Description:
This paper presents a new algorithm for the control of a snake-like robot with passive joints and active wheels. Each segment has four autonomously driven and steered wheels. The algorithm approximates the ideal solution in which all wheels on a segment have the same centre of curvature with wheel speeds, providing cooperative redundancy. Each hitch point joining segments traverses the same path, which is determined by an operator, prescribing the path curvature and front hitch speed. The numerical algorithm developed in this paper is simulation tested against a previously derived analytical solution for a predetermined path. Further simulations are carried out to show the effects of changing curvature and front hitch speed on hitch path, wheel angles and wheel speeds for a one, two and three segment robot.
Description:
This paper presents a new algorithm for the control of a snake-like robot with passive joints and active wheels. Each segment has four autonomously driven and steered wheels. The algorithm approximates the ideal solution in which all wheels on a segment have the same centre of curvature with wheel speeds, providing cooperative redundancy. Each hitch point joining segments traverses the same path, which is determined by an operator, prescribing the path curvature and front hitch speed. The numerical algorithm developed in this paper is simulation tested against a previously derived analytical solution for a predetermined path. Further simulations are carried out to show the effects of changing curvature and front hitch speed on hitch path, wheel angles and wheel speeds for a one, two and three segment robot.
Description:
Condition based maintenance (CBM) in the process industry helps in determining the residual life of equipment, avoiding sudden breakdown and facilitating the maintenance staff to schedule repairs by optimizing demand–supply relationships. One of the prevalent issues in CBM is to predict the residual life of the equipment. This paper proposes a novel framework to predict the remnant life of the equipment, called residual life prediction, based on optimally parameterized wavelet transform and multi-step support vector regression (RWMS). In optimally parameterized wavelet transform, a generalized criterion is proposed to select the wavelet decomposition level which works for all the applications; decomposition nodes are selected by characterizing their dominancy level based upon relative fault signature–signal energy contents. The prediction model is based on multi-step support vector regression to determine the nonlinear crack propagation in the rotary machine according to Paris’s fatigue model. The results both for the simulated as well as the actual vibration datasets validate the enhanced performance of RWMS in comparison with the existing techniques to predict the residual life of the equipment.
Description:
Wave energy is increasingly being seen as an alternative source of low-emissions power particularly for Australia, as Australia has abundant wave resources. This paper, using the performance characteristics of wave energy converters (WEC), resource measurements along Australia's southern coastline and global resource estimates, models projections of the future uptake of wave energy globally and in Australia. Globally, wave farms are projected to be installed up to a presumed maximum limit of 500 GW. In Australia there is more variability in the amount of wave farms installed, with differences across the different WEC. The outcome globally and in Australia depends on variations in the average power generated by the WEC and the carbon price path. In Australia, the majority of projected WEC installations are in the state of Victoria, which has relatively high demand and currently highly-emissions intensive sources of generation compared to states with better wave resources. When a dispatchable, or continuous power ability is added to wave energy's output the uptake of wave energy increases in Australia, in those states with the best resources. When the amount of wave energy extractable in any region is increased or decreased it has a large effect on output in Victoria in particular, which generates power from wave energy up to 30% of the total extractable resource under the dispatchable power scenario. Wave energy has the potential to make a significant contribution to electricity generation globally and in Australia. This paper is part two of a series of papers, the first being [1]. Highlights► Globally up to 500 GW of wave energy capacity is projected to be installed by 2050. ► In Australia the majority of wave farms are projected to be deployed in Victoria. ► A dispatchable power scenario increases wave energy generation by 22% in Australia. ► Wave energy could supply approx 11% of Australia's electricity generation by 2050.
Description:
This paper focuses on modeling of multi-junction solar cell (MJSC) to improve the conversion efficiency using MATLAB/Simulink software. The multi-junction photovoltaic (PV) cell is investigated to obtain its maximum performance compare to the conventional silicon PV cell. MATLAB/Simulink modeled results show that tandem cell can provide almost 3-times maximum power compared to the conventional PV cells. Maximum power point tracker (MPPT) has also been performed to improve the conversion efficiency of the PV systems. The MPPT is able to assist the PV cells to attain more power efficiently and deliver electricity to the grid.
Description:
This paper is concerned with the problem of passivity analysis and passification for a class of stochastic impulsive memristor-based piecewise linear (PWL) systems with mixed delays and nonlinearity disturbances. Based on the PWL memristor, a PWL system is set up. And some novel sufficient conditions are derived to ensure the passivity/passification performance, such that, for all admissible stochastic disturbances and nonlinearity, the closed-loop stochastic impulsive memristor-based PWL system is passive in the sense of expectation. Copyright (c) 2013 John Wiley & Sons, Ltd.
Description:
Limited transmission capacity may lead to network congestion which results in wind curtailment during periods of high availability of wind. Conventional congestion management techniques usually involve generation management which may not always benefit large wind farms. This paper investigates the problem in detail and presents an improved methodology to quantify the latent scheduling capacity of a power system taking into account stochastic variation in line-thermal rating, intermittency of wind, and mitigating the risk of network congestion associated with high penetration of wind. The mathematical model converts conventional thermal constraints to dynamic constraints by using a discretized stochastic penalty function with quadratic approximation of constraint relaxation risk. The uniqueness of the approach is that it can limit the generation to be curtailed or re-dispatch by dynamically enhancing the network latent capacity as per the need. The approach is aimed at strategic planning of power systems in the context of power systems with short to medium length lines with a priori known unit commitment decisions and uses stochastic optimization with a two stage recourse action. Results suggest that a considerable level of wind penetration is possible with dynamic line ratings, without adversely affecting the risk of network congestion.
Description:
Limited transmission capacity may lead to network congestion which results in wind curtailment during periods of high availability of wind. Conventional congestion management techniques usually involve generation management which may not always benefit large wind farms. This paper investigates the problem in detail and presents an improved methodology to quantify the latent scheduling capacity of a power system taking into account stochastic variation in line-thermal rating, intermittency of wind, and mitigating the risk of network congestion associated with high penetration of wind. The mathematical model converts conventional thermal constraints to dynamic constraints by using a discretized stochastic penalty function with quadratic approximation of constraint relaxation risk. The uniqueness of the approach is that it can limit the generation to be curtailed or re-dispatch by dynamically enhancing the network latent capacity as per the need. The approach is aimed at strategic planning of power systems in the context of power systems with short to medium length lines with a priori known unit commitment decisions and uses stochastic optimization with a two stage recourse action. Results suggest that a considerable level of wind penetration is possible with dynamic line ratings, without adversely affecting the risk of network congestion.
Description:
Predictive coding schemes, proposed in the literature, essentially model the residuals with discrete distributions. However, real-valued residuals can arise in predictive coding, for example, from the usage of an r order linear predictor specified by r real-valued coefficients. In this paper, we propose a symbol-by-symbol coding scheme for the Laplace distribution, which closely models the distribution of real-valued residuals in practice. To efficiently exploit the real-valued predictions at a given precision, the proposed scheme essentially combines the process of residual computation and coding, in contrast to conventional schemes that separate these two processes. In the context of adaptive predictive coding framework, where the source statistics must be learnt from the data, the proposed scheme has the advantage of lower 'model cost' as it involves learning only one parameter. In this paper, we also analyze the proposed parametric coding scheme to establish the relationship between the optimal value of the coding parameter and the scale parameter of the Laplace distribution. Our experimental results demonstrated the compression efficiency and computational simplicity of the proposed scheme in adaptive coding of residuals against the widely used arithmetic coding, Rice-Golomb coding, and the Merhav-Seroussi-Weinberger scheme adopted in JPEG-LS.
Description:
In the present study, the effects of graphene oxide (GO) nano-particles on performance and emissions of a diesel engine fueled with Oenothera lamarckiana biodiesel was investigated. Biodiesel was used in the blend of B20. The GO nano-particles with concentrations of 30, 60, and 90 ppm were considered for each fuel blend. Experiments were performed at a constant speed of 2100 rpm at loads of 0%, 25%, 50%, 75%, and 100%. Various parameters, such as power, exhaust gas temperature (EGT), carbon monoxide (CO), carbon dioxide (CO2), unburned hydrocarbons (UHCs), and nitrogen oxides (NOx), were investigated. Results showed that by using GO, power and EGT significantly increase. Furthermore, by using GO nanoparticles, significant reductions in CO (similar to 5%-22%) and UHC5 (similar to 17%-26%) were observed. However, under similar conditions, a slight increase in CO2 (similar to 7%-11%) and NOx (similar to 4%-9%) emissions observed. Finally, it can be concluded that nano-graphene oxide can be introduced as a suitable alternative fuel additive for Oenothera lamarckiana biodiesel blends. (C) 2019 Elsevier Ltd. All rights reserved.
Description:
Rotary machines, and limaçon machines in particular, offer a better power to weight ratio compared to reciprocating machines; however, leakage due to improper apex and side sealing have prevented rotary machines from thriving. In this paper, a modelling approach is presented to analyse the vibration of apex seal during the machine operation and the power loss caused by the seal friction. The seal and spring are modelled as a spring-mass system in which the seal deformation is negligible. The seal-groove relative positions have then been categorised into nine different possible cases based on the number of contact points between the seal and the seal groove. A case study has been presented to demonstrate the reliability of the model.