Marine Predator Algorithm based Cascaded PIDA Load Frequency Controller for Electric Power Systems with Wave Energy Conversion Systems

Marine Predators Algorithm (MPA) is a recent nature-inspired optimizer stemmed from widespread foraging mechanisms based on Lévy and Brownian movements in ocean predators. Due to its superb features, such as derivative-free, parameter-less, easy-to-use, flexible, and simplicity, MPA is quickly evolved for a wide range of optimization problems in a short period. Therefore, its impressive characteristics inspire this review to analyze and discuss the primary MPA research studies established. In this review paper, the growth of the MPA is analyzed based on 102 research papers to show its powerful performance. The MPA inspirations and its theoretical concepts are also illustrated, focusing on its convergence behaviour. Thereafter, the MPA versions suggested improving the MPA behaviour on connecting the search space shape of real-world optimization problems are analyzed. A plethora and diverse optimization applications have been addressed, relying on MPA as the main solver, which is also described and organized. In addition, a critical discussion about the convergence behaviour and the main limitation of MPA is given. The review is end-up highlighting the main findings of this survey and suggests some possible MPA-related improvements and extensions that can be carried out in the future.

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“…Original MPA MPA is proposed to optimize controller parameters for PIDA Load Frequency [159] Configuration of autonomous system…”

Section: Pida Load Frequency Controllermentioning

confidence: 99%

Marine Predators Algorithm: A Review

Al-Betar

Awadallah

Makhadmeh

et al. 2023

Arch Computat Methods Eng

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“…For ME, the qualified index of water pollution is an important goal of natural water environment quality management. Once a sudden risk of water pollution in the natural water ecological environment occurs in a region, under the combined driving force of various natural winds and water currents, The spread of pollutants may change in an instant, affecting the quality of water extracted by residents, thereby greatly increasing the management cost and difficulty of the ME environment [15][16].…”

Section: Frontiers In Ocean Engineeringmentioning

confidence: 99%

Ecological Problems in Ocean Engineering Construction Based on Fuzzy Recognition

2022

FOE

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With the development of large-scale building structures and complex structures such as hydraulic engineering and marine engineering (ME), their health during work is related to the vital interests of all parties. The combination of environmental corrosion, material aging, long-term effects of loads, and fatigue factors will cause structural damage, reduce the ability of the structure to resist external influences, and inevitably lead to a decline in the safety of structural performance. ME structures have been operating in harsh marine environments for a long time. Using intelligent monitoring technology to process monitoring signals can invert the damage status of the structure, or use a variety of information to evaluate the overall safety of the structure. The main purpose of this paper is to study the ecological problems in ME construction based on fuzzy identification. Based on fuzzy identification technology, this paper analyzes and studies the environmental risk events existing in the whole process of ME from planning, design, construction and operation, and establishes the risk assessment method and process for the environmental impact of ME construction. Experiments show that ME mainly affects the marine ecological environment from the following four aspects: 1) The occupation of engineering sediment and the impact of dredging operations on benthic organisms; 2) The impact of underwater blasting operations on aquatic organisms; 3) Suspended matter The impact on the marine ecological environment; 4) The impact of domestic sewage, machine sewage and other pollutants on the marine ecological environment.

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“…However, the performance efficacy of classical controllers is more likely to be dependent upon the optimization algorithms that have been deployed to optimize the controller gains. Several population-and stochastic-based searching algorithms reported in domain of LFC in optimizing classical controllers are chaotic atom search optimization (CASO) (Irudayaraj et al, 2022), many-objective optimization approach (MOOA) (Hajiakbari Fini et al, 2016), chaotic crow search (CCS) algorithm (Khokhar et al, 2021), gray wolf optimizer (GWO) (Sharma and Saikia, 2015), quadratic approach with pole compensator (QAWPC) (Hanwate and Hote, 2018), marine predator algorithm (MPA) (Yakout et al, 2021), Hooke-Jeeve's optimizer (HJO) (Chatterjee, 2010), quasioppositional harmony search algorithm (QOHSA) (Shankar and Mukherjee, 2016), chemical reaction optimizer (CRO) (Mohanty and Hota, 2018), hybrid artificial electric field algorithm (HAEFA) (Sai Kalyan et al, 2020), bacteria foraging optimization (BFOA) (Ali and Elazim, 2015), mine blast optimizer (MBO) (Alattar et al, 2019), particle swarm optimizer (PSO) (Magid and Abido, 2003), differential evolution (DE) (Kalyan and Suresh, 2021), combination of DE with pattern search (Sahu et al, 2015a) and AEFA (DE-AEFA) (Kalyan and Rao, 2021a), grasshopper optimizer (GHO), and cuckoo search approach (CSA) (Latif et al, 2018). Moreover, the conventional controllers exhibit efficacy in linearized models and could not maintain the stability of nonlinear interconnected power systems (IPS).…”

Section: Introductionmentioning

confidence: 99%

Seagull Optimization Algorithm–Based Fractional-Order Fuzzy Controller for LFC of Multi-Area Diverse Source System With Realistic Constraints

Kalyan¹,

Goud

Reddy

et al. 2022

Front. Energy Res.

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This study initiates the implementation of fractional-order (FO) fuzzy (F) PID (FOFPID) controller fine-tuned using a seagull optimization algorithm (SOA) for the study of load frequency control (LFC). Initially, the SOA-tuned FOFPID regulator is implemented on the widely utilized model of dual-area reheat-thermal system (DARTS), named test system-1 in this work for a perturbation of 10% step load (10% SLP) on area-1. Dynamical analysis of the DARTS system reveals the viability of the SOA-tuned FOFPID control scheme in regulating frequency deviations effectively compared to other control schemes covered in the literature. Later, the presented regulator is implemented on the multi-area diverse sources (MADS) system possessing realistic constraints in this study, termed test system-2. The sovereignty of the presented FOFPID controller is once again evidenced with controllers of PID/FOPID/FPID fine-tuned with the SOA approach. Moreover, the effect of considering practical realistic nonlinearity constraints such as communication time delays (CTDs) on MADS system performance is visualized and the necessity of its consideration is demonstrated. Furthermore, AC-DC lines are incorporated with the MADS system to enhance the performance under heavy-load disturbances and the robustness of the proposed regulatory mechanism is deliberated.

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Marine Predator Algorithm based Cascaded PIDA Load Frequency Controller for Electric Power Systems with Wave Energy Conversion Systems

Cited by 42 publications

References 23 publications

Marine Predators Algorithm: A Review

Marine Predators Algorithm: A Review

Ecological Problems in Ocean Engineering Construction Based on Fuzzy Recognition

Seagull Optimization Algorithm–Based Fractional-Order Fuzzy Controller for LFC of Multi-Area Diverse Source System With Realistic Constraints

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