A new metaphor-less algorithms for the photovoltaic cell parameter estimation

Premkumar, M.; Babu, Thanikanti Sudhakar; Subramaniam, Umashankar; Sowmya, R.

doi:10.1016/j.ijleo.2020.164559

Cited by 101 publications

(53 citation statements)

References 16 publications

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“…While a modification to the IEEE 30bus system by replacing the thermal generator at bus 5 with wind power generating source and the thermal generator at bus 8 with a photovoltaic (PV) power unit is considered. Optimal locations of the wind farm and PV power generation depend on several factors such as wind speed and solar radiation, respectively [58]. In this paper, the locations of wind and PV units are selected as in [34], with the aim of comparing the obtained results with those mentioned in [34].…”

Section: Vsimulation Results and Discussionmentioning

confidence: 99%

Optimal Reactive Power Dispatch With Time-Varying Demand and Renewable Energy Uncertainty Using Rao-3 Algorithm

et al. 2021

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The appropriate control and management of reactive power is of great relevance in the electrical reliability, stability, and security of power grids. This issue is considered in order to increase system efficiency and to maintain voltage under the acceptable value range. In this regard, novel technologies as FACTS, renewable energies, among others, are varying conventional grid behavior leading to unexpected limit capacity reaching due to large reactive power flow. Thus, optimal planning of this must be considered. This paper proposes a new application for a simple and easy implementation optimization algorithm, called Rao-3, to solve the constrained non-linear optimal reactive power dispatch problem. Moreover, the integration of solar and wind energy as the most applied technologies in electric power systems are exploited. Due to the continuous variation and the natural intermittence of wind speed and solar irradiance as well as load demand fluctuation, the uncertainties which have a global concern are investigated and considered in this paper. The proposed single-objective and multi-objective deterministic/stochastic optimal reactive power dispatch algorithms are validated using three standard test power systems, namely IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus. The simulation results show that the proposed optimal reactive power dispatch algorithms are superior compared with two recent algorithms (Artificial electric field algorithm (AEFA) and artificial Jellyfish Search (JS) algorithm) and other optimization algorithms used for solving the same problem. INDEX TERMS renewable energy; uncertainty; time-varying demand, optimal reactive power dispatch (ORPD); RAO algorithm, backward reduction algorithm

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Section: Vsimulation Results and Discussionmentioning

confidence: 99%

Optimal Reactive Power Dispatch With Time-Varying Demand and Renewable Energy Uncertainty Using Rao-3 Algorithm

et al. 2021

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“…In addition, Tables 3 and 4 illustrate the parameter estimation extracted of both recent and reported optimization techniques on PVSDM of PWP 201 polycrystalline Module. The reported optimization techniques, that are employed in this study, are particle swarm optimization (PSO) [51], grey wolf optimization (GWO) [52], particle swarm optimization based grey wolf optimization (PSOGWO) [53], slime mould optimization (SMA) [54], RAO optimizer [55], CS [56], JAYA Algorithm [57], performance-guided JAYA (PGJAYA) [58], teaching-learning-based artificial bee colony (TLABC) [59], simplified TLBO (STLBO) [60], covariance matrix based migration with biogeographybased optimization (CMM-BBO) [61], eagle based hybrid adaptive Neld-Mead simplex (EHA-NMS) [62], improved teaching learning based optimization (ITLBO) [63], selfadaptive TLBO (SATLBO) [64], grey wolf optimizer with cuckoo search (GWOCS) [65], hybrid Firefly and Pattern Search (HFAPS) [66], and Ant lion optimizer (ALO) [67]. Fig.…”

Section: ) Case 1: Pvsdmmentioning

confidence: 99%

A Forensic-Based Investigation Algorithm for Parameter Extraction of Solar Cell Models

et al. 2021

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“…The SDeM and DDeM are considered to be effective PV models as per the detailed study from various literature [68]- [71]. Therefore, this section of the paper discusses the mathematical modeling of different photovoltaic models.…”

Section: Modeling Of Photovoltaic Models and Problem Formulationmentioning

confidence: 99%

Identification of Solar Photovoltaic Model Parameters Using an Improved Gradient-Based Optimization Algorithm With Chaotic Drifts

Premkumar¹,

Jangir²,

Ramakrishnan³

et al. 2021

IEEE Access

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When discussing the commercial applications of photovoltaic (PV) systems, one of the most critical problems is to estimate the efficiency of a PV system because current (I)voltage (V) and power (P) voltage (V) characteristics are highly non-linear. It should be noted that most of the manufacturer's datasheets do not have complete information on the electrical equivalent parameters of PV systems that are necessary for simulating an effective PV module. Compared to conventional approaches, computational optimization and global research strategies are more acceptable as an effective alternative to parameter estimation of solar PV modules. Recently, a Gradient-based optimizer (GBO) is reported to solve the engineering design optimization problems. However, the basic GBO algorithm is stuck in local optima when handling complex non-linear problems. In this sense, this paper presents a new optimization technique called the Chaotic-GBO (CGBO) algorithm to derive the parameters of PV modules while offering precise I-V and P-V curves. To this end, the CGBO algorithm is based on a chaotic generator to obtain the PV parameters combined with the GBO algorithm. There are five case studies considered to validate the performance of the proposed CGBO algorithm. A quantitative and qualitative performance evaluation reveals that the proposed CGBO algorithm has improved results than other state-of-the-art algorithms in terms of accuracy and robustness when obtaining PV parameters. The average RMSE values and runtime of five case studies are equal to 9.8427E-04, 2.3700E-04, 2.4251E-03, 4.3524E-03 and 1. 8349E-03, and 18.44, 17.78, 18.18, 18.28 and 17.97, respectively. The results proved the superiority of the proposed CGBO algorithm over the different selected algorithms. For future research, this study will be backed up with external support at https://premkumarmanoharan.wixsite.com/mysite.

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A new metaphor-less algorithms for the photovoltaic cell parameter estimation

Cited by 101 publications

References 16 publications

Optimal Reactive Power Dispatch With Time-Varying Demand and Renewable Energy Uncertainty Using Rao-3 Algorithm

Optimal Reactive Power Dispatch With Time-Varying Demand and Renewable Energy Uncertainty Using Rao-3 Algorithm

A Forensic-Based Investigation Algorithm for Parameter Extraction of Solar Cell Models

Identification of Solar Photovoltaic Model Parameters Using an Improved Gradient-Based Optimization Algorithm With Chaotic Drifts

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