An improved TLBO with elite strategy for parameters identification of PEM fuel cell and solar cell models

Niu, Qun; Zhang, Hongyun; Kang, Li

doi:10.1016/j.ijhydene.2013.12.110

Cited by 233 publications

(109 citation statements)

References 38 publications

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“…Therefore, it is necessary to acquire the accurate measurements before estimating the model parameters [22]. Another important point is accurate mathematical modelling of I-V curve which is very essential for reflecting the electrical characteristics of solar cell models.…”

Section: Solar Cell Modellingmentioning

confidence: 99%

Global Best Algorithm Based Parameter Identification of Solar Cell Models

Turgut¹

2017

ijisae

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Effectivity of the solar energy systems is thoroughly dependent of successful modeling of the I-V characteristic curves. However, due to the lack of information about the precise model parameters those are profoundly involved in characterizing governing equations; an efficient design has not been accurately accomplished by researchers yet. This article proposes Global Best Algorithm (GBEST) in order to extract unknown parameters of solar cell models accurately. In order to test the performance of the proposed optimizer, nine different unconstrained optimization test functions are evaluated and their statistical results are compared with the recently developed metaheuristic algorithms. GBEST is applied on PV module, single and double diode models which are mathematically formulated as multi-dimensional and highly nonlinear in their nature. Results reveal that GBEST is superior to the other methods in terms of solution accuracy and efficiency.

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Section: Solar Cell Modellingmentioning

confidence: 99%

Global Best Algorithm Based Parameter Identification of Solar Cell Models

Turgut¹

2017

ijisae

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“…Teaching-learning based optimization (TLBO) is a new meta-heuristic algorithm proposed in 2011 and has been utilized in solving some engineering problems [18], [19], [20], [21]. Two phases are designed for each evolution iteration in the original TLBO, named teaching phase and learning phase respectively.…”

Section: Self-learning Teaching-learning Based Optimizationmentioning

confidence: 99%

A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

Yang

Niu

et al. 2014

J. Mod. Power Syst. Clean Energy

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111

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Economic and environmental load dispatch aims to determine the amount of electricity generated from power plants to meet load demand while minimizing fossil fuel costs and air pollution emissions subject to operational and licensing requirements. These two scheduling problems are commonly formulated with non-smooth cost functions respectively considering various effects and constraints, such as the valve point effect, power balance and ramp rate limits. The expected increase in plug-in electric vehicles is likely to see a significant impact on the power system due to high charging power consumption and significant uncertainty in charging times. In this paper, multiple electric vehicle charging profiles are comparatively integrated into a 24-hour load demand in an economic and environment dispatch model. Self-learning teaching-learning based optimization (TLBO) is employed to solve the non-convex non-linear dispatch problems. Numerical results on well-known benchmark functions, as well as test systems with different scales of generation units show the significance of the new scheduling method.

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“…The proposed LBEO was compared with six recent popular algorithms, which are GA-TB [5], PSO-IW [5], SaDE [5], GL-25 [5], HIS [5] and TLBO [5], respectively.…”

Section: Benchmark Testmentioning

confidence: 99%

“…In comparison with basic TLBO, the structure is further simplified, and the function evaluation number in each generation is significantly reduced. To evaluate the performance of LBEO, it is tested on three large scale benchmark functions, and compared with the basic TLBO and five commonly used methods, including GA-TB [5], PSO-IW [5], SaDE [5], GL-25 [5], HIS [5] and TLBO [5]. Then, LBEO is employed to solve the OPF problem in a standard IEEE 30-bus test system with the objective of the minimum total fuel cost.…”

Section: Introductionmentioning

confidence: 99%

Learning-Based Evolutionary Optimization for Optimal Power Flow

Niu

Peng

Zhang

2015

Intelligent Computing Theories and Methodologies

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Abstract. This paper proposes a learning-based evolutionary optimization (LBEO) for solving optimal power flow (OPF) problem. The LBEO is a simple and effective algorithm, which simplifies the structure of teaching-learning-based optimization (TLBO) and enhances the convergence speed. The performance of this method is implemented on IEEE 30-bus test system with the minimized fuel cost objective function, and the results show that LBEO is practicable for OPF problem compared with other methods in the literature.

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An improved TLBO with elite strategy for parameters identification of PEM fuel cell and solar cell models

Cited by 233 publications

References 38 publications

Global Best Algorithm Based Parameter Identification of Solar Cell Models

Global Best Algorithm Based Parameter Identification of Solar Cell Models

A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

Learning-Based Evolutionary Optimization for Optimal Power Flow

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