Researchers are increasingly using algorithms that are influenced by nature because of its ease and versatility, the key components of nature-inspired metaheuristic algorithms are investigated... Show More

Researchers are increasingly using algorithms that are influenced by nature because of its ease and versatility, the key components of nature-inspired metaheuristic algorithms are investigated, involving divergence and adoption, investigation and utilization, and dissemination techniques. Grey Wolf Optimizer (GWO), a relatively recent algorithm influenced by the dominance structure and poaching deportment of grey wolves, is a very popular technique for solving realistic mechanical and optical technical challenges. Half of the recurrence in the GWO are committed to the exploration and the other half to exploitation, ignoring the importance of maintaining the correct equilibrium to ensure a precise estimate of the global optimum. To address this flaw, a Multi-tiered GWO (MGWO) is formulated, that further accomplishes an appropriate equivalence among exploration and exploitation, resulting in optimal algorithm efficiency. In comparison to familiar optimization methods, simulations relying on benchmark functions exhibit the efficacy, performance, and stabilization of MGWO.

The renewable economic emission transmit is a significant and new assignment in the modern power system. This article develops oppositional grasshopper optimization algorithm (OGOA) which depends on... Show More

The renewable economic emission transmit is a significant and new assignment in the modern power system. This article develops oppositional grasshopper optimization algorithm (OGOA) which depends on the social dealings of the grasshopper in nature, to solve renewable energy based economic emission dispatch (EED) considering uncertainty in wind power availability and a carbon tax on emission from the thermal unit. To speed up the convergence speed and advance the simulation results, opposition based learning (OBL) is integrated with the fundamental GOA in OGOA algorithm. To show the nonlinearity of wind power availability the Weibull distribution is used. A standard system, containing of two wind farms and six thermal units is used for testing the dispatch model for three different loads. The statistical outcomes of the applied OGOA technique are compared with basic GOA and quantum-inspired particle swarm optimization (QPSO) optimization. It is observed OGOA is more skillful than basic GOA technique for significantly reducing the computation time and developing hopeful outcomes.

In this paper, a novel cascade control technique is proposed in order to identify the parameters of cascade controllers in a grid-connected photovoltaic (PV) system. Here, tuning of the inner and... Show More

In this paper, a novel cascade control technique is proposed in order to identify the parameters of cascade controllers in a grid-connected photovoltaic (PV) system. Here, tuning of the inner and outer loop controllers is done simultaneously by means of an optimized genetic algorithm-based fractional order PID (GA-FOPID) control. Simulations are conducted using Matlab/Simulink software under different operating conditions, namely under fast-changing weather conditions, sudden parametric variations, and voltage dip, for the purpose of verifying the effectiveness of the proposed control strategy. By comparing the results with recently published optimization techniques such as particle swarm optimization (PSO) and ant colony optimization (ACO), the superiority and effectiveness of the proposed GA-FOPID control have been proven.

Preliminary preparation of waste for anaerobic digestion at thermophilic temperature conditions is the most energy-intensive stage of the process of anaerobic bioconversion of production and... Show More

Preliminary preparation of waste for anaerobic digestion at thermophilic temperature conditions is the most energy-intensive stage of the process of anaerobic bioconversion of production and consumption waste organic matter, therefore, the search for ways to reduce energy consumption at this stage remains an urgent task. The article proposes a technological solution to maintain the temperature regime of the digester operation due to the utilization of existing waste low-grade energy sources using a compression heat pump. The flow diagram of the experimental biogas plant is shown and a description of its operation is given. The dependences of the absolute and specific rates of heating of the influent and cooling of the effluent on the initial temperature of the effluent are given. The principal possibility of maintaining the temperature regime in the digester is shown by using the heat recovery of the effluent using a compression heat pump.

Wind energy is generated via the use of wind blades, turbines and generators that are deployed over a given area. To achieve a higher energy and system reliability, the wind blade and other units of... Show More

Wind energy is generated via the use of wind blades, turbines and generators that are deployed over a given area. To achieve a higher energy and system reliability, the wind blade and other units of the system must be designed with suitable materials. In this paper however, a computational intelligent model based on an artificial neutral network has been propose for the evaluation of the reliability of the wind turbine blade designed with the FRP material. The simulation results show that there was a reduction in the training mean square error, testing (re–training) mean square error and validation mean square error, when the number of training epochs is increased by 50% such that the minimum mean square error and maximum mean square error were 0.0011 and 0.0061, respectively. The low validation mean square error in the simulation results implies that the developed artificial neural network has a good accuracy when determining the reliability and the failure probability of the wind turbine blade.