Applications of Flower Pollination Algorithm in Electrical Power Systems: A Review

Lalljith, Sahil; Fleming, Ismail; Pillay, Umeshan; Naicker, Kiveshen; Naidoo, Zachary; Saha, Akshay Kumar

doi:10.1109/access.2021.3138518

Cited by 16 publications

(8 citation statements)

References 117 publications

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“…In this way, fittest reproduction and pollination can be ensured. Flower reliability phenomena and the first rule of global pollination step can be mathematically [36,37] described as…”

Section: Basis Principle Of Fpamentioning

confidence: 99%

High-Resolution Direction of Arrival Estimation of Underwater Multitargets Using Swarming Intelligence of Flower Pollination Heuristics

Ahmed

Wang

et al. 2022

Shock and Vibration

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Developing the parameter estimation, particularly direction of arrival (DOA), utilizing the swarming intelligence-based flower pollination algorithm (FPA) is considered an optimistic solution. Therefore, in this paper, the features of FPA are applied for viable DOA in the case of several robust underwater scenarios. Moreover, acoustic waves impinging from the far-field multitarget are evaluated using the different number of hydrophones of uniform linear array (ULA). The measuring parameters like robustness against noise and element quantity, estimation accuracy, computation complexity, various numbers of hydrophones, variability analysis, frequency distribution and cumulative distribution function of root mean square error (RMSE), and resolution ability are applied for analyzing the performance of the proposed model with additive white Gaussian noise (AWGN). For this purpose, particle swarm optimization (PSO), minimum variance distortion-less response (MVDR), multiple signal classification (MUSIC), and estimation of signal parameter via rotational invariance technique (ESPRIT) standard counterparts are employed along with Crammer–Rao bound (CRB) to improve the worth of the proposed setup further. The proposed scheme for estimating the DOA generates efficient outcomes compared to the state-of-the-art algorithms over the Monte Carlo simulations.

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“…In this way, fittest reproduction and pollination can be ensured. Flower reliability phenomena and the first rule of global pollination step can be mathematically [36,37] described as…”

Section: Basis Principle Of Fpamentioning

confidence: 99%

High-Resolution Direction of Arrival Estimation of Underwater Multitargets Using Swarming Intelligence of Flower Pollination Heuristics

Ahmed

Wang

et al. 2022

Shock and Vibration

View full text Add to dashboard Cite

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“…iv) human-based algorithms that depend on human behavior such as Teaching Learning-Based Optimization (TLBO) and Tabu Search (TS). Compared to mathematical optimization techniques, metaheuristic optimization algorithms can solve complex optimization problems effectively and obtain the global optimal solution in less time [22]- [24]. These techniques are suitable to handle MG optimization problems due to their flexibility, robustness, efficiency, accuracy, and short convergence time [22].…”

Section: Optimization Techniquesmentioning

confidence: 99%

Sizing of Microgrid System Including Multi-Functional Battery Storage and Considering Uncertainties

et al. 2023

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Battery storage units (BSUs) are usually used to perform a single function in most planning studies related to microgrids (MGs). This paper presents an effective methodology to use the BSUs to perform multi-function including supply/demand matching and energy arbitrage. This is done according to a system policy containing all possible scenarios to fully utilize the BSUs to maximize the benefit. In the proposed work, the optimal sizing of the MG system under study containing wind turbines (WTs), photovoltaic system (PV), BSUs, and diesel units (DUs) is obtained. The main objectives of the proposed methodology are; 1) minimizing the total costs of the MG, 2) minimizing the harmful gas emissions, and 3) minimizing the accumulated power difference between the generation from renewable energy systems (RESs) and the demand. Due to the stochastic behavior of the output from the RESs, the uncertainties of wind speed, solar irradiance, and temperature are considered in the study. Two modes of operation of the MG (grid-connected and islanded) and the demand side management (DSM) are also considered. The problem is formulated as a constrained nonlinear optimization problem and is solved using two metaheuristic optimization algorithms, Moth-Flame Optimization (MFO) and Hybrid Firefly and Particle Swarm Optimization (HFPSO). Moreover, the uncertainties in the different parameters are considered by using the Latin Hypercube Sampling (LHS) method to generate samples of wind speed, solar irradiance, and temperature. To examine the proposed methodology, different case studies are presented and discussed. Moreover, the results of the used two algorithms, MFO and HFPSO, are compared to show their effectiveness in solving the proposed problem and assure the optimal solution. The optimization problem is implemented and solved using MATLAB software.

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“…The algorithm was presented by Xin-She Yang in 2012 [64]. The algorithm is a universal tool for optimization in different areas of science, economics and also social behavior studies [65][66][67][68]. It belongs to swarm-based metaheuristic algorithms.…”

Section: Nature Inspired Optimization Algorithm-the Flower Pollinatio...mentioning

confidence: 99%

Speed Control Based on State Vector Applied for Electrical Drive with Elastic Connection

et al. 2022

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The paper is focused on issues related to the control of electrical drives with oscillations of state variables. The main problem deals with the construction of the mechanical part, which contains elastic elements used as a coupling between the motor machine and the load. In such cases, strict tracking of the reference trajectory is difficult, so damping of the disturbances is necessary. For this purpose, the full state vector of the object is applied as the feedback signal for the speed controller. This method is efficient and relatively easy to implement (including the hardware part). However, the control accuracy is dependent on the quality of the parameters identification and the invariance of the object. Thus, two adaptive structures are proposed for the two-mass system. Moreover, selected coefficients were optimized using metaheuristic algorithms (symbiotic organism search and flower pollination algorithm). After presentation of the preliminaries and mathematical background, tests were conducted, and the numerical simulations are shown. Finally, the experimental verification for the 0.5 kW DC machines was performed. The results confirm the theoretical concept and the initial assumptions: the state controller leads to the precise control of the drive with a long shaft; recalculation of the parameters can improve the work of the drive under changes of time constants; modern design tools are appropriate for this application.

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Applications of Flower Pollination Algorithm in Electrical Power Systems: A Review

Abstract: This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.

Cited by 16 publications

References 117 publications

High-Resolution Direction of Arrival Estimation of Underwater Multitargets Using Swarming Intelligence of Flower Pollination Heuristics

High-Resolution Direction of Arrival Estimation of Underwater Multitargets Using Swarming Intelligence of Flower Pollination Heuristics

Sizing of Microgrid System Including Multi-Functional Battery Storage and Considering Uncertainties

Speed Control Based on State Vector Applied for Electrical Drive with Elastic Connection

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