2018
DOI: 10.1016/j.energy.2017.12.059
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A modified cat swarm optimization based maximum power point tracking method for photovoltaic system under partially shaded condition

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Cited by 59 publications
(27 citation statements)
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“…CSO successfully optimized the switching parameters of CSI and hence minimized the total harmonic distortion [52] Applied both CSO, PCSO, PSO-CFA, and ACO-ABC on distributed generation units on distribution networks IEEE 33-bus and IEEE 69-bus distribution systems were used in the simulation experiments and CSO outperformed the other algorithms [53] Applied MCSO on MPPT to achieve global maximum power point (GMPP) tracking MCSO outperformed PSO, MPSO, DE, GA, and HC algorithms [54] Applied BCSO to optimize the location of phasor measurement units and reduce the required number of PMUs IEEE 14-bus and IEEE 30-bus test systems were used in the simulation. BCSO outperformed BPSO, generalized integer linear programming, and effective data structure-based algorithm [55] Used CSO algorithm to identify the parameters of single and double diode models in solar cell system CSO outperformed PSO, GA, SA, PS, Newton, HS, GGHS, IGHS, ABSO, DE, and LMSA [56] Applied CSO and SVM to classify students' facial expression e results show 100% classification accuracy for the selected 9 face expressions [39] Applied CSO and SVM to classify students' facial expression e system achieved satisfactory results [40] Applied CSO-GA-PSOSVM to classify students' facial expression e system achieved 99% classification accuracy [23] Applied CSO, HCSO and ICSO in block matching for efficient motion estimation e system reduced computational complexity and provided faster convergence [16,17,57] Used CSO algorithm to retrieve watermarks similar to the original copy CSO outperformed PSO and PSO time-varying inertia weight factor algorithms [58,59] Sabah used EHCSO in an object-tracking system to obtain further efficiency and accuracy e system yielded desirable results in terms of efficiency and accuracy [60] Used BCSO as a band selection method for hyperspectral images BCSO outperformed PSO [61] Used CSO and multilevel thresholding for image segmentation CSO outperformed PSO [62] Used CSO and multilevel thresholding for image segmentation PSO outperformed CSO [63] Used CSO, ANN and wavelet entropy to build an AUD identification system.…”
Section: Purpose Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…CSO successfully optimized the switching parameters of CSI and hence minimized the total harmonic distortion [52] Applied both CSO, PCSO, PSO-CFA, and ACO-ABC on distributed generation units on distribution networks IEEE 33-bus and IEEE 69-bus distribution systems were used in the simulation experiments and CSO outperformed the other algorithms [53] Applied MCSO on MPPT to achieve global maximum power point (GMPP) tracking MCSO outperformed PSO, MPSO, DE, GA, and HC algorithms [54] Applied BCSO to optimize the location of phasor measurement units and reduce the required number of PMUs IEEE 14-bus and IEEE 30-bus test systems were used in the simulation. BCSO outperformed BPSO, generalized integer linear programming, and effective data structure-based algorithm [55] Used CSO algorithm to identify the parameters of single and double diode models in solar cell system CSO outperformed PSO, GA, SA, PS, Newton, HS, GGHS, IGHS, ABSO, DE, and LMSA [56] Applied CSO and SVM to classify students' facial expression e results show 100% classification accuracy for the selected 9 face expressions [39] Applied CSO and SVM to classify students' facial expression e system achieved satisfactory results [40] Applied CSO-GA-PSOSVM to classify students' facial expression e system achieved 99% classification accuracy [23] Applied CSO, HCSO and ICSO in block matching for efficient motion estimation e system reduced computational complexity and provided faster convergence [16,17,57] Used CSO algorithm to retrieve watermarks similar to the original copy CSO outperformed PSO and PSO time-varying inertia weight factor algorithms [58,59] Sabah used EHCSO in an object-tracking system to obtain further efficiency and accuracy e system yielded desirable results in terms of efficiency and accuracy [60] Used BCSO as a band selection method for hyperspectral images BCSO outperformed PSO [61] Used CSO and multilevel thresholding for image segmentation CSO outperformed PSO [62] Used CSO and multilevel thresholding for image segmentation PSO outperformed CSO [63] Used CSO, ANN and wavelet entropy to build an AUD identification system.…”
Section: Purpose Resultsmentioning
confidence: 99%
“…El-Ela et al [53] used CSO and PCSO to find the optimal place and size of distributed generation units on distribution networks. Guo et al [54] used MCSO algorithm to propose a novel maximum power point tracking (MPPT) approach to obtain global maximum power point (GMPP) tracking. Srivastava et al used BCSO algorithm to optimize the location of phasor measurement units and reduce the required number of PMUs [55].…”
mentioning
confidence: 99%
“…The non-linear characteristics of solar PV, and partial shading condition causes multiple peaks on the P-V characteristics. However, to reduce these multi-peaks into a global peak is a challenging task [7].…”
Section: Introductionmentioning
confidence: 99%
“…A PV module's output power is, however, plagued by irradiance levels, temperature, loads, and so on, which undoubtedly impair conversion efficiency. A number of studies in the literature have addressed such problems and improved the overall conversion efficiency of a PV system [5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27]. Ahmed et al proposed a design strategy of perturbation parameters based on the HC algorithm for PV MPPT control [5].…”
Section: Introductionmentioning
confidence: 99%
“…Zhang et al presented a novel memetic reinforcement learning (MRL) based MPPT scheme for PV systems [20]. Guo et al introduced a new MPPT method based on a modified cat swarm optimization (MCSO) to achieve MPP [21]. Mirza et al utilized a novel MPPT technique, including adaptive cuckoo search optimization algorithm (ACSOA), general regression neural network (GRNN) with fruit fly optimization algorithm (FFOA), and dragonfly optimization algorithm (DFOA), to track the MPP [22].…”
Section: Introductionmentioning
confidence: 99%