Combining Model-Based and Heuristic Techniques for Fast Tracking the Maximum-Power Point of Photovoltaic Systems

Hartmann, Lucas V.; Vitorino, Montiê A.; Corrêa, Maurício B. R.; Lima, A.M.N.

doi:10.1109/tpel.2012.2204408

Cited by 52 publications

(19 citation statements)

References 17 publications

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“…A PV module is used to convert sunlight into direct current and thus facilitates the conversion of light energy to electric energy. Based on the electronics theory of semiconductor p-n junction, it can be described by a current source [9].…”

Section: Model Of Photovoltaicmentioning

confidence: 99%

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Maximum Power Point Tracking of Photovoltaic Generators Partially Shaded Using a Hybrid Artificial Neural Network and Particle Swarm Optimization Algorithm

Said¹

2017

IJEPE

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This paper addresses the research methodology for Maximum Power Point Tracking (MPPT). Photovoltaic (PV)Generators may receive different level of solar irradiance and temperature, such as partially shaded by clouds, tree leaves or nearby building. Under partial shaded conditions, several peak power points can occur when the PV module is shaded, which would significantly reduce the energy produced by PV Generators without proper control. Therefore, a Maximum Power Point Tracking (MPPT) Algorithm is used to extract the maximum available PV power from the PV array. However, the common used conventional MPPT algorithms are unable to detect global peak (GP) power point with the presence of several local peaks (LP). In this paper, a hybrid Particle Swarm Optimization and Artificial Neural Network (PSO-ANN) algorithm is proposed to detect the global peak power. MATLAB/Simulink is used to simulate a PV system which consists of PV Generators, DC-DC boost converter, a hybrid PSO-ANN Algorithm, and a resistive load. The simulation results are compared and discussed. The proposed algorithm should perform well to detect the Global Peak of the PV array even under partial shaded conditions.

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Section: Model Of Photovoltaicmentioning

confidence: 99%

“…Artificial Neural Network (ANN) is an information processing paradigm, which is based on the functional concepts of biological nervous systems. It works best to deal with nonlinear dependence between the inputs and outputs [9,13]. In this study, a hybrid ANN and PSO method is proposed to extract the GP under partial shaded condition.…”

Section: Introductionmentioning

confidence: 99%

Maximum Power Point Tracking of Photovoltaic Generators Partially Shaded Using a Hybrid Artificial Neural Network and Particle Swarm Optimization Algorithm

Said¹

2017

IJEPE

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“…All the time it is necessary to track MPP due to deviation of hotness and irradiation of the array [49,50]. MPPT techniques to track MPP have been addressed and published over years of research [50][51][52][53]. Every MPPT method has its own merits and demerits like required sensors, complexity, cost, range of effectiveness, convergence speed, correct tracking when the irradiation and temperature change.…”

Section: Introductionmentioning

confidence: 99%

An Original Transformer and Switched-Capacitor (T & SC)-Based Extension for DC-DC Boost Converter for High-Voltage/Low-Current Renewable Energy Applications: Hardware Implementation of a New T & SC Boost Converter

et al. 2018

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“…They widely used techniques including Perturb and Observe (P&O) [5,6], Incremental Conductance (IC) [6], Hill Climbing (HC) [7,8], open-circuit voltage [8], and short-circuit current algorithm [8,11]. Recently, several artificial intelligent methods, i.e., Fuzzy Logic Controller (FLC) [9,10], Artificial Neural Network (ANN) [7,9,10] are explored. The above-mentioned conventional MPPT algorithms are not capable of tracking the true Maximum Power Point (MPP) if the PV array is partially shaded, such as covered by heavy clouds, falling tree leaves, birds' litters on the array or shaded by buildings [10] and so on.…”

Section: Introductionmentioning

confidence: 99%

Performance of Artificial Neural Network and Particle Swarm Optimization Technique based Maximum Power Point Tracking for Photovoltaic System Under Different Environmental Conditions

Said

Thiaw

2018

J. Phys.: Conf. Ser.

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Abstract. Photovoltaic (PV) array may receive different levels of solar irradiance and temperature under different environmental conditions, such as partially shaded by clouds or nearby building. However, all PV systems have two major drawbacks: the efficiency of PV power generation is very low and the output power of a PV system is nonlinear, which depends closely on weather conditions, such as ambient temperature and the solar irradiance. Hence, tracking the maximum power of the PV arrays at real time is very important to increase the whole system performance. Multiple peak power points occur when PV module is under partially shaded conditions, which would significantly reduce the energy produced by PV without proper control. Therefore, a Maximum Power Point Tracking (MPPT) algorithm is used to extract maximum available PV power from the PV array. However, most of the conventional MPPT algorithms are incapable to detect global peak (GP) power point with the presence of several local peaks (LP). A hybrid Artificial Neural Network and Particle Swarm Optimization (ANN-PSO) algorithm is proposed in this report to detect the global peak power. A PV system which consists of PV array, DC-DC boost converter, a hybrid ANN-PSO Algorithm, and a resistive load, is simulated using MATLAB/Simulink. The simulation results are carried out, compared and discussed. The proposed algorithm should perform well to detect the Global Peak of the PV array under different environmental conditions.

show abstract

Combining Model-Based and Heuristic Techniques for Fast Tracking the Maximum-Power Point of Photovoltaic Systems

Cited by 52 publications

References 17 publications

Maximum Power Point Tracking of Photovoltaic Generators Partially Shaded Using a Hybrid Artificial Neural Network and Particle Swarm Optimization Algorithm

Maximum Power Point Tracking of Photovoltaic Generators Partially Shaded Using a Hybrid Artificial Neural Network and Particle Swarm Optimization Algorithm

An Original Transformer and Switched-Capacitor (T & SC)-Based Extension for DC-DC Boost Converter for High-Voltage/Low-Current Renewable Energy Applications: Hardware Implementation of a New T & SC Boost Converter

Performance of Artificial Neural Network and Particle Swarm Optimization Technique based Maximum Power Point Tracking for Photovoltaic System Under Different Environmental Conditions

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