Optimal voltage control in distribution systems using PV generators

Recently, renewable energy technologies such as wind turbine generators and photovoltaic systems have been introduced as distributed generation. The connection of large number of distributed generators causes voltage deviation beyond the statutory range in a distribution system. In this paper, a methodology for voltage control in proposed by using the tap changing transformers and the inverters interfaced with the distributed generators. In the proposed method, information of the voltage and power is collected via a communication network. Based on these information, the optimal reference values are calculated at the control center, and sent to the transformers and the inverters. The proposed method accomplishes a coordinated operation among the control equipments and reduces the voltage deviation. Effectiveness of the proposed method is verified by the numerical simulation results.     

Tensile properties of hot rolled Mg–3Sn–1Ca alloy sheets at elevated temperatures

Mechanical behavior of hot rolled Mg–3Sn–1Ca (TX31) magnesium alloy sheets were studied in the temperature range 25–350 °C. The microstructure of the alloy consisted of the eutectic structure of α-Mg + Mg₂Sn and a dispersion of needle-like CaMgSn. The highest room-temperature ductility of 18% was obtained by hot rolling of the cast slabs at 440 °C, followed by annealing at 420 °C. The high temperature tensile deformation of the material was characterized by a decrease in work hardening exponent (n) and an increase in strain rate sensitivity index (m). These variations resulted in respective drops of proof stress and tensile strength from 126.5 MPa and 220 MPa at room temperature to 23.5 MPa and 29 MPa at 350 °C. This was in contrast to the ductility of the alloy which increased from 18% at room temperature to 56% at 350 °C. The observed variations in strength and ductility were ascribed to the activity of non-basal slip systems and dynamic recovery at high temperatures. The TX31 alloy showed lower strength than AZ31 magnesium alloy at low temperatures, while it exhibited superior strength at temperatures higher than 200 °C, mainly due to the presence of thermally stable CaMgSn particles.     

Artificial neural networks modeling of electrospinning of polyethylene oxide from aqueous acid acetic solution

The artificial neural networks (ANNs) were used to provide a model for investigating the relationships of the electrospinning parameters with the diameter of polyethylene oxide (PEO) nanofibers from acid acetic aqueous solution. The effects of four parameters including PEO concentration, acetic acid concentration, applied voltage, and temperature of the electrospinning media on the nanofibers mean diameter were investigated. To train, test, and valid the model, three datasets of the input variables with random values were prepared and the mean diameters obtained were taken as the output for the network. The datasets were analyzed by ANNs software and the correlation coefficient, R-squared (R²), between the predicted values of the nanofibers mean diameter and actual amount were obtained. The results demonstrate the capability of the ANNs model for predicting the nanofibers diameter. The 3-D plots generated from the model show complex and nonlinear relationships between the parameters and nanofibers diameter. From the model, increasing the PEO concentration above a critical point leads to a sharp increase in the nanofibers mean diameter. The effects of applied voltage and temperature are mainly dependent on the PEO concentration. The acetic acid concentration, in general shows a direct relation with the nanofibers mean diameter. The plots also show that to produce nanofibers with the lowest diameter, both the PEO concentration and AcOH concentration should be at lowest values regardless the applied voltage and temperature. In contrast, highest nanofibers diameters are obtained when the PEO concentration and AcOH concentration are at their high values. © 2012 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

Application of CFD for simulation of a baffled tubular membrane

Computational fluid dynamics (CFD) investigation of a tubular membrane channel containing a set of baffles was conducted for predicting turbulent flow. Simulation was performed using an array of baffles oriented either in the flow or in the reverse direction. A range of local parameters such as stream function, velocity, static pressure, wall shear stress, turbulent kinetic energy, and turbulent dissipation energy on the membrane surface was computed using CFD code FLUENT. The simulation results indicate that the presence of baffle can improve the local shear stress on the membrane surface and produces eddy activities which enhance the filtration performance. The observed flux enhancement can be attributed to the intense fluctuations of wall velocity and shear stress which can disrupt the growth of boundary layer on the membrane surface. The experimental evaluation was performed through cross flow microfiltration of titanium dioxide suspension which showed an acceptable agreement with the CFD predictions.     

Application of principal component-artificial neural network models for simultaneous determination of phenolic compounds by a kinetic spectrophotometric method

A multicomponent analysis method based on principal component analysis-artificial neural network models (PC-ANN) is proposed for the determination of phenolic compounds. The method relies on the oxidative coupling of phenols (phenol, 2 chlorophenol, 3-chlorophenol and 4-chlorophenol) to N,N-diethyl-p-phenylenediamine in the presence of hexacyanoferrate(III). The reaction monitored at analytical wavelength 680 nm of the dye formed. Phenols can be determined individually over the concentration range 0.1-7.0 microg ml(-1). Differences in the kinetic behavior of the four species were exploited by using PC-ANN, to resolve mixtures of phenol. After reducing the number of kinetic data using principal component analysis, an artificial neural network consisting of three layers of nodes was trained by applying a back-propagation learning rule. The optimized ANN allows the simultaneous quantitation of four analytes in mixtures with relative standard errors of prediction in the region of 5% for four species. The results show that PC-ANN is an efficient method for prediction of the four analytes.     

A fuzzy control strategy for power smoothing and grid dynamic response enrichment of a grid‐connected wind energy conversion system

This paper concentrates on the output power smoothing and the grid dynamic response enhancement of a grid‐interactive MW‐class permanent magnet synchronous generator‐based wind energy conversion system (WECS). A simple fuzzy controller method is applied to improve the overall performance of the WECS. The proposed method can retrieve the storing kinetic energy from the inertia of a wind turbine, perfectly. As a result, it can ensure a proficient power smoothing of the variable speed WECS. On the other hand, the grid side inverter is controlled by the fuzzy controller. This approach can reduce the fluctuation of DC link voltage and can deliver a smooth power to the power grid. The proposed method is compared with two other methods such as the maximum power point tracking control method and the without fuzzy controller method. A simple shunt circuit also includes in the DC link circuit. Therefore, during the system fault condition, the WECS can perform a stable operation. Effectiveness of the proposed method is verified by numerical simulations. Copyright © 2013 John Wiley & Sons, Ltd.     

Optimization of Emergency Ventilation Strategies in a Roadway Tunnel

In order to evaluate the effectiveness of in-place emergency ventilation strategies to control smoke spread in the event of a fire in a section of a roadway tunnel, both numerical and experimental studies were performed. The experimental study was conducted to provide the necessary initial and boundary conditions for the numerical phase of the investigation. A fire heat release rate of 1 MW was used in all fire tests. This fire heat release rate was selected to minimize the risk of damage to the tunnel and its associated systems while producing reliable data for visualizing the smoke movement in the tunnel. The numerical study used Computational Fluid Dynamics, Fire Dynamic Simulator version 4.0 to investigate smoke removal in the tunnel for large fire of 30 MW (bus or truck on fire). In total, four field fire tests and seven numerical simulations were conducted. Based on the study results, recommendations were made to optimize the ventilation scenarios in the tunnel section. This article presents the details of the study as well as the recommendations made.     

Modeling and Hardware Implementation on the FPGA of a Variable Structure Control Associated with a DTC-SVM of an Induction Motor

The conventional Direct Torque Control (DTC) is considered as a powerful approach which is widely used to control induction motors. However, this control approach suffers from several problems like undesirable torque and flux ripples and stator resistance variations, especially at low speed. To overcome these problems, a Variable Structure Control associated with a DTC based on Space Vector Modulation (VSC-DTC-SVM) is proposed in this paper to reduce ripples and enhance the system robustness. Due to the complex scheme of the suggested VSC-DTC-SVM approach, its implementation on software solutions such as microcontrollers and digital signal processors requires a high sampling period. In fact, this period creates delay in applying the inverter switching states which results undesirable ripples and distortions in the torque and the stator current, respectively. This delay is inevitable, and thus the VSC-DTC-SVM performances are affected and the sampling frequency is limited. These limitations in digital control are mainly caused by the calculation speed which depends on the complexity of the algorithm and the serial processing of the software solutions. To cope with this problem, a programmable digital circuit such as the FPGA is chosen to preserve the performances of the VSC-DTC-SVM in spite of its complexity, thanks to its parallel processing. To illustrate this, a hardware implementation of the VSC-DTC-SVM of an induction motor on the FPGA is presented and analyzed. The VSC-DTC-SVM performances in terms of ripples, under stator resistance variation, are presented by a theoretical study, a digital simulation and a hardware co-simulation using an FPGA Virtex 5.     

Optimization of economic load dispatch of higher order general cost polynomials and its sensitivity using modified particle swarm optimization

This paper presents a modified particle swarm optimization (MPSO) for constrained economic load dispatch (ELD) problem. Real cost functions are more complex than conventional second order cost functions when multi-fuel operations, valve-point effects, accurate curve fitting, etc., are considering in deregulated changing market. The proposed modified particle swarm optimization (PSO) consists of problem dependent variable number of promising values (in velocity vector), unit vector and error–iteration dependent step length. It reliably and accurately tracks a continuously changing solution of the complex cost function and no extra concentration/effort is needed for the complex higher order cost polynomials in ELD. Constraint management is incorporated in the modified PSO. The modified PSO has balance between local and global searching abilities, and an appropriate fitness function helps to converge it quickly. To avoid the method to be frozen, stagnated/idle particles are reset. Sensitivity of the higher order cost polynomials is also analyzed visually to realize the importance of the higher order cost polynomials for the optimization of ELD. Finally, benchmark data sets and methods are used to show the effectiveness of the proposed method.