Photovoltaic dynamic MPPT on a moving vehicle

Ko, Shih Hung; Chao, Ru-Min

doi:10.1016/j.solener.2012.03.013

Cited by 38 publications

(13 citation statements)

References 13 publications

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“…PV energy is one of the most promising renewable energy resources and it is much clean, inexhaustible and free to harvest [1]. Several applications employing this technology have been developed such as satellite power systems, solar power generations, solar battery charging stations and solar vehicles [2][3][4][5]. The main disadvantage of PV is the low efficiency of energy conversion as compared with other alternative resources.…”

Section: Introductionmentioning

confidence: 99%

Development of adaptive perturb and observe‐fuzzy control maximum power point tracking for photovoltaic boost dc–dc converter

Zainuri¹,

Radzi²,

Soh³

et al. 2014

IET Renewable Power Generation

234

100

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This study presents an adaptive perturb and observe (P&O)-fuzzy control maximum power point tracking (MPPT) for photovoltaic (PV) boost dc-dc converter. P&O is known as a very simple MPPT algorithm and used widely. Fuzzy logic is also simple to be developed and provides fast response. The proposed technique combines both of their advantages. It should improve MPPT performance especially with existing of noise. For evaluation and comparison analysis, conventional P&O and fuzzy logic control algorithms have been developed too. All the algorithms were simulated in MATLAB-Simulink, respectively, together with PV module of Kyocera KD210GH-2PU connected to PV boost dc-dc converter. For hardware implementation, the proposed adaptive P&O-fuzzy control MPPT was programmed in TMS320F28335 digital signal processing board. The other two conventional MPPT methods were also programmed for comparison purpose. Performance assessment covers overshoot, time response, maximum power ratio, oscillation and stability as described further in this study. From the results and analysis, the adaptive P&O-fuzzy control MPPT shows the best performance with fast time response, less overshoot and more stable operation. It has high maximum power ratio as compared to the other two conventional MPPT algorithms especially with existing of noise in the system at low irradiance.

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Section: Introductionmentioning

confidence: 99%

Development of adaptive perturb and observe‐fuzzy control maximum power point tracking for photovoltaic boost dc–dc converter

Zainuri¹,

Radzi²,

Soh³

et al. 2014

IET Renewable Power Generation

234

100

View full text Add to dashboard Cite

show abstract

“…The quadratic maximization (QM) algorithm for maximum power point tracking was developed previously and applied to a small experimental solar boat [12,13]. It has been proven to be quite efficient for fast changing insolation and temperature variation conditions if compared with other MPPT techniques, such as the perturb and observation method.…”

Section: Modified Quadratic Maximization Mppt Algorithmmentioning

confidence: 99%

“…Lastly, a solar-power boat is used to demonstrate a complicated standalone PV application, since the vessel in a rolling and a pitching motion encounters fast changing of solar insolation intensity over the PV panels on board. Previously, a technique called the dynamic MPPT process in conjunction with the QM method was developed which is very suitable for marine applications [13]. The solar power is further fed into the power system of the ship, increasing its overall green energy efficiency.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a Distributed Photovoltaic System in Grid-Connected and Standalone Applications by Different MPPT Algorithms

Chao

Lin

et al. 2018

Energies

Self Cite

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Due to the shortage of fossil fuel and the environmental pollution problem, solar energy applications have drawn a lot of attention worldwide. This paper reports the use of the latest patented distributed photovoltaic (PV) power system design, including the two possible maximum power point tracking (MPPT) algorithms, a power optimizer, and a PV power controller, in grid-connected and standalone applications. A distributed PV system with four amorphous silicon thin-film solar panels is used to evaluate both the quadratic maximization (QM) and the Steepest descent (SD) MPPT algorithms. The system's design is different for the QM or the SD MPPT algorithm being used. The test result for the grid-connected silicon-based PV panels will also be reported. Considering the settling time for the power optimizer to be 20 ms, the test result shows that the tracking time for the QM method is close to 200 ms, which is faster when compared with the SD method whose tracking time is 500 ms. Besides this, the use of the QM method provides a more stable power output since the tracking is restricted by a local power optimizer rather than the global tracking the SD method uses. For a standalone PV application, a solar-powered boat design with 18 PV panels using a cascaded MPPT controller is introduced, and it provides flexibility in system design and the effective use of photovoltaic energy.

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“…Along with the significant increasing of global energy demand, the need of renewable energy is incredibly raised due to the considerations of environmental impact and sustainable development [1]. At the same time, PV generation have been rapidly increased for the past two decades and become one of the most important clean resources because of its desirable characteristics such as high availability, scalability, energy independency, and low maintenance cost [2][3][4][5]. These remarkable advantages make it even more promising for large scale utility application.…”

Section: Introductionmentioning

confidence: 99%

A Rprop-Neural-Network-Based PV Maximum Power Point Tracking Algorithm with Short-Circuit Current Limitation

Cui

Duan

et al. 2019

2019 IEEE Power &Amp; Energy Society Innovative Smart Grid Technologies Conference (ISGT)

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This paper proposes a resilient-backpropagationneural-network-(Rprop-NN) based algorithm for Photovoltaic (PV) maximum power point tracking (MPPT). A supervision mechanism is proposed to calibrate the Rprop-NN-MPPT reference and limit short-circuit current caused by incorrect prediction. Conventional MPPT algorithms (e.g., perturb and observe (P&O), hill climbing, and incremental conductance (Inc-Cond) etc.) are trial-and-error-based, which may result in steadystate oscillations and loss of tracking direction under fast changing ambient environment. In addition, partial shading is also a challenge due to the difficulty of finding the global maximum power point on a multi-peak characteristic curve. As an attempt to address the aforementioned issues, a novel Rprop-NN MPPT algorithm is developed and elaborated in this work. Multiple case studies are carried out to verify the effectiveness of proposed algorithm. Index Terms-Rprop Neural network, maximum power point tracking, short-circuit current, partial shading, PV.I.

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Photovoltaic dynamic MPPT on a moving vehicle

Cited by 38 publications

References 13 publications

Development of adaptive perturb and observe‐fuzzy control maximum power point tracking for photovoltaic boost dc–dc converter

Development of adaptive perturb and observe‐fuzzy control maximum power point tracking for photovoltaic boost dc–dc converter

Evaluation of a Distributed Photovoltaic System in Grid-Connected and Standalone Applications by Different MPPT Algorithms

A Rprop-Neural-Network-Based PV Maximum Power Point Tracking Algorithm with Short-Circuit Current Limitation

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