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An effective hybrid approach based control for the MPPT of PV system under partial shading condition for indoor energy harvesting in smart homes

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Abstract

Renewable radiation is a natural, natural resource that is simultaneously permanent and unrenewable. The two key elements that influence the performance of a PV system are irradiation and temperature. Partial shade, on the other hand, causes a nonlinear maximum power point tracking (MPPT) issue in PV systems. The PV array’s P-V characteristics have more than one MPP when exposed to non-uniform solar irradiation. This situation makes tracking MPP more difficult and reduces the PV system’s efficiency. This article proposes a hybrid optimization of Aquila optimization (AO) and Bird Swarm Algorithm (BSA) assisted MPPT controller to monitor peak energy in various weather conditions for the MPPT of PV System under partial shading condition for Indoor energy harvesting in smart homes. Finally, the proposed Bird Swarm Fostered Aquila Optimization (BSFAO) assisted MPPT control is compared to that of the standard methods like perturb and observe (P&O), Harris Hawks Optimization (HHO), Bird Swarm Algorithm (BSA), Aquila Optimization (AO). The proposed BSFAO method displays higher MPPT performance and a rapid convergence at the global maxima. Thus, the suggested hybrid (BSFAO) formed MPPT approach provides faster MPPT. The results proved that the suggested BSFAO model has achieved enhanced power tracking efficiency of 99.61% for every shading design when compared to the existing methodology and attained a reduced computational burden (3717.63) approximately, for all shading conditions respectively.

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No new data were generated or analysed in support of this research.

Abbreviations

BSFAO:

Bird Swarm Fostered Aquila Optimization

SPV:

Solar Photo Voltaic

INC:

Incremental Conductance

P&O:

Perturb-and-Observe

MPPT:

Maximum Power Point tracking

MPP:

Maximum Power Point

GWO:

Grey Wolf Optimization

IBA:

Improved Bat Algorithm

APPSO:

Adaptive Perceptive Particle Swarm Optimization

AO:

Aquila Optimization

BSA:

Bird Swarm Optimization

GMMP:

Global Maximum Power Point

PSO:

Particle Swarm Optimization

CSA:

Crow Search Algorithm

ACOA:

Adaptive Cuckoo Search Optimization Algorithm

FFOA:

Fruit Fly Optimization Algorithm

DFO:

Dragon Fly Optimization

PSC:

Partial Shading Condition

NN:

Neural Network

MP&O:

Modified Perturb & Observe method

DRL:

Deep Reinforcement Learning

MPSO:

Modified Particle Swarm Optimization

PWM:

Pulse Width Modulation

GM:

Global Maximum

OF:

Objectice Function

RES:

Renewable Energy Resources

LMPP:

Local Maximum Power point

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  1. Department of Electrical and Electronics Engineering, VIT University, Bhopal, Madhya Pradesh, India

    Nilesh Mendhe & Abhay Vidyarthi

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  1. Nilesh Mendhe
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Mendhe, N., Vidyarthi, A. An effective hybrid approach based control for the MPPT of PV system under partial shading condition for indoor energy harvesting in smart homes. Multimed Tools Appl 82, 46717–46740 (2023). https://doi.org/10.1007/s11042-023-15069-7

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