Salp Swarm Optimization Algorithm for Estimating the Parameters of Photovoltaic Panels Based on the Three-Diode Model

Round 1

Reviewer 1 Report

The paper presented a mathematical three-diode model for a PV includes multiple unknow parameters and a salp swarm algorithm (SSA) based method is designed to estimate the parameters. The effectiveness of the proposed method is validated by simulations and also compared the SSA with PSO, CGA, and SCA algorithms. The reviewer has some minor comments:

1) Please highlight the contributions of this work and clearly present the main contributions of this work in introduction section.

2) The computation times of SSA and other comparing methods are suggested to provide in the simulation results.

Author Response

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Reviewer 2 Report

The authors provided a Salp Swarm Optimization Algorithm for Estimating the Parameters of Photovoltaic Panels Based on the Three-Diode Model. There are some comments that should be addressed:

• In Abstract ' photovoltaic panels (PVs)' is not correct. It should be written as 'photovoltaic (PV) panels'. In the latter parts of the Abstract: ' to estimate the parameters of a three-diode model of a photovoltaic panel'; photovoltaic should be replaced with PV.
• The authors mentioned:  the SSA, was measured by a fair comparison; how many percent? (mention in %)
• The number of keywords should be a maximum of 6.
• In line 39, what does ' their PV behavior' mean?
• In line 101, it is the first time that SSA appears in the text but its full form is missing.
• Is there any literature on using SSA? If yes, they should be discussed in the literature.
• For equation 3, is there any reference?
• In Tables 7 and 8, if experiments have been done, how the radiation levels have been set?
• If the results of this work are compared with others, the value will be enhanced.
• The conclusion should be enhanced by reporting values obtained from the results.

Author Response

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Reviewer 3 Report

Authors attempted to present a methodology based on the equivalent mathematical three-diode model of a PV panel and the salp swarm optimization method to solve the parameter estimation. The organization of the paper is proper. The paper is well-presented. The following issues should be addressed before it is considered for publication:

* The studied parameters and their range should be given in the last passage of introduction for ease understanding of the paper. 

* The assumptions made for the analysis should be given.

* Please briefly explain in the text the rationale behind selection of solar insolation. 

* It should be stressed out the contribution of the study on the predictions of electric output. 

* The quality of results and discussion section is satisfactorily. However, the obtained results (improvements provided by the proposed methods) should be compared with the literature. 

* The limitation of the model should be mentioned. 

* The conclusions drawn sound very general. They must be supported by the quantitative data. The accuracy of proposed model should be given quantitatively.

Based on the above observations, my overall opinion is that the paper deserves to be published after minor revision. 

Author Response

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Reviewer 4 Report

Salp swarm optimization algorithm has been proposed for estimating the
parameters of photovoltaic panels Based on the three-diode model. However, this optimization algorithm has already been used for three-diode model in the paper, "Reliable applied objective for identifying simple and detailed photovoltaic models using modern metaheuristics: Comparative study" https://doi.org/10.1016/j.enconman.2020.113279. As the proposed metaheuristic optimization algorithm has already been used for the same application, the presented work clearly lacks novelty and it cannot be considered for publication.

  

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Reviewer 5 Report

This paper introduces a mathematical three-diode model of a PV that includes the following parameters: photoinduced current, diode saturation currents, ideality factors, serial resistance, and parallel resistance. Two panels were considered in the experiments, they are: Kyocera KC200GT and Solarex MSX60.

The proposed algorithm is compared against CGA, PSO and SCA and the results show it outperforms them in terms of accuracy.

1. In table 4 the parameters of the four algorithms were given (number of particles and stopping criteria). How were these parameters selected? One may wonder whether the accuracy of each algorithm can be increased by increasing the number of particles and/or the number of iterations. In particular, the PSO algorithm has a lower number of particles than the others. What is the accuracy of PSO when the number of particles is increased to more than 70?

2. Ensure that all the algorithms are well-tuned in order to perform a fair comparison and to obtain meaningful results. Parameters' tuning has not been satisfactorily discussed.

3. A fair comparison between the algorithms should include the execution time. SSA is truly outperforming the others only if a higher accuracy is achieved with similar computational cost. Please include computation time in the results' tables.

4. The paper should provide more insights about the possible reasons why the proposed SSA method is returning a better model than the others in terms of RMSE and STD deviation, in almost all the analysed cases. This could be better explained referring to the way the algorithm works, e.g. how the initial population is generated and evolves through the iterations.

Author Response

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Round 2

Reviewer 2 Report

The authors have properly addressed all of my comments.

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Reviewer 4 Report

Rebuttal is not satisfactory and the paper clearly lacks novelty. It cannot be considered for publication without significant contribution. 

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