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Deep reinforced learning-based inductively coupled DSTATCOM under load uncertainties

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Abstract

Concerning the power quality issues in the power distribution network due to load uncertainties and improper impedance matching of the inductances, deep reinforced learning (DRL)-based inductively coupled DSTATCOM (IC-DSTATCOM) is proposed. First, by analyzing the impedance matching principle, the expression of source, load and filter current is derived with the help of inductive filtering transformer. And second, an individual DRL subnet structure is accumulated for each phase using mathematical equations to perform the better dynamic response. A 10-kVA, 230-V, 50-Hz prototype direct coupled distributed static compensator (DC-DSTATCOM) and IC-DSTATCOM experimental setup is buit to verify the experimental performance under uncertainties of loading. The IC-DSTATCOM is augmented better dynamic performance in terms of harmonics curtailment, improvement in power factor, load balancing, potential regulation, etc. The benchmark IEEE-519-2017, IEC-61727 and IEC-61000-1 grid code are used to evaluate the effectiveness of the simulation and experimental study.

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Data availability

The raw processed data required to reproduce the above findings cannot be shared at this time due to legal/ethical reasons.

Abbreviations

w p a, w p b and w p c :

Weighting values of active component of load current

w q a, w q b and w q c :

Weighting values of reactive component of load current

u p a, u p b and u p c :

In phase unit voltage template

u q a, u q b and u q c :

Quadrature unit voltage template

w cp and w cq :

Output of DC and AC side PI controller

w sp and w sq :

Total active and reactive components

w a :

Mean active weight

w r :

Mean reactive weight

i r a, i r b and i r c :

Total reactive weight

v s a, v s b and v s c :

Supply voltages

i l a, i l b and i l c :

Load currents

i s a, i s b and i s c :

Supply currents

i a a, i a b and i a c :

Total active weight

\(i_{{{\text{s}}a}}^{*} ,i_{{{\text{s}}b}}^{*} ,i_{{{\text{s}}c}}^{*}\) :

Reference supply currents

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Funding

This study was not funded by any external agency.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, SRM University, Amaravathi, Andhra Pradesh, 522502, India

    Mrutyunjaya Mangaraj, Tousif Khan Nizami, Satyavir Singh & Arghya Chakravarty

  2. Department of Electrical Engineering, Qatar University, 2713, Doha, Qatar

    S. M. Muyeen

  3. Department of Electronics & Communication Engineering, Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram, Chennai, 600127, India

    B. Chitti Babu

Authors
  1. Mrutyunjaya Mangaraj
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  2. S. M. Muyeen
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  3. B. Chitti Babu
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  4. Tousif Khan Nizami
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  5. Satyavir Singh
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  6. Arghya Chakravarty
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Contributions

Dr. Mrutyunjaya Mangaraj has conducted the experimental analysis Prof. S M Muyeen has reviewed the paper and supervised the research as required Prof. B Chitti Babu has analyzed the MATLAB simulation study for the different scenarios Dr. N Toushif Khan has written the overall manuscript based upon simulation and experimental study Dr. S Singh has contributed for overall correction and formatting as per journal guidelines Dr. A Chakravarty cross-checked the grammatical error and plagiarism.

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Correspondence to Mrutyunjaya Mangaraj.

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Mangaraj, M., Muyeen, S.M., Babu, B.C. et al. Deep reinforced learning-based inductively coupled DSTATCOM under load uncertainties. Electr Eng (2024). https://doi.org/10.1007/s00202-024-02446-0

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