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This research proposes an improved energy management strategy (EMS) for a fuel cell hybrid power system for an electric aircraft based on a recently developed coyote optimization algorithm (COA). The suggested hybrid system consists of fuel cells and an energy storage system (ESS) to supply the required load in stable conditions. The distribution and performance of the hybrid electrical power system are determined by various energy sources. Consequently, having the best energy management system is essential for completing this work. The suggested EMS’s main objectives are to reduce hydrogen energy utilization and increase power source longevity. The proposed coyote optimization algorithm with external energy maximization strategy (COA-EEMS) and coyote optimization algorithm with equivalent consumption minimisation strategy (COA-ECMS) are tested with the help of the Opal-RT 5700 real-time HIL simulator and MATLAB/Simulink. The proposed algorithms confirm their robustness and higher efficiency by minimizing hydrogen fuel consumption compared to existing algorithms. The merits of the proposed algorithms are presented in detailed and compared with existing algorithms.

This paper presents novel models of Soft Computing (SC) Maximum Power Point Tracking (MPPT) controllers for Photo-Voltaic Water Pumping Systems(PVWPS) Based on DC Motor. Also paper discusses and evaluates the performance of the proposed models. The proposed methodology is mainly based on duty cycle prediction of the cuk converter to track the maximum power point (MPP) of the PV array. Indeed of the maximum water flow rate of PVWPS at varying climate conditions must ensure that the motor-pump set operates at MPP. The analytical modeling for the proposed MPPT controller has been implemented to predict the optimal duty cycle of the converter for MPPT achievement within the different operating conditions of solar radiation, ambient temperature, and total static head. Also, different Soft Computing (SC) Algorithms have been implemented and evaluated based on Particle Swarm Optimization (PSO) algorithm, Mine Blast Algorithm (MBA), and β -hill climbing ( β -HC) algorithm. A comparative study between SC Algorithms, analytical modeling, and experimental works are given. Also evaluations of SC Algorithms are done to verify SC Algorithms’ accuracy. This evaluation study has proven the significant improvement of daily water flow rate and also has specified the appropriateness of the SC algorithm for MPPT for a PVWPS.

The passive power distribution networks are prone to imperfect voltage profile and higher power losses, especially at the far end of long feeders. The capacitor placement is studied in this article using a novel Mine Blast Algorithm (MBA). The voltage profile improvement and reduction in the net annual cost are also considered along with minimizing the power loss. The optimization problem is formulated and solved in two steps. Firstly, the Voltage Stability Index (VSI) is used to rank the nodes for placement of the capacitors. Secondly, from the priority list of nodes in the previous step, the MBA is utilized to provide the optimal location and sizes of the capacitors ensuring loss minimization, voltage profile improvement, and reduced net annual cost. Finally, the results are tested on 33 and 69 radial node systems in MATLAB. The results for the considered variables are presented which show a significant improvement in active and reactive power loss reduction and voltage profile with lesser reactive power injection.

The shape optimization of mechanical and automotive component plays a crucial role in the development of automotive technology. Presently, the use of derivative-free metaheuristics in combination with finite element analysis for mechanical component design is one of the most focused on topics due to its simplicity and effectiveness. In this research paper, the mine blast algorithm (MBA) is used to solve the problem of shape optimization for a vehicle door hinge to prove how the MBA can be used for solving shape optimization problems in designing electrical vehicles. The results show the advantage of the MBA for designing optimal components in the automotive industry.

Solving practical mechanical problems is considered as a real challenge for evaluating the efficiency of newly developed algorithms. The present article introduces a comparative study on the application of ten recent meta-heuristic approaches to optimize the design of six mechanical engineering optimization problems. The algorithms are: the artificial bee colony (ABC), particle swarm optimization (PSO) algorithm, moth-flame optimization (MFO), ant lion optimizer (ALO), water cycle algorithm (WCA), evaporation rate WCA (ER-WCA), grey wolf optimizer (GWO), mine blast algorithm (MBA), whale optimization algorithm (WOA) and salp swarm algorithm (SSA). The performances of the algorithms are tested quantitatively and qualitatively using convergence speed, solution quality, and the robustness. The experimental results on the six mechanical problems demonstrate the efficiency and the ability of the algorithms used in this article. British Association for Psychopharmacology Benemérita Universidad Autónoma de Puebla

Frequency control represents a critically significant issue for the enhancement of the dynamic performance of isolated micro grids. The micro grid system studied here was a wind−diesel system. A new and robust optimization technique called the mine blast algorithm (MBA) was designed for tuning the PID (proportional−integral−differential) gains of the blade pitch controller of the wind turbine side and the gains of the superconducting magnetic energy storage (SMES) controller. SMES was implemented to release and absorb active power quickly in order to achieve a balance between generation and load power, and thereby control system frequency. The minimization of frequency and output wind power deviations were considered as objective functions for the PID controller of the wind turbine, and the diesel frequency and power deviations were used as objective functions for optimizing the SMES controller gains. Different case studies were considered by applying disturbances in input wind, load power, and wind gust, and sensitivity analysis was conducted by applying harsh conditions with varying fluid coupling parameter of the wind−diesel hybrid system. The proposed MBA−SMES was compared with MBA (tuned PID pitch controller) and classical PI control systems in the Matlab environment. Simulation results showed that the MBA−SMES scheme damped the oscillations in the system output responses and improved the system performance by reducing the overshoot by 75% and 36% from classical and MBA-based systems, respectively, reduced the settling time by 45% compared to other systems, and set the final steady-state error of the frequency deviation to zero compared to other systems. The proposed scheme was extremely robust to disturbances and parameter variations.

This research investigates the performance improvements of induction motor (IM) drive supplied by a hybrid wind/battery storage system based on the optimal mine blast algorithm. This is done by using IM field-oriented speed control as an independent dynamic load and a battery as a storage power unit. To ensure the accuracy and quality of energy equilibrium in autonomous wind power systems, there is an urgent and necessary demand for battery storage units. So in principle, the overall system of the complete model is configured to be combined with an uncontrolled rectifier, a steady magnetic synchronous generator (PMSG), a buck converter, and a lead&ndash, acid battery (LAB) in addition to an induction motor. According to the imposed controller, the loads required power can be obtained effectively by the prescribed battery storage and the wind generation units. Considering the suggested vector control to adjust the induction motor velocity through a three-phase inverter, it will be obtained 220 V/50 Hz, which is the principal target of this research. Moreover, the system with the proposed optimal control is compared with the optimal control based on genetic algorithm (GA) and the classical PID control. It is, therefore, possible to conclude that the results of numerical calculations and simulations illustrate that the studied system has strong achievement and perfect predictor of the electric parameter waveforms.