A New Analytical MPPT-Based Induction Motor Drive for Solar PV Water Pumping System With Battery Backup

Shukla, Saurabh; Singh, Bhim; Shaw, Priyabrata; Al‐Durra, Ahmed; EL-Fouly, Tarek H. M.; El-Saadany, Ehab F.

doi:10.1109/tie.2021.3091929

Cited by 34 publications

(8 citation statements)

References 27 publications

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“…However, performances of the P&O are dependent on the tracking step-size; the larger step-size is, the better the speed of tracking response will be, but a larger step-size selection results in oscillations at steady state. [4] It must be pointed out that it is difficult to set the step size by comprehensively considering the tracking accuracy and corresponding speed.…”

Section: Variable Step Size Perturbation Observation Algorithmmentioning

confidence: 99%

Maximum Power Point Tracking Control Based on Variable Step Size Perturbation Observation Method

Ma¹,

Zhang²,

Hou³

et al. 2023

Atlantis Highlights in Computer Sciences

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In the whole life cycle of the evaluation, construction, grid connection, operation and maintenance and sale of power plant projects, the calculation of power generation with artificial intelligence technology as the core is the top priority. Maximum power points tracking techniques are widely utilized in photovoltaic systems to operate at the peak power of PV array which depends on solar and ambient temperature. the perturbation observation method algorithm is the most applied maximum power point tracking control scheme in photovoltaic applications for its simplicity and ease of implementation. But conventional perturbation and observation method suffers from steady state oscillations. To avoid this, this paper emphasizes on a variable step perturbation and observation which can able to track the maximum power point rapidly with less steady state oscillation as compared with conventional perturbation and observation algorithm. The efficacy of the proposed method is verified considering both simulation and experimental results by modelling.

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Section: Variable Step Size Perturbation Observation Algorithmmentioning

confidence: 99%

Maximum Power Point Tracking Control Based on Variable Step Size Perturbation Observation Method

Ma¹,

Zhang²,

Hou³

et al. 2023

Atlantis Highlights in Computer Sciences

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“…In fact, in accordance with this, it is possible to establish a law, function of the time derivative of the power and voltage that defines the MPPT. This can be ensured by the control law for the reference current definition expressed by (4).…”

Section: Control Of the Proposed Systemmentioning

confidence: 99%

“…One of the applications in which this system is extremely important but where there are problems regarding the electrical power supply is in rural and remote areas. In order to overcome these problems these systems have been associated with solar photovoltaic (PV) supply [1][2][3][4]. The system consists of several components, being the electrical machine one of them.…”

Section: Introductionmentioning

confidence: 99%

A SRM for a PV Powered Water Pumping System Based on a Multilevel Converter and DC/DC Dual Output Converter

Foito

Pires

Cordeiro

et al. 2022

2022 7th International Conference on Smart and Sustainable Technologies (SpliTech)

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This paper focuses on a proposal for a system based on a photovoltaic (PV) supply for a powered water pumping. The system consists in a switched reluctance machine (SRM) controlled by a multilevel converter and fed by PV panels associated to a DC/DC converter. The multilevel power converter proposed to control the SRM was designed to minimize the switches and to support the balance of the two input capacitors. The DC/DC converter consists in a hybrid solution that merges a Buck-Boost converter with a Sepic converter. They use a topology solution in which the input current presents a reduced ripple and only requires one switch. This DC/DC converter is also characterized by a dual output to adapt to the multilevel converter. The control system and a maximum power point tracking (MPPT) algorithm are also presented. The operation of this system will be verified by tests that are done by computer simulations.

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“…Fossil fuels have been in the depletion stage in the preceding decades, producing greenhouse gases and global warming [1,2]. Therefore, nowadays, renewable energy is an appropriate alternative for fossil fuels to produce electric power.…”

Section: Introductionmentioning

confidence: 99%

An adaptive TS‐fuzzy model based RBF neural network learning for grid integrated photovoltaic applications

Priyadarshi¹,

Sanjeevikumar

Bhaskar

et al. 2022

IET Renewable Power Gen

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In this research work, an adaptive TS‐fuzzy based RBF neural network (ATSFRBFNN) algorithm based maximum power point tracker (MPPT) is employed for grid integrated photovoltaic (PV) system. The novel learning algorithm provides accurate and rapid PV power tracking under fluctuating solar insolation. However, an improved damping circulating current limiting inverter controller is employed to generate gating signal of voltage source inverter and to deliver reduced harmonic constant, less power loss, mitigation of power quality issues regulation of Dc‐link voltage utilization for grid integrated PV system under steady, and transient weather conditions. The experimental results justify the improved system performance under varying operating conditions. The behaviour of novel hybrid PV MPPT structure is compared with TS‐fuzzy, RBFNN, and GA‐RBF for higher tracking efficacy, least tracking deviation, accurate responses, and zero steady‐state power oscillation around MPP region.

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A New Analytical MPPT-Based Induction Motor Drive for Solar PV Water Pumping System With Battery Backup

Cited by 34 publications

References 27 publications

Maximum Power Point Tracking Control Based on Variable Step Size Perturbation Observation Method

Maximum Power Point Tracking Control Based on Variable Step Size Perturbation Observation Method

A SRM for a PV Powered Water Pumping System Based on a Multilevel Converter and DC/DC Dual Output Converter

An adaptive TS‐fuzzy model based RBF neural network learning for grid integrated photovoltaic applications

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