Photovoltaic array characteristics with partial shading (PS) have multiple maximum power points (MPPs), and conventional algorithms have difficulties in tracking accurate global maximum power points (GMPPs). This stud...
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Photovoltaic array characteristics with partial shading (PS) have multiple maximum power points (MPPs), and conventional algorithms have difficulties in tracking accurate global maximum power points (GMPPs). This study proposes a MPP tracking (MPPT) method based on improved watercycleoptimization for fast-tracking the GMPP under PS conditions, along with a new strategy to enhance the convergence speed of the MPPT method during load variations. The experimental setup included a dc-dc single-ended primary inductance converter (SEPIC) and digital signal processing and control engineering (DSPACE) controller to assess the performance of the proposed method. The proposed method was also compared with the conventional watercycleoptimization and six MPPT algorithms. The experimental results showed that the proposed method obtained an average tracking efficiency of 99.92% and a tracking time of 0.475 s for all PS tests. Moreover, it achieved a GMPP in a single perturbation step when the load change occurred, reducing the power loss in the photovoltaic (PV) system. The comparison showed that the proposed method performed better than the other MPPT methods in terms of tracking efficiency, convergence speed, and ease of implementation. This method could be utilized to implement developed PV systems with minimal losses.
In this study, a framework to circumvent the difficulties in selecting a proper flood routing method was established by employing two different multi-criteria decision analysis (MCDA) tools, namely, TOPSIS and PROMETH...
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In this study, a framework to circumvent the difficulties in selecting a proper flood routing method was established by employing two different multi-criteria decision analysis (MCDA) tools, namely, TOPSIS and PROMETHEE, with definite decisive criteria such as the error metrics, the number of model parameters, and the model background, under three scenarios. For eight distinct flood datasets, the parameters of 10 different Muskingum models were determined using the water cycle optimization algorithm (WCOA) and the performance of each model was ranked by both MCDA tools considering the hydrograph types of flood datasets, labeled as smooth single peak, non-smooth single peak, multi-peak, and irregular. The results indicate that both tools were compatible by giving similar model results in the rankings of almost all scenarios that include different weights in the criteria. The ranking results from both tools also showed that the routing application in single-peak hydrographs was examined better with empirical models that have a high number of parameters;however, complex hydrographs that have more than one peak with irregular limps can be assessed better using the physical-based routing model that has fewer parameters. The proposed approach serves as an extensive analysis in finding a good agreement between measured and routed hydrographs for flood modelers about the estimation capabilities of commonly used Muskingum models considering the importance of correlation, model complexity, and hydrograph characteristics.
Hybrid heuristic algorithm (HA), an innovative technique in the machine learning field, enhances the accuracy of reference evapotranspiration (ETo) prediction, which is of paramount significance for regional water man...
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Hybrid heuristic algorithm (HA), an innovative technique in the machine learning field, enhances the accuracy of reference evapotranspiration (ETo) prediction, which is of paramount significance for regional water management, agricultural planning, and irrigation designing. However, the new hybrid HA techniques, namely Moth-Flame optimizationalgorithm (MFO) and water cycle optimization algorithm (WCA) are rarely applied to estimate ETo in the earlier literature. Therefore, this study assessed prediction and the estimation abilities of a novel hybrid adaptive neuro-fuzzy inference system (ANFIS-WCAMFO) for monthly ETo of Dhaka and Mymensing stations with data-limited humid regions of south-central Bangladesh. Prediction precision of the ANFIS-WCAMFO model is compared with other state-of-art models, i.e., ANFIS-WCA and ANFIS-MFO using a 4-fold cross-validation method including root-mean-square-error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency (NSE), and coefficient of determination (R2). Nine input combinations of meteorological datasets, including extraterrestrial radiation (Ra), solar radiation (Rs), maximum and minimum temperatures (Tmax and Tmin), relative humidity (RH), and wind speed (U2), were employed for model training and testing purposes. The ANFIS-WCAMFO performed superior to the other state-of-arts methods in estimating monthly ETo in all input combinations. The ANFIS-WCA, ANFIS-MFO, and ANFIS-WCAMFO hybrid models for estimating ETo improved RMSE as 2.7%, 6.9%, and 15.1% for Dhaka station and 0.6%, 7.3%, 12.4% for Mymensingh station, respectively. The use of the Ra variable with the temperature inputs considerably improved the models' accuracy in ETo;improvements in RMSE, MAE, NSE, and R-2 of the hybrid ANFIS-WCMFO models were 26.6%, 30.9%, 17.6%, and 8.2% for Dhaka station and by 28.8%, 34.2%, 18.8% and 22.1% for Mymensingh station, respectively. The proposed hybrid neuro-fuzzy model has been suggested as a promising technique due t
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