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Performance improvement of hybrid renewable energy sources connected to the grid using artificial neural network and sliding mode control

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Abstract

The main purpose of this paper to compare and analyze three types of controllers in the three phases DC–AC inverters in hybrid renewable energy source (HRES) systems. To achieve this, two modern controllers are developed and compared based on sliding mode control (SMC) and artificial neural network techniques. The HRESs comprise photovoltaic (PV), wind turbines, battery storage systems, and transmission lines connected to infinite bus bars via a step-up transformer. The developed controllers at the inverter side utilize both voltage control and current regulation. A DC–DC boost converter is employed to set up a voltage demand at the point of common coupling (PCC). Then, the formulation of an HRES with the developed controllers is presented. The developed controllers are considered to operate under various solar radiations, temperatures, and wind speed loading conditions. The HRESs with the developed controllers are simulated via MATLAB/Simulink to verify the effectiveness of the developed controllers. The obtained results demonstrate that adaptive SMC and artificial ANN control techniques give better results in terms of input power, output power, current, and voltage when compared to classic PI control.

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Abbreviations

I :

Output current of a PV module (V)

I ph :

Photo-generated current (A)

I sc :

PV cell short circuit current (A)

I c :

PV cell output current (A)

I o :

Reverse saturation current (A)

G :

Solar radiation (W/m2)

K :

Boltzmann’s constant (J/K)

T :

PV cell temperature (K)

T r :

Reference temperature (K)

V c :

PV cell output voltage (V)

R s :

Series resistance of a PV cell (ohm)

R sh :

Shunt resistance of a PV cell (ohm)

V :

PV module output voltage (V)

V oc :

PV cell open-circuit voltage (V)

P :

Output power of a PV module (W)

V d and V q :

Stator voltages in the d-q axis (V)

ω s :

Angular velocity of the synchronously rotating reference frame (rad/s)

\({C}_{\mathrm{p}}\) :

Power coefficient of a wind turbine

\({P}_{\mathrm{m}}\) :

Mechanical power of a wind turbine (W)

\(\dot{m}\) :

Air mass (kg/s)

\(\rho\) :

Air density (kg/m3)

V w :

Wind speed (m/s)

A :

Rotor blade area (m2)

R :

Wind turbine rotor radius (m)

λ r :

Tip speed ratio

T m :

Mechanical torque (N·m)

Β :

Pitch angle of blades (deg)

ω m :

Wind turbine rotational speed (rad/s)

K cp :

Power coefficient constant of a wind turbine

N :

Rotational speed (rpm)

V S :

Converter input voltage (V)

V O :

Boost converter output voltage (V)

D :

Duty cycle of a boost converter

K p :

Proportional control

K i :

Integral control

G(s):

Functions of the variable s

RMSE:

Root mean square error

RE:

Relative error

R cor :

Correlation coefficient

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Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Engineering, Assiut University, Assiut, Egypt

    Ahmed Elnozahy, Ali M. Yousef & Farag K. Abo-Elyousr

  2. Electrical Department, Faculty of Technology and Education, Sohag University, Sohag, Egypt

    Moayed Mohamed

  3. Electrical Department, Faculty of Technology and Education, Suez University, Suez, Egypt

    Saad A. Mohamed Abdelwahab

  4. High Institute of Electronic Engineering, Ministry of Higher Education, Bilbis-Sharqiya, Egypt

    Saad A. Mohamed Abdelwahab

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  1. Ahmed Elnozahy
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  2. Ali M. Yousef
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  4. Moayed Mohamed
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  5. Saad A. Mohamed Abdelwahab
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Correspondence to Saad A. Mohamed Abdelwahab.

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Elnozahy, A., Yousef, A.M., Abo-Elyousr, F.K. et al. Performance improvement of hybrid renewable energy sources connected to the grid using artificial neural network and sliding mode control. J. Power Electron. 21, 1166–1179 (2021). https://doi.org/10.1007/s43236-021-00242-8

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  • DOI: https://doi.org/10.1007/s43236-021-00242-8

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