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Modeling and simulation of novel dynamic control strategy for PV–wind hybrid power system using FGS−PID and RBFNSM methods

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Abstract

During the past years, hybrid solar-wind power systems containing photovoltaic (PV) and wind generators are used to minimize the intermittency problem of renewable power generation units. The improved modeling and control schemes for a grid-tied hybrid PV–wind system is presented in the current research work. The maximum power point tracking namely “MPPT” algorithm together with controlling the pitch angle are used, respectively, for the PV system and wind power generation to attain the maximum power for any given external weather conditions. A radial basis function network sliding mode known as the RBFNSM method is used to control the pitch angle in the wind energy system, while the PV system uses a proportional–integral–derivative controller equipped with the fuzzy gain scheduling in order to enhance the transient state and mitigate the settling time to ensure the stability of the mentioned system. To test the suggested control scheme’s effectiveness, MATLAB simulations are carried out under various scenarios of the wind speed as well as solar irradiation. The obtained results show the efficiency of the adaptive MPPT method to harness the highest power under very challenging scenarios. The merits of the developed schemes are quickly and precisely tracking the maximum power output of the hybrid PV–wind system. Besides, the power flowing between the utility grid and the hybrid source with a fast transient response and improved stability performance is effectively controlled using the offered schemes.

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Abbreviations

PV:

Photovoltaic

WT:

Wind turbine

MPPT:

Maximum power point tracking

RBFNSM:

Radial basis function network-sliding mode

PID:

Proportional–integral–derivative

FGS:

Fuzzy gain scheduling

VSC:

Voltage source converter

HC:

Hill climbing

P&O:

Perturb and observe

INC:

Incremental conductance

FLC:

Fuzzy logic controllers

ANFIS:

Adaptive neuro fuzzy inference system

PSO:

Particle swarm optimization

GA:

Genetic algorithm

TSA:

Tabu search algorithm

BFA:

Bacterial foraging algorithm

ANA:

Ant colony algorithm

ABC:

Artificial bee colony

Z–N:

Ziegler–Nichols

PSF:

Power signal feedback

TSR:

Tip speed ratio

RBFN:

Radial basis function network

HCS:

Hill climb searching

SMV:

Sliding mode variable

ANN:

Artificial neural network

DFIG:

Doubly fed induction generator

STC:

Standard test condition

DC:

Direct current

PQ:

Power quality

PWM:

Pulse width modulation

OD:

Ordinary differential

FIS:

Fuzzy inference system

PCC:

Point of common coupling

DG:

Distributed generation

PLL:

Phase-locked loop

ud/uq :

Grid voltage

i ref :

Current reference

R s :

Series resistances

R sh :

Shunt resistances

I ph :

Photocurrent of the PV cell

T :

Temperature

n :

Ideality factor of p–n junction

IPVO :

Reverse saturation current

K :

Boltzmann constant

q :

Electronic charge

t 1 :

Startup time

t 2 :

Closedown time

P :

PV’s output power

S :

Radiation

D :

Duty cycle

V out :

Output voltage

V s :

Input voltage

T i :

Integral time

T d :

Derivative time

G 0 :

Nominal value of the control

E (k):

Input of fuzzy rules

CE (k):

Output of fuzzy rules

ρ :

Air density

A :

Area swept by blades

\( \upsilon \) :

Wind velocity

Pa :

Aerodynamic power

CP :

Power coefficient

β′:

Pitch angle of the blade

λ :

Speed ratio of tip

ω r :

Turbine speed

P ref :

Wind output power

P out :

Real output power of wind

e :

Tracking error

\( s_{1}^{1} (k) \) :

Switching surface

c j :

Central vector of jth

b j :

Base width constant of jth

\( y_{0}^{3} (k) \) :

Output of the RBFNSM

ω :

Rotation frequency

θ :

Rotation angle

id/iq :

Inverter current

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Acknowledgement

This work is supported by the Natural Science Foundation of Guangdong Province under Grant 2018A0303130115 and the National Science and Technology Major Project(2017ZX05018- 003) of China.

Author information

Authors and Affiliations

  1. College of Geophysics, Chengdu University of Technology, Chengdu, 610059, China

    Di Wu

  2. BGP Sci & Tech. Park, Huayang East Road, Zhuozhou, 072751, Hebei, China

    Di Wu

  3. Department of Information Technology, College of Science and Technology, University of Human Development, Sulaymaniyah, Kurdistan Region, Iraq

    Goran Saman Nariman

  4. Department of Communication Engineering, Sulaimani Polytechnic University, Sulaymaniyah, Kurdistan Region, Iraq

    Salim Qadir Mohammed

  5. Institute of Computing Science and Technology, Guangzhou University, Guangzhou, 510006, China

    Zehui Shao

  6. Department of Electrical Engineering, Saveh Branch, Islamic Azad University, Saveh, Iran

    Alireza Rezvani

  7. Department of Electrical and Computer Engineering, University of North Carolina at Charlotte, Charlotte, NC, USA

    Saeed Mohajeryami

Authors
  1. Di Wu
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  2. Goran Saman Nariman
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  3. Salim Qadir Mohammed
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  4. Zehui Shao
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  5. Alireza Rezvani
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  6. Saeed Mohajeryami
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Correspondence to Alireza Rezvani.

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Appendix

Appendix

See Table 8.

Table 8 Description of the detailed model
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Wu, D., Nariman, G.S., Mohammed, S.Q. et al. Modeling and simulation of novel dynamic control strategy for PV–wind hybrid power system using FGS−PID and RBFNSM methods. Soft Comput 24, 8403–8425 (2020). https://doi.org/10.1007/s00500-019-04408-2

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