Integrated evolutionary neural network approach with distributed computing for congestion management

Electric supply industry is facing deregulation all over the world. Under deregulated power supply scenario, power transmission congestion has become more intensified and recurrent, as compared to conventional regulated power system. Congestion may lead to violation of voltage or transmission capacity limits, thus threatens the power system security and reliability. Also the growing congestion may lead to unanticipated divergent electricity pricing. Owing to these facts congestion management has become a crucial issue in the deregulated power system scenario.Fast and precise prediction of nodal congestion prices in real time deregulated/spot power market may enable market participants and system operators to keep pace with the congestion by taking preventive measures like transaction rescheduling, bids (both for supplying and consuming electricity) modification, regulated dispatch of electric power, etc. This paper proposes an integrated evolutionary neural network (ENN) approach to predict nodal congestion prices (NCPs) for congestion management in spot power market. Distributed computing is employed to tackle the heterogeneity of the data in the prediction of NCP values. Developed ENNs have been trained and tested under distributed computing environment, using a message passing paradigm. Proposed hybrid approach for NCP prediction is demonstrated on a 6-bus test power system with and without distributed computing. The proposed approach not only demonstrated the computing efficiency of the developed ENN model over the conventional optimal power flow method but also shows the time saving aspect of distributed computing.     

Improvement of hydrogen storage properties of TiCrV alloy by Zr substitution for Ti

The effects of Zr substitution for Ti on the hydrogen absorption–desorption characteristics of Ti_1−xZr_xCrV alloys (x = 0, 0.05, 0.1 and 1.0) have been investigated. The crystal structure, maximum hydrogen absorption capacity, kinetics and hydrogen desorption properties have been studied in detail. While TiCrV crystallizes in body centered cubic (BCC) structure, ZrCrV is a C15 cubic Laves phase compound and the intermediate compositions with 5 and 10 at% Zr substitutions for Ti (x = 0.05 and 0.1) show the presence of a small amount of ZrCr₂ Laves phase along with the main BCC phase. The pressure–composition isotherms have been studied at room temperature. TiCrV shows separation of TiH₂ phase on cycling. A small amount of Zr substitution for Ti is found to have advantageous effects on the hydrogen absorption properties of TiCrV as it suppresses TiH₂ phase separation and decreases hysteresis. It is found that the hydrogen absorption capacity of Ti_1−xZr_xCrV decreases as the Zr content increases due to the increased fraction of Laves phase. Temperature-programmed desorption studies have been carried out on the saturated hydrides in order to find the relative desorption temperatures.     

Microhardness of laser ablated alumina coating on Ti-6Al-4V

Alumina coated on Titanium alloys find wide tribological applications due to the improvement in hardness of substrate. This paper presents the effect of deposition of alumina by pulsed laser ablation on Vickers hardness of Ti-6Al-4V substrate. Nd: YAG laser of wavelength 1064nm is used with sintered alumina disc as target for ablation. The variation of Vickers microhardness with load in Ti-6Al-4V shows indentation size effect. Proportional Specimen Resistance (P.S.R) model is applied to separate load -independent hardness from the load -dependent hardness. Composite hardness of Alumina coated Ti-6Al-4V is measured for different laser processing conditions. The film hardness has been separated from the composite hardness of the film-substrate system by the use of an approach based on the law of area of mixtures model taking into an account of ISE, due to proportional specimen resistance of the material to indentation. Film hardness of different films produced by varying the target — substrate distance is presented. As the film thickness increases its hardness decreases as compared to bulk hardness. These studies will be useful in the selection of appropriate coating thickness and substrate hardness to achieve a required composite hardness in the design and production of wear-resistant parts of engineering devices.     

Effect of ink organics on cambering of an Ag-metallized low temperature co-fired ceramics (LTCC)

Ceramic technology has had an important role in microelectronics since 1960s and ceramic seems to be a continuously developing, mature technology. Recently, development of low temperature co-fired ceramic technology (LTCC) has been geared up due to the huge demand of miniaturisation of electronic components. New materials are being developed for extending the demand of wide range of dielectric properties of LTCC, minimization of shrinkage, cambering of LTCC, high quality of conductors and patterning etc. This paper deals with formulation of silver conductor inks for LTCC and the effect of ink organics on the LTCC in particular to cambering/warpage, microstructure development were studied and presented.

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Location Estimation Based on Semi-Supervised Locally Linear Embedding (SSLLE) Approach for Indoor Wireless Networks

Location aware computing is popularized and location information use has important due to huge application of mobile computing devices and local area wireless networks. In this paper, we have proposed a method based on Semi-supervised Locally Linear Embedding for indoor wireless networks. Previous methods for location estimation in indoor wireless networks require a large amount of labeled data for learning the radio map. However, labeled instances are often difficult, expensive, or time consuming to obtain, as they require great efforts, meanwhile unlabeled data may be relatively easy to collect. So, the use of semi-supervised learning is more feasible. In the experiment 101 access points (APs) have been deployed so, the RSS vector received by the mobile station has large dimensions (i.e. 101). At first, we use Locally Linear Embedding to reduce the dimensions of data, and then we use semi-supervised learning algorithm to learn the radio map. The algorithm performs nonlinear mapping between the received signal strengths from nearby access points and the user??s location. It is shown that the proposed scheme has the advantage of robustness and scalability, and is easy in training and implementation. In addition, the scheme exhibits superior performance in the nonline-of-sight (NLOS) situation. Experimental results are presented to demonstrate the feasibility of the proposed SSLLE algorithm.     

An improved bacterial foraging algorithm for combined static/dynamic environmental economic dispatch

Economic dispatch is carried out at the energy control center to find out the optimal output of thermal generating units such that power balance criterion is met, unit operating limits are satisfied and the fuel cost is minimized. With growing environmental awareness and strict government regulations throughout the world, it has become essential to optimize not only the total fuel cost but also the harmful emissions, both, under static as well as dynamic conditions. The static environment economic dispatch finds the optimal output of generating units for a fixed load demand at a given time, while the dynamic environmental economic dispatch schedules the output of online generators with changing power demands over a certain time period (normally one day) so as to minimize these two conflicting objectives, simultaneously. In this paper, the price penalty factor approach is employed for simultaneous minimization of cost and emission. The generator ramp rate constraints, non-convex and discontinuous nature of cost function and the large number of generators in practical power plants, make this problem very difficult to solve. Here, a fuzzy ranking approach is employed to identify the solution which offers the best compromise between cost and emission objectives.     

Adaptive dehazing control factor based fast single image dehazing

The single image dehazing is performed using atmospheric scattering model (ASM). The ASM is based on transmission and atmospheric light. Thus, accurate estimation of transmission is essential for quality single image dehazing. Single image dehazing is of prime focus in research nowadays. The proposed work presents a fast and accurate method for single image dehazing. The proposed method works in two folds; (i) An adaptive dehazing control factor is proposed to estimate accurate transmission, which is based on difference of maximum and minimum color channel of hazy image, and (ii) a mathematical model to compute probability of a pixel to be at short distance is presented, which is utilized to locate haziest region of the image to compute the value of atmospheric light. The proposed method obtains visually compelling results, and recovers the information content (such as structural similarity, color, and visibility) accurately. The computation speed and accuracy of the proposed method is proved using quantitative and qualitative comparison of results with state of the art dehazing methods.

     

A Survey on Path Planning Techniques for Mobile Sink in IoT-Enabled Wireless Sensor Networks

Wireless Personal Communications - Recently, the Internet of Things (IoT) had emerged very rapidly and became the most important technology in today’s era. In an IoT-based environment, every...     

A review on rendezvous based data acquisition methods in wireless sensor networks with mobile sink

Wireless Networks - Solutions for energy hole problem in wireless sensor networks (WSNs) have been excessively explored using mobile sink (MS). Although, MS provides a considerable amount of energy...