Wind Energy Potential at Badin and Pasni Costal Line of Pakistan

Unfortunately, Pakistan is facing an acute energy crisis since the past decade due to the increasing population growth and is heavily dependent on imports of fossil fuels. The shortage of the electricity is 14-18 hours in rural areas and 8-10 hours in urban areas. This situation has been significantly affecting the residential, industrial and commercial sectors in the country. At this time, it is immense challenges for the government to keep the power supply provision continue in the future for the country. In this situation, it has been the increased research to explore renewable energy resources in the country to fulfill the deficit scenario in the state. The renewable energy sector has not penetrated in the energy mix, currently in the upcoming markets. This paper highlights the steps taken by the country in the past and is taking steps at the present time to get rid of from the existing energy crisis when most urban areas are suffering from power outages for 12 hours on regular basis. Until 2009, no single grid interconnected wind established, but now the circumstances are changing significantly and wind farms are contributing to the national grid is the reality now. The initiation of the three wind farms interconnection network and many others in the pipeline are going to be operational soon. The federal policy on wind energy system has recently changed. Surprisingly, the continuing schemes of the wind farm are getting slow. This paper reviews developments in the wind energy sector in the country and lists some suggestions that can contribute to improving the penetration of wind energy in the national energy sector.Article History: Received Dec 16th 2016; Received in revised form May 15th 2017; Accepted June 18th 2017; Available onlineHow to Cite This Article: Kaloi,G.S., Wang, J., Baloch, M.H and Tahir, S. (2017) Wind Energy Potential at Badin and Pasni Costal Line Pakistan. Int. Journal of Renewable Energy Development, 6(2), 103-110.https://doi.org/10.14710/ijred.6.2.103-11

Predicting the effect of voids on mechanical properties of woven composites.

An accurate yet easy to use methodology for determining the effective mechanical properties of woven fabric reinforced composites is presented. The approach involves generating a representative unit cell geometry based on randomly selected 2D orthogonal slices from a 3D X-ray micro-tomographic scan. Thereafter, the finite element mesh is generated from this geometry. Analytical and statistical micromechanics equations are then used to calculate effective input material properties for the yarn and resin regions within the FE mesh. These analytical expressions account for the effect of resin volume fraction within the yarn (due to infiltration during curing) as well as the presence of voids within the composite. The unit cell model is then used to evaluate the effective properties of the composite.DelPHE 780 Project funded by UK Department of International Development (DFID), through British Council managed DelPHE scheme

Deep convolutional neural network with 2D spectral energy maps for fault diagnosis of gearboxes under variable speed.

For industrial safety, correct classification of gearbox fault conditions is necessary. One of the most crucial tasks in data-driven fault diagnosis is determining the best set of features by analyzing the statistical parameters of the signals. However, under variable speed conditions, these statistical parameters are incapable of uncovering the dynamic characteristics of different fault conditions of gearboxes. Later, several deep learning algorithms are used to improve the performance of the feature selection process, but domain knowledge expertise is still necessary. In this paper, a combination domain knowledge analysis and a deep neural network is proposed. By using the input acoustic emission (AE) signal, a two-dimensional spectrum energy map (2D AE-SEM) is created to form an identical fault pattern for various speed conditions of gearboxes. Then, a deep convolutional neural network (DCNN) is proposed to investigate the detailed structure of the 2D input for final fault classification. This 2D AE-SEM offers a graphical depiction of acoustic emission spectral characteristics. Our proposed system offers vigorous and dynamic classification performance through the proposed DCNN with a high diagnostic fault classification accuracy of 96.37% in all considered scenarios