Design and Implementation of a Simulator for Photovoltaic Modules

Tang, Kuang-Hui; Chao, Kuei‐Hsiang; Chao, Yuan-Wei; Chen, Jyun-Ping

doi:10.1155/2012/368931

Cited by 19 publications

(17 citation statements)

References 14 publications

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“…The I-V characteristic curve produced by the PV emulator should be similar with the actual PV module characteristics [4,12]. The dynamic response of the PV emulator should close to 10 th of a microsecond to obtain better results from MPPT devices and power conditioning system [7,13].…”

Section: Resultsmentioning

confidence: 93%

An Adaptive Controller for Photovoltaic Emulator using Artificial Neural Network

Ayop

Tan

2017

IJEECS

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The photovoltaic (PV) Keywords: PV, PI Controller, Interleaved Buck Converter, Multiple Stage FilterCopyright © 2017 Institute of Advanced Engineering and Science. All rights reserved. IntroductionThe uses of renewable energy become more popular especially the solar energy due to the increases in the performance of the photovoltaic (PV) module and the power converter. The power produces by the PV module is affected by the weather condition and requires the maximum power point tracking (MPPT) devices and power conditioning system to retrieve the maximum power from the PV module. However, the development of MPPT requires a full control of the factor that affecting the PV module which is the irradiance and module temperature. However, this experimental setup is complex, which requires a large space for the PV module. Moreover, the repetitive testing conditions are difficult to achieve. Due to these drawbacks, the MPPT device tested using the PV emulator would be more cost effective and time-saving.The common PV emulator consists of a conventional buck converter with the proportional-integral (PI) controller and a reference input from the PV model [1][2][3]. Although the conventional buck converter is simple and requires a low number of components, the dynamic response is slow due to a large capacitance in order to maintain a small voltage ripple. The multiple-stage interleaved buck converter improves the reliability, efficiency, and transient response of the PV emulator [4]. The interleaved converter produces a lower inductor current ripple. As a result, a smaller capacitance is required to maintain similar ripple factor thus producing a faster transient response. The two-stage LC filter (TSLCF) is also introduced in the PV emulator application to improve the bandwidth of the PV emulator system [5].The dynamic response of the PV emulator varies depending on output resistance [3], [6][7]. A low load resistance causes the overshoot at the output voltage and output current of the PV emulator. On the other hand, a large load resistance causes the PV emulator to response slower. This problem is overcome by implementing the fuzzy PI controller that adapts to the output resistance [8]. However, the nonlinear characteristic of the PV module also affects the dynamic response of the PV emulator [6]. The overdamped PV emulator output is required to prevent instability of the PV emulator output. Therefore, the dynamic response of the PV emulator becomes slow. To overcome this problem, the control system for the PV emulator not only considers the output resistance, but also the irradiance and temperature. However, by consideration these factors into the controller of the PV emulator, the controller for the PV

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Section: Resultsmentioning

confidence: 93%

An Adaptive Controller for Photovoltaic Emulator using Artificial Neural Network

Ayop

Tan

2017

IJEECS

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“…Due to their unstable and intermittent characteristics, PV panels cannot provide a constant or stable power output. Thus, to achieve PV energy conversion, PV panels often adopt chargers and batteries [3][4][5][6][7][8][9][10].…”

Section: Open Accessmentioning

confidence: 99%

“…The advantage of the perturbation-and-observation method is that MPPT controller only requires a few parameters to easily measure and control the maximum power point. Therefore, it is often applied to PV panels for enhancing the power capacity [5][6][7][8][9].…”

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confidence: 99%

A Reflex Charger with ZVS and Non-Dissipative Cells for Photovoltaic Energy Conversion

Tsai

Kuo

et al. 2015

Energies

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Abstract:In this paper, a reflex charger with zero-voltage-switching (ZVS) and non-dissipative cells for photovoltaic (PV) energy conversion is presented. The proposed reflex charger has the following advantages: (1) A ZVS cell is incorporated to reduce the switching losses of the main and auxiliary switches. Therefore, the conversion efficiency of the proposed reflex charger can be improved significantly; (2) A non-dissipative charging/discharging reflex cell is used to increase charging efficiency and extend lifecycle of battery. Finally, a prototype reflex charger with ZVS and non-dissipative cells is built and implemented. Experimental results are presented to verify the performance and the feasibility of the proposed reflex charger for PV energy conversion.

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“…For the past decades, many MPPT algorithms have been proposed, in which many centered around obtaining optimum maximum power point [2][3][4][5][6]. Among the renowned power maximizing methods are Perturb and Observe (P&O) and/or hill climbing and incremental conductance (IC) [7][8][9]. These techniques, nonetheless, fail to track maximum power point when the insolation level is not consistent for all PV solar cells or the panel is partially shaded.…”

Section: Introductionmentioning

confidence: 99%

An Improved PSO-Based MPPT Control Strategy for Photovoltaic Systems

Abdulkadir

Yatim

Yusuf

2014

International Journal of Photoenergy

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This paper presents a control strategy proposed for power maximizing which is a critical mechanism to ensure power track is maximized. Many tracking algorithms have been proposed for this purpose. One of the more commonly used techniques is the incremental conductance method. In this paper, an improved particle swarm optimization- (IPSO-) based MPPT technique for photovoltaic system operating under varying environmental conditions is proposed. The approach of linearly decreasing scheme for weighting factor and cognitive and social parameter is modified. The proposed control scheme can overcome deficiency and accelerate convergence of the IPSO-based MPPT algorithm. The approach is not only capable of tracking the maximum power point under uniform insolation state, but also able to find the maximum power point under fast changing nonuniform insolation conditions. The photovoltaic systematic process with control schemes is created using MATLAB Simulink to verify the effectiveness with several simulations being carried out and then compared with the conventional incremental conductance technique. Lastly, the effectiveness of the intended techniques is proven using real data obtained form previous literature. With the change in insolation and temperature portrait, it produces exceptional MPPT maximization. This shows that optimum performance is achieved using the intended method compared to the typical method.

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Design and Implementation of a Simulator for Photovoltaic Modules

Cited by 19 publications

References 14 publications

An Adaptive Controller for Photovoltaic Emulator using Artificial Neural Network

An Adaptive Controller for Photovoltaic Emulator using Artificial Neural Network

A Reflex Charger with ZVS and Non-Dissipative Cells for Photovoltaic Energy Conversion

An Improved PSO-Based MPPT Control Strategy for Photovoltaic Systems

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