Maximum Power Point Tracking Using Modified Butterfly Optimization Algorithm for Partial Shading, Uniform Shading, and Fast Varying Load Conditions

Shams, Immad; Mekhilef, Saad; Soon, Tey Kok

doi:10.1109/tpel.2020.3029607

Cited by 137 publications

(88 citation statements)

References 26 publications

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“…But PSO and Jaya algorithms show more oscillations during the tracking period and PSO needs comparatively larger time and more iterations to reach the GMPP. The proposed method compares with MBOA [25], as MBOA depends on one tunning parameter and one random number, whereas the proposed method does not require any tunning parameters and the random number, this makes the proposed algorithm more robust in implementation. Moreover, the average efficiency of the proposed method is almost near to that of MBOA, which is 99.45% and the response is fast to the sudden load variations.…”

Section: Comparative Studymentioning

confidence: 99%

“…Nevertheless, this method may still take significant time compared to the conventional methods and highly dependent on the initial candidate. Meta-heuristic-based modified butterfly algorithm (MBOA) [25] and a radial movement optimization (ARMO) [26] MPPT tracking algorithm were proposed optimal tracking under partial shading conditions and fast varying loads. But the tracking speed and accuracy of these methods are highly sensitive for the optimization parameter which should be properly selected for best results.…”

Section: Introductionmentioning

confidence: 99%

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A Bayesian fusion technique for maximum power point tracking under partial shading condition

Gugulothu

Bhookya

2021

SN Appl. Sci.

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In this paper, a Bayesian fusion technique (BFT) based on maximum power point tracking (MPPT) is developed for the photovoltaic (PV) system that can exhibit faster and accurate tracking under partially shaded conditions (PSCs). Although the conventional hill-climbing algorithms have fast tracking capabilities, they are prone to steady-state oscillations and may not guarantee global peak under partially shaded conditions. Contrarily, the meta-heuristic-based techniques may promise a global peak solution, but they are computationally inefficient and take significant time for tracking. To address this problem, a BFT is proposed which combines the solutions obtained from conventional incremental conductance algorithm and Jaya optimization algorithm to produce better responses under various PSCs. The effectiveness of the proposed BFT-based MPPT is evaluated by comparing it with various MPPT methods, viz. incremental conductance, particle swarm optimization (PSO), and Jaya optimization algorithms in MATLAB/Simulink environment. From the various case studies carried, the overall average tracking speed with more than 99% accuracy is less than 0.25 s and having minimum steady-state oscillations. Even under the wide range of partially shaded conditions, the proposed method exhibited superior MPPT compared to the existing methods with tracking speed less than 0.1 s to achieve 99.8% tracking efficiency. A detailed comparison table is provided by comparing with other popular existing MPPT methodologies.

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Section: Comparative Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Bayesian fusion technique for maximum power point tracking under partial shading condition

Gugulothu

Bhookya

2021

SN Appl. Sci.

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“…AI consists of fuzzy logic [25] and artificial neural networks [26], [27]. EC includes the genetic algorithm (GA) [28], particle swarm optimisation (PSO) [29], [30], cuckoo search (CS) [31], fractional chaotic FPA [32], flow regime algorithm, social mimic optimization, Rao algorithm, ant colony optimisation (ACO) [33], [34], butterfly optimization algorithm [35], [36], grasshopper optimization algorithm [37], bat algorithm [38], metaphorless algorithms [39] and many more.…”

Section: Introductionmentioning

confidence: 99%

An Effective Salp Swarm Based MPPT for Photovoltaic Systems Under Dynamic and Partial Shading Conditions

et al. 2021

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This study proposes a duty cycle-based direct search method that capitalizes on a bioinspired optimization algorithm known as the salp swarm algorithm (SSA). The goal is to improve the tracking capability of the maximum power point (MPP) controller for optimum power extraction from a photovoltaic system under dynamic environmental conditions. The performance of the proposed SSA is tested under a transition between uniform irradiances and a transition between partial shading (PS) conditions with a focus on convergence speed, fast and accurate tracking, reduce high initial exploration oscillation, and low steady-state oscillation at MPP. Simulation results demonstrate the superiority of the proposed SSA algorithm in terms of tracking performance. The performance of the SSA method is better than the conventional (hill-climbing) and among other popular metaheuristic methods. Further validation of the SSA performance is conducted via experimental studies involving a DC-DC buck-boost converter driven by TMS320F28335 DSP on the Texas Instruments Experimenter Kit platform. Hardware results show that the proposed SSA method aligns with the simulation in terms of fast-tracking, convergence speed, and satisfactory accuracy under PS and dynamic conditions. The proposed SSA method tracks maximum power with high efficiency through its superficial structures and concepts, as well as its easy implementation. Moreover, the SSA maintains a steady-state oscillation at a minimum level to improve the overall energy yield. It thus compensates for the shortcomings of other existing methods.

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“…Further optimized bioinspired algorithms [29]- [31] include improved PSO [32], chaotic flower pollination algorithm [2] and improved bat algorithm [33]. Modified butterfly optimization algorithm is one of the bio-inspired algorithms that has been hybridized with a constant impedance method to improve the response time to one second [34]. Some other methods that have been combined into hybrid algorithms are PSO along with Distributed Evaluation [5], ANN with conventional and modified P&O or INC [19], [20].…”

Section: Introductionmentioning

confidence: 99%

Optimized Global Maximum Power Point Tracking of Photovoltaic Systems Based on Rectangular Power Comparison

Bharadwaj

2021

IEEE Access

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Global maximum power point tracking (GMPPT) refers to the extraction of the maximum power from photovoltaic (PV) modules in real time under changing ambient conditions. Due to the installation of PV systems in densely built-up areas, partial shading scenarios are commonplace. Commercially established GMPPTs suffer from low tracking speeds and inefficiency. A novel GMPPT algorithm is proposed here based on the rectangular power comparison (RPC), which exploits the fundamental relationship between the shading factor, the bypass diode voltage and the global maximum power point. The entire theoretical formulation of RPC is presented systematically for the first time. This method boasts of increased conversion speeds owing to the precomputation of the module voltage versus the shading factor correlations using the regression of diode model from the experimentally obtained bypass diode characteristics. The proposed method is simple to implement with the computational complexity of order n, which represents the number of uniquely shaded PV modules in a series string. The proposed approach addresses the much-needed intersection problem between the distributed and centralized PV systems and therefore targets PV strings which are most common in residential and small to medium scale commercial PV installations world over. The proposed approach is validated with the in-house developed prototype hardware set-up and software control implementation giving a 99% tracking efficiency with a recorded tracking time of 10 ms. The experimental results show 50 times improvement in speed and 95% increase in power gain as compared to the other popular existing methods namely scanning based GMPPT and local MPPT methods respectively, with negligible computational burden and less than 0.5% added cost to the conventional PV energy conversion system. INDEX TERMS Efficiency optimization, Global maximum power point tracking, Partial shading, Photovoltaic module measurements and Solar energy conversion.

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Maximum Power Point Tracking Using Modified Butterfly Optimization Algorithm for Partial Shading, Uniform Shading, and Fast Varying Load Conditions

Cited by 137 publications

References 26 publications

A Bayesian fusion technique for maximum power point tracking under partial shading condition

A Bayesian fusion technique for maximum power point tracking under partial shading condition

An Effective Salp Swarm Based MPPT for Photovoltaic Systems Under Dynamic and Partial Shading Conditions

Optimized Global Maximum Power Point Tracking of Photovoltaic Systems Based on Rectangular Power Comparison

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