Accuracy of fuel cell characteristics mainly depends on its parameters. Generally, estimation is done in a curve fitting procedure. In this paper, the problem of fuel cell parameter extraction is formulated as an objective function and solved using simple Genetic Algorithm. Explanations about the new formulation and its implementation using GA is elaborated. The candidature of the proposed method is explained. The results are compared with existing curve fitting method. © 2015 The Authors. Published by Elsevier Ltd.

Balasubramanian V

Department of Information Security

School of Computer Science and Engineering

Vellore Campus

Rajasekar N

Department of Energy and Power Electronics

School of Electrical Engineering

Jacob B

Priya K

Sangeetha K

Babu T.S.

Fulltext

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Energy Procedia

The widespread use of Proton Exchange Membrane fuel cell for its unique advantages compelled researchers for precise modelling of its characteristics. Since, modelling becomes extremely important for better understanding, simulation, design, analysis and development of high efficiency fuel cell system. However, due to its non-linearity, multivariate and strongly coupled characteristics; mathematical modelling based on empirical equations was widely adopted. But, the shortage of data, complexity in modelling, and number of unknown parameters favored the use of optimization methods. Many optimization methods have been endeavored to model Proton Exchange Membrane fuel cell characteristics. However, no prior attempt has been made to consolidate the contributions. Hence, this paper comprehensively describes and discusses the various Artificial Intelligence/bio inspired methods applied for fuel cell parameter estimation problem. The methods background theory and its application to the problem is elaborated. It is envisioned that, this review will be a one stop solution to the researchers and engineers working in the area of fuel cell systems. © 2018 Elsevier Ltd

Renewable and Sustainable Energy Reviews

A comprehensive review on parameter estimation techniques for Proton Exchange Membrane fuel cell modelling

The Proportional Integral Derivative (PID) controllers are extensively used as the controllers for controlling the system outputs. The performance of the system is mainly depends on the parameters of the PID controller. To tune these parameters, various techniques are available, and in these methods, there exists drawbacks. To overcome these drawbacks in this paper, the tuning of PID parameters are done by using genetic algorithm, and it is effectively tested on linear inverted pendulum system (LIP). In addition, extensive simulations are performed by varying parameters of the pendulum. For brevity, comparison is made with Z–N methods of tuning, and it is observed that GA-tuned PID controller is giving better performance. © 2018, Springer Nature Singapore Pte Ltd.

Advances in Systems, Control and Automation Lecture Notes in Electrical Engineering

Tuning PID Controller for Inverted Pendulum Using Genetic Algorithm

In recent years, continuous depletion of conventional energy resources and the fluctuating fuel cost have motivated research on alternative source for power generation. To effectively harvest power from the green energy resources, extensive research have been carried out in literature. Solar energy being the foremost among all other renewable energy resources has evolved as a reliable substitute for the conventional power generation. Unlike other sources, real time research on Photovoltaic (PV) Systems is a difficult assignment as it necessitates an accurate PV emulator that can precisely replicate the nonlinear characteristics of a PV cell. PV Researchers have developed emulators of different type utilizing various configurations. This paper attempts to present a comprehensive review on different PV emulator topologies so as to gather all the research activities that are scattered in this arena under a common umbrella. Further this paper provides a detailed analysis of each technique emphasizing on its (i) Realization cost (ii) Accuracy (iii) Level of complexity (iv) Sensitiveness to varying environmental conditions (v) Hardware implementation and (vi) Efficiency. © 2017 Elsevier Ltd

Analysis on solar PV emulators: A review

The inaccurate I-V curve generation in solar PV modeling introduces less efficiency and on the other hand, accurate simulation of PV characteristics becomes a mandatory obligation before experimental validation. Although many optimization methods in literature have attempted to extract accurate PV parameters, all of these methods do not guarantee their convergence to the global optimum. Hence, the authors of this paper have proposed a new hybrid Bee pollinator Flower Pollination Algorithm (BPFPA) for the PV parameter extraction problem. The PV parameters for both single diode and double diode are extracted and tested under different environmental conditions. For brevity, the I01, I02, Ipvfor double diode and I0,Ipvfor single diode models are calculated analytically where the remaining parameters ‘Rs, Rp, a1, a2’ are optimized using BPFPA method. It is found that, the proposed Bee Pollinator method has all the scope to create exploration and exploitation in the control variable to yield a less RMSE value even under lower irradiated conditions. Further for performance validation, the parameters arrived via BPFPA method is compared with Genetic Algorithm (GA), Pattern Search (PS), Harmony Search (HS), Flower Pollination Algorithm (FPA) and Artificial Bee Swarm Optimization (ABSO). In addition, various outcomes of PV modeling and different parameters influencing the accurate PV modeling are critically analyzed. © 2016 Elsevier Ltd

Energy Conversion and Management

A new hybrid bee pollinator flower pollination algorithm for solar PV parameter estimation

over a decade, the research interest in solar energy is considerably increased, since it is one of the most promising alternate for decaying fossil fuel. Among the research thrusts, the accurate modeling of solar I-V characteristics is given prior importance. In this article, the authors have proposed a new FPA based solar PV parameter extraction. To avoid computational burden and complexity, single diode modeling is preferred. Solar PV parameters are computed using FPA method. The computed root mean square error and relative error is compared with existing simulated annealing, Pattern search, Genetic algorithm and particle swam optimization techniques. © 2016 IEEE.

2016 IEEE 6th International Conference on Power Systems (ICPS)

Solar PV parameter extraction using FPA

The abundance and non-polluting nature of solar energy has aroused the interest of many researchers. This worldwide attention of photovoltaic panels has led to the need of generating accurate model for solar photovoltaic (PV) module before proceeding to the installation part. However, accurate modeling of solar PV characteristics is difficult; since the manufacturer's datasheet provides only four values such as Vmp, Imp, Voc, and Isc. Further, for accurate modeling precise estimation of model parameters at different environmental conditions are very essential. On the other hand, optimization technique is a very powerful tool to obtain solutions to complex non-linear problems. Hence, in this paper, Bacterial Foraging Algorithm is proposed to model the solar PV characteristics accurately. A new equation has been evolved to determine the values of Voc, Vmp accurately; since these values decides the closeness of the simulated characteristics. Model parameters are extracted for three different types of solar PV panels. A systematic evaluation and performance comparison of Bacterial Foraging Algorithm with other optimization techniques such as Genetic Algorithm and Artificial Immune System has been done and the best computational technique is derived based on performance criteria such as accuracy, consistency, speed of convergence and absolute error. Extensive computations are carried out for the proposed method, as well as for Genetic Algorithm and Artificial Immune System to substantiate the findings. © 2013 Elsevier Ltd.

Solar Energy

Bacterial Foraging Algorithm based solar PV parameter estimation

International Journal of Electrical Power & Energy Systems

Artificial immune system-based parameter extraction of proton exchange membrane fuel cell

International Journal of Hydrogen Energy

Validation of genetic algorithm results in a fuel cell model

Journal of Power Sources

Improvement of proton exchange membrane fuel cell electrical performance by optimization of operating parameters and electrodes preparation

A comparative study of approaches to direct methanol fuel cells modelling

Critical Evaluation of Genetic Algorithm Based Fuel Cell Parameter Extraction

Journal	Data powered by TypesetEnergy Procedia
Publisher	Data powered by TypesetElsevier BV
ISSN	1876-6102
Open Access	Yes