The penetration of renewable distributed generations (RDGs) into traditional distribution systems (TDSs) remedies many of its deficiencies and shortcomings. Also, it provides mutual technical, economic and environmental benefits for both electricity companies and their customers as well. With a 25% load increase for the standard IEEE 30-bus system, buses 19, 26 and 30 have the lowest voltage magnitudes among all buses. Therefore, these weak buses are selected initially to allocate RDGs. Three cases, namely, one RDG allocated, two RDGs allocated and three RDGs allocated, of RDGs insertion are covered. A novel crow search algorithm auto-drive particle swarm optimization (CSA-PSO) technique is proposed for the first time in this study to specify the optimal allocation, sizing, and number of RDGs based on the total cost and power losses minimization objectives. A new reduction percent formula is used to estimate the reduction in total cost and the total power losses. These will help us to discern between the best cases based on total cost minimization and those based on total power losses minimization to pick up the best among all best cases. In brief, RDGs allocated on buses 19 and 30 is the best among all cases based on total cost reduction and total power losses reduction. Therefore, buses 19 and 30 are recommended to allocate a wind farm and a solar photovoltaic, respectively based on technical and economic issues. Finally, the simulation findings revealed the superiority of the CSA-PSO algorithm in solving the optimal power flow problem with RDGs compared to the state-of-the-art metaheuristic techniques.

Large-scale photovoltaic system (PV) installation can affect power system operation, stability, and reliability because of the non-linear characteristic of the PV system installation. DC/AC and DC/DC converters are the major devices use in connecting PV into the grid. These converters are liable to power quality problem if the proper control mechanism is not adopted. This study presents an optimal control technique to improve dynamic operation of PV grid-connected system. An optimal control method with use of Manta ray foraging optimization (MRFO), is implemented as a control strategy for tuning the proportional–integral (PI) controllers of DC/DC and DC/AC converters for the integration of the PV system into the grid. The MRFO is chosen because of its ease of implementation and requirement of less adjusting parameters. The effectiveness of the proposed technique is studied under irradiance variation. The obtained results demonstrate the superior performance of the MRFO over five other metaheuristic algorithms (i.e., grey wolf optimization, whale optimization, grasshopper optimization, atom search optimization, and salp swarm algorithm) in terms of convergence rate and optimal global solution capture. The entire simulation model is established using MATLAB editor and Simulink. The acquired transient result shows the functionality and viability of the MRFO approach.

Electroencephalogram (EEG) signals reflect the activities or electrical disturbances in neurons in the human brain. Therefore, these signals are vital for diagnosing certain brain disorders. This study mainly focused on the diagnosis of epilepsy and autism spectrum disorders (ASDs) through the analysis and processing of EEGs. In this study, artifacts were removed from the EEG datasets using Independent Component Analysis and were filtered using a fifth-order band-pass Butterworth filter to remove interference and noise. Next, new methods were used to extract the features of EEGs using common spatial pattern (CSP). It is known that conventional CSP uses variance. However, here the use of entropy, energy, and band power with CSP was proposed to extract features of EEGs. Then, in our investigation, four techniques were employed for classification, namely, linear discriminant analysis, support vector machine, k-nearest neighbor (KNN), and artificial neural network, with the aim of comparing the proposed methods and recommending the optimal combination for the diagnosis of epilepsy and ASDs. Finally, the effects of segment length, frequency band, and reduction number on the results were investigated. Two EEG datasets were employed to verify the proposed methods: the King Abdulaziz University dataset (for ASD) and the MIT dataset (for epilepsy). The results indicated that the extracted features based on CSP and band LBP produced the best performance and that the combination of CSP-LBP-KNN provided the best performance with average classification accuracy of approximately 98.46% and 98.62% for diagnosing ASDs and epilepsy, respectively.

This paper presents an optimum sizing methodology to optimize the hybrid energy system (HES) configuration based on genetic algorithm. The proposed optimization model has been applied to evaluate the techno-economic prospective of the HES to meet the load demand of a remote village in the northern part of Saudi Arabia. The optimum configuration is not achieved only by selecting the combination with the lowest cost but also by finding a suitable renewable energy fraction that satisfies load demand requirements with zero rejected loads. Moreover, the economic, technical and environmental characteristics of nine different HES configurations were investigated and weighed against their performance. The simulation results indicated that the optimum wind turbine (WT) selection is not affected only by the WT speed parameters or by the WT rated power but also by the desired renewable energy fraction. It was found that the rated speed of the WT has a significant effect on optimum WT selection, whereas the WT rated power has no consistent effect on optimal WT selection. Moreover, the results clearly indicated that the HES consisting of photovoltaics (PV), WT, battery bank (Batt) and diesel generator (DG) has superiority over all the nine systems studied here in terms of economical and environmental performance. The PV/Batt/DG hybrid system is only feasible when wind resource is very limited and solar energy density is high. On the other hand, the WT/Batt/DG hybrid system is only feasible at high wind speed and low solar energy density. It was also found that the inclusion of batteries reduced the required DG and hence reduced fuel consumption and operating and maintenance cost. Copyright © 2014 John Wiley & Sons, Ltd.

This paper presents a novel minimum seeking algorithm referred to as the Hunger Games Search (HGS) algorithm. The HGS is used to obtain optimal values in the model describing proton exchange membrane fuel cells (PEMFCs). The PEMFC model has many parameters that are linked in a nonlinear manner, as well as a set of constraints. The HGS was used with the aforementioned model to test its performance against nonlinear models. The main aim of the optimization problem was to obtain accurate values of PEMFC parameters. The proposed heuristic algorithm was used with two commercial PEMFCs: the Ballard Mark V and the BCS 500 W. The simulation results obtained using the HGS-based model were compared to the experimental results. The effectiveness of the proposed model was verified under various temperature and partial pressure conditions. The numerical output results of the HGS-based fuel cell model were compared with other optimization algorithm-based models with respect to their efficiency. Moreover, the parametric t-test and other statistical analysis methods were employed to check the robustness of the proposed algorithm under various independent runs. Using the proposed HGS-based PEMFC model, a model with very high precision could be obtained, affecting the operation and control of the fuel cells in the simulation analyses.