A modified particle swarm optimization rat search algorithm and its engineering application.

Solar energy generation requires photovoltaic (PV) systems to be optimised, regulated, and simulated with efficiency. The performance of PV systems is greatly impacted by the fluctuation and occasionally restricted accessibility of model parameters, which makes it difficult to identify these characteristics over time. To extract the features of solar modules and build highly accurate models for PV system modelling, control, and optimisation, current-voltage data collecting is essential. To overcome these difficulties, the modified particle swarm optimization rat search algorithm is presented in this manuscript. The modified rat search algorithm is incorporated to increase the PSO algorithm's accuracy and efficiency, which leads to better outcomes. The RSA mechanism increases both the population's diversity and the quality of exploration. For triple diode model of both monocrystalline and polycrystalline, PSORSA has showed exceptional performance in comparison to other algorithm i.e. RMSE for monocrystalline is 3.21E-11 and for polycrystalline is 1.86E-11. Similar performance can be observed from the PSORSA for four diode model i.e. RMSE for monocrystalline is 4.14E-09 and for polycrystalline is 4.72E-09. The findings show that PSORSA outperforms the most advanced techniques in terms of output, accuracy, and dependability. As a result, PSORSA proves to be a trustworthy instrument for assessing solar cell and PV module data.

Sustainable development of fuel cell using enhanced weighted mean of vectors algorithm.

Using the mathematical model of a Direct Methanol Fuel Cell (DMFC) stack, a new optimum approach is presented for estimating the seven unknown parameters i.e., ( e o , α , R , j e i d , C 1 , ß ,req) optimally. Specifically, a method is proposed for minimization of the Sum of Squared Errors (SSE) associated with the estimated polarization profile, based on the experimental data from simulations. The Enhanced Weighted mean of vectors (EINFO) algorithm is a novel metaheuristic method that is proposed to achieve this goal. An analysis of the results of this method is then compared to various metaheuristic algorithms such as the Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Dragonfly Algorithm (DA), Atom Search Optimization (ASO), and Weighted mean of vectors (INFO) well known in literature. As a final step to confirm the proposed approach's effectiveness, the sensitivity analysis is carried out using temperature changes, along with comparison against different approaches described in the literature to demonstrate its superiority. After comparison of parameter estimation and different operating temperature a non-parametric test is also performed and compared with the rest of the metaheuristic algorithms used in the manuscript. From these tests it is concluded that the proposed algorithm is superior to the rest of the compared algorithms.

Parameter estimation of proton exchange membrane fuel cell using a novel meta-heuristic algorithm.

In recent years, proton exchange membrane fuel cells (PEMFCs) have been known to be a viable method for meeting the electrical energy needs, thereby enhancing the overall reliability of renewable energy systems. PEMFCs demonstrate various promising attributes like pollution-free, totally sustainable, non-self-discharging. These need hydrogen as fuel, and air for their operation, while the final product is pure water only. Thus, under varying operating conditions, the appropriate modeling and parameter optimization of PEMFCs have gained considerable importance in recent times. The evolutionary optimization approaches had been utilized in recent past for estimating PEMFCs parameters as exact modeling of the same does not exist in the literature. For the evaluation of PEMFCs performance criteria, a newly proposed algorithm is developed in this manuscript i.e. black widow optimization (BWO). Firstly, the performance of this proposed algorithm is checked by complex benchmark results. After that, this proposed algorithm is applied to extract the parameters of PEMFCs models under different operating temperatures. The parameter optimization results are obtained using BWO and are further compared with those obtained with five other algorithms, i.e., particle swarm optimization (PSO), multi-verse optimizer (MVO), sine cosine algorithm (SCA), whale optimization algorithm (WOA), and grey wolf optimization (GWO). The complete error analysis is carried out for the two data sheets of the PEMFCs to establish the superiority of BWO. It has been observed that the developed proposed algorithm gives better results when compared to those obtained with rest of the algorithms considered in this work. After calculating the error, non-parametric test is performed which suggests that the BWO is better than the rest of the compared algorithms.

Hydrogen fuel and fuel cell technology for cleaner future: a review.

One of the main problems facing our planetary bodies is unexpected and sudden climate change due to continuously increasing global energy demand, which currently is being met by fossil fuels. Hydrogen is considered as one of the major energy solutions of the twenty-first century, capable of meeting future energy needs. Being 61a zero-emission fuel, it could reduce environmental impacts and craft novel energy opportunities. Hydrogen through fuel cells can be used in transport and distributed heating, as well as in energy storage systems. The transition from fossil-based fuels to hydrogen requires intensive research to overcome scientific and socio-economic barriers. The purpose of this paper is to reflect the current state, related issues, and projection of hydrogen and fuel elements within the conceptual framework of 61a future sustainable energy vision. An attempt has been made to compile in this paper the past hydrogen-related technologies, present challenges, and role of hydrogen in the future.