Parameter Extraction of Photovoltaic Models Using a Dynamic Self-Adaptive and Mutual- Comparison Teaching-Learning-Based Optimization

Li, Lei; Xiong, Guojiang; Yuan, Xufeng; Zhang, Jing; Chen, Jinlong

doi:10.1109/access.2021.3069748

Cited by 28 publications

(14 citation statements)

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“…The TLBA has been previously applied in an efficient way for several engineering optimization problems [28]. Some examples of these successful implementations are reactive power control in electrical systems [29], service restoration in distribution feeders [30], Tsallis-entropy-based feature selection classification [31], generation expansion-planning problem [32], design of passive filters [33], dissimilar resistance spot-welding process [34], water supply pipe condition prediction [35], robot manipulator calibration [36], harmonic elimination in multi-level inverters [37], operation analysis of a grid-connected photovoltaic (PV) with battery system [38] and parameter extraction of PV modules [39,40]. The abovementioned advantages of the TLBA and its successful applications in a wide array of engineering problems are the main reasons for the selection of the TLBA in this article.…”

Section: An Adaptive Algorithm With Quadratic and Polyhedral Relaxationsmentioning

confidence: 99%

A Multi-Objective Teaching–Learning Studying-Based Algorithm for Large-Scale Dispatching of Combined Electrical Power and Heat Energies

et al. 2022

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This paper proposes a multi-objective teaching–learning studying-based algorithm (MTLSBA) to handle different objective frameworks for solving the large-scale Combined Heat and Power Economic Environmental Dispatch (CHPEED) problem. It aims at minimizing the fuel costs and emissions by managing the power-only, CHP and heat-only units. TLSBA is a modified version of TLBA to increase its global optimization performance by merging a new studying strategy. Based on this integrated tactic, every participant gathers knowledge from someone else randomly to improve his position. The position is specified as the vector of the design variables, which are the power and heat outputs from the power-only, CHP and heat-only units. TLSBA has been upgraded to include an extra Pareto archiving to capture and sustain the non-dominated responses. The objective characteristic is dynamically adapted by systematically modifying the shape of the applicable objective model. Likewise, a decision-making approach based on the fuzzy concept is used to select the most suitable CHPEED solution for large-scale dispatching of combined electrical power and heat energies. The proposed MTLSBA is assigned to multiple testing of 5-unit, 7-unit and 96-unit systems. It is contrasted with other reported techniques in the literature. According to numerical data, the suggested MTLSBA outperforms the others in terms of effectiveness and robustness indices. For the 5-unit system, the proposed MTLSBA achieves improvement in the fuel costs of 0.6625% and 0.3677% and reduction in the emissions of 2.723% and 7.4669% compared to non-dominated sorting genetic algorithm (NSGA-II) and strength Pareto evolutionary algorithm (SPEA 2), respectively. For the 7-unit system, the proposed MTLSBA achieves improvement in the fuel costs of 2.927% and 3.041% and reduction in the emissions of 40.156% and 40.050% compared to NSGA-II and SPEA 2, respectively.

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Section: An Adaptive Algorithm With Quadratic and Polyhedral Relaxationsmentioning

confidence: 99%

A Multi-Objective Teaching–Learning Studying-Based Algorithm for Large-Scale Dispatching of Combined Electrical Power and Heat Energies

et al. 2022

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“…Iterative [4], [6], [11]- [14]; iterative for online operation [30]; non-iterative requiring more information & simplifications [9]; non-iterative but suited for offline operation [10]; iterative heuristic methods using GA [20] and PSO [21], and iterative meta-heuristic methods using enhanced FPA [25], improved OTSA [26], CGBO [27], and DMTLBO [28].…”

Section: Sdmmentioning

confidence: 99%

“…Heuristic methods including iterative pattern search [23], improved multiswarm PSO iterative algorithm [24]; and iterative meta-heuristic methods using enhanced FPA [25], CGBO [27], DMTLBO [28], and improved learning search algorithm [29].…”

Section: Ddmmentioning

confidence: 99%

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Fast Online Parameter Identification for Current Source Operated PV Modules in DC Microgrids

2022

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This paper presents a non-iterative online approach to identify the modeling parameters of a photovoltaic (PV) module. It is motivated by the fact that accurate and reliable modeling of distributed energy resources (DERs) in DC Microgrids improves their stability and efficiency under a wide range of operational conditions. In particular, the case where PV modules are used as DERs in their current-source region is considered. The proposed method addresses the limitations associated with parameter identification in these settings. Specifically, the method works under varying temperature and insolation conditions relying only on current and voltage sensors that already exist in the power electronic converters tying the PV DER to the Microgrid. Also, its algorithm is practical and reliable as it does not rely on a priori knowledge or an initial guess, and it is non-iterative, so it does not risk divergence (or require proof of convergence) as other iterative algorithms. Moreover, it is both fast and of a low computational complexity, which enables its implementation on microcontrollers within PV DER systems. The development of this method is detailed in the paper along with its application steps to facilitate its adoption. Furthermore, an experimental setup was used to test the proposed method under different ambient conditions and demonstrated its efficacy with algorithm execution times of under 1 second and high modeling accuracy on a microcontroller. INDEX TERMS Photovoltaic system modeling, DC microgrids, parameter identification, maximum power point, non-iterative techniques. NOMENCLATURE

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“…Likewise, their analysis of the resulting errors in the V-I or V-P profiles was not clear. Other techniques were also applied in [23][24][25][26][27][28]: the tree growth algorithm (TGA), dynamic self-adaptive and mutual-comparison teaching-learning-based optimization, the moth-flame optimization algorithm, improved gray wolf optimization, and the SSA. These implementations, as the previous ones, are commonly used for cells or PV modules, where restrictions to the search space are evident, without convincing statistical results or tuning the optimization techniques to be able to determine the minimization of the objective function in the best conditions of each optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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Due to the the lack of information about parameters in the datasheets of photovoltaic (PV) panels, it is difficult to study their modeling because PV behavior is based on voltage–current (V-I) data, which present a highly nonlinear relationship. To solve this difficulty, this study presents a mathematical three-diode model of a PV panel that includes multiple unknown parameters: photoinduced current, saturation currents of the three diodes, three ideality factors, serial resistance, and parallel resistance. These parameters should be estimated in the three-diode model of a PV panel to obtain the actual values that represent the voltage–current profile or the voltage–power profile (because of its visual simplicity) of the PV panel under analysis. In order to solve this problem, this paper proposes a new application of the salp swarm algorithm (SSA) to estimate the parameters of a three-diode model of a PV panel. Two test scenarios were implemented with two different PV panels, i.e., Kyocera KC200GT and Solarex MSX60, which generate different power levels and are widely used for commercial purposes. The results of the simulations were obtained using different irradiance levels. The proposed PV model was evaluated based on the experimental results of the PV modules analyzed in this paper. The efficiency of the optimization technique proposed here, i.e., SSA, was measured by a fair comparison between its numerical results and those of other optimization techniques tuned to obtain the best response in terms of the objective function.

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Parameter Extraction of Photovoltaic Models Using a Dynamic Self-Adaptive and Mutual- Comparison Teaching-Learning-Based Optimization

Abstract: He is currently an engineer with the Guizhou Electric Power Grid Dispatching and Control Center, Guiyang, China. His research interests include power system operation and dispatching.

Cited by 28 publications

References 77 publications

A Multi-Objective Teaching–Learning Studying-Based Algorithm for Large-Scale Dispatching of Combined Electrical Power and Heat Energies

A Multi-Objective Teaching–Learning Studying-Based Algorithm for Large-Scale Dispatching of Combined Electrical Power and Heat Energies

Fast Online Parameter Identification for Current Source Operated PV Modules in DC Microgrids

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

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