Genetic Algorithms for Optimal Reactive Power Compensation of a Power System with Wind Generators based on Artificial Neural Networks

In this paper, we develop a method to maintain an acceptable voltages profile and minimization of active losses of a power system including wind generators in real time. These tasks are ensured by acting on capacitor and inductance benches implemented in the consuming nodes. To solve this problem, we minimize an objective function associated to active losses under constraints imposed on the voltages and the reactive productions of the various benches. The minimization procedure was realised by the use of genetic algorithms (GA). The major disadvantage of this technique is that it requires a significant computing time thus not making it possible to deal with the problem in real time. After a training phase, a neural model has the capacity to provide a good estimation of the voltages, the reactive productions and the losses for forecast curves of the load and the wind speed, in real time

Modeliranje umjetne neuronske mreže višesustavnom dinamičkom adsorpcijom organskih onečišćujućih tvari na aktivnom ugljenu

The aim of this work was to model multi-system dynamic adsorption using an artificial intelligence technique. A set of data points, collected from scientific papers containing the dynamic adsorption kinetics on activated carbon, was used to build the artificial neural network (ANN). The studied parameters were molar mass, initial concentration, flow rate, bed height, particle diameter, BET surface area, average pore diameter, time, and concentration of dimensionless effluents. Results showed that the optimized ANN was obtained with a high correlation coefficient, R = 0.997, a root mean square error of RMSE = 0.029, and a mean absolute deviation of AAD (%) = 1.810 during the generalisation phase. Furthermore, a sensitivity analysis was also conducted using the inverse artificial neural network method to study the effect of all the inputs on the dynamic adsorption. Also in this work, the traceability of the estimated results was conducted by developing a graphical user interface. This work is licensed under a Creative Commons Attribution 4.0 International License.Cilj ovog rada bio je modelirati višesustavnu dinamičku adsorpciju tehnikom umjetne inteligencije. Za izradu umjetne neuronske mreže (ANN) upotrijebljen je skup podataka prikupljen iz znanstvenih radova koji sadrže kinetiku dinamičke adsorpcije na aktivnom ugljenu. Ispitivani parametri bili su: molarna masa, početna koncentracija, brzina protoka, visina sloja, promjer čestica, površina BET, prosječni promjer pora, vrijeme i koncentracija bezdimenzijskih otpadnih voda. Rezultati su pokazali da je tijekom faze generalizacije dobiven optimiran ANN s visokim koeficijentom korelacije, R = 0,997, korijenom srednje kvadratne pogreške RMSE = 0,029 i srednjim apsolutnim odstupanjem AAD (%) = 1,810. Dodatno, provedena je i analiza osjetljivosti primjenom metode inverzne umjetne neuronske mreže kako bi se proučio učinak svih ulaza na dinamičku adsorpciju. U radu je provedena i sljedivost procijenjenih rezultata razvojem grafičkog korisničkog sučelja. Ovo djelo je dano na korištenje pod licencom Creative Commons Imenovanje 4.0 međunarodna

Kritična svojstva i acentrični čimbenici modeliranja čistih spojeva primjenom modela QSPR-SVM i algoritma Dragonfly

This work aimed to model the critical pressure, temperature, volume properties, and acentric factors of 6700 pure compounds based on five relevant descriptors and two thermodynamic properties. To that end, four methods were used, namely, multi-linear regression (MLR), artificial neural networks (ANNs), support vector machines (SVMs) using sequential minimal optimisation (SMO), and hybrid SVM with Dragonfly optimisation algorithm (SVM-DA) to model each property. The results suggested that hybrid SVM-DA had better prediction performance compared to the other models in terms of average absolute relative deviation (AARD%) of {0.7551, 1.962, 1.929, and 2.173} and R2 of {0.9699, 0.9673, 0.9856, and 0.9766} for critical temperature, critical pressure, critical volume, and acentric factor, respectively. The developed models can be used to estimate the property of newly designed compounds only from their molecular structure.Cilj ovog rada bio je modeliranje kritičnog tlaka, temperature, volumnih svojstava i acentričnih čimbenika 6700 čistih spojeva na temelju pet relevantnih deskriptora i dva termodinamička svojstva. U tu svrhu primijenjene su četiri metode: višestruka linearna regresija (MLR), umjetna neuronska mreža (ANN), metoda potpornih vektora (SVM) i algoritam optimizacije Dragonfly (SVM-DA), koji se za modeliranje svakog svojstva koriste sekvencijalnom minimalnom optimizacijom (SMO) i hibridnim SVM-om. Rezultati su pokazali da hibridni SVM-DA daje bolje predviđanje u odnosu na ostale modele u smislu postotka prosječnog apsolutnog relativnog odstupanja (AARD%) od {0,7551, 1,962, 1,929 i 2,173} i R2 od {0,9699, 0,9673, 0,9856, i 0,9766} za kritičnu temperaturu, kritični tlak, kritični volumen i acentrični faktor. Razvijeni modeli mogu se primjenjivati za procjenu svojstava novodizajniranih spojeva samo iz njihove molekularne strukture

A study on the characteristics of Algerian Hassi-Messaoud asphaltenes:Algerian Hassi-Messaoud asphaltenes: solubility and precipitation

This study focuses on detailed characterizations of asphaltene fractions extracted from the Algerian Hassi-Messaoud oil field. It was found that the extracted asphaltenes are not completely soluble in toluene, instead two fractions of asphaltenes were obtained upon solubilizing the heptane-precipitated neat asphaltenes in toluene. Extensive characterizations of the toluene-soluble and insoluble fractions were carried out using elemental analysis, Fourier transform infrared (FTIR), thermogravimetric analysis (TGA), X-ray diffraction (XRD) and solid-state nuclear magnetic resonance (ssNMR). It was suggested that the high oxygen content and uneven compositional structures are the main contributors to asphaltene instability. The toluene-insoluble fractions were found to have higher polarity and aromaticity as well as more oxygen content than the neat asphaltenes and toluene-soluble fractions

Analyzing the Effects of MBPSS on the Transit Stability and High-Level Integration of Wind Farms during Fault Conditions

As the demand for renewable energy continues to increase, wind power has emerged as a prominent source of clean energy. However, incorporating wind energy into the power generation system at a high level can significantly impact the dynamic performance of the power system, resulting in increased uncertainties during operation. This study investigated the effectiveness of the Multi-Band Power System Stabilizer (MBPSS), a new power system stabilizer, in suppressing dynamic oscillations in a multi-machine power system connected to a wind farm. This research focused on analyzing the transient stability of a nine-bus network, commonly known as the Western System Coordinating Council (WSCC), integrated with a Doubly Fed Induction Generator (DFIG) using MATLAB/Simulink. The study evaluated the dynamic performance of the proposed system under fault conditions, including Line-to-Line-to-Line-to-Ground (LLLG) faults. Simulation results showed that MBPSS effectively dampened oscillations and improved the stability of the power system, even in the presence of severe faults and high-level integration of wind farms

Estimation of Properties of Liquid-Vapor Mixture of Some Refrigerants at High Pressure for Solar-Photovoltaic Refrigeration

Abstract. In this work, a hybrid method based on neural network and particle swarm optimization is applied to literature data to develop and validate a model that can predict with precision vapor-liquid equilibrium data for the binary systems (hexafluoroethane (R116(1)), 1,1,1,2-tetrafluoroethane (R134a) and R1234ze) . ANN was used for modelling the non-linear process. The PSO was used for two purposes: replacing the standard back propagation in training the ANN and optimizing the process. The training and validation strategy has been focused on the use of a validation agreement vector, determined from linear regression analysis of the predicted versus experimental outputs, as an indication of the predictive ability of the neural network model. Statistical analysis of the predictability of the optimized neural network model shows excellent agreement with experimental data (coefficient of correlation equal to 0.998). Furthermore, the comparison in terms of average relative deviation (AARD%) between, the predicted results for the whole temperature and pressure range shows that the ANN-PSO model can predict far better the mixture properties than cubic equations of state

Mechanical Behavior Analysis of a Friction Stir Welding (FSW) for Welded Joint Applied to Polymer Materials

Welding is a technique of fusion joining the material involving a process of inter-molecular diffusion adhesion. Polymer welding is an assembly method among several known assembly techniques such as gluing. This welding process applies to thermoplastics; they have the rheological or softening characteristics during melting. This process is fast and controlled in order to obtain a solid and durable mechanical connection on the series parts. This study focuses on the weldability of high density polyethylene (HDPE) using the friction stir welding technique. A parametric choice was made to optimize the operating parameters namely the shape of the welding tool, the speed of rotation and the speed of advance of the tool.  Monotonic tensile tests were used to compare the mechanical characteristics between a HDPE test specimen and a specimen taken from an FSW weldment. It emerges from this study that the FSW welding introduces a weakening of the joints characterized by a clear decrease of the deformation at break

New Construction of a Chaotic Generator on the Lorenz Attractor

it be known that the chaotic phenomena can be obtained from relatively simple systems that are governed by a small number of variables. The system will then be deterministic, although its behaviour is unforeseeable. The chaotic generator hereby suggested is implemented under the 7.0 version of MATLAB software. It makes use exclusively, of the fundamental properties of chaotic systems; that are sensitivity to initial conditions and equations of strange attractor. All is done in order to set up systems with protected transmissions. As a matter of fact and in the long term, the unforeseeable behaviour of such systems is very much related to the extreme sensitivity of initials conditions. Another fundamental property is that the chaotic system is characterized by a strange attractor, within the space of state