The hybridisation of two or more algorithms is recently emerging to detect superior solutions to the optimization troubles. In this study, a new hybrid cuckoo search algorithm and grey wolf optimiser (CSA-GWO) optimis...
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The hybridisation of two or more algorithms is recently emerging to detect superior solutions to the optimization troubles. In this study, a new hybrid cuckoo search algorithm and grey wolf optimiser (CSA-GWO) optimisation technique is exercised and exhibited to optimally design and tune the controller parameters installed in the voltage source converter (VSC) of an offshore wind farm (OWF). One of the widely used control strategies for VSC is the proportional-integral (PI) closed-loop control system. The new hybrid optimisation algorithm is used to design and tune the PI controllers' parameters to improve the performance of OWF. It shall be mentioned that these parameters are usually hard to obtain owing to the high level of embedded non-linearity in such energy systems. The performance of such optimally designed PI controllers is presented in both dynamic and transient conditions. To examine the realistic stability of the proposed algorithm, real wind speed pattern has been captured from Egypt wind farm at Zafarrana and simulated. The obtained results from this new hybridoptimisation CSA -GWO control system reflect its superiority over other traditional algorithms, such as genetic algorithm, especially during symmetrical and unsymmetrical faults. CSA-GWO algorithm was examined using MATLAB/Simulink.
A microstrip antenna array (MAA) loading with metasurface is designed and fabricated for ultra-wideband radar cross-section (RCS) reduction while preserving its radiation characteristics. A new physical mechanism base...
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A microstrip antenna array (MAA) loading with metasurface is designed and fabricated for ultra-wideband radar cross-section (RCS) reduction while preserving its radiation characteristics. A new physical mechanism based on size adjustment of the multiple basic unit cells is proposed for ultra-wideband manipulation of electromagnetic waves. To obtain the excellent RCS performance, the selection of the basic unit cells is optimised by the hybrid optimisation algorithm. Both simulated and measured results indicate that the proposed MAA can reduce the RCS significantly in an ultra-wide frequency band from 6.2 to 27.3 GHz under x- and y-polarised normal incidence. The radiation characteristics of the MAA are well preserved simultaneously.
In general, WSN maintains the chief support of cloud-assisted internet of things (CIoT). This paper intends to implement a WSN-assisted CIoT model which involves two processes: one is optimal cluster head selection, a...
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In general, WSN maintains the chief support of cloud-assisted internet of things (CIoT). This paper intends to implement a WSN-assisted CIoT model which involves two processes: one is optimal cluster head selection, and the other is optimal shortest path selection. The main intent of this optimal cluster head selection is to select a cluster head using a hybrid optimisation algorithm with the objective of minimising the distance between each IoT sensor node and cluster head and consumed energy. Moreover, the same hybridalgorithm is used in the optimal shortest path selection process. The beneficial concepts of two well-performing meta-heuristic algorithms like deer hunting optimisationalgorithm (DHOA), and particle swarm optimisation (PSO) are merged to frame a hybridalgorithm termed as particle swarm-based deer hunting optimisationalgorithm (PS-DHOA) to be suited for both cluster head selection and route selection. Developed model has been validated through effective performance analysis.
Reference crop evapotranspiration (ETO) is a key factor for estimating crop water requirements, which guide agricultural irrigation. To improve the accuracy of predicting ETO in different climate zones in China that l...
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Reference crop evapotranspiration (ETO) is a key factor for estimating crop water requirements, which guide agricultural irrigation. To improve the accuracy of predicting ETO in different climate zones in China that lack meteorological data, an ETO hybrid model based on K-nearest neighbour (KNN) machine learning algorithm for extracting factor contribution rates is proposed in this study. Meteorological factors with large contribution rates were selected as input, and a prediction model for ETO was established using the Elman daily ETO prediction model. The ETO prediction model was optimised using three optimisationalgorithms [Genetic optimization algorithm (GA), Cuckoo optimization algorithm (CS) and Whale optimization algorithm (WOA)] to improve the accuracy of ETO prediction. The results revealed that surface radiation (Rs) is the most important factor in estimating ETO (contribution rate = 0.392-0.626), followed by temperature factors (T;including maximum, minimum, and average temperatures). And each model has the highest accuracy with the input combination of Rs and T. For the different machine learning models, the CS-Elman model had the highest accuracy (RMSE = 0.468-2.235, R-2 = 0.567-0.928, MAE = 0.363-1.343, and NSE = 0.345-0.923), and the machine learning model had higher accuracy than the experience model. The CS-Elman model performed more favourably in tropical monsoon and subtropical monsoon regions than that in other areas, and the model performed best at the junction of two climatic zones. The results can provide a theoretical basis for high-precision prediction of ETO in different climate zones in China.
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