A novel ant colony optimization-based maximum power point tracking for photovoltaic systems under partially shaded conditions

Jiang, Lijing; Maskell, Douglas L.; Patra, J.C.

doi:10.1016/j.enbuild.2012.12.001

Cited by 317 publications

(122 citation statements)

References 18 publications

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“…To obtain m I sc, m detecting points located in the current source area of each module need to be determined. First, the detecting point is set as U det (1) =1V, and the others are set as Equation (19):…”

Section: Mpf For a Pv String Under Module-level Pscsmentioning

confidence: 99%

“…To heighten the efficiency of PV systems under PSCs, researchers have proposed a variety of intelligent algorithms to extract the GMPP in recent years, including particle swarm optimization (PSO) [17]- [18], ant colony optimization (ACO) [19], firefly algorithm (FA) [20]- [21], cuckoo search (CS) [22]- [23], artificial bee colony (ABC) [24], etc. These intelligent algorithms are capable of tracking the GMPP.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Combining Model-based and Heuristic Techniques for Fast Tracking the Global Maximum Power Point of a Photovoltaic String

Shi¹,

Xue²,

Ling³

et al. 2017

Journal of Power Electronics

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Under partial shading conditions (PSCs), multiple maximums may be exhibited on the P-U curve of string inverter photovoltaic (PV) systems. Under such conditions, heuristic methods are invalid for extracting a global maximum power point (GMPP); intelligent algorithms are time-consuming; and model-based methods are complex and costly. To overcome these shortcomings, a novel hybrid MPPT (MPF-IP&O) based on a model-based peak forecasting (MPF) method and an improved perturbation and observation (IP&O) method is proposed. The MPF considers the influence of temperature and does not require solar radiation measurements. In addition, it can forecast all of the peak values of the PV string without complex computation under PSCs, and it can determine the candidate GMPP after a comparison. Hence, the MPF narrows the searching range tremendously and accelerates the convergence to the GMPP. Additionally, the IP&O with a successive approximation strategy searches for the real GMPP in the neighborhood of the candidate one, which can significantly enhance the tracking efficiency. Finally, simulation and experiment results show that the proposed method has a higher tracking speed and accuracy than the perturbation and observation (P&O) and particle swarm optimization (PSO) methods under PSCs.

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Section: Mpf For a Pv String Under Module-level Pscsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combining Model-based and Heuristic Techniques for Fast Tracking the Global Maximum Power Point of a Photovoltaic String

Shi¹,

Xue²,

Ling³

et al. 2017

Journal of Power Electronics

View full text Add to dashboard Cite

show abstract

“…In ant colony optimization technique based MPPT controller (Jianga et al 2013), the tracking efficiency is quite good for slow changes in irradiation level, whereas, the tracking efficiency is very poor for rapidly changing irradiation levels. An artificial bee colony optimization technique based MPPT controller has been implemented in (Fathy et al 2015) to track the GMPP and to mitigate power loss in shaded modules of PV array.…”

Section: Introductionmentioning

confidence: 99%

“…Artificial neural network (ANN) and fuzzy logic controller (FLC) based MPPT algorithms are considered to be part of artificial intelligent (AI) techniques (Lin et al 2011, Khateb et al 2014. The MPPT algorithms based on nature inspired optimization techniques are genetic algorithm (Larbes et al 2009), particle swarm optimization technique (Liu et al 2012), ant colony optimization (Jianga et al 2013), artificial bee colony (Benyoucef et al 2015), and grey wolf optimization technique (Mohanty et al 2016). The P&O method is easier to implement, but this algorithm fails to track MPP and will result in oscillation at steady state point.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Maximum Power Point Tracking Algorithms Under Varying Irradiation

Naick

Chatterjee

2017

Int. J. Renew. Energy Dev.

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Photovoltaic (PV) system is one of the reliable alternative sources of energy and its contribution in energy sector is growing rapidly. The performance of PV system depends upon the solar insolation, which will be varying throughout the day, season and year. The biggest challenge is to obtain the maximum power from PV array at varying insolation levels. The maximum power point tracking (MPPT) controller, in association with tracking algorithm will act as a principal element in driving the PV system at maximum power point (MPP). In this paper, the simulation model has been developed and the results were compared for perturb and observe, incremental conductance, extremum seeking control and fuzzy logic controller based MPPT algorithms at different irradiation levels on a 10 KW PV array. The results obtained were analysed in terms of convergence rate and their efficiency to track the MPP.Article History: Received 3rd Oct 2016; Received in revised form 6th January 2017; Accepted 10th February 2017; Available onlineHow to Cite This Article: Naick, B. K., Chatterjee, T. K. & Chatterjee, K. (2017) Performance Analysis of Maximum Power Point Tracking Algorithms Under Varying Irradiation. Int Journal of Renewable Energy Development, 6(1), 65-74.http://dx.doi.org/10.14710/ijred.6.1.65-74

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“…Several authors proposed MPPT algorithms based on Particle Swarm Optimization -Liu et al 2012, Artificial Bee Colony (Sundareswaran et al 2015), Ant Colony Optimization (Jiang et al 2013), Cuckoo Search (Ahmed et al 2014), Firefly (Sundareswaran et al 2014), Grey Wolf Optimizer (Satyajit et al 2016) and Whale Optimization Algorithm (Santhan et al 2016). All these algorithms differ noticeably in terms of accuracy, efficiency, tracking time and complexity (Jordehi 2016).…”

Section: Introductionmentioning

confidence: 99%

Enhanced Grey Wolf Optimizer Based MPPT Algorithm of PV System Under Partial Shaded Condition

Cherukuri

Rayapudi

2017

Int. J. Renew. Energy Dev.

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ABSTRACT:Partial shading condition is one of the adverse phenomena which effects the power output of photovoltaic (PV) systems due to inaccurate tracking of global maximum power point. Conventional Maximum Power Point Tracking (MPPT) techniques like Perturb and Observe, Incremental Conductance and Hill Climbing can track the maximum power point effectively under uniform shaded condition, but fails under partial shaded condition. An attractive solution under partial shaded condition is application of meta-heuristic algorithms to operate at global maximum power point. Hence in this paper, an Enhanced Grey Wolf Optimizer (EGWO) based maximum power point tracking algorithm is proposed to track the global maximum power point of PV system under partial shading condition. A Mathematical model of PV system is developed under partial shaded condition using single diode model and EGWO is applied to track global maximum power point. The proposed method was programmed in MATLAB environment and simulations are carried out on 4S and 2S2P PV configurations for dynamically changing shading patterns. The results of the proposed method were analyzed and compared with GWO and PSO algorithms. It was observed that proposed method is effective in tracking global maximum power point with more accuracy in less computation time compared to other methods.

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A novel ant colony optimization-based maximum power point tracking for photovoltaic systems under partially shaded conditions

Cited by 317 publications

References 18 publications

Combining Model-based and Heuristic Techniques for Fast Tracking the Global Maximum Power Point of a Photovoltaic String

Combining Model-based and Heuristic Techniques for Fast Tracking the Global Maximum Power Point of a Photovoltaic String

Performance Analysis of Maximum Power Point Tracking Algorithms Under Varying Irradiation

Enhanced Grey Wolf Optimizer Based MPPT Algorithm of PV System Under Partial Shaded Condition

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