Robust control of an isolated hybrid wind-diesel power system using Linear Quadratic Gaussian approach

This paper presents the application of the Linear Quadratic Gaussian (LQG) controller for voltage and frequency regulation of an isolated hybrid wind-diesel scheme. The scheme essentially consists of a vertical axis wind turbine driving a self-excited induction generator connected via an asynchronous (AC-DC-AC) link to a synchronous generator driven by a diesel engine. The synchronous generator is equipped with a voltage regulator and a static exciter. The wind generator and the synchronous generator together cater for the local load and power requirement. However, the load bus voltage and frequency are governed by the synchronous generator. The control objective aims to regulate the load voltage and frequency. This is accomplished via controlling the field voltage and rotational speed of the synchronous generator. The complete nonlinear dynamic model of the system has been described and linearized around an operating point. The standard Kalman filter technique has been employed to estimate the full states of the system. The computational burden has been minimized to a great extent by computing the optimal state feedback gains and the Kalman state space model off-line. The proposed controller has the advantages of robustness, fast response and good performance. The hybrid wind diesel energy scheme with the proposed controller has been tested through a step change in both wind speed and load impedance. Simulation results show that accurate tracking performance of the proposed hybrid wind diesel energy system has been achieved.     

Distributed data-driven observer for linear time invariant systems

This paper is concerned with distributed data-driven observer design problem. The existing data-driven observers rely on a common assumption that all the information about the system, and the calculations based upon this information are centralized. Therefore the resulting algorithms cannot be applied to the distributed systems in which each local observer receives only a part of the output signal. On the other hand, traditional model-based distributed state estimation methods generally assume that the processes are decomposed according to the known process models, while in data-driven approaches there is no such information available. The main goal of this paper is to extend the centralized data-driven observer design approach to the distributed framework. The stability of the proposed data-driven distributed observer is also proved analytically. A quadruple-tank process is simulated to demonstrate the performance of the proposed scheme.     

Electrical double-layer capacitors: evaluation of ageing phenomena during cycle life testing

This paper represents an assessment of the main ageing phenomena in electrical double-layer capacitors (EDLCs). In this study the cycle life of the EDLC cells with a rated capacitance of 1,600 F has been investigated at different ambient temperatures and current rates. From the experimental results we can observe that the impact of the high ambient temperature is significant on the cycle life of the cells. Moreover, the results also show the negative impact of the current rate. The internal resistance tests showed that the increase of the resistance is much higher than the decrease of the capacitance. Thus, the ageing of the EDLC during cycling was clearly non-linear. Further the EIS measurements indicated the higher increase of the imaginary part of the impedance at low frequencies during cycling, which indicates the capacitance fade.     

One-Pot,Three-Component Synthesis of 2,3-Dihydro-4(1H)-Quinazolinones Catalyzed by Perchlorated Zirconia (HClO<Subscript>4</Subscript>/ZrO<Subscript>2</Subscript>) Nanoparticles as a Solid Acid Catalyst

Mono and disubstituted 2,3-dihydroquinazolin-4(1H)-ones were obtained in good yields via a one-pot, three component reaction of isatoic anhydride and aromatic aldehydes with ammonium acetate or primary amines in the presence of perchlorated zirconia (HClO₄/ZrO₂) nano particles as an efficient solid acid catalyst under solvent-free conditions. Simple workup and reusability of the catalyst are advantages of this method.     

Sustainable optimization of waste management network over extended planning time horizon

This study proposes a multiperiod mixed integer linear programming model for the management of a single municipal solid waste (MSW) treatment plant with sustainability as the objective. Discrete and continuous variables define the capacity selections for diverse MSW technologies, and the operation of the MSW network, respectively. The economic target is considered to maximize the net present value. The environmental impact is the minimization of a normalized environmental objective function (NEOF). The social target is the maximization of jobs. An interesting feature about the research work is the requirement of biodrying technologies for MSW moisture content control. Due to the conflicted nature among the sustainability components, a multiobjective optimization (MO) is carried out to find the Pareto optimal solutions. The MO results show that the Pareto optimal solutions vary around profit range of $4.9–8.5 billion, NEOF impact range of 3.2–3.6 units, and social benefit range of 2700–4828 jobs.     

Supertough (Polyamide 6)/(acrylonitrile butadiene rubber) nano alloy through in situ polymerization of caprolactam in the presence of acrylonitrile butadiene rubber nanophase

In this work, polymerization of caprolactam (CL) was carried out in the presence of acrylonitrile butadiene rubber (NBR) during the reactive melt‐mixing process. During shear mixing, NBR particles swelled and dissolved in the molten CL, which led to separation and distribution of rubber particles to nanoscale in the dissolution stage. Then, in an internal mixer, supertough Polyamide 6 was prepared via melt polymerization of CL/NBR mixture, sodium caprolactam as a catalyst, and hexamethylene diisocyanate as an activator. The effects of various concentrations of catalyst and activator on the initiation time of the reaction were determined. Physical and mechanical properties of different formulations prepared via reactive melt blending were determined by tensile and impact measurements, differential scanning calorimetry, Fourier‐transform infrared spectroscopy, X‐ray scattering techniques, transmission electron microscopy, and dynamic mechanical thermal analysis. Experimental results showed that a recipe with 3% nitrile rubber in a CL/NBR mixture enhances the physical and mechanical properties the best, compared with other formulations. This condition led to the formation of NBR nanospheres during melt polymerization of Polyamide 6 as well. J. VINYL ADDIT. TECHNOL., 21:116–121, 2015. © 2014 Society of Plastics Engineers     

Applying gravitational search algorithm in the QoS-based Web service selection problem

With the growing use of service-oriented architecture for designing next generation software systems,the service composition problem and its execution complexity have become even more important in resp...     

Prediction of phosphorus content of electroless nickel–phosphorous coatings using artificial neural network modeling

A multilayer feedforward neural network with two hidden layers was designed and developed for prediction of the phosphorus content of electroless Ni–P coatings. The input parameters of the network were the pH, metal turnover, and loading of an electroless bath. The output parameter was the phosphorus content of the electroless Ni–P coatings. The temperature and molar rate of the bath were constant ( 91^° \textC, 0.4 \textNi^{\text + +} /\textH₂ \textPO₂ ^{- -} 91^\circ {\text{C}},\:0.4\,{\text{Ni}}^{{{\text{ + + }}}} /{\text{H}}_{2} {\text{PO}}_{2}^{{ - - }} ). The network was trained and tested using the data gathered from our own experiments. The goal of the study was to estimate the accuracy of this type of neural network in prediction of the phosphorus content. The study result shows that this type of network has high accuracy even when the number of hidden neurons is very low. Some comparison between the network’s predictions and own experimental data are given.