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This paper presents the reconfiguration of control circuit designed to control four-quadrant chopper placed in the variable speed drive system (VSDS)'s DC-link. The purpose of this design is to reduce the overall total harmonic distortion THD% of input current, and the ripple factor (RF) of the DC-link current in this system. Both of Grey Wolf Algo...
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... The dominant approach today is to use nature-inspired algorithms to solve these complex problems. These techniques have gained popularity due to their simplicity, prompting researchers to innovate and introduce novel methods [21]. ...
... GWO has proven to be effective in various engineering and optimization challenges, showcasing its ability to find optimal solutions. Consequently, the strengths of GWO have made it a popular choice for optimizing the sizing of grid-connected bifacial PV systems [42]. These strengths include its nature-inspired optimization strategy, ability to balance exploration and exploitation, efficient convergence, scalability, adaptability, and successful track record in solving intricate optimization problems. ...
The shift towards renewable energies is driven by the shortage of fossil fuels for electricity generation and the associated harmful impacts. Grid-connected PV systems are a reliable and effective choice for power production across different uses, making them a key player in the global renewable energy landscape. Consequently, the careful selection of components for these systems is a crucial and widely studied aspect in this area of research. This paper introduced using gray wolf optimization algorithm GWO & whale optimization algorithm WOA for determining the optimal number of grid - connected bifacial photovoltaic PV systems in Babylon Hilla. The considered factors included available space, desired energy production, radiation, dihedral factor, budget constraints, and grid connectivity requirements. The mathematical formulation of the problem and implementation details of the algorithms are presented. In addition, two cases studied are performed one for a residential area, and the other for a single house. The results demonstrated the efficiency and effectiveness of both algorithms in identifying optimal solutions for determining the size of systems in the area under study. However, the WOA surpassed the GWO in meeting the optimization criteria. The proper selection of these systems resulted in higher power generation, lower costs, improved energy management, and the advancement of sustainable solar energy solutions.
... In this algorithm, a solution to the optimization problem is represented by an empire, while imperialists and colonies symbolize specific features or components of the solution. The algorithm involves a process of competition and assimilation in which stronger imperialists capture weaker colonies, ultimately leading to global convergence [49]- [58]. ICA has shown remarkable performance in fields as diverse as engineering, finance and bioinformatics, making it a promising tool for tackling real-world problems [59]- [62]. ...
... To overcome this problem, the grey wolf optimizer (GWO) algorithm shall be adapted for rapid decisionmaking by the continuous decrease of search space (Nadweh et al. 2020;Mirjalili et al. 2014;Jegha et al. 2020). However, the GWO is not capable of balancing between exploring and exploiting search space (Mittal et al. 2016). ...
An adjustable pump speed drive is commonly employed to control the speed of the pump motor, achieve the appropriate flow rate, and maintain the fluid level. The electrical motor, power electronics converter, and control are the main elements of the pump motor drive (PMD). In terms of energy efficiency and reliability, pump drives with permanent magnet synchronous motors (PMSMs) and sensorless control have more alluring qualities. To increase the efficiency of PMSM-PMD, the optimum controls including loss minimization and modified grey wolf optimizer (mGWO) are employed. The model reference adaptive system (MRAS) control is often employed for sensorless PMSM-PMD owing to its simplicity, reliability, and good response. The PMSM core loss equivalent parameters are precisely analyzed in this article. Also, the loss model that considers core loss is used to calculate the link between power loss and reference d-axis stator current (Ids). Further, for enhancing the efficiency, an optimal Ids* value is injected in field orientation control (FOC). This proposed scheme increases the efficiency of the PMSM pump drive by up to 1.5 percent as compared to the conventional FOC strategy. A 2.2-kW PMSM drive is tested with the proposed control strategy using real-time interfacing controller dSPACE 1202 MicroLabBox. Also, the obtained results are validated in Matlab/Simulink environment.
... GWO has been used to increase the performance of an optimized model of hybrid kernel function relevance vector machine (HKRVM) for battery prognostics and health management [22], solve problems related to load frequency control in high scale power systems [23], reconfigure the control circuit designed to keep the four-layer chopper placed on the DC connection of the variable speed drive system (VSDS) under control and has yielded better results than previous methods and practices used in the system [24]. ...
... Table 1 presents the binary and ternary models explored in the various areas of engineering applications. As observed, GWO algorithm with or without having been explored in abundant waste oil [18], energy management scheme [20], MRCD [21], HFD [22], power scheduling [23], COVID-19 epidemic prediction [24], wind speed prediction [25], solar irradiance [26], healthcare districting modelling [27], solar photovoltaic system [28], cetane improver concentration [29], compressive strength of the concretes [30], Rafsanjan pistachio shells [31], industrial variable drive systems [32], spatial probability of landslide [33], estimation of landslide susceptibility [34], U-shaped robotic assembly line [35], Soil moisture simulation [36], coal-deformation temperature [37], biochar yield [38], converted plug-in hybrid electric vehicle [39], combined heat and power [40], MSCB dryer [41], flexible job shops [42], drying features of tarragon [43], Design of truss structures [44], fuel cell [45], heat exchanger [46], engineering problems [47], spinning mills [48]. A perusal of the comprehensive reviews shows that the GWO has not been adopted to predict the yield of TSOME. ...
Modelling and enhancing the production of green diesel in biodiesel industries have been hampered by the failure of the conventional approach to pursue space with continuous convergence velocity, being entombed in local minima, and maintaining unwavering resolutions. The study presented for the first time the optimization protocol for the development of biodiesel production from tobacco seed oil (TSO) on the batch reactor aided by the unique Grey Wolf Optimizer-Response Surface Methodology-Artificial Neural Network (GWO-RSM-ANN) techniques. Lower calorific value (LCV), higher calorific value (HCV), and specific heat capacity (Cp) correlations were postulated for tobacco seed oil methyl ester (TSOME/B100/TSOB) and diesel blends. RSM, ANN, and GWO approaches were used to model TSOME's main production yield. The ASTM test methods were used to examine the significant basic properties of the fuel categories, while the LCV and HCV were detected using standard procedures. Maximum TSOME yield (90.2%) was obtained at methanol/TSO molar ratio of 5.95, KOH content of 1.15 wt. %, and methylic duration of 77.6 min. The ANN model configuration (3-15-1) that was developed showed more adaptability and nonlinearity. The estimated coefficient of determination (R2) of 0.9999, mean average error (MAE) of 0.00035, and RMSE of 0.00105 for the GWO model compared to those of R2 of 0.9825, MAE of 1.3145, and RMSE of 1.7087 for RSM model; and R2 of 0.9976, MAE of 0.2405, and RMSE of 0.6381 for ANN model vindicate the superiority of GWO model over the RSM and ANN models. The major fuel properties agreed with the ranges of the ASTMD6751 and EN 14214 specifications. The LCV, HCV, and Cp are also correlated with the TSOME fraction through the linear equations. There were excellent correlations between the analyzed and calculated values for the LCVs and HCVs. The maximum absolute error between the measured and estimated LCV and HCV are 0.108% for 20%TSOME (20% TSOME +80% diesel fuel), and 0.17% for pure diesel, respectively. The model and correlations can offer biodiesel and automobile industries with database information.
... CNN-GWO (AUC = 0.876, RMSE = 0.08) outperformed the CNN-ICA model (AUC = 0.852, RMSE = 0.09). The finding is well in line with previous studies (Nadweh et al., 2020;Nosratabadi et al., 2020;Panahi et al., 2021b). Generally, the prediction ability of deep learning algorithm can vary based on the model structure, the proper selection of inputs, the quantity and quality of the dataset, and the optimization of the model's parameters (Asim et al., 2018). ...
Landslides are a geological hazard that can pose a serious threat to human health and the environment of highlands or mountain slopes. Landslide susceptibility mapping is an essential tool for predicting and mitigating landslides. This study aimed to investigate the application of deep learning algorithms based on convolutional neural networks (CNNs) with metaheuristic optimization algorithms, namely the grey wolf optimizer (GWO) and imperialist competitive algorithm (ICA), to landslide susceptibility mapping. The study area was Icheon City, South Korea, for which an accurate landslide inventory dataset was available. The landslide inventory map was prepared and randomly divided into datasets of 70% for training and 30% for validation. Additionally, 18 landslide-related factors, including geo-environmental and topo-hydrological factors, were considered as predictive variables. The models were compared using area under the curve (AUC) values in receiver operating characteristic (ROC) curve analysis. The validation results showed that optimized models based on CNN-GWO (AUC = 0.876, RMSE = 0.08) and CNN-ICA (AUC = 0.852, RMSE = 0.09) outperformed the standalone CNN model (AUC = 0.847, RMSE = 0.12). Nevertheless, the CNN model outperformed previous research that used a machine learning algorithm alone. Thus, the deep learning algorithm with optimization algorithms proposed in this study can generate more suitable models for landslide susceptibility mapping in the study area due to its improved accuracy.
... Consequently, research has been dedicated towards averting the premature convergence issue. Recent research entailing modification and application of the PSO algorithm in various problems can be found in [14,15,16,17,18,19,20,21,22]. ...
Collaborative Beamforming (CBF) is an essential tool towards increasing transmission range in Wireless Sensor Networks (WSNs). Owing to the random and complex nature of WSNs, development and use of improved metaheuristic algorithms in CBF is of essence. Particle Swarm Optimization (PSO) algorithm is a good candidate for use in CBF owing to its simplicity and low computation complexity. However, the basic PSO algorithm suffers from premature convergence particularly in highly multimodal functions (typical of CBF). This paper delves into the development and application of an improved Particle Swarm Optimization (PSO) algorithm in CBF. A new fuzzy-logic based confidence and inertia weight parameters adaptation scheme has been developed with an aim of enhancing exploration and exploitation capabilities of the PSO algorithm. Normalized particle quality and iteration count have been used as the inputs to the designed fuzzy-logic inference system. The fuzzy logic based parameters adaptation scheme has been implemented in the form of a lookup table to minimize “on-line" computation complexity. Furthermore, a particle culling/ re-initialization procedure is utilized at half the number of maximum iterations to enhance overall swarm diversity. The modified PSO algorithm has been christened Culled Fuzzy Adaptive Particle Swarm Optimization (CFAPSO) algorithm. The developed CFAPSO algorithm is noted to outperform other metaheuristic algorithms in a statistical performance analysis procedure (on the basis of a set of standard unimodal and multimodal functions). Upon application to CBF, the CFAPSO algorithm is found to generate a beamsteering outcome statistically identical to that of conventional beamsteering.
... Among them, Particle Swarm Optimization (PSO) [25] and Grey Wolf Optimization (GWO) [32] have shown their reliability to solve real optimization problems where the objective function is not linear. In particular, the works in [17,33] only considered these two algorithms to configure a DC/DC power converter. The review presented in [34] show that PSO algorithms are still investigated to tune the power converters of microgrids. ...
This research focuses on a photovoltaic system that powers an Electric Vehicle when moving in realistic scenarios with partial shading conditions. The main goal is to find an efficient control scheme to allow the solar generator producing the maximum amount of power achievable. The first contribution of this paper is the mathematical modelling of the photovoltaic system, its function and its features, considering the synthesis of the step-up converter and the maximum power point tracking analysis. This research looks at two intelligent control strategies to get the most power out, even with shading areas. Specifically, we show how to apply two evolutionary algorithms for this control. They are the “particle swarm optimization method” and the “grey wolf optimization method”. These algorithms were tested and evaluated when a battery storage system in an Electric Vehicle is fed through a photovoltaic system. The Simulink/Matlab tool is used to execute the simulation phases and to quantify the performances of each of these control systems. Based on our simulation tests, the best method is identified.
Grey Wolf Optimizer (GWO) tends to converge prematurely when dealing with multimodal problems. Using the benefits of hybridizing algorithm to boost the performance of GWO is a recent trend. Therefore, a novel improved GWO called collaboration-based Hybrid GWO-SCA optimizer (cHGWOSCA) is developed. Given the powerful exploration of Sine Cosine Algorithm (SCA), SCA is incorporated into the position update of leading wolves in GWO. Then a collaboration between the personal best and leading wolves is applied in the hybridized position update, which can improve the global exploration. To balance the exploitation, weight-based individual position update and crossover with personal best are used to guide the exploitation of promising areas. The factor a→ modified by a sine function is employed to equilibrate exploration and exploitation. In addition, the global convergence of cHGWOSCA is proved. IEEE CEC 2013, 2014 and 2019 are applied to verify the validity of cHGWOSCA. PV model parameter extraction and three constrained engineering design problems are used to further demonstrate the performance of cHGWOSCA. Experimental results indicate that cHGWOSCA is a high-performing algorithm in global optimization.
