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Power Quality Enhancement in Grid-Connected PV/Wind/Battery Using UPQC: Atom Search Optimization

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Abstract

Nowadays, the integration of hybrid renewable energy system (HRES) in grid connected load system are encouraged to increase reliability and reduce losses. The HRES system is connected to the grid system to meet required load demand and the integrated design creates the power quality (PQ) issues in the system due to non-linear load, critical load and unbalanced load conditions. Hence, in this paper, atom search optimization (ASO) with unified power quality conditioner (UPQC) is designed to solve the PQ issues in HRES system. The main objective of the work is the mitigation of PQ issues and compensate load demand in HRES system. The PQ issue problems are solved with the help of UPQC device in the system. The UPQC performance is increased by introducing fractional order proportional integral derivative (FOPID) with ASO based controller in series and shunt active power filter to mitigate PQ issues of current and voltage. Initially, HRES is designed with photovoltaic (PV) system, wind turbine (WT) and battery energy storage system (BESS) which connected with the load system. To analysis the presentation of the proposed controller structure, the non-linear load is connected with the system to create PQ issues in the system. The PQ issues are mitigated and load demand is reimbursed with the assistance of HRES system. The proposed method is employed in the MATLAB/Simulink platform and performances were analysed. Three different cases are used to validate the performance of the proposed method such as Sag, Swell, and disturbances. Additionally, total harmonic distortion (THD) is analysed. The proposed method is compared with existing methods of proportional integral (PI) controller, gravitational search algorithm (GSA), biogeography based optimisation (BBO), grey wolf optimization (GWO), extended search algorithm (ESA), random forest algorithm (RFA) and genetic algorithm (GA).

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Abbreviations

ASO:

Atom search optimization

BBO:

Biogeography based optimisation

BESS:

Battery energy storage system

ESA:

Extended search algorithm

FACT:

Flexible AC transmission systems

FOPID:

Functional order proportional integral derivative

GA:

Genetic algorithm

GSA:

Gravitational search algorithm

GWO:

Grey wolf optimization

HRES:

Hybrid renewable energy system

PI:

Proportional integral

PQ:

Power quality

PV:

Photovoltaic

RES:

Renewable energy resources

RFA:

Random forest algorithm

THD:

Total harmonic distortion

UPQC:

Unified power quality conditioner

WT:

Wind turbine

\(Q\) :

Electron charge

\(k\) :

Boltzmann’s constant

\(a\) :

Diode ideality factor

\(t\) :

Temperature in Kelvin

\({R}_{SE}\) :

Series resistance

\({R}_{SH}\) :

Shunt resistance

\({I}_{Sc}\) :

Current

\({V}_{P}\) :

The voltage of the cell

\({P}_{PV}\left(t\right)\) :

Power of PV

\({N}_{pv}\left(t\right)\) :

Number of cells in the PV array

\({I}_{pv}\left(t\right)\) :

Current of PV

\({V}_{pv}\left(t\right)\) :

Voltage of PV

\({P}_{WT}^{CO}\) :

Wind turbine power at cutout voltage

\(V\left(t\right)\) :

Wind speed at time \(t\)

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

Nominal wind speed

\({V}_{cutout}\) :

Cut out speed in the wind turbine

\({V}_{cutin }\) :

Cut in speed of wind turbine

\({P}_{WT}^{MAX}\) :

Maximum power of wind turbine

\(DOD\) :

Depth of discharge rate of battery

\({\eta }^{B}\) :

Efficiency of battery

\({\eta }^{I}\) :

Inverter efficiency

\({P}^{L}\) :

Demand power

\({P}_{L}\left(t\right)\) :

Load demand of the system

\({B}^{P}\) :

Battery power

\(\mu\) :

Battery self-discharge rate

\({I}^{ih}\) :

The output current of shunt active filter

\({I}^{iL}\) :

Load current

\({I}^{is}\) :

Line current

\({V}^{ih}:\) :

The output voltage of the series active filter

\({e}^{i}\) :

Source voltage

\({L}^{s}\) :

The inductance of the transmission line

\({R}^{s}\) :

The resistance of the transmission line

\(P\left(c\right)\) :

Active power of the series filter

\(Q\left(c\right)\) :

Reactive power of series filter

\({V}^{d}\) :

Direct axes voltage

\({V}^{q}\) :

Quadrature axes voltage

\({V}^{a},{V}^{b},{V}^{c}\) :

Three-phase voltages

\({V}^{d\left(ac\right)}\) :

Ac component voltage

\({V}^{d(dC)}\) :

Dc component voltage

\({V}^{Ra}\), \({V}^{Rb}\), \({V}^{Rc}\) :

Three-phase reference voltages

\({I}^{l\alpha }\), \({I}^{l\beta }\) :

Phase neutral currents

\({I}^{La};{I}^{Lb};{I}^{Lc}\) :

Three-phase load currents

\({V}^{s\alpha };{V}^{s\beta }\) :

Phase neutral voltages

\({V}^{sa};{V}^{sb};{V}^{sc}\) :

Three-phase supply voltages

\({I}^{Ra};{I}^{Rb};{I}^{Rc}\) :

Reference current of shunt active power filter

\(G\left(s\right)\) :

The transfer function of FOPID controller in HRES system

\(u(s)\) :

Controller output

\(e(s)\) :

Error signal of HRES system

\({K}_{p}\) :

Proportional parameters

\({K}_{i}\) :

Integral parameters

\({K}_{d}\) :

Derivative parameters

\({D}^{-\lambda }\), \({D}^{\mu }\) :

Fractional order parameters

\(e\left(s\right)\) :

Error signal

\({R}^{ab}\) :

Position of ath atom in 3D space

\(\sigma\) :

Length scale

\({\left(\frac{\sigma }{R}\right)}^{12}\) :

Repulsive interactions

\({\left(\frac{\sigma }{R}\right)}^{6}\) :

Attractive interactions

\(\varepsilon\) :

Depth of the potential

\(LB\) :

Lower bound

\(UB\) :

Upper bound

D:

Measurement of the search space

\({X}_{a}^{D}\) :

Position of ath atom in ASO

\({Fit}_{best}^{a}\left(T\right)\) :

Minimum fitness value

\({Fit}_{worst}^{a}\left(T\right)\) :

Maximum fitness value at the Tth iteration

\({Fit}^{a}\left(T\right)\) :

Fitness function value of the Tth iteration of ith atom

C:

Neighbours

\({random}_{b}\) :

Random number in [0, 1]

\(\beta\) :

Multiplier weight

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Goud, B.S., Rao, B.L. Power Quality Enhancement in Grid-Connected PV/Wind/Battery Using UPQC: Atom Search Optimization. J. Electr. Eng. Technol. 16, 821–835 (2021). https://doi.org/10.1007/s42835-020-00644-x

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