Novel Grass Hopper optimization based MPPT of PV systems for complex partial shading conditions

Mansoor, Majad; Mirza, Adeel Feroz; Ling, Qiang; Javed, Muhammad Yaqoob

doi:10.1016/j.solener.2020.01.070

Cited by 139 publications

(58 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The effectiveness of NBGOA was evaluated using 20 datasets with various sizes taken from the UCI datasets repository in comparison with five well-regarded optimization techniques in the feature selection field. Simulation results revealed that BGOA [78] NBGOA [79] BGOA [67] ECGOAs [80] LMGOA [81] ECGOA [82] CGOA [83] CGOA [84] SFECGOAs [85] OLCGOA [86] ECGOAs [87] ECAGOA [88] IGOA [70] EGOA [89] PGOA [90] LGOA [91] IGOA [92] AGOA [93] MI-LFGOA [94] LGOA [95] GOA_EPD [65] DJGOA [96] DQBGOA_MR [97] Fuzzy GOA [98] GO-FLC [99] EGOA-FC [100] AGOA [69] AGOA [101] GHO [102] self-adaptive GOA [103] OGOA [104] OBLGOA [105] IGOA [106] MOGOA [75] MOGOA [76] MOGOA [66] MOGOA [107] MOGOA [108] MOGOA [109] LWSGOA [110] MGOA [111] GOFS [112] PCA-GOA [113] OGOA [114] IGOA [115] Fractional-GOA…”

Section: ) Binary Grasshopper Optimization Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Grasshopper Optimization Algorithm: Theory, Variants, and Applications

et al. 2021

View full text Add to dashboard Cite

Grasshopper Optimization Algorithm (GOA) is a recent swarm intelligence algorithm inspired by the foraging and swarming behavior of grasshoppers in nature. The GOA algorithm has been successfully applied to solve various optimization problems in several domains and demonstrated its merits in the literature. This paper proposes a comprehensive review of GOA based on more than 120 scientific articles published by leading publishers: IEEE, Springer, Elsevier, IET, Hindawi, and others. It provides the GOA variants, including multi-objective and hybrid variants. It also discusses the main applications of GOA in various fields such as scheduling, economic dispatch, feature selection, load frequency control, distributed generation, wind energy system, and other engineering problems. Finally, the paper provides some possible future research directions in this area.

show abstract

Section: ) Binary Grasshopper Optimization Algorithmmentioning

confidence: 99%

“…Mansoor et al [102] proposed an adaptive GOA (GHO) for solving the maximum power point tracking (MPPT) problem fast varying irradiance and partial shading conditions. An adaptive search and skip method were introduced.…”

Section: ) Adaptive Grasshopper Optimization Algorithmmentioning

confidence: 99%

Grasshopper Optimization Algorithm: Theory, Variants, and Applications

et al. 2021

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…On the other hand, many intelligent optimization algorithms have been applied to MPPT of PV cells. In (Mansoor et al, 2020), a grass hopper optimization (GHO) algorithm was proposed to solve the problem of MPPT of PV cells in complex environments. The article compared several traditional methods under different conditions and points out the shortcomings of the prior techniques in dealing with complex partial shading conditions.…”

Section: Introductionmentioning

confidence: 99%

A Novel Maximum Power Point Tracking Strategy Based on Enhanced Real-Time Adaptive Step-Size Modified Control for Photovoltaic Systems

Zhou

Zhang

et al. 2021

Front. Energy Res.

View full text Add to dashboard Cite

With the development of society, the demand for energy keeps increasing. Solar energy has received widespread concern for its renewable and environmentally friendly advantages. As one of the most efficient solar energy devices, the output power of photovoltaic (PV) cells is easily affected by the external environment. In order to solve the problem of the maximum power output of PV cells, this paper proposed a maximum power point tracking (MPPT) method. Based on the online particle swarm optimization (PSO) variable step length algorithm, the pulse width modulation (PWM) control module parameters are set according to the parameters of the PV cells’ output voltage. By dynamically adjusting the output voltage step of the PV cells online, the output of the PV cells is stabilized near the maximum power point (MPP). The simulation results concluded that the method and model could accurately adjust the output voltage according to the external environment changes in real time and reduce the voltage fluctuation at the MPP, providing a new idea to solve the problem of MPPT of PV cells.

show abstract

Novel Grass Hopper optimization based MPPT of PV systems for complex partial shading conditions

Cited by 139 publications

References 51 publications

Grasshopper Optimization Algorithm: Theory, Variants, and Applications

Grasshopper Optimization Algorithm: Theory, Variants, and Applications

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

A Novel Maximum Power Point Tracking Strategy Based on Enhanced Real-Time Adaptive Step-Size Modified Control for Photovoltaic Systems

Contact Info

Product

Resources

About