Modified Salp Swarm Optimization for Parameter Estimation of Solar PV Models

Yaghoubi, M.; Eslami, Mahdiyeh; Noroozi, Mohammad; Mohammadi, Hamed; Kamari, Osman; Sivaprakasam, P.

doi:10.1109/access.2022.3213746

Cited by 37 publications

(14 citation statements)

References 77 publications

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“…The settings of the Swarm MVMO algorithm shown in Tab. 1 were based on the recommendations of Rueda and Erlich (2013) for the algorithm setup and Yaghoubi et al (2022) for the parameters search limits. In this study, all simulations ran independently 30 times in Matlab R2022b using an 11th Gen Intel® Core™ i5-11400 @ 2.60 GHz 2.59 GHz, 24 GB RAM with Windows(R) 11, 64 bits.…”

Section: Resultsmentioning

confidence: 99%

Parameter Estimation Of Photovoltaic Cell Using Swarm MVMO

SANTOS,

VARGAS,

CARI

2023

Anais Do 8° Congresso Brasileiro De Geração Distribuída (CBGD 2023)

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Photovoltaic solar energy has proven to be an important source in the energy transition process. However, the non-linear nature of the models used to estimate parameters makes the work difficult in an attempt to improve the efficiency of the systems. This work proposes the parameter estimation of the RTC France photovoltaic cell using the Single-diode model and the Swarm Mean-Variance Mapping Optimization algorithm. The results obtained show that the proposed algorithm is good enough for this purpose, as it achieved the same error levels as other algorithms reported in the literature and the estimated parameters achieved a good fit in the I -V and P -V curves.

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Section: Resultsmentioning

confidence: 99%

Parameter Estimation Of Photovoltaic Cell Using Swarm MVMO

SANTOS,

VARGAS,

CARI

2023

Anais Do 8° Congresso Brasileiro De Geração Distribuída (CBGD 2023)

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“…KOA evaluates the fitness of the initial set and repeats until the termination requirement is met. Since the word "time" is frequently applied in solar systems 39 and cosmology, it is employed in place of "iteration" in the present investigation.…”

Section: Overview Of the Kepler Optimization Algorithm (Koa)mentioning

confidence: 99%

Optimization of a photovoltaic/wind/battery energy-based microgrid in distribution network using machine learning and fuzzy multi-objective improved Kepler optimizer algorithms

Duan,

Eslami,

Khajehzadeh

et al. 2024

Sci Rep

Self Cite

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In this study, a fuzzy multi-objective framework is performed for optimization of a hybrid microgrid (HMG) including photovoltaic (PV) and wind energy sources linked with battery energy storage (PV/WT/BES) in a 33-bus distribution network to minimize the cost of energy losses, minimizing the voltage oscillations as well as power purchased minimization from the HMG incorporated forecasted data. The variables are microgrid optimal location and capacity of the HMG components in the network which are determined through a multi-objective improved Kepler optimization algorithm (MOIKOA) modeled by Kepler’s laws of planetary motion, piecewise linear chaotic map and using the FDMT. In this study, a machine learning approach using a multilayer perceptron artificial neural network (MLP-ANN) has been used to forecast solar radiation, wind speed, temperature, and load data. The optimization problem is implemented in three optimization scenarios based on real and forecasted data as well as the investigation of the battery's depth of discharge in the HMG optimization in the distribution network and its effects on the different objectives. The results including energy losses, voltage deviations, and purchased power from the HMG have been presented. Also, the MOIKOA superior capability is validated in comparison with the multi-objective conventional Kepler optimization algorithm, multi-objective particle swarm optimization, and multi-objective genetic algorithm in problem-solving. The findings are cleared that microgrid multi-objective optimization in the distribution network considering forecasted data based on the MLP-ANN causes an increase of 3.50%, 2.33%, and 1.98%, respectively, in annual energy losses, voltage deviation, and the purchased power cost from the HMG compared to the real data-based optimization. Also, the outcomes proved that increasing the battery depth of discharge causes the BES to have more participation in the HMG effectiveness on the distribution network objectives and affects the network energy losses and voltage deviation reduction.

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“…There are two articles [71,72] applying the improved SSA and ARSO to the parameter identification of the SDM, DDM, and Photowatt-PWP201 modules. Using the DDM as an example, the RMSEs of the improved methods in these two publications can reach 9.83 × 10 −4 and 9.82 × 10 −4 , respectively, while the improved method in this paper can reach 3.20 × 10 −3 and the standard DA reaches 4.37 × 10 −3 .…”

Section: Parameter Lb Ubmentioning

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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Modified Salp Swarm Optimization for Parameter Estimation of Solar PV Models

Cited by 37 publications

References 77 publications

Parameter Estimation Of Photovoltaic Cell Using Swarm MVMO

Parameter Estimation Of Photovoltaic Cell Using Swarm MVMO

Optimization of a photovoltaic/wind/battery energy-based microgrid in distribution network using machine learning and fuzzy multi-objective improved Kepler optimizer algorithms

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

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