scholarly journals A Robust Maximum Power Point Tracking Control Method for a PEM Fuel Cell Power System

2018 ◽  
Vol 8 (12) ◽  
pp. 2449 ◽  
Author(s):  
Mohamed Derbeli ◽  
Oscar Barambones ◽  
Lassaad Sbita

Taking into account the limited capability of proton exchange membrane fuel cells (PEMFCs) to produce energy, it is mandatory to provide solutions, in which an efficient power produced by PEMFCs can be attained. The maximum power point tracker (MPPT) plays a considerable role in the performance improvement of the PEMFCs. Conventional MPPT algorithms showed good performances due to their simplicity and easy implementation. However, oscillations around the maximum power point and inefficiency in the case of rapid change in operating conditions are their main drawbacks. To this end, a new MPPT scheme based on a current reference estimator is presented. The main goal of this work is to keep the PEMFCs functioning at an efficient power point. This goal is achieved using the backstepping technique, which drives the DC–DC boost converter inserted between the PEMFC and the load. The stability of the proposed algorithm is demonstrated by means of Lyapunov analysis. To verify the ability of the proposed method, an extensive simulation test is executed in a Matlab–Simulink T M environment. Compared with the well-known proportional–integral (PI) controller, results indicate that the proposed backstepping technique offers rapid and adequate converging to the operating power point.

2021 ◽  
Vol 24 (1) ◽  
pp. 43-48
Author(s):  
Abdelghani Harrag

This paper presents a new neural network single sensor maximum power point tracking algorithm controlling the DC-DC boost converter to guarantee the transfer of the proton exchange membrane fuel cell maximum generated power to the load. The implemented neural network single sensor controller has been developed and trained firstly in offline mode using single sensor maximum power point tracking data obtained previously; and secondly used in online mode to track the maximum output power of the fuel cell power system. Comparative simulation results prove the superiority of the proposed neural network single sensor maximum power point compared to the single sensor one especially in transit response reducing by the way the overshoot and the tracking time which leads to an overall energy losses reduction. In addition, the implemented neural network single sensor MPPT employs only one sensor which will reduce the complexity and the cost of PEM fuel cell power system. To our knowledge, this study is a pioneering work using a neural network single sensor controller as PEM fuel cell MPPT.


Author(s):  
Badreddine KANOUNI ◽  
◽  
Abd Essalam BADOUD ◽  
Saad MEKHILEF ◽  
◽  
...  

Fuel cells output power depends on the operating conditions, including cell temperature, oxygen pressure, hydrogen pressure, tempureter . In each particular condition, there is only one unique operating point for a fuel cell system with the maximum output. Thus, a maximum power point tracking (MPPT) controller is needed to increase the efficiency of the PEMFC systems. In this paper an efficient method fuzzy logic controller is proposed for MPPT of the proton exchange membrane (PEM) fuel cells, boost converter. FLC adjusts the operating point of the PEM fuel cell to the maximum power by tuning of the boost converter duty cycle. To demonstrate the performance of the proposed algorithm, simulation results are sumulated in two cases, in normel condution and variation in temperature .the FLC algorithm with fast convergence, high accuracy and very low power fluctuations tracks the maximum power point of the fuel cell system


Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 483
Author(s):  
Novie Ayub Windarko ◽  
Muhammad Nizar Habibi ◽  
Bambang Sumantri ◽  
Eka Prasetyono ◽  
Moh. Zaenal Efendi ◽  
...  

During its operation, a photovoltaic system may encounter many practical issues such as receiving uniform or non-uniform irradiance caused mainly by partial shading. Under uniform irradiance a photovoltaic panel has a single maximum power point. Conversely under non-uniform irradiance, a photovoltaic panel has several local maximum power points and a single global maximum power point. To maximize energy production, a maximum power point tracker algorithm is commonly implemented to achieve the maximum power operating point of the photovoltaic panel. However, the performance of the algorithm will depend on operating conditions such as variation in irradiance. Presently, most of existing maximum power point tracker algorithms work only in a single condition: either uniform or non-uniform irradiance. This paper proposes a new maximum power point tracker algorithm for photovoltaic power generation that is designed to work under uniform and partial shading irradiance conditions. Additionally, the proposed maximum power point tracker algorithm aims to provide: (1) a simple math algorithm to reduce computational load, (2) fast tracking by evaluating progress for every single executed duty cycle, (3) without random steps to prevent jumping duty cycle, and (4) smooth variable steps to increase accuracy. The performances of the proposed algorithm are evaluated by three conditions of uniform and partial shading irradiance where a targeted maximum power point is located: (1) far from, (2) near, and (3) laid between initial positions of particles. The simulation shows that the proposed algorithm successfully tracks the maximum power point by resulting in similar power values in those three conditions. The proposed algorithm could handle the partial shading condition by avoiding the local maxima power point and finding the global maxima power point. Comparisons of the proposed algorithm and other well-known algorithms such as differential evolution, firefly, particle swarm optimization, and grey wolf optimization are provided to show the superiority of the proposed algorithm. The results show the proposed algorithm has better performance by providing faster tracking, faster settling time, higher accuracy, minimum oscillation and jumping duty cycle, and higher energy harvesting.


Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1359
Author(s):  
Anindya-Sundar Jana ◽  
Hwa-Dong Liu ◽  
Shiue-Der Lu ◽  
Chang-Hua Lin

The traditional perturbation and observation (P&O) maximum power point tracking (MPPT) algorithm of a structure is simple and low-cost. However, the P&O algorithm is prone to divergence under solar radiation when the latter varies rapidly and the P&O algorithm cannot track the maximum power point (MPP) under partial shading conditions (PSCs). This study proposes an algorithm from the P&O algorithm combined with the solar radiation value detection scheme, where the solar radiation value detection is based on the solar photovoltaic (SPV) module equivalent conductance threshold control (CTC). While the proposed algorithm can immediately judge solar radiation, it also has suitable control strategies to achieve the high efficiency of MPPT especially for the rapid change in solar radiation and PSCs. In the actual test of the proposed algorithm and the P&O algorithm, the MPPT efficiency of the proposed algorithm could reach 99% under solar radiation, which varies rapidly, and under PSCs. However, in the P&O algorithm, the MPPT efficiency was 96% under solar radiation, which varies rapidly, while the MPPT efficiency was only 80% under PSCs. Furthermore, in verifying the experimental results, the proposed algorithm’s performance was higher than the P&O algorithm.


2021 ◽  
pp. 69-76
Author(s):  
Mourad Talbi ◽  
Nawel Mensia ◽  
Hatem Ezzaouia

Nowadays, renewable energy resources play an important role in replacing conventional fossil fuel energy resources. Solar photovoltaic (PV) energy is a very promising renewable energy resource, which rapidly grew in the past few years. The main problem of the solar photovoltaic is with the variation of the operating conditions of the array, the voltage at which maximum power can be obtained from it likewise changes. In this paper, is first performed the modelling of a solar PV panel using MATLAB/Simulink. After that, a maximum power point tracking (MPPT) technique based on artificial neural network (ANN) is applied in order to control the DC-DC boost converter. This MPPT controller technique is evaluated and compared to the “perturb and observe” technique (P&O). The simulation results show that the proposed MPPT technique based on ANN gives faster response than the conventional P&O technique, under rapid variations of operating conditions. This comparative study is made in terms of temporal variations of the duty cycle (D), the output power ( out P ), the output current ( out I ), the efficiency, and the reference current ( ref I ). The efficiency, D, out P , and out I are the output of the boost DC-DC, and ref I is itsinput. The different temporal variations of the efficiency, D, ref I , out P , and out I (for the two cases: the first case, when T = 25°C and G =1000 W/m2 and the second case, when T and G are variables), show negligible oscillations around the maximum power point. The used MPPT controller based on ANN has a convergence time better than conventional P&O technique.


Author(s):  
Jianxin Liu ◽  
Tiebiao Zhao ◽  
YangQuan Chen

Proton Exchange Membrane FC (PEMFC) is widely recognized as a potentially renewable and green energy source based on hydrogen. Maximum power point tracking (MPPT) is one of the most important working conditions to be considered. In order to improve the searching performance such as convergence and robustness under disturbance and uncertainty, a kind of fractional order low pass filter (FOLPF) is applied for the MPPT controller design based on general Extremum Seeking Control (ESC). The controller is designed with FOLPF and high pass filter (HPF) substituting the normal LPF and HPF in the original ESC design. With this FOLPF ESC, better convergence and smooth performance is gained while maintaining the robust specifications. Simulation results are included to validate the proposed new FOLPF ESC scheme under disturbance and comparisons between FOLPF ESC and general ESC method are also provided.


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