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Optimization of ANFIS controller for solar/battery sources fed UPQC using an hybrid algorithm

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Abstract

This study introduces an integrated power quality (PQ) conditioner, referred to as UPQC, that is linked with photovoltaic (PV) and battery energy systems (BSS) in order to address and solve PQ issues. It is proposed to employ the Levenberg–Marquardt (LM) backpropagation (LMBP) trained artificial neural network control (ANNC) technique for generating reference signal for converters in UPQC. This approach eliminates the need for traditional abc to dq0 to αβ conversions. Additionally, the hybrid algorithm (FFHSA) in combination of harmony search algorithm (HSA), and firefly algorithm (FFA) is also implemented for the optimal selection of adaptive neuro-fuzzy interface system (ANFIS) parameters to maintain direct current link capacitor voltage (DLCV) constant. The prime goal of the developed hybrid ANNC-FFHSA is to stabilize the DLCV with low settling time during load and solar irradiation (G), Temperature (T) changes, minimization of distortions in the source current signal to diminish total harmonic distortion (THD) in turn boosting the power factor (PF), suppression of fluctuations like disturbances, swell, sag and unbalances in the supply voltage. The suggested method is validated by four test cases with several combinations of variable irradiation (G), temperature and loads. On the other hand, to reveal the superiority of the developed method, the comparison is carried out with the genetic algorithm (GA) and Ant colony algorithm (ACA) along with instantaneous power (p–q) and Synchronous reference frame (SRF) conventional methods. The proposed approach significantly diminishes the total harmonic distortion to values of 3.61%, 3.48%, 3.48%, and 4.51%, which are notably lower compared to the values reported in the existing literature and also improves the power factor to almost unity. The design and implementation of this method were carried out using MATLAB/Simulink software.

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Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Sreenidhi Institute of Science & Technology, Hyderabad, Telangana, 501301, India

    Koganti Srilakshmi

  2. Department of Electrical and Electronic Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Kanuru, Vijayawada, Andhra Pradesh, India

    Gummadi Srinivasa Rao

  3. Department of Electrical and Electronics Engineering, R.V.R. & J.C. College of Engineering, Guntur, Andhra Pradesh, India

    Katragadda Swarnasri

  4. Department of Electrical and Electronics Engineering, R. K. College of Engineering, Vijayawada, Andhra Pradesh, India

    Sai Ram Inkollu

  5. Department of Electrical and Electronics Engineering, Chaitanya Bharathi Institute of Technology, Hyderabad, Telangana, India

    Krishnaveni Kondreddi

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

    Praveen Kumar Balachandran

  7. Department of Electrical and Electronics Engineering, Faculty of Engineering and Architectures, Nisantasi University, 34398, Istanbul, Turkey

    Ilhami Colak

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  1. Koganti Srilakshmi
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Contributions

Data curation was done by KOS, GSR, KAS, SRI, KK and PKB; formal analysis was done by KOS and IC; funding acquisition was done by IC and PKB; methodology was done by KOS and GSR; project administration was done by PKB; resources were done by SRI and KK; supervision was done by PKB and KAS; and writing–original draft were done by KOS, SRI PKB. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Praveen Kumar Balachandran.

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Srilakshmi, K., Rao, G.S., Swarnasri, K. et al. Optimization of ANFIS controller for solar/battery sources fed UPQC using an hybrid algorithm. Electr Eng (2024). https://doi.org/10.1007/s00202-023-02185-8

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