Artificial Bee Colony Based Solar PV System with Z-Source Multilevel Inverter

Minai, Ahmad Faiz; Usmani, Tahsin; Iqbal, Atif; Mallick, M.A.

doi:10.1109/icaccm50413.2020.9213060

Cited by 21 publications

(8 citation statements)

References 10 publications

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“…Each learner is assigned a value grade for each subject that corresponds to a possible alternative, and the quality of the proposed answer is reflected in the overall mean grade. The greatest response for the entire population is represented by the instructor [57].…”

Section: New Generation Optimization Techniquesmentioning

confidence: 99%

Optimal Sizing, Control, and Management Strategies for Hybrid Renewable Energy Systems: A Comprehensive Review

et al. 2022

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To meet the expanding energy demand, all available energy sources must be utilized. Renewable energies are both eternal and natural, but their major downside is their inconsistency. Due to the rising costs of fossil fuels and the CO2 they emit, hybrid renewable energy (HRE) sources have gained popularity as an alternative in remote and rural areas. To address this issue, a hybrid renewable energy system (HRES) can be developed by combining several energy sources. In order to build modern electrical grids that have advantages for the economy, environment, and society, the hybrid system is preferable. A summary of various optimization methods (modeling techniques) of an HRES is presented in this paper. This study offers an in-depth analysis of the best sizing, control methodologies, and energy management strategies, along with the incorporation of various renewable energy sources to form a hybrid system. Modern hybrid renewable energy system utilities rely more on an optimal design to reduce the cost function. Reviews of several mathematical models put out by various academicians are presented in this work. These models were created based on reliability analyses incorporating design factors, objective functions, and economics. The reader will get familiar with numerous system modelling optimization strategies after reading this study, and they will be able to compare different models based on their cost functions. Numerous modeling approaches and software simulation tools have been created to aid stakeholders in the planning, research, and development of HRES. The optimal use of renewable energy potential and the meticulous creation of applicable designs are closely tied to the full analysis of these undoubtedly complicated systems. In this field, as well, several optimization restrictions and objectives have been applied. Overall, the optimization, sizing, and control of HRES are covered in this paper with the energy management strategies.

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Section: New Generation Optimization Techniquesmentioning

confidence: 99%

Optimal Sizing, Control, and Management Strategies for Hybrid Renewable Energy Systems: A Comprehensive Review

et al. 2022

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“…In some applications, the hysteresis band is limited to some small values and should not exceed a limit, and it is not possible to limit the switching frequency and the THD of the load current after applying hysteresis band control, as shown in Figure 9a. Several solutions were also proposed in literature, such as the use of constant switching frequency described in [29,30]. In Figure 9b, the THD of the output voltage decreases sharply when the hysteresis Total harmonic distortion percentage of voltage and current with varying load have been shown in Figure 10a at hysteresis bandwidth of 0.03.…”

Section: Sno Parameter Simulationmentioning

confidence: 99%

“…Many AI techniques are used in tunings, such as neural networks, fuzzy neural logic [26] and fuzzy system, to find the optimal value of PI constants. Nature inspired techniques such as genetic algorithm (GA) [27,28], particles swarm optimization (PSO) [29], bee algorithm (BA) [30] and bat algorithm (BAT) [31] have gained interest in achieving high efficiency and finding an optimal solution to the problem [32].…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm Based PI Control with 12-Band Hysteresis Current Control of an Asymmetrical 13-Level Inverter

et al. 2021

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In this paper, a twelve-band hysteresis control is applied to a recent thirteen-level asymmetrical inverter topology by employing a robust proportional-integral (PI) controller whose parameters are decided online by genetic algorithm (GA). The asymmetrical inverter topology can generate thirteen levels of output voltage incorporating only ten switches and exhibits boosting capability. A 12-band hysteresis current control strategy is applied to ensure the satisfactory operation of the inverter. It is designed to provide a sinusoidal line current at the unity power factor. The tuning of the PI controller is achieved by a nature inspired GA. Comparative analysis of the results obtained after application of the GA and the conventional Ziegler–Nichols method is also performed. The efficacy of the proposed control on WE topology is substantiated in the MATLAB Simulink environment and was further validated through experimental/real-time implementation using DSC TMS320F28379D and Typhoon HIL real-time emulator (Typhoon-HIL-402).

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“…These techniques also improve the response of steady-state and the dynamic response of the system [22]. Different optimization techniques like genetic algorithm [23], Particle swarm optimization (PSO) [24], Bee algorithm [25], BAT algorithm [26] and Differential evolution [27] are being employed in obtaining near-to-exact solutions. These techniques give a fast dynamic response within a few iterations and control the inverter more effectively.…”

Section: Introductionmentioning

confidence: 99%

Thirteen-Level UXE-Type Inverter With 12-Band Hysteresis Current Control Employing PSO Based PI Controller

et al. 2022

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This work presents hysteresis control applied to UXE-type inverter topology with a PI (Proportional Integral) controller, where thegains are derived by Particle Swarm Optimization (PSO). The investigated UXE inverter can generate a thirteen level output voltage waveform, which results in lower switching losses. It can boost the output voltage to 1.5 times the applied input DC source voltage. Satisfactory inverter operation is ensured by employing twelve-band hysteresis current control. The tuning of the PI controller required for the closed-loop hysteresis current control is achieved by nature-inspired PSO algorithm. A comparative analysis is also performed after obtaining the results applying natureinspired PSO and conventional Ziegler Nichols (ZN) methods. The effectiveness of the control strategy is verified in the MATLAB Simulink and further validated in experimentusing TMS320F28379D and also with Typhoon Hardware in Loop Technology.

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Artificial Bee Colony Based Solar PV System with Z-Source Multilevel Inverter

Cited by 21 publications

References 10 publications

Optimal Sizing, Control, and Management Strategies for Hybrid Renewable Energy Systems: A Comprehensive Review

Optimal Sizing, Control, and Management Strategies for Hybrid Renewable Energy Systems: A Comprehensive Review

Genetic Algorithm Based PI Control with 12-Band Hysteresis Current Control of an Asymmetrical 13-Level Inverter

Thirteen-Level UXE-Type Inverter With 12-Band Hysteresis Current Control Employing PSO Based PI Controller

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