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Solar photovoltaic converter controller using opposition-based reinforcement learning with butterfly optimization algorithm under partial shading conditions

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Abstract

The major use of a power point tracking controller is to maximize or enhance the power generation in photovoltaic systems. These systems are steered to operate and maximize the power point. Under partial shading conditions, the power points may vary or fluctuate between global maxima and local maxima. This fluctuation leads to a decrease in energy or energy loss. Hence, to address the fluctuation issue and its variations, a new hybridized maximum power point tracking technique based on an opposition-based reinforcement learning approach with a butterfly optimization algorithm has been proposed. The proposed methodology has been tested on 6S, 3S2P and 2S3P photo-voltaic configurations under different shading conditions. Performance comparison and analysis have been presented with a butterfly optimization algorithm, grey wolf optimization algorithm, whale optimization algorithm, and particle swarm optimization-based maximum power point tracking techniques. Experimental results show that the proposed method performs better adaptation than the conventional approaches and mitigates the load variation convergence and frequent exploration and exploitation patterns.

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Abbreviations

OBRL:

opposition-based reinforcement learning

BOA:

butterfly optimization algorithm

GWO:

grey-wolf optimization algorithm

WOA:

whale optimization algorithm

PSO:

particle swarm optimization

6S:

six series

3S2P:

three series two parallel

2S3P:

two series three parallel

MPPT:

maximum power point tracking

MPP:

maximum power point

PV:

photo-voltaic

DC:

direct current

f(t):

fragrance fitness function

I(t):

stimulus intensity

C:

sensory modality

⏀i(t):

duty cycle of the ith butterfly

∅Gbest(t):

global best duty cycle

Dutympp :

maximum power point at a particular duty cycle

∅besti(t):

best duty cycle at ith level or operation

∅s(t):

duty cycle at a sample

Pmpp :

power at the maximum power point

Pbesti(t):

best fit power generated at ith operation

Pi(t):

power at ith operation

s :

random number

n :

number of operations or executions

tmax:

maximum number of operations

Vmpp :

maximum power point voltage

Vpv :

voltage generated at photo-voltaic systems

∅best(t):

best location of a butterfly

α:

power exponent

λD:

duty cycle difference between two butterflies

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Acknowledgements

The authors are thankful to the Deanship of Scientific Research at Najran University for funding this work, under the Research Groups Funding program grant code (NU/RG/SERC/12/7).

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Authors and Affiliations

  1. Department of Electrical Engineering, College of Engineering, Najran University, Najran, 11001, Saudi Arabia

    Belqasem Aljafari

  2. Department of Electrical and Electronics Engineering, Vardhaman College of Engineering, Hyderabad, 501218, India

    Praveen Kumar Balachandran

  3. Center for Electric Mobility, Department of Electrical and Electronics Engineering , SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, India

    Devakirubakaran Samithas

  4. Department of Electrical and Electronics Engineering, Chaitanya Bharathi Institute of Technology (CBIT), Hyderabad, 500075, India

    Sudhakar Babu Thanikanti

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Conceptualization: Praveen Kumar Balachandran ; methodology: Devakirubakaran Samithas and Sudhakar Babu Thanikanti; formal analysis and investigation: Belqasem Aljafari ; writing—original draft preparation: Praveen Kumar Balachandran ; writing—review and editing: Sudhakar Babu Thanikanti and Devakirubakaran Samithas ; resources: Belqasem Aljafari ; supervision: Sudhakar Babu Thanikanti. All authors read and approved the final manuscript.

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Aljafari, B., Balachandran, P.K., Samithas, D. et al. Solar photovoltaic converter controller using opposition-based reinforcement learning with butterfly optimization algorithm under partial shading conditions. Environ Sci Pollut Res 30, 72617–72640 (2023). https://doi.org/10.1007/s11356-023-27261-1

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