Search results

This paper aims to design an optimum vertical axis wind turbine (VAWT) and assess its techno-economic performance for wind energy harvesting at high-speed railway in Malaysia.

This project adopted AutoCAD and ANSYS modeling tools to design and optimize the blade of the turbine. The site selected has a railway of 30 km with six stops. The vertical turbines are placed 1 m apart from each other considering the optimum tip speed ratio. The power produced and net present value had been analyzed to evaluate its techno-economic viability.

Computational fluid dynamics (CFD) analysis of National Advisory Committee for Aeronautics (NACA) 0020 blade has been carried out. For a turbine with wind speed of 50 m/s and swept area of 8 m², the power generated is 245 kW. For eight trains that operate for 19 h/day with an interval of 30 min in nonpeak hours and 15 min in peak hours, total energy generated is 66 MWh/day. The average cost saved by the train stations is RM 16.7 mil/year with battery charging capacity of 12 h/day.

Wind energy harvesting is not commonly used in Malaysia due to its low wind speed ranging from 1.5 to 4.5 m/s. Conventional wind turbine requires a minimum cut-in wind speed of 11 m/s to overcome the inertia and starts generating power. Hence, this paper proposes an optimum design of VAWT to harvest an unconventional untapped wind sources from railway. The research finding complements the alternate energy harvesting technologies which can serve as reference for countries which experienced similar geographic constraints.

The purpose of this paper is to investigate the characteristics of PCGEs used in a small‐scale windmill in terms of the number of PCGEs.

Experiments were performed in cases where one, two, or four PCGEs are attached to the frame of the windmill, with optimization of different gap distances between exciting and secondary magnets carried out to determine the optimal configuration for generating the peak voltage and harvesting the maximum wind energy for the same range of wind speeds.

The experimental results show that the prototype can harvest energy in urban regions with low wind speeds and convert the wasted wind energy into electricity for city use.

The experimental results show that the prototype can harvest energy in urban regions with low wind speeds and convert the wasted wind energy into electricity for city use.

Current technology uses large windmills that operate in remote regions and have complex generating mechanisms such as towers, blades gears, speed controls, magnets, and coils. In a city, wind energy that would otherwise be wasted can be claimed and stored for later use. The purpose of this paper is to introduce a small‐scale windmill that can work in urban areas.

The device uses a piezo‐composite generating element (PCGE) to generate the electric power. The PCGE is composed of layers of carbon/epoxy, lead zirconate titanate (PZT) ceramic, and glass/epoxy cured at an elevated temperature. Previous work by the authors had proved that the PCGE can produce high performance energy harvesting.

In the prototype, the PCGE performed as a secondary beam element. One end of the PCGE is attached to the frame of the device. Additionally, the fan blade rotates in the direction of the wind and hits the other end of the PCGE. When the PCGE is excited, the effects of the beam's deformation enable it to generate electric power. The power generation and battery charging capabilities of the proposed device were tested, and the results show that the prototype can harvest energy in urban regions using minor wind movement.

The paper presents a prototype that uses a PCGE for harvesting wind energy in urban areas. The PCGE has the potential of being used as a generator for harvesting energy from sources such as machine vibration, body motion, wind, and ocean waves. The PCGE design is flexible: the ply orientation and the size of the prepreg layers can be changed. Generating elements with a specific stacking sequence can be used for scavenging energy in a wide range of applications such as network sensors, portable electronics, and microelectromechanical systems.

For decades, continuous research work is going on to maximize the power harvested from the sun; however, there is only a limited analysis on exploiting the microwatt output power from indoor lightings. Microelectronic system has power demand in the µW range, and therefore, indoor photovoltaics would be appropriate for micro-energy harvesting appliances. “Energy harvesting is defined as the transfer process by which energy source is acquired from the ambient energy, stored in energy storage element and powered to the target systems”. The theory of energy harvesting is: gathering energy from surroundings and offering technological solutions such as solar energy harvesting, wind energy collection and vibration energy harvesting. “The solar cell or photovoltaic cell (PV), is a device that converts light into electric current using the photoelectric effect”. Factors such as light source, temperature, circuit connection, light intensity, angle and height can manipulate the functions of PV cells. Among these, the most noticeable factor is the light intensity that has a major impact on the operations of solar panels.

This paper aims to design an enhanced prediction model on illuminance or irradiance by an optimized artificial neural network (ANN). The input attributes or the features considered here are temperatures, maxim, TSL, VI, short circuit current, open-circuit voltage, maximum power point (MPP) voltage, MPP current and MPP power, respectively. To enhance the performance of the prediction model, the weights of ANN are optimally tuned by a new self-improved brain storm optimization (SI-BSO) model.

The superiority of the implemented work is compared and proved over the conventional models in terms of error analysis and prediction analysis. Accordingly, the presented approach was analysed and its superiority was proved over other conventional schemes such as ANN, ANN-Levenberg–Marquardt (LM), adaptive-network-based fuzzy inference system (ANFIS) and brainstorm optimization (BSO). In addition, analysis was held with respect to error measures such as mean absolute relative error (MARE), mean square root error (MSRE), mean absolute error and mean absolute percentage error. Moreover, prediction analysis was also performed that revealed the betterment of the presented model. More particularly, the proposed ANN + SI-BSO model has attained minimal error for all measures when compared to the existing schemes. More particularly, on considering the MARE, the adopted model for data set 1 was 23.61%, 48.12%, 79.39% and 90.86% better than ANN, ANN-LM, ANFIS and BSO models, respectively. Similarly, on considering data set 2, the MSRE of the implemented model was 99.87%, 70.69%, 99.57% and 94.74% better than ANN, ANN-LM, ANFIS and BSO models, respectively. Thus, the enhancement of the presented ANN + SI-BSO scheme has been validated effectively.

This work has established an improved illuminance/irradiance prediction model using the optimization concept. Here, the attributes, namely, temperature, maxim, TSL, VI, Isc, Voc, Vmpp, Impp and Pmpp were given as input to ANN, in which the weights were chosen optimally. For the optimal selection of weights, a novel ANN + SI-BSO model was established, which was an improved version of the BSO model.

A simple economic analysis has revealed that in order for wind energy to be a viable alternative, wind-turbines (convertors of wind energy into electrical energy) must be able to operate for at least 20 years, with only regular maintenance. However, wind-turbines built nowadays do not generally possess this level of reliability and durability. Specifically, due to the malfunction and failure of drive-trains/gear-boxes, many wind-turbines require major repairs after only three to five years in service. The paper aims to discuss these issues.

The subject of the present work is the so-called white etch cracking, one of the key processes responsible for the premature failure of gear-box roller-bearings. To address this problem, a multi-physics computational methodology is developed and used to analyze the problem of wind-turbine gear-box roller-bearing premature-failure. The main components of the proposed methodology include the analyses of: first, hydrogen dissolution and the accompanying grain-boundary embrittlement phenomena; second, hydrogen diffusion from the crack-wake into the adjacent unfractured material; third, the inter-granular fracture processes; and fourth, the kinematic and structural response of the bearing under service-loading conditions.

The results obtained clearly revealed the operation of the white-etch cracking phenomenon in wind-turbine gear-box roller-bearings and its dependence on the attendant loading and environmental conditions.

The present work attempts to make a contribution to the resolution of an important problem related to premature-failure and inferior reliability of wind-turbine gearboxes.

Wind energy is one of the most promising and the fastest growing alternative-energy production technologies, which have been developed in response to stricter environmental regulations, the depletion of fossil-fuel reserves, and the world's ever-growing energy needs. This form of alternative energy is projected to provide 20 percent of the US energy needs by 2030. For economic reasons, wind turbines (articulated structures that convert wind energy into electrical energy) are expected to operate, with only regular maintenance, for at least 20 years. However, some key wind turbine components (especially the gearbox) tend to wear down, malfunction and fail in a significantly shorter time, often three to five years after installation, causing an increase in the wind-energy cost and in the cost of ownership of the wind turbine. Clearly, to overcome this problem, a significant increase in long-term gearbox reliability needs to be achieved.

While purely empirical efforts aimed at identifying shortcomings in the current design of the gearboxes are of critical importance, the present work demonstrates that the use of advanced computational engineering analyses, like the finite-element stress analysis and a post-processing fatigue-life assessment analysis, can also be highly beneficial.

The results obtained in the present work clearly revealed how a variety of normal operating and extreme wind-loading conditions can influence the service-life of a wind-turbine gearbox in the case when the service-life is controlled by the gear-tooth bending-fatigue.

The present work attempts to make a contribution to the resolution of an important problem related to premature-failure and inferior reliability of wind-turbine gearboxes.

No carbon gas is released into the atmosphere in electricity production with wind energy. Therefore, this type of energy is significantly environmentally friendly. On the other hand, thanks to wind energy investments, a country can produce its own energy. This reduces the country’s foreign dependency on energy. Due to these positive aspects, increasing wind energy investments is very important for both the social and economic development of the country. The aim of this study is to define the most appropriate government support to improve wind energy investments. In this context, four different criteria are defined in this regard by making a detailed literature review. In the analysis process, fuzzy AHP methodology is considered. Within this framework, the comparative evaluations of three different experts are obtained. The findings indicate that providing loans with low-interest rate is the most appropriate government support for the wind energy investors. Additionally, it is also defined that tax deduction is another important issue for this purpose. However, it is also identified that appropriate location supply and purchase guarantee of the product produced have lower importance in comparison with the others. It is strongly recommended that governments should provide loans to the wind energy investors. This strategy can minimize the problem of high initial cost so that it can be much easier to attract the attention of the investors.

Does social-isolation deflate observed preferences for public goods? Using a voting referendum elicitation mechanism, List et al. (2004) document a 30% reduction in affirmative voting when votes are privately, rather than publicly, cast for a public good. We replicate this work and then add to the exercise by examining the role that group size plays in generating social-pressure bias – an extension we motivate with a structural model. Having replicated to the extent possible the List et al.’s consequential private and public treatments, we find that when group size is large (N = 60), social isolation reduces affirmative votes by roughly 30%, a result that does not carry over to the small-group (N = 30) session.

This paper aims to deal with the development of a software tool to simulate and study vehicle – road interaction (VRI) to quantify the forces induced and energy released from vehicles to the road pavement, in different vehicle motion scenarios, and the energy absorbed by the road surface, speed reducers or a specific energy harvester surface or device. The software tool also enables users to quantify the energetic efficiency of the process.

Existing software tools were analysed and its limitations were identified in terms of performing energetic analysis on the interaction between the vehicle and the road pavement elements, such as speed reducers or energy harvest devices. The software tool presented in this paper intends to overcome those limitations and precisely quantify the energy transfer.

Different vehicle models and VRI models were evaluated, allowing to conclude about each model precision: bicycle car model has a 60 per cent higher precision when compared with quarter-car model, and contact patch analysis model has a 67 per cent higher precision than single force analysis model. Also, a technical study was performed for different equipment surface shapes and displacements, concluding that these variables have a great influence on the energy released by the vehicle and on the energy harvested by the equipment surface.

The developed software tool allows to study VRI with a higher precision than existing tools, especially when energetic analyses are performed and when speed reduction or energy harvesting devices are applied on the pavement.

The purpose of this paper is to analyze the wind energy technologies using the social network analysis based on patent information. Analysis of patent documents with social network analysis is used to identify the most influential and connected technologies in the field of wind energy.

In the literature, patent data are often used to evaluate technologies. Patents related to wind energy technologies are obtained from the United States Patent and Trademark Office database and the relationships among sub-technologies based on Corporate Patent Classification (CPC) codes are analyzed in this study. The results of two-phase algorithm for mining high average-utility itemsets algorithm, which is one of the utility mining algorithm in data mining, is used to find associations among wind energy technologies for social network analysis.

The results of this study show that it is very important to focus on wind motors and technologies related to energy conversion or management systems reducing greenhouse gas emissions. The results of this study imply that Y02E, F03D and F05B CPC codes are the most influential CPC codes based on social network analysis.

Analysis of patent documents with social network analysis for technology evaluation is extremely limited in the literature. There is no research related to the analysis of patent documents with social network analysis, in particular CPC codes, for wind energy technology. This paper fills this gap in the literature. This study explores technologies related to wind energy technologies and identifies the most influential wind energy technologies in practice. This study also extracts useful information and knowledge to identify core corporate patent class (es) in the field of wind energy technology.