Marginal Power-Based Maximum Power Point Tracking Control of Photovoltaic System Under Partially Shaded Condition

Ghasemi, Mohamad Amin; Foroushani, Hossein Mohammadian; Blaabjerg, Frede

doi:10.1109/tpel.2019.2952972

Cited by 26 publications

(8 citation statements)

References 31 publications

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“…According to the external environment, the output power of solar cell varies with the operating point of voltage and current, but the output power has a maximum value, and at this time, the photoelectric conversion efficiency reaches the peak [1]. erefore, photovoltaic development should be specialized in maximum power point tracking control to raise the efficiency in solar power stations [2].…”

Section: Introductionmentioning

confidence: 99%

Vehicle‐Mounted Photovoltaic System Energy Management in Intelligent Transportation Systems: A Maximum Power Point Tracking Control

2021

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Electric vehicles have become the main contributor in terms of reducing fuel consumption and CO2 emission. Although the government is vigorously promoting the use of electric vehicles worldwide, the range anxiety still impedes the rapid development of electric vehicles, especially when air-conditioning also adds battery power consumption and aggravates the range anxiety. To this end, this paper proposes an improved vehicle-mounted photovoltaic system energy management in intelligent transportation systems, which is a maximum power point tracking control system. Meanwhile, since the power of solar panels is usually relatively small and the power changes at any time, low power density and poor controllability are difficult to avoid. In order to solve this problem, this paper offers a tracking control method to improve the output efficiency of solar panels. For improving photovoltaic conversion efficiency and maximizing output power, traditional photovoltaic power panels are often dominated by a centralized maximum power point tracing control, which is named MPPT. Although the cost under this case is lower, the output power of all photovoltaic panels cannot be maximized under the condition of uneven illumination or local mismatch. To improve the situation, a micro-scale inverter is proposed to provide MPPT control of photovoltaic modules, which can effectively improve the output power of each photovoltaic panel. Moreover, our MPPT algorithm is applicable to cloud shadow, building shadow, and shade, and it is more suitable for the car roof. Firstly, the Diode 5-parameter model is used to deduce the I-U equation of the photovoltaic module considering shadow shading, and then the real-time 5 parameter equation is formed by using the measured data group and selected. The reasonable initial value is used to iteratively solve the real-time value of 5 parameters, which is further to judge the masking situation. The maximum power point (MPP) is solved directly by the mathematical method based on the mathematical model of I-U relation mathematics, and the DC-DC circuit is used to adjust the running point to MPP. Unlike the traditional MPPT method, the method in this paper is based on the physical model of solar cells, and MPP tracking is based on mathematical methods. Based on this, it does not need to cause multiple interference to the circuit, and the tracking efficiency is high. Finally, the relative experimental results are provided to verify the performance of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Vehicle‐Mounted Photovoltaic System Energy Management in Intelligent Transportation Systems: A Maximum Power Point Tracking Control

2021

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“…MPPT can be executed after comparing two power points before and after the implementation [10]. This intelligent sampling program based on the method of open-circuit voltage judges each sample of the marginal array power and then searches the MPP [11]. A hybrid tracking technique was proposed to offer ample trade-off between the perturb and observe (P&O) and accelerate soft calculating technique via accurately searching the MPP [12].…”

Section: Introductionmentioning

confidence: 99%

A Novel Photovoltaic Module Quick Regulate MPPT Algorithm for Uniform Irradiation and Partial Shading Conditions

Liu

Lee

et al. 2021

Processes

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This study proposed a new photovoltaic module quick regulate (PVM-QR) maximum power point tracking (MPPT) algorithm, which can eliminate the disturbance problem of the hill-climbing (HC) algorithm, especially under low irradiance level and partial shading conditions (PSC). This proposed algorithm has the advantage that it only uses the detection photovoltaic module (PVM) impedance and the open-circuit voltage to simply and quickly calculate the PVM temperature, the irradiance level, and then the key factor parameter u. To achieve the MPPT quickly and accurately, this proposed algorithm is developed for the prediction of the best MPPT duty cycle based on the irradiance level, parameter u, and load. This study verified the proposed MPPT by the measurement results, where the HC algorithm MPPT has 95% efficiency at 0.55 kW/m2 but diverges at 0.2 kW/m2 under uniform irradiation conditions (UIC), and the proposed MPPT algorithm has an improved efficiency (99%) under the same conditions. Moreover, the proposed MPPT algorithm has 99% efficiency under PSC, while the HC algorithm MPPT’s efficiency is 66%. This study implemented a simple-design circuit with the proposed MPPT algorithm for UIC and PSC, where the actual experiment results can also verify that the proposed algorithm performs better than the HC algorithm.

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“…A modified version of I&C algorithm using the initial value incremental conductance strategy for global MPPT is presented in [17]. Adaptive neuro-fuzzy inference system-PSO based hybrid algorithm for accurate and fast-tracking of MPP with zero oscillation tracking [18], Marginal power-based MPP [19], capacitor selection method [20], combined PSO and perturb and observe method [21], modified genetic algorithm [22] for tracking the global MPPT in Solar-PV system are also reported in literature.…”

Section: Introductionmentioning

confidence: 99%

Incremental Conductance Based Particle Swarm Optimization Algorithm for Global Maximum Power Tracking of Solar-PV under Nonuniform Operating Conditions

Chawda

Mahela²,

Gupta

et al. 2020

Applied Sciences

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In practical operating conditions, the Solar-Photo Voltaic (SPV) system experiences multifarious irradiation and temperature levels, which generate power with multiple peaks. This is considered as the nonuniform operating condition (NUOC). This requires accurate tracking of global power peaks to achieve maximum power from SPV, which is a challenging task. Hence, this paper presents an incremental Conductance based Particle Swarm Optimization (ICPSO) algorithm for accurate tracking of maximum global power from active power multiple peaks generated by the SPV. The proposed algorithm continuously adjusts the individual particle’s weight component, which depends on its distance from the global best position during the tracking process. The proposed algorithm has the merit of continuous adjustment of weight components which reduces active power oscillations at the optimal global position area. Proposed ICPSO algorithm has been successfully designed and implemented for Solar-photo voltaic (PV) under nonuniform operating condition. It is established that the proposed algorithm enhances the output power of the Solar-PV up to 7% with the maximum power tracking of 0.1 s compared to other maximum power point tracking algorithms.

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Marginal Power-Based Maximum Power Point Tracking Control of Photovoltaic System Under Partially Shaded Condition

Cited by 26 publications

References 31 publications

Vehicle‐Mounted Photovoltaic System Energy Management in Intelligent Transportation Systems: A Maximum Power Point Tracking Control

Vehicle‐Mounted Photovoltaic System Energy Management in Intelligent Transportation Systems: A Maximum Power Point Tracking Control

A Novel Photovoltaic Module Quick Regulate MPPT Algorithm for Uniform Irradiation and Partial Shading Conditions

Incremental Conductance Based Particle Swarm Optimization Algorithm for Global Maximum Power Tracking of Solar-PV under Nonuniform Operating Conditions

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