Novel design of artificial ecosystem optimizer for large-scale optimal reactive power dispatch problem with application to Algerian electricity grid

Mouassa, Souhil; Jurado, Francisco; Bouktir, Tarek; Raja, Muhammad Asif Zahoor

doi:10.1007/s00521-020-05496-0

Cited by 18 publications

(5 citation statements)

References 46 publications

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“…The original algorithm, AEO, is a meta-heuristic algorithm inspired by the energy flow dynamics observed in ecosystems on Earth [44]. Several real-world engineering applications have already employed the AEO algorithm such as the optimal parameter identification strategy of the lithium-ion (Li-ion) battery model [45], reconfiguration of electrical distribution network-based DG and capacitors allocations [46], proton exchange membrane fuel cells model [47], optimizing of position and operational power of battery energy storage system on the distribution network considering distributed generations [48], techno-economic evaluation of hybrid energy systems [49], and large-scale optimal reactive power dispatch problem with application to Algerian electricity grid [50]. The AEO algorithm exhibits poor exploitation, despite its abovementioned applications, because of its stochastic nature [51].…”

Section: Contributionsmentioning

confidence: 99%

Optimizing photovoltaic models: A leader artificial ecosystem approach for accurate parameter estimation of dynamic and static three diode systems

Hassan,

Kamel,

Ramadan

et al. 2024

IET Generation Trans & Dist

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The utilization of accurate models is crucial in the various stages of development for photovoltaic (PV) systems. Modelling these systems effectively allows developers to assess new modifications prior to the manufacturing phase, resulting in cost and time savings. This research paper presents a viable approach to accurately estimate both static and dynamic PV models. The proposed estimation method relies on a novel and enhanced optimization algorithm called leader artificial ecosystem‐based optimization (LAEO), which improves upon the original artificial ecosystem‐based optimization (AEO). The proposed LAEO algorithm integrates the adaptive probability (AP) and leader‐based mutation‐selection strategies to enhance the search capability, improve the balance between exploration and exploitation, and overcome local optima. To evaluate the effectiveness of LAEO, it was tested on 23 different benchmark functions. Additionally, LAEO was applied to estimate the parameters of static three‐diode PV models, as well as integral‐order and fractional‐order dynamic models. This paper showcases practical implementations of photovoltaic (PV) parameter estimation in various scenarios, including the static three‐diode model, dynamic integral order model (IOM), and fractional order model (FOM). The results were assessed from various angles to examine the precision, performance, and stability of the LAEO algorithm.

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Section: Contributionsmentioning

confidence: 99%

Optimizing photovoltaic models: A leader artificial ecosystem approach for accurate parameter estimation of dynamic and static three diode systems

Hassan,

Kamel,

Ramadan

et al. 2024

IET Generation Trans & Dist

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“…In this paper, the basic idea of particle swarm algorithm is introduced into the bacterial foraging algorithm to construct a hybrid bacterial foraging optimization algorithm, which has good search speed and accuracy, can effectively make up for the defects of slow BFO operation and low accuracy of PSO operation, avoid the problem of local convergence, and is suitable for solving the optimization of complex functions [ 21 ]. Bacterial Foraging Optimization-Particle Swarm Optimization (BFO-PSO) has the advantages of fast global search and high operational accuracy compared to similar population intelligence algorithms such as Fruit Fly Optimization Algorithm (FOA) [ 22 ] and Ant Colony Optimization (ACO) [ 23 ], so this paper proposes the bacterial particle swarm optimization algorithm for the combination of parameters of VMD [ k , ɑ] for simultaneous optimization search. The Particle Swarm finds the individual optimal position p best and the population optimal position g best by updating its velocity and position, and the function iteration terminates when both are in the same position.…”

Section: Improved Vmd Noise Reductionmentioning

confidence: 99%

Residual current detection method based on improved VMD-BPNN

Bai,

Zhang,

Wang

et al. 2024

PLoS ONE

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To further enhance the residual current detection capability of low-voltage distribution networks, an improved adaptive residual current detection method that combines variational modal decomposition (VMD) and BP neural network (BPNN) is proposed. Firstly, the method employs the envelope entropy as the adaptability function, optimizes the [k, ɑ] combination value of the VMD decomposition using the bacterial foraging-particle swarm algorithm (BFO-PSO), and utilizes the interrelation number R as the classification index with the Least Mean Square Algorithm (LMS) to classify, filter, and extract the effective signal from the decomposed signal. Then, the extracted signals are detected by BPNN, and the training data are utilized to predict the residual current signals. Simulation and experimental data demonstrate that the proposed algorithm exhibits strong robustness and high detection accuracy. With an ambient noise of 10dB, the signal-to-noise ratio is 16.3108dB, the RMSE is 0.4359, and the goodness-of-fit is 0.9627 after processing by the algorithm presented in this paper, which are superior to the Variational Modal Decomposition-Long Short-Term Memory (VMD-LSTM) and Normalized-Least Mean Square (N-LMS) detection methods. The results were also statistically analyzed in conjunction with the Kolmogorov-Smirnov test, which demonstrated significance at the experimental data level, indicating the high accuracy of the algorithms presented in this paper and providing a certain reference for new residual current protection devices for biological body electrocution.

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“…al. 2020b ), feature selection (Sahlol et al 2020b ), Fuel Cell Dynamic Model (Rizk-Allah and El-Fergany 2021a ), Photovoltaic Systems (Yousri et al 2020b ), PID Controller (Ćalasan et al 2020 )), Power Dispatch Problem (Mouassa et al 2021 ) Zhao et. al.…”

Section: Survey On Recent Nature-inspired Optimization Algorithmsmentioning

confidence: 99%

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

2022

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Multilevel Thresholding (MLT) is considered as a significant and imperative research field in image segmentation that can efficiently resolve difficulties aroused while analyzing the segmented regions of multifaceted images with complicated nonlinear conditions. MLT being a simple exponential combinatorial optimization problem is commonly phrased by means of a sophisticated objective function requirement that can only be addressed by nondeterministic approaches. Consequently, researchers are engaging Nature-Inspired Optimization Algorithms (NIOA) as an alternate methodology that can be widely employed for resolving problems related to MLT. This paper delivers an acquainted review related to novel NIOA shaped lately in last three years (2019–2021) highlighting and exploring the major challenges encountered during the development of image multi-thresholding models based on NIOA.

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Novel design of artificial ecosystem optimizer for large-scale optimal reactive power dispatch problem with application to Algerian electricity grid

Cited by 18 publications

References 46 publications

Optimizing photovoltaic models: A leader artificial ecosystem approach for accurate parameter estimation of dynamic and static three diode systems

Optimizing photovoltaic models: A leader artificial ecosystem approach for accurate parameter estimation of dynamic and static three diode systems

Residual current detection method based on improved VMD-BPNN

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

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