Design of Cascaded PI-Fractional Order PID Controller for Improving the Frequency Response of Hybrid Microgrid System Using Gorilla Troops Optimizer

Ali, Moetasem; Kotb, Hossam; AboRas, Kareem M.; Abbasy, Nabil H.

doi:10.1109/access.2021.3125317

Cited by 69 publications

(48 citation statements)

References 45 publications

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“…The accuracy and efficiency of the GTO were demonstrated [31]. The optimizer is simple for engineering applications and does not require many adjustments [27].…”

Section: Introductionmentioning

confidence: 92%

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A3C-TL-GTO: Alzheimer Automatic Accurate Classification Using Transfer Learning and Artificial Gorilla Troops Optimizer

Baghdadi

Malki

Balaha

et al. 2022

Sensors

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Alzheimer’s disease (AD) is a chronic disease that affects the elderly. There are many different types of dementia, but Alzheimer’s disease is one of the leading causes of death. AD is a chronic brain disorder that leads to problems with language, disorientation, mood swings, bodily functions, memory loss, cognitive decline, mood or personality changes, and ultimately death due to dementia. Unfortunately, no cure has yet been developed for it, and it has no known causes. Clinically, imaging tools can aid in the diagnosis, and deep learning has recently emerged as an important component of these tools. Deep learning requires little or no image preprocessing and can infer an optimal data representation from raw images without prior feature selection. As a result, they produce a more objective and less biased process. The performance of a convolutional neural network (CNN) is primarily affected by the hyperparameters chosen and the dataset used. A deep learning model for classifying Alzheimer’s patients has been developed using transfer learning and optimized by Gorilla Troops for early diagnosis. This study proposes the A3C-TL-GTO framework for MRI image classification and AD detection. The A3C-TL-GTO is an empirical quantitative framework for accurate and automatic AD classification, developed and evaluated with the Alzheimer’s Dataset (four classes of images) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The proposed framework reduces the bias and variability of preprocessing steps and hyperparameters optimization to the classifier model and dataset used. Our strategy, evaluated on MRIs, is easily adaptable to other imaging methods. According to our findings, the proposed framework was an excellent instrument for this task, with a significant potential advantage for patient care. The ADNI dataset, an online dataset on Alzheimer’s disease, was used to obtain magnetic resonance imaging (MR) brain images. The experimental results demonstrate that the proposed framework achieves 96.65% accuracy for the Alzheimer’s Dataset and 96.25% accuracy for the ADNI dataset. Moreover, a better performance in terms of accuracy is demonstrated over other state-of-the-art approaches.

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“…The accuracy and efficiency of the GTO were demonstrated [31]. The optimizer is simple for engineering applications and does not require many adjustments [27].…”

Section: Introductionmentioning

confidence: 92%

“…Male gorilla groups are also present. The silver hair that emerges on the silverback's back during puberty gives it its name [27,31]. It has a lifespan of about 12 years.…”

Section: Introductionmentioning

confidence: 99%

A3C-TL-GTO: Alzheimer Automatic Accurate Classification Using Transfer Learning and Artificial Gorilla Troops Optimizer

Baghdadi

Malki

Balaha

et al. 2022

Sensors

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“…Researchers in the field of LFC have used a variety of optimization techniques, such as Artificial Electric Field Algorithm (AEFA) [18] FOTID controller optimized with Path Finder Algorithm (FPA) [19], Fitness Dependent Optimizer (FDO) [20], Grasshopper Optimization Algorithm (GOA) [21], Firefly Algorithm (FA) [22], Harries Hawks Optimizer (HHO) [23], Frog Leaping Algorithm (FLA) [24], Marine Predator Algorithm (MPA) [25], Imperialist Competitive Algorithm (ICA) [26], Gray Wolf Optimizer (GWO) [27], Sine Cosine Algorithm (SCA) [28], Fuzzy PID controller optimized with Improved Ant Colony Optimization (IACO) [29], Atomic Search Optimization (ASO) [30], Improved Fitness Dependent Optimizer (I-FDO) [31], Hybrid PSO based Gravitational Search Optimization (GSO) [32], Volleyball Premier League (VPL) based optimized fractional order control [33], Hybrid Differential Evolution (DE) with Pattern Search (PS) algorithm [? ], Hybrid Teaching-Learning Optimization with PS [34], Water Cycle Algorithm (WCA) based optimized I-TD controller [35] , Gorilla Troops Optimizer [36] and Salp Swarm Algorithm (SSA) [37] algorithm. However, most of the above algorithms have the drawbacks of parametric sensitivity, premature convergence, and complex computation.…”

Section: A Literature Reviewmentioning

confidence: 99%

Optimized Fractional Order Integral-Tilt Derivative Controller for Frequency Regulation of Interconnected Diverse Renewable Energy Resources

et al. 2022

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The interconnection of renewable energy systems, which are complex nonlinear systems, often results in power fluctuations in the interconnection line and high system frequency due to insufficient damping in extreme and dynamic loading situations. To solve this problem, load frequency control ensures nominal operating frequency and orderly fluctuation of grid interconnection power by delivering highquality electric power to energy consumers through efficient and intelligent control systems. To introduce the frequency control of power systems, this paper presents a novel control technique of Fractional Order Integral-Tilt Derivative with Filter (FOI-TDN) controller optimized by the current soft computing technique of hybrid Sine-Cosine algorithm with Fitness Dependent Optimizer (hSC-FDO). For more realistic analysis, practical constraints with nonlinear features, such as controller deadband, communication time delay, boiler dynamics, and generation rate constraint are embedded in the given system model. The proposed approach outperforms some recently developed heuristic approaches such as fitnessdependent optimizer, firefly algorithm, and particle swarm optimization for the interconnected power system of two areas with multiple generating units in terms of minimum undershoot, overshoot, and settling time. To improve the system performance, capacitive energy storage devices are used in each area and Thyristor control phase shifter is used in the interconnection line of the power system. The potential of the hSC-FDO-based FOI-TDN is demonstrated by comparing it with conventional FOTID/FOPID/PID controllers for two areas with multiple power generators IPS. Finally, a robustness analysis is performed to determine the robustness of the presented control system by varying the system loads and system parameters.

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“…Results showed that the proposed SA-based TID controller exhibited better performance under different test systems than other controllers. Ali et al [35] employed Gorilla Troops Optimizer (GTO) to optimise the parameters of a cascaded Proportional Integral-Fractional Order Proportional-Integral-Derivative (PI-FOPID) controller for a two-area hybrid microgrid system. A comparative study involving the optimisation of the control model parameters has been performed in [36].…”

Section: Literature Reviewmentioning

confidence: 99%

An enhanced flower pollinated algorithm with a modified fluctuation rate for global optimisation and load frequency control system

Oladipo

Sun

Wang

2022

IET Renewable Power Gen

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This paper proposes a new hybridised approach comprising the flower pollination algorithm and pathfinder algorithm (FPAPFA), in order to address optimisation problems and for load frequency control system. Although the FPA is a popular algorithm that has been widely used in diverse applications, its implementation is met with the tendency to be trapped in local optimal due to an imbalance between the exploration and exploitation process. Consequently, the FPA's exploration functionality can be enhanced by using the PFA features to shift certain pollens to moderately enhanced locations rather than leading them to random positions. Furthermore, a modified fluctuation rate is incorporated into the PFA to reinforce the exploitative competence of the FPAPFA. Compared to other popular techniques, the proposed algorithm's performance was evaluated against 23 standard mathematical optimisation functions and statistically tested using the Wilcoxon rank-sum and Friedman rank tests. Moreover, the FPAPFA is applied to regulate two unequal multiarea interconnected power systems with different generating units (thermal, hydro, diesel, and wind power plants) while also integrating redox flow batteries (RFBs) and interline power flow controller (IPFC). Simulation results show that the proposed FPAPFA delivered better results than other algorithms with improved convergence speed, stability, and robustness.

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Design of Cascaded PI-Fractional Order PID Controller for Improving the Frequency Response of Hybrid Microgrid System Using Gorilla Troops Optimizer

Cited by 69 publications

References 45 publications

A3C-TL-GTO: Alzheimer Automatic Accurate Classification Using Transfer Learning and Artificial Gorilla Troops Optimizer

A3C-TL-GTO: Alzheimer Automatic Accurate Classification Using Transfer Learning and Artificial Gorilla Troops Optimizer

Optimized Fractional Order Integral-Tilt Derivative Controller for Frequency Regulation of Interconnected Diverse Renewable Energy Resources

An enhanced flower pollinated algorithm with a modified fluctuation rate for global optimisation and load frequency control system

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