Artificial bee colony algorithm based on a new local search approach for parameter estimation of photovoltaic systems

Tefek, Mehmet Fatih

doi:10.1007/s10825-021-01796-3

Cited by 16 publications

(4 citation statements)

References 48 publications

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“…ey put forward a risk assessment method that comprehensively considers the attack difficulty, attack consequence severity, path length, and the importance of target nodes. ey search through heuristic algorithms to obtain the approximate maximum risk coefficient in the attack graph, avoiding the exponential time complexity problem of accurately searching the attack path [6]. Vajjha and Sushma proposed an electricity network security intrusion detection method in cloud environment based on feature selection.…”

Section: Literature Reviewmentioning

confidence: 99%

Electricity Network Security Monitoring Based on Bee Colony Algorithm

Su¹,

Zhang²

2022

International Transactions on Electrical Energy Systems

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In order to effectively detect and discover network threats in the initial stage, this study proposes an electricity network security intrusion detection method based on feature selection. A heuristic feature selection algorithm based on the bee colony algorithm is proposed to overcome the shortcomings of existing feature evaluation methods. The algorithm uses average mutual information to measure the importance of features and more truly reflects the relationship between the selected features, the selected features, and the classification labels. Aiming at the problem that the algorithm is easy to fall into local optimization, a heuristic random search algorithm is proposed, which iteratively optimizes to generate smaller feature subsets, and improves the speed and accuracy of intrusion detection. The experimental results show that compared with the traditional algorithm, the proposed method can effectively evaluate the risk of attack path on the selected experimental data set, and the gap between the generation strategy and the optimal strategy is reduced by 71.3%, which enhances the practicability of the attack graph analysis method in a large-scale network environment. Conclusion. This method has good scalability and can be applied to large-scale network environments. It can effectively obtain attack paths that are more in line with the real threat situation in an acceptable time, so as to effectively find the network threats.

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Section: Literature Reviewmentioning

confidence: 99%

Electricity Network Security Monitoring Based on Bee Colony Algorithm

Su¹,

Zhang²

2022

International Transactions on Electrical Energy Systems

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“…HS has better convergence speed compared to FA. The artificial bee colony (ABC) [64,119,120] algorithm shows better accuracy and convergence speed compared to HS, GA, BFA and PSO. However, it shows convergence failure in the case of repeated progression.…”

Section: Performance Analysis Of the Reported Algorithmsmentioning

confidence: 99%

A Critical Review on the Estimation Techniques of the Solar PV Cell’s Unknown Parameters

et al. 2022

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To meet the exponentially growing demand for clean and green energy, the solar photovoltaic (PV) system’s importance is increasing day by day, for which PV modeling is considered to be one of the most important work in the current state-of-the-art methods. To effectively model a PV system, accurate PV parameter estimation is of the utmost importance. In line with this, although the values of some of the parameters are provided in the manufacturer’s datasheet, the values of unknown parameters, such as shunt resistance, series resistance, the diode ideality factor, photo-generated current and diode saturation current, are not provided. To estimate these values a lot of algorithms are already reported in the literature. After careful observation of all the reported algorithms, a few best-reported algorithms are identified and their performances are compared with respect to accuracy, convergence issues, computational complexity and thermal stability. All kind of algorithms, such as numerical, analytical and evolutionary algorithms, are considered in this study, and only the best reported algorithms are considered for the comparison.

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“…Additionally, deterministic methods are founded on gradient computation [14]; one of its characteristics is its extreme sensitivity to initial conditions [15]. The above-mentioned problems are addressed at present using a variety of metaheuristic algorithms [16], including the partial swarm optimization (PSO) [17,18], symbiotic organisms search (SOS) algorithm [19], grasshopper optimization algorithm (GOA) [20,21], lion swarm optimization (LSO) [22], butterfly optimization algorithm (BOA) [23], sparrow search algorithm (SSA) [24], African vultures optimization (AVO) [25], Harris hawk optimization (HHO) [26,27], chicken swarm optimization (CSO) [28,29], artificial bee colony (ABC) algorithm [30,31], marine predators algorithm (MPA) [32,33], golden search optimization (GSO) [34], and firefly algorithm (FA) [35]. Because of their advantages of simplicity and powerful search capabilities, these population-based methods have received extensive attention.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid-Strategy-Improved Dragonfly Algorithm for the Parameter Identification of an SDM

Zhao,

Zhang,

et al. 2023

Sustainability

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As primary components of solar power applications, photovoltaic cells have promising development prospects. Due to the characteristics of PV cells, the identification of parameters for circuit models has become a research focus. Among the various methods of parameter estimations, metaheuristic algorithms have attracted significant interest. In this paper, a hybrid-strategy-improved dragonfly algorithm (HIDA) is proposed to meet the demand for high parameter-identification accuracy. Tent chaotic mapping generates the initial position of individual dragonflies and aids in increasing the population diversity. Individual dragonflies can adapt their updated positions to various scenarios using the adjacent position decision approach. The whale optimization algorithm fusion strategy incorporates the spiral bubble-net attack mechanism into the dragonfly algorithm to improve the optimization-seeking precision. Moreover, the optimal position perturbation strategy reduces the frequency of the HIDA falling into local optima from the perspective of an optimal solution. The effectiveness of the HIDA was evaluated using function test experiments and engineering application experiments. Seven unimodal and five multimodal benchmark test functions in 50, 120, and 200 dimensions were used for the function test experiments, while five CEC2013 functions and seven CEC2014 functions were also selected for the experiments. In the engineering application experiments, the HIDA was applied to the single-diode model (SDM), engineering model, double-diode model (DDM), triple-diode model (TDM), and STM-40/36 parameter identification, as well as to the solution of seven classical engineering problems. The experimental results all verify the good performance of the HIDA with high stability, a wide application range, and high accuracy.

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Artificial bee colony algorithm based on a new local search approach for parameter estimation of photovoltaic systems

Cited by 16 publications

References 48 publications

Electricity Network Security Monitoring Based on Bee Colony Algorithm

Electricity Network Security Monitoring Based on Bee Colony Algorithm

A Critical Review on the Estimation Techniques of the Solar PV Cell’s Unknown Parameters

A Hybrid-Strategy-Improved Dragonfly Algorithm for the Parameter Identification of an SDM

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