This study explores the smoothing effect on the uncertainty of the wind power fluctuations that affect a power system at points of common coupling. Set pair analysis is applied to evaluate the ...similarity between the power fluctuations of a single wind turbine and those of all aggregated wind turbines, based on which a quantitative index describing the wind power smoothing effect is proposed. The smoothing effect characteristics of a wind farm cluster are investigated for different numbers of wind turbines, different wind speeds, different seasons, and multiple sampling intervals. A significant smoothing effect is usually observed at a shorter sampling interval and a higher wind speed, and the smoothing effect index increases with an increase in the numbers of wind turbines and farms. Additionally, the correlation between the smoothing effect of aggregated wind farms and the forecast accuracy for the corresponding aggregated power output is examined. The experimental results indicate that the wind power forecast accuracy varies with the smoothing effect index, which is influenced by the number of wind farms. Furthermore, the aggregated output from a wind farm cluster with a higher smoothing effect index exhibits better forecasting performance than that from a single wind farm, showing that the trend of the wind power series becomes smoother due to the smoothing effect, thus enabling one-step-ahead wind power forecasting with higher accuracy.
•Developed a novel micro combustor for portable thermoelectric power generation.•Thermal and power characteristics of the system are investigated in detail.•The proposed system size is compatible ...with a conventional electrochemical battery.•The proposed system offers high power density with high conversion efficiency.
An innovative method to use combustion-based source for portable power generation for Microelectromechanical systems with significant improvement in power density and conversion efficiency has been reported in this study. A triple microcombustor configuration with backward facing steps and associated provision for efficient heat recirculation is proposed for flame stabilization. The presence of three parallel combustion zones increases the combustion intensity compared to a single combustor configuration and enhances the overall thermal performance. A novel technique to extract maximum heat from the hot combustion products for thermoelectric power generation is implemented. The thermoelectric modules are mounted to facilitate direct contact of the module surface with the hot combustion products. A significant improvement in heat transfer rate from the hot combustion gases to the thermoelectric modules (~16 times) is achieved. An electric power output of 4.9 W with overall conversion efficieny of 5.09% at a mixture velocity of 10 m/s and a mixture equivalence ratio of 0.9 is achieved. This signifies an improvement of ~10–20% in overall conversion efficiency as compared to previously reported systems.
The power output delivered from a photovoltaic module highly depends on the amount of irradiance, which reaches the solar cells. Many factors determine the ideal output or optimum yield in a ...photovoltaic module. However, the environment is one of the contributing parameters which directly affect the photovoltaic performance. The authors review and evaluate key contributions to the understanding, performance effects, and mitigation of power loss due to soiling on a solar panel. Electrical characteristics of PV (Voltage and current) are discussed with respect to shading due to soiling. Shading due to soiling is divided in two categories, namely, soft shading such as air pollution, and hard shading which occurs when a solid such as accumulated dust blocks the sunlight. The result shows that soft shading affects the current provided by the PV module, but the voltage remains the same. In hard shading, the performance of the PV module depends on whether some cells are shaded or all cells of the PV module are shaded. If some cells are shaded, then as long as the unshaded cells receive solar irradiance, there will be some output although there will be a decrease in the voltage output of the PV module. This study also present a few cleaning method to prevent from dust accumulation on the surface of solar arrays.
The efficiency and power output of a PV module decrease at the peak of sunlight due to energy loss as heat energyand this reduces the module power output. Multi-concept cooling technique, a concept ...that involves three types of passive cooling, namely conductive cooling, air passive cooling and water passive cooling has the potential to tackle this challenge. The experiment was set up using two solar panels of 250 watts each with both modules mounted at a height of 37 cm to create room for air-cooling, with the application of water-cooling at the surface of one of the PV modules to reduce the surface temperature to 2̊0 C. The rear of the same module attached to an aluminium, Al heat sink. The other module also mounted was without water-cooling and Al heat sink attachment. The Al heat sink comprises aluminium plate attached with aluminium fins to aid cooling, and water at a reduced temperature achieved with the introduction blocks of ice facilitated the module surface cooling. Analysis of the power output achieved, carried out with the help of the equation for PV array power output with a derating factor of 80%. The experiment recorded an increase in output power of 20.96 watts, and an increase in efficiency of not less than 3% achieved thus making the module more efficient and productive.
•Prediction of power production of photovoltaic module considering ambient weather conditions.•Predictive models have been developed using both artificial neural network and regression ...analysis.•Solar irradiation, ambient and module temperature are key factors and important variables to estimate PV power generation.•Performance of developed models was evaluated and compared to other models in the literature.
This paper proposes artificial neural network (ANN) and regression models for photovoltaic modules power output predictions and investigates the effects of climatic conditions and operating temperature on the estimated output. The models use six days of experimental data creating a large dataset of 172,800 × 7. After data preprocessing, the appropriate attributes were selected as inputs and taken into account as features; solar irradiation, ambient air and module temperature, wind speed, and relative humidity, while the power generation as a target. In light of these data, the effect of training algorithm on the predictive performance of the ANN model was investigated. Results show that solar irradiation, ambient and module temperatures are key factors in predicting PV module power generation, as these variables are strongly correlated with PV power output. Moreover, the Levenberg-Marquardt algorithm was found to be the best training procedure. The ANN model demonstrated higher accuracy than the developed multiple linear regression models. However, the proposed Rational-Power-Law (RPL) and Power-Law (PL) models were able to capture the nonlinearity in the system, as assessed by coefficient of determination (R2) and the Mean Absolute Error (MAE), and successfully supplied a very high level of precision. The ANN, and both RPL and PL models provided comparable performance, attaining an R2 of 0.997, 0.998 and 0.996, and a MAE of 1.998, 1.156, and 1.242, respectively, when compared to experimental results. Furthermore, models proposed in this study were evaluated and compared with others available in literature and have demonstrated superior performance and better accuracy.
Precise prediction of wave energy is indispensable and holds immense promise as ocean waves have a power capacity of 30–40 kW/m along the coast. Utilising this energy source does not generate harmful ...emissions, making it a superior substitute for fossil fuel-based energy. The computational expense associated with simulating and computing intricate hydrodynamic interactions in wave farms restricts optimisation methods to a few thousand evaluations and makes a challenging situation for training in deep neural prediction models. To address this issue, we propose a new solution: a Meta-learner gradient boosting method that employs four multi-layer convolutional dense neural network surrogate models combined with an optimised extreme gradient boosting. In order to train and validate the predictive model, we used four wave farm datasets, including the absorbed power outputs and 2D coordinates of wave energy converters (WECs) located along the southern coast of Australia, Adelaide, Sydney, Perth and Tasmania. Furthermore, the capability of the transfer learning strategy is evaluated. The WECs used in this study are of the fully submerged three-tether converter type, similar to the CETO prototype. The effectiveness of the proposed approach is assessed by comparing it with 15 well-established and effective machine learning (ML) methods. The experimental findings indicate that the proposed model is competitive with other ML and deep learning approaches, exhibiting considerable accuracy of 88.8%, 90.0%, 90.3%, and 84.4% in Adelaide, Perth, Sydney and Tasmania and improved robustness in predicting wave farm power output.
Ballistic performances are determined by both the maximal lower limb power output (
) and their individual force-velocity (F-v) mechanical profile, especially the F-v imbalance (
): difference ...between the athlete's actual and optimal profile. An optimized training should aim to increase
and/or reduce
. The aim of this study was to test whether an individualized training program based on the individual F-v profile would decrease subjects' individual
and in turn improve vertical jump performance.
was used as the reference to assign participants to different training intervention groups. Eighty four subjects were assigned to three groups: an "optimized" group divided into velocity-deficit, force-deficit, and well-balanced sub-groups based on subjects'
, a "non-optimized" group for which the training program was not specifically based on
and a control group. All subjects underwent a 9-week specific resistance training program. The programs were designed to reduce
for the optimized groups (with specific programs for sub-groups based on individual
values), while the non-optimized group followed a classical program exactly similar for all subjects. All subjects in the three optimized training sub-groups (velocity-deficit, force-deficit, and well-balanced) increased their jumping performance (12.7 ± 5.7% ES = 0.93 ± 0.09, 14.2 ± 7.3% ES = 1.00 ± 0.17, and 7.2 ± 4.5% ES = 0.70 ± 0.36, respectively) with jump height improvement for all subjects, whereas the results were much more variable and unclear in the non-optimized group. This greater change in jump height was associated with a markedly reduced
for both force-deficit (57.9 ± 34.7% decrease in
) and velocity-deficit (20.1 ± 4.3%) subjects, and unclear or small changes in
(-0.40 ± 8.4% and +10.5 ± 5.2%, respectively). An individualized training program specifically based on
(gap between the actual and optimal F-v profiles of each individual) was more efficient at improving jumping performance (i.e., unloaded squat jump height) than a traditional resistance training common to all subjects regardless of their
. Although improving both
and
has to be considered to improve ballistic performance, the present results showed that reducing
without even increasing
lead to clearly beneficial jump performance changes. Thus,
could be considered as a potentially useful variable for prescribing optimal resistance training to improve ballistic performance.
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