An efficient salp swarm-inspired algorithm for parameters identification of photovoltaic cell models

By R. Abbassi, A. Abbassi, Ali Asghar Heidari, Seyedali Mirjalili. Energy Conversion and Management, 2019

367 citations recorded by the source corpus.

Abstract Solar Photovoltaic systems (SPVSs) are becoming one of the most popular renewable energy technology for generating significant share of electric power. With the consistent growth of SPVSs applications, the challenge of parameters estimation of photovoltaic cells has drawn the attention of researchers and industrialists and gained immense momentum for SPVSs modeling. This paper proposes an efficient approach based on Salp Swarm Algorithm (SSA) for extracting the parameters of the electrical equivalent circuit of PV cell based double-diode model. The experimental and comparative results demonstrate that SSA is highly competitive with the results of two algorithms that have never been used before for the PV cell parameter extraction namely Sine Cosine Algorithm (SCA) and Virus Colony Search Algorithm (VCS). SSA is also significantly better than three well-established parameter extraction algorithms namely Ant Lion Optimizer (ALO), Gravitational Search Algorithm (GSA) and Whale Optimization Algorithm (WOA). Several evaluation criteria including Mean Square Error (MSE), Absolute Error (AE) and statistical criterion show that the SSA algorithm provides the highest value of accuracy and has merits in designing SPVSs.

Subfields: Artificial Intelligence, Energy Engineering and Power Technology, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/62be3a79436c97027251e776a2f55ae404d1e1ee

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Huiling Chen
2. Ali Asghar Heidari
3. Majdi M. Mafarja
4. Mohammad Shehab
5. Mohammad Alshinwan
6. Hamzeh Alabool
7. Ren-jing Liu
8. Yanan Zhang

In Renewable Energy, Sustainability and the Environment

1. Mohammad Alshinwan
2. Hamzeh Alabool
3. Mohammad Shehab
4. Laith Mohammad Abualigah

Citations in the corpus

• Salp swarm algorithm: a comprehensive survey (2019, methodology): “An evaluation model is implemented in [70] to estimate the parameter of photovoltaic cells system.”
• Metaphor-free dynamic spherical evolution for parameter estimation of photovoltaic modules (2021, methodology): “Abbassi et al. (2020) Opposition-based learning modified salp swarm algorithm (OLMSSA) OLMSSA is highly competitive and better than the reported results. Ridha et al. (2020) Boosted Harris hawk’s optimization (BHHO) BHHO is valuable for dealing with optimal parameters of double and three diodes PV c”
• Boosted binary Harris hawks optimizer and feature selection (2020, background): “…of its effectiveness and simplicity, SSA has been widely utilized to tackle various scenarios such as sensor deployment [24], photovoltaic systems [93], variable speed wind generators [94], feature selection [55, 95–98], polymer exchange membrane fuel cell models [99], and CMOS integrated circuit…”