improved methodology combining the simplex-based linear programming with the particle swarm optimisation approach is employed to implement an integrated power management system. The energy scheduling is done by assuming the consumption profile of a smart city. two scenarios of energy management have been suggested to illustrate the behaviour of cost and gas emissions for an optimised energy management. The results showed the reliability of the energy management system using an improvemed approach in scheduling of the energy flows for the microgrid producers, limiting the utility’s cost versus an experiment that had already been done for a similar system using the identical data. The outcome of the computation identified the ideal set points of the power generators in a smart city supplied by a microgrid, while guaranteeing the comfort of the customers i.e without intermetency in the supply, also, reducing the emissions of greenhouse gases and providing an optimal exploitation cost for all smart city users. Morover, the proposed energy management system gave an inverse relation between economic and environmental aspects, in fact, a multi-objective optimization approach is performed as a continuation of the work proposed in this paperinfo:eu-repo/semantics/publishedVersio

Particle swarm optimization for micro-grid power management and load scheduling

A smart power management strategy is needed to economically manage local production and consumption while maintaining the balance between supply and demand. Finding the best-distributed generators’ set-points and the best city demand scheduling can lead to moderate and judicious use out of critical moments without compromising smart city residents’ comfort. This paper aimed at applying the Particle Swarm Optimization (PSO) to minimize the operating cost of the consumed energy in a smart city supplied by a micro-grid. Two PSO algorithms were developed in two steps to find the optimal operating set-points. The first PSO algorithm led to the optimal set-points powers of all micro-grid generators that can satisfy the non-shiftable needs of the smart city demand with a low operating cost. While the second PSO algorithm aimed at scheduling the shiftable city demand in order to avoid peak hours when the operating cost is high. The results showed that the operating costs during the day were remarkably reduced by using optimal distributed generators’ set-points and scheduling shiftable loads out of peaks hours. To conclude, the main advantages of the proposed methodology are the improvement in the local energy efficiency of the micro-grid and the reduction in the energy consumption costs

Probabilistic Modelling of Compressive Strength of Concrete Using Response Surface Methodology and Neural Networks

International audienceIn this paper we aim to achieve a probabilistic modelling of the compressive strength of concrete using three Response Surface Models (RSM) and the Artificial Neural Network method (ANN). The Input random variables for the three RSM and for the ANN are: cement content, water content, measure of slump and air content, while the output for all the models is the compressive strength of concrete at 28 days. 2 More than 800 cylindrical specimens 16x32 cm were tested. These experimental data are used to check the reliability of the suggested probabilistic models and their capability of prediction. It is shown that the use of these new RSM is as simple as that of any of the basic formulas, yet they provide an improved tool for the prediction of concrete strength and for concrete proportioning. We also show that the concrete compressive strength could be readily and accurately estimated from the established ANN

Optimal energy management of microgrid using multi-objective optimisation approach

The use of several distributed generators as well as the energy storage system in a local microgrid require an energy management system to maximize system efficiency, by managing generation and loads. The main purpose of this work is to find the optimal set-points of distributed generators and storage devices of a microgrid, minimizing simultaneously the energy costs and the greenhouse gas emissions. A multi-objective approach called Pareto-search Algorithm based on direct multi-search is proposed to ensure optimal management of the microgrid. According to the non-dominated resulting points, several scenarios are proposed and compared. The effectiveness of the algorithm is validated, giving a compromised choice between two criteria: energy cost and GHG emissions.info:eu-repo/semantics/publishedVersio

Effet du dosage en superplastifiant sur les caractéristiques rhéologiques des bétons autoplaçants

L’utilisation des superplastifiants dans les bétons autoplaçants (BAP) est indispensable pour garantir la fluidité recherchée. A travers ce travail expérimental, nous visons à étudier l’effet du dosage en superplastifiant sur les caractéristiques des bétons autoplaçants (BAP). Les propriétés rhéologiques des mélanges sont étudiées au moyen d’un rhéomètre de forte capacité de couple muni d’une géométrie couette. En complément, les essais de la norme NF 206/CN ont été réalisés afin d’établir une comparaison avec les essais sur rhéomètre. Les résultats ont montré que les superplastifiants améliorent l’ouvrabilité des BAP en diminuant significativement les seuils de cisaillement statiques et dynamiques. Cependant, leur effet sur la viscosité plastique dépend fortement du dosage utilisé. Par ailleurs l’augmentation du dosage en superplastifiant s’accompagne toujours par une diminution de la stabilité du mélange, ce qui peut engendrer des pertes dans la résistance mécanique

Smart microgrid management: a hybrid optimisation approach

The association of distributed generators, energy storage systems and controllable loads close to the energy consumers gave place to a small-scale electrical network called microgrid. The stochastic behavior of renewable energy sources, as well as the demand variation, can lead in some cases to problems related to the reliability of the microgrid system. On the other hand, the market price of electricity from mainly non-renewable sources becomes a concern for a simple consumer due to its high costs. An innovative optimization method, combining linear programming, based on the simplex method, with the particle swarm optimisation algorithm is used to develop an energy management system. The management is performed considering a smart city’s consumption profile, two management scenarios have been proposed to characterize the relation price versus gas emissions for optimal energy management. The simulation results have demonstrated the reliability of the optimisation approach on the energy management system in the optimal scheduling of the microgrid generators power flows, having achieved a better energy price compared to a previous study with the same data. The computational results identified the optimal set-points of generators in a smart city supplied by a microgrid while ensuring consumer comfort, minimising greenhouse gas emissions and guarantee an appropriate operating price for all consumers in the smart city. The energy management system based on the proposed optimisation approach gave an inverse correlation between economic and environmental aspects, in fact, a multi-objective optimisation approach is performed as a continuation of the work proposed in this paper.This work has been supported by Fundação La Caixa and FCT — Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/05757/2020info:eu-repo/semantics/publishedVersio