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Shunt compensation using Deep Belief Learning Network Based Inductively Coupled DSTATCOM

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Abstract

The directly coupled distributed static compensator (DC-DSTATCOM) is often utilized to achieve better power quality (PQ) in the power distribution network (PDN). However, this compensator faced challenges like poor adaptability performance and more maintenance costs due to the integration of several types of energy resources. To overcome the above-said limitations, the inductively coupled distributed static compensator (IC-DSTATCOM) using Deep Belief Learning Network (DBLN) technique is proposed. The power transfer capability of the IC-DSTATCOM is examined by considering the impedance matching principle of the distributed static compensator (DSTATCOM), source and load. Besides this, the dependent parameters are combined with the convergence factor and learning rate to achieve the approximate tuned weight by using the suggested learning mechanism. The generalized mathematical equations are illustrated using MATLAB/Simulink to generate the switching pulses. The simulation studies of both DC-DSTATCOM & IC-DSTATCOM are performed to evaluate the transient behaviour and robustness under different states of loading. The proposed system is augmented with a superior performance in terms of harmonics curtailment, improvement in power factor (p.f), load balancing, potential regulation etc. The international standard regulatory guidelines IEEE-519–2017 and IEC- 61,000–1 are imposed to evaluate the effectiveness of the simulation and d-SPACE-based experimental study.

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Data availability

The authors declare that the data supporting the findings of this study are available within the paper and its Supplementary Information files.

Abbreviations

\({N}_{1}, {N}_{1}, {N}_{3}\) :

Number of the turns of the primary, secondary and tertiary windings

\({i}_{sap}, {i}_{sbp}, {i}_{scp}\) :

Current in the primary winding of IFCT of a, b & c-phase

\({i}_{las}, {i}_{lbs}, {i}_{lcs}\) :

Current in the secondary winding of IFCT of a, b & c-phase

\({i}_{caf}, {i}_{cbf}, {i}_{ccf}\) :

Current in the tertiary winding of IFCT of a, b & c-phase

\({Z}_{sa}, {Z}_{sb}, {Z}_{sc}\) :

Total impedance of source and IFCT primary winding of a, b & c-phase

\({W}_{qa}^{n},{W}_{qb}^{n},{W}_{qc}^{n}\) :

Reactive component of load current

\({W}_{qa}^{i},{W}_{qb}^{i},{W}_{qc}^{i}\) :

Reactive ith component of load current

\({W}_{qa}^{n-1},{W}_{qb}^{n-1},{W}_{qc}^{n-1}\) :

Previous reactive component of load current

\({h}_{k}^{i-1}\) :

Previous ith component of somatic weight

\({b}_{j}^{i}\) :

E ith component of synaptic weight

\({W}_{pa}^{n},{W}_{pb}^{n},{W}_{pc}^{n}\) :

Active component of load current

\({W}_{pa}^{i},{W}_{pb}^{i},{W}_{pc}^{i}\) :

Active ith component of load current

\({W}_{pa}^{n-1},{W}_{pb}^{n-1},{W}_{pc}^{n-1}\) :

Previous active component of load current

\(\sigma\) :

Step size

\({u}_{pa}^{n},{u}_{pb}^{n},{u}_{pc}^{n}\) :

In-phase unit voltage templates of the nth component

\({u}_{qa}^{n},{u}_{qb}^{n},{u}_{qc}^{n}\) :

Quadrature unit voltage templates of the nth component

\({v}_{sa}, {v}_{sb}, {v}_{sc}\) :

Phase voltage

\({v}_{t}\) :

PCC voltage

\({w}_{cp}\) :

Output weight of DC side PI controller

\({w}_{sp}\) :

Total reactive weight of the reference source current

\({w}_{a}\) :

Active average value

\({k}_{pa}\) :

DC side Proportional controller

\({k}_{ia}\) :

DC side Integral controller

\({w}_{cq}\) :

Output weight of AC side PI controller

\({k}_{pr}\) :

AC side Proportional controller

\({k}_{ir}\) :

AC side Integral controller

\({v}_{te}\) :

Error voltage

\({w}_{sq}\) :

Total reactive weight of the reference source current

\({w}_{r}\) :

Reactive average value

\({i}_{aa},{i}_{ab}, {i}_{ac}\) :

Active part of the reference source currents

\({i}_{ra}, {i}_{rb}, {i}_{rc}\) :

Reactive part of the reference source currents

\({i}_{sa}^{*}, {i}_{sb}^{*}, {i}_{sc}^{*}\) :

Total reference source currents

CBEMA:

Computer Business Equipment and Manufacturing Association

CB:

Circuit Breaker

DBLN:

Deep Belief Learning Network

DSTATCOM:

Distributed Static Compensator

DC-DSTATCOM:

Directly Coupled Distributed Static Compensator

FW:

Filtering Winding

HCC:

Hysteresis Current Controller

IFCT:

Inductively Filtering Converting Transformer

IGBT:

Insulated Gate Bipolar Transistor

NN:

Neural Network

PCC:

Point of Common Coupling

PDN:

Power Distribution Network

P.F:

Power Factor

PQ:

Power Quality

PW:

Primary Winding

SW:

Secondary Winding

THD:

Total Harmonic Distortion

VSI:

Voltage Source Inverter

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Authors and Affiliations

  1. Department of Electrical & Electronics Engineering, Tolani Maritime Institute, Pune, Maharashtra, 410507, India

    Jogeswara Sabat

  2. Department of Electrical & Electronics Engineering, SRM University Amaravati, Amaravati, Andhra Pradesh, 522240, India

    Mrutyunjaya Mangaraj

  3. Department of Electrical and Electronics Engineering, LIET, Vizianagaram, Andhra Pradesh, 535005, India

    Praveen Kumar Yadav Kundala, K. Subbaramaiah, B. V. S. Acharyulu & T. Papinaidu

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  1. Jogeswara Sabat
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  2. Mrutyunjaya Mangaraj
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Correspondence to Jogeswara Sabat.

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Sabat, J., Mangaraj, M., Kundala, P.K.Y. et al. Shunt compensation using Deep Belief Learning Network Based Inductively Coupled DSTATCOM. Energy Syst (2023). https://doi.org/10.1007/s12667-023-00647-3

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